(define-module (guix-cran packages s)
  #:use-module (guix packages)
  #:use-module (guix download)
  #:use-module (guix build-system r)
  #:use-module ((guix licenses)
                #:prefix license:)
  #:use-module (gnu packages statistics)
  #:use-module (gnu packages cran)
  #:use-module (gnu packages bioconductor)
  #:use-module (gnu packages gcc)
  #:use-module (gnu packages maths)
  #:use-module (gnu packages python)
  #:use-module (gnu packages cmake)
  #:use-module (gnu packages multiprecision)
  #:use-module (gnu packages pkg-config)
  #:use-module (gnu packages version-control)
  #:use-module (gnu packages web)
  #:use-module (gnu packages autotools)
  #:use-module (gnu packages algebra)
  #:use-module (gnu packages compression)
  #:use-module (gnu packages java)
  #:use-module (gnu packages docker)
  #:use-module (gnu packages tbb)
  #:use-module (gnu packages ghostscript)
  #:use-module (gnu packages geo)
  #:use-module (gnu packages ssh)
  #:use-module (gnu packages tls)
  #:use-module (gnu packages haskell-xyz)
  #:use-module (gnu packages video)
  #:use-module (gnu packages sqlite)
  #:use-module (gnu packages machine-learning)
  #:use-module (gnu packages perl)
  #:use-module (guix-cran packages z)
  #:use-module (guix-cran packages y)
  #:use-module (guix-cran packages x)
  #:use-module (guix-cran packages w)
  #:use-module (guix-cran packages v)
  #:use-module (guix-cran packages u)
  #:use-module (guix-cran packages t)
  #:use-module (guix-cran packages r)
  #:use-module (guix-cran packages q)
  #:use-module (guix-cran packages p)
  #:use-module (guix-cran packages o)
  #:use-module (guix-cran packages n)
  #:use-module (guix-cran packages m)
  #:use-module (guix-cran packages l)
  #:use-module (guix-cran packages k)
  #:use-module (guix-cran packages j)
  #:use-module (guix-cran packages i)
  #:use-module (guix-cran packages h)
  #:use-module (guix-cran packages g)
  #:use-module (guix-cran packages f)
  #:use-module (guix-cran packages e)
  #:use-module (guix-cran packages d)
  #:use-module (guix-cran packages c)
  #:use-module (guix-cran packages b)
  #:use-module (guix-cran packages a))

(define-public r-syt
  (package
    (name "r-syt")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "syt" version))
       (sha256
        (base32 "1xpvvknyv5c0kvvipmyzrzrvqj51kkp86yl5gkgxfzs570zafwzp"))))
    (properties `((upstream-name . "syt")))
    (build-system r-build-system)
    (propagated-inputs (list r-partitions r-matrix))
    (home-page "https://github.com/stla/syt")
    (synopsis "Young Tableaux")
    (description
     "Deals with Young tableaux (field of combinatorics).  For standard Young tabeaux,
performs enumeration, counting, random generation, the Robinson-Schensted
correspondence, and conversion to and from paths on the Young lattice.  Also
performs enumeration and counting of semistandard Young tableaux, enumeration of
skew semistandard Young tableaux, enumeration of Gelfand-Tsetlin patterns, and
computation of Kostka numbers.")
    (license license:gpl3)))

(define-public r-systemicrisk
  (package
    (name "r-systemicrisk")
    (version "0.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "systemicrisk" version))
       (sha256
        (base32 "05isv0wdx98s824nk0v8r3mqvmnrmsq7ffki40092snsfda3451z"))))
    (properties `((upstream-name . "systemicrisk")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-lpsolve))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=systemicrisk")
    (synopsis "Systemic Risk and Network Reconstruction")
    (description
     "Analysis of risk through liability matrices.  Contains a Gibbs sampler for
network reconstruction, where only row and column sums of the liabilities matrix
as well as some other fixed entries are observed, following the methodology of
Gandy&Veraart (2016) <doi:10.1287/mnsc.2016.2546>.  It also incorporates models
that use a power law distribution on the degree distribution.")
    (license license:gpl3)))

(define-public r-systemicr
  (package
    (name "r-systemicr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SystemicR" version))
       (sha256
        (base32 "1vk08w734zxvrwzd81iy0azwcj53k6wvgc1qlaxw96hirdwdg6zr"))))
    (properties `((upstream-name . "SystemicR")))
    (build-system r-build-system)
    (propagated-inputs (list r-xts r-quantreg r-matrix r-igraph))
    (home-page "https://cran.r-project.org/package=SystemicR")
    (synopsis "Monitoring Systemic Risk")
    (description
     "The past decade has demonstrated an increased need to better understand risks
leading to systemic crises.  This framework offers scholars, practitioners and
policymakers a useful toolbox to explore such risks in financial systems.
Specifically, this framework provides popular econometric and network measures
to monitor systemic risk and to measure the consequences of regulatory
decisions.  These systemic risk measures are based on the frameworks of Adrian
and Brunnermeier (2016) <doi:10.1257/aer.20120555> and Billio, Getmansky, Lo and
Pelizzon (2012) <doi:10.1016/j.jfineco.2011.12.010>.")
    (license license:gpl3)))

(define-public r-systemfit
  (package
    (name "r-systemfit")
    (version "1.1-30")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "systemfit" version))
       (sha256
        (base32 "1qhw8gda85lliqhj67zsgva4cb6wihr5hks1c9c34y0n3ywgp52r"))))
    (properties `((upstream-name . "systemfit")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich r-matrix r-mass r-lmtest r-car))
    (native-inputs (list r-knitr))
    (home-page "https://r-forge.r-project.org/projects/systemfit/")
    (synopsis "Estimating Systems of Simultaneous Equations")
    (description
     "Econometric estimation of simultaneous systems of linear and nonlinear equations
using Ordinary Least Squares (OLS), Weighted Least Squares (WLS), Seemingly
Unrelated Regressions (SUR), Two-Stage Least Squares (2SLS), Weighted Two-Stage
Least Squares (W2SLS), and Three-Stage Least Squares (3SLS) as suggested, e.g.,
by Zellner (1962) <doi:10.2307/2281644>, Zellner and Theil (1962)
<doi:10.2307/1911287>, and Schmidt (1990) <doi:10.1016/0304-4076(90)90127-F>.")
    (license license:gpl2+)))

(define-public r-sysrecon
  (package
    (name "r-sysrecon")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Sysrecon" version))
       (sha256
        (base32 "01vbkv707m31bf69j3vxfiwgvaz56v2yr6da0j0796rcmca1d4b8"))))
    (properties `((upstream-name . "Sysrecon")))
    (build-system r-build-system)
    (propagated-inputs (list r-tm
                             r-stringr
                             r-snowballc
                             r-rlang
                             r-rcolorbrewer
                             r-plyr
                             r-patchwork
                             r-magrittr
                             r-ggtree
                             r-ggplot2
                             r-dplyr
                             r-ape))
    (home-page "https://oyshilin.github.io/sysrecon/")
    (synopsis "Systematical Metabolic Reconstruction")
    (description
     "In the past decade, genome-scale metabolic reconstructions have widely been used
to comprehend the systems biology of metabolic pathways within an organism.
Different GSMs are constructed using various techniques that require distinct
steps, but the input data, information conversion and software tools are neither
concisely defined nor mathematically or programmatically formulated in a
context-specific manner.The tool that quantitatively and qualitatively specifies
each reconstruction steps and can generate a template list of reconstruction
steps dynamically selected from a reconstruction step reservoir, constructed
based on all available published papers.")
    (license license:gpl3)))

(define-public r-syslognet
  (package
    (name "r-syslognet")
    (version "0.1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "syslognet" version))
       (sha256
        (base32 "0nwni2mn7w43pfasqa2dwy1l6r0ymlkwlsyd5q47fajdr8v1ir0q"))))
    (properties `((upstream-name . "syslognet")))
    (build-system r-build-system)
    (home-page "https://github.com/philaris/syslognet")
    (synopsis "Send Log Messages to Remote 'syslog' Server")
    (description
     "Send syslog protocol messages to a remote syslog server specified by host name
and TCP network port.")
    (license license:expat)))

(define-public r-sysid
  (package
    (name "r-sysid")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sysid" version))
       (sha256
        (base32 "0fr9gf5yjin3zvz850z4r4pqc1r4mwx8d46sl64i4csdm9qnqagy"))))
    (properties `((upstream-name . "sysid")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-tframe
                             r-signal
                             r-reshape2
                             r-polynom
                             r-ggplot2
                             r-bitops))
    (home-page "https://cran.r-project.org/package=sysid")
    (synopsis "System Identification in R")
    (description
     "This package provides functions for constructing mathematical models of
dynamical systems from measured input-output data.")
    (license license:gpl3)))

(define-public r-syscselection
  (package
    (name "r-syscselection")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SyScSelection" version))
       (sha256
        (base32 "16zx507hlzhdxbh6gzndbgmzpyp6vc59h441l4gv8lx58zi9kyjd"))))
    (properties `((upstream-name . "SyScSelection")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SyScSelection")
    (synopsis "Systematic Scenario Selection for Stress Testing")
    (description
     "Quasi-Monte-Carlo algorithm for systematic generation of shock scenarios from an
arbitrary multivariate elliptical distribution.  The algorithm selects a
systematic mesh of arbitrary fineness that approximately evenly covers an
isoprobability ellipsoid in d dimensions (Flood, Mark D. & Korenko, George G.
(2013) <doi:10.1080/14697688.2014.926018>).  This package is the R analogy to
the Matlab code published by Flood & Korenko in above-mentioned paper.")
    (license license:cc0)))

(define-public r-syrup
  (package
    (name "r-syrup")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "syrup" version))
       (sha256
        (base32 "008h2fzj620fh4gkbfjs40yk2mq047512djraqkj501s80x0rvib"))))
    (properties `((upstream-name . "syrup")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-vctrs
                             r-tibble
                             r-rlang
                             r-purrr
                             r-ps
                             r-dplyr
                             r-callr
                             r-bench))
    (home-page "https://github.com/simonpcouch/syrup")
    (synopsis "Measure Memory and CPU Usage for Parallel R Code")
    (description
     "Measures memory and CPU usage of R code by regularly taking snapshots of calls
to the system command ps'.  The package provides an entry point (albeit coarse)
to profile usage of system resources by R code run in parallel.")
    (license license:expat)))

(define-public r-synthtools
  (package
    (name "r-synthtools")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SynthTools" version))
       (sha256
        (base32 "19vasz3nnm5vgwqs6g2a5bibmk44br1bxga5zavsx938x3jw9gnd"))))
    (properties `((upstream-name . "SynthTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-magrittr r-dplyr))
    (home-page "https://cran.r-project.org/package=SynthTools")
    (synopsis
     "Tools and Tests for Experiments with Partially Synthetic Data Sets")
    (description
     "This package provides a set of functions to support experimentation in the
utility of partially synthetic data sets.  All functions compare an observed
data set to one or a set of partially synthetic data sets derived from the
observed data to (1) check that data sets have identical attributes, (2)
calculate overall and specific variable perturbation rates, (3) check for
potential logical inconsistencies, and (4) calculate confidence intervals and
standard errors of desired variables in multiple imputed data sets.  Confidence
interval and standard error formulas have options for either synthetic data sets
or multiple imputed data sets.  For more information on the formulas and methods
used, see Reiter & Raghunathan (2007) <doi:10.1198/016214507000000932>.")
    (license license:gpl2+)))

(define-public r-synthpop
  (package
    (name "r-synthpop")
    (version "1.8-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "synthpop" version))
       (sha256
        (base32 "0ljq3vhprzxj0p5s57pr223n3mxbiwz0p5bdvjcdkw4xykx7zkka"))))
    (properties `((upstream-name . "synthpop")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-stringr
                             r-rpart
                             r-rmutil
                             r-ranger
                             r-randomforest
                             r-proto
                             r-polspline
                             r-plyr
                             r-party
                             r-nnet
                             r-mipfp
                             r-mass
                             r-lattice
                             r-ggplot2
                             r-foreign
                             r-classint
                             r-broman))
    (home-page "https://www.synthpop.org.uk/")
    (synopsis
     "Generating Synthetic Versions of Sensitive Microdata for Statistical Disclosure Control")
    (description
     "This package provides a tool for producing synthetic versions of microdata
containing confidential information so that they are safe to be released to
users for exploratory analysis.  The key objective of generating synthetic data
is to replace sensitive original values with synthetic ones causing minimal
distortion of the statistical information contained in the data set.  Variables,
which can be categorical or continuous, are synthesised one-by-one using
sequential modelling.  Replacements are generated by drawing from conditional
distributions fitted to the original data using parametric or classification and
regression trees models.  Data are synthesised via the function syn() which can
be largely automated, if default settings are used, or with methods defined by
the user.  Optional parameters can be used to influence the disclosure risk and
the analytical quality of the synthesised data.  For a description of the
implemented method see Nowok, Raab and Dibben (2016)
<doi:10.18637/jss.v074.i11>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-synthetic
  (package
    (name "r-synthetic")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SynthETIC" version))
       (sha256
        (base32 "1nrqfy2gl2x9fz5vh7c3rlnk1zw9pghv1fqlsndzs5nwr5ww8db5"))))
    (properties `((upstream-name . "SynthETIC")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-magrittr r-ggplot2 r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/agi-lab/SynthETIC")
    (synopsis "Synthetic Experience Tracking Insurance Claims")
    (description
     "Creation of an individual claims simulator which generates various features of
non-life insurance claims.  An initial set of test parameters, designed to
mirror the experience of an Auto Liability portfolio, were set up and applied by
default to generate a realistic test data set of individual claims (see
vignette).  The simulated data set then allows practitioners to back-test the
validity of various reserving models and to prove and/or disprove certain
actuarial assumptions made in claims modelling.  The distributional assumptions
used to generate this data set can be easily modified by users to match their
experiences.  Reference: Avanzi B, Taylor G, Wang M, Wong B (2020)
\"@code{SynthETIC}: an individual insurance claim simulator with feature control\"
<@code{arXiv:2008.05693>}.")
    (license license:gpl3)))

(define-public r-synthesizer
  (package
    (name "r-synthesizer")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "synthesizer" version))
       (sha256
        (base32 "0x1px8dzihrrr5gg0pzwjnxda5zfhck3c3rj605wrgjps16zdj41"))))
    (properties `((upstream-name . "synthesizer")))
    (build-system r-build-system)
    (propagated-inputs (list r-randomforest))
    (native-inputs (list r-simplermarkdown))
    (home-page "https://github.com/markvanderloo/synthesizer")
    (synopsis
     "Synthesize Data Based on Empirical Quantile Functions and Rank Order Matching")
    (description
     "Data is synthesized using a combination of inverse transform sampling from the
empirical quantile functions for each variable, and then copying the rank order
structure from the original dataset.  The package also includes a number of
functions to measure the utility of synthesized datasets.")
    (license (license:fsdg-compatible "EUPL"))))

(define-public r-synthesisr
  (package
    (name "r-synthesisr")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "synthesisr" version))
       (sha256
        (base32 "0b50d5hfkscggn2f6h8wyljkar08r8cbc0a4l12f809w5hdnjc63"))))
    (properties `((upstream-name . "synthesisr")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringdist))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=synthesisr")
    (synopsis "Import, Assemble, and Deduplicate Bibliographic Datasets")
    (description
     "This package provides a critical first step in systematic literature reviews and
mining of academic texts is to identify relevant texts from a range of sources,
particularly databases such as Web of Science or Scopus'.  These databases often
export in different formats or with different metadata tags.  synthesisr expands
on the tools outlined by Westgate (2019) <doi:10.1002/jrsm.1374> to import
bibliographic data from a range of formats (such as bibtex', ris', or ciw') in a
standard way, and allows merging and deduplication of the resulting dataset.")
    (license license:gpl3)))

(define-public r-synthesis
  (package
    (name "r-synthesis")
    (version "1.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "synthesis" version))
       (sha256
        (base32 "0zsk3y322qzmq3rcclka176xbqfwqkg2r4p3k0cx057dkn4v7866"))))
    (properties `((upstream-name . "synthesis")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/zejiang-unsw/synthesis#readme")
    (synopsis "Generate Synthetic Data from Statistical Models")
    (description
     "Generate synthetic time series from commonly used statistical models, including
linear, nonlinear and chaotic systems.  Applications to testing methods can be
found in Jiang, Z., Sharma, A., & Johnson, F. (2019)
<doi:10.1016/j.advwatres.2019.103430> and Jiang, Z., Sharma, A., & Johnson, F.
(2020) <doi:10.1029/2019WR026962> associated with an open-source tool by Jiang,
Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020)
<doi:10.1016/j.envsoft.2020.104907>.")
    (license license:gpl3+)))

(define-public r-synthacs
  (package
    (name "r-synthacs")
    (version "1.7.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "synthACS" version))
       (sha256
        (base32 "1s4jawg2g0zy41y0ajacki4nif9m3r0hnaz63yhapcnizc37lyy1"))))
    (properties `((upstream-name . "synthACS")))
    (build-system r-build-system)
    (propagated-inputs (list r-retry r-rcpp r-data-table r-acs))
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "https://cran.r-project.org/package=synthACS")
    (synopsis
     "Synthetic Microdata and Spatial MicroSimulation Modeling for ACS Data")
    (description
     "This package provides access to curated American Community Survey (ACS) base
tables via a wrapper to library(acs).  Builds synthetic micro-datasets at any
user-specified geographic level with ten default attributes; and, conducts
spatial microsimulation modeling (SMSM) via simulated annealing.  SMSM is
conducted in parallel by default.  Lastly, we provide functionality for
data-extensibility of micro-datasets <doi:10.18637/jss.v104.i07>.")
    (license license:expat)))

(define-public r-synth
  (package
    (name "r-synth")
    (version "1.1-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Synth" version))
       (sha256
        (base32 "0vn4ww719q849fgq86g4xi6lb4fy081svz17b0ivqn7f7aqwnb0r"))))
    (properties `((upstream-name . "Synth")))
    (build-system r-build-system)
    (propagated-inputs (list r-rgenoud r-optimx r-kernlab))
    (home-page "https://web.stanford.edu/~jhain/")
    (synopsis "Synthetic Control Group Method for Comparative Case Studies")
    (description
     "This package implements the synthetic control group method for comparative case
studies as described in Abadie and Gardeazabal (2003) and Abadie, Diamond, and
Hainmueller (2010, 2011, 2014).  The synthetic control method allows for effect
estimation in settings where a single unit (a state, country, firm, etc.) is
exposed to an event or intervention.  It provides a data-driven procedure to
construct synthetic control units based on a weighted combination of comparison
units that approximates the characteristics of the unit that is exposed to the
intervention.  A combination of comparison units often provides a better
comparison for the unit exposed to the intervention than any comparison unit
alone.")
    (license license:gpl2+)))

(define-public r-syntenyplotter
  (package
    (name "r-syntenyplotter")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "syntenyPlotteR" version))
       (sha256
        (base32 "00w380hcw7zfsr4lgzlp57l7rcl280n3y5m8xsp7n2f5csf8kq5q"))))
    (properties `((upstream-name . "syntenyPlotteR")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=syntenyPlotteR")
    (synopsis "Genome Synteny Visualization")
    (description
     "Draw syntenic relationships between genome assemblies.  There are 3 functions
which take a tab delimited file containing alignment data for syntenic blocks
between genomes to produce either a linear alignment plot, an evolution highway
style plot, or a painted ideogram representing syntenic relationships.  There is
also a function to convert alignment data in the DESCHRAMBLER/@code{inferCAR}
format to the required data structure.")
    (license license:expat)))

(define-public r-syntaxr
  (package
    (name "r-syntaxr")
    (version "0.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "syntaxr" version))
       (sha256
        (base32 "1iik9b3x9jx7w31j7hyi3sixg30l5a6w40svhqj1xlchsk60yykn"))))
    (properties `((upstream-name . "syntaxr")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr))
    (home-page "https://github.com/greenmeen/syntaxr")
    (synopsis "An 'SPSS' Syntax Generator for Multi-Variable Manipulation")
    (description
     "This package provides a set of functions for generating SPSS syntax files from
the R environment.")
    (license license:expat)))

(define-public r-synr
  (package
    (name "r-synr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "synr" version))
       (sha256
        (base32 "160lkj0jhazh717rm340aivbrq67wcdj1c19hs9vza43f65x78i3"))))
    (properties `((upstream-name . "synr")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-dbscan r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://datalowe.github.io/synr/")
    (synopsis "Explore and Process Synesthesia Consistency Test Data")
    (description
     "Explore synesthesia consistency test data, calculate consistency scores, and
classify participant data as valid or invalid.")
    (license license:expat)))

(define-public r-synmicrodata
  (package
    (name "r-synmicrodata")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "synMicrodata" version))
       (sha256
        (base32 "03fx8lzg90p8cw237lgn6yqxnxlknximpyafx6jnd9yh2sc3ccjb"))))
    (properties `((upstream-name . "synMicrodata")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=synMicrodata")
    (synopsis "Synthetic Microdata Generator")
    (description
     "This tool fits a non-parametric Bayesian model called a \"hierarchically coupled
mixture model with local dependence (HCMM-LD)\" to the original microdata in
order to generate synthetic microdata for privacy protection.  The
non-parametric feature of the adopted model is useful for capturing the joint
distribution of the original input data in a highly flexible manner, leading to
the generation of synthetic data whose distributional features are similar to
that of the input data.  The package allows the original input data to have
missing values and impute them with the posterior predictive distribution, so no
missing values exist in the synthetic data output.  The method builds on the
work of Murray and Reiter (2016) <doi:10.1080/01621459.2016.1174132>.")
    (license license:gpl3+)))

(define-public r-synlik
  (package
    (name "r-synlik")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "synlik" version))
       (sha256
        (base32 "0pslag0nb9v872yr383chjdavz8hv9m0ba3pvxhfpshxsfzczcca"))))
    (properties `((upstream-name . "synlik")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-matrix))
    (native-inputs (list r-knitr))
    (home-page "http://mfasiolo.github.io/synlik_release/")
    (synopsis "Synthetic Likelihood Methods for Intractable Likelihoods")
    (description
     "Framework to perform synthetic likelihood inference for models where the
likelihood function is unavailable or intractable.")
    (license license:gpl2+)))

(define-public r-syndi
  (package
    (name "r-syndi")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SynDI" version))
       (sha256
        (base32 "15vrp4qhgf0689h0jqygrdl1nl0xipwmy29dpfa8lx1vd1pb7dnn"))))
    (properties `((upstream-name . "SynDI")))
    (build-system r-build-system)
    (propagated-inputs (list r-stackimpute
                             r-randomforest
                             r-mvtnorm
                             r-mice
                             r-mass
                             r-magrittr
                             r-knitr
                             r-dplyr
                             r-broom
                             r-boot
                             r-arm))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/umich-biostatistics/SynDI")
    (synopsis "Synthetic Data Integration")
    (description
     "Regression inference for multiple populations by integrating summary-level data
using stacked imputations.  Gu, T., Taylor, J.M.G. and Mukherjee, B. (2021) A
synthetic data integration framework to leverage external summary-level
information from heterogeneous populations <@code{arXiv:2106.06835>}.")
    (license license:gpl2)))

(define-public r-syncsa
  (package
    (name "r-syncsa")
    (version "1.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SYNCSA" version))
       (sha256
        (base32 "0n9za302x638s666xs86zy94qch9wn0rln8xy6x9jymhhq9zn0kj"))))
    (properties `((upstream-name . "SYNCSA")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan r-rcpparmadillo r-permute r-fd))
    (home-page "https://cran.r-project.org/package=SYNCSA")
    (synopsis
     "Analysis of Functional and Phylogenetic Patterns in Metacommunities")
    (description
     "Analysis of metacommunities based on functional traits and phylogeny of the
community components.  The functions that are offered here implement for the R
environment methods that have been available in the SYNCSA application written
in C++ (by Valerio Pillar, available at
<http://ecoqua.ecologia.ufrgs.br/SYNCSA.html>).")
    (license license:gpl2)))

(define-public r-syncrng
  (package
    (name "r-syncrng")
    (version "1.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SyncRNG" version))
       (sha256
        (base32 "0ndfd1ikd7ha30b2fk2qh27idnkga7fbarw7cm4rzj3bhdami55m"))))
    (properties `((upstream-name . "SyncRNG")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SyncRNG")
    (synopsis "Synchronized Tausworthe RNG for R and Python")
    (description "Generate the same random numbers in R and Python.")
    (license license:gpl2)))

(define-public r-syncmove
  (package
    (name "r-syncmove")
    (version "0.1-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SyncMove" version))
       (sha256
        (base32 "1jlnsj5v8y5pijfkww7ng7nkwvj93naw29wcxxj130ww5qk7qk1z"))))
    (properties `((upstream-name . "SyncMove")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SyncMove")
    (synopsis
     "Subsample Temporal Data to Synchronal Events and Compute the MCI")
    (description
     "The function @code{syncSubsample} subsamples temporal data of different entities
so that the result only contains synchronal events.  The function mci calculates
the Movement Coordination Index (MCI, see reference on help page for function
mci') of a data set created with the function @code{syncSubsample}'.")
    (license license:gpl2)))

(define-public r-synchwave
  (package
    (name "r-synchwave")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SynchWave" version))
       (sha256
        (base32 "1y53235523z0nf4pmskdiz3xq4p8hdzvf5jy4ml4isj6nqzy6b88"))))
    (properties `((upstream-name . "SynchWave")))
    (build-system r-build-system)
    (propagated-inputs (list r-fields))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=SynchWave")
    (synopsis "Synchrosqueezed Wavelet Transform")
    (description
     "The synchrosqueezed wavelet transform is implemented.  The package is a
translation of MATLAB Synchrosqueezing Toolbox, version 1.1 originally developed
by Eugene Brevdo (2012).  The C code for curve_ext was authored by Jianfeng Lu,
and translated to Fortran by Dongik Jang.  Synchrosqueezing is based on the
papers: [1] Daubechies, I., Lu, J. and Wu, H. T. (2011) Synchrosqueezed wavelet
transforms: An empirical mode decomposition-like tool.  Applied and
Computational Harmonic Analysis, 30.  243-261. [2] Thakur, G., Brevdo, E.,
Fukar, N. S. and Wu, H-T. (2013) The Synchrosqueezing algorithm for time-varying
spectral analysis: Robustness properties and new paleoclimate applications.
Signal Processing, 93, 1079-1094.")
    (license license:lgpl2.0+)))

(define-public r-synchrony
  (package
    (name "r-synchrony")
    (version "0.3.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "synchrony" version))
       (sha256
        (base32 "1zpxg4dkxnvafiyp0j00wig5ymj10bzfg2376x56rzpnabhg57hi"))))
    (properties `((upstream-name . "synchrony")))
    (build-system r-build-system)
    (home-page "http://github.com/tgouhier/synchrony")
    (synopsis
     "Methods for Computing Spatial, Temporal, and Spatiotemporal Statistics")
    (description
     "This package provides methods for computing spatial, temporal, and
spatiotemporal statistics as described in Gouhier and Guichard (2014)
<doi:10.1111/2041-210X.12188>.  These methods include empirical univariate,
bivariate and multivariate variograms; fitting variogram models; phase locking
and synchrony analysis; generating autocorrelated and cross-correlated matrices.")
    (license license:gpl2+)))

(define-public r-syn
  (package
    (name "r-syn")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "syn" version))
       (sha256
        (base32 "12apdjbsazwfxizsw4dj0y3vkdjmki8dzpyv3bi9gnhpysdd1jzx"))))
    (properties `((upstream-name . "syn")))
    (build-system r-build-system)
    (home-page "http://syn.njtierney.com/")
    (synopsis "Creates Synonyms From Target Words")
    (description
     "Generates synonyms from a given word drawing from a synonym list from the moby
project <http://moby-thesaurus.org/>.")
    (license license:gpl3)))

(define-public r-symts
  (package
    (name "r-symts")
    (version "1.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SymTS" version))
       (sha256
        (base32 "0v1vgsxq101fdxawdsdk2wa2llp79hzvmdphflnj8iblj8ncm6vc"))))
    (properties `((upstream-name . "SymTS")))
    (build-system r-build-system)
    (inputs (list gsl))
    (home-page "https://cran.r-project.org/package=SymTS")
    (synopsis "Symmetric Tempered Stable Distributions")
    (description
     "This package contains methods for simulation and for evaluating the pdf, cdf,
and quantile functions for symmetric stable, symmetric classical tempered
stable, and symmetric power tempered stable distributions.")
    (license license:gpl3+)))

(define-public r-symsem
  (package
    (name "r-symsem")
    (version "0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "symSEM" version))
       (sha256
        (base32 "0zk9a1l1pcyjg43j8jafbms072kzz6qqz514a0kbnyg7ja0bfrmj"))))
    (properties `((upstream-name . "symSEM")))
    (build-system r-build-system)
    (inputs (list python))
    (propagated-inputs (list r-openmx r-metasem r-caracas))
    (home-page "https://github.com/mikewlcheung/symsem")
    (synopsis "Symbolic Computation for Structural Equation Models")
    (description
     "This package provides a collection of functions for symbolic computation using
the caracas package for structural equation models and other statistical
analyses.  Among its features is the ability to calculate the model-implied
covariance (and correlation) matrix and the sampling covariance matrix of
variable functions using the delta method.")
    (license license:gpl2+)))

(define-public r-symptomcheckr
  (package
    (name "r-symptomcheckr")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "symptomcheckR" version))
       (sha256
        (base32 "0mhcn5df3nlli55wv50mmc5gk9yjkgazvagivwk0k033f0k73cjl"))))
    (properties `((upstream-name . "symptomcheckR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-irr r-ggpubr r-ggplot2 r-dplyr))
    (home-page "https://github.com/ma-kopka/symptomcheckR")
    (synopsis "Analyzing and Visualizing Symptom Checker Performance")
    (description
     "Easily analyze and visualize the performance of symptom checkers.  This package
can be used to gain comprehensive insights into the performance of single
symptom checkers or the performance of multiple symptom checkers.  It can be
used to easily compare these symptom checkers across several metrics to gain an
understanding of their strengths and weaknesses.  The metrics are developed in
Kopka et al. (2023) <doi:10.1177/20552076231194929>.")
    (license license:gpl3)))

(define-public r-symphony
  (package
    (name "r-symphony")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "symphony" version))
       (sha256
        (base32 "0bgdhah202sh5mjbzqzk14vkl6xwdxxhri0ik03xh654b4s8hscc"))))
    (properties `((upstream-name . "symphony")))
    (build-system r-build-system)
    (propagated-inputs (list r-uwot
                             r-tidyr
                             r-tibble
                             r-rlang
                             r-rcpparmadillo
                             r-rcpp
                             r-rcolorbrewer
                             r-rann
                             r-purrr
                             r-matrix
                             r-magrittr
                             r-irlba
                             r-harmony
                             r-ggplot2
                             r-dplyr
                             r-data-table
                             r-class))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=symphony")
    (synopsis "Efficient and Precise Single-Cell Reference Atlas Mapping")
    (description
     "This package implements the Symphony single-cell reference building and query
mapping algorithms and additional functions described in Kang et al
<https://www.nature.com/articles/s41467-021-25957-x>.")
    (license license:gpl3+)))

(define-public r-symmoments
  (package
    (name "r-symmoments")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "symmoments" version))
       (sha256
        (base32 "0wl68nsvydfhlbyaj54alj31dphqigyp1s4h2xdapxj4zvwf2sws"))))
    (properties `((upstream-name . "symmoments")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-multipol r-cubature r-combinat))
    (home-page "https://cran.r-project.org/package=symmoments")
    (synopsis
     "Symbolic Central and Noncentral Moments of the Multivariate Normal Distribution")
    (description
     "Symbolic central and non-central moments of the multivariate normal
distribution.  Computes a standard representation, @code{LateX} code, and values
at specified mean and covariance matrices.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-symmetry
  (package
    (name "r-symmetry")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "symmetry" version))
       (sha256
        (base32 "01sk0v6kh9qi3phl51wk83c5v413ab23qmcqjd3hl2x4hn2rv2z6"))))
    (properties `((upstream-name . "symmetry")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=symmetry")
    (synopsis "Testing for Symmetry of Data and Model Residuals")
    (description
     "Implementations of a large number of tests for symmetry and their bootstrap
variants, which can be used for testing the symmetry of random samples around a
known or unknown mean.  Functions are also there for testing the symmetry of
model residuals around zero.  Currently, the supported models are linear models
and generalized autoregressive conditional heteroskedasticity (GARCH) models
(fitted with the @code{fGarch} package).  All tests are implemented using the
Rcpp package which ensures great performance of the code.")
    (license license:expat)))

(define-public r-symengine
  (package
    (name "r-symengine")
    (version "0.2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "symengine" version))
       (sha256
        (base32 "1vwgd3bclsc93s1688xnf65f1hy2l7qn3g25rqsyqshdd5w2hzw9"))))
    (properties `((upstream-name . "symengine")))
    (build-system r-build-system)
    (inputs (list mpfr gmp cmake))
    (propagated-inputs (list r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/symengine/symengine.R")
    (synopsis "Interface to the 'SymEngine' Library")
    (description
     "This package provides an R interface to @code{SymEngine}
<https://github.com/symengine/>, a standalone C++ library for fast symbolic
manipulation.  The package has functionalities for symbolic computation like
calculating exact mathematical expressions, solving systems of linear equations
and code generation.")
    (license license:gpl2+)))

(define-public r-symdmatrix
  (package
    (name "r-symdmatrix")
    (version "2.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "symDMatrix" version))
       (sha256
        (base32 "0xmpw37qkbadqp1idv8qmazdmmv0sd4fwgihg1b97hxzqhsxw0fh"))))
    (properties `((upstream-name . "symDMatrix")))
    (build-system r-build-system)
    (propagated-inputs (list r-linkedmatrix r-ff r-bit))
    (home-page "https://github.com/QuantGen/symDMatrix")
    (synopsis "Partitioned Symmetric Matrices")
    (description
     "This package provides a matrix-like class to represent a symmetric matrix
partitioned into file-backed blocks.")
    (license license:expat)))

(define-public r-symbolicqspray
  (package
    (name "r-symbolicqspray")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "symbolicQspray" version))
       (sha256
        (base32 "0l7lhpxnbbfgwypyqy3sy13vcjv63fv707i5d4r9gh2almk373s7"))))
    (properties `((upstream-name . "symbolicQspray")))
    (build-system r-build-system)
    (inputs (list mpfr gmp))
    (propagated-inputs (list r-rcppcgal
                             r-rcpp
                             r-ratioofqsprays
                             r-qspray
                             r-gmp
                             r-bh))
    (native-inputs (list pkg-config))
    (home-page "https://github.com/stla/symbolicQspray")
    (synopsis
     "Multivariate Polynomials with Symbolic Parameters in their Coefficients")
    (description
     "Introduces the @code{symbolicQspray} objects.  Such an object represents a
multivariate polynomial whose coefficients are fractions of multivariate
polynomials with rational coefficients.  The package allows arithmetic on such
polynomials.  It is based on the qspray and @code{ratioOfQsprays} packages.
Some functions for qspray polynomials have their counterpart for
@code{symbolicQspray} polynomials.  A @code{symbolicQspray} polynomial should
not be seen as a polynomial on the field of fractions of rational polynomials,
but should rather be seen as a polynomial with rational coefficients depending
on some parameters, symbolically represented, with a dependence given by
fractions of rational polynomials.")
    (license license:gpl3)))

(define-public r-symbolicdeterminants
  (package
    (name "r-symbolicdeterminants")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SymbolicDeterminants" version))
       (sha256
        (base32 "0hf9b9g2vgpyqbxgi5rkkls2b8vw1zrwl8lymwvwb6y1vwfl9pym"))))
    (properties `((upstream-name . "SymbolicDeterminants")))
    (build-system r-build-system)
    (inputs (list gmp))
    (propagated-inputs (list r-fs r-arrangements))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SymbolicDeterminants")
    (synopsis "Symbolic Representation of Matrix Determinant")
    (description
     "This package creates a numeric guide for writing the formula for the determinant
of a square matrix (a detguide) as a function of the elements of the matrix and
writes out that formula, the symbolic representation.")
    (license license:expat)))

(define-public r-symbolicda
  (package
    (name "r-symbolicda")
    (version "0.7-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "symbolicDA" version))
       (sha256
        (base32 "1x1qwrf587lgp9ciakrhiy3wj4g90x4g7r784rr2b97g1xwysjff"))))
    (properties `((upstream-name . "symbolicDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml
                             r-shapes
                             r-rsda
                             r-e1071
                             r-clustersim
                             r-cluster
                             r-ade4))
    (home-page "http://keii.ue.wroc.pl/symbolicDA/")
    (synopsis "Analysis of Symbolic Data")
    (description
     "Symbolic data analysis methods: importing/exporting data from ASSO XML Files,
distance calculation for symbolic data (Ichino-Yaguchi, de Carvalho measure),
zoom star plot, 3d interval plot, multidimensional scaling for symbolic interval
data, dynamic clustering based on distance matrix, HI@code{NoV} method for
symbolic data, Ichino's feature selection method, principal component analysis
for symbolic interval data, decision trees for symbolic data based on optimal
split with bagging, boosting and random forest approach (+visualization), kernel
discriminant analysis for symbolic data, Kohonen's self-organizing maps for
symbolic data, replication and profiling, artificial symbolic data generation.
(Milligan, G.W., Cooper, M.C. (1985) <doi:10.1007/BF02294245>, Breiman, L.
(1996), <doi:10.1007/BF00058655>, Hubert, L., Arabie, P. (1985),
<doi:10.1007%2FBF01908075>, Ichino, M., & Yaguchi, H. (1994),
<doi:10.1109/21.286391>, Rand, W.M. (1971) <doi:10.1080/01621459.1971.10482356>,
Calinski, T., Harabasz, J. (1974) <doi:10.1080/03610927408827101>, Breckenridge,
J.N. (2000) <doi:10.1207/S15327906MBR3502_5>, Groenen, P.J.F, Winsberg, S.,
Rodriguez, O., Diday, E. (2006) <doi:10.1016/j.csda.2006.04.003>, Walesiak, M.,
Dudek, A. (2008) <doi:10.1007/978-3-540-78246-9_11>, Dudek, A. (2007),
<doi:10.1007/978-3-540-70981-7_4>).")
    (license license:gpl2+)))

(define-public r-symbol-equation-gpt
  (package
    (name "r-symbol-equation-gpt")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "symbol.equation.gpt" version))
       (sha256
        (base32 "0h1301406spbl7yz0i0b1vqqrf5zd0yxvxx3ljsi5pj4mdhp5j32"))))
    (properties `((upstream-name . "symbol.equation.gpt")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinystoreplus
                             r-shiny
                             r-rstudioapi
                             r-r2symbols
                             r-nextgenshinyapps
                             r-markdown
                             r-htmltools))
    (native-inputs (list r-knitr))
    (home-page "https://symbols-ui.obi.obianom.com")
    (synopsis "Simple User Interface to Build Equations and Add Symbols")
    (description
     "Powerful user interface for adding symbols, smileys, arrows, building
mathematical equations using @code{LaTeX} or r2symbols'.  Built for use in
development of Markdown and Shiny Outputs.")
    (license license:expat)))

(define-public r-sym-arma
  (package
    (name "r-sym-arma")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sym.arma" version))
       (sha256
        (base32 "13fhmgzd50mwlnz9m6nnn4arr4jdqf5h7dgli5lk08da9wx6cy69"))))
    (properties `((upstream-name . "sym.arma")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sym.arma")
    (synopsis "Autoregressive and Moving Average Symmetric Models")
    (description
     "This package provides functions for fitting the Autoregressive and Moving
Average Symmetric Model for univariate time series introduced by Maior and
Cysneiros (2018), <doi:10.1007/s00362-016-0753-z>.  Fitting method: conditional
maximum likelihood estimation.  For details see: Wei (2006), Time Series
Analysis: Univariate and Multivariate Methods, Section 7.2.")
    (license license:gpl2)))

(define-public r-sylly-en
  (package
    (name "r-sylly-en")
    (version "0.1-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sylly.en" version))
       (sha256
        (base32 "0jw48qin172vv7skcpvcpi9wzgav5l9afzb09hhgcxvivbz6dl93"))))
    (properties `((upstream-name . "sylly.en")))
    (build-system r-build-system)
    (propagated-inputs (list r-sylly))
    (home-page "http://reaktanz.de/?c=hacking&s=koRpus")
    (synopsis "Language Support for 'sylly' Package: English")
    (description
     "Adds support for the English language to the sylly package.  Due to some
restrictions on CRAN, the full package sources are only available from the
project homepage.  To ask for help, report bugs, suggest feature improvements,
or discuss the global development of the package, please consider subscribing to
the @code{koRpus-dev} mailing list (<http://korpusml.reaktanz.de>).")
    (license license:gpl3+)))

(define-public r-sylly
  (package
    (name "r-sylly")
    (version "0.1-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sylly" version))
       (sha256
        (base32 "0f03k9d15fkym14y94jb7rkh228blc76jlm7pk229b44hcbh3mmm"))))
    (properties `((upstream-name . "sylly")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://reaktanz.de/?c=hacking&s=sylly")
    (synopsis "Hyphenation and Syllable Counting for Text Analysis")
    (description
     "This package provides the hyphenation algorithm used for @code{TeX'/'LaTeX} and
similar software, as proposed by Liang (1983, <https://tug.org/docs/liang/>).
Mainly contains the function hyphen() to be used for hyphenation/syllable
counting of text objects.  It was originally developed for and part of the
@code{koRpus} package, but later released as a separate package so it's lighter
to have this particular functionality available for other packages.  Support for
various languages needs be added on-the-fly or by plugin packages
(<https://undocumeantit.github.io/repos/>); this package does not include any
language specific data.  Due to some restrictions on CRAN, the full package
sources are only available from the project homepage.  To ask for help, report
bugs, request features, or discuss the development of the package, please
subscribe to the @code{koRpus-dev} mailing list (<http://korpusml.reaktanz.de>).")
    (license license:gpl3+)))

(define-public r-syllogi
  (package
    (name "r-syllogi")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "syllogi" version))
       (sha256
        (base32 "1rdgkc4vncx74awznjkfldma5n7jbv2cd7sapyl064vlh0hhrlqf"))))
    (properties `((upstream-name . "syllogi")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=syllogi")
    (synopsis "Collection of Data Sets for Teaching Purposes")
    (description
     "Collection (syllogi in greek) of real and fictitious data sets for teaching
purposes.  The datasets were manually entered by the author from the respective
references as listed in the individual dataset documentation.  The fictions
datasets are the creation of the author, that he has found useful for teaching
statistics.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-syllabifyr
  (package
    (name "r-syllabifyr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "syllabifyr" version))
       (sha256
        (base32 "1z61afxx2bm37hxvv1fl3x7ibhkp3h1ivl3k9prqzsaw4j592vfx"))))
    (properties `((upstream-name . "syllabifyr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-tibble r-stringr r-purrr r-dplyr))
    (home-page "https://cran.r-project.org/package=syllabifyr")
    (synopsis "Syllabifier for CMU Dictionary Transcriptions")
    (description
     "This package implements tidy syllabification of transcription.  Based on
@@kylebgorman's python implementation
<https://github.com/kylebgorman/syllabify>.")
    (license license:expat)))

(define-public r-sylcount
  (package
    (name "r-sylcount")
    (version "0.2-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sylcount" version))
       (sha256
        (base32 "046d76qwiszpkwiljnigbnj8cvfp66b0a8c50g9aww8a8p44mb87"))))
    (properties `((upstream-name . "sylcount")))
    (build-system r-build-system)
    (home-page "https://github.com/wrathematics/sylcount")
    (synopsis "Syllable Counting and Readability Measurements")
    (description
     "An English language syllable counter, plus readability score measure-er.  For
readability, we support Flesch Reading Ease and Flesch-Kincaid Grade Level
('Kincaid et al'.  1975)
<https://stars.library.ucf.edu/cgi/viewcontent.cgi?article=1055&context=istlibrary>,
Automated Readability Index ('Senter and Smith 1967)
<https://apps.dtic.mil/sti/citations/AD0667273>, Simple Measure of Gobbledygook
(@code{McLaughlin} 1969), and Coleman-Liau (Coleman and Liau 1975)
<doi:10.1037/h0076540>.  The package has been carefully optimized and should be
very efficient, both in terms of run time performance and memory consumption.
The main methods are vectorized by document, and scores for multiple documents
are computed in parallel via @code{OpenMP}'.")
    (license (license:fsdg-compatible "BSD 2-clause License + file LICENSE"))))

(define-public r-swtools
  (package
    (name "r-swtools")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SWTools" version))
       (sha256
        (base32 "10d9wgw2h1kykl46apaymfrriypf8xpixvk6za427xgya7krkz6l"))))
    (properties `((upstream-name . "SWTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-sf
                             r-segmented
                             r-rmarkdown
                             r-rlang
                             r-readr
                             r-prettymapr
                             r-magrittr
                             r-lubridate
                             r-jsonlite
                             r-hydrotsm
                             r-httr
                             r-ggspatial
                             r-ggpubr
                             r-ggplot2
                             r-forcats
                             r-dplyr))
    (home-page "https://github.com/matt-s-gibbs/SWTools")
    (synopsis "Helper Tools for Australian Hydrologists")
    (description
     "This package provides functions to speed up work flow for hydrological analysis.
 Focused on Australian climate data (SILO climate data), hydrological models
(@code{eWater} Source) and in particular South Australia
(<https://water.data.sa.gov.au> hydrological data).")
    (license license:gpl3)))

(define-public r-swmprextension
  (package
    (name "r-swmprextension")
    (version "2.2.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SWMPrExtension" version))
       (sha256
        (base32 "107v5fh3h03dk9ma67hj8988hfmswrl9lfijhjv5b4ww73n2v6x7"))))
    (properties `((upstream-name . "SWMPrExtension")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-swmpr
                             r-sf
                             r-scales
                             r-rlang
                             r-rcolorbrewer
                             r-purrr
                             r-officer
                             r-magrittr
                             r-lubridate
                             r-ggthemes
                             r-ggplot2
                             r-ggimage
                             r-flextable
                             r-envstats
                             r-dplyr
                             r-curl
                             r-broom))
    (home-page "https://cran.r-project.org/package=SWMPrExtension")
    (synopsis "Functions for Analyzing and Plotting Estuary Monitoring Data")
    (description
     "This package provides tools for performing routine analysis and plotting tasks
with environmental data from the System Wide Monitoring Program of the National
Estuarine Research Reserve System <https://cdmo.baruch.sc.edu/>.  This package
builds on the functionality of the SWMPr package
<https://cran.r-project.org/package=SWMPr>, which is used to retrieve and
organize the data.  The combined set of tools address common challenges
associated with continuous time series data for environmental decision making,
and are intended for use in annual reporting activities.  References: Beck,
Marcus W. (2016) <ISSN
2073-4859><https://journal.r-project.org/archive/2016-1/beck.pdf> Rudis, Bob
(2014)
<https://rud.is/b/2014/11/16/moving-the-earth-well-alaska-hawaii-with-r/>.
United States Environmental Protection Agency (2015)
<https://cfpub.epa.gov/si/si_public_record_Report.cfm?Lab=OWOW&@code{dirEntryId=327030>}.")
    (license license:cc0)))

(define-public r-swmpr
  (package
    (name "r-swmpr")
    (version "2.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SWMPr" version))
       (sha256
        (base32 "15193hv4hvr46l42qvpi5pd187bjjcmrs7akm13vm3nr6nlvzpkx"))))
    (properties `((upstream-name . "SWMPr")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xml
                             r-tidyr
                             r-tictoc
                             r-suncalc
                             r-reshape2
                             r-rcolorbrewer
                             r-openair
                             r-oce
                             r-lattice
                             r-httr
                             r-gridextra
                             r-ggplot2
                             r-dplyr
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "http://fawda123.github.io/SWMPr/")
    (synopsis "Retrieving, Organizing, and Analyzing Estuary Monitoring Data")
    (description
     "This package provides tools for retrieving, organizing, and analyzing
environmental data from the System Wide Monitoring Program of the National
Estuarine Research Reserve System <https://cdmo.baruch.sc.edu/>.  These tools
address common challenges associated with continuous time series data for
environmental decision making.")
    (license license:cc0)))

(define-public r-switchselection
  (package
    (name "r-switchselection")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "switchSelection" version))
       (sha256
        (base32 "0lgicakwcmvjhvy1sqvv4rasxyyx2s1gadxnz81s7ii1865lbryk"))))
    (properties `((upstream-name . "switchSelection")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mnorm r-hpa r-gena))
    (home-page "https://cran.r-project.org/package=switchSelection")
    (synopsis "Endogenous Switching and Sample Selection Regression Models")
    (description
     "Estimate the parameters of multivariate endogenous switching and sample
selection models using methods described in Newey (2009)
<doi:10.1111/j.1368-423X.2008.00263.x>, E. Kossova, B. Potanin (2018)
<https://ideas.repec.org/a/ris/apltrx/0346.html>, E. Kossova, L. Kupriianova, B.
Potanin (2020) <https://ideas.repec.org/a/ris/apltrx/0391.html> and E. Kossova,
B. Potanin (2022) <https://ideas.repec.org/a/ris/apltrx/0455.html>.")
    (license license:gpl2+)))

(define-public r-switchr
  (package
    (name "r-switchr")
    (version "0.14.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "switchr" version))
       (sha256
        (base32 "1q1rnwm0g5kk5z1szb0f9bvaqgpdnj22lvfnychb7jdhriwmn1zw"))))
    (properties `((upstream-name . "switchr")))
    (build-system r-build-system)
    (inputs (list subversion git))
    (propagated-inputs (list r-rjsonio r-rcurl))
    (home-page "https://github.com/gmbecker/switchr")
    (synopsis
     "Installing, Managing, and Switching Between Distinct Sets of Installed Packages")
    (description
     "This package provides an abstraction for managing, installing, and switching
between sets of installed R packages.  This allows users to maintain multiple
package libraries simultaneously, e.g. to maintain strict,
package-version-specific reproducibility of many analyses, or work within a
development/production release paradigm.  Introduces a generalized package
installation process which supports multiple repository and non-repository
sources and tracks package provenance.")
    (license license:artistic2.0)))

(define-public r-switchcase
  (package
    (name "r-switchcase")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "switchcase" version))
       (sha256
        (base32 "0h31nzyf7dzalgl65cw0k1fxylbmx78l45r5id2wcqdym23dv9a9"))))
    (properties `((upstream-name . "switchcase")))
    (build-system r-build-system)
    (home-page "https://github.com/jsugarelli/switchcase/")
    (synopsis "Simple and Flexible Switch-Case Construct for the 'R' Language")
    (description
     "This package provides a switch-case construct for R', as it is known from other
programming languages.  It allows to test multiple, similar conditions in an
efficient, easy-to-read manner, so nested if-else constructs can be avoided.
The switch-case construct is designed as an R function that allows to return
values depending on which condition is met and lets the programmer flexibly
decide whether or not to leave the switch-case construct after a case block has
been executed.")
    (license license:gpl3)))

(define-public r-switchboard
  (package
    (name "r-switchboard")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "switchboard" version))
       (sha256
        (base32 "1qh6cfm5h0j17ldljpgj400f2dhcfaa4x1157d46cyfawfdscvc5"))))
    (properties `((upstream-name . "switchboard")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=switchboard")
    (synopsis "An Agile Widget Engine for Real-Time, Dynamic Visualizations")
    (description
     "An unsorted collection of visualization widgets rendered in
Tcl/Tk'<https://www.tcl.tk/> to generate agile dashboards for your iterative
simulations.  Widgets include progress bars, counters, eavesdroppers, injectors,
switches, and sliders for dynamic manipulation and visualization of simulation
parameters.")
    (license license:gpl2+)))

(define-public r-swissparl
  (package
    (name "r-swissparl")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "swissparl" version))
       (sha256
        (base32 "0rj01qcg0vh3vwrbag42w7mvb9ld800hzn4lszmbiv13yifgi8ys"))))
    (properties `((upstream-name . "swissparl")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-purrr
                             r-magrittr
                             r-jsonlite
                             r-httr
                             r-ggplot2
                             r-dplyr
                             r-crayon))
    (home-page "https://www.parlament.ch/en/services/open-data-webservices")
    (synopsis "Interface to the Webservices of the Swiss Parliament")
    (description
     "Retrieves the most important data on parliamentary activities of the Swiss
Federal Assembly via an open, machine-readable interface (see
<https://ws.parlament.ch/odata.svc/>).")
    (license license:gpl2+)))

(define-public r-swissair
  (package
    (name "r-swissair")
    (version "1.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SwissAir" version))
       (sha256
        (base32 "03fj00py8h4pb10br2pnaxh00dcfpcrh1yyld3if7cv0dwc76xvc"))))
    (properties `((upstream-name . "SwissAir")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SwissAir")
    (synopsis
     "Air Quality Data of Switzerland for One Year in 30 Min Resolution")
    (description
     "Ozone, NOx (= Sum of nitrogen monoxide and nitrogen dioxide), nitrogen monoxide,
ambient temperature, dew point, wind speed and wind direction at 3 sites around
lake of Lucerne in Central Switzerland in 30 min time resolution for year 2004.")
    (license license:gpl3+)))

(define-public r-swirlify
  (package
    (name "r-swirlify")
    (version "0.5.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "swirlify" version))
       (sha256
        (base32 "1bpg6yz2adgspjlwsf2q83j1bq0iyvp9938smgc41861nsclngwr"))))
    (properties `((upstream-name . "swirlify")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-whisker
                             r-swirl
                             r-stringr
                             r-shinyace
                             r-shiny
                             r-rmarkdown
                             r-readr
                             r-base64enc))
    (home-page "http://swirlstats.com")
    (synopsis "Toolbox for Writing 'swirl' Courses")
    (description
     "This package provides a set of tools for writing and sharing interactive courses
to be used with swirl.")
    (license license:expat)))

(define-public r-swirl
  (package
    (name "r-swirl")
    (version "2.4.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "swirl" version))
       (sha256
        (base32 "0cqyn2rnqfkb2wza4zdr1kcms5l0igbq79xh5i7765agdday2z9w"))))
    (properties `((upstream-name . "swirl")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-testthat
                             r-stringr
                             r-rcurl
                             r-httr
                             r-digest))
    (home-page "http://swirlstats.com")
    (synopsis "Learn R, in R")
    (description
     "Use the R console as an interactive learning environment.  Users receive
immediate feedback as they are guided through self-paced lessons in data science
and R programming.")
    (license license:expat)))

(define-public r-swiper
  (package
    (name "r-swiper")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "swipeR" version))
       (sha256
        (base32 "1wj878y15z9d6ijyv4pvff3vcdn78ybs4rqzhqdfrn47fa99zmdv"))))
    (properties `((upstream-name . "swipeR")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-rchoicedialogs r-htmlwidgets r-htmltools
                             r-base64enc))
    (native-inputs (list esbuild))
    (home-page "https://github.com/stla/swipeR")
    (synopsis "Carousels using the 'JavaScript' Library 'Swiper'")
    (description
     "Create carousels using the @code{JavaScript} library Swiper and the package
htmlwidgets'.  The carousels can be displayed in the RStudio viewer pane, in
Shiny applications and in R markdown documents.  The package also provides a
RStudio addin allowing to choose image files and to display them in the viewer
pane.")
    (license license:gpl3)))

(define-public r-swimplot
  (package
    (name "r-swimplot")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "swimplot" version))
       (sha256
        (base32 "02w1l67hvz8gha8gcqxcvcr8g2azidn6bvw76h3k75p9rvq8sgpp"))))
    (properties `((upstream-name . "swimplot")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-ggplot2 r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=swimplot")
    (synopsis "Tools for Creating Swimmers Plots using 'ggplot2'")
    (description
     "Used for creating swimmers plots with functions to customize the bars, add
points, add lines, add text, and add arrows.")
    (license license:gpl3)))

(define-public r-swimmer
  (package
    (name "r-swimmer")
    (version "0.14.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SwimmeR" version))
       (sha256
        (base32 "17ri50k88absi7ijj1w478aahl9hx1ajq16gk9807m9q83pq25yg"))))
    (properties `((upstream-name . "SwimmeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-stringr
                             r-rvest
                             r-readr
                             r-purrr
                             r-pdftools
                             r-magrittr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SwimmeR")
    (synopsis "Data Import, Cleaning, and Conversions for Swimming Results")
    (description
     "The goal of the @code{SwimmeR} package is to provide means of acquiring, and
then analyzing, data from swimming (and diving) competitions.  To that end
@code{SwimmeR} allows results to be read in from .html sources, like Hy-Tek real
time results pages, .pdf files, ISL results, Omega results, and (on a
development basis) .hy3 files.  Once read in, @code{SwimmeR} can convert
swimming times (performances) between the computationally useful format of
seconds reported to the 100ths place (e.g. 95.37), and the conventional
reporting format (1:35.37) used in the swimming community. @code{SwimmeR} can
also score meets in a variety of formats with user defined point values, convert
times between courses ('LCM', SCM', SCY') and draw single elimination brackets,
as well as providing a suite of tools for working cleaning swimming data.  This
is a developmental package, not yet mature.")
    (license license:expat)))

(define-public r-swim
  (package
    (name "r-swim")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SWIM" version))
       (sha256
        (base32 "1pfrwxn4rha7a68b82lq18x6acyhjy9d8nn2hdwd9hhf576vkvhg"))))
    (properties `((upstream-name . "SWIM")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2
                             r-rdpack
                             r-plyr
                             r-nleqslv
                             r-hmisc
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/spesenti/SWIM")
    (synopsis "Scenario Weights for Importance Measurement")
    (description
     "An efficient sensitivity analysis for stochastic models based on Monte Carlo
samples.  Provides weights on simulated scenarios from a stochastic model, such
that stressed random variables fulfil given probabilistic constraints (e.g.
specified values for risk measures), under the new scenario weights.  Scenario
weights are selected by constrained minimisation of the relative entropy to the
baseline model.  The SWIM package is based on Pesenti S.M., Millossovich P.,
Tsanakas A. (2019) \"Reverse Sensitivity Testing: What does it take to break the
model\" <openaccess.city.ac.uk/id/eprint/18896/> and Pesenti S.M. (2021) \"Reverse
Sensitivity Analysis for Risk Modelling\"
<https://www.ssrn.com/abstract=3878879>.")
    (license license:gpl3)))

(define-public r-swgee
  (package
    (name "r-swgee")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "swgee" version))
       (sha256
        (base32 "1a6zr4nkpj52b0rk01qbwzi8y0c798wlxzw69wyyv576k148rgfq"))))
    (properties `((upstream-name . "swgee")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-geepack r-gee))
    (home-page "https://cran.r-project.org/package=swgee")
    (synopsis
     "Simulation Extrapolation Inverse Probability Weighted Generalized Estimating Equations")
    (description
     "Simulation extrapolation and inverse probability weighted generalized estimating
equations method for longitudinal data with missing observations and measurement
error in covariates.  References: Yi, G. Y. (2008)
<doi:10.1093/biostatistics/kxm054>; Cook, J. R. and Stefanski, L. A. (1994)
<doi:10.1080/01621459.1994.10476871>; Little, R. J. A. and Rubin, D. B. (2002,
ISBN:978-0-471-18386-0).")
    (license license:gpl3)))

(define-public r-swfscmisc
  (package
    (name "r-swfscmisc")
    (version "1.6.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "swfscMisc" version))
       (sha256
        (base32 "1qc2d3ykzxqqnjdq93j9nvkb8gfkvyr2x1l657q7jh1wcj8b7754"))))
    (properties `((upstream-name . "swfscMisc")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-spatstat-geom
                             r-sf
                             r-runjags
                             r-rlang
                             r-modeest
                             r-magrittr
                             r-kknn
                             r-hdinterval
                             r-ggrepel
                             r-ggplot2
                             r-dplyr
                             r-abind))
    (home-page "https://github.com/EricArcher/swfscMisc")
    (synopsis "Miscellaneous Functions for Southwest Fisheries Science Center")
    (description
     "Collection of conversion, analytical, geodesic, mapping, and plotting functions.
 Used to support packages and code written by researchers at the Southwest
Fisheries Science Center of the National Oceanic and Atmospheric Administration.")
    (license license:gpl2+)))

(define-public r-swfscdas
  (package
    (name "r-swfscdas")
    (version "0.6.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "swfscDAS" version))
       (sha256
        (base32 "0xyhaabbcmb3hja6pylnmr2vwxqhsxa47ih8chdwcnrz5lqz9inw"))))
    (properties `((upstream-name . "swfscDAS")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-swfscmisc
                             r-sf
                             r-rlang
                             r-readr
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://swfsc.github.io/swfscDAS/")
    (synopsis "Processing DAS Data Files")
    (description
     "Process and summarize DAS data files.  These files are typically, but do not
have to be DAS
<https://swfsc-publications.fisheries.noaa.gov/publications/TM/SWFSC/NOAA-TM-NMFS-SWFSC-305.PDF>
data produced by the Southwest Fisheries Science Center (SWFSC) program
@code{WinCruz}'.  This package standardizes and streamlines basic DAS data
processing, and includes a PDF with the DAS data format requirements expected by
the package.")
    (license (license:fsdg-compatible "Apache License (== 2)"))))

(define-public r-swfscairdas
  (package
    (name "r-swfscairdas")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "swfscAirDAS" version))
       (sha256
        (base32 "0y52ishci4djp1izanzh6psrmnqrpcgvkq28vxs61gdgrgxqp5lk"))))
    (properties `((upstream-name . "swfscAirDAS")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-swfscmisc
                             r-swfscdas
                             r-stringr
                             r-rlang
                             r-readr
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://swfsc.github.io/swfscAirDAS/")
    (synopsis "Southwest Fisheries Science Center Aerial DAS Data Processing")
    (description
     "Process and summarize aerial survey DAS data (@code{AirDAS})
<https://swfsc-publications.fisheries.noaa.gov/publications/TM/SWFSC/NOAA-TM-NMFS-SWFSC-185.PDF>
collected using an aerial survey program from the Southwest Fisheries Science
Center (SWFSC)
<https://www.fisheries.noaa.gov/west-coast/science-data/california-current-marine-mammal-assessment-program>.
 PDF files detailing the relevant @code{AirDAS} data formats are included in
this package.")
    (license (license:fsdg-compatible "Apache License (== 2)"))))

(define-public r-swephr
  (package
    (name "r-swephr")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "swephR" version))
       (sha256
        (base32 "10ra6m8arjpgypj15diailnrkgrvkxl3v0xji7v29ci7n03xa46r"))))
    (properties `((upstream-name . "swephR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rstub/swephR/")
    (synopsis "High Precision Swiss Ephemeris")
    (description
     "The Swiss Ephemeris (version 2.10.03) is a high precision ephemeris based upon
the DE431 ephemerides from NASA's JPL. It covers the time range 13201 BCE to
17191 CE. This package uses the semi-analytic theory by Steve Moshier.  For
faster and more accurate calculations, the compressed Swiss Ephemeris data is
available in the @code{swephRdata} package.  To access this data package, run
install.packages(\"@code{swephRdata}\", repos = \"https://rstub.r-universe.dev\",
type = \"source\")'.  The size of the @code{swephRdata} package is approximately
115 MB. The user can also use the original JPL DE431 data.")
    (license (license:fsdg-compatible "AGPL"))))

(define-public r-sweidnumbr
  (package
    (name "r-sweidnumbr")
    (version "1.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sweidnumbr" version))
       (sha256
        (base32 "0w7q69whpwwi1d75jyglgc2qxvgg9v7xgs7jxgmqzl23z87nsjqp"))))
    (properties `((upstream-name . "sweidnumbr")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-lubridate r-checkmate))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/rOpenGov/sweidnumbr")
    (synopsis "Handling of Swedish Identity Numbers")
    (description
     "Structural handling of identity numbers used in the Swedish administration such
as personal identity numbers ('personnummer') and organizational identity
numbers ('organisationsnummer').")
    (license license:bsd-2)))

(define-public r-sweepdiscovery
  (package
    (name "r-sweepdiscovery")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SweepDiscovery" version))
       (sha256
        (base32 "1302bhkbgb898jjjpmn4gvfj3qid54bbk24d41ssz55cbyaqsimm"))))
    (properties `((upstream-name . "SweepDiscovery")))
    (build-system r-build-system)
    (propagated-inputs (list r-randomforest))
    (home-page "https://cran.r-project.org/package=SweepDiscovery")
    (synopsis "Selective Sweep Discovery Tool")
    (description
     "Selective sweep is a biological phenomenon in which genetic variation between
neighboring beneficial mutant alleles is swept away due to the effect of genetic
hitchhiking.  Detection of selective sweep is not well acquainted as well as it
is a laborious job.  This package is a user friendly approach for detecting
selective sweep in genomic regions.  It uses a Random Forest based machine
learning approach to predict selective sweep from VCF files as an input.  Input
of this function, train data and new data, can be computed using the project
<https://github.com/@code{AbhikSarkar1999/SweepDiscovery>} in @code{GitHub}'.
This package has been developed by using the concept of Pavlidis and Alachiotis
(2017) <doi:10.1186/s40709-017-0064-0>.")
    (license license:gpl3)))

(define-public r-sweep
  (package
    (name "r-sweep")
    (version "0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sweep" version))
       (sha256
        (base32 "1h9skkcpygj6y8hw3zawcdhvhjyqrr41fqn8jyf9wyvmhz6n84fb"))))
    (properties `((upstream-name . "sweep")))
    (build-system r-build-system)
    (propagated-inputs (list r-timetk
                             r-tidyverse
                             r-tidyr
                             r-tidyquant
                             r-tibble
                             r-rlang
                             r-lubridate
                             r-forecast
                             r-dplyr
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/business-science/sweep")
    (synopsis "Tidy Tools for Forecasting")
    (description
     "Tidies up the forecasting modeling and prediction work flow, extends the broom
package with sw_tidy', sw_glance', sw_augment', and sw_tidy_decomp functions for
various forecasting models, and enables converting forecast objects to \"tidy\"
data frames with sw_sweep'.")
    (license license:gpl3+)))

(define-public r-sweater
  (package
    (name "r-sweater")
    (version "0.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sweater" version))
       (sha256
        (base32 "045cyhdsnrhkcpgpgv7s209wn2pa9plj7pnlmmnfiz7dy67ikkr3"))))
    (properties `((upstream-name . "sweater")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp
                             r-quanteda
                             r-purrr
                             r-proxy
                             r-liblinear
                             r-data-table
                             r-combinat
                             r-cli))
    (home-page "https://github.com/gesistsa/sweater")
    (synopsis "Speedy Word Embedding Association Test and Extras Using R")
    (description
     "Conduct various tests for evaluating implicit biases in word embeddings: Word
Embedding Association Test (Caliskan et al., 2017),
<doi:10.1126/science.aal4230>, Relative Norm Distance (Garg et al., 2018),
<doi:10.1073/pnas.1720347115>, Mean Average Cosine Similarity (Mazini et al.,
2019) <@code{arXiv:1904.04047>}, @code{SemAxis} (An et al., 2018)
<@code{arXiv:1806.05521>}, Relative Negative Sentiment Bias (Sweeney & Najafian,
2019) <doi:10.18653/v1/P19-1162>, and Embedding Coherence Test (Dev & Phillips,
2019) <@code{arXiv:1901.07656>}.")
    (license license:gpl3+)))

(define-public r-swdpwr
  (package
    (name "r-swdpwr")
    (version "1.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "swdpwr" version))
       (sha256
        (base32 "1c1l0qmphd2xjm7v8fkvr354yn5blm7w8n8s3xg3vidnr9x39ysx"))))
    (properties `((upstream-name . "swdpwr")))
    (build-system r-build-system)
    (propagated-inputs (list r-spatstat-random))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=swdpwr")
    (synopsis "Power Calculation for Stepped Wedge Cluster Randomized Trials")
    (description
     "To meet the needs of statistical power calculation for stepped wedge cluster
randomized trials, we developed this software.  Different parameters can be
specified by users for different scenarios, including: cross-sectional and
cohort designs, binary and continuous outcomes, marginal (GEE) and conditional
models (mixed effects model), three link functions (identity, log, logit links),
with and without time effects (the default specification assumes no-time-effect)
under exchangeable, nested exchangeable and block exchangeable correlation
structures.  Unequal numbers of clusters per sequence are also allowed.  The
methods included in this package: Zhou et al. (2020)
<doi:10.1093/biostatistics/kxy031>, Li et al. (2018) <doi:10.1111/biom.12918>.
Supplementary documents can be found at:
<https://ysph.yale.edu/cmips/research/software/study-design-power-calculation/swdpwr/>.
 The Shiny app for swdpwr can be accessed at:
<https://jiachenchen322.shinyapps.io/swdpwr_shinyapp/>.  The package also
includes functions that perform calculations for the intra-cluster correlation
coefficients based on the random effects variances as input variables for
continuous and binary outcomes, respectively.")
    (license license:gpl3)))

(define-public r-swdft
  (package
    (name "r-swdft")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "swdft" version))
       (sha256
        (base32 "0529rw8nsl48hwiy741alvrzg5d5q1nhnjshrsfwchm5kdv0igva"))))
    (properties `((upstream-name . "swdft")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=swdft")
    (synopsis "Sliding Window Discrete Fourier Transform (SWDFT)")
    (description
     "This package implements the Sliding Window Discrete Fourier Transform (SWDFT).
Also provides statistical methods based on the SWDFT, and graphical tools to
display the outputs.")
    (license license:expat)))

(define-public r-swcrtdesign
  (package
    (name "r-swcrtdesign")
    (version "4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "swCRTdesign" version))
       (sha256
        (base32 "0q0f6f2fhjgqwvlhs14x6vz1fjq4iadx2g7cypifzpr1pq474nwf"))))
    (properties `((upstream-name . "swCRTdesign")))
    (build-system r-build-system)
    (propagated-inputs (list r-lmertest r-lme4))
    (home-page "https://cran.r-project.org/package=swCRTdesign")
    (synopsis "Stepped Wedge Cluster Randomized Trial (SW CRT) Design")
    (description
     "This package provides a set of tools for examining the design and analysis
aspects of stepped wedge cluster randomized trials (SW CRT) based on a repeated
cross-sectional sampling scheme (Hussey MA and Hughes JP (2007) Contemporary
Clinical Trials 28:182-191. <doi:10.1016/j.cct.2006.05.007>).")
    (license license:gpl2)))

(define-public r-swatches
  (package
    (name "r-swatches")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "swatches" version))
       (sha256
        (base32 "1kivscgka8gn44rjj0s5sjan1s04znnyn0nd37a74zaik0hm8zs0"))))
    (properties `((upstream-name . "swatches")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-stringr r-pack r-httr r-colorspace))
    (home-page "https://github.com/hrbrmstr/swatches")
    (synopsis "Read, Inspect, and Manipulate Color Swatch Files")
    (description
     "There are numerous places to create and download color palettes.  These are
usually shared in Adobe swatch file formats of some kind.  There is also often
the need to use standard palettes developed within an organization to ensure
that aesthetics are carried over into all projects and output.  Now there is a
way to read these swatch files in R and avoid transcribing or converting color
values by hand or or with other programs.  This package provides functions to
read and inspect Adobe Color ('ACO'), Adobe Swatch Exchange ('ASE'), GIMP
Palette ('GPL'), @code{OpenOffice} palette ('SOC') files and KDE Palette
('colors') files.  Detailed descriptions of Adobe Color and Swatch Exchange file
formats as well as other swatch file formats can be found at
<http://www.selapa.net/swatches/colors/fileformats.php>.")
    (license license:expat)))

(define-public r-swarmverse
  (package
    (name "r-swarmverse")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "swaRmverse" version))
       (sha256
        (base32 "1c5mwncc4ms651gcil2yskm1fzkcj9na0y25k3q9bp56cf0vvmbx"))))
    (properties `((upstream-name . "swaRmverse")))
    (build-system r-build-system)
    (propagated-inputs (list r-trackdf r-swarm r-rtsne r-pbapply r-geosphere))
    (native-inputs (list r-knitr))
    (home-page "https://marinapapa.github.io/swaRmverse/")
    (synopsis "Swarm Space Creation")
    (description
     "This package provides a pipeline for the comparative analysis of collective
movement data (e.g. fish schools, bird flocks, baboon troops) by processing
2-dimensional positional data (x,y,t) from GPS trackers or computer vision
tracking systems, discretizing events of collective motion, calculating a set of
established metrics that characterize each event, and placing the events in a
multi-dimensional swarm space constructed from these metrics.  The swarm space
concept, the metrics and data sets included are described in: Papadopoulou
Marina, Furtbauer Ines, O'Bryan Lisa R., Garnier Simon, Georgopoulou Dimitra G.,
Bracken Anna M., Christensen Charlotte and King Andrew J. (2023)
<doi:10.1098/rstb.2022.0068>.")
    (license license:gpl3)))

(define-public r-swarm
  (package
    (name "r-swarm")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "swaRm" version))
       (sha256
        (base32 "0p29dyig4zyrjp5gbm17q4s4g2jxl2idq6cqp6zyhsr0n197d38h"))))
    (properties `((upstream-name . "swaRm")))
    (build-system r-build-system)
    (propagated-inputs (list r-splancs r-mass r-lubridate r-geosphere))
    (native-inputs (list r-knitr))
    (home-page "https://swarm-lab.github.io/swaRm/")
    (synopsis "Processing Collective Movement Data")
    (description
     "Function library for processing collective movement data (e.g. fish schools,
ungulate herds, baboon troops) collected from GPS trackers or computer vision
tracking software.")
    (license license:gpl3)))

(define-public r-swaprinc
  (package
    (name "r-swaprinc")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "swaprinc" version))
       (sha256
        (base32 "0r90w387chh4xgqn7jfi2wawsgr8faansp94hrp4vkhdmbbsb86z"))))
    (properties `((upstream-name . "swaprinc")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-rlang
                             r-magrittr
                             r-lme4
                             r-gifi
                             r-dplyr
                             r-broom-mixed
                             r-broom))
    (home-page "https://github.com/mncube/swaprinc")
    (synopsis "Swap Principal Components into Regression Models")
    (description
     "Obtaining accurate and stable estimates of regression coefficients can be
challenging when the suggested statistical model has issues related to
multicollinearity, convergence, or overfitting.  One solution is to use
principal component analysis (PCA) results in the regression, as discussed in
Chan and Park (2005) <doi:10.1080/01446190500039812>.  The swaprinc() package
streamlines comparisons between a raw regression model with the full set of raw
independent variables and a principal component regression model where principal
components are estimated on a subset of the independent variables, then swapped
into the regression model in place of those variables.  The swaprinc() function
compares one raw regression model to one principal component regression model,
while the compswap() function compares one raw regression model to many
principal component regression models.  Package functions include parameters to
center, scale, and undo centering and scaling, as described by Harvey and Hansen
(2022)
<https://cran.r-project.org/package=@code{LearnPCA/vignettes/Vig_03_Step_By_Step_PCA.pdf>}.
 Additionally, the package supports using Gifi methods to extract principal
components from categorical variables, as outlined by Rossiter (2021)
<https://www.css.cornell.edu/faculty/dgr2/_static/files/R_html/@code{NonlinearPCA.html#2_Package>}.")
    (license license:expat)))

(define-public r-swamp
  (package
    (name "r-swamp")
    (version "1.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "swamp" version))
       (sha256
        (base32 "1mj6cqrc19p07v051fg6ikhhbccsg2lvz7wjiakaswqqm0269843"))))
    (properties `((upstream-name . "swamp")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-impute r-gplots r-amap))
    (home-page "https://cran.r-project.org/package=swamp")
    (synopsis
     "Visualization, Analysis and Adjustment of High-Dimensional Data in Respect to Sample Annotations")
    (description
     "Collection of functions to connect the structure of the data with the
information on the samples.  Three types of associations are covered: 1.  linear
model of principal components.  2.  hierarchical clustering analysis.  3.
distribution of features-sample annotation associations.  Additionally, the
inter-relation between sample annotations can be analyzed.  Simple methods are
provided for the correction of batch effects and removal of principal
components.")
    (license license:gpl2+)))

(define-public r-swag
  (package
    (name "r-swag")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "swag" version))
       (sha256
        (base32 "1w6schbj5gw7278krjzkcwafz3wv7vp7cfsvy17c0i1b5g3qqkpd"))))
    (properties `((upstream-name . "swag")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/SMAC-Group/SWAG-R-Package/")
    (synopsis "Sparse Wrapper Algorithm")
    (description
     "An algorithm that trains a meta-learning procedure that combines screening and
wrapper methods to find a set of extremely low-dimensional attribute
combinations.  This package works on top of the caret package and proceeds in a
forward-step manner.  More specifically, it builds and tests learners starting
from very few attributes until it includes a maximal number of attributes by
increasing the number of attributes at each step.  Hence, for each fixed number
of attributes, the algorithm tests various (randomly selected) learners and
picks those with the best performance in terms of training error.  Throughout,
the algorithm uses the information coming from the best learners at the previous
step to build and test learners in the following step.  In the end, it outputs a
set of strong low-dimensional learners.")
    (license license:gpl2+)))

(define-public r-swa
  (package
    (name "r-swa")
    (version "0.8.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "swa" version))
       (sha256
        (base32 "1zy1a6c17vmdkmkwp8vv3niww2zs1m0hylsa0b2cz68vfhnmpim4"))))
    (properties `((upstream-name . "swa")))
    (build-system r-build-system)
    (propagated-inputs (list r-rocr r-reshape r-ggplot2))
    (home-page "https://cran.r-project.org/package=swa")
    (synopsis "Subsampling Winner Algorithm for Classification")
    (description
     "This algorithm conducts variable selection in the classification setting.  It
repeatedly subsamples variables and runs linear discriminant analysis (LDA) on
the subsampled variables.  Variables are scored based on the AUC and the
t-statistics.  Variables then enter a competition and the semi-finalist
variables will be evaluated in a final round of LDA classification.  The
algorithm then outputs a list of variable selected.  Qiao, Sun and Fan (2017)
<http://people.math.binghamton.edu/qiao/swa.html>.")
    (license license:gpl3)))

(define-public r-svyweight
  (package
    (name "r-svyweight")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svyweight" version))
       (sha256
        (base32 "1pg22gc8798dn9r8gphzlagryybb8gv6l18x650wq82mirnvgl6h"))))
    (properties `((upstream-name . "svyweight")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey r-gdata))
    (home-page "https://cran.r-project.org/package=svyweight")
    (synopsis "Quick and Flexible Survey Weighting")
    (description
     "Quickly and flexibly calculates weights for survey data, in order to correct for
survey non-response or other sampling issues.  Uses rake weighting, a common
technique also know as rim weighting or iterative proportional fitting.  This
technique allows for weighting on multiple variables, even when the interlocked
distribution of the two variables is not known.  Interacts with Thomas Lumley's
survey package, as described in Lumley, Thomas (2011, ISBN:978-1-118-21093-2).
Adds additional functionality, more adaptable syntax, and error-checking to the
base weighting functionality in survey.'.")
    (license license:gpl3)))

(define-public r-svyvgam
  (package
    (name "r-svyvgam")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svyVGAM" version))
       (sha256
        (base32 "01y372p52xjd7j20xk5gwxkl8sfnwdvixx3pb3f6r407ca7l330h"))))
    (properties `((upstream-name . "svyVGAM")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam r-survey))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=svyVGAM")
    (synopsis "Design-Based Inference in Vector Generalised Linear Models")
    (description
     "This package provides inference based on the survey package for the wide range
of parametric models in the VGAM package.")
    (license license:gpl3)))

(define-public r-svyvarsel
  (package
    (name "r-svyvarsel")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svyVarSel" version))
       (sha256
        (base32 "1sfgifyjnfmf8piwpzhgnl0vdrv6nj1raab2aj6ajrxr6va780kh"))))
    (properties `((upstream-name . "svyVarSel")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey r-glmnet))
    (home-page "https://cran.r-project.org/package=svyVarSel")
    (synopsis "Variable Selection for Complex Survey Data")
    (description
     "Fit design-based linear and logistic elastic nets with complex survey data
considering the sampling design when defining training and test sets using
replicate weights.  Methods implemented in this package are described in: A.
Iparragirre, T. Lumley, I. Barrio, I. Arostegui (2024) <doi:10.1002/sta4.578>.")
    (license license:gpl3+)))

(define-public r-svynom
  (package
    (name "r-svynom")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SvyNom" version))
       (sha256
        (base32 "0s9gn10v6a8n6898g0gmdrw51z7y98klji59sn06dfbsxlswlv11"))))
    (properties `((upstream-name . "SvyNom")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-survey r-rms r-hmisc))
    (home-page "https://github.com/MSKCC-Epi-Bio/SvyNom")
    (synopsis "Nomograms for Right-Censored Outcomes from Survey Designs")
    (description
     "Builds, evaluates and validates a nomogram with survey data and right-censored
outcomes.  As described in Capanu (2015) <doi:10.18637/jss.v064.c01>, the
package contains functions to create the nomogram, validate it using bootstrap,
as well as produce the calibration plots.")
    (license license:gpl2)))

(define-public r-svylme
  (package
    (name "r-svylme")
    (version "1.5-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svylme" version))
       (sha256
        (base32 "1bbg6ha0crg166856h65aly9l5x8y18wv39jymbaczqhs0ba5w1s"))))
    (properties `((upstream-name . "svylme")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey r-minqa r-matrix r-lme4))
    (home-page "https://cran.r-project.org/package=svylme")
    (synopsis "Linear Mixed Models for Complex Survey Data")
    (description
     "Linear mixed models for complex survey data, by pairwise composite likelihood,
as described in Lumley & Huang (2023) <@code{arXiv:2311.13048>}.  Supports
nested and crossed random effects, and correlated random effects as in genetic
models.  Allows for multistage sampling and for other designs where pairwise
sampling probabilities are specified or can be calculated.")
    (license license:gpl3)))

(define-public r-svydiags
  (package
    (name "r-svydiags")
    (version "0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svydiags" version))
       (sha256
        (base32 "13rmy02f5d2ysbi5r9w9mhnyqrsvikhqwjk1d3dgkxgvz9lyqas0"))))
    (properties `((upstream-name . "svydiags")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey r-matrix r-mass))
    (home-page "https://cran.r-project.org/package=svydiags")
    (synopsis "Regression Model Diagnostics for Survey Data")
    (description
     "Diagnostics for fixed effects linear regression models fitted with survey data.
Extensions of standard diagnostics to complex survey data are included:
standardized residuals, leverages, Cook's D, dfbetas, dffits, condition indexes,
and variance inflation factors as found in Li and Valliant (Surv.  Meth., 2009,
35(1), pp.  15-24; Jnl.  of Off.  Stat., 2011, 27(1), pp.  99-119; Jnl.  of Off.
 Stat., 2015, 31(1), pp.  61-75); Liao and Valliant (Surv.  Meth., 2012, 38(1),
pp.  53-62; Surv.  Meth., 2012, 38(2), pp.  189-202).  Variance inflation
factors are also computed for some general linear models (binomial, gaussian,
poisson, quasibinomial, and quasipoisson) as described in Liao (U. Maryland
thesis, 2010).")
    (license license:gpl3)))

(define-public r-svycdiff
  (package
    (name "r-svycdiff")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svycdiff" version))
       (sha256
        (base32 "1hfgrcrkwgsi3d71aih9w866cwnidhkl45bnw29g0gz3wyvxj9gs"))))
    (properties `((upstream-name . "svycdiff")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey r-numderiv r-betareg))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/salernos/svycdiff")
    (synopsis "Controlled Difference Estimation for Complex Surveys")
    (description
     "Estimates the population average controlled difference for a given outcome
between levels of a binary treatment, exposure, or other group membership
variable of interest for clustered, stratified survey samples where sample
selection depends on the comparison group.  Provides three methods for
estimation, namely outcome modeling and two factorizations of inverse
probability weighting.  Under stronger assumptions, these methods estimate the
causal population average treatment effect.  Salerno et al., (2024)
<doi:10.48550/@code{arXiv.2406.19597>}.")
    (license license:gpl3+)))

(define-public r-svwidgets
  (package
    (name "r-svwidgets")
    (version "0.9-45")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svWidgets" version))
       (sha256
        (base32 "08cpsy4abypjnv33arsgxk5i0gpdvdzj60wa1b22ca1n7d35mc80"))))
    (properties `((upstream-name . "svWidgets")))
    (build-system r-build-system)
    (propagated-inputs (list r-svmisc))
    (home-page "http://www.sciviews.org/SciViews-R")
    (synopsis "Management of GUI Widgets, Windows, and Other GUI Resources")
    (description
     "High level management of widgets, windows and other graphical resources.")
    (license license:gpl2)))

(define-public r-svunit
  (package
    (name "r-svunit")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svUnit" version))
       (sha256
        (base32 "08nmlbdrmmz04ira30721l99i74w07w2avdr4pypswf2sq1nqgi6"))))
    (properties `((upstream-name . "svUnit")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/SciViews/svUnit")
    (synopsis "'SciViews' - Unit, Integration and System Testing")
    (description
     "This package provides a complete unit test system and functions to implement its
GUI part.")
    (license license:gpl2)))

(define-public r-svtools
  (package
    (name "r-svtools")
    (version "0.9-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svTools" version))
       (sha256
        (base32 "040wr8cr2p35x6pr515bprmsw8ysajx4df5f2njz3ily61xf9szi"))))
    (properties `((upstream-name . "svTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-svmisc r-codetools))
    (home-page "http://www.sciviews.org/SciViews-R")
    (synopsis "Wrappers for Tools in Other Packages for IDE Friendliness")
    (description
     "Set of tools aimed at wrapping some of the functionalities of the packages
tools, utils and codetools into a nicer format so that an IDE can use them.")
    (license license:gpl2)))

(define-public r-svsweave
  (package
    (name "r-svsweave")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svSweave" version))
       (sha256
        (base32 "00w5ykqcx7zzx95pjih16w4rz4fcg6fbq57i0bj0rvbbw9dp3nz7"))))
    (properties `((upstream-name . "svSweave")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmarkdown r-knitr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/SciViews/svSweave")
    (synopsis
     "'SciViews' - 'Sweave', 'Knitr' and R Markdown Companion Functions")
    (description
     "This package provides functions to enumerate and reference figures, tables and
equations in R Markdown documents that do not support these features (thus not
bookdown or quarto'.  Supporting functions for using Sweave and Knitr with
@code{LyX}'.")
    (license license:gpl2)))

(define-public r-svsocket
  (package
    (name "r-svsocket")
    (version "1.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svSocket" version))
       (sha256
        (base32 "1ir9fpw1v5n4ygyydclh2g0bijzd7lm7nl8nfg7d0f2bhp3p18s7"))))
    (properties `((upstream-name . "svSocket")))
    (build-system r-build-system)
    (propagated-inputs (list r-svmisc))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/SciViews/svSocket")
    (synopsis "'SciViews' - Socket Server")
    (description
     "This package provides a socket server allows to connect clients to R.")
    (license license:gpl2)))

(define-public r-svs
  (package
    (name "r-svs")
    (version "3.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svs" version))
       (sha256
        (base32 "0hfs0dif4b9ssi98mv2aa849blgdgvphymafabmsix4nkdjqzi2k"))))
    (properties `((upstream-name . "svs")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-gtools))
    (home-page "https://cran.r-project.org/package=svs")
    (synopsis "Tools for Semantic Vector Spaces")
    (description
     "Various tools for semantic vector spaces, such as correspondence analysis
(simple, multiple and discriminant), latent semantic analysis, probabilistic
latent semantic analysis, non-negative matrix factorization, latent class
analysis, EM clustering, logratio analysis and log-multiplicative (association)
analysis.  Furthermore, there are specialized distance measures, plotting
functions and some helper functions.")
    (license license:gpl3)))

(define-public r-svrpath
  (package
    (name "r-svrpath")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svrpath" version))
       (sha256
        (base32 "18h40555jmqpbsnhlwkvc5fl7d0r2bgscdv8amjvhjxmx9y952a2"))))
    (properties `((upstream-name . "svrpath")))
    (build-system r-build-system)
    (propagated-inputs (list r-svmpath r-quadprog))
    (home-page "https://cran.r-project.org/package=svrpath")
    (synopsis "The SVR Path Algorithm")
    (description
     "Computes the entire solution paths for Support Vector Regression(SVR) with
respect to the regularization parameter, lambda and epsilon in epsilon-intensive
loss function, efficiently.  We call each path algorithm svrpath and epspath.
See Wang, G. et al (2008) <doi:10.1109/TNN.2008.2002077> for details regarding
the method.")
    (license license:gpl2+)))

(define-public r-svrep
  (package
    (name "r-svrep")
    (version "0.6.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svrep" version))
       (sha256
        (base32 "110vbkzrlyx8f9arqw39jxw2z4lpggcjq180radyxz2a2is7lwvc"))))
    (properties `((upstream-name . "svrep")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey r-mvtnorm r-matrix r-dbi))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://bschneidr.github.io/svrep/")
    (synopsis
     "Tools for Creating, Updating, and Analyzing Survey Replicate Weights")
    (description
     "This package provides tools for creating and working with survey replicate
weights, extending functionality of the survey package from Lumley (2004)
<doi:10.18637/jss.v009.i08>.  Implements bootstrap methods for complex surveys,
including the generalized survey bootstrap as described by Beaumont and Patak
(2012) <doi:10.1111/j.1751-5823.2011.00166.x>.  Methods are provided for
applying nonresponse adjustments to both full-sample and replicate weights as
described by Rust and Rao (1996) <doi:10.1177/096228029600500305>.  Implements
methods for sample-based calibration described by Opsomer and Erciulescu (2021)
<https://www150.statcan.gc.ca/n1/pub/12-001-x/2021002/article/00006-eng.htm>.
Diagnostic functions are included to compare weights and weighted estimates from
different sets of replicate weights.")
    (license license:gpl3+)))

(define-public r-svplots
  (package
    (name "r-svplots")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svplots" version))
       (sha256
        (base32 "0fa3mnsdaz85dy7rr6w454z516simj1zrf08sfbm05rp6jr3c7ws"))))
    (properties `((upstream-name . "svplots")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=svplots")
    (synopsis "Sample Variance Plots (Sv-Plots)")
    (description
     "Two versions of sample variance plots, Sv-plot1 and Sv-plot2, will be provided
illustrating the squared deviations from sample variance.  Besides indicating
the contribution of squared deviations for the sample variability, these plots
are capable of detecting characteristics of the distribution such as symmetry,
skewness and outliers.  A remarkable graphical method based on Sv-plot2 can
determine the decision on testing hypotheses over one or two population means.
In sum, Sv-plots will be appealing visualization tools.  Complete description of
this methodology can be found in the article, Wijesuriya (2020)
<doi:10.1080/03610918.2020.1851716>.")
    (license license:gpl3)))

(define-public r-svn
  (package
    (name "r-svn")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SVN" version))
       (sha256
        (base32 "0n93lqb1f2s2qvzc8lrcqmr0fhd57xqbi0ymrqqmll31ns679m92"))))
    (properties `((upstream-name . "SVN")))
    (build-system r-build-system)
    (propagated-inputs (list r-memoise r-igraph r-data-table))
    (home-page "https://cran.r-project.org/package=SVN")
    (synopsis "Statistically Validated Networks")
    (description
     "Determines networks of significant synchronization between the discrete states
of nodes; see Tumminello et al <doi:10.1371/journal.pone.0017994>.")
    (license license:gpl2+)))

(define-public r-svmpath
  (package
    (name "r-svmpath")
    (version "0.970")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svmpath" version))
       (sha256
        (base32 "19b5hfqqsh072l5mbkafm5pjd6qhyj20qd2vq6zl5jjmc45r72w1"))))
    (properties `((upstream-name . "svmpath")))
    (build-system r-build-system)
    (propagated-inputs (list r-kernlab))
    (home-page "http://www.jmlr.org/papers/volume5/hastie04a/hastie04a.pdf")
    (synopsis "The SVM Path Algorithm")
    (description
     "Computes the entire regularization path for the two-class svm classifier with
essentially the same cost as a single SVM fit.")
    (license license:gpl2)))

(define-public r-svmmaj
  (package
    (name "r-svmmaj")
    (version "0.2.9.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SVMMaj" version))
       (sha256
        (base32 "11ii0kg47iwrc8aq7qbrg0hpd6aj30ra8rgjpv2nwq0ffsw5jwa2"))))
    (properties `((upstream-name . "SVMMaj")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-reshape2
                             r-kernlab
                             r-gridextra
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list))
    (home-page "https://cran.r-project.org/package=SVMMaj")
    (synopsis "Implementation of the SVM-Maj Algorithm")
    (description
     "This package implements the SVM-Maj algorithm to train data with support vector
machine <doi:10.1007/s11634-008-0020-9>.  This algorithm uses two efficient
updates, one for linear kernel and one for the nonlinear kernel.")
    (license license:gpl2)))

(define-public r-svmd
  (package
    (name "r-svmd")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SVMD" version))
       (sha256
        (base32 "0g6cp3zazfx70c0ahfymwxwsinkhrd0hcvh464sch8k77m81asbf"))))
    (properties `((upstream-name . "SVMD")))
    (build-system r-build-system)
    (propagated-inputs (list r-vmdecomp))
    (home-page "https://cran.r-project.org/package=SVMD")
    (synopsis "Spearman Variational Mode Decomposition")
    (description
     "In practice, it is difficult to determine the number of decomposition modes, K,
for Variational Mode Decomposition (VMD).  To overcome this issue, this study
offers Spearman Variational Mode Decomposition (SVMD), a method that uses the
Spearman correlation coefficient to calculate the ideal mode number.  Unlike the
Pearson correlation coefficient, which only returns a perfect value when X and Y
are linearly connected, the Spearman correlation can be calculated without
knowing the probability distributions of X and Y. The Spearman correlation
coefficient, also called Spearman's rank correlation coefficient, is a subset of
a wider correlation coefficient.  As VMD decomposes a signal, the Spearman
correlation coefficient between the reconstructed and original sequences rises
as the mode number K increases.  Once the signal has been fully decomposed,
subsequent increases in K cause the correlation to gradually level off.  When
the correlation reaches a specific level, VMD is said to have adequately
decomposed the signal.  Numerous experiments revealed that a threshold of 0.997
produces the best denoising effect, so the threshold is set at 0.997.  This
package has been developed using concept of Yang et al.
(2021)<doi:10.1016/j.aej.2021.01.055>.")
    (license license:gpl3)))

(define-public r-svkomodo
  (package
    (name "r-svkomodo")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svKomodo" version))
       (sha256
        (base32 "0h5p1c6wfyvnygd5cwxvwd8qgs9svd70cyr7sqm41zy3d70mavds"))))
    (properties `((upstream-name . "svKomodo")))
    (build-system r-build-system)
    (propagated-inputs (list r-svmisc))
    (home-page "https://github.com/SciViews/svKomodo")
    (synopsis "'SciViews' - Functions to Interface with Komodo IDE")
    (description
     "R-side code to implement an R editor and IDE in Komodo IDE with the
@code{SciViews-K} extension.")
    (license license:gpl2)))

(define-public r-svines
  (package
    (name "r-svines")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svines" version))
       (sha256
        (base32 "0pmipkywpffs5zkvgdncz4ila6wndhvcm73bl73ncg7jqrrsi86r"))))
    (properties `((upstream-name . "svines")))
    (build-system r-build-system)
    (propagated-inputs (list r-wdm
                             r-univariateml
                             r-rvinecopulib
                             r-rcppthread
                             r-rcppeigen
                             r-rcpp
                             r-fgarch
                             r-bh
                             r-assertthat))
    (home-page "https://github.com/tnagler/svines")
    (synopsis "Stationary Vine Copula Models")
    (description
     "This package provides functionality to fit and simulate from stationary vine
copula models for time series, see Nagler et al. (2022)
<doi:10.1016/j.jeconom.2021.11.015>.")
    (license license:gpl3)))

(define-public r-svide
  (package
    (name "r-svide")
    (version "0.9-54")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svIDE" version))
       (sha256
        (base32 "1a03xpjx22xhxfqkgcm1pmal0dlp544hvnckj0p6sy4y56hmsfik"))))
    (properties `((upstream-name . "svIDE")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml r-svmisc))
    (home-page "http://www.sciviews.org/SciViews-R")
    (synopsis
     "Functions to Ease Interactions Between R and IDE or Code Editors")
    (description
     "Function for the GUI API to interact with external IDE/code editors.")
    (license license:gpl2)))

(define-public r-svhttp
  (package
    (name "r-svhttp")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svHttp" version))
       (sha256
        (base32 "1j0lb2fy8hp63g6zyryk9w50fzgxa5gjh0g0f9dc2z6i4n3fqfsh"))))
    (properties `((upstream-name . "svHttp")))
    (build-system r-build-system)
    (propagated-inputs (list r-svmisc))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/SciViews/svHttp")
    (synopsis "'SciViews' - HTTP Server")
    (description
     "This package provides a simple HTTP server allows to connect GUI clients to R.")
    (license license:gpl2)))

(define-public r-svgviewr
  (package
    (name "r-svgviewr")
    (version "1.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svgViewR" version))
       (sha256
        (base32 "0nsm397i1p0z1bbp92k6r12fgkxbgn624wvdwh5r7ydpiam6pvg8"))))
    (properties `((upstream-name . "svgViewR")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-rjson r-rcpp))
    (native-inputs (list esbuild))
    (home-page "https://aaronolsen.github.io/tutorials/visualization3d.html")
    (synopsis "3D Animated Interactive Visualizations Using SVG and WebGL")
    (description
     "This package creates 3D animated, interactive visualizations that can be viewed
in a web browser.")
    (license license:gpl2+)))

(define-public r-svgtools
  (package
    (name "r-svgtools")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svgtools" version))
       (sha256
        (base32 "0jp53x1nlclwsg5adyr1764car0z52rjq95ppijfkqi7yrbbi6ja"))))
    (properties `((upstream-name . "svgtools")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-stringr r-rsvg r-magick))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://cran.r-project.org/package=svgtools")
    (synopsis "Manipulate SVG (Template) Files of Charts")
    (description
     "The purpose of this package is to manipulate SVG files that are templates of
charts the user wants to produce.  In vector graphics one copes with
x-/y-coordinates of elements (e.g. lines, rectangles, text).  Their scale is
often dependent on the program that is used to produce the graphics.  In applied
statistics one usually has numeric values on a fixed scale (e.g. percentage
values between 0 and 100) to show in a chart.  Basically, svgtools transforms
the statistical values into coordinates and widths/heights of the vector
graphics.  This is done by @code{stackedBar}() for bar charts, by
@code{linesSymbols}() for charts with lines and/or symbols (dot markers) and
@code{scatterSymbols}() for scatterplots.")
    (license license:gpl3)))

(define-public r-svgpanzoom
  (package
    (name "r-svgpanzoom")
    (version "0.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svgPanZoom" version))
       (sha256
        (base32 "1iglgwbyxf5q4716h9ba2cfmxh1qq16zwy54krnawg4580v8ydh2"))))
    (properties `((upstream-name . "svgPanZoom")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-htmlwidgets))
    (native-inputs (list esbuild))
    (home-page "https://github.com/timelyportfolio/svgPanZoom")
    (synopsis "R 'Htmlwidget' to Add Pan and Zoom to Almost any R Graphic")
    (description
     "This htmlwidget provides pan and zoom interactivity to R graphics, including
base', lattice', and ggplot2'.  The interactivity is provided through the
svg-pan-zoom.js library.  Various options to the widget can tailor the pan and
zoom experience to nearly any user desire.")
    (license license:expat)))

(define-public r-svenssonm
  (package
    (name "r-svenssonm")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svenssonm" version))
       (sha256
        (base32 "0pvay9vfrcafaq270pq6glp2b74zdfgldksl13cwdb0wk7qx7731"))))
    (properties `((upstream-name . "svenssonm")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=svenssonm")
    (synopsis "Svensson's Method")
    (description
     "Obtain parameters of Svensson's Method, including percentage agreement,
systematic change and individual change.  Also, the contingency table can be
generated.  Svensson's Method is a rank-invariant nonparametric method for the
analysis of ordered scales which measures the level of change both from
systematic and individual aspects.  For the details, please refer to Svensson E.
Analysis of systematic and random differences between paired ordinal categorical
data [dissertation].  Stockholm: Almqvist & Wiksell International; 1993.")
    (license license:gpl3)))

(define-public r-svdnf
  (package
    (name "r-svdnf")
    (version "0.1.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SVDNF" version))
       (sha256
        (base32 "00jrdj6shbkihvnjldhiwn976nadsxw9b7lj5ivlbbfbd2ah4x1r"))))
    (properties `((upstream-name . "SVDNF")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-xts r-rcpp))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=SVDNF")
    (synopsis "Discrete Nonlinear Filtering for Stochastic Volatility Models")
    (description
     "This package implements the discrete nonlinear filter (DNF) of Kitagawa (1987)
<doi:10.1080/01621459.1987.10478534> to a wide class of stochastic volatility
(SV) models with return and volatility jumps following the work of BÃ©gin and
Boudreault (2021) <doi:10.1080/10618600.2020.1840995> to obtain likelihood
evaluations and maximum likelihood parameter estimates.  Offers several built-in
SV models and a flexible framework for users to create customized models by
specifying drift and diffusion functions along with an arrival distribution for
the return and volatility jumps.  Allows for the estimation of factor models
with stochastic volatility (e.g., heteroskedastic volatility CAPM) by
incorporating expected return predictors.  Also includes functions to compute
filtering and prediction distribution estimates, to simulate data from built-in
and custom SV models with jumps, and to forecast future returns and volatility
values using Monte Carlo simulation from a given SV model.")
    (license license:gpl3)))

(define-public r-svdialogstcltk
  (package
    (name "r-svdialogstcltk")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svDialogstcltk" version))
       (sha256
        (base32 "03rxw1m1ixisj0z9csmn3xff3rl1wqdvkcl1x7kmx64d0x6jwyfy"))))
    (properties `((upstream-name . "svDialogstcltk")))
    (build-system r-build-system)
    (propagated-inputs (list r-svgui r-svdialogs))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/SciViews/svDialogstcltk")
    (synopsis "'SciViews' - Standard Dialog Boxes using Tcl/Tk")
    (description
     "Reimplementation of the @code{svDialogs} dialog boxes in Tcl/Tk.")
    (license license:gpl2)))

(define-public r-svd
  (package
    (name "r-svd")
    (version "0.5.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svd" version))
       (sha256
        (base32 "050m8hjddgygm7q1lvz2lqvq9r6xrbi4cy4kpbvypnwdf1w4x6ah"))))
    (properties `((upstream-name . "svd")))
    (build-system r-build-system)
    (native-inputs (list gfortran))
    (home-page "https://github.com/asl/svd")
    (synopsis "Interfaces to Various State-of-Art SVD and Eigensolvers")
    (description
     "R bindings to SVD and eigensolvers (PROPACK, @code{nuTRLan}).")
    (license license:bsd-3)))

(define-public r-svars
  (package
    (name "r-svars")
    (version "1.3.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "svars" version))
       (sha256
        (base32 "1y8klpj4b1dcba0d3b3kr29n8ji8pfis7v311ph0bx4j1mwilc9g"))))
    (properties `((upstream-name . "svars")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-vars
                             r-strucchange
                             r-steadyica
                             r-reshape2
                             r-rcpparmadillo
                             r-rcpp
                             r-pbapply
                             r-ggplot2
                             r-expm
                             r-deoptim
                             r-copula
                             r-clue))
    (home-page "https://cran.r-project.org/package=svars")
    (synopsis "Data-Driven Identification of SVAR Models")
    (description
     "This package implements data-driven identification methods for structural vector
autoregressive (SVAR) models as described in Lange et al. (2021)
<doi:10.18637/jss.v097.i05>.  Based on an existing VAR model object (provided by
e.g. VAR() from the vars package), the structural impact matrix is obtained via
data-driven identification techniques (i.e.  changes in volatility (Rigobon, R.
(2003) <doi:10.1162/003465303772815727>), patterns of GARCH (Normadin, M.,
Phaneuf, L. (2004) <doi:10.1016/j.jmoneco.2003.11.002>), independent component
analysis (Matteson, D. S, Tsay, R. S., (2013)
<doi:10.1080/01621459.2016.1150851>), least dependent innovations (Herwartz, H.,
Ploedt, M., (2016) <doi:10.1016/j.jimonfin.2015.11.001>), smooth transition in
variances (Luetkepohl, H., Netsunajev, A. (2017)
<doi:10.1016/j.jedc.2017.09.001>) or non-Gaussian maximum likelihood (Lanne, M.,
Meitz, M., Saikkonen, P. (2017) <doi:10.1016/j.jeconom.2016.06.002>)).")
    (license license:expat)))

(define-public r-svalues
  (package
    (name "r-svalues")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sValues" version))
       (sha256
        (base32 "0830f4w6jgpiysk0vz0bxaxwa8m3k94fgj9l3qb42r3jjf9cdn29"))))
    (properties `((upstream-name . "sValues")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=sValues")
    (synopsis "Measures of the Sturdiness of Regression Coefficients")
    (description
     "This package implements the s-values proposed by Ed.  Leamer.  It provides a
context-minimal approach for sensitivity analysis using extreme bounds to assess
the sturdiness of regression coefficients.")
    (license license:gpl3)))

(define-public r-susy
  (package
    (name "r-susy")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SUSY" version))
       (sha256
        (base32 "0l0mxvas80d9n3vv9bqxw2dk8q1ikwg616hraprcs70aabh7717w"))))
    (properties `((upstream-name . "SUSY")))
    (build-system r-build-system)
    (home-page "https://wtschacher.github.io/SUSY/")
    (synopsis "Surrogate Synchrony")
    (description
     "Computes synchrony as windowed cross-correlation based on two-dimensional time
series in a text file you can upload.  SUSY works as described in Tschacher &
Meier (2020) <doi:10.1080/10503307.2019.1612114>.")
    (license license:gpl2)))

(define-public r-susographql
  (package
    (name "r-susographql")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "susographql" version))
       (sha256
        (base32 "1drh8vqkdmvd9v80ibjx3jhnp5f8ggs445mr6r9j69ygw259npbk"))))
    (properties `((upstream-name . "susographql")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-stringr
                             r-rlang
                             r-readr
                             r-lubridate
                             r-jsonlite
                             r-httr2
                             r-glue
                             r-data-table
                             r-curl
                             r-cli))
    (home-page "https://michael-cw.github.io/susographql/")
    (synopsis "Comprehensive Interface to the Survey Solutions 'GraphQL' API")
    (description
     "This package provides a complete suite of tools for interacting with the Survey
Solutions @code{GraphQL} API <https://demo.mysurvey.solutions/graphql/>.  This
package encompasses all currently available queries and mutations, including the
latest features for map uploads.  It is built on the modern httr2 package,
offering a streamlined and efficient interface without relying on external
@code{GraphQL} client packages.  In addition to core API functionalities, the
package includes a range of helper functions designed to facilitate the use of
available query filters.")
    (license license:gpl3+)))

(define-public r-susier
  (package
    (name "r-susier")
    (version "0.12.35")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "susieR" version))
       (sha256
        (base32 "0fhdc3lkhl4s9h2ng72gypy2z3hgk6566jfhwhsfbddyzd22drpd"))))
    (properties `((upstream-name . "susieR")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape
                             r-mixsqp
                             r-matrixstats
                             r-matrix
                             r-ggplot2
                             r-crayon))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/stephenslab/susieR")
    (synopsis "Sum of Single Effects Linear Regression")
    (description
     "This package implements methods for variable selection in linear regression
based on the \"Sum of Single Effects\" (@code{SuSiE}) model, as described in Wang
et al (2020) <DOI:10.1101/501114> and Zou et al (2021)
<DOI:10.1101/2021.11.03.467167>.  These methods provide simple summaries, called
\"Credible Sets\", for accurately quantifying uncertainty in which variables
should be selected.  The methods are motivated by genetic fine-mapping
applications, and are particularly well-suited to settings where variables are
highly correlated and detectable effects are sparse.  The fitting algorithm, a
Bayesian analogue of stepwise selection methods called \"Iterative Bayesian
Stepwise Selection\" (IBSS), is simple and fast, allowing the @code{SuSiE} model
be fit to large data sets (thousands of samples and hundreds of thousands of
variables).")
    (license license:bsd-3)))

(define-public r-survtrunc
  (package
    (name "r-survtrunc")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurvTrunc" version))
       (sha256
        (base32 "0zkjlndmg0rc7ycqy2v42nyzs4sd9z1y9y9kwfdci9qrc88nnfmn"))))
    (properties `((upstream-name . "SurvTrunc")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "https://cran.r-project.org/package=SurvTrunc")
    (synopsis "Analysis of Doubly Truncated Data")
    (description
     "Package performs Cox regression and survival distribution function estimation
when the survival times are subject to double truncation.  In case that the
survival and truncation times are quasi-independent, the estimation procedure
for each method involves inverse probability weighting, where the weights
correspond to the inverse of the selection probabilities and are estimated using
the survival times and truncation times only.  A test for checking this
independence assumption is also included in this package.  The functions
available in this package for Cox regression, survival distribution function
estimation, and testing independence under double truncation are based on the
following methods, respectively: Rennert and Xie (2018)
<doi:10.1111/biom.12809>, Shen (2010) <doi:10.1007/s10463-008-0192-2>, Martin
and Betensky (2005) <doi:10.1198/016214504000001538>.  When the survival times
are dependent on at least one of the truncation times, an EM algorithm is
employed to obtain point estimates for the regression coefficients.  The
standard errors are calculated using the bootstrap method.  See Rennert and Xie
(2022) <doi:10.1111/biom.13451>.  Both the independent and dependent cases
assume no censoring is present in the data.  Please contact Lior Rennert
<liorr@@clemson.edu> for questions regarding function @code{coxDT} and Yidan Shi
<yidan.shi@@pennmedicine.upenn.edu> for questions regarding function
@code{coxDTdep}.")
    (license license:gpl2)))

(define-public r-survstan
  (package
    (name "r-survstan")
    (version "0.0.7.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survstan" version))
       (sha256
        (base32 "1yd634a4bqv5sfmsp2n4xxjjn23ms67iw3zzaa0yqzwldh09z6ng"))))
    (properties `((upstream-name . "survstan")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-survival
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rlang
                             r-rdpack
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-purrr
                             r-mass
                             r-gridextra
                             r-ggplot2
                             r-generics
                             r-future
                             r-foreach
                             r-extradistr
                             r-dplyr
                             r-dofuture
                             r-broom
                             r-bh
                             r-actuar))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/fndemarqui/survstan")
    (synopsis "Fitting Survival Regression Models via 'Stan'")
    (description
     "Parametric survival regression models under the maximum likelihood approach via
Stan'.  Implemented regression models include accelerated failure time models,
proportional hazards models, proportional odds models, accelerated hazard
models, Yang and Prentice models, and extended hazard models.  Available
baseline survival distributions include exponential, Weibull, log-normal,
log-logistic, gamma, generalized gamma, rayleigh, Gompertz and fatigue
(Birnbaum-Saunders) distributions.  References: Lawless (2002)
<ISBN:9780471372158>; Bennett (1982) <doi:10.1002/sim.4780020223>; Chen and
Wang(2000) <doi:10.1080/01621459.2000.10474236>; Demarqui and Mayrink (2021)
<doi:10.1214/20-BJPS471>.")
    (license license:expat)))

(define-public r-survspearman
  (package
    (name "r-survspearman")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survSpearman" version))
       (sha256
        (base32 "1b6nj2ikf931926a47a441icbrfjcadwxxnkc35c9yz8nw1kn1ii"))))
    (properties `((upstream-name . "survSpearman")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=survSpearman")
    (synopsis "Nonparametric Spearman's Correlation for Survival Data")
    (description
     "Nonparametric estimation of Spearman's rank correlation with bivariate survival
(right-censored) data as described in Eden, S.K., Li, C., Shepherd B.E. (2021),
Nonparametric Estimation of Spearman's Rank Correlation with Bivariate Survival
Data, Biometrics (under revision).  The package also provides functions that
visualize bivariate survival data and bivariate probability mass function.")
    (license license:gpl2+)))

(define-public r-survsparse
  (package
    (name "r-survsparse")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurvSparse" version))
       (sha256
        (base32 "14v0319w2k6b5cmpfrcxc2h3558lrpf101mhbpkqy0k4ahsb0sax"))))
    (properties `((upstream-name . "SurvSparse")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-purrr
                             r-nloptr
                             r-nleqslv
                             r-mass
                             r-gaussquad
                             r-foreach
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=SurvSparse")
    (synopsis "Survival Analysis with Sparse Longitudinal Covariates")
    (description
     "Survival analysis with sparse longitudinal covariates under right censoring
scheme.  Different hazards models are involved.  Please cite the manuscripts
corresponding to this package: Sun, Z. et al. (2022)
<doi:10.1007/s10985-022-09548-6>, Sun, Z. and Cao, H. (2023)
<@code{arXiv:2310.15877>} and Sun, D. et al. (2023) <@code{arXiv:2308.15549>}.")
    (license license:gpl3)))

(define-public r-survsnp
  (package
    (name "r-survsnp")
    (version "0.26")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survSNP" version))
       (sha256
        (base32 "0fvmqlzg6j43d5bwvn70n4630n396wrykl5blkszadj3l118rmp4"))))
    (properties `((upstream-name . "survSNP")))
    (build-system r-build-system)
    (inputs (list gsl))
    (propagated-inputs (list r-xtable r-survival r-rcpp r-lattice r-foreach))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=survSNP")
    (synopsis "Power Calculations for SNP Studies with Censored Outcomes")
    (description
     "Conduct asymptotic and empirical power and sample size calculations for
Single-Nucleotide Polymorphism (SNP) association studies with right censored
time to event outcomes.")
    (license license:gpl3)))

(define-public r-survsim
  (package
    (name "r-survsim")
    (version "1.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survsim" version))
       (sha256
        (base32 "0gv54bd94a6vdz8li0j409qv7r0h4d681l1p9hj9ilzk7y5bfm70"))))
    (properties `((upstream-name . "survsim")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod r-eha))
    (home-page "https://cran.r-project.org/package=survsim")
    (synopsis "Simulation of Simple and Complex Survival Data")
    (description
     "Simulation of simple and complex survival data including recurrent and multiple
events and competing risks.  See MoriÃ±a D, Navarro A. (2014)
<doi:10.18637/jss.v059.i02> and MoriÃ±a D, Navarro A. (2017)
<doi:10.1080/03610918.2016.1175621>.")
    (license license:gpl2+)))

(define-public r-survsens
  (package
    (name "r-survsens")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survSens" version))
       (sha256
        (base32 "08xh0jh99xq8lnw8xskmyan78mbk3n2sbjrfpwz7zwmfx4wbip35"))))
    (properties `((upstream-name . "survSens")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-reshape2 r-metr r-interp r-ggplot2))
    (home-page "https://github.com/Rong0707/survSens")
    (synopsis "Sensitivity Analysis with Time-to-Event Outcomes")
    (description
     "This package performs a dual-parameter sensitivity analysis of treatment effect
to unmeasured confounding in observational studies with either survival or
competing risks outcomes.  Huang, R., Xu, R. and Dulai, P.S.(2020)
<doi:10.1002/sim.8672>.")
    (license license:gpl2)))

(define-public r-survsakk
  (package
    (name "r-survsakk")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survSAKK" version))
       (sha256
        (base32 "0j2avaf08jn7llbik1pfjlbficn79s7x2by1fyni3l7bjk6lhpb1"))))
    (properties `((upstream-name . "survSAKK")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (native-inputs (list r-knitr))
    (home-page "https://sakk-statistics.github.io/survSAKK/")
    (synopsis "Create Publication Ready Kaplan-Meier Plots")
    (description
     "Incorporate various statistics and layout customization options to enhance the
efficiency and adaptability of the Kaplan-Meier plots.")
    (license license:gpl2+)))

(define-public r-survrm2perm
  (package
    (name "r-survrm2perm")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survRM2perm" version))
       (sha256
        (base32 "0js9sgslxzzs8k94k9kdmbmwp1ckk9mlr8sndk17dg24raks4zmz"))))
    (properties `((upstream-name . "survRM2perm")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "https://cran.r-project.org/package=survRM2perm")
    (synopsis "Permutation Test for Comparing Restricted Mean Survival Time")
    (description
     "This package performs the permutation test using difference in the restricted
mean survival time (RMST) between groups as a summary measure of the survival
time distribution.  When the sample size is less than 50 per group, it has been
shown that there is non-negligible inflation of the type I error rate in the
commonly used asymptotic test for the RMST comparison.  Generally, permutation
tests can be useful in such a situation.  However, when we apply the permutation
test for the RMST comparison, particularly in small sample situations, there are
some cases where the survival function in either group cannot be defined due to
censoring in the permutation process.  Horiguchi and Uno (2020)
<doi:10.1002/sim.8565> have examined six workable solutions to handle this
numerical issue.  It performs permutation tests with implementation of the six
methods outlined in the paper when the numerical issue arises during the
permutation process.  The result of the asymptotic test is also provided for a
reference.")
    (license license:gpl2)))

(define-public r-survrm2adapt
  (package
    (name "r-survrm2adapt")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survRM2adapt" version))
       (sha256
        (base32 "1abi73a0z5g2khkmrgywrba6j9k6k7vpnskjcbqqq21qr5j32crv"))))
    (properties `((upstream-name . "survRM2adapt")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mvtnorm))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=survRM2adapt")
    (synopsis
     "Flexible and Coherent Test/Estimation Procedure Based on Restricted Mean Survival Times")
    (description
     "Estimates the restricted mean survival time (RMST) with the time window [0,
tau], where tau is adaptively selected from the procedure, proposed by Horiguchi
et al. (2018) <doi:10.1002/sim.7661>.  It also estimates the RMST with the time
window [tau1, tau2], where tau1 is adaptively selected from the procedure,
proposed by Horiguchi et al. (2023) <doi:10.1002/sim.9662>.")
    (license license:gpl2)))

(define-public r-survrm2
  (package
    (name "r-survrm2")
    (version "1.0-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survRM2" version))
       (sha256
        (base32 "0ybq1238paar6dsf26cl9yfhkk88lqxiksvk5i4dymmx7xbcx7fw"))))
    (properties `((upstream-name . "survRM2")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=survRM2")
    (synopsis "Comparing Restricted Mean Survival Time")
    (description
     "This package performs two-sample comparisons using the restricted mean survival
time (RMST) as a summary measure of the survival time distribution.  Three kinds
of between-group contrast metrics (i.e., the difference in RMST, the ratio of
RMST and the ratio of the restricted mean time lost (RMTL)) are computed.  It
performs an ANCOVA-type covariate adjustment as well as unadjusted analyses for
those measures.")
    (license license:gpl2)))

(define-public r-survregcenscov
  (package
    (name "r-survregcenscov")
    (version "1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurvRegCensCov" version))
       (sha256
        (base32 "02bg0iaqh98gwbzl24dy4piyxfqb3fmn2yc0pkd7y5ygchpfm4ak"))))
    (properties `((upstream-name . "SurvRegCensCov")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-numderiv))
    (home-page "https://cran.r-project.org/package=SurvRegCensCov")
    (synopsis
     "Weibull Regression for a Right-Censored Endpoint with Interval-Censored Covariate")
    (description
     "The function @code{SurvRegCens}() of this package allows estimation of a Weibull
Regression for a right-censored endpoint, one interval-censored covariate, and
an arbitrary number of non-censored covariates.  Additional functions allow to
switch between different parametrizations of Weibull regression used by
different R functions, inference for the mean difference of two arbitrarily
censored Normal samples, and estimation of canonical parameters from censored
samples for several distributional assumptions.  Hubeaux, S. and Rufibach, K.
(2014) <@code{arXiv:1402.0432>}.")
    (license license:gpl2+)))

(define-public r-survpresmooth
  (package
    (name "r-survpresmooth")
    (version "1.1-11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survPresmooth" version))
       (sha256
        (base32 "1rgz4g4irm6kg1ysb1sz9jar5i7l87blfhlhcmsg5bjbw0ih5xbn"))))
    (properties `((upstream-name . "survPresmooth")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=survPresmooth")
    (synopsis "Presmoothed Estimation in Survival Analysis")
    (description
     "Presmoothed estimators of survival, density, cumulative and non-cumulative
hazard functions with right-censored survival data.  For details, see
Lopez-de-Ullibarri and Jacome (2013) <doi:10.18637/jss.v054.i11>.")
    (license license:gpl2+)))

(define-public r-survpen
  (package
    (name "r-survpen")
    (version "1.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survPen" version))
       (sha256
        (base32 "1gihr41a7smq4ql52gah249g3q2zd5zf5qiwpbxi7imgfzkmbzza"))))
    (properties `((upstream-name . "survPen")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod r-rcppeigen r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/fauvernierma/survPen")
    (synopsis
     "Multidimensional Penalized Splines for Survival and Net Survival Models")
    (description
     "Fits hazard and excess hazard models with multidimensional penalized splines
allowing for time-dependent effects, non-linear effects and interactions between
several continuous covariates.  In survival and net survival analysis, in
addition to modelling the effect of time (via the baseline hazard), one has
often to deal with several continuous covariates and model their functional
forms, their time-dependent effects, and their interactions.  Model
specification becomes therefore a complex problem and penalized regression
splines represent an appealing solution to that problem as splines offer the
required flexibility while penalization limits overfitting issues.  Current
implementations of penalized survival models can be slow or unstable and
sometimes lack some key features like taking into account expected mortality to
provide net survival and excess hazard estimates.  In contrast, @code{survPen}
provides an automated, fast, and stable implementation (thanks to explicit
calculation of the derivatives of the likelihood) and offers a unified framework
for multidimensional penalized hazard and excess hazard models. @code{survPen}
may be of interest to those who 1) analyse any kind of time-to-event data:
mortality, disease relapse, machinery breakdown, unemployment, etc 2) wish to
describe the associated hazard and to understand which predictors impact its
dynamics.  See Fauvernier et al. (2019a) <doi:10.21105/joss.01434> for an
overview of the package and Fauvernier et al. (2019b) <doi:10.1111/rssc.12368>
for the method.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-survparamsim
  (package
    (name "r-survparamsim")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survParamSim" version))
       (sha256
        (base32 "1dmxvdz1pp6k9rpln8w1pq1bkjmjx2jhym526c9bkl48xnla1nbw"))))
    (properties `((upstream-name . "survParamSim")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-survival
                             r-rlang
                             r-purrr
                             r-mvtnorm
                             r-magrittr
                             r-lifecycle
                             r-ggplot2
                             r-forcats
                             r-dplyr
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/yoshidk6/survParamSim")
    (synopsis "Parametric Survival Simulation with Parameter Uncertainty")
    (description
     "Perform survival simulation with parametric survival model generated from
survreg function in survival package.  In each simulation coefficients are
resampled from variance-covariance matrix of parameter estimates to capture
uncertainty in model parameters.  Prediction intervals of Kaplan-Meier estimates
and hazard ratio of treatment effect can be further calculated using simulated
survival data.")
    (license license:gpl3)))

(define-public r-survout
  (package
    (name "r-survout")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survout" version))
       (sha256
        (base32 "1ypxhdirppr6061zk1is473b8k8yy3m14ij4fa66rczs8gqy5sj2"))))
    (properties `((upstream-name . "survout")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-survival r-openxlsx r-dplyr r-cmprsk))
    (home-page "https://cran.r-project.org/package=survout")
    (synopsis "Excel Conversion of R Surival Analysis Output")
    (description
     "Simple and quick method of exporting the most often used survival analysis
results to an Excel sheet.")
    (license license:gpl3)))

(define-public r-survobj
  (package
    (name "r-survobj")
    (version "3.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survobj" version))
       (sha256
        (base32 "0vxv34sj15077jgiq1jfw7wdi0j396scp7ngz9nzg7zq1icg214q"))))
    (properties `((upstream-name . "survobj")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-survival r-ggplot2 r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://johnaponte.github.io/survobj/")
    (synopsis "Objects to Simulate Survival Times")
    (description
     "Generate objects that simulate survival times.  Random values for the
distributions are generated using the method described by Bender (2003)
<https://epub.ub.uni-muenchen.de/id/eprint/1716> and Leemis (1987) in Operations
Research, 35(6), 892â894.")
    (license license:gpl3+)))

(define-public r-survml
  (package
    (name "r-survml")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survML" version))
       (sha256
        (base32 "10fz2k7c3q94qb5rvhabcyxpyn1flj0xjfsf7h5pxqz6hx221hpn"))))
    (properties `((upstream-name . "survML")))
    (build-system r-build-system)
    (propagated-inputs (list r-superlearner r-iso))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/cwolock/survML")
    (synopsis
     "Flexible Estimation of Conditional Survival Functions Using Machine Learning")
    (description
     "This package provides tools for flexible estimation of conditional survival
functions using off-the-shelf machine learning tools.  Implements both global
and local survival stacking.  See Wolock CJ, Gilbert PB, Simon N, and Carone M
(2024) <doi:10.1080/10618600.2024.2304070>.")
    (license license:gpl3+)))

(define-public r-survmixer
  (package
    (name "r-survmixer")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survmixer" version))
       (sha256
        (base32 "00s06h7nn8li4d2kn4yzlnj3px1wkpvnx5w73y98cvrcwm0ad71g"))))
    (properties `((upstream-name . "survmixer")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=survmixer")
    (synopsis
     "Design of Clinical Trials with Survival Endpoints Based on Binary Responses")
    (description
     "Sample size and effect size calculations for survival endpoints based on mixture
survival-by-response model.  The methods implemented can be found in Bofill,
Shen & GÃ³mez (2021) <@code{arXiv:2008.12887>}.")
    (license license:gpl3)))

(define-public r-survmi
  (package
    (name "r-survmi")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurvMI" version))
       (sha256
        (base32 "0qgsgz11vz73v83bkxrrjwk7i26rllkxk97if3nw2rgl9jnmiih0"))))
    (properties `((upstream-name . "SurvMI")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-survival))
    (home-page "https://cran.r-project.org/package=SurvMI")
    (synopsis "Multiple Imputation Method in Survival Analysis")
    (description
     "In clinical trials, endpoints are sometimes evaluated with uncertainty.
Adjudication is commonly adopted to ensure the study integrity.  We propose to
use multiple imputation (MI) introduced by Robin (1987)
<doi:10.1002/9780470316696> to incorporate these uncertainties if reasonable
event probabilities were provided.  The method has been applied to Cox
Proportional Hazard (PH) model, Kaplan-Meier (KM) estimation and Log-rank test
in this package.  Moreover, weighted estimations discussed in Cook (2004)
<doi:10.1016/S0197-2456(00)00053-2> were also implemented with weights
calculated from event probabilities.  In conclusion, this package can handle
time-to-event analysis if events presented with uncertainty by different
methods.")
    (license license:gpl2)))

(define-public r-survmetrics
  (package
    (name "r-survmetrics")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurvMetrics" version))
       (sha256
        (base32 "14hpvqg0k3ww5qla4s08nrqwcy27y76kfp7b0g1hzs3vv046fb7i"))))
    (properties `((upstream-name . "SurvMetrics")))
    (build-system r-build-system)
    (propagated-inputs (list r-survminer r-survival r-randomforestsrc r-pec
                             r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/skyee1/SurvMetrics")
    (synopsis "Predictive Evaluation Metrics in Survival Analysis")
    (description
     "An implementation of popular evaluation metrics that are commonly used in
survival prediction including Concordance Index, Brier Score, Integrated Brier
Score, Integrated Square Error, Integrated Absolute Error and Mean Absolute
Error.  For a detailed information, see (Ishwaran H, Kogalur UB, Blackstone EH
and Lauer MS (2008) <doi:10.1214/08-AOAS169>) and (Moradian H, Larocque D and
Bellavance F (2017) <doi:10.1007/s10985-016-9372-1>) for different evaluation
metrics.")
    (license license:gpl2+)))

(define-public r-survma
  (package
    (name "r-survma")
    (version "1.6.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurvMA" version))
       (sha256
        (base32 "1nypjx7ds135ffgyxvjrqfqk0cissw27dmslqf11m9jajm7brqja"))))
    (properties `((upstream-name . "SurvMA")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-quadprog r-pec r-maxlik))
    (home-page "<https://github.com/Stat-WangXG/SurvMA>")
    (synopsis
     "Model Averaging Prediction of Personalized Survival Probabilities")
    (description
     "Provide model averaging-based approaches that can be used to predict
personalized survival probabilities.  The key underlying idea is to approximate
the conditional survival function using a weighted average of multiple candidate
models.  Two scenarios of candidate models are allowed: (Scenario 1) partial
linear Cox model and (Scenario 2) time-varying coefficient Cox model.  A
reference of the underlying methods is Li and Wang (2023)
<doi:10.1016/j.csda.2023.107759>.")
    (license license:gpl2+)))

(define-public r-survlong
  (package
    (name "r-survlong")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurvLong" version))
       (sha256
        (base32 "004s62c5w4lrp55g883iz8plviaij86m5wiaxcmqxaf5709sbk9g"))))
    (properties `((upstream-name . "SurvLong")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SurvLong")
    (synopsis
     "Analysis of Proportional Hazards Model with Sparse Longitudinal Covariates")
    (description
     "This package provides kernel weighting methods for estimation of proportional
hazards models with intermittently observed longitudinal covariates.  Cao H.,
Churpek M. M., Zeng D., and Fine J. P. (2015)
<doi:10.1080/01621459.2014.957289>.")
    (license license:gpl2)))

(define-public r-survivor
  (package
    (name "r-survivor")
    (version "2.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survivoR" version))
       (sha256
        (base32 "1jc6qfms8awd49vgv9ncdbaqmm7c9ynx9azgbdcbvpp9pb9f3glc"))))
    (properties `((upstream-name . "survivoR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-shinyjs
                             r-shinycssloaders
                             r-shiny
                             r-readr
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-glue
                             r-ggplot2
                             r-dt
                             r-dplyr
                             r-crayon))
    (home-page "https://github.com/doehm/survivoR")
    (synopsis
     "Data from all Seasons of Survivor (US) TV Series in Tidy Format")
    (description
     "Several datasets which detail the results and events of each season of Survivor.
 This includes details on the cast, voting history, immunity and reward
challenges, jury votes and viewers.  This data is useful for practicing data
wrangling, graph analytics and analysing how each season of Survivor played out.
 Includes ggplot2 scales and colour palettes for visualisation.")
    (license license:expat)))

(define-public r-survivalvignettes
  (package
    (name "r-survivalvignettes")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survivalVignettes" version))
       (sha256
        (base32 "140pwwgfi0h3gh33vwka3fxrpaxzgzf06g3g19nnwmpn4b8xm061"))))
    (properties `((upstream-name . "survivalVignettes")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bethatkinson/survivalVignettes")
    (synopsis "Survival Analysis Vignettes and Optional Datasets")
    (description
     "Vignettes for the survival package.  Split from the survival package since the
vignettes were getting large.  Also, since survival is a recommended package it
cannot make use of other packages outside of base+recommended (e.g. rmarkdown').")
    (license license:lgpl2.0+)))

(define-public r-survivaltests
  (package
    (name "r-survivaltests")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurvivalTests" version))
       (sha256
        (base32 "1yhl9pg5dbfkqrkl5i1da7c8zhianj2qgbs5arfk6lj6w3vqmnb9"))))
    (properties `((upstream-name . "SurvivalTests")))
    (build-system r-build-system)
    (propagated-inputs (list r-weibullness r-ggplot2))
    (home-page "https://cran.r-project.org/package=SurvivalTests")
    (synopsis "Survival Tests for One-Way Layout")
    (description
     "This package performs survival analysis for one-way layout.  The package
includes the generalized test for survival ANOVA (Tsui and Weerahandi (1989)
<doi:10.2307/2289949> and (Weerahandi, 2004; ISBN:978-0471470175)).  It also
performs pairwise comparisons and graphical approaches.  Moreover, it assesses
the weibullness of data in each group via test.  The package computes mean and
confidence interval under Weibull distribution.")
    (license license:gpl2+)))

(define-public r-survivalsvm
  (package
    (name "r-survivalsvm")
    (version "0.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survivalsvm" version))
       (sha256
        (base32 "1b4hrdrqq1z0kfn8vpdwg54388m25df2s6w9i574x3mkxkmkjkga"))))
    (properties `((upstream-name . "survivalsvm")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-quadprog
                             r-pracma
                             r-matrix
                             r-kernlab
                             r-hmisc))
    (home-page "https://github.com/imbs-hl/survivalsvm")
    (synopsis "Survival Support Vector Analysis")
    (description
     "This package performs support vectors analysis for data sets with survival
outcome.  Three approaches are available in the package: The regression approach
takes censoring into account when formulating the inequality constraints of the
support vector problem.  In the ranking approach, the inequality constraints set
the objective to maximize the concordance index for comparable pairs of
observations.  The hybrid approach combines the regression and ranking
constraints in the same model.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-survivalsl
  (package
    (name "r-survivalsl")
    (version "0.96")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survivalSL" version))
       (sha256
        (base32 "0mjaklq2zkz35z4y1w11yv5pc520a3xkinrynkkmrpdmvhlwjsk9"))))
    (properties `((upstream-name . "survivalSL")))
    (build-system r-build-system)
    (propagated-inputs (list r-survivalplann
                             r-survival
                             r-rpart
                             r-randomforestsrc
                             r-mass
                             r-hdnom
                             r-glmnetutils
                             r-glmnet
                             r-flexsurv
                             r-dplyr
                             r-date
                             r-caret))
    (home-page "https://cran.r-project.org/package=survivalSL")
    (synopsis "Super Learner for Survival Prediction from Censored Data")
    (description
     "Several functions and S3 methods to construct a super learner in the presence of
censored times-to-event and to evaluate its prognostic capacities.")
    (license license:gpl2+)))

(define-public r-survivalrec
  (package
    (name "r-survivalrec")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survivalREC" version))
       (sha256
        (base32 "14b4zzdjhk258lsy6dhlgdpkpyry6bd01dncrm9g0qj36hlwll3d"))))
    (properties `((upstream-name . "survivalREC")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-kernsmooth))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=survivalREC")
    (synopsis
     "Nonparametric Estimation of the Distribution of Gap Times for Recurrent Events")
    (description
     "This package provides estimates for the bivariate and trivariate distribution
functions and bivariate and trivariate survival functions for censored gap
times.  Two approaches, using existing methodologies, are considered: (i) the
Lin's estimator, which is based on the extension the Kaplan-Meier estimator of
the distribution function for the first event time and the Inverse Probability
of Censoring Weights for the second time (Lin DY, Sun W, Ying Z (1999)
<doi:10.1093/biomet/86.1.59> and (ii) another estimator based on Kaplan-Meier
weights (Una-Alvarez J, Meira-Machado L (2008)
<https://w3.math.uminho.pt/~lmachado/Biometria_conference.pdf>).  The proposed
methods are the landmark estimators based on subsampling approach, and the
estimator based on weighted cumulative hazard estimator.  The package also
provides nonparametric estimator conditional to a given continuous covariate.
All these methods have been submitted to be published.")
    (license license:gpl3)))

(define-public r-survivalplann
  (package
    (name "r-survivalplann")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survivalPLANN" version))
       (sha256
        (base32 "0vkpclsirb37a0xdzbbk7z0ajnp398l12mwkrxh2nprngsnfvywn"))))
    (properties `((upstream-name . "survivalPLANN")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-nnet))
    (home-page "https://cran.r-project.org/package=survivalPLANN")
    (synopsis "Neural Networks to Predict Survival")
    (description
     "Several functions and S3 methods to predict survival by using neural networks.
We implemented Partial Logistic Artificial Neural Networks (PLANN) as proposed
by Biganzoli et al. (1998) <https://pubmed.ncbi.nlm.nih.gov/9618776>.")
    (license license:gpl2+)))

(define-public r-survivalmpldc
  (package
    (name "r-survivalmpldc")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survivalMPLdc" version))
       (sha256
        (base32 "1wqlzpwm1p7nq19l3d7c93zv6fnn0b1w24kak9zwsgyhar3f5lv9"))))
    (properties `((upstream-name . "survivalMPLdc")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-splines2 r-matrixcalc r-copula))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=survivalMPLdc")
    (synopsis
     "Penalised Likelihood for Survival Analysis with Dependent Censoring")
    (description
     "Fitting Cox proportional hazard model under dependent right censoring using
copula and maximum penalised likelihood methods.")
    (license license:gpl3)))

(define-public r-survivalmpl
  (package
    (name "r-survivalmpl")
    (version "0.2-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survivalMPL" version))
       (sha256
        (base32 "1dxn9s68f545chqg1kfdys0gr419w259b1156g92mql899fm77sq"))))
    (properties `((upstream-name . "survivalMPL")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mass))
    (home-page "https://cran.r-project.org/package=survivalMPL")
    (synopsis "Penalised Maximum Likelihood for Survival Analysis Models")
    (description
     "Estimate the regression coefficients and the baseline hazard of proportional
hazard Cox models with left, right or interval censored survival data using
maximum penalised likelihood.  A non-parametric smooth estimate of the baseline
hazard function is provided.")
    (license license:lgpl2.0+)))

(define-public r-survivalmodels
  (package
    (name "r-survivalmodels")
    (version "0.1.191")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survivalmodels" version))
       (sha256
        (base32 "0iwbpfaqr0yl0ryi1d6y343cprm9ncn8c4qfp5q2vj193pzv10q0"))))
    (properties `((upstream-name . "survivalmodels")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://github.com/RaphaelS1/survivalmodels/")
    (synopsis "Models for Survival Analysis")
    (description
     "Implementations of classical and machine learning models for survival analysis,
including deep neural networks via keras and tensorflow'.  Each model includes a
separated fit and predict interface with consistent prediction types for
predicting risk or survival probabilities.  Models are either implemented from
Python via reticulate <https://CRAN.R-project.org/package=reticulate>, from code
in @code{GitHub} packages, or novel implementations using Rcpp
<https://CRAN.R-project.org/package=Rcpp>.  Neural networks are implemented from
the Python package pycox <https://github.com/havakv/pycox>.")
    (license license:expat)))

(define-public r-survivalclusteringtree
  (package
    (name "r-survivalclusteringtree")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurvivalClusteringTree" version))
       (sha256
        (base32 "1wxyk0b22igv319dpidm0b1wjp675k9z1araqzdd140lxan8nndf"))))
    (properties `((upstream-name . "SurvivalClusteringTree")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rcpparmadillo
                             r-rcpp
                             r-gridtext
                             r-formula-tools
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SurvivalClusteringTree")
    (synopsis "Clustering Analysis Using Survival Tree and Forest Algorithms")
    (description
     "An outcome-guided algorithm is developed to identify clusters of samples with
similar characteristics and survival rate.  The algorithm first builds a random
forest and then defines distances between samples based on the fitted random
forest.  Given the distances, we can apply hierarchical clustering algorithms to
define clusters.  Details about this method is described in
<https://github.com/luyouepiusf/@code{SurvivalClusteringTree>}.")
    (license license:gpl2+)))

(define-public r-survivalanalysis
  (package
    (name "r-survivalanalysis")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survivalAnalysis" version))
       (sha256
        (base32 "0s4cihmjhg94zcbvr7c1pxdjyc0rpbqlw1gni5xkdhqp4lnqmygf"))))
    (properties `((upstream-name . "survivalAnalysis")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidytidbits
                             r-tidyr
                             r-tibble
                             r-survminer
                             r-survival
                             r-stringr
                             r-scales
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-gridextra
                             r-ggplot2
                             r-forcats
                             r-dplyr
                             r-cowplot))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=survivalAnalysis")
    (synopsis
     "High-Level Interface for Survival Analysis and Associated Plots")
    (description
     "This package provides a high-level interface to perform survival analysis,
including Kaplan-Meier analysis and log-rank tests and Cox regression.  Aims at
providing a clear and elegant syntax, support for use in a pipeline, structured
output and plotting.  Builds upon the survminer package for Kaplan-Meier plots
and provides a customizable implementation for forest plots.  Kaplan & Meier
(1958) <doi:10.1080/01621459.1958.10501452> Cox (1972) <JSTOR:2985181> Peto &
Peto (1972) <JSTOR:2344317>.")
    (license license:gpl3)))

(define-public r-survival-svb
  (package
    (name "r-survival-svb")
    (version "0.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survival.svb" version))
       (sha256
        (base32 "13lipzn1cnkd5dcg8n69cdghycifiin3jnjvgzsv0g60l1jk0lmp"))))
    (properties `((upstream-name . "survival.svb")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rcppeigen r-rcpp r-glmnet))
    (home-page "https://github.com/mkomod/survival.svb")
    (synopsis "Fit High-Dimensional Proportional Hazards Models")
    (description
     "Implementation of methodology designed to perform: (i) variable selection, (ii)
effect estimation, and (iii) uncertainty quantification, for high-dimensional
survival data.  Our method uses a spike-and-slab prior with Laplace slab and
Dirac spike and approximates the corresponding posterior using variational
inference, a popular method in machine learning for scalable conditional
inference.  Although approximate, the variational posterior provides excellent
point estimates and good control of the false discovery rate.  For more
information see Komodromos et al. (2021) <@code{arXiv:2112.10270>}.")
    (license license:gpl3)))

(define-public r-survimchd
  (package
    (name "r-survimchd")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurviMChd" version))
       (sha256
        (base32 "1lwbs9b8kc6lhlfjziphmdhm7h8p6d4fbhihsmjdi8lwngvpgxx8"))))
    (properties `((upstream-name . "SurviMChd")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjags r-r2jags r-dplyr))
    (home-page "https://cran.r-project.org/package=SurviMChd")
    (synopsis
     "High Dimensional Survival Data Analysis with Markov Chain Monte Carlo")
    (description
     "High dimensional survival data analysis with Markov Chain Monte Carlo(MCMC).
Currently supports frailty data analysis.  Allows for Weibull and Exponential
distribution.  Includes function for interval censored data.")
    (license license:gpl3)))

(define-public r-survidm
  (package
    (name "r-survidm")
    (version "1.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survidm" version))
       (sha256
        (base32 "0ass7ffpk70ph7dcdjm2f6xyzqaavlkhr1pcij2vcasiy4v1vyiv"))))
    (properties `((upstream-name . "survidm")))
    (build-system r-build-system)
    (propagated-inputs (list r-tpmsm
                             r-survival
                             r-rcpp
                             r-plotly
                             r-np
                             r-kernsmooth
                             r-gridextra
                             r-ggplot2
                             r-foreach
                             r-dorng
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=survidm")
    (synopsis "Inference and Prediction in an Illness-Death Model")
    (description
     "Newly developed methods for the estimation of several probabilities in an
illness-death model.  The package can be used to obtain nonparametric and
semiparametric estimates for: transition probabilities, occupation
probabilities, cumulative incidence function and the sojourn time distributions.
 Additionally, it is possible to fit proportional hazards regression models in
each transition of the Illness-Death Model.  Several auxiliary functions are
also provided which can be used for marginal estimation of the survival
functions.")
    (license license:gpl3)))

(define-public r-survidinri
  (package
    (name "r-survidinri")
    (version "1.1-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survIDINRI" version))
       (sha256
        (base32 "1cl2dlxya3aqhmj7gwk5s5wfdrk5in8if04hm0bjg4ivcrw4m9r6"))))
    (properties `((upstream-name . "survIDINRI")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-survc1))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=survIDINRI")
    (synopsis
     "IDI and NRI for Comparing Competing Risk Prediction Models with Censored Survival Data")
    (description
     "This package performs inference for a class of measures to compare competing
risk prediction models with censored survival data.  The class includes the
integrated discrimination improvement index (IDI) and category-less net
reclassification index (NRI).")
    (license license:gpl2)))

(define-public r-survhidim
  (package
    (name "r-survhidim")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurvHiDim" version))
       (sha256
        (base32 "1kix00zfrbx11pwx2dfag8vz5nvf8siw2kbxi8g4fb7xib9wh2na"))))
    (properties `((upstream-name . "SurvHiDim")))
    (build-system r-build-system)
    (propagated-inputs (list r-useful
                             r-tidyverse
                             r-survival
                             r-readr
                             r-rdpack
                             r-igraph
                             r-glmnet
                             r-factoextra))
    (home-page "https://cran.r-project.org/package=SurvHiDim")
    (synopsis "High Dimensional Survival Data Analysis")
    (description
     "High dimensional time to events data analysis with variable selection technique.
 Currently support LASSO, clustering and Bonferroni's correction.")
    (license license:gpl3)))

(define-public r-survhe
  (package
    (name "r-survhe")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survHE" version))
       (sha256
        (base32 "02nznc00jwgvpfyn95fhaxr2hsznmdplwjysi0bg722lhlp2w2bn"))))
    (properties `((upstream-name . "survHE")))
    (build-system r-build-system)
    (propagated-inputs (list r-xlsx
                             r-tidyr
                             r-tibble
                             r-rms
                             r-ggplot2
                             r-flexsurv
                             r-dplyr))
    (home-page "https://github.com/giabaio/survHE")
    (synopsis "Survival Analysis in Health Economic Evaluation")
    (description
     "This package contains a suite of functions for survival analysis in health
economics.  These can be used to run survival models under a frequentist (based
on maximum likelihood) or a Bayesian approach (both based on Integrated Nested
Laplace Approximation or Hamiltonian Monte Carlo).  To run the Bayesian models,
the user needs to install additional modules (packages), i.e. @code{survHEinla}
and @code{survHEhmc}'.  These can be installed using remotes::install_github
from their @code{GitHub} repositories:
(<https://github.com/giabaio/@code{survHEhmc>} and
<https://github.com/giabaio/@code{survHEinla/>} respectively). @code{survHEinla}
is based on the package INLA, which is available for download at
<https://inla.r-inla-download.org/R/stable/>.  The user can specify a set of
parametric models using a common notation and select the preferred mode of
inference.  The results can also be post-processed to produce probabilistic
sensitivity analysis and can be used to export the output to an Excel file (e.g.
for a Markov model, as often done by modellers and practitioners).
<doi:10.18637/jss.v095.i14>.")
    (license license:gpl3+)))

(define-public r-surveyvoi
  (package
    (name "r-surveyvoi")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "surveyvoi" version))
       (sha256
        (base32 "0lkws90jlvr4l40l8h7fkck0skpf9yp4989a3bsqvm670l8qvc1w"))))
    (properties `((upstream-name . "surveyvoi")))
    (build-system r-build-system)
    (inputs (list mpfr
                  jags
                  gmp
                  fftw
                  automake
                  autoconf))
    (propagated-inputs (list r-xgboost
                             r-withr
                             r-vegan
                             r-tibble
                             r-sf
                             r-scales
                             r-rsymphony
                             r-rcppeigen
                             r-rcppalgos
                             r-rcpp
                             r-progress
                             r-poissonbinomial
                             r-plyr
                             r-nloptr
                             r-matrix
                             r-groupdata2
                             r-dplyr
                             r-doparallel
                             r-assertthat))
    (native-inputs (list pkg-config r-knitr))
    (home-page "https://prioritizr.github.io/surveyvoi/")
    (synopsis "Survey Value of Information")
    (description
     "Decision support tool for prioritizing sites for ecological surveys based on
their potential to improve plans for conserving biodiversity (e.g. plans for
establishing protected areas).  Given a set of sites that could potentially be
acquired for conservation management, it can be used to generate and evaluate
plans for surveying additional sites.  Specifically, plans for ecological
surveys can be generated using various conventional approaches (e.g. maximizing
expected species richness, geographic coverage, diversity of sampled
environmental algorithms.  After generating such survey plans, they can be
evaluated using conditions) and maximizing value of information.  Please note
that several functions depend on the Gurobi optimization software (available
from <https://www.gurobi.com>).  Additionally, the JAGS software (available from
<https://mcmc-jags.sourceforge.io/>) is required to fit hierarchical generalized
linear models.  For further details, see Hanson et al. (2022)
<doi:10.1111/1365-2664.14309>.")
    (license license:gpl3)))

(define-public r-surveytable
  (package
    (name "r-surveytable")
    (version "0.9.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "surveytable" version))
       (sha256
        (base32 "1l76dqg6dansl47fqc55yq70hd43cjqwqjkd1sfpg0n75jdcsbaa"))))
    (properties `((upstream-name . "surveytable")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey
                             r-magrittr
                             r-lifecycle
                             r-kableextra
                             r-huxtable
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://cdcgov.github.io/surveytable/")
    (synopsis "Formatted Survey Estimates")
    (description
     "Short and understandable commands that generate tabulated, formatted, and
rounded survey estimates.  Mostly a wrapper for the survey package (Lumley
(2004) <doi:10.18637/jss.v009.i08> <https://CRAN.R-project.org/package=survey>)
that identifies low-precision estimates using the National Center for Health
Statistics (NCHS) presentation standards (Parker et al. (2017)
<https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf>, Parker et al. (2023)
<doi:10.15620/cdc:124368>).")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-surveysd
  (package
    (name "r-surveysd")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "surveysd" version))
       (sha256
        (base32 "16l17ying9xshfn563kp7nn3hdqrg03ny578sgi08b7zfds5yf8b"))))
    (properties `((upstream-name . "surveysd")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-laeken r-ggplot2 r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/statistikat/surveysd")
    (synopsis
     "Survey Standard Error Estimation for Cumulated Estimates and their Differences in Complex Panel Designs")
    (description
     "Calculate point estimates and their standard errors in complex household surveys
using bootstrap replicates.  Bootstrapping considers survey design with a
rotating panel.  A comprehensive description of the methodology can be found
under <https://statistikat.github.io/surveysd/articles/methodology.html>.")
    (license license:gpl2+)))

(define-public r-surveyprev
  (package
    (name "r-surveyprev")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "surveyPrev" version))
       (sha256
        (base32 "1vp4v27vj9w0vsab6jjy9hvx36lrn01qkx3d0idmx0zwbd0bxy50"))))
    (properties `((upstream-name . "surveyPrev")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyverse
                             r-survey
                             r-summer
                             r-stringr
                             r-spdep
                             r-sp
                             r-sjlabelled
                             r-sf
                             r-rdhs
                             r-raster
                             r-naniar
                             r-matrixstats
                             r-labelled
                             r-ggplot2
                             r-dplyr
                             r-data-table))
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "https://github.com/richardli/surveyPrev")
    (synopsis
     "Mapping the Prevalence of Binary Indicators using Survey Data in Small Areas")
    (description
     "This package provides a pipeline to perform small area estimation and prevalence
mapping of binary indicators using health and demographic survey data, described
in Fuglstad et al. (2022) <doi:10.48550/@code{arXiv.2110.09576>} and Wakefield
et al. (2020) <doi:10.1111/insr.12400>.")
    (license license:gpl2+)))

(define-public r-surveyplanning
  (package
    (name "r-surveyplanning")
    (version "4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "surveyplanning" version))
       (sha256
        (base32 "19ydgcwyvkhknf4qmlw7j25skcj9a4rd6s8j5n7bdqpljl60zync"))))
    (properties `((upstream-name . "surveyplanning")))
    (build-system r-build-system)
    (propagated-inputs (list r-laeken r-data-table))
    (home-page "https://csblatvia.github.io/surveyplanning/")
    (synopsis "Survey Planning Tools")
    (description
     "This package provides tools for sample survey planning, including sample size
calculation, estimation of expected precision for the estimates of totals, and
calculation of optimal sample size allocation.")
    (license license:gpl2+)))

(define-public r-surveygraph
  (package
    (name "r-surveygraph")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "surveygraph" version))
       (sha256
        (base32 "06izdycgrggzgyf730fkrd1m3d9a8k2f4fvs9hhihsbkcns2kpgi"))))
    (properties `((upstream-name . "surveygraph")))
    (build-system r-build-system)
    (home-page "https://surveygraph.ie/")
    (synopsis "Network Representations of Attitudes")
    (description
     "This package provides a tool for computing network representations of attitudes,
extracted from tabular data such as sociological surveys.  Development of
surveygraph software and training materials was initially funded by the European
Union under the ERC Proof-of-concept programme (ERC, Attitude-Maps-4-All,
project number: 101069264).  Views and opinions expressed are however those of
the author(s) only and do not necessarily reflect those of the European Union or
the European Research Council Executive Agency.  Neither the European Union nor
the granting authority can be held responsible for them.")
    (license license:expat)))

(define-public r-surveyexplorer
  (package
    (name "r-surveyexplorer")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "surveyexplorer" version))
       (sha256
        (base32 "0znrisi4y2nkvxmqv6909mfz8cap9wmq32jafafnmihc99k7a6kq"))))
    (properties `((upstream-name . "surveyexplorer")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-stringr
                             r-scales
                             r-rlang
                             r-purrr
                             r-gt
                             r-ggupset
                             r-ggplot2
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=surveyexplorer")
    (synopsis "Quickly Explore Complex Survey Data")
    (description
     "Visualize and tabulate single-choice, multiple-choice, matrix-style questions
from survey data.  Includes ability to group cross-tabulations, frequency
distributions, and plots by categorical variables and to integrate survey
weights.  Ideal for quickly uncovering descriptive patterns in survey data.")
    (license license:expat)))

(define-public r-surveydefense
  (package
    (name "r-surveydefense")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurveyDefense" version))
       (sha256
        (base32 "13x89nnqlycdvdya8p0qphsmq4kf94m6g3s4dy1npnhddjww277s"))))
    (properties `((upstream-name . "SurveyDefense")))
    (build-system r-build-system)
    (propagated-inputs (list r-flextable r-dplyr))
    (home-page "https://cran.r-project.org/package=SurveyDefense")
    (synopsis "Survey Defense Tool")
    (description
     "This tool is designed to analyze up to 5 Fraud Detection Questions integrated
into a survey, focusing on potential fraudulent participants to clean the survey
dataset from potential fraud.  Fraud Detection Questions and further information
available at <https://surveydefense.org>.")
    (license license:gpl3)))

(define-public r-surveydata
  (package
    (name "r-surveydata")
    (version "0.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "surveydata" version))
       (sha256
        (base32 "0wrp92j50a19is4xvh5s82zkp3rvn46gi2344pxinppfax0kjf2h"))))
    (properties `((upstream-name . "surveydata")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-scales
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-ggplot2
                             r-dt
                             r-dplyr
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/andrie/surveydata")
    (synopsis "Tools to Work with Survey Data")
    (description
     "Data obtained from surveys contains information not only about the survey
responses, but also the survey metadata, e.g. the original survey questions and
the answer options.  The surveydata package makes it easy to keep track of this
metadata, and to easily extract columns with specific questions.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-surveycv
  (package
    (name "r-surveycv")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "surveyCV" version))
       (sha256
        (base32 "183ybmfin4xmpsmssw7hskj1rgwpabpa616bzxlir27kygyrswbz"))))
    (properties `((upstream-name . "surveyCV")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey r-magrittr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ColbyStatSvyRsch/surveyCV/")
    (synopsis "Cross Validation Based on Survey Design")
    (description
     "This package provides functions to generate K-fold cross validation (CV) folds
and CV test error estimates that take into account how a survey dataset's
sampling design was constructed (SRS, clustering, stratification, and/or unequal
sampling weights).  You can input linear and logistic regression models, along
with data and a type of survey design in order to get an output that can help
you determine which model best fits the data using K-fold cross validation.  Our
paper on \"K-Fold Cross-Validation for Complex Sample Surveys\" by Wieczorek,
Guerin, and @code{McMahon} (2022) <doi:10.1002/sta4.454> explains why differing
how we take folds based on survey design is useful.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-surveycc
  (package
    (name "r-surveycc")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurveyCC" version))
       (sha256
        (base32 "04p0g3dffk4khb2f2i9zzcqk8y1d13vyaf2vbn37zpc1c4zd9pvn"))))
    (properties `((upstream-name . "SurveyCC")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey r-candisc))
    (home-page "https://github.com/237triangle/SurveyCC")
    (synopsis "Canonical Correlation for Survey Data")
    (description
     "This package performs canonical correlation for survey data, including multiple
tests of significance for secondary canonical correlations.  A key feature of
this package is that it incorporates survey data structure directly in a novel
test of significance via a sequence of simple linear regression models on the
canonical variates.  See reference - Cruz-Cano, Cohen, and Mead-Morse (2024)
\"Canonical Correlation Analysis of Survey data: the @code{SurveyCC} R package\"
The R Journal under review.")
    (license license:expat)))

(define-public r-surveybootstrap
  (package
    (name "r-surveybootstrap")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "surveybootstrap" version))
       (sha256
        (base32 "1ci6gqs1kiizpmddkw9r5nsq9wqdhha3kxhnn6b28fczrsypilf8"))))
    (properties `((upstream-name . "surveybootstrap")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rcpparmadillo
                             r-rcpp
                             r-purrr
                             r-plyr
                             r-functional
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=surveybootstrap")
    (synopsis "Bootstrap with Survey Data")
    (description
     "This package implements different kinds of bootstraps to estimate sampling
variation from survey data with complex designs.  Includes the rescaled
bootstrap described in Rust and Rao (1996) <doi:10.1177/096228029600500305> and
Rao and Wu (1988) <doi:10.1080/01621459.1988.10478591>.")
    (license license:expat)))

(define-public r-survexp-fr
  (package
    (name "r-survexp-fr")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survexp.fr" version))
       (sha256
        (base32 "0ci65w3w06kwab98q9gb7nb7smq9r25qfibkgfilsg1i5li6p9h5"))))
    (properties `((upstream-name . "survexp.fr")))
    (build-system r-build-system)
    (propagated-inputs (list r-writexls r-survival))
    (home-page "https://cran.r-project.org/package=survexp.fr")
    (synopsis "Relative Survival, AER and SMR Based on French Death Rates")
    (description
     "It computes Relative survival, AER and SMR based on French death rates.")
    (license license:gpl2+)))

(define-public r-survex
  (package
    (name "r-survex")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survex" version))
       (sha256
        (base32 "09kksjpfikw96njblplgqn33lhfvbpqzsid35cs3wmxhi2xv5fbf"))))
    (properties `((upstream-name . "survex")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-pec
                             r-patchwork
                             r-kernelshap
                             r-ggplot2
                             r-dalex))
    (home-page "https://modeloriented.github.io/survex/")
    (synopsis "Explainable Machine Learning in Survival Analysis")
    (description
     "Survival analysis models are commonly used in medicine and other areas.  Many of
them are too complex to be interpreted by human.  Exploration and explanation is
needed, but standard methods do not give a broad enough picture.  survex
provides easy-to-apply methods for explaining survival models, both complex
black-boxes and simpler statistical models.  They include methods specific to
survival analysis such as @code{SurvSHAP(t}) introduced in Krzyzinski et al.,
(2023) <doi:10.1016/j.knosys.2022.110234>, @code{SurvLIME} described in Kovalev
et al., (2020) <doi:10.1016/j.knosys.2020.106164> as well as extensions of
existing ones described in Biecek et al., (2021) <doi:10.1201/9780429027192>.")
    (license license:gpl3+)))

(define-public r-surveval
  (package
    (name "r-surveval")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurvEval" version))
       (sha256
        (base32 "0xhlfzcyniwy1wb68cdq3in9kq5w5m4439jgjmbrcrhplxab1cm0"))))
    (properties `((upstream-name . "SurvEval")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rcpp))
    (home-page "https://cran.r-project.org/package=SurvEval")
    (synopsis "Methods for the Evaluation of Survival Models")
    (description
     "This package provides predictive accuracy tools to evaluate time-to-event
survival models.  This includes calculating the concordance probability estimate
that incorporates the follow-up time for a particular study developed by Devlin,
Gonen, Heller (2020)<doi:10.1007/s10985-020-09503-3>.  It also evaluates the
concordance probability estimate for nested Cox proportional hazards models
using a projection-based approach by Heller and Devlin (under review).")
    (license license:gpl2+)))

(define-public r-surveltest
  (package
    (name "r-surveltest")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survELtest" version))
       (sha256
        (base32 "03jnj88z45iw9v1628bz99608q2jwbqw6cfsvqal2jj4392fz428"))))
    (properties `((upstream-name . "survELtest")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-plyr r-nloptr r-iso))
    (home-page "https://github.com/news11/survELtest")
    (synopsis "Comparing Multiple Survival Functions with Crossing Hazards")
    (description
     "Computing the one-sided/two-sided integrated/maximally selected EL statistics
for simultaneous testing, the one-sided/two-sided EL tests for pointwise
testing, and an initial test that precedes one-sided testing to exclude the
possibility of crossings or alternative orderings among the survival functions.")
    (license license:gpl2+)))

(define-public r-surveillance
  (package
    (name "r-surveillance")
    (version "1.24.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "surveillance" version))
       (sha256
        (base32 "0lqh2c1qkf3qgxdyvrziha9hpl7xgmd40szd4adp5jgn3lvi0zng"))))
    (properties `((upstream-name . "surveillance")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-spatstat-geom
                             r-sp
                             r-polycub
                             r-nlme
                             r-matrix
                             r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://surveillance.R-Forge.R-project.org/")
    (synopsis
     "Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena")
    (description
     "Statistical methods for the modeling and monitoring of time series of counts,
proportions and categorical data, as well as for the modeling of continuous-time
point processes of epidemic phenomena.  The monitoring methods focus on
aberration detection in count data time series from public health surveillance
of communicable diseases, but applications could just as well originate from
environmetrics, reliability engineering, econometrics, or social sciences.  The
package implements many typical outbreak detection procedures such as the
(improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of
Hoehle and Paul (2008) <doi:10.1016/j.csda.2008.02.015>.  A novel CUSUM approach
combining logistic and multinomial logistic modeling is also included.  The
package contains several real-world data sets, the ability to simulate outbreak
data, and to visualize the results of the monitoring in a temporal, spatial or
spatio-temporal fashion.  A recent overview of the available monitoring
procedures is given by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>.  For
the retrospective analysis of epidemic spread, the package provides three
endemic-epidemic modeling frameworks with tools for visualization, likelihood
inference, and simulation.  hhh4() estimates models for (multivariate) count
time series following Paul and Held (2011) <doi:10.1002/sim.4177> and Meyer and
Held (2014) <doi:10.1214/14-AOAS743>. @code{twinSIR}() models the
susceptible-infectious-recovered (SIR) event history of a fixed population, e.g,
epidemics across farms or networks, as a multivariate point process as proposed
by Hoehle (2009) <doi:10.1002/bimj.200900050>.  twinstim() estimates
self-exciting point process models for a spatio-temporal point pattern of
infective events, e.g., time-stamped geo-referenced surveillance data, as
proposed by Meyer et al. (2012) <doi:10.1111/j.1541-0420.2011.01684.x>.  A
recent overview of the implemented space-time modeling frameworks for epidemic
phenomena is given by Meyer et al. (2017) <doi:10.18637/jss.v077.i11>.")
    (license license:gpl2)))

(define-public r-surveil
  (package
    (name "r-surveil")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "surveil" version))
       (sha256
        (base32 "18q7i3pi5b4nx24zfkcswvdivqa7s0zri1gfimygyaz4mdpza1y1"))))
    (properties `((upstream-name . "surveil")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tidybayes
                             r-stanheaders
                             r-scales
                             r-rstantools
                             r-rstan
                             r-rlang
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-gridextra
                             r-ggplot2
                             r-ggdist
                             r-dplyr
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://connordonegan.github.io/surveil/")
    (synopsis "Time Series Models for Disease Surveillance")
    (description
     "Fits time trend models for routine disease surveillance tasks and returns
probability distributions for a variety of quantities of interest, including
age-standardized rates, period and cumulative percent change, and measures of
health inequality.  The models are appropriate for count data such as disease
incidence and mortality data, employing a Poisson or binomial likelihood and the
first-difference (random-walk) prior for unknown risk.  Optionally add a
covariance matrix for multiple, correlated time series models.  Inference is
completed using Markov chain Monte Carlo via the Stan modeling language.
References: Donegan, Hughes, and Lee (2022) <doi:10.2196/34589>; Stan
Development Team (2021) <https://mc-stan.org>; Theil (1972, ISBN:0-444-10378-3).")
    (license license:gpl3+)))

(define-public r-survdisc
  (package
    (name "r-survdisc")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurvDisc" version))
       (sha256
        (base32 "0ajvnm0a4krbm0m584bg58hd7dzl0f2rz5as1zsajx8agywb5qmv"))))
    (properties `((upstream-name . "SurvDisc")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-simex
                             r-nlme
                             r-mvtnorm
                             r-mass
                             r-cubature))
    (home-page "https://cran.r-project.org/package=SurvDisc")
    (synopsis "Discrete Time Survival and Longitudinal Data Analysis")
    (description
     "Various functions for discrete time survival analysis and longitudinal analysis.
 SIMEX method for correcting for bias for errors-in-variables in a mixed effects
model.  Asymptotic mean and variance of different proportional hazards test
statistics using different ties methods given two survival curves and censoring
distributions.  Score test and Wald test for regression analysis of grouped
survival data.  Calculation of survival curves for events defined by the
response variable in a mixed effects model crossing a threshold with or without
confirmation.")
    (license license:gpl2)))

(define-public r-survcurve
  (package
    (name "r-survcurve")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survCurve" version))
       (sha256
        (base32 "0zspyxzn99ljiqp1111202714rqs4vg6j2rv66qv8ilxajvdxxas"))))
    (properties `((upstream-name . "survCurve")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=survCurve")
    (synopsis "Plots Survival Curves Element by Element")
    (description
     "Plots survival models from the survival package.  Additionally, it plots curves
of multistate models from the mstate package.  Typically, a plot is drawn by the
sequence survplot(), @code{confIntArea}(), @code{survCurve}() and
@code{nrAtRisk}().  The separation of the plot in this 4 functions allows for
great flexibility to make a custom plot for publication.")
    (license license:expat)))

(define-public r-survcorr
  (package
    (name "r-survcorr")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurvCorr" version))
       (sha256
        (base32 "0pccjc9zdvbb869wfywm23l63h1bpi5px4q5x7fq54pm0iyi1k73"))))
    (properties `((upstream-name . "SurvCorr")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-fields))
    (home-page "https://cran.r-project.org/package=SurvCorr")
    (synopsis "Correlation of Bivariate Survival Times")
    (description
     "Estimates correlation coefficients with associated confidence limits for
bivariate, partially censored survival times.  Uses the iterative multiple
imputation approach proposed by Schemper, Kaider, Wakounig and Heinze (2013)
<doi:10.1002/sim.5874>.  Provides a scatterplot function to visualize the
bivariate distribution, either on the original time scale or as copula.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-survcompare
  (package
    (name "r-survcompare")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survcompare" version))
       (sha256
        (base32 "1g5gzs61srzx4glrxgfr70vwmx96mvf6lbca43srskn1f8n93k14"))))
    (properties `((upstream-name . "survcompare")))
    (build-system r-build-system)
    (propagated-inputs (list r-timeroc r-survival r-randomforestsrc r-glmnet
                             r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=survcompare")
    (synopsis
     "Nested Cross-Validation to Compare Cox-PH, Cox-Lasso, Survival Random Forests")
    (description
     "This package performs repeated nested cross-validation for Cox Proportionate
Hazards, Cox Lasso, Survival Random Forest, and their ensemble.  Returns
internally validated concordance index, time-dependent area under the curve,
Brier score, calibration slope, and statistical testing of non-linear ensemble
outperforming the baseline Cox model.  In this, it helps researchers to quantify
the gain of using a more complex survival model, or justify its redundancy.
Equally, it shows the performance value of the non-linear and interaction terms,
and may highlight the need of further feature transformation.  Further details
can be found in Shamsutdinova, Stamate, Roberts, & Stahl (2022) \"Combining Cox
Model and Tree-Based Algorithms to Boost Performance and Preserve
Interpretability for Health Outcomes\" <doi:10.1007/978-3-031-08337-2_15>, where
the method is described as Ensemble 1.")
    (license license:gpl3+)))

(define-public r-survc1
  (package
    (name "r-survc1")
    (version "1.0-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survC1" version))
       (sha256
        (base32 "1ays0qlrafg1p0rvxq119skj0ciz4knxycrgrb93nikl4snmkkvw"))))
    (properties `((upstream-name . "survC1")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=survC1")
    (synopsis
     "C-Statistics for Risk Prediction Models with Censored Survival Data")
    (description
     "This package performs inference for C of risk prediction models with censored
survival data, using the method proposed by Uno et al. (2011)
<doi:10.1002/sim.4154>.  Inference for the difference in C between two competing
prediction models is also implemented.")
    (license license:gpl2)))

(define-public r-survbootoutliers
  (package
    (name "r-survbootoutliers")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survBootOutliers" version))
       (sha256
        (base32 "1gfh2n1kd1m4bpd1j7islm5ar1qmzycnmfpb8zl8ghv0z5rf5f09"))))
    (properties `((upstream-name . "survBootOutliers")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "https://github.com/jonydog/survBootOutliers")
    (synopsis
     "Concordance Based Bootstrap Methods for Outlier Detection in Survival Analysis")
    (description
     "Three new methods to perform outlier detection in a survival context.  In total
there are six methods provided, the first three methods are traditional
residual-based outlier detection methods, the second three are the
concordance-based.  Package developed during the work on the two following
publications: Pinto J., Carvalho A. and Vinga S. (2015)
<doi:10.5220/0005225300750082>; Pinto J.D., Carvalho A.M., Vinga S. (2015)
<doi:10.1007/978-3-319-27926-8_22>.")
    (license license:gpl2)))

(define-public r-survawkmt2
  (package
    (name "r-survawkmt2")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survAWKMT2" version))
       (sha256
        (base32 "08v7gd51fqjj66b504apgplnarlbspsf2146gpwvkqli6h5a4rsf"))))
    (properties `((upstream-name . "survAWKMT2")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "https://cran.r-project.org/package=survAWKMT2")
    (synopsis "Two-Sample Tests Based on Differences of Kaplan-Meier Curves")
    (description
     "Tests for equality of two survival functions based on integrated weighted
differences of two Kaplan-Meier curves.")
    (license license:gpl2)))

(define-public r-survauc
  (package
    (name "r-survauc")
    (version "1.3-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survAUC" version))
       (sha256
        (base32 "18j3fmn0ac97pl3bwnj0iamgcnva0198g7g09gdqml1dp9vh4k8y"))))
    (properties `((upstream-name . "survAUC")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rms))
    (home-page "https://cran.r-project.org/package=survAUC")
    (synopsis "Estimators of Prediction Accuracy for Time-to-Event Data")
    (description
     "This package provides a variety of functions to estimate time-dependent
true/false positive rates and AUC curves from a set of censored survival data.")
    (license license:gpl2)))

(define-public r-survah
  (package
    (name "r-survah")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "survAH" version))
       (sha256
        (base32 "1cs3h2gc7i5lpy98lq6vi70k207x42nl1swdp5qs602zfbivx56k"))))
    (properties `((upstream-name . "survAH")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=survAH")
    (synopsis "Survival Data Analysis using Average Hazard")
    (description
     "This package performs two-sample comparisons based on average hazard with
survival weight (AHSW) or general censoring-free incidence rate (CFIR) proposed
by Uno and Horiguchi (2023) <doi:10.1002/sim.9651>.")
    (license license:gpl2)))

(define-public r-surv2samplecomp
  (package
    (name "r-surv2samplecomp")
    (version "1.0-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "surv2sampleComp" version))
       (sha256
        (base32 "0pk70q33il9clz0ikzx1cypg5ygnsqp5hgk67xczzbidksjzs4dn"))))
    (properties `((upstream-name . "surv2sampleComp")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-plotrix r-flexsurv))
    (home-page "https://cran.r-project.org/package=surv2sampleComp")
    (synopsis
     "Inference for Model-Free Between-Group Parameters for Censored Survival Data")
    (description
     "This package performs inference of several model-free group contrast measures,
which include difference/ratio of cumulative incidence rates at given time
points, quantiles, and restricted mean survival times (RMST).  Two kinds of
covariate adjustment procedures (i.e., regression and augmentation) for
inference of the metrics based on RMST are also included.")
    (license license:gpl2)))

(define-public r-surtvep
  (package
    (name "r-surtvep")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "surtvep" version))
       (sha256
        (base32 "0mmpyjv8cyayqpkkr7lh31s7bascv6i3h3774sfc015lzjpwl22f"))))
    (properties `((upstream-name . "surtvep")))
    (build-system r-build-system)
    (inputs (list zlib))
    (propagated-inputs (list r-tibble
                             r-rlang
                             r-rcpparmadillo
                             r-rcpp
                             r-ggpubr
                             r-ggplot2))
    (native-inputs (list pkg-config))
    (home-page "https://github.com/UM-KevinHe/surtvep")
    (synopsis
     "Cox Non-Proportional Hazards Model with Time-Varying Coefficients")
    (description
     "Fit Cox non-proportional hazards models with time-varying coefficients.  Both
unpenalized procedures (Newton and proximal Newton) and penalized procedures
(P-splines and smoothing splines) are included using B-spline basis functions
for estimating time-varying coefficients.  For penalized procedures, cross
validations, @code{mAIC}, TIC or GIC are implemented to select tuning
parameters.  Utilities for carrying out post-estimation visualization,
summarization, point-wise confidence interval and hypothesis testing are also
provided.  For more information, see Wu et al. (2022) <doi:
10.1007/s10985-021-09544-2> and Luo et al. (2023)
<doi:10.1177/09622802231181471>.")
    (license license:gpl3)))

(define-public r-surtex
  (package
    (name "r-surtex")
    (version "0.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "suRtex" version))
       (sha256
        (base32 "0xcy3x1079v10bn3n3y6lxignb9n3h57w4hhrvzi5y14x05jjyda"))))
    (properties `((upstream-name . "suRtex")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=suRtex")
    (synopsis "LaTeX descriptive statistic reporting for survey data")
    (description
     "@code{suRtex} was designed for easy descriptive statistic reporting of
categorical survey data (e.g., Likert scales) in @code{LaTeX}. @code{suRtex}
takes a matrix or data frame and produces the @code{LaTeX} code necessary for a
sideways table creation.  Mean, median, standard deviation, and sample size are
optional.")
    (license license:gpl3)))

(define-public r-surrosurvroc
  (package
    (name "r-surrosurvroc")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "surrosurvROC" version))
       (sha256
        (base32 "1dpqr9dqzckgvdfrb2ndxazra0lbp6db9wbjch4vig3kyz6nyw4y"))))
    (properties `((upstream-name . "surrosurvROC")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "https://cran.r-project.org/package=surrosurvROC")
    (synopsis "Surrogate Survival ROC")
    (description
     "Nonparametric and semiparametric estimations of the time-dependent ROC curve for
an incomplete failure time data with surrogate failure time endpoints.")
    (license license:gpl2+)))

(define-public r-surrosurv
  (package
    (name "r-surrosurv")
    (version "1.1.26")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "surrosurv" version))
       (sha256
        (base32 "1zkrbc71n06i0fccrv5f15rnkbp3cb8lnizh4z16mmqgvn35hg1j"))))
    (properties `((upstream-name . "surrosurv")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-parfm
                             r-optimx
                             r-mvmeta
                             r-msm
                             r-matrix
                             r-mass
                             r-lme4
                             r-eha
                             r-copula))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/Oncostat/surrosurv")
    (synopsis
     "Evaluation of Failure Time Surrogate Endpoints in Individual Patient Data Meta-Analyses")
    (description
     "This package provides functions for the evaluation of surrogate endpoints when
both the surrogate and the true endpoint are failure time variables.  The
approaches implemented are: (1) the two-step approach (Burzykowski et al, 2001)
<DOI:10.1111/1467-9876.00244> with a copula model (Clayton, Plackett, Hougaard)
at the first step and either a linear regression of log-hazard ratios at the
second step (either adjusted or not for measurement error); (2) mixed
proportional hazard models estimated via mixed Poisson GLM (Rotolo et al, 2017
<DOI:10.1177/0962280217718582>).")
    (license license:gpl2)))

(define-public r-surrogatetest
  (package
    (name "r-surrogatetest")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurrogateTest" version))
       (sha256
        (base32 "0vs1ii3l6i6cmswyv6xjbya5filfixppyzmnw099g9s4x8kn0kq7"))))
    (properties `((upstream-name . "SurrogateTest")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "https://cran.r-project.org/package=SurrogateTest")
    (synopsis
     "Early Testing for a Treatment Effect using Surrogate Marker Information")
    (description
     "This package provides functions to test for a treatment effect in terms of the
difference in survival between a treatment group and a control group using
surrogate marker information obtained at some early time point in a
time-to-event outcome setting.  Nonparametric kernel estimation is used to
estimate the test statistic and perturbation resampling is used for variance
estimation.  More details will be available in the future in: Parast L, Cai T,
Tian L (2019) ``Using a Surrogate Marker for Early Testing of a Treatment
Effect\" Biometrics, 75(4):1253-1263. <doi:10.1111/biom.13067>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-surrogatersq
  (package
    (name "r-surrogatersq")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurrogateRsq" version))
       (sha256
        (base32 "1g77cwp5cdldhqlp3k7f7pzxjww2pxgr842qd3fr2k0gzg2s246a"))))
    (properties `((upstream-name . "SurrogateRsq")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-progress r-passo r-mass))
    (native-inputs (list r-r-rsp))
    (home-page "https://xiaorui.site/SurrogateRsq/")
    (synopsis
     "Goodness-of-Fit Analysis for Categorical Data using the Surrogate R-Squared")
    (description
     "To assess and compare the models goodness of fit, R-squared is one of the most
popular measures.  For categorical data analysis, however, no universally
adopted R-squared measure can resemble the ordinary least square (OLS) R-squared
for linear models with continuous data.  This package implement the surrogate
R-squared measure for categorical data analysis, which is proposed in the study
of Dungang Liu, Xiaorui Zhu, Brandon Greenwell, and Zewei Lin (2022)
<doi:10.1111/bmsp.12289>.  It can generate a point or interval measure of the
surrogate R-squared.  It can also provide a ranking measure of the percentage
contribution of each variable to the overall surrogate R-squared.  This ranking
assessment allows one to check the importance of each variable in terms of their
explained variance.  This package can be jointly used with other existing R
packages for variable selection and model diagnostics in the model-building
process.")
    (license license:gpl2+)))

(define-public r-surrogateregression
  (package
    (name "r-surrogateregression")
    (version "0.6.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurrogateRegression" version))
       (sha256
        (base32 "0cjw780wzvxp6q4q8m57d3qzv7ivk884h6s6n9walnd8rvrfj414"))))
    (properties `((upstream-name . "SurrogateRegression")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SurrogateRegression")
    (synopsis "Surrogate Outcome Regression Analysis")
    (description
     "This package performs estimation and inference on a partially missing target
outcome (e.g. gene expression in an inaccessible tissue) while borrowing
information from a correlated surrogate outcome (e.g. gene expression in an
accessible tissue).  Rather than regarding the surrogate outcome as a proxy for
the target outcome, this package jointly models the target and surrogate
outcomes within a bivariate regression framework.  Unobserved values of either
outcome are treated as missing data.  In contrast to imputation-based inference,
no assumptions are required regarding the relationship between the target and
surrogate outcomes.  Estimation in the presence of bilateral outcome missingness
is performed via an expectation conditional maximization either algorithm.  In
the case of unilateral target missingness, estimation is performed using an
accelerated least squares procedure.  A flexible association test is provided
for evaluating hypotheses about the target regression parameters.  For
additional details, see: @code{McCaw} ZR, Gaynor SM, Sun R, Lin X: \"Leveraging a
surrogate outcome to improve inference on a partially missing target outcome\"
<doi:10.1111/biom.13629>.")
    (license license:gpl3)))

(define-public r-surrogaterank
  (package
    (name "r-surrogaterank")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurrogateRank" version))
       (sha256
        (base32 "0q90qq3apn8qs6rlmw0vj3iv89mpm85wah2xqjdwvbf8k7hwkgrm"))))
    (properties `((upstream-name . "SurrogateRank")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SurrogateRank")
    (synopsis "Rank-Based Test to Evaluate a Surrogate Marker")
    (description
     "Uses a novel rank-based nonparametric approach to evaluate a surrogate marker in
a small sample size setting.  Details are described in Parast et al (2024)
<doi:10.1093/biomtc/ujad035>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-surrogateoutcome
  (package
    (name "r-surrogateoutcome")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurrogateOutcome" version))
       (sha256
        (base32 "1wiqn9gfvj225dvmp49fyybp77np507s04f28a9wa9jiyvqf7m97"))))
    (properties `((upstream-name . "SurrogateOutcome")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "https://cran.r-project.org/package=SurrogateOutcome")
    (synopsis
     "Estimation of the Proportion of Treatment Effect Explained by Surrogate Outcome Information")
    (description
     "This package provides functions to estimate the proportion of treatment effect
on a censored primary outcome that is explained by the treatment effect on a
censored surrogate outcome/event.  All methods are described in detail in
Parast, Tian, Cai (2020) \"Assessing the Value of a Censored Surrogate Outcome\"
<doi:10.1007/s10985-019-09473-1>.  The main functions are (1) R.q.event() which
calculates the proportion of the treatment effect (the difference in restricted
mean survival time at time t) explained by surrogate outcome information
observed up to a selected landmark time, (2) R.t.estimate() which calculates the
proportion of the treatment effect explained by primary outcome information only
observed up to a selected landmark time, and (3) IV.event() which calculates the
incremental value of the surrogate outcome information.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-surrogatebma
  (package
    (name "r-surrogatebma")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurrogateBMA" version))
       (sha256
        (base32 "19q63rdrm4y03xmjmjqvsq8s1fx48jalxshzrdyzms9dacbshr0z"))))
    (properties `((upstream-name . "SurrogateBMA")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsurrogate
                             r-rcppnumerical
                             r-rcppeigen
                             r-rcpp
                             r-mvtnorm
                             r-mcmcpack))
    (home-page "https://cran.r-project.org/package=SurrogateBMA")
    (synopsis
     "Flexible Evaluation of Surrogate Markers with Bayesian Model Averaging")
    (description
     "This package provides functions to estimate the proportion of treatment effect
explained by the surrogate marker using a Bayesian Model Averaging approach.
Duan and Parast (2023) <doi:10.1002/sim.9986>.")
    (license license:gpl2+)))

(define-public r-surrogate
  (package
    (name "r-surrogate")
    (version "3.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Surrogate" version))
       (sha256
        (base32 "1r4i12f6kmbszrm8v17gp2396ybwv913ngr3mnhqmvsl91fnpwi6"))))
    (properties `((upstream-name . "Surrogate")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-survival
                             r-rvinecopulib
                             r-rms
                             r-purrr
                             r-pbapply
                             r-optimx
                             r-numderiv
                             r-nlme
                             r-msm
                             r-mbess
                             r-maxlik
                             r-mass
                             r-logistf
                             r-lme4
                             r-latticeextra
                             r-lattice
                             r-ks
                             r-flexsurv
                             r-extradistr
                             r-dplyr))
    (home-page "https://github.com/florianstijven/Surrogate-development")
    (synopsis "Evaluation of Surrogate Endpoints in Clinical Trials")
    (description
     "In a clinical trial, it frequently occurs that the most credible outcome to
evaluate the effectiveness of a new therapy (the true endpoint) is difficult to
measure.  In such a situation, it can be an effective strategy to replace the
true endpoint by a (bio)marker that is easier to measure and that allows for a
prediction of the treatment effect on the true endpoint (a surrogate endpoint).
The package Surrogate allows for an evaluation of the appropriateness of a
candidate surrogate endpoint based on the meta-analytic, information-theoretic,
and causal-inference frameworks.  Part of this software has been developed using
funding provided from the European Union's Seventh Framework Programme for
research, technological development and demonstration (Grant Agreement no
602552), the Special Research Fund (BOF) of Hasselt University (BOF-number:
BOF2OCPO3), @code{GlaxoSmithKline} Biologicals, Baekeland Mandaat
(HBC.2022.0145), and Johnson & Johnson Innovative Medicine.")
    (license license:gpl2+)))

(define-public r-surreal
  (package
    (name "r-surreal")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "surreal" version))
       (sha256
        (base32 "1d1hymkb8asqrhjz7c1shf3mm3c2y0d9pbjapfq5xz52vp01sawb"))))
    (properties `((upstream-name . "surreal")))
    (build-system r-build-system)
    (propagated-inputs (list r-png))
    (home-page "https://github.com/coatless-rpkg/surreal")
    (synopsis "Create Datasets with Hidden Images in Residual Plots")
    (description
     "This package implements the \"Residual (Sur)Realism\" algorithm described by
Stefanski (2007) <doi:10.1198/000313007X190079> to generate datasets that reveal
hidden images or messages in their residual plots.  It offers both predefined
datasets and tools to embed custom text or images into residual structures.
Allowing users to create intriguing visual demonstrations for teaching model
diagnostics.")
    (license license:gpl3+)))

(define-public r-surfrough
  (package
    (name "r-surfrough")
    (version "0.0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurfRough" version))
       (sha256
        (base32 "1nvia5y38y2hx3w3k89cf4hrpl44sj23444xdr4vbpifrgy6qgpf"))))
    (properties `((upstream-name . "SurfRough")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra))
    (home-page "https://github.com/strevisani/SurfRough")
    (synopsis "Calculate Surface/Image Texture Indexes")
    (description
     "This package provides methods for the computation of surface/image texture
indices using a geostatistical based approach (Trevisani et al. (2023)
<doi:10.1016/j.geomorph.2023.108838>).  It provides various functions for the
computation of surface texture indices (e.g., omnidirectional roughness and
roughness anisotropy), including the ones based on the robust MAD estimator.
The kernels included in the software permit also to calculate the surface/image
texture indices directly from the input surface (i.e., without de-trending)
using increments of order 2.  It also provides the new radial roughness index
(RRI), representing the improvement of the popular topographic roughness index
(TRI).  The framework can be easily extended with ad-hoc surface/image texture
indices.")
    (license license:expat)))

(define-public r-surfacetortoise
  (package
    (name "r-surfacetortoise")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SurfaceTortoise" version))
       (sha256
        (base32 "0l1r495qnqfr3izngv7dmnb13dcp5736khgwmkm7hkxw8fjpzc6j"))))
    (properties `((upstream-name . "SurfaceTortoise")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-sf r-gstat))
    (home-page "https://CRAN.R-project.org/package=SurfaceTortoise")
    (synopsis "Find Optimal Sampling Locations Based on Spatial Covariate(s)")
    (description
     "Create sampling designs using the surface reconstruction algorithm.  Original
method by: Olsson, D. 2002.  A method to optimize soil sampling from ancillary
data.  Poster presenterad at: NJF seminar no.  336, Implementation of Precision
Farming in Practical Agriculture, 10-12 June 2002, Skara, Sweden.")
    (license license:expat)))

(define-public r-surface
  (package
    (name "r-surface")
    (version "0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "surface" version))
       (sha256
        (base32 "1s5z7krv7kaxdkx4vsvp405bb1a13lkbfcmcf1lp0ia46vqdiph3"))))
    (properties `((upstream-name . "surface")))
    (build-system r-build-system)
    (propagated-inputs (list r-phytools r-ouch r-mass r-geiger r-ape))
    (home-page "https://www.otago.ac.nz/ecoevotago/research/code/index.html")
    (synopsis "Fitting Hansen Models to Investigate Convergent Evolution")
    (description
     "This data-driven phylogenetic comparative method fits stabilizing selection
models to continuous trait data, building on the ouch methodology of Butler and
King (2004) <doi:10.1086/426002>.  The main functions fit a series of Hansen
models using stepwise AIC, then identify cases of convergent evolution where
multiple lineages have shifted to the same adaptive peak.  For more information
see Ingram and Mahler (2013) <doi:10.1111/2041-210X.12034>.")
    (license license:gpl2+)))

(define-public r-surf-vs
  (package
    (name "r-surf-vs")
    (version "1.1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SuRF.vs" version))
       (sha256
        (base32 "0yhd6wsifmlr39fxs46b93y0bpqrv5c0zc4sjs2jpdrzivwfq1j5"))))
    (properties `((upstream-name . "SuRF.vs")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-glmnet r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SuRF.vs")
    (synopsis "Subsampling Ranking Forward Selection (SuRF)")
    (description
     "This package performs variable selection based on subsampling, ranking forward
selection.  Details of the method are published in Lihui Liu, Hong Gu, Johan Van
Limbergen, Toby Kenney (2020) @code{SuRF}: A new method for sparse variable
selection, with application in microbiome data analysis Statistics in Medicine
40 897-919 <doi:10.1002/sim.8809>.  Xo is the matrix of predictor variables.  y
is the response variable.  Currently only binary responses using logistic
regression are supported.  X is a matrix of additional predictors which should
be scaled to have sum 1 prior to analysis.  fold is the number of folds for
cross-validation.  Alpha is the parameter for the elastic net method used in the
subsampling procedure: the default value of 1 corresponds to LASSO. prop is the
proportion of variables to remove in the each subsample.  weights indicates
whether observations should be weighted by class size.  When the class sizes are
unbalanced, weighting observations can improve results.  B is the number of
subsamples to use for ranking the variables.  C is the number of permutations to
use for estimating the critical value of the null distribution.  If the
@code{doParallel} package is installed, the function can be run in parallel by
setting ncores to the number of threads to use.  If the default value of 1 is
used, or if the @code{doParallel} package is not installed, the function does
not run in parallel.  display.progress indicates whether the function should
display messages indicating its progress.  family is a family variable for the
glm() fitting.  Note that the glmnet package does not permit the use of
nonstandard link functions, so will always use the default link function.
However, the glm() fitting will use the specified link.  The default is binomial
with logistic regression, because this is a common use case.  pval is the
p-value for inclusion of a variable in the model.  Under the null case, the
number of false positives will be geometrically distributed with this as
probability of success, so if this parameter is set to p, the expected number of
false positives should be p/(1-p).")
    (license license:gpl3)))

(define-public r-surelda
  (package
    (name "r-surelda")
    (version "0.1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sureLDA" version))
       (sha256
        (base32 "09ls5gv9jip2pw38qw3v2ag91fgmmkq989pyr8c8j92nbwk6i3hr"))))
    (properties `((upstream-name . "sureLDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-proc
                             r-matrix
                             r-map
                             r-glmnet
                             r-foreach
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/celehs/sureLDA")
    (synopsis "Novel Multi-Disease Automated Phenotyping Method for the EHR")
    (description
     "This package provides a statistical learning method to simultaneously predict a
range of target phenotypes using codified and natural language processing
(NLP)-derived Electronic Health Record (EHR) data.  See Ahuja et al (2020) JAMIA
<doi:10.1093/jamia/ocaa079> for details.")
    (license license:gpl3)))

(define-public r-sure
  (package
    (name "r-sure")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sure" version))
       (sha256
        (base32 "0gwr2j321i2vq98rin5b1m4sl123dm1nih1ghcmj0zd1wd6x5x54"))))
    (properties `((upstream-name . "sure")))
    (build-system r-build-system)
    (propagated-inputs (list r-gridextra r-goftest r-ggplot2))
    (home-page "https://github.com/AFIT-R/sure")
    (synopsis "Surrogate Residuals for Ordinal and General Regression Models")
    (description
     "An implementation of the surrogate approach to residuals and diagnostics for
ordinal and general regression models; for details, see Liu and Zhang (2017)
<doi:10.1080/01621459.2017.1292915>.  These residuals can be used to construct
standard residual plots for model diagnostics (e.g., residual-vs-fitted value
plots, residual-vs-covariate plots, Q-Q plots, etc.).  The package also provides
an autoplot function for producing standard diagnostic plots using ggplot2
graphics.  The package currently supports cumulative link models from packages
MASS', ordinal', rms', and VGAM'.  Support for binary regression models using
the standard glm function is also available.")
    (license license:gpl2+)))

(define-public r-surbayes
  (package
    (name "r-surbayes")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "surbayes" version))
       (sha256
        (base32 "0n01g84cwq5aj4qafk1zf60ka7s9z3q2gkgzp5xw9dx5lgfh7dgb"))))
    (properties `((upstream-name . "surbayes")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlist r-rcpparmadillo r-rcpp r-matrix))
    (home-page "https://github.com/ethan-alt/surbayes")
    (synopsis "Bayesian Analysis of Seemingly Unrelated Regression Models")
    (description
     "Implementation of the direct Monte Carlo approach of Zellner and Ando (2010)
<doi:10.1016/j.jeconom.2010.04.005> to sample from posterior of Seemingly
Unrelated Regression (SUR) models.  In addition, a Gibbs sampler is implemented
that allows the user to analyze SUR models using the power prior.")
    (license license:gpl2+)))

(define-public r-sur
  (package
    (name "r-sur")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sur" version))
       (sha256
        (base32 "0rp5pwx3m6nzcy73wzmxpsxp9pc7swj2w5b82m0zdxq5kv9hsjsl"))))
    (properties `((upstream-name . "sur")))
    (build-system r-build-system)
    (propagated-inputs (list r-learnr))
    (home-page "https://cran.r-project.org/package=sur")
    (synopsis "Companion to \"Statistics Using R: An Integrative Approach\"")
    (description
     "Access to the datasets and many of the functions used in \"Statistics Using R: An
Integrative Approach\".  These datasets include a subset of the National
Education Longitudinal Study, the Framingham Heart Study, as well as several
simulated datasets used in the examples throughout the textbook.  The functions
included in the package reproduce some of the functionality of Stata that is not
directly available in R'.  The package also contains a tutorial on basic data
frame management, including how to handle missing data.")
    (license license:gpl2+)))

(define-public r-supportr
  (package
    (name "r-supportr")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "supportR" version))
       (sha256
        (base32 "0z18rmfn5hmrirks6arkpia9biiq2b6r57sak9yhk6j68yhbkjvs"))))
    (properties `((upstream-name . "supportR")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-tidyr
                             r-stringr
                             r-stringi
                             r-scales
                             r-rmarkdown
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-lifecycle
                             r-googledrive
                             r-gh
                             r-ggplot2
                             r-dplyr
                             r-data-tree))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/njlyon0/supportR")
    (synopsis "Support Functions for Wrangling and Visualization")
    (description
     "Suite of helper functions for data wrangling and visualization.  The only theme
for these functions is that they tend towards simple, short, and
narrowly-scoped.  These functions are built for tasks that often recur but are
not large enough in scope to warrant an ecosystem of interdependent functions.")
    (license license:expat)))

(define-public r-support-ces
  (package
    (name "r-support-ces")
    (version "0.7-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "support.CEs" version))
       (sha256
        (base32 "09snvzvskjhw0l6g1gm5jl7c7idmincmqld1bqhcdinsqiq15h1d"))))
    (properties `((upstream-name . "support.CEs")))
    (build-system r-build-system)
    (propagated-inputs (list r-simex r-mass r-doe-base))
    (home-page "https://cran.r-project.org/package=support.CEs")
    (synopsis
     "Basic Functions for Supporting an Implementation of Choice Experiments")
    (description
     "This package provides basic functions that support an implementation of
(discrete) choice experiments (CEs).  CEs is a question-based survey method
measuring people's preferences for goods/services and their characteristics.
Refer to Louviere et al. (2000) <doi:10.1017/CBO9780511753831> for details on
CEs, and Aizaki (2012) <doi:10.18637/jss.v050.c02> for the package.")
    (license license:gpl2+)))

(define-public r-support-bws3
  (package
    (name "r-support-bws3")
    (version "0.2-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "support.BWS3" version))
       (sha256
        (base32 "074h4c0c42mq6b7kkv1slwsjx0ig9i2mxqf1sqf90c6afsdkzmg3"))))
    (properties `((upstream-name . "support.BWS3")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=support.BWS3")
    (synopsis "Tools for Case 3 Best-Worst Scaling")
    (description
     "This package provides basic functions that support an implementation of
multi-profile case (Case 3) best-worst scaling (BWS).  Case 3 BWS is a
question-based survey method to elicit people's preferences for attribute
levels.  Case 3 BWS constructs various combinations of attribute levels
(profiles) and then asks respondents to select the best and worst profiles in
each choice set.  A main function creates a dataset for the analysis from the
choice sets and the responses to the questions.  For details on Case 3 BWS,
refer to Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.")
    (license license:gpl2+)))

(define-public r-support-bws2
  (package
    (name "r-support-bws2")
    (version "0.4-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "support.BWS2" version))
       (sha256
        (base32 "1zhhgd24r1xsrcf9av8hg7sc5pp165kff44lzzvhh79rl8rzrmyy"))))
    (properties `((upstream-name . "support.BWS2")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=support.BWS2")
    (synopsis "Tools for Case 2 Best-Worst Scaling")
    (description
     "This package provides three basic functions that support an implementation of
Case 2 (profile case) best-worst scaling.  The first is to convert an orthogonal
main-effect design into questions, the second is to create a dataset suitable
for analysis, and the third is to calculate count-based scores.  For details,
see Aizaki and Fogarty (2019) <doi:10.1016/j.jocm.2019.100171>.")
    (license license:gpl2+)))

(define-public r-support-bws
  (package
    (name "r-support-bws")
    (version "0.4-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "support.BWS" version))
       (sha256
        (base32 "0l47a4llraybpfghw8zhb51rs7c0wnlprpmjhc6jb2nclygzc704"))))
    (properties `((upstream-name . "support.BWS")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=support.BWS")
    (synopsis "Tools for Case 1 Best-Worst Scaling")
    (description
     "This package provides basic functions that support an implementation of object
case (Case 1) best-worst scaling: a function for converting a two-level
orthogonal main-effect design/balanced incomplete block design into questions;
two functions for creating a data set suitable for analysis; a function for
calculating count-based scores; a function for calculating shares of preference;
and a function for generating artificial responses to questions.  See Louviere
et al. (2015) <doi:10.1017/CBO9781107337855> for details on best-worst scaling,
and Aizaki and Fogarty (2023) <doi:10.1016/j.jocm.2022.100394> for the package.")
    (license license:gpl2+)))

(define-public r-suppdata
  (package
    (name "r-suppdata")
    (version "1.1-9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "suppdata" version))
       (sha256
        (base32 "0h8yl68x61r8dwz6jdx0mss9n2d6nxcxhacbmfkjfmxlyb7rsgd6"))))
    (properties `((upstream-name . "suppdata")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-rcrossref r-jsonlite r-httr))
    (native-inputs (list r-knitr))
    (home-page "https://docs.ropensci.org/suppdata/")
    (synopsis "Downloading Supplementary Data from Published Manuscripts")
    (description
     "Downloads data supplementary materials from manuscripts, using papers DOIs as
references.  Facilitates open, reproducible research workflows: scientists
re-analyzing published datasets can work with them as easily as if they were
stored on their own computer, and others can track their analysis workflow
painlessly.  The main function suppdata() returns a (temporary) location on the
user's computer where the file is stored, making it simple to use suppdata()
with standard functions like read.csv().")
    (license license:expat)))

(define-public r-supmz
  (package
    (name "r-supmz")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SupMZ" version))
       (sha256
        (base32 "1n81wqacrpk5x6i1pd1zsjivv2jk41743kg1h7gnp0rb3k58vp7r"))))
    (properties `((upstream-name . "SupMZ")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr r-dplyr))
    (home-page "https://github.com/myaseen208/SupMZ")
    (synopsis "Detecting Structural Change with Heteroskedasticity")
    (description
     "Calculates the sup MZ value to detect the unknown structural break points under
Heteroskedasticity as given in Ahmed et al. (2017) (<DOI:
10.1080/03610926.2016.1235200>).")
    (license license:gpl2)))

(define-public r-supervisedprim
  (package
    (name "r-supervisedprim")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "supervisedPRIM" version))
       (sha256
        (base32 "1j5gsy119pvrhkkg048lyk6hjvn9x1bhmfy5g824gj3k1w5slrib"))))
    (properties `((upstream-name . "supervisedPRIM")))
    (build-system r-build-system)
    (propagated-inputs (list r-prim))
    (home-page "https://github.com/dashaub/supervisedPRIM")
    (synopsis
     "Supervised Classification Learning and Prediction using Patient Rule Induction Method (PRIM)")
    (description
     "The Patient Rule Induction Method (PRIM) is typically used for \"bump hunting\"
data mining to identify regions with abnormally high concentrations of data with
large or small values.  This package extends this methodology so that it can be
applied to binary classification problems and used for prediction.")
    (license license:gpl3)))

(define-public r-superranker
  (package
    (name "r-superranker")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SuperRanker" version))
       (sha256
        (base32 "02yvx1lg2drhb111jcq90hq0vnycwp3mssclvazgcfyw9z7rnzwg"))))
    (properties `((upstream-name . "SuperRanker")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-prodlim))
    (home-page "https://cran.r-project.org/package=SuperRanker")
    (synopsis "Sequential Rank Agreement")
    (description
     "This package provides tools for analysing the agreement of two or more rankings
of the same items.  Examples are importance rankings of predictor variables and
risk predictions of subjects.  Benchmarks for agreement are computed based on
random permutation and bootstrap.  See EkstrÃ¸m CT, Gerds TA, Jensen, AK (2018).
\"Sequential rank agreement methods for comparison of ranked lists.\"
_Biostatistics_, *20*(4), 582-598 <doi:10.1093/biostatistics/kxy017> for more
information.")
    (license license:gpl2+)))

(define-public r-superpower
  (package
    (name "r-superpower")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Superpower" version))
       (sha256
        (base32 "0bv2jx3hadp5d9c3q790in3m728c8jc1nkp9wqvzcdip1d29hyin"))))
    (properties `((upstream-name . "Superpower")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-reshape2
                             r-mvtnorm
                             r-mass
                             r-magrittr
                             r-gridextra
                             r-ggplot2
                             r-emmeans
                             r-dplyr
                             r-afex))
    (native-inputs (list r-knitr))
    (home-page "https://aaroncaldwell.us/SuperpowerBook/")
    (synopsis "Simulation-Based Power Analysis for Factorial Designs")
    (description
     "This package provides functions to perform simulations of ANOVA designs of up to
three factors.  Calculates the observed power and average observed effect size
for all main effects and interactions in the ANOVA, and all simple comparisons
between conditions.  Includes functions for analytic power calculations and
additional helper functions that compute effect sizes for ANOVA designs,
observed error rates in the simulations, and functions to plot power curves.
Please see Lakens, D., & Caldwell, A. R. (2021). \"Simulation-Based Power
Analysis for Factorial Analysis of Variance Designs\".
<doi:10.1177/2515245920951503>.")
    (license license:expat)))

(define-public r-superpixelimagesegmentation
  (package
    (name "r-superpixelimagesegmentation")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SuperpixelImageSegmentation" version))
       (sha256
        (base32 "01j8p1vfnswy41b7nzmwml4aq8s00xwdk3zjcc1b6n9bqv0cgnaz"))))
    (properties `((upstream-name . "SuperpixelImageSegmentation")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-r6
                             r-openimager
                             r-lattice
                             r-clusterr))
    (home-page "https://github.com/mlampros/SuperpixelImageSegmentation")
    (synopsis "Superpixel Image Segmentation")
    (description
     "Image Segmentation using Superpixels, Affinity Propagation and Kmeans
Clustering.  The R code is based primarily on the article \"Image Segmentation
using SLIC Superpixels and Affinity Propagation Clustering, Bao Zhou,
International Journal of Science and Research (IJSR), 2013\"
<https://www.ijsr.net/archive/v4i4/SUB152869.pdf>.")
    (license license:gpl3)))

(define-public r-superpc
  (package
    (name "r-superpc")
    (version "1.12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "superpc" version))
       (sha256
        (base32 "0n68qafsww00q0wx37ms3rimrlhbp085ywvv2k5vw951j4z8ivfb"))))
    (properties `((upstream-name . "superpc")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "http://www-stat.stanford.edu/~tibs/superpc")
    (synopsis "Supervised Principal Components")
    (description
     "Does prediction in the case of a censored survival outcome, or a regression
outcome, using the \"supervised principal component\" approach.  Superpc is
especially useful for high-dimensional data when the number of features p
dominates the number of samples n (p >> n paradigm), as generated, for instance,
by high-throughput technologies.")
    (license (list license:gpl3+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-supernova
  (package
    (name "r-supernova")
    (version "3.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "supernova" version))
       (sha256
        (base32 "14wnn0vmswc8drrn28385zman5rm2qq4phxfp5kd2k37q2gn9mpj"))))
    (properties `((upstream-name . "supernova")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tibble
                             r-stringr
                             r-rlang
                             r-purrr
                             r-pillar
                             r-cli))
    (home-page "https://github.com/UCLATALL/supernova")
    (synopsis "Judd, McClelland, & Ryan Formatting for ANOVA Output")
    (description
     "This package produces ANOVA tables in the format used by Judd,
@code{McClelland}, and Ryan (2017, ISBN: 978-1138819832) in their introductory
textbook, Data Analysis.  This includes proportional reduction in error and
formatting to improve ease the transition between the book and R.")
    (license license:gpl3+)))

(define-public r-superml
  (package
    (name "r-superml")
    (version "0.5.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "superml" version))
       (sha256
        (base32 "0r2xp520j8w395b272681g9d7vzinmf4cl627xxcsw8575k89lpf"))))
    (properties `((upstream-name . "superml")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-r6
                             r-metrics
                             r-data-table
                             r-bh
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/saraswatmks/superml")
    (synopsis
     "Build Machine Learning Models Like Using Python's Scikit-Learn Library in R")
    (description
     "The idea is to provide a standard interface to users who use both R and Python
for building machine learning models.  This package provides a scikit-learn's
fit, predict interface to train machine learning models in R.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-supermice
  (package
    (name "r-supermice")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "superMICE" version))
       (sha256
        (base32 "0wf043vyd89a5jdvm128rqmfrnm9c0lhir35jyzy20rm75aqqh1w"))))
    (properties `((upstream-name . "superMICE")))
    (build-system r-build-system)
    (propagated-inputs (list r-superlearner r-mice))
    (home-page "https://cran.r-project.org/package=superMICE")
    (synopsis "SuperLearner Method for MICE")
    (description
     "Adds a Super Learner ensemble model method (using the @code{SuperLearner}
package) to the mice package.  Laqueur, H. S., Shev, A. B., Kagawa, R. M. C.
(2021) <doi:10.1093/aje/kwab271>.")
    (license license:gpl3)))

(define-public r-supergauss
  (package
    (name "r-supergauss")
    (version "2.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SuperGauss" version))
       (sha256
        (base32 "174gicpk2jwbzg39m44sxp7506cja9cnn95r287sq7049lrj302a"))))
    (properties `((upstream-name . "SuperGauss")))
    (build-system r-build-system)
    (inputs (list fftw))
    (propagated-inputs (list r-rcppeigen r-rcpp r-r6 r-fftw))
    (native-inputs (list pkg-config r-knitr))
    (home-page "https://cran.r-project.org/package=SuperGauss")
    (synopsis
     "Superfast Likelihood Inference for Stationary Gaussian Time Series")
    (description
     "Likelihood evaluations for stationary Gaussian time series are typically
obtained via the Durbin-Levinson algorithm, which scales as O(n^2) in the number
of time series observations.  This package provides a \"superfast\" O(n log^2 n)
algorithm written in C++, crossing over with Durbin-Levinson around n = 300.
Efficient implementations of the score and Hessian functions are also provided,
leading to superfast versions of inference algorithms such as Newton-Raphson and
Hamiltonian Monte Carlo.  The C++ code provides a Toeplitz matrix class packaged
as a header-only library, to simplify low-level usage in other packages and
outside of R.")
    (license license:gpl3)))

(define-public r-superexacttest
  (package
    (name "r-superexacttest")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SuperExactTest" version))
       (sha256
        (base32 "0i3pk2c2ipnc7mq5w4d4nl2l9pj1ijynm72n4jnlmhrs16ka88i1"))))
    (properties `((upstream-name . "SuperExactTest")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mw201608/SuperExactTest/")
    (synopsis "Exact Test and Visualization of Multi-Set Intersections")
    (description
     "Identification of sets of objects with shared features is a common operation in
all disciplines.  Analysis of intersections among multiple sets is fundamental
for in-depth understanding of their complex relationships.  This package
implements a theoretical framework for efficient computation of statistical
distributions of multi-set intersections based upon combinatorial theory, and
provides multiple scalable techniques for visualizing the intersection
statistics.  The statistical algorithm behind this package was published in Wang
et al. (2015) <doi:10.1038/srep16923>.")
    (license license:gpl3)))

(define-public r-superdiag
  (package
    (name "r-superdiag")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "superdiag" version))
       (sha256
        (base32 "0zfy9ain6agh276av0p66mks3rap42n8yma0dr6qbc14z3nh8w0m"))))
    (properties `((upstream-name . "superdiag")))
    (build-system r-build-system)
    (propagated-inputs (list r-coda))
    (home-page "https://cran.r-project.org/package=superdiag")
    (synopsis
     "Comprehensive Test Suite for Testing Markov Chain Nonconvergence")
    (description
     "The superdiag package provides a comprehensive test suite for testing Markov
Chain nonconvergence.  It integrates five standard empirical MCMC convergence
diagnostics (Gelman-Rubin, Geweke, Heidelberger-Welch, Raftery-Lewis, and
Hellinger distance) and plotting functions for trace plots and density
histograms.  The functions of the package can be used to present all diagnostic
statistics and graphs at once for conveniently checking MCMC nonconvergence.")
    (license license:gpl2+)))

(define-public r-supercompress
  (package
    (name "r-supercompress")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "supercompress" version))
       (sha256
        (base32 "1kz04m37r1g4lx4y0kzjn77p3wra2rq21y93v9llikycsh6vzwp1"))))
    (properties `((upstream-name . "supercompress")))
    (build-system r-build-system)
    (propagated-inputs (list r-fnn))
    (home-page "https://cran.r-project.org/package=supercompress")
    (synopsis "Supervised Compression of Big Data")
    (description
     "This package provides a supervised compression method that incorporates the
response for reducing big data to a carefully selected subset.  Please see
Joseph and Mak (2021) <doi:10.1002/sam.11508>.  This research is supported by a
U.S. National Science Foundation (NSF) grant CMMI-1921646.")
    (license license:gpl2+)))

(define-public r-supercells
  (package
    (name "r-supercells")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "supercells" version))
       (sha256
        (base32 "1r08mny63w9gichi78jfif2jbpl484p5gdl4yb8h5c2l70r7q258"))))
    (properties `((upstream-name . "supercells")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-sf r-philentropy r-future-apply r-cpp11))
    (home-page "https://jakubnowosad.com/supercells/")
    (synopsis "Superpixels of Spatial Data")
    (description
     "This package creates superpixels based on input spatial data.  This package
works on spatial data with one variable (e.g., continuous raster), many
variables (e.g., RGB rasters), and spatial patterns (e.g., areas in categorical
rasters).  It is based on the SLIC algorithm (Achanta et al. (2012)
<doi:10.1109/TPAMI.2012.120>), and readapts it to work with arbitrary
dissimilarity measures.")
    (license license:gpl3+)))

(define-public r-supercell
  (package
    (name "r-supercell")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SuperCell" version))
       (sha256
        (base32 "01zdh9fz174rys9vixcpskxyva511p7xww3xlkmsp01j4prg0gba"))))
    (properties `((upstream-name . "SuperCell")))
    (build-system r-build-system)
    (propagated-inputs (list r-weights
                             r-weightedcluster
                             r-umap
                             r-scales
                             r-rtsne
                             r-rlang
                             r-rann
                             r-proxy
                             r-plyr
                             r-plotfunctions
                             r-patchwork
                             r-matrixstats
                             r-matrix
                             r-irlba
                             r-igraph
                             r-hmisc
                             r-ggplot2
                             r-entropy
                             r-dbscan
                             r-corpcor))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SuperCell")
    (synopsis
     "Simplification of scRNA-Seq Data by Merging Together Similar Cells")
    (description
     "Aggregates large single-cell data into metacell dataset by merging together gene
expression of very similar cells. @code{SuperCell} uses velocyto.R
<doi:10.1038/s41586-018-0414-6> <https://github.com/velocyto-team/velocyto.R>
for RNA velocity.  We also recommend installing scater Bioconductor package
<doi:10.18129/B9.bioc.scater>
<https://bioconductor.org/packages/release/bioc/html/scater.html>.")
    (license license:gpl3)))

(define-public r-superbiclust
  (package
    (name "r-superbiclust")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "superbiclust" version))
       (sha256
        (base32 "0mk3d820skp99hl8ilwic2fz90yb9dwy2jq3vfjb726gj1qich3b"))))
    (properties `((upstream-name . "superbiclust")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-biclust))
    (home-page "https://cran.r-project.org/package=superbiclust")
    (synopsis
     "Generating Robust Biclusters from a Bicluster Set (Ensemble Biclustering)")
    (description
     "Biclusters are submatrices in the data matrix which satisfy certain conditions
of homogeneity.  Package contains functions for generating robust biclusters
with respect to the initialization parameters for a given bicluster solution
contained in a bicluster set in data, the procedure is also known as ensemble
biclustering.  The set of biclusters is evaluated based on the similarity of its
elements (the overlap), and afterwards the hierarchical tree is constructed to
obtain cut-off points for the classes of robust biclusters.  The result is a
number of robust (or super) biclusters with none or low overlap.")
    (license license:gpl2+)))

(define-public r-superb
  (package
    (name "r-superb")
    (version "0.95.15")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "superb" version))
       (sha256
        (base32 "1lirfmsrkbssfg0kwlfw67g8hi4j2g71c7q177hvahpw90ajr6gf"))))
    (properties `((upstream-name . "superb")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-shinybs
                             r-shiny
                             r-rdpack
                             r-plyr
                             r-mass
                             r-lsr
                             r-ggplot2
                             r-foreign))
    (native-inputs (list r-knitr))
    (home-page "https://dcousin3.github.io/superb/")
    (synopsis "Summary Plots with Adjusted Error Bars")
    (description
     "Computes standard error and confidence interval of various descriptive
statistics under various designs and sampling schemes.  The main function,
@code{superbPlot}(), return a plot. @code{superbData}() returns a dataframe with
the statistic and its precision interval so that other plotting package can be
used.  See Cousineau and colleagues (2021) <doi:10.1177/25152459211035109> or
Cousineau (2017) <doi:10.5709/acp-0214-z> for a review as well as Cousineau
(2005) <doi:10.20982/tqmp.01.1.p042>, Morey (2008)
<doi:10.20982/tqmp.04.2.p061>, Baguley (2012) <doi:10.3758/s13428-011-0123-7>,
Cousineau & Laurencelle (2016) <doi:10.1037/met0000055>, Cousineau & O'Brien
(2014) <doi:10.3758/s13428-013-0441-z>, Calderini & Harding
<doi:10.20982/tqmp.15.1.p001> for specific references.")
    (license license:gpl3)))

(define-public r-supclust
  (package
    (name "r-supclust")
    (version "1.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "supclust" version))
       (sha256
        (base32 "1iar957gdarp1m677fjmph0ll8zsqiy2bzkzx03gv37k56w1acpd"))))
    (properties `((upstream-name . "supclust")))
    (build-system r-build-system)
    (propagated-inputs (list r-rpart r-class))
    (home-page "https://github.com/mmaechler/supclust")
    (synopsis "Supervised Clustering of Predictor Variables Such as Genes")
    (description
     "Methodology for supervised grouping aka \"clustering\" of potentially many
predictor variables, such as genes etc, implementing algorithms PELORA and
WILMA'.")
    (license license:gpl3)))

(define-public r-sunsvoc
  (package
    (name "r-sunsvoc")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SunsVoc" version))
       (sha256
        (base32 "08qmkssxa75q2c8vl4i0560bazqjw1v00h6yl5zjwrxklws2m57p"))))
    (properties `((upstream-name . "SunsVoc")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-dplyr
                             r-ddiv
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SunsVoc")
    (synopsis "Constructing Suns-Voc from Outdoor Time-Series I-V Curves")
    (description
     "Suns-Voc (or Isc-Voc) curves can provide the current-voltage (I-V)
characteristics of the diode of photovoltaic cells without the effect of series
resistance.  Here, Suns-Voc curves can be constructed with outdoor time-series
I-V curves [1,2,3] of full-size photovoltaic (PV) modules instead of having to
be measured in the lab.  Time series of four different power loss modes can be
calculated based on obtained Isc-Voc curves.  This material is based upon work
supported by the U.S. Department of Energy's Office of Energy Efficiency and
Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement
Number DE-EE0008172.  Jennifer L. Braid is supported by the U.S. Department of
Energy (DOE) Office of Energy Efficiency and Renewable Energy administered by
the Oak Ridge Institute for Science and Education (ORISE) for the DOE. ORISE is
managed by Oak Ridge Associated Universities (ORAU) under DOE contract number
DE-SC0014664. [1] Wang, M. et al, 2018. <doi:10.1109/PVSC.2018.8547772>. [2]
Walters et al, 2018 <doi:10.1109/PVSC.2018.8548187>. [3] Guo, S. et al, 2016.
<doi:10.1117/12.2236939>.")
    (license license:bsd-3)))

(define-public r-sundialr
  (package
    (name "r-sundialr")
    (version "0.1.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sundialr" version))
       (sha256
        (base32 "1qzak0zrdj2ygw4h6cn28z6q5qxaam8dybqblmf17xq2lkvn2bpn"))))
    (properties `((upstream-name . "sundialr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sn248/sundialr")
    (synopsis
     "An Interface to 'SUNDIALS' Ordinary Differential Equation (ODE) Solvers")
    (description
     "This package provides a way to call the functions in SUNDIALS C ODE solving
library (<https://computing.llnl.gov/projects/sundials>).  Currently the serial
version of ODE solver, CVODE', sensitivity calculator CVODES and differential
algebraic solver IDA from the SUNDIALS library are implemented.  The package
requires ODE to be written as an R or Rcpp function and does not require the
SUNDIALS library to be installed on the local machine.")
    (license license:bsd-3)))

(define-public r-sunclarco
  (package
    (name "r-sunclarco")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Sunclarco" version))
       (sha256
        (base32 "0jv92a4ciwkm0wa4irgb6ql3vf6v6p24fmbnk24pqp5p8drp8g5v"))))
    (properties `((upstream-name . "Sunclarco")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "https://cran.r-project.org/package=Sunclarco")
    (synopsis "Survival Analysis using Copulas")
    (description
     "Survival analysis for unbalanced clusters using Archimedean copulas (Prenen et
al. (2016) <DOI:10.1111/rssb.12174>).")
    (license license:gpl3)))

(define-public r-suncalc
  (package
    (name "r-suncalc")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "suncalc" version))
       (sha256
        (base32 "1xkci2sbcf5x1wn2jqzbfzc5dly331dhihrxjlmdiic247blwqyp"))))
    (properties `((upstream-name . "suncalc")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr r-lubridate r-data-table))
    (home-page "https://github.com/datastorm-open/suncalc")
    (synopsis
     "Compute Sun Position, Sunlight Phases, Moon Position and Lunar Phase")
    (description
     "Get sun position, sunlight phases (times for sunrise, sunset, dusk, etc.), moon
position and lunar phase for the given location and time.  Most calculations are
based on the formulas given in Astronomy Answers articles about position of the
sun and the planets : <https://www.aa.quae.nl/en/reken/zonpositie.html>.")
    (license (list license:gpl2
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-sunburstr
  (package
    (name "r-sunburstr")
    (version "2.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sunburstR" version))
       (sha256
        (base32 "1rlfmw79krdahm7c03xw1hbzmxvaqbghl90bqiqa66k7mn9d25z6"))))
    (properties `((upstream-name . "sunburstR")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-htmlwidgets r-htmltools r-dplyr r-d3r))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://github.com/timelyportfolio/sunburstR")
    (synopsis "Sunburst 'Htmlwidget'")
    (description
     "Make interactive d3.js sequence sunburst diagrams in R with the convenience and
infrastructure of an htmlwidget'.")
    (license license:expat)))

(define-public r-sumsome
  (package
    (name "r-sumsome")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sumSome" version))
       (sha256
        (base32 "1a70kxvp9d9hn38jnvgk934vwbz4bjb6k4kcngp6rp9gv9h2bh2r"))))
    (properties `((upstream-name . "sumSome")))
    (build-system r-build-system)
    (propagated-inputs (list r-rnifti r-rcpp r-pari r-aribrain))
    (home-page "https://github.com/annavesely/sumSome")
    (synopsis "Permutation True Discovery Guarantee by Sum-Based Tests")
    (description
     "It allows to quickly perform permutation-based closed testing by sum-based
global tests, and construct lower confidence bounds for the TDP, simultaneously
over all subsets of hypotheses.  As a main feature, it produces simultaneous
lower confidence bounds for the proportion of active voxels in different
clusters for @code{fMRI} cluster analysis.  Details may be found in Vesely,
Finos, and Goeman (2020) <@code{arXiv:2102.11759>}.")
    (license license:gpl2+)))

(define-public r-sumr
  (package
    (name "r-sumr")
    (version "0.4.15")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sumR" version))
       (sha256
        (base32 "1s4id6yrwib0b6l1a3pkihmqmishwjkg6pz25mr17aqmb8k400yy"))))
    (properties `((upstream-name . "sumR")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrixstats))
    (home-page "https://cran.r-project.org/package=sumR")
    (synopsis "Approximate Summation of Series")
    (description
     "Application of theoretical results which ensure that the summation of an
infinite discrete series is within an arbitrary margin of error of its true
value.  The C code under the hood is shared through header files to allow users
to sum their own low level functions as well.  Based on the paper by Braden
(1992) <doi: 10.2307/2324995>.")
    (license license:gpl3+)))

(define-public r-summer
  (package
    (name "r-summer")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SUMMER" version))
       (sha256
        (base32 "0x93mppdaah7sznzk1rrw76ia07fnjmb676q1rrya6bzkv5fbf5z"))))
    (properties `((upstream-name . "SUMMER")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-terra
                             r-survival
                             r-survey
                             r-spdep
                             r-sp
                             r-shadowtext
                             r-sf
                             r-scales
                             r-reshape2
                             r-rcolorbrewer
                             r-matrix
                             r-haven
                             r-ggridges
                             r-ggplot2
                             r-fields
                             r-data-table))
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "https://github.com/richardli/SUMMER")
    (synopsis
     "Small-Area-Estimation Unit/Area Models and Methods for Estimation in R")
    (description
     "This package provides methods for spatial and spatio-temporal smoothing of
demographic and health indicators using survey data, with particular focus on
estimating and projecting under-five mortality rates, described in Mercer et al.
(2015) <doi:10.1214/15-AOAS872>, Li et al. (2019)
<doi:10.1371/journal.pone.0210645>, Wu et al. (DHS Spatial Analysis Reports No.
21, 2021), and Li et al. (2023) <@code{arXiv:2007.05117>}.")
    (license license:gpl2+)))

(define-public r-summclust
  (package
    (name "r-summclust")
    (version "0.7.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "summclust" version))
       (sha256
        (base32 "1cdzv5bm3l8fnnpk0c94y32ss1pmx7dddks33ha9r2yqw7p9xwvq"))))
    (properties `((upstream-name . "summclust")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-mass
                             r-generics
                             r-dreamerr
                             r-collapse
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://s3alfisc.github.io/summclust/")
    (synopsis
     "Module to Compute Influence and Leverage Statistics for Regression Models with Clustered Errors")
    (description
     "Module to compute cluster specific information for regression models with
clustered errors, including leverage and influence statistics.  Models of type
lm and fixest'(from the stats and fixest packages) are supported.  summclust
implements similar features as the user-written summclust.ado Stata module
(@code{MacKinnon}, Nielsen & Webb, 2022; <@code{arXiv:2205.03288v1>}).")
    (license license:expat)))

(define-public r-summarylasso
  (package
    (name "r-summarylasso")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SummaryLasso" version))
       (sha256
        (base32 "1gb1g1xgda5psq93vy50yfr0cbhvymliblpkxz7ck1nwpp65n9py"))))
    (properties `((upstream-name . "SummaryLasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-gtools))
    (home-page "https://cran.r-project.org/package=SummaryLasso")
    (synopsis "Building Polygenic Risk Score Using GWAS Summary Statistics")
    (description
     "Shrinkage estimator for polygenic risk prediction models based on summary
statistics of genome-wide association studies.")
    (license license:gpl3)))

(define-public r-summariser
  (package
    (name "r-summariser")
    (version "2.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "summariser" version))
       (sha256
        (base32 "0zliqy8dlcr2yy4ky8k68qn256xvpla8fcv7via19y40bmla37ic"))))
    (properties `((upstream-name . "summariser")))
    (build-system r-build-system)
    (propagated-inputs (list r-dplyr))
    (home-page "https://github.com/condwanaland/summariser")
    (synopsis "Easy Calculation and Visualisation of Confidence Intervals")
    (description
     "This package provides functions to speed up the exploratory analysis of simple
datasets using dplyr'.  Functions are provided to do the common tasks of
calculating confidence intervals.")
    (license license:gpl3)))

(define-public r-sumfregat
  (package
    (name "r-sumfregat")
    (version "1.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sumFREGAT" version))
       (sha256
        (base32 "10clll3c0ws2mccdp9grz3qw75kv7ydffak2vv9ijky8n1b39qa7"))))
    (properties `((upstream-name . "sumFREGAT")))
    (build-system r-build-system)
    (propagated-inputs (list r-seqminer r-matrix r-gbj))
    (home-page "https://cran.r-project.org/package=sumFREGAT")
    (synopsis "Fast Region-Based Association Tests on Summary Statistics")
    (description
     "An adaptation of classical region/gene-based association analysis techniques to
the use of summary statistics (P values and effect sizes) and correlations
between genetic variants as input.  It is a tool to perform the most popular and
efficient gene-based tests using the results of genome-wide association
(meta-)analyses without having the original genotypes and phenotypes at hand.
See for details: Svishcheva et al (2019) Gene-based association tests using GWAS
summary statistics.  Bioinformatics.  Belonogova et al (2022) @code{SumSTAAR}: A
flexible framework for gene-based association studies using GWAS summary
statistics.  PLOS Comp Biol.")
    (license license:gpl3)))

(define-public r-sumcaver1
  (package
    (name "r-sumcaver1")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SumcaVer1" version))
       (sha256
        (base32 "0czswr99s0i2q942yld0qqyhaxyxjv5wa8lqv8nk59y9phwss904"))))
    (properties `((upstream-name . "SumcaVer1")))
    (build-system r-build-system)
    (propagated-inputs (list r-psych r-lme4))
    (home-page "https://cran.r-project.org/package=SumcaVer1")
    (synopsis
     "Mean Square Prediction Error Estimation in Small Area Estimation")
    (description
     "Estimation of mean squared prediction error of a small area predictor is
provided.  In particular, the recent method of Simple, Unified, Monte-Carlo
Assisted approach for the mean squared prediction error estimation of small area
predictor is provided.  We also provide other existing methods of mean squared
prediction error estimation such as jackknife method for the mixed logistic
model.")
    (license license:expat)))

(define-public r-sugrrants
  (package
    (name "r-sugrrants")
    (version "0.2.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sugrrants" version))
       (sha256
        (base32 "1jqns2q7gaya88z7zll4diy7p98ycaqgxmq2aqha6dqs8sqqhj3r"))))
    (properties `((upstream-name . "sugrrants")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-lubridate r-gtable r-ggplot2 r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://pkg.earo.me/sugrrants/")
    (synopsis "Supporting Graphs for Analysing Time Series")
    (description
     "This package provides ggplot2 graphics for analysing time series data.  It aims
to fit into the tidyverse and grammar of graphics framework for handling
temporal data.")
    (license license:gpl3+)))

(define-public r-suggests
  (package
    (name "r-suggests")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "suggests" version))
       (sha256
        (base32 "05z2winamc8rdvkvhsbiz7r183qh8balrhv9r2sr3crn0rh2fm62"))))
    (properties `((upstream-name . "suggests")))
    (build-system r-build-system)
    (home-page "https://github.com/owenjonesuob/suggests")
    (synopsis "Declare when Suggested Packages are Needed")
    (description
     "By adding dependencies to the \"Suggests\" field of a package's DESCRIPTION file,
and then declaring that they are needed within any dependent functionality, it
is often possible to significantly reduce the number of \"hard\" dependencies
required by a package.  This package provides a minimal way to declare when a
suggested package is needed.")
    (license license:expat)))

(define-public r-sugarbag
  (package
    (name "r-sugarbag")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sugarbag" version))
       (sha256
        (base32 "0xgazjr55cdjcxngpfasq17pc72lkg2p9jfzfpmwkjrpvsqw8dff"))))
    (properties `((upstream-name . "sugarbag")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-sf
                             r-rmapshaper
                             r-rlang
                             r-purrr
                             r-progress
                             r-geosphere
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://srkobakian.github.io/sugarbag/")
    (synopsis "Create Tessellated Hexagon Maps")
    (description
     "Create a hexagon tile map display from spatial polygons.  Each polygon is
represented by a hexagon tile, placed as close to it's original centroid as
possible, with a focus on maintaining spatial relationship to a focal point.
Developed to aid visualisation and analysis of spatial distributions across
Australia, which can be challenging due to the concentration of the population
on the coast and wide open interior.")
    (license license:expat)))

(define-public r-sufficientforecasting
  (package
    (name "r-sufficientforecasting")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sufficientForecasting" version))
       (sha256
        (base32 "1fsai7dfcvyhvjy0y3nmsdwcn1pbzki3qqswdfsbg4vcn2dxw5kv"))))
    (properties `((upstream-name . "sufficientForecasting")))
    (build-system r-build-system)
    (propagated-inputs (list r-gam))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/JingFu1224/sufficientForecasting")
    (synopsis "Sufficient Forecasting using Factor Models")
    (description
     "The sufficient forecasting (SF) method is implemented by this package for a
single time series forecasting using many predictors and a possibly nonlinear
forecasting function.  Assuming that the predictors are driven by some latent
factors, the SF first conducts factor analysis and then performs sufficient
dimension reduction on the estimated factors to derive predictive indices for
forecasting.  The package implements several dimension reduction approaches,
including principal components (PC), sliced inverse regression (SIR), and
directional regression (DR).  Methods for dimension reduction are as described
in: Fan, J., Xue, L. and Yao, J. (2017) <doi:10.1016/j.jeconom.2017.08.009>,
Luo, W., Xue, L., Yao, J. and Yu, X. (2022) <doi:10.1093/biomet/asab037> and Yu,
X., Yao, J. and Xue, L. (2022) <doi:10.1080/07350015.2020.1813589>.")
    (license license:gpl3+)))

(define-public r-suessr
  (package
    (name "r-suessr")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SuessR" version))
       (sha256
        (base32 "1rh792x7dyfnhnw6g0f6hb5wqw58j6j11k9xib11ba256qrnvwgc"))))
    (properties `((upstream-name . "SuessR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SuessR")
    (synopsis
     "Suess and Laws Corrections for Marine Stable Carbon Isotope Data")
    (description
     "Generates region-specific Suess and Laws corrections for stable carbon isotope
data from marine organisms collected between 1850 and 2022.  Version 0.1.5 of
@code{SuessR} contains four built-in regions: the Bering Sea ('Bering Sea'), the
Aleutian archipelago ('Aleutian Islands'), the Gulf of Alaska ('Gulf of
Alaska'), and the subpolar North Atlantic ('Subpolar North Atlantic').  Users
can supply their own environmental data for regions currently not built into the
package to generate corrections for those regions.")
    (license license:expat)))

(define-public r-sudokualt
  (package
    (name "r-sudokualt")
    (version "0.2-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sudokuAlt" version))
       (sha256
        (base32 "1y3li64xqag2ia8dg7q49jmlb1awl92rz6xa9zsbf0s9jman9wj9"))))
    (properties `((upstream-name . "sudokuAlt")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=sudokuAlt")
    (synopsis "Tools for Making and Spoiling Sudoku Games")
    (description
     "This package provides tools for making, retrieving, displaying and solving
sudoku games.  This package is an alternative to the earlier sudoku-solver
package, sudoku'.  The present package uses a slightly different algorithm, has
a simpler coding and presents a few more sugar tools, such as plot and print
methods.  Solved sudoku games are of some interest in Experimental Design as
examples of Latin Square designs with additional balance constraints.")
    (license license:gpl2+)))

(define-public r-sudoku
  (package
    (name "r-sudoku")
    (version "2.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sudoku" version))
       (sha256
        (base32 "1z130cx30wdqch0a4f27aa9v3v29hx6d5zav308abx42s4ilysbs"))))
    (properties `((upstream-name . "sudoku")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sudoku")
    (synopsis "Sudoku Puzzle Generator and Solver")
    (description
     "Generates, plays, and solves Sudoku puzzles.  The GUI @code{playSudoku}() needs
package \"tkrplot\" if you are not on Windows.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-suddengains
  (package
    (name "r-suddengains")
    (version "0.7.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "suddengains" version))
       (sha256
        (base32 "0mrnj9sjgk66riqz8a8kcbkvzlv3jdvlkqsmv8z7pj74yqrr99sf"))))
    (properties `((upstream-name . "suddengains")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-scales
                             r-rlang
                             r-readr
                             r-psych
                             r-patchwork
                             r-naniar
                             r-magrittr
                             r-ggrepel
                             r-ggplot2
                             r-forcats
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://milanwiedemann.github.io/suddengains/")
    (synopsis "Identify Sudden Gains in Longitudinal Data")
    (description
     "Identify sudden gains based on the three criteria outlined by Tang and
@code{DeRubeis} (1999) <doi:10.1037/0022-006X.67.6.894> to a selection of
repeated measures.  Sudden losses, defined as the opposite of sudden gains can
also be identified.  Two different datasets can be created, one including all
sudden gains/losses and one including one selected sudden gain/loss for each
case.  It can extract scores around sudden gains/losses.  It can plot the
average change around sudden gains/losses and trajectories of individual cases.")
    (license license:expat)))

(define-public r-sudachir
  (package
    (name "r-sudachir")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sudachir" version))
       (sha256
        (base32 "09ry00c3yhrz8fiyxdxgj9r3x7kldsl6fv35mmi5ysc2549kbcfb"))))
    (properties `((upstream-name . "sudachir")))
    (build-system r-build-system)
    (inputs (list python))
    (propagated-inputs (list r-tidyselect
                             r-tibble
                             r-rlang
                             r-reticulate
                             r-purrr
                             r-magrittr
                             r-glue
                             r-dplyr
                             r-cli))
    (home-page "https://github.com/uribo/sudachir")
    (synopsis "R Interface to 'Sudachi'")
    (description
     "Interface to Sudachi <https://github.com/@code{WorksApplications/Sudachi>}, a
Japanese morphological analyzer.  This is a port of what is available in Python.")
    (license license:asl2.0)))

(define-public r-success
  (package
    (name "r-success")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "success" version))
       (sha256
        (base32 "14y6farbnjxd0jq3g4vzj6y1xivcibgfic3w4s2jir1wq10ixf5x"))))
    (properties `((upstream-name . "success")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rfast
                             r-plotly
                             r-pbapply
                             r-matrixcalc
                             r-matrix
                             r-ggrepel
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/d-gomon/success")
    (synopsis "Survival Control Charts Estimation Software")
    (description
     "Quality control charts for survival outcomes.  Allows users to construct the
Continuous Time Generalized Rapid Response CUSUM (CGR-CUSUM)
<doi:10.1093/biostatistics/kxac041>, the Biswas & Kalbfleisch (2008)
<doi:10.1002/sim.3216> CUSUM, the Bernoulli CUSUM and the risk-adjusted funnel
plot for survival data <doi:10.1002/sim.1970>.  These procedures can be used to
monitor survival processes for a change in the failure rate.")
    (license license:gpl3+)))

(define-public r-subvis
  (package
    (name "r-subvis")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SubVis" version))
       (sha256
        (base32 "1nb3zgm6i5lwfwdrn8mk3wkg8a4ldfvs27ai8v46l4316qc1fa9p"))))
    (properties `((upstream-name . "SubVis")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-biostrings))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SubVis")
    (synopsis
     "Visual Exploration of Protein Alignments Resulting from Multiple Substitution Matrices")
    (description
     "Substitution matrices are important parameters in protein alignment algorithms.
These matrices represent the likelihood that an amino acid will be substituted
for another during mutation.  This tool allows users to apply predefined and
custom matrices and then explore the resulting alignments with interactive
visualizations. @code{SubVis} requires the availability of a web browser.")
    (license license:gpl3)))

(define-public r-subtypedrug
  (package
    (name "r-subtypedrug")
    (version "0.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SubtypeDrug" version))
       (sha256
        (base32 "1r0sjzjmllicqmqv5phxpqcndh84kjqc779js32rlwbw0x279xhw"))))
    (properties `((upstream-name . "SubtypeDrug")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-rvest
                             r-pheatmap
                             r-igraph
                             r-gsva
                             r-chemminer
                             r-biocgenerics))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SubtypeDrug")
    (synopsis "Prioritization of Candidate Cancer Subtype Specific Drugs")
    (description
     "This package provides a systematic biology tool was developed to prioritize
cancer subtype-specific drugs by integrating genetic perturbation, drug action,
biological pathway, and cancer subtype.  The capabilities of this tool include
inferring patient-specific subpathway activity profiles in the context of gene
expression profiles with subtype labels, calculating differentially expressed
subpathways based on cultured human cells treated with drugs in the @code{cMap}
(connectivity map) database, prioritizing cancer subtype specific drugs
according to drug-disease reverse association score based on subpathway, and
visualization of results (Castelo (2013) <doi:10.1186/1471-2105-14-7>; Han et al
(2019) <doi:10.1093/bioinformatics/btz894>; Lamb and Justin (2006)
<doi:10.1126/science.1132939>).  Please cite using
<doi:10.1093/bioinformatics/btab011>.")
    (license license:gpl2+)))

(define-public r-subts
  (package
    (name "r-subts")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SubTS" version))
       (sha256
        (base32 "0vd3pb7y341rki0yjnvmzdlq49sk481g79k82pvryavfk7485hid"))))
    (properties `((upstream-name . "SubTS")))
    (build-system r-build-system)
    (propagated-inputs (list r-tweedie r-gsl r-copula))
    (home-page "https://cran.r-project.org/package=SubTS")
    (synopsis
     "Positive Tempered Stable Distributions and Related Subordinators")
    (description
     "This package contains methods for the simulation of positive tempered stable
distributions and related subordinators.  Including classical tempered stable,
rapidly deceasing tempered stable, truncated stable, truncated tempered stable,
generalized Dickman, truncated gamma, generalized gamma, and p-gamma.  For
details, see Dassios et al (2019) <doi:10.1017/jpr.2019.6>, Dassios et al (2020)
<doi:10.1145/3368088>, Grabchak (2021) <doi:10.1016/j.spl.2020.109015>.")
    (license license:gpl3+)))

(define-public r-subtite
  (package
    (name "r-subtite")
    (version "4.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SubTite" version))
       (sha256
        (base32 "13fh24w7nvx5xqb0yvn8cackj576dspnrszdqv1gd0i3irzlyqya"))))
    (properties `((upstream-name . "SubTite")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=SubTite")
    (synopsis "Subgroup Specific Optimal Dose Assignment")
    (description
     "Chooses subgroup specific optimal doses in a phase I dose finding clinical trial
allowing for subgroup combination and simulates clinical trials under the
subgroup specific time to event continual reassessment method.  Chapple, A.G.,
Thall, P.F. (2018) <doi:10.1002/pst.1891>.")
    (license license:gpl2)))

(define-public r-subspace
  (package
    (name "r-subspace")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "subspace" version))
       (sha256
        (base32 "0p2j0lnwj3ym1v4xla6r97zjikb8alnibdc690xn9c0z21hmv43v"))))
    (properties `((upstream-name . "subspace")))
    (build-system r-build-system)
    (inputs (list openjdk))
    (propagated-inputs (list r-stringr r-rjava r-ggvis r-colorspace))
    (home-page "https://cran.r-project.org/package=subspace")
    (synopsis "Interface to OpenSubspace")
    (description
     "An interface to @code{OpenSubspace}', an open source framework for evaluation
and exploration of subspace clustering algorithms in WEKA (see
<http://dme.rwth-aachen.de/de/opensubspace> for more information).  Also
performs visualization.")
    (license license:gpl2)))

(define-public r-subsemble
  (package
    (name "r-subsemble")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "subsemble" version))
       (sha256
        (base32 "125msb0krcdj6jbdvzdl75179ajakb1l0xal45bp38am8w62a6zz"))))
    (properties `((upstream-name . "subsemble")))
    (build-system r-build-system)
    (propagated-inputs (list r-superlearner))
    (home-page "https://github.com/ledell/subsemble")
    (synopsis
     "An Ensemble Method for Combining Subset-Specific Algorithm Fits")
    (description
     "The Subsemble algorithm is a general subset ensemble prediction method, which
can be used for small, moderate, or large datasets.  Subsemble partitions the
full dataset into subsets of observations, fits a specified underlying algorithm
on each subset, and uses a unique form of k-fold cross-validation to output a
prediction function that combines the subset-specific fits.  An oracle result
provides a theoretical performance guarantee for Subsemble.  The paper,
\"Subsemble: An ensemble method for combining subset-specific algorithm fits\" is
authored by Stephanie Sapp, Mark J. van der Laan & John Canny (2014)
<doi:10.1080/02664763.2013.864263>.")
    (license license:asl2.0)))

(define-public r-subscreen
  (package
    (name "r-subscreen")
    (version "3.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "subscreen" version))
       (sha256
        (base32 "0vxwyx08bjha7vgv3lcgigar43f74fr3w7wna6z8viawh3bc7k96"))))
    (properties `((upstream-name . "subscreen")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinywidgets
                             r-shinyjs
                             r-shiny
                             r-ranger
                             r-purrr
                             r-plyr
                             r-jsonlite
                             r-dt
                             r-dplyr
                             r-data-table
                             r-colourpicker
                             r-bsplus))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=subscreen")
    (synopsis "Systematic Screening of Study Data for Subgroup Effects")
    (description
     "Identifying outcome relevant subgroups has now become as simple as possible! The
formerly lengthy and tedious search for the needle in a haystack will be
replaced by a single, comprehensive and coherent presentation.  The central
result of a subgroup screening is a diagram in which each single dot stands for
a subgroup.  The diagram may show thousands of them.  The position of the dot in
the diagram is determined by the sample size of the subgroup and the statistical
measure of the treatment effect in that subgroup.  The sample size is shown on
the horizontal axis while the treatment effect is displayed on the vertical
axis.  Furthermore, the diagram shows the line of no effect and the overall
study results.  For small subgroups, which are found on the left side of the
plot, larger random deviations from the mean study effect are expected, while
for larger subgroups only small deviations from the study mean can be expected
to be chance findings.  So for a study with no conspicuous subgroup effects, the
dots in the figure are expected to form a kind of funnel.  Any deviations from
this funnel shape hint to conspicuous subgroups.  This approach was presented in
Muysers (2020) <doi:10.1007/s43441-019-00082-6> and referenced in Ballarini
(2020) <doi:10.1002/pst.2012>.  New to version 3 is the Automatic Screening of
one- or MUlti-factorial Subgroups (ASMUS) for documentation of the structured
review of subgroup findings.")
    (license license:gpl3)))

(define-public r-subscore
  (package
    (name "r-subscore")
    (version "3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "subscore" version))
       (sha256
        (base32 "01y7bp7nj0p47n69pxvj2gyrcvgj617ymhwhxlqp0jrkns1wqjy5"))))
    (properties `((upstream-name . "subscore")))
    (build-system r-build-system)
    (propagated-inputs (list r-sirt
                             r-ltm
                             r-irtoys
                             r-ctt
                             r-cocor
                             r-boot))
    (home-page "https://cran.r-project.org/package=subscore")
    (synopsis
     "Computing Subscores in Classical Test Theory and Item Response Theory")
    (description
     "This package provides functions for computing test subscores using different
methods in both classical test theory (CTT) and item response theory (IRT).
This package enables three types of subscoring methods within the framework of
CTT and IRT, including (1) Wainer's augmentation method (Wainer et.  al., 2001)
<doi:10.4324/9781410604729>, (2) Haberman's subscoring methods (Haberman, 2008)
<doi:10.3102/1076998607302636>, and (3) Yen's objective performance index (OPI;
Yen, 1987)
<https://www.ets.org/research/policy_research_reports/publications/paper/1987/hrap>.
 It also includes functions to compute Proportional Reduction of Mean Squared
Errors (PRMSEs) in Haberman's methods which are used to examine whether test
subscores are of added value.  In addition, the package includes a function to
assess the local independence assumption of IRT with Yen's Q3 statistic (Yen,
1984 <doi:10.1177/014662168400800201>; Yen, 1993
<doi:10.1111/j.1745-3984.1993.tb00423.x>).")
    (license license:gpl2+)))

(define-public r-subrank
  (package
    (name "r-subrank")
    (version "0.9.9.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "subrank" version))
       (sha256
        (base32 "1sg1krlzck5m7g9klppxcwpbj6dnwlnc1pjvyyyw0hb5raz102w1"))))
    (properties `((upstream-name . "subrank")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=subrank")
    (synopsis "Computes Copula using Ranks and Subsampling")
    (description
     "Estimation of copula using ranks and subsampling.  The main feature of this
method is that simulation studies show a low sensitivity to dimension, on
realistic cases.")
    (license license:gpl3+)))

(define-public r-subpathwaylnce
  (package
    (name "r-subpathwaylnce")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SubpathwayLNCE" version))
       (sha256
        (base32 "051csjavr9549y54yirfdn266i7swsvpbcakhziyz4sl4afwx5kl"))))
    (properties `((upstream-name . "SubpathwayLNCE")))
    (build-system r-build-system)
    (propagated-inputs (list r-rbgl r-igraph r-graph r-biasedurn))
    (home-page "https://cran.r-project.org/package=SubpathwayLNCE")
    (synopsis
     "Identify Signal Subpathways Competitively Regulated by LncRNAs Based on ceRNA Theory")
    (description
     "Identify dysfunctional subpathways competitively regulated by @code{lncRNAs}
through integrating @code{lncRNA-mRNA} expression profile and pathway
topologies.")
    (license license:gpl2+)))

(define-public r-submax
  (package
    (name "r-submax")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "submax" version))
       (sha256
        (base32 "0b9x55gqzw20smr3hx9izvyr0i8zv0h03hadn4c7ks7w8ga5i0kj"))))
    (properties `((upstream-name . "submax")))
    (build-system r-build-system)
    (propagated-inputs (list r-sensitivityfull r-mvtnorm))
    (home-page "https://cran.r-project.org/package=submax")
    (synopsis
     "Effect Modification in Observational Studies Using the Submax Method")
    (description
     "Effect modification occurs if a treatment effect is larger or more stable in
certain subgroups defined by observed covariates.  The submax or
subgroup-maximum method of Lee et al. (2017) <@code{arXiv:1702.00525>} does an
overall test and separate tests in subgroups, correcting for multiple testing
using the joint distribution.")
    (license license:gpl2)))

(define-public r-subgxe
  (package
    (name "r-subgxe")
    (version "0.9.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "subgxe" version))
       (sha256
        (base32 "0faww87ncylqixb640hc2axvn4i50d2r2ggngmd7s9wazbb6sd55"))))
    (properties `((upstream-name . "subgxe")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/umich-cphds/subgxe")
    (synopsis "Combine Multiple GWAS by Using Gene-Environment Interactions")
    (description
     "Classical methods for combining summary data from genome-wide association
studies (GWAS) only use marginal genetic effects and power can be compromised in
the presence of heterogeneity.  subgxe is a R package that implements p-value
assisted subset testing for association (@code{pASTA}), a method developed by Yu
et al. (2019) <doi:10.1159/000496867>. @code{pASTA} generalizes association
analysis based on subsets by incorporating gene-environment interactions into
the testing procedure.")
    (license license:gpl3)))

(define-public r-subgrpid
  (package
    (name "r-subgrpid")
    (version "0.12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SubgrpID" version))
       (sha256
        (base32 "0x25n5gm1fl5s157g5a7jszy1rj16151r0jpf61vvsw0c9c58707"))))
    (properties `((upstream-name . "SubgrpID")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rpart
                             r-matrix
                             r-mass
                             r-glmnet
                             r-ggplot2))
    (home-page "https://cran.r-project.org/package=SubgrpID")
    (synopsis "Patient Subgroup Identification for Clinical Drug Development")
    (description
     "Implementation of Sequential BATTing (bootstrapping and aggregating of
thresholds from trees) for developing threshold-based multivariate
(prognostic/predictive) biomarker signatures.  Variable selection is
automatically built-in.  Final signatures are returned with interaction plots
for predictive signatures.  Cross-validation performance evaluation and testing
dataset results are also output.  Detail algorithms are described in Huang et al
(2017) <doi:10.1002/sim.7236>.")
    (license license:gpl2+)))

(define-public r-subgroup
  (package
    (name "r-subgroup")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "subgroup" version))
       (sha256
        (base32 "1n3qw7vih1rngmp4fwjbs050ngby840frj28i8x7d7aa52ha2syf"))))
    (properties `((upstream-name . "subgroup")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=subgroup")
    (synopsis
     "Methods for exploring treatment effect heterogeneity in subgroup analysis of clinical trials")
    (description
     "This package produces various measures of expected treatment effect
heterogeneity under an assumption of homogeneity across subgroups.  Graphical
presentations are created to compare these expected differences with the
observed differences.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-subformula
  (package
    (name "r-subformula")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "subformula" version))
       (sha256
        (base32 "0nsl0nzj79z6aib12b5qijyab0yxlljfssiq9p8xgcgbxy4a7syy"))))
    (properties `((upstream-name . "subformula")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/JonasMoss/subformula")
    (synopsis "Create Subformulas of a Formula")
    (description
     "This package provides a formula sub is a subformula of formula if all the terms
on the right hand side of sub are terms of formula and their left hand sides are
identical.  This package aids in the creation of subformulas.")
    (license license:expat)))

(define-public r-subdetect
  (package
    (name "r-subdetect")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "subdetect" version))
       (sha256
        (base32 "0p9153dhxf9k5qs554vdcgpdsqas41ylc5qb2i2j76z8nvi0v9hm"))))
    (properties `((upstream-name . "subdetect")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=subdetect")
    (synopsis "Detect Subgroup with an Enhanced Treatment Effect")
    (description
     "This package provides a test for the existence of a subgroup with enhanced
treatment effect.  And, a sample size calculation procedure for the subgroup
detection test.")
    (license license:gpl3)))

(define-public r-subcopem2d
  (package
    (name "r-subcopem2d")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "subcopem2D" version))
       (sha256
        (base32 "06wwd847g9pxd0z2a8494h3nc9s280a3s1510bir24m3z7w1pqf3"))))
    (properties `((upstream-name . "subcopem2D")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=subcopem2D")
    (synopsis "Bivariate Empirical Subcopula")
    (description
     "Calculate empirical subcopula and dependence measures from a given bivariate
sample, and Bernstein copula approximations.")
    (license license:gpl3)))

(define-public r-stylo
  (package
    (name "r-stylo")
    (version "0.7.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stylo" version))
       (sha256
        (base32 "00iw5yr6qnf01hd0a9dsqwjv2cdhq82pfj5wiy1fz0jg0q1ghh9n"))))
    (properties `((upstream-name . "stylo")))
    (build-system r-build-system)
    (propagated-inputs (list r-tsne
                             r-tcltk2
                             r-pamr
                             r-lattice
                             r-e1071
                             r-class
                             r-ape))
    (home-page "https://github.com/computationalstylistics/stylo")
    (synopsis "Stylometric Multivariate Analyses")
    (description
     "Supervised and unsupervised multivariate methods, supplemented by GUI and some
visualizations, to perform various analyses in the field of computational
stylistics, authorship attribution, etc.  For further reference, see Eder et al.
(2016), <https://journal.r-project.org/archive/2016/RJ-2016-007/index.html>.
You are also encouraged to visit the Computational Stylistics Group's website
<https://computationalstylistics.github.io/>, where a reasonable amount of
information about the package and related projects are provided.")
    (license license:gpl3+)))

(define-public r-stylest2
  (package
    (name "r-stylest2")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stylest2" version))
       (sha256
        (base32 "1r8vasbhynw7bv94j2wrh7l37552x77m1p7jaig1cr6s4r53flvg"))))
    (properties `((upstream-name . "stylest2")))
    (build-system r-build-system)
    (propagated-inputs (list r-quanteda r-matrix))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://cran.r-project.org/package=stylest2")
    (synopsis "Estimating Speakers of Texts")
    (description
     "Estimates the authors or speakers of texts.  Methods developed in Huang, Perry,
and Spirling (2020) <doi:10.1017/pan.2019.49>.  The model is built on a Bayesian
framework in which the distinctiveness of each speaker is defined by how
different, on average, the speaker's terms are to everyone else in the corpus of
texts.  An optional cross-validation method is implemented to select the subset
of terms that generate the most accurate speaker predictions.  Once a set of
terms is selected, the model can be estimated.  Speaker distinctiveness and term
influence can be recovered from parameters in the model using package functions.
 Once fitted, the model can be used to predict authorship of new texts.")
    (license license:gpl3)))

(define-public r-stxplore
  (package
    (name "r-stxplore")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stxplore" version))
       (sha256
        (base32 "1bnpp53s7r8ghvbxvnlk103h66a55xf7y9fcdhvmfgf31d2x5dsd"))))
    (properties `((upstream-name . "stxplore")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stars
                             r-spacetime
                             r-sp
                             r-rlang
                             r-rcolorbrewer
                             r-magrittr
                             r-lubridate
                             r-gstat
                             r-gridextra
                             r-ggridges
                             r-ggplot2
                             r-ggmap
                             r-fields))
    (native-inputs (list r-knitr))
    (home-page "https://sevvandi.github.io/stxplore/")
    (synopsis "Exploration of Spatio-Temporal Data")
    (description
     "This package provides a set of statistical tools for spatio-temporal data
exploration.  Includes simple plotting functions, covariance calculations and
computations similar to principal component analysis for spatio-temporal data.
Can use both dataframes and stars objects for all plots and computations.  For
more details refer Spatio-Temporal Statistics with R (Christopher K. Wikle,
Andrew Zammit-Mangion, Noel Cressie, 2019, ISBN:9781138711136).")
    (license license:gpl3+)))

(define-public r-stv
  (package
    (name "r-stv")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "STV" version))
       (sha256
        (base32 "02cd78z2k08gg5kskw1m53dc2xyl9f8qhcnhvy9n90ckakw6h19h"))))
    (properties `((upstream-name . "STV")))
    (build-system r-build-system)
    (home-page "https://github.com/jayemerson/STV")
    (synopsis "Single Transferable Vote Counting")
    (description
     "Implementations of the Single Transferable Vote counting system.  By default, it
uses the Cambridge method for surplus allocation and Droop method for quota
calculation.  Fractional surplus allocation and the Hare quota are available as
options.")
    (license license:lgpl3)))

(define-public r-stutetest
  (package
    (name "r-stutetest")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StuteTest" version))
       (sha256
        (base32 "1gkhs3f3pa96a80pipndlxbrwfn9x1l6xzkqmbd6781d90nnk939"))))
    (properties `((upstream-name . "StuteTest")))
    (build-system r-build-system)
    (propagated-inputs (list r-rnames r-rcpparmadillo r-rcpp r-plm r-dplyr))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=StuteTest")
    (synopsis "Stute (1997) Linearity Test")
    (description
     "Non-parametric test, originally proposed by Stute (1997)
<https://www.jstor.org/stable/2242560>, that the expectation of a dependent
variable Y given an independent variable D is linear in D.")
    (license license:expat)))

(define-public r-studystrap
  (package
    (name "r-studystrap")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "studyStrap" version))
       (sha256
        (base32 "0lvzqnq40j0xn74qrj0pqhr8zz34mbhfd7dg8bnadscsvj5ffm4c"))))
    (properties `((upstream-name . "studyStrap")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyverse
                             r-tibble
                             r-pls
                             r-nnls
                             r-matrixcorrelation
                             r-dplyr
                             r-cca
                             r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=studyStrap")
    (synopsis "Study Strap and Multi-Study Learning Algorithms")
    (description
     "This package implements multi-study learning algorithms such as merging, the
study-specific ensemble (trained-on-observed-studies ensemble) the study strap,
the covariate-matched study strap, covariate-profile similarity weighting, and
stacking weights.  Embedded within the caret framework, this package allows for
a wide range of single-study learners (e.g., neural networks, lasso, random
forests).  The package offers over 20 default similarity measures and allows for
specification of custom similarity measures for covariate-profile similarity
weighting and an accept/reject step.  This implements methods described in
Loewinger, Kishida, Patil, and Parmigiani. (2019) <doi:10.1101/856385>.")
    (license license:expat)))

(define-public r-studentlife
  (package
    (name "r-studentlife")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "studentlife" version))
       (sha256
        (base32 "1xvlcwg7ckxsm5hb5ax8r3hzdqnx61ax59ini5xc5jrsmir13g67"))))
    (properties `((upstream-name . "studentlife")))
    (build-system r-build-system)
    (propagated-inputs (list r-visdat
                             r-tidyr
                             r-tibble
                             r-skimr
                             r-readr
                             r-r-utils
                             r-purrr
                             r-jsonlite
                             r-ggplot2
                             r-dplyr
                             r-crayon))
    (home-page "https://github.com/Frycast/studentlife")
    (synopsis "Tidy Handling and Navigation of the Student-Life Dataset")
    (description
     "Download, navigate and analyse the Student-Life dataset.  The Student-Life
dataset contains passive and automatic sensing data from the phones of a class
of 48 Dartmouth college students.  It was collected over a 10 week term.
Additionally, the dataset contains ecological momentary assessment results along
with pre-study and post-study mental health surveys.  The intended use is to
assess mental health, academic performance and behavioral trends.  The raw
dataset and additional information is available at
<https://studentlife.cs.dartmouth.edu/>.")
    (license license:gpl3)))

(define-public r-stuart
  (package
    (name "r-stuart")
    (version "0.10.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stuart" version))
       (sha256
        (base32 "025zd8253x6s8y78vhmd7f4ws9x96y7qlad1k5n0whpm1z3i48sl"))))
    (properties `((upstream-name . "stuart")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=stuart")
    (synopsis "Subtests Using Algorithmic Rummaging Techniques")
    (description
     "Construct subtests from a pool of items by using ant-colony-optimization,
genetic algorithms, brute force, or random sampling.  Schultze (2017)
<doi:10.17169/refubium-622>.")
    (license license:gpl3)))

(define-public r-sts
  (package
    (name "r-sts")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sts" version))
       (sha256
        (base32 "10jza1c0813yhjybw4g9fq21m7ypwhzzp1v9yf26ah4b828zb2m8"))))
    (properties `((upstream-name . "sts")))
    (build-system r-build-system)
    (propagated-inputs (list r-stm
                             r-slam
                             r-rcpparmadillo
                             r-rcpp
                             r-mvtnorm
                             r-matrixstats
                             r-matrix
                             r-glmnet
                             r-foreach
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=sts")
    (synopsis
     "Estimation of the Structural Topic and Sentiment-Discourse Model for Text Analysis")
    (description
     "The Structural Topic and Sentiment-Discourse (STS) model allows researchers to
estimate topic models with document-level metadata that determines both topic
prevalence and sentiment-discourse.  The sentiment-discourse is modeled as a
document-level latent variable for each topic that modulates the word frequency
within a topic.  These latent topic sentiment-discourse variables are controlled
by the document-level metadata.  The STS model can be useful for regression
analysis with text data in addition to topic modelingâs traditional use of
descriptive analysis.  The method was developed in Li and Mankad (2024)
<doi:10.2139/ssrn.4020651>.")
    (license license:expat)))

(define-public r-strvalidator
  (package
    (name "r-strvalidator")
    (version "2.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "strvalidator" version))
       (sha256
        (base32 "0s7n26in5fb6lxgffx01743vd4854qg4wclqgsa6mwnrqg8ihg4y"))))
    (properties `((upstream-name . "strvalidator")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-plyr
                             r-plotly
                             r-mass
                             r-gwidgets2tcltk
                             r-gwidgets2
                             r-gtable
                             r-gridextra
                             r-ggplot2
                             r-dt
                             r-dplyr
                             r-data-table))
    (home-page "https://sites.google.com/site/forensicapps/strvalidator")
    (synopsis "Process Control and Validation of Forensic STR Kits")
    (description
     "An open source platform for validation and process control.  Tools to analyze
data from internal validation of forensic short tandem repeat (STR) kits are
provided.  The tools are developed to provide the necessary data to conform with
guidelines for internal validation issued by the European Network of Forensic
Science Institutes (ENFSI) DNA Working Group, and the Scientific Working Group
on DNA Analysis Methods (SWGDAM).  A front-end graphical user interface is
provided.  More information about each function can be found in the respective
help documentation.")
    (license license:gpl2)))

(define-public r-structuremc
  (package
    (name "r-structuremc")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StructureMC" version))
       (sha256
        (base32 "1pj3inznw6flvmk22dzjcfjnwjhx7r98ngckz0axf6ipp3hb85jn"))))
    (properties `((upstream-name . "StructureMC")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrixcalc r-mass))
    (home-page "https://cran.r-project.org/package=StructureMC")
    (synopsis "Structured Matrix Completion")
    (description
     "This package provides an efficient method to recover the missing block of an
approximately low-rank matrix.  Current literature on matrix completion focuses
primarily on independent sampling models under which the individual observed
entries are sampled independently.  Motivated by applications in genomic data
integration, we propose a new framework of structured matrix completion (SMC) to
treat structured missingness by design [Cai T, Cai TT, Zhang A (2016)
<doi:10.1080/01621459.2015.1021005>].  Specifically, our proposed method aims at
efficient matrix recovery when a subset of the rows and columns of an
approximately low-rank matrix are observed.  The main function in our package,
smc.FUN(), is for recovery of the missing block A22 of an approximately low-rank
matrix A given the other blocks A11, A12, A21.")
    (license license:gpl2+)))

(define-public r-structuraldecompose
  (package
    (name "r-structuraldecompose")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StructuralDecompose" version))
       (sha256
        (base32 "1iw4knii94wajignrq0c91d66b6bc2qm019ny86jyyj5nd9nxkwz"))))
    (properties `((upstream-name . "StructuralDecompose")))
    (build-system r-build-system)
    (propagated-inputs (list r-strucchange r-segmented r-changepoint))
    (native-inputs (list r-knitr))
    (home-page "https://allen-1242.github.io/StructuralDecompose/")
    (synopsis "Decomposes a Level Shifted Time Series")
    (description
     "Explains the behavior of a time series by decomposing it into its trend,
seasonality and residuals.  It is built to perform very well in the presence of
significant level shifts.  It is designed to play well with any breakpoint
algorithm and any smoothing algorithm.  Currently defaults to lowess for
smoothing and strucchange for breakpoint identification.  The package is useful
in areas such as trend analysis, time series decomposition, breakpoint
identification and anomaly detection.")
    (license license:expat)))

(define-public r-structree
  (package
    (name "r-structree")
    (version "1.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "structree" version))
       (sha256
        (base32 "178x6maghjaw9lkyjr84pq916vbq0ldh5gqia0r0vqs8k8dvna9i"))))
    (properties `((upstream-name . "structree")))
    (build-system r-build-system)
    (propagated-inputs (list r-penalized r-mgcv r-lme4))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=structree")
    (synopsis "Tree-Structured Clustering")
    (description
     "Tree-structured modelling of categorical predictors (Tutz and Berger (2018),
<doi:10.1007/s11634-017-0298-6>) or measurement units (Berger and Tutz (2018),
<doi:10.1080/10618600.2017.1371030>).")
    (license license:gpl3)))

(define-public r-strucchangercpp
  (package
    (name "r-strucchangercpp")
    (version "1.5-4-1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "strucchangeRcpp" version))
       (sha256
        (base32 "15csz77hfii23dv8rrsabwilq8z0fa5f0vp6nhwh2nmww4y2fm7v"))))
    (properties `((upstream-name . "strucchangeRcpp")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-sandwich r-rcpparmadillo r-rcpp))
    (home-page "https://github.com/bfast2/strucchangeRcpp/")
    (synopsis
     "Testing, Monitoring, and Dating Structural Changes: C++ Version")
    (description
     "This package provides a fast implementation with additional experimental
features for testing, monitoring and dating structural changes in (linear)
regression models. @code{strucchangeRcpp} features tests/methods from the
generalized fluctuation test framework as well as from the F test (Chow test)
framework.  This includes methods to fit, plot and test fluctuation processes
(e.g. cumulative/moving sum, recursive/moving estimates) and F statistics,
respectively.  These methods are described in Zeileis et al. (2002)
<doi:10.18637/jss.v007.i02>.  Finally, the breakpoints in regression models with
structural changes can be estimated together with confidence intervals, and
their magnitude as well as the model fit can be evaluated using a variety of
statistical measures.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-strs
  (package
    (name "r-strs")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "strs" version))
       (sha256
        (base32 "072abw4am93h51zd4yi48fprvgg61rzif259i1150aav2n43aklw"))))
    (properties `((upstream-name . "strs")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringi))
    (home-page "https://github.com/pythonicr/strs")
    (synopsis "'Python' Style String Functions")
    (description
     "This package provides a comprehensive set of string manipulation functions based
on those found in Python without relying on reticulate'.  It provides functions
that intend to (1) make it easier for users familiar with Python to work with
strings, (2) reduce the complexity often associated with string operations, (3)
and enable users to write more readable and maintainable code that manipulates
strings.")
    (license license:expat)))

(define-public r-stroupglmm
  (package
    (name "r-stroupglmm")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StroupGLMM" version))
       (sha256
        (base32 "1vif5kwra18kq83f56rkcshqzmddmfl1ln6rwx54c0hwhpvnhszm"))))
    (properties `((upstream-name . "StroupGLMM")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey
                             r-scatterplot3d
                             r-phia
                             r-parameters
                             r-nlme
                             r-mutoss
                             r-mass
                             r-magrittr
                             r-lmertest
                             r-lattice
                             r-ggplot2
                             r-emmeans
                             r-dplyr
                             r-car
                             r-broom-mixed
                             r-aod))
    (home-page "https://cran.r-project.org/package=StroupGLMM")
    (synopsis
     "R Codes and Datasets for Generalized Linear Mixed Models: Modern Concepts, Methods and Applications by Walter W. Stroup")
    (description
     "R Codes and Datasets for Stroup, W. W. (2012).  Generalized Linear Mixed Models
Modern Concepts, Methods and Applications, CRC Press.")
    (license license:gpl3)))

(define-public r-stroke
  (package
    (name "r-stroke")
    (version "23.9.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stRoke" version))
       (sha256
        (base32 "0rlhi8cj1gb5cpvpds4hr6m1g4g53yqlzrvl5gzn3xkiyd5i3kx3"))))
    (properties `((upstream-name . "stRoke")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rankinplot
                             r-mass
                             r-lubridate
                             r-gtsummary
                             r-ggplot2
                             r-dplyr
                             r-calendar))
    (native-inputs (list r-knitr))
    (home-page "https://agdamsbo.github.io/stRoke/")
    (synopsis "Clinical Stroke Research")
    (description
     "This is an R-toolbox of custom functions for convenient data management and
analysis in clinical health research and teaching.  The package is mainly
collected for personal use, but any use beyond that is encouraged.  This package
has migrated functions from agdamsbo/@code{daDoctoR}', and new functions has
been added.  Version follows months and year.  See NEWS/Changelog for release
notes.  This package includes sampled data from the TALOS trial (Kraglund et al
(2018) <doi:10.1161/STROKEAHA.117.020067>).  The win_prob() function is based on
work by Zou et al (2022) <doi:10.1161/STROKEAHA.121.037744>.  The age_calc()
function is based on work by Becker (2020) <doi:10.18637/jss.v093.i02>.")
    (license license:gpl3)))

(define-public r-strmps
  (package
    (name "r-strmps")
    (version "0.5.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "STRMPS" version))
       (sha256
        (base32 "0vlmhrna0laqzjpbg4sgnscrli3cly2lc5d69n6iqfl213zsfz78"))))
    (properties `((upstream-name . "STRMPS")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-shortread
                             r-purrr
                             r-iranges
                             r-dplyr
                             r-biostrings))
    (home-page "https://cran.r-project.org/package=STRMPS")
    (synopsis
     "Analysis of Short Tandem Repeat (STR) Massively Parallel Sequencing (MPS) Data")
    (description
     "Loading, identifying, aggregating, manipulating, and analysing short tandem
repeat regions of massively parallel sequencing data in forensic genetics.
STRMPS can work with the package ST@code{RaitRazoR} (an R interface to the
ST@code{RaitRazor} commandline tool) for added speed.  ST@code{RaitRazoR} only
works on linux and can found at <https://github.com/svilsen/ST@code{RaitRazoR>}.
 The analyses and framework implemented in this package relies on the papers of
Vilsen et al. (2017) <doi:10.1016/j.fsigen.2017.01.017> and Vilsen et al. (2018)
<doi:10.1016/j.fsigen.2018.04.003>.  Lastly, note that the parallelisation in
the package relies on mclapply() and, thus, speed-ups will only be seen on UNIX
based systems.")
    (license license:expat)))

(define-public r-stripless
  (package
    (name "r-stripless")
    (version "1.0-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stripless" version))
       (sha256
        (base32 "08mdp7kq6r5bk77j09477d1dnn7iwa346pr24b5bqsxwnbknyrsr"))))
    (properties `((upstream-name . "stripless")))
    (build-system r-build-system)
    (propagated-inputs (list r-lattice))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=stripless")
    (synopsis
     "Structured Trellis Displays Without Strips for Lattice Graphics")
    (description
     "For making Trellis-type conditioning plots without strip labels.  This is useful
for displaying the structure of results from factorial designs and other studies
when many conditioning variables would clutter the display with layers of
redundant strip labels.  Settings of the variables are encoded by layout and
spacing in the trellis array and decoded by a separate legend.  The
functionality is implemented by a single S3 generic strucplot() function that is
a wrapper for the Lattice package's xyplot() function.  This allows access to
all Lattice graphics capabilities in the usual way.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-strip
  (package
    (name "r-strip")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "strip" version))
       (sha256
        (base32 "1j3kq6w8k66z45rpd1cgxplpnldzbyqklgs4bnbv906pyd8wk9ak"))))
    (properties `((upstream-name . "strip")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlist))
    (home-page "https://github.com/paulponcet/strip")
    (synopsis "Lighten your R Model Outputs")
    (description
     "The strip function deletes components of R model outputs that are useless for
specific purposes, such as predict[ing], print[ing], summary[izing], etc.")
    (license license:expat)))

(define-public r-stringx
  (package
    (name "r-stringx")
    (version "0.2.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stringx" version))
       (sha256
        (base32 "0rcbbc1x07am5ag5fnirvyg7r640pp9cs2vqzs9jlx8xx385p4md"))))
    (properties `((upstream-name . "stringx")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringi))
    (home-page "https://stringx.gagolewski.com/")
    (synopsis "Replacements for Base String Functions Powered by 'stringi'")
    (description
     "English is the native language for only 5% of the World population.  Also, only
17% of us can understand this text.  Moreover, the Latin alphabet is the main
one for merely 36% of the total.  The early computer era, now a very long time
ago, was dominated by the US. Due to the proliferation of the internet,
smartphones, social media, and other technologies and communication platforms,
this is no longer the case.  This package replaces base R string functions (such
as grep(), tolower(), sprintf(), and strptime()) with ones that fully support
the Unicode standards related to natural language and date-time processing.  It
also fixes some long-standing inconsistencies, and introduces some new, useful
features.  Thanks to ICU (International Components for Unicode) and stringi',
they are fast, reliable, and portable across different platforms.")
    (license license:gpl2+)))

(define-public r-stringstatic
  (package
    (name "r-stringstatic")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stringstatic" version))
       (sha256
        (base32 "0kj85yas07l9xl49l2j0bk42ymdg57fmgyk45wwl0kmxp146skfq"))))
    (properties `((upstream-name . "stringstatic")))
    (build-system r-build-system)
    (home-page "https://github.com/rossellhayes/stringstatic")
    (synopsis "Dependency-Free String Operations")
    (description
     "This package provides drop-in replacements for functions from the stringr
package, with the same user interface.  These functions have no external
dependencies and can be copied directly into your package code using the
staticimports package.")
    (license license:cc0)))

(define-public r-stringmagic
  (package
    (name "r-stringmagic")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stringmagic" version))
       (sha256
        (base32 "134g9c358d3730fvkhcwcrhy4w7ig614jcnxrygakrcvm55c1lgf"))))
    (properties `((upstream-name . "stringmagic")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://lrberge.github.io/stringmagic/")
    (synopsis "Character String Operations and Interpolation, Magic Edition")
    (description
     "This package performs complex string operations compactly and efficiently.
Supports string interpolation jointly with over 50 string operations.  Also
enhances regular string functions (like grep() and co).  See an introduction at
<https://lrberge.github.io/stringmagic/>.")
    (license license:gpl2+)))

(define-public r-stringformattr
  (package
    (name "r-stringformattr")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stringformattr" version))
       (sha256
        (base32 "0x56k30clj5ajk0qg5sr8b9l0asz6ldivwr1ddy1vp1djliih1fx"))))
    (properties `((upstream-name . "stringformattr")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr))
    (home-page "https://cran.r-project.org/package=stringformattr")
    (synopsis "Dynamic String Formatting")
    (description
     "Pass named and unnamed character vectors into specified positions in strings.
This represents an attempt to replicate some of python's string formatting.")
    (license license:expat)))

(define-public r-string2path
  (package
    (name "r-string2path")
    (version "0.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "string2path" version))
       (sha256
        (base32 "1grp1r2dgng1p436g1hx01w8m3a8r37x30q9b38lfrjsmzqqzgzp"))))
    (properties `((upstream-name . "string2path")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-tibble r-cli))
    (home-page "https://yutannihilation.github.io/string2path/")
    (synopsis "Rendering Font into 'data.frame'")
    (description
     "Extract glyph information from font data, and translate the outline curves to
flattened paths or tessellated polygons.  The converted data is returned as a
data.frame in easy-to-plot format.")
    (license license:expat)))

(define-public r-string2adjmatrix
  (package
    (name "r-string2adjmatrix")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "String2AdjMatrix" version))
       (sha256
        (base32 "058r30kjjna02nmd6ngqfcnn1yk5390i20xvl6qb5qk3bcp2wmxn"))))
    (properties `((upstream-name . "String2AdjMatrix")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr))
    (home-page "https://cran.r-project.org/package=String2AdjMatrix")
    (synopsis "Creates an Adjacency Matrix from a List of Strings")
    (description
     "Takes a list of character strings and forms an adjacency matrix for the times
the specified characters appear together in the strings provided.  For use in
social network analysis and data wrangling.  Simple package, comprised of three
functions.")
    (license license:gpl3)))

(define-public r-strider
  (package
    (name "r-strider")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "strider" version))
       (sha256
        (base32 "114j0qpdyg8rrhj68zq596pgc4h7ja39n0rfxf0x9qg7imhaj1h6"))))
    (properties `((upstream-name . "strider")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/thk686/strider")
    (synopsis "Strided Iterator and Range")
    (description
     "The strided iterator adapts multidimensional buffers to work with the C++
standard library and range-based for-loops.  Given a pointer or iterator into a
multidimensional data buffer, one can generate an iterator range using
make_strided to construct strided versions of the standard library's begin and
end.  For constructing range-based for-loops, a strided_range class is provided.
 These help authors to avoid integer-based indexing, which in some cases can
impede algorithm performance and introduce indexing errors.  This library exists
primarily to expose the header file to other R projects.")
    (license license:expat)))

(define-public r-stressstrength
  (package
    (name "r-stressstrength")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StressStrength" version))
       (sha256
        (base32 "1fq26bzmwsk4nzz9bgasyxz9alw8x2ssahz67mham7vw483w79pd"))))
    (properties `((upstream-name . "StressStrength")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=StressStrength")
    (synopsis
     "Computation and Estimation of Reliability of Stress-Strength Models")
    (description
     "Reliability of (normal) stress-strength models and for building two-sided or
one-sided confidence intervals according to different approximate procedures.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-stressr
  (package
    (name "r-stressr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stressr" version))
       (sha256
        (base32 "00b93gfh1jd5r7i3dhsfqjidrczf693kyqlsa1krdndg8f0jkyj7"))))
    (properties `((upstream-name . "stressr")))
    (build-system r-build-system)
    (propagated-inputs (list r-xts r-xml r-latticeextra r-lattice))
    (home-page "https://github.com/mrbcuda/stressr")
    (synopsis "Fetch and plot financial stress index and component data")
    (description
     "Forms queries to submit to the Cleveland Federal Reserve Bank web site's
financial stress index data site.  Provides query functions for both the
composite stress index and the components data.  By default the download
includes daily time series data starting September 25, 1991.  The functions
return a class of either type easing or cfsi which contain a list of items
related to the query and its graphical presentation.  The list includes the time
series data as an xts object.  The package provides four lattice time series
plots to render the time series data in a manner similar to the bank's own
presentation.")
    (license license:expat)))

(define-public r-stressor
  (package
    (name "r-stressor")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stressor" version))
       (sha256
        (base32 "1bm7f5mz529c0r1qq33z7di0d1c77yhpnrnmywlkx9ccvq7dicyc"))))
    (properties `((upstream-name . "stressor")))
    (build-system r-build-system)
    (inputs (list python))
    (propagated-inputs (list r-reticulate r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=stressor")
    (synopsis "Algorithms for Testing Models under Stress")
    (description
     "Traditional model evaluation metrics fail to capture model performance under
less than ideal conditions.  This package employs techniques to evaluate models
\"under-stress\".  This includes testing models extrapolation ability, or testing
accuracy on specific sub-samples of the overall model space.  Details describing
stress-testing methods in this package are provided in Haycock (2023)
<doi:10.26076/2am5-9f67>.  The other primary contribution of this package is
provided to R users access to the Python library @code{PyCaret}
<https://pycaret.org/> for quick and easy access to auto-tuned machine learning
models.")
    (license license:expat)))

(define-public r-stressaddition
  (package
    (name "r-stressaddition")
    (version "3.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stressaddition" version))
       (sha256
        (base32 "14ggy4ka9kll723jdir22g5l5xkwdckvd08gg6rxsmxryhznqd4a"))))
    (properties `((upstream-name . "stressaddition")))
    (build-system r-build-system)
    (propagated-inputs (list r-plotrix r-drc))
    (home-page "https://git.ufz.de/oekotox/stressaddition")
    (synopsis "Modelling Tri-Phasic Concentration-Response Relationships")
    (description
     "The stress addition approach is an alternative to the traditional concentration
addition or effect addition models.  It allows the modelling of tri-phasic
concentration-response relationships either as single toxicant experiments, in
combination with an environmental stressor or as mixtures of two toxicants.  See
Liess et al. (2019) <doi:10.1038/s41598-019-51645-4> and Liess et al. (2020)
<doi:10.1186/s12302-020-00394-7>.")
    (license license:gpl3)))

(define-public r-streg
  (package
    (name "r-streg")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StReg" version))
       (sha256
        (base32 "0cwrizyk4l82alfwijpljwhcjhgcimrf1vv8wsz7azwwiqzb0cns"))))
    (properties `((upstream-name . "StReg")))
    (build-system r-build-system)
    (propagated-inputs (list r-tseries r-numderiv r-mcmcpack r-matlab
                             r-adgoftest))
    (home-page "https://cran.r-project.org/package=StReg")
    (synopsis "Student's t Regression Models")
    (description
     "It contains functions to estimate multivariate Student's t dynamic and static
regression models for given degrees of freedom and lag length.  Users can also
specify the trends and dummies of any kind in matrix form.  Poudyal, N., and
Spanos, A. (2022) <doi:10.3390/econometrics10020017>.  Spanos, A. (1994)
<http://www.jstor.org/stable/3532870>.")
    (license license:gpl2)))

(define-public r-streetscape
  (package
    (name "r-streetscape")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "streetscape" version))
       (sha256
        (base32 "0dyvliwf4wz348xlwc36dimv0v0nfvs0lskcgzmy0g7rbd2xikan"))))
    (properties `((upstream-name . "streetscape")))
    (build-system r-build-system)
    (propagated-inputs (list r-superpixelimagesegmentation
                             r-sp
                             r-sf
                             r-rlang
                             r-reticulate
                             r-quickpwcr
                             r-pbmcapply
                             r-parallelly
                             r-osmdata
                             r-openimager
                             r-mapview
                             r-httr
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=streetscape")
    (synopsis "Collect And Investigate Street Views For Urban Science")
    (description
     "This package provides a collection of functions to search and download street
view imagery ('Mapilary <https://www.mapillary.com/developer/api-documentation>)
and to extract, quantify, and visualize visual features.  Moreover, there are
functions provided to generate Qualtrics survey in TXT format using the
collection of street views for various research purposes.")
    (license license:gpl3)))

(define-public r-streammoa
  (package
    (name "r-streammoa")
    (version "1.3-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "streamMOA" version))
       (sha256
        (base32 "12fa19z31a9046xn6v5w0kj4pmprdfmzakiwikg3y0k31fpjhqk6"))))
    (properties `((upstream-name . "streamMOA")))
    (build-system r-build-system)
    (inputs (list openjdk))
    (propagated-inputs (list r-stream r-rjava r-proxy))
    (home-page "https://cran.r-project.org/package=streamMOA")
    (synopsis "Interface for MOA Stream Clustering Algorithms")
    (description
     "Interface for data stream clustering algorithms implemented in the MOA (Massive
Online Analysis) framework (Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard
Pfahringer (2010).  MOA: Massive Online Analysis, Journal of Machine Learning
Research 11: 1601-1604).")
    (license license:gpl3)))

(define-public r-streammetabolism
  (package
    (name "r-streammetabolism")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StreamMetabolism" version))
       (sha256
        (base32 "13f5z4d04pq5jb8gpk8rh79i2f5b6bqma1p3a3zqcw74wxdhk8hg"))))
    (properties `((upstream-name . "StreamMetabolism")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-suntools r-chron))
    (home-page "https://github.com/ssefick/StreamMetabolism")
    (synopsis "Calculate Single Station Metabolism from Diurnal Oxygen Curves")
    (description
     "I provide functions to calculate Gross Primary Productivity, Net Ecosystem
Production, and Ecosystem Respiration from single station diurnal Oxygen curves.")
    (license license:gpl3+)))

(define-public r-streamdepletr
  (package
    (name "r-streamdepletr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "streamDepletr" version))
       (sha256
        (base32 "0pa0hsnhscsa98xkqzg7l83yagzq1r6m250m3m4r79baab8ndqp4"))))
    (properties `((upstream-name . "streamDepletr")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-rmpfr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/FoundrySpatial/streamDepletr")
    (synopsis "Estimate Streamflow Depletion Due to Groundwater Pumping")
    (description
     "Implementation of analytical models for estimating streamflow depletion due to
groundwater pumping, and other related tools.  Functions are broadly split into
two groups: (1) analytical streamflow depletion models, which estimate
streamflow depletion for a single stream reach resulting from groundwater
pumping; and (2) depletion apportionment equations, which distribute estimated
streamflow depletion among multiple stream reaches within a stream network.  See
Zipper et al. (2018) <doi:10.1029/2018WR022707> for more information on
depletion apportionment equations and Zipper et al. (2019)
<doi:10.1029/2018WR024403> for more information on analytical depletion
functions, which combine analytical models and depletion apportionment
equations.")
    (license license:bsd-3)))

(define-public r-streamdag
  (package
    (name "r-streamdag")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "streamDAG" version))
       (sha256
        (base32 "1zzpjp1k0x0kh4jja8x0yjv35pygp5qb8ivqqg7wc7dr5plv87y5"))))
    (properties `((upstream-name . "streamDAG")))
    (build-system r-build-system)
    (propagated-inputs (list r-plotrix r-igraph r-asbio))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=streamDAG")
    (synopsis "Analytical Methods for Stream DAGs")
    (description
     "This package provides indices and tools for directed acyclic graphs (DAGs),
particularly DAG representations of intermittent streams.  A detailed
introduction to the package can be found in the publication: \"Non-perennial
stream networks as directed acyclic graphs: The R-package @code{streamDAG}\" (Aho
et al., 2023) <doi:10.1016/j.envsoft.2023.105775>, and in the introductory
package vignette.")
    (license license:gpl2+)))

(define-public r-streambugs
  (package
    (name "r-streambugs")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "streambugs" version))
       (sha256
        (base32 "0j7fi5rvs2apkzgyk9idpdcbxaa7kqj650gycfsq2md23fl75jpw"))))
    (properties `((upstream-name . "streambugs")))
    (build-system r-build-system)
    (propagated-inputs (list r-desolve))
    (home-page "https://www.eawag.ch/en/department/siam/projects/streambugs/")
    (synopsis
     "Parametric Ordinary Differential Equations Model of Growth, Death, and Respiration of Macroinvertebrate and Algae Taxa")
    (description
     "Numerically solve and plot solutions of a parametric ordinary differential
equations model of growth, death, and respiration of macroinvertebrate and algae
taxa dependent on pre-defined environmental factors.  The model (version 1.0) is
introduced in Schuwirth, N. and Reichert, P., (2013) <DOI:10.1890/12-0591.1>.
This package includes model extensions and the core functions introduced and
used in Schuwirth, N. et al. (2016) <DOI:10.1111/1365-2435.12605>, Kattwinkel,
M. et al. (2016) <DOI:10.1021/acs.est.5b04068>, Mondy, C. P., and Schuwirth, N.
(2017) <DOI:10.1002/eap.1530>, and Paillex, A. et al. (2017)
<DOI:10.1111/fwb.12927>.")
    (license license:gpl3)))

(define-public r-stream
  (package
    (name "r-stream")
    (version "2.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stream" version))
       (sha256
        (base32 "0qpn6rf0rp1nklarw0jkdlvpalcnvvjs26h8slnqkgrkb1rr0vhc"))))
    (properties `((upstream-name . "stream")))
    (build-system r-build-system)
    (propagated-inputs (list r-rpart
                             r-rcpp
                             r-proxy
                             r-mlbench
                             r-mass
                             r-magrittr
                             r-fpc
                             r-dbscan
                             r-clustergeneration
                             r-cluster
                             r-clue
                             r-bh))
    (home-page "https://github.com/mhahsler/stream")
    (synopsis "Infrastructure for Data Stream Mining")
    (description
     "This package provides a framework for data stream modeling and associated data
mining tasks such as clustering and classification.  The development of this
package was supported in part by NSF IIS-0948893, NSF CMMI 1728612, and NIH
R21HG005912.  Hahsler et al (2017) <doi:10.18637/jss.v076.i14>.")
    (license license:gpl3)))

(define-public r-streak
  (package
    (name "r-streak")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "STREAK" version))
       (sha256
        (base32 "1gi228y3rn187pkr74abiydlkdwaahd9b9nn5dn5433y69kwnkw5"))))
    (properties `((upstream-name . "STREAK")))
    (build-system r-build-system)
    (propagated-inputs (list r-vam r-speck r-seurat r-matrix r-ckmeans-1d-dp))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=STREAK")
    (synopsis
     "Receptor Abundance Estimation using Feature Selection and Gene Set Scoring")
    (description
     "This package performs receptor abundance estimation for single cell
RNA-sequencing data using a supervised feature selection mechanism and a
thresholded gene set scoring procedure.  Seurat's normalization method is
described in: Hao et al., (2021) <doi:10.1016/j.cell.2021.04.048>, Stuart et
al., (2019) <doi:10.1016/j.cell.2019.05.031>, Butler et al., (2018)
<doi:10.1038/nbt.4096> and Satija et al., (2015) <doi:10.1038/nbt.3192>.  Method
for reduced rank reconstruction and rank-k selection is detailed in: Javaid et
al., (2022) <doi:10.1101/2022.10.08.511197>.  Gene set scoring procedure is
described in: Frost et al., (2020) <doi:10.1093/nar/gkaa582>.  Clustering method
is outlined in: Song et al., (2020) <doi:10.1093/bioinformatics/btaa613> and
Wang et al., (2011) <doi:10.32614/RJ-2011-015>.")
    (license license:gpl2+)))

(define-public r-stray
  (package
    (name "r-stray")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stray" version))
       (sha256
        (base32 "1pjw1bdkqsp085lrwca1psma95ncapfz8a5iampxgghydkhh3dm8"))))
    (properties `((upstream-name . "stray")))
    (build-system r-build-system)
    (propagated-inputs (list r-pcapp r-ks r-ggplot2 r-fnn r-colorspace))
    (home-page "https://cran.r-project.org/package=stray")
    (synopsis "Anomaly Detection in High Dimensional and Temporal Data")
    (description
     "This is a modification of HDoutliers package.  The HDoutliers algorithm is a
powerful unsupervised algorithm for detecting anomalies in high-dimensional
data, with a strong theoretical foundation.  However, it suffers from some
limitations that significantly hinder its performance level, under certain
circumstances.  This package implements the algorithm proposed in Talagala,
Hyndman and Smith-Miles (2019) <@code{arXiv:1908.04000>} for detecting anomalies
in high-dimensional data that addresses these limitations of HDoutliers
algorithm.  We define an anomaly as an observation that deviates markedly from
the majority with a large distance gap.  An approach based on extreme value
theory is used for the anomalous threshold calculation.")
    (license license:gpl2)))

(define-public r-straweib
  (package
    (name "r-straweib")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "straweib" version))
       (sha256
        (base32 "0hi59p580yg6rj72grq4jx6grv7lmpi2cimp0rbxhkjpj6i1hav9"))))
    (properties `((upstream-name . "straweib")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=straweib")
    (synopsis "Stratified Weibull Regression Model")
    (description
     "The main function is icweib(), which fits a stratified Weibull proportional
hazards model for left censored, right censored, interval censored, and
non-censored survival data.  We parameterize the Weibull regression model so
that it allows a stratum-specific baseline hazard function, but where the
effects of other covariates are assumed to be constant across strata.  Please
refer to Xiangdong Gu, David Shapiro, Michael D. Hughes and Raji Balasubramanian
(2014) <doi:10.32614/RJ-2014-003> for more details.")
    (license license:gpl2+)))

(define-public r-stratvns
  (package
    (name "r-stratvns")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stratvns" version))
       (sha256
        (base32 "01s3p9mml04pqfd7fydkmqdk0qf5jmzz3b3q3x5vavlma6fv8nwi"))))
    (properties `((upstream-name . "stratvns")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr r-partitions r-multalloc))
    (home-page "https://cran.r-project.org/package=stratvns")
    (synopsis "Optimal Stratification in Stratified Sampling")
    (description
     "An Optimization Algorithm Applied to Stratification Problem.This function aims
at constructing optimal strata with an optimization algorithm based on a global
optimisation technique called vns.")
    (license license:gpl2)))

(define-public r-stratus
  (package
    (name "r-stratus")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "STraTUS" version))
       (sha256
        (base32 "0n3i7gbycp1f10058h9xlh3yk21w2izdb3xgs793mmh1i1sjy394"))))
    (properties `((upstream-name . "STraTUS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppalgos
                             r-phangorn
                             r-igraph
                             r-gmp
                             r-ggtree
                             r-ggplot2
                             r-ape))
    (home-page "http://github.com/mdhall272/STraTUS/")
    (synopsis
     "Enumeration and Uniform Sampling of Transmission Trees for a Known Phylogeny")
    (description
     "For a single, known pathogen phylogeny, provides functions for enumeration of
the set of compatible epidemic transmission trees, and for uniform sampling from
that set.  Optional arguments allow for incomplete sampling with a known number
of missing individuals, multiple sampling, and known infection time limits.
Always assumed are a complete transmission bottleneck and no superinfection or
reinfection.  See Hall and Colijn (2019) <doi:10.1093/molbev/msz058> for
methodology.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-stratsel
  (package
    (name "r-stratsel")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StratSel" version))
       (sha256
        (base32 "0hd6q8s8aiymcd33kv0gd0a4wzr86xxdzwn18jgpyk9q8pmg8gvl"))))
    (properties `((upstream-name . "StratSel")))
    (build-system r-build-system)
    (propagated-inputs (list r-pbivnorm r-mnormt r-memisc r-mass r-formula))
    (home-page "https://cran.r-project.org/package=StratSel")
    (synopsis "Strategic Selection Estimator")
    (description
     "This package provides functions to estimate a strategic selection estimator.  A
strategic selection estimator is an agent error model in which the two random
components are not assumed to be orthogonal.  In addition this package provides
generic functions to print and plot objects of its class as well as the
necessary functions to create tables for @code{LaTeX}.  There is also a function
to create dyadic data sets.")
    (license license:gpl2+)))

(define-public r-stratpal
  (package
    (name "r-stratpal")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StratPal" version))
       (sha256
        (base32 "0bmlblmd5lm203b5zqf20akc24xd9ra4kas9kd16pxw482s7fvyw"))))
    (properties `((upstream-name . "StratPal")))
    (build-system r-build-system)
    (propagated-inputs (list r-paleots r-admtools))
    (native-inputs (list r-knitr))
    (home-page "https://mindthegap-erc.github.io/StratPal/")
    (synopsis "Stratigraphic Paleobiology Modeling Pipelines")
    (description
     "The fossil record is a joint expression of ecological, taphonomic, evolutionary,
and stratigraphic processes (Holland and Patzkowsky, 2012, ISBN:978-0226649382).
 This package allowing to simulate biological processes in the time domain
(e.g., trait evolution, fossil abundance), and examine how their expression in
the rock record (stratigraphic domain) is influenced based on age-depth models,
ecological niche models, and taphonomic effects.  Functions simulating common
processes used in modeling trait evolution or event type data such as first/last
occurrences are provided and can be used standalone or as part of a pipeline.
The package comes with example data sets and tutorials in several vignettes,
which can be used as a template to set up one's own simulation.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-stratigrapher
  (package
    (name "r-stratigrapher")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StratigrapheR" version))
       (sha256
        (base32 "10cyv9cddi6lsy1ah3dhvf93gd5mal4frcdwy2zqq50apmmrx5l9"))))
    (properties `((upstream-name . "StratigrapheR")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml
                             r-stringr
                             r-shiny
                             r-reshape
                             r-dplyr
                             r-diagram))
    (home-page "https://cran.r-project.org/package=StratigrapheR")
    (synopsis "Integrated Stratigraphy")
    (description
     "Includes bases for litholog generation: graphical functions based on R base
graphics, interval management functions and svg importation functions among
others.  Also include stereographic projection functions, and other functions
made to deal with large datasets while keeping options to get into the details
of the data.  When using for publication please cite Sebastien Wouters,
Anne-Christine Da Silva, Frederic Boulvain and Xavier Devleeschouwer, 2021.  The
R Journal 13:2, 153-178.  The palaeomagnetism functions are based on: Tauxe, L.,
2010.  Essentials of Paleomagnetism.  University of California Press.
<https://earthref.org/@code{MagIC/books/Tauxe/Essentials/>}; Allmendinger, R.
W., Cardozo, N. C., and Fisher, D., 2013, Structural Geology Algorithms: Vectors
& Tensors: Cambridge, England, Cambridge University Press, 289 pp.; Cardozo, N.,
and Allmendinger, R. W., 2013, Spherical projections with OSXStereonet:
Computers & Geosciences, v.  51, no.  0, p.  193 - 205, <doi:
10.1016/j.cageo.2012.07.021>.")
    (license license:gpl3)))

(define-public r-stratifyr
  (package
    (name "r-stratifyr")
    (version "1.0-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stratifyR" version))
       (sha256
        (base32 "1kbpsyhx081rqr7q7gyjw377zlvvravsslpnykl2d16nap0rg0sq"))))
    (properties `((upstream-name . "stratifyR")))
    (build-system r-build-system)
    (propagated-inputs (list r-zipfr r-triangle r-mc2d r-fitdistrplus r-actuar))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=stratifyR")
    (synopsis "Optimal Stratification of Univariate Populations")
    (description
     "The stratification of univariate populations under stratified sampling designs
is implemented according to Khan et al. (2002) <doi:10.1177/0008068320020518>
and Khan et al. (2015) <doi:10.1080/02664763.2015.1018674> in this library.  It
determines the Optimum Strata Boundaries (OSB) and Optimum Sample Sizes (OSS)
for the study variable, y, using the best-fit frequency distribution of a survey
variable (if data is available) or a hypothetical distribution (if data is not
available).  The method formulates the problem of determining the OSB as
mathematical programming problem which is solved by using a dynamic programming
technique.  If a dataset of the population is available to the surveyor, the
method estimates its best-fit distribution and determines the OSB and OSS under
Neyman allocation directly.  When the dataset is not available, stratification
is made based on the assumption that the values of the study variable, y, are
available as hypothetical realizations of proxy values of y from recent surveys.
 Thus, it requires certain distributional assumptions about the study variable.
At present, it handles stratification for the populations where the study
variable follows a continuous distribution, namely, Pareto, Triangular,
Right-triangular, Weibull, Gamma, Exponential, Uniform, Normal, Log-normal and
Cauchy distributions.")
    (license license:gpl2)))

(define-public r-stratifiedyh
  (package
    (name "r-stratifiedyh")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stratifiedyh" version))
       (sha256
        (base32 "03hj2zap6cks74nk3al66dpphlinp10bi1pzpcc51ycb1n95gw95"))))
    (properties `((upstream-name . "stratifiedyh")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=stratifiedyh")
    (synopsis "Stratified Sampling and Labeling of Data in R")
    (description
     "This package provides functions for stratified sampling and assigning custom
labels to data, ensuring randomness within groups.  The package supports various
sampling methods such as stratified, cluster, and systematic sampling.  It
allows users to apply transformations and customize the sampling process.  This
package can be useful for statistical analysis and data preparation tasks.")
    (license license:expat)))

(define-public r-stratifiedrf
  (package
    (name "r-stratifiedrf")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StratifiedRF" version))
       (sha256
        (base32 "0a5djia6xacs17nnr5knr1acwzicrffz73q9nvnhdmihazq8v27d"))))
    (properties `((upstream-name . "StratifiedRF")))
    (build-system r-build-system)
    (propagated-inputs (list r-dplyr r-c50))
    (home-page "https://cran.r-project.org/package=StratifiedRF")
    (synopsis "Builds Trees by Sampling Variables in Groups")
    (description
     "Random Forest-like tree ensemble that works with groups of predictor variables.
When building a tree, a number of variables is taken randomly from each group
separately, thus ensuring that it considers variables from each group for the
splits.  Useful when rows contain information about different things (e.g. user
information and product information) and it's not sensible to make a prediction
with information from only one group of variables, or when there are far more
variables from one group than the other and it's desired to have groups appear
evenly on trees.  Trees are grown using the C5.0 algorithm rather than the usual
CART algorithm.  Supports parallelization (multithreaded), missing values in
predictors, and categorical variables (without doing One-Hot encoding in the
processing).  Can also be used to create a regular (non-stratified) Random
Forest-like model, but made up of C5.0 trees and with some additional control
options.  As it's built with C5.0 trees, it works only for classification (not
for regression).")
    (license license:gpl3)))

(define-public r-stratifiedmedicine
  (package
    (name "r-stratifiedmedicine")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StratifiedMedicine" version))
       (sha256
        (base32 "0q1d5jar5x2v9bsrfzv73wirgbkvy16ibzq93qv9b6c4zj3q75r7"))))
    (properties `((upstream-name . "StratifiedMedicine")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-ranger
                             r-partykit
                             r-mvtnorm
                             r-glmnet
                             r-ggplot2
                             r-ggparty
                             r-dplyr
                             r-coin))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/thomasjemielita/StratifiedMedicine")
    (synopsis "Stratified Medicine")
    (description
     "This package provides a toolkit for stratified medicine, subgroup
identification, and precision medicine.  Current tools include (1) filtering
models (reduce covariate space), (2) patient-level estimate models
(counterfactual patient-level quantities, such as the conditional average
treatment effect), (3) subgroup identification models (find subsets of patients
with similar treatment effects), and (4) treatment effect estimation and
inference (for the overall population and discovered subgroups).  These tools
can be customized and are directly used in PRISM (patient response identifiers
for stratified medicine; Jemielita and Mehrotra 2019 <@code{arXiv:1912.03337>}.
This package is in beta and will be continually updated.")
    (license license:gpl3)))

(define-public r-stratifiedbalancing
  (package
    (name "r-stratifiedbalancing")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StratifiedBalancing" version))
       (sha256
        (base32 "0pklnm0q0imdhyzhwyb4i0j441dkk4k6qqlx8d4q2xnl62drqnvv"))))
    (properties `((upstream-name . "StratifiedBalancing")))
    (build-system r-build-system)
    (propagated-inputs (list r-plyr r-bnlearn))
    (home-page "https://cran.r-project.org/package=StratifiedBalancing")
    (synopsis "Stratified Covariate Balancing")
    (description
     "This package performs Stratified Covariate Balancing with Markov blanket feature
selection and use of synthetic cases.  See Alemi et al. (2016)
<DOI:10.1111/1475-6773.12628>.")
    (license license:gpl2+)))

(define-public r-stratification
  (package
    (name "r-stratification")
    (version "2.2-7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stratification" version))
       (sha256
        (base32 "0vsra08lbq2nabd9lizakgpvn5lrp0lc961jvpns59l0b4wvdqab"))))
    (properties `((upstream-name . "stratification")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=stratification")
    (synopsis "Univariate Stratification of Survey Populations")
    (description
     "Univariate stratification of survey populations with a generalization of the
Lavallee-Hidiroglou method of stratum construction.  The generalized method
takes into account a discrepancy between the stratification variable and the
survey variable.  The determination of the optimal boundaries also incorporate,
if desired, an anticipated non-response, a take-all stratum for large units, a
take-none stratum for small units, and a certainty stratum to ensure that some
specific units are in the sample.  The well known cumulative root frequency rule
of Dalenius and Hodges and the geometric rule of Gunning and Horgan are also
implemented.")
    (license license:gpl2)))

(define-public r-strathe2e2
  (package
    (name "r-strathe2e2")
    (version "3.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StrathE2E2" version))
       (sha256
        (base32 "10y6bagqg1h57c8g7m837qidrf7wdf8r0jcgqwrcgkr1kbxywfg9"))))
    (properties `((upstream-name . "StrathE2E2")))
    (build-system r-build-system)
    (propagated-inputs (list r-netindices r-desolve))
    (native-inputs (list r-knitr))
    (home-page
     "https://gitlab.com/MarineResourceModelling/StrathE2E/StrathE2E2")
    (synopsis "End-to-End Marine Food Web Model")
    (description
     "This package provides a dynamic model of the big-picture, whole ecosystem
effects of hydrodynamics, temperature, nutrients, and fishing on continental
shelf marine food webs.  The package is described in: Heath, M.R., Speirs, D.C.,
Thurlbeck, I. and Wilson, R.J. (2020) <doi:10.1111/2041-210X.13510>
@code{StrathE2E2}: An R package for modelling the dynamics of marine food webs
and fisheries.  8pp.")
    (license license:gpl2+)))

(define-public r-stratest
  (package
    (name "r-stratest")
    (version "1.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stratEst" version))
       (sha256
        (base32 "0m41866xr21h4mih4z98y6pcrp2ws0mxxx157vdhbv7rhlh5djvp"))))
    (properties `((upstream-name . "stratEst")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://github.com/fdvorak/stratEst")
    (synopsis "Strategy Estimation")
    (description
     "Variants of strategy estimation (Dal Bo & Frechette, 2011,
<doi:10.1257/aer.101.1.411>), including the model with parameters for the choice
probabilities of the strategies (Breitmoser, 2015, <doi:10.1257/aer.20130675>),
and the model with individual level covariates for the selection of strategies
by individuals (Dvorak & Fehrler, 2018, <doi:10.2139/ssrn.2986445>).")
    (license license:gpl3)))

(define-public r-strategy
  (package
    (name "r-strategy")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Strategy" version))
       (sha256
        (base32 "0phja1r0qfvcswvw5w1x6ny86p84wkqb029fdqgw10djdm9xp0f2"))))
    (properties `((upstream-name . "Strategy")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-xts))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=Strategy")
    (synopsis "Generic Framework to Analyze Trading Strategies")
    (description
     "Users can build and test customized quantitative trading strategies.  Some
quantitative trading strategies are already implemented, e.g. various
moving-average filters with trend following approaches.  The implemented class
called \"Strategy\" allows users to access several methods to analyze performance
figures, plots and backtest the strategies.  Furthermore, custom strategies can
be added, a generic template is available.  The custom strategies require a
certain input and output so they can be called from the Strategy-constructor.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-strategicplayers
  (package
    (name "r-strategicplayers")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "strategicplayers" version))
       (sha256
        (base32 "0fpmmnv6w9zd17gbk44kkwx2znvdgqkclgjpahalns8abqm1cvyl"))))
    (properties `((upstream-name . "strategicplayers")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=strategicplayers")
    (synopsis "Strategic Players")
    (description
     "Identifies individuals in a social network who should be the intervention
subjects for a network intervention in which you have a group of targets, a
group of avoiders, and a group that is neither.")
    (license license:gpl3)))

(define-public r-stratbr
  (package
    (name "r-stratbr")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stratbr" version))
       (sha256
        (base32 "15vkymmc61yz9szhfhc5663hfyqvh499ahwhr9mv1lhv5bikk0kb"))))
    (properties `((upstream-name . "stratbr")))
    (build-system r-build-system)
    (propagated-inputs (list r-stratification r-snowfall r-rglpk))
    (home-page "https://cran.r-project.org/package=stratbr")
    (synopsis "Optimal Stratification in Stratified Sampling")
    (description
     "An Optimization Algorithm Applied to Stratification Problem.This function aims
at constructing optimal strata with an optimization algorithm based on a global
optimisation technique called Biased Random Key Genetic Algorithms.")
    (license license:gpl2)))

(define-public r-stratastats
  (package
    (name "r-stratastats")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stratastats" version))
       (sha256
        (base32 "038gm0253kb980w1b9hzf11fjg76hlavihhyb0jsgrcfsxag93v7"))))
    (properties `((upstream-name . "stratastats")))
    (build-system r-build-system)
    (propagated-inputs (list r-gt r-abind))
    (home-page "https://cran.r-project.org/package=stratastats")
    (synopsis "Stratified Analysis of 2x2 Contingency Tables")
    (description
     "Offers a comprehensive approach for analysing stratified 2x2 contingency tables.
 It facilitates the calculation of odds ratios, 95% confidence intervals, and
conducts chi-squared, Cochran-Mantel-Haenszel, Mantel-Haenszel, and
Breslow-Day-Tarone tests.  The package is particularly useful in fields like
epidemiology and social sciences where stratified analysis is essential.  The
package also provides interpretative insights into the results, aiding in the
understanding of statistical outcomes.")
    (license license:gpl2+)))

(define-public r-stratamatch
  (package
    (name "r-stratamatch")
    (version "0.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stratamatch" version))
       (sha256
        (base32 "1z395355kac0wd4f0m1ijh851m046ra8h1wam04if4qp00ychajp"))))
    (properties `((upstream-name . "stratamatch")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rlang r-magrittr r-hmisc r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/raikens1/stratamatch")
    (synopsis "Stratification and Matching for Large Observational Data Sets")
    (description
     "This package provides a pilot matching design to automatically stratify and
match large datasets.  The manual_stratify() function allows users to manually
stratify a dataset based on categorical variables of interest, while the
auto_stratify() function does automatically by allocating a held-aside (pilot)
data set, fitting a prognostic score (see Hansen (2008)
<doi:10.1093/biomet/asn004>) on the pilot set, and stratifying the data set
based on prognostic score quantiles.  The strata_match() function then does
optimal matching of the data set in parallel within strata.")
    (license license:gpl3)))

(define-public r-stratallo
  (package
    (name "r-stratallo")
    (version "2.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stratallo" version))
       (sha256
        (base32 "0v29c8qva2clmhdyrj7sn4cqx0wq00vjhnlh355iirm8vi9nhky0"))))
    (properties `((upstream-name . "stratallo")))
    (build-system r-build-system)
    (propagated-inputs (list r-lifecycle r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/wwojciech/stratallo")
    (synopsis "Optimum Sample Allocation in Stratified Sampling")
    (description
     "This package provides functions in this package provide solution to classical
problem in survey methodology - an optimum sample allocation in stratified
sampling.  In this context, the optimum allocation is in the classical
Tschuprow-Neyman's sense and it satisfies additional lower or upper bounds
restrictions imposed on sample sizes in strata.  There are few different
algorithms available to use, and one them is based on popular sample allocation
method that applies Neyman allocation to recursively reduced set of strata.
This package also provides the function that computes a solution to the minimum
cost allocation problem, which is a minor modification of the classical optimum
sample allocation.  This problem lies in the determination of a vector of strata
sample sizes that minimizes total cost of the survey, under assumed fixed level
of the stratified estimator's variance.  As in the case of the classical optimum
allocation, the problem of minimum cost allocation can be complemented by
imposing upper-bounds constraints on sample sizes in strata.")
    (license license:gpl2)))

(define-public r-strata-maxcombo
  (package
    (name "r-strata-maxcombo")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "strata.MaxCombo" version))
       (sha256
        (base32 "1iafnxa0z0rsal3v1y1l9ddzdgi594abhc4jkrsqp33316cmm2az"))))
    (properties `((upstream-name . "strata.MaxCombo")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://cran.r-project.org/package=strata.MaxCombo")
    (synopsis "Stratified Max-Combo Test")
    (description
     "Non-proportional hazard (NPH) is commonly observed in immuno-oncology studies,
where the survival curves of the treatment and control groups show delayed
separation.  To properly account for NPH, several statistical methods have been
developed.  One such method is Max-Combo test, which is a straightforward and
flexible hypothesis testing method that can simultaneously test for constant,
early, middle, and late treatment effects.  However, the majority of the
Max-Combo test performed in clinical studies are unstratified, ignoring the
important prognostic stratification factors.  To fill this gap, we have
developed an R package for stratified Max-Combo testing that accounts for
stratified baseline factors.  Our package explores various methods for
calculating combined test statistics, estimating joint distributions, and
determining the p-values.")
    (license license:gpl2)))

(define-public r-strat
  (package
    (name "r-strat")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "strat" version))
       (sha256
        (base32 "1axxrp750kjzcgxjdqfscjmryv1mkv4l23zk8k23z8l4ymhd2f5w"))))
    (properties `((upstream-name . "strat")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-hmisc))
    (home-page "https://github.com/xiangzhou09/strat")
    (synopsis "An Implementation of the Stratification Index")
    (description
     "An implementation of the stratification index proposed by Zhou (2012)
<DOI:10.1177/0081175012452207>.  The package provides two functions, srank,
which returns stratum-specific information, including population share and
average percentile rank; and strat, which returns the stratification index and
its approximate standard error.  When a grouping factor is specified, strat also
provides a detailed decomposition of the overall stratification into
between-group and within-group components.")
    (license license:gpl3+)))

(define-public r-strap
  (package
    (name "r-strap")
    (version "1.6-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "strap" version))
       (sha256
        (base32 "12cxdm3mmyw1bqyic03axckzy7la10m05a0bi5nsc1fvs1p1w268"))))
    (properties `((upstream-name . "strap")))
    (build-system r-build-system)
    (propagated-inputs (list r-pbapply r-geoscale r-ape))
    (home-page "https://github.com/graemetlloyd/strap")
    (synopsis "Stratigraphic Tree Analysis for Palaeontology")
    (description
     "This package provides functions for the stratigraphic analysis of phylogenetic
trees.")
    (license license:gpl2+)))

(define-public r-stranslate
  (package
    (name "r-stranslate")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stranslate" version))
       (sha256
        (base32 "10a2xrpg1vw9mci9w9hwcjnic942s3j5658wd5nw14ndxm1xhv79"))))
    (properties `((upstream-name . "stranslate")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-knitr r-crayon))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sigbertklinke/stranslate")
    (synopsis "Simple Translation Between Different Languages")
    (description
     "Message translation is often managed with po files and the gettext programme,
but sometimes another solution is needed.  In contrast to po files, a more
flexible approach is used as in the Fluent <https://projectfluent.org/> project
with R Markdown snippets.  The key-value approach allows easier handling of the
translated messages.")
    (license license:gpl3)))

(define-public r-strand
  (package
    (name "r-strand")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "strand" version))
       (sha256
        (base32 "0gd9nnbdilvk4bszg77bxilan6qpg1np7c38w1b7j5sr3r30y5in"))))
    (properties `((upstream-name . "strand")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-tidyr
                             r-tibble
                             r-rlang
                             r-rglpk
                             r-r6
                             r-matrix
                             r-lubridate
                             r-ggplot2
                             r-dplyr
                             r-arrow))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/strand-tech/strand")
    (synopsis "Framework for Investment Strategy Simulation")
    (description
     "This package provides a framework for performing discrete (share-level)
simulations of investment strategies.  Simulated portfolios optimize exposure to
an input signal subject to constraints such as position size and factor
exposure.  For background see L. Chincarini and D. Kim (2010,
ISBN:978-0-07-145939-6) \"Quantitative Equity Portfolio Management\".")
    (license license:gpl3)))

(define-public r-strainranking
  (package
    (name "r-strainranking")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StrainRanking" version))
       (sha256
        (base32 "1r8avm9xajqmsy4x7pgh1yaffq7wl5i5kg75kdilydwmii6n19i0"))))
    (properties `((upstream-name . "StrainRanking")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=StrainRanking")
    (synopsis "Ranking of Pathogen Strains")
    (description
     "Regression-based ranking of pathogen strains with respect to their contributions
to natural epidemics, using demographic and genetic data sampled in the curse of
the epidemics.  This package also includes the GMCPIC test.")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-str2str
  (package
    (name "r-str2str")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "str2str" version))
       (sha256
        (base32 "1hzi8ns2v9y0mzv59aafh5gq4vyza2qbsmaxpj945nmfi4x3qqwi"))))
    (properties `((upstream-name . "str2str")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape r-plyr r-checkmate r-abind))
    (home-page "https://cran.r-project.org/package=str2str")
    (synopsis "Convert R Objects from One Structure to Another")
    (description
     "Offers a suite of functions for converting to and from (atomic) vectors,
matrices, data.frames, and (3D+) arrays as well as lists of these objects.  It
is an alternative to the base R as.<str>.<method>() functions (e.g.,
as.data.frame.array()) that provides more useful and/or flexible restructuring
of R objects.  To do so, it only works with common structuring of R objects
(e.g., data.frames with only atomic vector columns).")
    (license license:gpl2+)))

(define-public r-str
  (package
    (name "r-str")
    (version "0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stR" version))
       (sha256
        (base32 "1ihhz62ajkiq4d5s9zvqfp2h4k7hwgynfdy7walrhpxlcb9pjgay"))))
    (properties `((upstream-name . "stR")))
    (build-system r-build-system)
    (propagated-inputs (list r-sparsem r-quantreg r-matrix r-forecast
                             r-foreach))
    (native-inputs (list r-knitr))
    (home-page "https://pkg.robjhyndman.com/stR/")
    (synopsis "Seasonal Trend Decomposition Using Regression")
    (description
     "This package provides methods for decomposing seasonal data: STR (a
Seasonal-Trend time series decomposition procedure based on Regression) and
Robust STR. In some ways, STR is similar to Ridge Regression and Robust STR can
be related to LASSO. They allow for multiple seasonal components, multiple
linear covariates with constant, flexible and seasonal influence.  Seasonal
patterns (for both seasonal components and seasonal covariates) can be
fractional and flexible over time; moreover they can be either strictly periodic
or have a more complex topology.  The methods provide confidence intervals for
the estimated components.  The methods can also be used for forecasting.")
    (license license:gpl3)))

(define-public r-stppsim
  (package
    (name "r-stppsim")
    (version "1.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stppSim" version))
       (sha256
        (base32 "0rrridj6r354lz3fbpyzhbvbb72cif0yxmd7d5ghbv2ig9dwz310"))))
    (properties `((upstream-name . "stppSim")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-terra
                             r-stringr
                             r-splancs
                             r-spatstat-geom
                             r-sparr
                             r-sp
                             r-simriv
                             r-sf
                             r-raster
                             r-progressr
                             r-otusummary
                             r-magrittr
                             r-lubridate
                             r-leaflet
                             r-ks
                             r-gstat
                             r-ggplot2
                             r-geosphere
                             r-future-apply
                             r-dplyr
                             r-data-table
                             r-cowplot
                             r-chron))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MAnalytics/stppSim")
    (synopsis "Spatiotemporal Point Patterns Simulation")
    (description
     "Generates artificial point patterns marked by their spatial and temporal
signatures.  The resulting point cloud may exhibit inherent interactions between
both signatures.  The simulation integrates microsimulation (Holm, E.,
(2017)<doi:10.1002/9781118786352.wbieg0320>) and agent-based models (Bonabeau,
E., (2002)<doi:10.1073/pnas.082080899>), beginning with the configuration of
movement characteristics for the specified agents (referred to as walkers') and
their interactions within the simulation environment.  These interactions
(Quaglietta, L. and Porto, M., (2019)<doi:10.1186/s40462-019-0154-8>) result in
specific spatiotemporal patterns that can be visualized, analyzed, and used for
various analytical purposes.  Given the growing scarcity of detailed
spatiotemporal data across many domains, this package provides an alternative
data source for applications in social and life sciences.")
    (license license:gpl3)))

(define-public r-stpphawkes
  (package
    (name "r-stpphawkes")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stpphawkes" version))
       (sha256
        (base32 "1mfhvfjlshhigyl5bfagjiv3q8dizqf89ikyifkchylni5szblbs"))))
    (properties `((upstream-name . "stpphawkes")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppprogress
                             r-rcppgsl
                             r-rcpparmadillo
                             r-rcpp
                             r-interp
                             r-extradistr))
    (home-page "https://cran.r-project.org/package=stpphawkes")
    (synopsis "Missing Data for Marked Hawkes Process")
    (description
     "Estimation of model parameters for marked Hawkes process.  Accounts for missing
data in the estimation of the parameters.  Technical details found in (Tucker et
al., 2019 <DOI:10.1016/j.spasta.2018.12.004>).")
    (license license:expat)))

(define-public r-stpp
  (package
    (name "r-stpp")
    (version "2.0-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stpp" version))
       (sha256
        (base32 "1xb80nbfx7f1frzcw61fr2vnb8br8355f7aaifrjfz6vclf2ax1z"))))
    (properties `((upstream-name . "stpp")))
    (build-system r-build-system)
    (propagated-inputs (list r-splancs
                             r-spatstat-univar
                             r-spatstat-random
                             r-spatstat-geom
                             r-spatstat-explore
                             r-rpanel
                             r-rgl
                             r-plot3d
                             r-kernsmooth
                             r-gridextra
                             r-ggplot2))
    (native-inputs (list r-knitr gfortran))
    (home-page "https://cran.r-project.org/package=stpp")
    (synopsis
     "Space-Time Point Pattern Simulation, Visualisation and Analysis")
    (description
     "Many of the models encountered in applications of point process methods to the
study of spatio-temporal phenomena are covered in stpp'.  This package provides
statistical tools for analyzing the global and local second-order properties of
spatio-temporal point processes, including estimators of the space-time
inhomogeneous K-function and pair correlation function.  It also includes tools
to get static and dynamic display of spatio-temporal point patterns.  See
Gabriel et al (2013) <doi:10.18637/jss.v053.i02>.")
    (license license:gpl3)))

(define-public r-stpm
  (package
    (name "r-stpm")
    (version "1.7.12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stpm" version))
       (sha256
        (base32 "16kp69356bj1s26iwwysxdyr87kd3jqhj2gmmz4vc003ai63ga6y"))))
    (properties `((upstream-name . "stpm")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-sas7bdat
                             r-rcpparmadillo
                             r-rcpp
                             r-nloptr
                             r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=stpm")
    (synopsis
     "Stochastic Process Model for Analysis of Longitudinal and Time-to-Event Outcomes")
    (description
     "Utilities to estimate parameters of the models with survival functions induced
by stochastic covariates.  Miscellaneous functions for data preparation and
simulation are also provided.  For more information, see: (i)\"Stochastic model
for analysis of longitudinal data on aging and mortality\" by Yashin A. et al.
(2007), Mathematical Biosciences, 208(2), 538-551,
<DOI:10.1016/j.mbs.2006.11.006>; (ii) \"Health decline, aging and mortality: how
are they related?\" by Yashin A. et al. (2007), Biogerontology 8(3), 291(302),
<DOI:10.1007/s10522-006-9073-3>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-stplanr
  (package
    (name "r-stplanr")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stplanr" version))
       (sha256
        (base32 "111hhmixq3h2f4hrzpldzkq3wwipwggalavy8wszkwbs3l35h487"))))
    (properties `((upstream-name . "stplanr")))
    (build-system r-build-system)
    (propagated-inputs (list r-sfheaders
                             r-sf
                             r-rlang
                             r-rcpp
                             r-pbapply
                             r-od
                             r-nabor
                             r-magrittr
                             r-lwgeom
                             r-jsonlite
                             r-httr
                             r-geosphere
                             r-dplyr
                             r-data-table
                             r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ropensci/stplanr")
    (synopsis "Sustainable Transport Planning")
    (description
     "This package provides tools for transport planning with an emphasis on spatial
transport data and non-motorized modes.  The package was originally developed to
support the Propensity to Cycle Tool', a publicly available strategic cycle
network planning tool (Lovelace et al.  2017) <doi:10.5198/jtlu.2016.862>, but
has since been extended to support public transport routing and accessibility
analysis (Moreno-Monroy et al.  2017) <doi:10.1016/j.jtrangeo.2017.08.012> and
routing with locally hosted routing engines such as OSRM (Lowans et al.  2023)
<doi:10.1016/j.enconman.2023.117337>.  The main functions are for creating and
manipulating geographic \"desire lines\" from origin-destination (OD) data
(building on the od package); calculating routes on the transport network
locally and via interfaces to routing services such as
<https://cyclestreets.net/> (Desjardins et al.  2021)
<doi:10.1007/s11116-021-10197-1>; and calculating route segment attributes such
as bearing.  The package implements the travel flow aggregration method
described in Morgan and Lovelace (2020) <doi:10.1177/2399808320942779> and the
OD jittering method described in Lovelace et al. (2022)
<doi:10.32866/001c.33873>.  Further information on the package's aim and scope
can be found in the vignettes and in a paper in the R Journal (Lovelace and
Ellison 2018) <doi:10.32614/RJ-2018-053>, and in a paper outlining the landscape
of open source software for geographic methods in transport planning (Lovelace,
2021) <doi:10.1007/s10109-020-00342-2>.")
    (license license:expat)))

(define-public r-stpga
  (package
    (name "r-stpga")
    (version "5.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "STPGA" version))
       (sha256
        (base32 "0mwjv9r7x925ljmbwk2fl0xvf2n2hnf5n5z5p5rxr57ywvirqw1b"))))
    (properties `((upstream-name . "STPGA")))
    (build-system r-build-system)
    (propagated-inputs (list r-scatterplot3d r-scales r-emoa r-algdesign))
    (home-page "https://cran.r-project.org/package=STPGA")
    (synopsis "Selection of Training Populations by Genetic Algorithm")
    (description
     "Combining Predictive Analytics and Experimental Design to Optimize Results.  To
be utilized to select a test data calibrated training population in high
dimensional prediction problems and assumes that the explanatory variables are
observed for all of the individuals.  Once a \"good\" training set is identified,
the response variable can be obtained only for this set to build a model for
predicting the response in the test set.  The algorithms in the package can be
tweaked to solve some other subset selection problems.")
    (license license:gpl3)))

(define-public r-stosim
  (package
    (name "r-stosim")
    (version "0.0.15")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stosim" version))
       (sha256
        (base32 "09fxi9wv47fvc59jnr9y6kmc2gzvxs28hwf15h3f62vvh3v9ksn6"))))
    (properties `((upstream-name . "stosim")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=stosim")
    (synopsis
     "Stochastic Simulator for Reliability Modeling of Repairable Systems")
    (description
     "This package provides a toolkit for Reliability Availability and Maintainability
(RAM) modeling of industrial process systems.")
    (license license:gpl3+)))

(define-public r-storywranglr
  (package
    (name "r-storywranglr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "storywranglr" version))
       (sha256
        (base32 "07w3dwv2naqbbdzjcv58cisj7cv1rbhrk96a30q8ww11rf953rj4"))))
    (properties `((upstream-name . "storywranglr")))
    (build-system r-build-system)
    (propagated-inputs (list r-urltools r-tibble r-jsonlite r-httr r-dplyr))
    (home-page "https://github.com/chris31415926535/storywranglr")
    (synopsis "Explore Twitter Trends with the 'Storywrangler' API")
    (description
     "An interface to explore trends in Twitter data using the Storywrangler
Application Programming Interface (API), which can be found here:
<https://github.com/janeadams/storywrangler>.")
    (license license:expat)))

(define-public r-story
  (package
    (name "r-story")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stoRy" version))
       (sha256
        (base32 "05lji6gl8nzkxhygfvfm7alk5yp5gw1dkkr5r993vnj67m10pcbq"))))
    (properties `((upstream-name . "stoRy")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tidyjson
                             r-tibble
                             r-stringr
                             r-rlang
                             r-readr
                             r-rappdirs
                             r-r6
                             r-purrr
                             r-lifecycle
                             r-httr
                             r-fansi
                             r-dplyr
                             r-crayon
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/theme-ontology/stoRy")
    (synopsis "Download, Explore, and Analyze Literary Theme Ontology Data")
    (description
     "Download, explore, and analyze Literary Theme Ontology themes and thematically
annotated story data.  To learn more about the project visit
<https://github.com/theme-ontology/theming> and <https://www.themeontology.org>.")
    (license license:gpl3)))

(define-public r-stortingscrape
  (package
    (name "r-stortingscrape")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stortingscrape" version))
       (sha256
        (base32 "0a3i9dfd1mp1xmwcslvrlphf0j0z1b45hpaz9ady73kr7d5zs2j8"))))
    (properties `((upstream-name . "stortingscrape")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rvest r-httr2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/martigso/stortingscrape")
    (synopsis "Access Data from the Norwegian Parliament API")
    (description
     "This package provides functions for retrieving general and specific data from
the Norwegian Parliament, through the Norwegian Parliament API at
<https://data.stortinget.no>.")
    (license license:gpl3+)))

(define-public r-storr
  (package
    (name "r-storr")
    (version "1.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "storr" version))
       (sha256
        (base32 "0qszdq16bf0dbss33zi63ciwmkgjsyc0c3akrr2kl14w3ncw6922"))))
    (properties `((upstream-name . "storr")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6 r-digest))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/richfitz/storr")
    (synopsis "Simple Key Value Stores")
    (description
     "This package creates and manages simple key-value stores.  These can use a
variety of approaches for storing the data.  This package implements the base
methods and support for file system, in-memory and DBI-based database stores.")
    (license license:expat)))

(define-public r-stormr
  (package
    (name "r-stormr")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StormR" version))
       (sha256
        (base32 "0b425783y36lqlydxmm95lwcxb6xf893sfpa9wjfn976bqa6kpvv"))))
    (properties `((upstream-name . "StormR")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-terra
                             r-stringr
                             r-sf
                             r-rworldmap
                             r-ncdf4
                             r-maps
                             r-leaflet))
    (native-inputs (list r-knitr))
    (home-page "https://umr-amap.github.io/StormR/")
    (synopsis
     "Analyzing the Behaviour of Wind Generated by Tropical Storms and Cyclones")
    (description
     "Set of functions to quantify and map the behaviour of winds generated by
tropical storms and cyclones in space and time.  It includes functions to
compute and analyze fields such as the maximum sustained wind field, power
dissipation index and duration of exposure to winds above a given threshold.  It
also includes functions to map the trajectories as well as characteristics of
the storms.")
    (license license:gpl3+)))

(define-public r-storm
  (package
    (name "r-storm")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Storm" version))
       (sha256
        (base32 "1fg8y9my9yp6px1gh43mr3m2s2z262mzq03pj52mqg3n186vk8z3"))))
    (properties `((upstream-name . "Storm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjson r-permute))
    (home-page "https://cran.r-project.org/package=Storm")
    (synopsis "Write Storm Bolts in R using the Storm Multi-Language Protocol")
    (description
     "Storm is a distributed real-time computation system.  Similar to how Hadoop
provides a set of general primitives for doing batch processing, Storm provides
a set of general primitives for doing real-time computation. .  Storm includes a
\"Multi-Language\" (or \"Multilang\") Protocol to allow implementation of Bolts and
Spouts in languages other than Java.  This R extension provides implementations
of utility functions to allow an application developer to focus on
application-specific functionality rather than Storm/R communications plumbing.")
    (license license:gpl2+)))

(define-public r-stops
  (package
    (name "r-stops")
    (version "1.8-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stops" version))
       (sha256
        (base32 "0nc4c371rq144f1ci5ppxnbjr9qchp2a2knsp3hiyyvg2mfax241"))))
    (properties `((upstream-name . "stops")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-tgp
                             r-smacofx
                             r-smacof
                             r-scagnostics
                             r-registry
                             r-pso
                             r-pomp
                             r-nloptr
                             r-minerva
                             r-energy
                             r-diceoptim
                             r-dicekriging
                             r-dfoptim
                             r-cordillera
                             r-cmaes
                             r-clue
                             r-acepack))
    (native-inputs (list r-r-rsp))
    (home-page "https://r-forge.r-project.org/projects/stops/")
    (synopsis "Structure Optimized Proximity Scaling")
    (description
     "This package provides methods that use flexible variants of multidimensional
scaling (MDS) which incorporate parametric nonlinear distance transformations
and trade-off the goodness-of-fit fit with structure considerations to find
optimal hyperparameters, also known as structure optimized proximity scaling
(STOPS) (Rusch, Mair & Hornik, 2023,<doi:10.1007/s11222-022-10197-w>).  The
package contains various functions, wrappers, methods and classes for fitting,
plotting and displaying different 1-way MDS models with ratio, interval, ordinal
optimal scaling in a STOPS framework.  These cover essentially the functionality
of the package smacofx, including Torgerson (classical) scaling with power
transformations of dissimilarities, SMACOF MDS with powers of dissimilarities,
Sammon mapping with powers of dissimilarities, elastic scaling with powers of
dissimilarities, spherical SMACOF with powers of dissimilarities, (ALSCAL)
s-stress MDS with powers of dissimilarities, r-stress MDS, MDS with powers of
dissimilarities and configuration distances, elastic scaling powers of
dissimilarities and configuration distances, Sammon mapping powers of
dissimilarities and configuration distances, power stress MDS (POST-MDS),
approximate power stress, Box-Cox MDS, local MDS, Isomap, curvilinear component
analysis (CLCA), curvilinear distance analysis (CLDA) and sparsified (power)
multidimensional scaling and (power) multidimensional distance analysis
(experimental models from smacofx influenced by CLCA).  All of these models can
also be fit by optimizing over hyperparameters based on goodness-of-fit fit only
(i.e., no structure considerations).  The package further contains functions for
optimization, specifically the adaptive Luus-Jaakola algorithm and a wrapper for
Bayesian optimization with treed Gaussian process with jumps to linear models,
and functions for various c-structuredness indices.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-stoppingrule
  (package
    (name "r-stoppingrule")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stoppingrule" version))
       (sha256
        (base32 "026464lkdxi6s79csb1fgm705yvszxxwq6993p92843zr344cffi"))))
    (properties `((upstream-name . "stoppingrule")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma r-matrixstats))
    (home-page "https://cran.r-project.org/package=stoppingrule")
    (synopsis "Create and Evaluate Stopping Rules for Safety Monitoring")
    (description
     "This package provides functions for creating, displaying, and evaluating
stopping rules for safety monitoring in clinical studies.  Implements stopping
rule methods described in Goldman (1987) <doi:10.1016/0197-2456(87)90153-X>;
Geller et al. (2003, ISBN:9781135524388); Ivanova, Qaqish, and Schell (2005)
<doi:10.1111/j.1541-0420.2005.00311.x>; Chen and Chaloner (2006)
<doi:10.1002/sim.2429>; and Kulldorff et al. (2011)
<doi:10.1080/07474946.2011.539924>.")
    (license license:gpl3)))

(define-public r-stopp
  (package
    (name "r-stopp")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stopp" version))
       (sha256
        (base32 "09cvh2h8c91zb8iak7g4j0sznv8kxbn0r2xjl36p663mqk2lb5ss"))))
    (properties `((upstream-name . "stopp")))
    (build-system r-build-system)
    (propagated-inputs (list r-stpp
                             r-stlnpp
                             r-splancs
                             r-spatstat-utils
                             r-spatstat-univar
                             r-spatstat-random
                             r-spatstat-model
                             r-spatstat-linnet
                             r-spatstat-geom
                             r-spatstat-explore
                             r-sparr
                             r-plot3d
                             r-optimx
                             r-mgcv
                             r-mass
                             r-kernsmooth
                             r-fields))
    (home-page "https://cran.r-project.org/package=stopp")
    (synopsis
     "Spatio-Temporal Point Pattern Methods, Model Fitting, Diagnostics, Simulation, Local Tests")
    (description
     "Toolbox for different kinds of spatio-temporal analyses to be performed on
observed point patterns, following the growing stream of literature on point
process theory.  This R package implements functions to perform different kinds
of analyses on point processes, proposed in the papers (Siino, Adelfio, and
Mateu 2018<doi:10.1007/s00477-018-1579-0>; Siino et al.
2018<doi:10.1002/env.2463>; Adelfio et al.
2020<doi:10.1007/s00477-019-01748-1>; DâAngelo, Adelfio, and Mateu
2021<doi:10.1016/j.spasta.2021.100534>; DâAngelo, Adelfio, and Mateu
2022<doi:10.1007/s00362-022-01338-4>; DâAngelo, Adelfio, and Mateu
2023<doi:10.1016/j.csda.2022.107679>).  The main topics include modeling,
statistical inference, and simulation issues on spatio-temporal point processes
on Euclidean space and linear networks.")
    (license license:gpl2+)))

(define-public r-stopes
  (package
    (name "r-stopes")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "STOPES" version))
       (sha256
        (base32 "0dlr9hasily74wlwb44qadql9qdii326xhdnam1anszsvijzs4dk"))))
    (properties `((upstream-name . "STOPES")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-glmnet r-cvtools r-changepoint))
    (home-page "https://cran.r-project.org/package=STOPES")
    (synopsis "Selection Threshold Optimized Empirically via Splitting")
    (description
     "This package implements variable selection procedures for low to moderate size
generalized linear regressions models.  It includes the STOPES functions for
linear regression (Capanu M, Giurcanu M, Begg C, Gonen M, Optimized variable
selection via repeated data splitting, Statistics in Medicine, 2020,
19(6):2167-2184) as well as subsampling based optimization methods for
generalized linear regression models (Marinela Capanu, Mihai Giurcanu, Colin B
Begg, Mithat Gonen, Subsampling based variable selection for generalized linear
models).")
    (license license:gpl2)))

(define-public r-stopdetection
  (package
    (name "r-stopdetection")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stopdetection" version))
       (sha256
        (base32 "0hgz18fiqp6ykv9h711fbjx234cjiwa6gifdgg460b0pib8hc66i"))))
    (properties `((upstream-name . "stopdetection")))
    (build-system r-build-system)
    (propagated-inputs (list r-lubridate r-geodist r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/daniellemccool/stopdetection")
    (synopsis
     "Stop Detection in Timestamped Trajectory Data using Spatiotemporal Clustering")
    (description
     "Trajectory data formed by human or animal movement is often marked by periods of
movement interspersed with periods of standing still.  It is often of interest
to researchers to separate geolocation trajectories of latitude/longitude points
by clustering consecutive locations to produce a model of this behavior.  This
package implements the Stay Point detection algorithm originally described in Ye
(2009) <doi:10.1109/MDM.2009.11> that uses time and distance thresholds to
characterize spatial regions as stops'.  This package also implements the
concept of merging described in Montoliu (2013) <doi:10.1007/s11042-011-0982-z>
as stay point region estimation, which allows for clustering of temporally
adjacent stops for which distance between the midpoints is less than the
provided threshold.  GPS-like data from various sources can be used, but the
temporal thresholds must be considered with respect to the sampling interval,
and the spatial thresholds must be considered with respect to the measurement
error.")
    (license license:gpl3+)))

(define-public r-stokes
  (package
    (name "r-stokes")
    (version "1.2-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stokes" version))
       (sha256
        (base32 "02pia2szr5mbl3cyr6agiydq5py0sjariws3mhaqmcni8infapvh"))))
    (properties `((upstream-name . "stokes")))
    (build-system r-build-system)
    (propagated-inputs (list r-spray r-permutations r-partitions r-disordr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/RobinHankin/stokes")
    (synopsis "The Exterior Calculus")
    (description
     "This package provides functionality for working with tensors, alternating forms,
wedge products, Stokes's theorem, and related concepts from the exterior
calculus.  Uses @code{disordR} discipline (Hankin, 2022,
<doi:10.48550/ARXIV.2210.03856>).  The canonical reference would be M. Spivak
(1965, ISBN:0-8053-9021-9) \"Calculus on Manifolds\".  To cite the package in
publications please use Hankin (2022) <doi:10.48550/ARXIV.2210.17008>.")
    (license license:gpl2)))

(define-public r-stoichcalc
  (package
    (name "r-stoichcalc")
    (version "1.1-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stoichcalc" version))
       (sha256
        (base32 "077mp485i844cdmjxy0spvh1scgakrwlf16qd0k5fn3h9lkgyg1c"))))
    (properties `((upstream-name . "stoichcalc")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=stoichcalc")
    (synopsis "R Functions for Solving Stoichiometric Equations")
    (description
     "Given a list of substance compositions, a list of substances involved in a
process, and a list of constraints in addition to mass conservation of
elementary constituents, the package contains functions to build the substance
composition matrix, to analyze the uniqueness of process stoichiometry, and to
calculate stoichiometric coefficients if process stoichiometry is unique. (See
Reichert, P. and Schuwirth, N., A generic framework for deriving process
stoichiometry in enviromental models, Environmental Modelling and Software 25,
1241-1251, 2010 for more details.).")
    (license license:gpl2+)))

(define-public r-stodom
  (package
    (name "r-stodom")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stodom" version))
       (sha256
        (base32 "065m94xp0qsh00f451a90g6dn8p9iksjqj5w7dzyjcbqxvmbadww"))))
    (properties `((upstream-name . "stodom")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-tibble r-pracma r-ggplot2 r-dplyr))
    (home-page "https://cran.r-project.org/package=stodom")
    (synopsis "Estimating Consistent Tests for Stochastic Dominance")
    (description
     "Stochastic dominance tests help ranking different distributions.  The package
implements the consistent test for stochastic dominance by Barrett and Donald
(2003) <doi:10.1111/1468-0262.00390>.  Specifically, it implements Barrett and
Donald's Kolmogorov-Smirnov type tests for first- and second-order stochastic
dominance based on bootstrapping 2 and 1.")
    (license license:expat)))

(define-public r-stocks
  (package
    (name "r-stocks")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stocks" version))
       (sha256
        (base32 "0b6rl7pfkgzbpwnv7zzkr36hs5f1zb60d16ijslf7g9m25vvcyg6"))))
    (properties `((upstream-name . "stocks")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-ttr
                             r-rcpp
                             r-rcolorbrewer
                             r-rbenchmark
                             r-quantmod
                             r-lubridate
                             r-hmisc
                             r-dvmisc))
    (home-page "https://cran.r-project.org/package=stocks")
    (synopsis "Stock Market Analysis")
    (description
     "This package provides functions for analyzing stocks or other investments.  Main
features are loading and aligning historical data for ticker symbols,
calculating performance metrics for individual funds or portfolios (e.g.
annualized growth, maximum drawdown, Sharpe/Sortino ratio), and creating graphs.
 C++ code is used to improve processing speed where possible.")
    (license license:gpl3)))

(define-public r-stockr
  (package
    (name "r-stockr")
    (version "1.0.76")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stockR" version))
       (sha256
        (base32 "1n1xnz97x82bc9n930cwm11sl8r40224psya5z29a2zp449a5540"))))
    (properties `((upstream-name . "stockR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-rcolorbrewer r-gtools))
    (native-inputs (list r-knitr gfortran))
    (home-page "https://cran.r-project.org/package=stockR")
    (synopsis "Identifying Stocks in Genetic Data")
    (description
     "This package provides a mixture model for clustering individuals (or sampling
groups) into stocks based on their genetic profile.  Here, sampling groups are
individuals that are sure to come from the same stock (e.g. breeding adults or
larvae).  The mixture (log-)likelihood is maximised using the EM-algorithm after
finding good starting values via a K-means clustering of the genetic data.
Details can be found in: Foster, S. D.; Feutry, P.; Grewe, P. M.; Berry, O.;
Hui, F. K. C. & Davies (2020) <doi:10.1111/1755-0998.12920>.")
    (license license:gpl2+)))

(define-public r-stockdistfit
  (package
    (name "r-stockdistfit")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StockDistFit" version))
       (sha256
        (base32 "02af493w8mhkqpwa1h2wvga19rz22b09z2j69yn8r74jfdfj08wz"))))
    (properties `((upstream-name . "StockDistFit")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xts
                             r-quantmod
                             r-magrittr
                             r-ghyp
                             r-fitdistrplus
                             r-fgarch
                             r-fbasics
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=StockDistFit")
    (synopsis "Fit Stock Price Distributions")
    (description
     "The @code{StockDistFit} package provides functions for fitting probability
distributions to stock price data.  The package uses maximum likelihood
estimation to find the best-fitting distribution for a given stock.  It also
offers a function to fit several distributions to one or more assets and compare
the distribution with the Akaike Information Criterion (AIC) and then pick the
best distribution.  References are as follows: Siew et al. (2008)
<https://www.jstage.jst.go.jp/article/jappstat/37/1/37_1_1/_pdf/-char/ja> and
Benth et al. (2008)
<https://books.google.co.ke/books?hl=en&lr=&id=MH@code{NpDQAAQBAJ&oi=fnd&pg=PR7&dq=Stochastic+modeling+of+commodity+prices+using+the+Variance+Gamma+(VG)+model.+&ots=YNIL2QmEYg&sig=XZtGU0lp4oqXHVyPZ-O8x5i7N3w&redir_esc=y#v=onepage&q&f=false>}.")
    (license license:gpl3+)))

(define-public r-stockanalyst
  (package
    (name "r-stockanalyst")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stockAnalyst" version))
       (sha256
        (base32 "1pqghpyvwylc00hcnqgcr03js3dz6id012ixn5gj3d3wqyh008bl"))))
    (properties `((upstream-name . "stockAnalyst")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=stockAnalyst")
    (synopsis "Equity Valuation using Methods of Fundamental Analysis")
    (description
     "This package provides methods of Fundamental Analysis for Valuation of Equity
included here serve as a quick reference for undergraduate courses on Stock
Valuation and Chartered Financial Analyst Levels 1 and 2 Readings on Equity
Valuation.  Jerald E. Pinto (âEquity Asset Valuation (4th Edition)â, 2020,
ISBN: 9781119628194).  Chartered Financial Analyst Institute (\"Chartered
Financial Analyst Program Curriculum 2020 Level I Volumes 1-6. (Vol.  4, pp.
445-491)\", 2019, ISBN: 9781119593577).  Chartered Financial Analyst Institute
(\"Chartered Financial Analyst Program Curriculum 2020 Level II Volumes 1-6.
(Vol.  4, pp.  197-447)\", 2019, ISBN: 9781119593614).")
    (license license:gpl3)))

(define-public r-stochvoltmb
  (package
    (name "r-stochvoltmb")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stochvolTMB" version))
       (sha256
        (base32 "07m3qz8j1sw37lrxzmbkbifd3nsdvd2blbca8vr1aq5bbafr1g1b"))))
    (properties `((upstream-name . "stochvolTMB")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmb
                             r-sn
                             r-rcppeigen
                             r-mass
                             r-ggplot2
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/JensWahl/stochvolTMB")
    (synopsis "Likelihood Estimation of Stochastic Volatility Models")
    (description
     "Parameter estimation for stochastic volatility models using maximum likelihood.
The latent log-volatility is integrated out of the likelihood using the Laplace
approximation.  The models are fitted via TMB (Template Model Builder)
(Kristensen, Nielsen, Berg, Skaug, and Bell (2016) <doi:10.18637/jss.v070.i05>).")
    (license license:gpl3)))

(define-public r-stochvol
  (package
    (name "r-stochvol")
    (version "3.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stochvol" version))
       (sha256
        (base32 "1cr8rn4nzdbvgjhsahyijn2kgbndiyvycbn6rjxc0x5prc99j52w"))))
    (properties `((upstream-name . "stochvol")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-coda))
    (native-inputs (list r-knitr))
    (home-page "https://gregorkastner.github.io/stochvol/")
    (synopsis
     "Efficient Bayesian Inference for Stochastic Volatility (SV) Models")
    (description
     "Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV)
models with and without asymmetry (leverage) via Markov chain Monte Carlo (MCMC)
methods.  Methodological details are given in Kastner and FrÃ¼hwirth-Schnatter
(2014) <doi:10.1016/j.csda.2013.01.002> and Hosszejni and Kastner (2019)
<doi:10.1007/978-3-030-30611-3_8>; the most common use cases are described in
Hosszejni and Kastner (2021) <doi:10.18637/jss.v100.i12> and Kastner (2016)
<doi:10.18637/jss.v069.i05> and the package examples.")
    (license license:gpl2+)))

(define-public r-stochqn
  (package
    (name "r-stochqn")
    (version "0.1.2-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stochQN" version))
       (sha256
        (base32 "1lfzivl745dj94kap103arz0zy75wk69kc6v84xaaxk7ad3icnaj"))))
    (properties `((upstream-name . "stochQN")))
    (build-system r-build-system)
    (home-page "https://github.com/david-cortes/stochQN")
    (synopsis "Stochastic Limited Memory Quasi-Newton Optimizers")
    (description
     "Implementations of stochastic, limited-memory quasi-Newton optimizers, similar
in spirit to the LBFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno)
algorithm, for smooth stochastic optimization.  Implements the following
methods: @code{oLBFGS} (online LBFGS) (Schraudolph, N.N., Yu, J. and Guenter,
S., 2007 <http://proceedings.mlr.press/v2/schraudolph07a.html>), SQN (stochastic
quasi-Newton) (Byrd, R.H., Hansen, S.L., Nocedal, J. and Singer, Y., 2016
<@code{arXiv:1401.7020>}), @code{adaQN} (adaptive quasi-Newton) (Keskar, N.S.,
Berahas, A.S., 2016, <@code{arXiv:1511.01169>}).  Provides functions for easily
creating R objects with partial_fit/predict methods from some given
objective/gradient/predict functions.  Includes an example stochastic logistic
regression using these optimizers.  Provides header files and registered C
routines for using it directly from C/C++.")
    (license license:bsd-2)))

(define-public r-stochlab
  (package
    (name "r-stochlab")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stochLAB" version))
       (sha256
        (base32 "1gixw8rjhivcz71ypmbn0cs27gi5bsmmm37nsx0psas7nvw8zwxi"))))
    (properties `((upstream-name . "stochLAB")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-pracma
                             r-msm
                             r-magrittr
                             r-logr
                             r-glue
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/HiDef-Aerial-Surveying/stochLAB")
    (synopsis "Stochastic Collision Risk Model")
    (description
     "Collision Risk Models for avian fauna (seabird and migratory birds) at offshore
wind farms.  The base deterministic model is derived from Band (2012)
<https://tethys.pnnl.gov/publications/using-collision-risk-model-assess-bird-collision-risks-offshore-wind-farms>.
 This was further expanded on by Masden (2015) <doi:10.7489/1659-1> and code
used here is heavily derived from this work with input from Dr A. Cook at the
British Trust for Ornithology.  These collision risk models are useful for
marine ornithologists who are working in the offshore wind industry,
particularly in UK waters.  However, many of the species included in the
stochastic collision risk models can also be found in the North Atlantic in the
United States and Canada, and could be applied there.")
    (license license:gpl3+)))

(define-public r-stochblock
  (package
    (name "r-stochblock")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StochBlock" version))
       (sha256
        (base32 "0g0l36imgw3bvqj5g45abwicswcyqprzbkas9pzgha8dsc5s12w3"))))
    (properties `((upstream-name . "StochBlock")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-foreach
                             r-dorng
                             r-doparallel
                             r-blockmodeling))
    (home-page "https://cran.r-project.org/package=StochBlock")
    (synopsis "Stochastic Blockmodeling of One-Mode and Linked Networks")
    (description
     "Stochastic blockmodeling of one-mode and linked networks as implemented in
Å kulj and Å½iberna (2022) <doi:10.1016/j.socnet.2022.02.001>.  The optimization
is done via CEM (Classification Expectation Maximization) algorithm that can be
initialized by random partitions or the results of k-means algorithm.  The
development of this package is financially supported by the Slovenian Research
Agency (<https://www.arrs.si/>) within the research programs P5-0168 and the
research projects J7-8279 (Blockmodeling multilevel and temporal networks) and
J5-2557 (Comparison and evaluation of different approaches to blockmodeling
dynamic networks by simulations with application to Slovenian co-authorship
networks).")
    (license license:gpl2+)))

(define-public r-stocc
  (package
    (name "r-stocc")
    (version "1.31")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stocc" version))
       (sha256
        (base32 "0d0n9nrcva0vradippq30y6dc6klwd1mllnzpnzq9rczpz5cw7mw"))))
    (properties `((upstream-name . "stocc")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm r-rarpack r-matrix r-fields r-coda))
    (home-page "https://cran.r-project.org/package=stocc")
    (synopsis "Fit a Spatial Occupancy Model via Gibbs Sampling")
    (description
     "Fit a spatial-temporal occupancy models using a probit formulation instead of a
traditional logit model.")
    (license license:cc0)))

(define-public r-stmr
  (package
    (name "r-stmr")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "STMr" version))
       (sha256
        (base32 "0xkjlp51985pjld7ra8alcifbbmf4j7kdyg8hrcwqbc15b6m5qmg"))))
    (properties `((upstream-name . "STMr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-quantreg
                             r-nlme
                             r-minpack-lm
                             r-magrittr
                             r-ggplot2
                             r-ggfittext
                             r-dplyr))
    (home-page "https://mladenjovanovic.github.io/STMr/")
    (synopsis "Strength Training Manual R-Language Functions")
    (description
     "Strength training prescription using percent-based approach requires numerous
computations and assumptions.  STMr package allow users to estimate individual
reps-max relationships, implement various progression tables, and create
numerous set and rep schemes.  The STMr package is originally created as a tool
to help writing JovanoviÄ M. (2020) Strength Training Manual
<ISBN:979-8604459898>.")
    (license license:expat)))

(define-public r-stmotif
  (package
    (name "r-stmotif")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "STMotif" version))
       (sha256
        (base32 "04cmghys37abxjkcj6hm4nxq5mjiwy93v8xhl6gacmahjr4fj0dl"))))
    (properties `((upstream-name . "STMotif")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-reshape2 r-rcolorbrewer r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/heraldoborges/STMotif/wiki")
    (synopsis "Discovery of Motifs in Spatial-Time Series")
    (description
     "Allow to identify motifs in spatial-time series.  A motif is a previously
unknown subsequence of a (spatial) time series with relevant number of
occurrences.  For this purpose, the Combined Series Approach (CSA) is used.")
    (license license:expat)))

(define-public r-stmosim
  (package
    (name "r-stmosim")
    (version "3.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StMoSim" version))
       (sha256
        (base32 "1y4lnbpgdza6r6w3q161nlmg8775g3zv323amcwxcj41r4v41p96"))))
    (properties `((upstream-name . "StMoSim")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppparallel r-rcpp))
    (home-page "https://cran.r-project.org/package=StMoSim")
    (synopsis "Quantile-Quantile Plot with Several Gaussian Simulations")
    (description "Plots a QQ-Norm Plot with several Gaussian simulations.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-stmomo
  (package
    (name "r-stmomo")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StMoMo" version))
       (sha256
        (base32 "1c4v2gjipq1y4crc6bqvz2x5c02bl10mh9jacg8dqb2dffsh5y44"))))
    (properties `((upstream-name . "StMoMo")))
    (build-system r-build-system)
    (propagated-inputs (list r-rootsolve
                             r-reshape2
                             r-rcolorbrewer
                             r-mass
                             r-gnm
                             r-forecast
                             r-fields
                             r-fanplot))
    (native-inputs (list r-knitr))
    (home-page "http://github.com/amvillegas/StMoMo")
    (synopsis "Stochastic Mortality Modelling")
    (description
     "Implementation of the family of generalised age-period-cohort stochastic
mortality models.  This family of models encompasses many models proposed in the
actuarial and demographic literature including the Lee-Carter (1992)
<doi:10.2307/2290201> and the Cairns-Blake-Dowd (2006)
<doi:10.1111/j.1539-6975.2006.00195.x> models.  It includes functions for
fitting mortality models, analysing their goodness-of-fit and performing
mortality projections and simulations.")
    (license license:gpl2+)))

(define-public r-stminsights
  (package
    (name "r-stminsights")
    (version "0.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stminsights" version))
       (sha256
        (base32 "1yx87ia1i0p7cavxikz4dvy2j0ymnqpwqmdhci5x7yzsb0arl4kf"))))
    (properties `((upstream-name . "stminsights")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidygraph
                             r-tibble
                             r-stringr
                             r-stm
                             r-shinyjs
                             r-shinydashboard
                             r-shinybs
                             r-shiny
                             r-scales
                             r-readr
                             r-purrr
                             r-igraph
                             r-huge
                             r-ggrepel
                             r-ggraph
                             r-ggplot2
                             r-dt
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/cschwem2er/stminsights")
    (synopsis "'Shiny' Application for Inspecting Structural Topic Models")
    (description
     "This app enables interactive validation, interpretation and visualization of
structural topic models from the stm package by Roberts and others (2014)
<doi:10.1111/ajps.12103>.  It also includes helper functions for model
diagnostics and extracting data from effect estimates.")
    (license license:expat)))

(define-public r-stmgui
  (package
    (name "r-stmgui")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stmgui" version))
       (sha256
        (base32 "1fn2m5l7y3p1riw39g6r3hpjxdccygih7yipp7dvc4msl9fm4h5c"))))
    (properties `((upstream-name . "stmgui")))
    (build-system r-build-system)
    (propagated-inputs (list r-tm
                             r-stm
                             r-shinythemes
                             r-shinyjs
                             r-shinybs
                             r-shiny
                             r-markdown))
    (home-page "https://cran.r-project.org/package=stmgui")
    (synopsis "Shiny Application for Creating STM Models")
    (description
     "This package provides an application that acts as a GUI for the stm text
analysis package.")
    (license license:expat)))

(define-public r-stmgp
  (package
    (name "r-stmgp")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stmgp" version))
       (sha256
        (base32 "1yf0hr5gaql424ikjm9ra50vz4ppfsshhf89xphl7c9v9a6bjvw0"))))
    (properties `((upstream-name . "stmgp")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=stmgp")
    (synopsis
     "Rapid and Accurate Genetic Prediction Modeling for Genome-Wide Association or Whole-Genome Sequencing Study Data")
    (description
     "Rapidly build accurate genetic prediction models for genome-wide association or
whole-genome sequencing study data by smooth-threshold multivariate genetic
prediction (STMGP) method.  Variable selection is performed using marginal
association test p-values with an optimal p-value cutoff selected by Cp-type
criterion.  Quantitative and binary traits are modeled respectively via linear
and logistic regression models.  A function that works through PLINK software
(Purcell et al.  2007 <DOI:10.1086/519795>, Chang et al.  2015
<DOI:10.1186/s13742-015-0047-8>) <https://www.cog-genomics.org/plink2> is
provided.  Covariates can be included in regression model.")
    (license license:gpl2+)))

(define-public r-stmcorrviz
  (package
    (name "r-stmcorrviz")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stmCorrViz" version))
       (sha256
        (base32 "1a4pckrbzsihyf1bqvw3cl0hxrc4yq1pnkgxgf4b8jday6zkxwcv"))))
    (properties `((upstream-name . "stmCorrViz")))
    (build-system r-build-system)
    (propagated-inputs (list r-tm r-stm r-snowballc r-jsonlite))
    (home-page "https://cran.r-project.org/package=stmCorrViz")
    (synopsis "Tool for Structural Topic Model Visualizations")
    (description
     "Generates an interactive visualization of topic correlations/ hierarchy in a
Structural Topic Model (STM) of Roberts, Stewart, and Tingley.  The package
performs a hierarchical clustering of topics which are then exported to a JSON
object and visualized using D3.")
    (license license:gpl2+)))

(define-public r-stltdnn
  (package
    (name "r-stltdnn")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stlTDNN" version))
       (sha256
        (base32 "1j2f9kxlry15xp7bc03h5jr7v8gzq8frdxyzhq9s78m3d3dbixf4"))))
    (properties `((upstream-name . "stlTDNN")))
    (build-system r-build-system)
    (propagated-inputs (list r-nnfor r-forecast))
    (home-page "https://cran.r-project.org/package=stlTDNN")
    (synopsis "STL Decomposition and TDNN Hybrid Time Series Forecasting")
    (description
     "Implementation of hybrid STL decomposition based time delay neural network model
for univariate time series forecasting.  For method details see Jha G K, Sinha,
K (2014). <doi:10.1007/s00521-012-1264-z>, Xiong T, Li C, Bao Y (2018).
<doi:10.1016/j.neucom.2017.11.053>.")
    (license license:gpl3)))

(define-public r-stlplus
  (package
    (name "r-stlplus")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stlplus" version))
       (sha256
        (base32 "14728xsm982z9sg4rbqg307pbwqlsiyzj8z3sr9wr6fi0dayf6z5"))))
    (properties `((upstream-name . "stlplus")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaimpute r-rcpp r-lattice))
    (home-page "https://github.com/hafen/stlplus")
    (synopsis "Enhanced Seasonal Decomposition of Time Series by Loess")
    (description
     "Decompose a time series into seasonal, trend, and remainder components using an
implementation of Seasonal Decomposition of Time Series by Loess (STL) that
provides several enhancements over the STL method in the stats package.  These
enhancements include handling missing values, providing higher order (quadratic)
loess smoothing with automated parameter choices, frequency component smoothing
beyond the seasonal and trend components, and some basic plot methods for
diagnostics.")
    (license license:bsd-3)))

(define-public r-stlnpp
  (package
    (name "r-stlnpp")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stlnpp" version))
       (sha256
        (base32 "1r80qzrvz5nmspark5xk59wi3r1kxjhrqpscwqrab9lnl7cal76s"))))
    (properties `((upstream-name . "stlnpp")))
    (build-system r-build-system)
    (propagated-inputs (list r-spatstat-univar
                             r-spatstat-random
                             r-spatstat-linnet
                             r-spatstat-geom
                             r-spatstat-explore
                             r-spatstat))
    (home-page "https://cran.r-project.org/package=stlnpp")
    (synopsis "Spatio-Temporal Analysis of Point Patterns on Linear Networks")
    (description
     "Statistical analysis of spatio-temporal point processes on linear networks.
This packages provides tools to visualise and analyse spatio-temporal point
patterns on linear networks using first- and second-order summary statistics.")
    (license license:gpl2+)))

(define-public r-stlelm
  (package
    (name "r-stlelm")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stlELM" version))
       (sha256
        (base32 "1q3lcfqq06jfky388v7iilnnqbr5f4gq451xkc2kl04yl0dwcv2h"))))
    (properties `((upstream-name . "stlELM")))
    (build-system r-build-system)
    (propagated-inputs (list r-nnfor r-forecast))
    (home-page "https://cran.r-project.org/package=stlELM")
    (synopsis "Hybrid Forecasting Model Based on STL Decomposition and ELM")
    (description
     "Univariate time series forecasting with STL decomposition based Extreme Learning
Machine hybrid model.  For method details see Xiong T, Li C, Bao Y (2018).
<doi:10.1016/j.neucom.2017.11.053>.")
    (license license:gpl3)))

(define-public r-stlarima
  (package
    (name "r-stlarima")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stlARIMA" version))
       (sha256
        (base32 "1bh88dd9md8w8qhmha02v2b7cz0pfk6yrc5bhxj8xv9cwanl7g6r"))))
    (properties `((upstream-name . "stlARIMA")))
    (build-system r-build-system)
    (propagated-inputs (list r-forecast))
    (home-page "https://cran.r-project.org/package=stlARIMA")
    (synopsis "STL Decomposition and ARIMA Hybrid Forecasting Model")
    (description
     "Univariate time series forecasting with STL decomposition based auto regressive
integrated moving average (ARIMA) hybrid model.  For method details see Xiong T,
Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.")
    (license license:gpl3)))

(define-public r-stinepack
  (package
    (name "r-stinepack")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stinepack" version))
       (sha256
        (base32 "11l68f01k2qqxkhsxysindpmcs93v5axsq8ilgm05zb46297lv2k"))))
    (properties `((upstream-name . "stinepack")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=stinepack")
    (synopsis "Stineman, a Consistently Well Behaved Method of Interpolation")
    (description
     "This package provides a consistently well behaved method of interpolation based
on piecewise rational functions using Stineman's algorithm.")
    (license license:gpl2+)))

(define-public r-stima
  (package
    (name "r-stima")
    (version "1.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stima" version))
       (sha256
        (base32 "0455disf5lpgxns0mwgmqbglyr9ih2v85vqp7hmardq028axwxrk"))))
    (properties `((upstream-name . "stima")))
    (build-system r-build-system)
    (propagated-inputs (list r-rpart))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=stima")
    (synopsis "Simultaneous Threshold Interaction Modeling Algorithm")
    (description
     "Regression trunk model estimation proposed by Dusseldorp and Meulman (2004)
<doi:10.1007/bf02295641> and Dusseldorp, Conversano, Van Os (2010)
<doi:10.1198/jcgs.2010.06089>, integrating a regression tree and a multiple
regression model.")
    (license license:gpl2)))

(define-public r-stim
  (package
    (name "r-stim")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stim" version))
       (sha256
        (base32 "0a2lcyhh12jzg2znyc648vs8pl19098wz3z60kgd5r9jns707ca4"))))
    (properties `((upstream-name . "stim")))
    (build-system r-build-system)
    (propagated-inputs (list r-ryacas r-lavaan))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=stim")
    (synopsis
     "Incorporating Stability Information into Cross-Sectional Estimates")
    (description
     "The goal of stim is to provide a function for estimating the Stability Informed
Model.  The Stability Informed Model integrates stability information (how much
a variable correlates with itself in the future) into cross-sectional estimates.
 Wysocki and Rhemtulla (2022) <https://psyarxiv.com/vg5as>.")
    (license license:expat)))

(define-public r-stilt
  (package
    (name "r-stilt")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stilt" version))
       (sha256
        (base32 "0b1bs849hw2wcvaldybxk8h8wgwl6p7x4i7winncvngmhplgrvx4"))))
    (properties `((upstream-name . "stilt")))
    (build-system r-build-system)
    (propagated-inputs (list r-fields))
    (home-page "https://cran.r-project.org/package=stilt")
    (synopsis "Separable Gaussian Process Interpolation (Emulation)")
    (description
     "This package provides functions for simplified emulation of time series computer
model output in model parameter space using Gaussian processes.  Stilt can be
used more generally for Kriging of spatio-temporal fields.  There are functions
to predict at new parameter settings, to test the emulator using
cross-validation (which includes information on 95% confidence interval
empirical coverage), and to produce contour plots over 2D slices in model
parameter space.")
    (license license:gpl3)))

(define-public r-sticsrfiles
  (package
    (name "r-sticsrfiles")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SticsRFiles" version))
       (sha256
        (base32 "1afn65z2pz99k3a1fq8w9jjy05bvsdy7p8f829iap1jaahnbz14m"))))
    (properties `((upstream-name . "SticsRFiles")))
    (build-system r-build-system)
    (propagated-inputs (list r-xslt
                             r-xml2
                             r-xml
                             r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rstudioapi
                             r-rlang
                             r-lubridate
                             r-lifecycle
                             r-dplyr
                             r-data-table
                             r-curl
                             r-crayon
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/SticsRPacks/SticsRFiles")
    (synopsis "Read and Modify 'STICS' Input/Output Files")
    (description
     "Manipulating input and output files of the STICS crop model.  Files are either
@code{JavaSTICS} XML files or text files used by the model fortran executable.
Most basic functionalities are reading or writing parameter names and values in
both XML or text input files, and getting data from output files.  Advanced
functionalities include XML files generation from XML templates and/or
spreadsheets, or text files generation from XML files by using xslt
transformation.")
    (license license:lgpl3+)))

(define-public r-sticr
  (package
    (name "r-sticr")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "STICr" version))
       (sha256
        (base32 "0ai5hgq6r3jzklz1wsd3dj5psc4n0rzqlnzrhdqblvbcmrqmhynn"))))
    (properties `((upstream-name . "STICr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-stringr r-lubridate r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/HEAL-KGS/STICr")
    (synopsis
     "Process Stream Temperature, Intermittency, and Conductivity (STIC) Sensor Data")
    (description
     "This package provides a collection of functions for processing raw data from
Stream Temperature, Intermittency, and Conductivity (STIC) loggers.  STICr
(pronounced \"sticker\") includes functions for tidying, calibrating, classifying,
and doing quality checks on data from STIC sensors.  Some package functionality
is described in Wheeler/Zipper et al. (2023) <doi:10.31223/X5636K>.")
    (license license:agpl3+)))

(define-public r-stickyr
  (package
    (name "r-stickyr")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stickyr" version))
       (sha256
        (base32 "0a1jpkphh538wf1bgcm1qyppi2mp99v9d4qa3nlgxf18hnga3jvg"))))
    (properties `((upstream-name . "stickyr")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidyselect
                             r-tibble
                             r-rlang
                             r-purrr
                             r-pillar
                             r-generics
                             r-ellipsis
                             r-dplyr))
    (home-page "https://github.com/UchidaMizuki/stickyr")
    (synopsis "Data Frames with Persistent Columns and Attributes")
    (description
     "This package provides data frames that hold certain columns and attributes
persistently for data processing in dplyr'.")
    (license license:expat)))

(define-public r-sticky
  (package
    (name "r-sticky")
    (version "0.5.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sticky" version))
       (sha256
        (base32 "1szfpay9cpdwq6v5fmnjyr00r4zhz6zd2rrl3h88v9l5xpfafi4w"))))
    (properties `((upstream-name . "sticky")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=sticky")
    (synopsis "Persist Attributes Across Data Operations")
    (description
     "In base R, object attributes are lost when objects are modified by common data
operations such as subset, filter, slice, append, extract etc.  This packages
allows objects to be marked as sticky and have attributes persisted during these
operations or when inserted into or extracted from list-like or table-like
objects.")
    (license (list license:gpl2
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-stgs
  (package
    (name "r-stgs")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "STGS" version))
       (sha256
        (base32 "0xfwd0jsg3glbr3c5l5yyri4is5kg77ivgr6qlzaxp6p8q7yfzqf"))))
    (properties `((upstream-name . "STGS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rrblup r-randomforest r-kernlab r-glmnet r-brnn))
    (home-page "https://cran.r-project.org/package=STGS")
    (synopsis "Genomic Selection using Single Trait")
    (description
     "Genomic Selection (GS) is a latest development in animal and plant breeding
where whole genome markers information is used to predict genetic merit of an
individuals in a practical breeding programme.  GS is one of the promising tool
for improving genetic gain in animal and plants in todayâs scenario.  This
package is basically developed for genomic predictions by estimating marker
effects.  These marker effects further used for calculation of genotypic merit
of individual i.e.  genome estimated breeding values (GEBVs).  Genomic selection
may be based on single trait or multi traits information.  This package performs
genomic selection only for single traits hence named as STGS i.e.  single trait
genomic selection.  STGS is a comprehensive package which gives single step
solution for genomic selection based on most commonly used statistical methods.")
    (license license:gpl3)))

(define-public r-stgam
  (package
    (name "r-stgam")
    (version "0.0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stgam" version))
       (sha256
        (base32 "0p6w0yb0cyc4zpsb7nwkxh8s7vlssprb5ssg0rfbmh83n37faqb4"))))
    (properties `((upstream-name . "stgam")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-mgcv
                             r-metr
                             r-magrittr
                             r-glue
                             r-ggplot2
                             r-foreach
                             r-dplyr
                             r-doparallel
                             r-cowplot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/lexcomber/stgam")
    (synopsis
     "Spatially and Temporally Varying Coefficient Models Using Generalized Additive Models")
    (description
     "This package provides a framework for specifying spatially, temporally and
spatially-and-temporally varying coefficient models using Generalized Additive
Models with Gaussian Process smooths.  The smooths are parameterised with
location and / or time attributes.  Importantly the framework supports the
investigation of the presence and nature of any space-time dependencies in the
data, allows the user to evaluate different model forms (specifications) and to
pick the most probable model or to combine multiple varying coefficient models
using Bayesian Model Averaging.  For more details see: Brunsdon et al (2023)
<doi:10.4230/LIPIcs.GIScience.2023.17>, Comber et al (2023)
<doi:10.4230/LIPIcs.GIScience.2023.22> and Comber et al (2024)
<doi:10.1080/13658816.2023.2270285>.")
    (license license:expat)))

(define-public r-stfts
  (package
    (name "r-stfts")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "STFTS" version))
       (sha256
        (base32 "1cv2gc9334fbsmmwhqskfssaql6kr9i680p2zkwhm5735dr1h49h"))))
    (properties `((upstream-name . "STFTS")))
    (build-system r-build-system)
    (propagated-inputs (list r-e1071))
    (home-page "https://cran.r-project.org/package=STFTS")
    (synopsis "Statistical Tests for Functional Time Series")
    (description
     "This package provides a collection of statistical hypothesis tests of functional
time series.  While it will include more tests when the related literature are
enriched, this package contains the following key tests: functional stationarity
test, functional trend stationarity test, functional unit root test, to name a
few.")
    (license license:gpl2)))

(define-public r-stfit
  (package
    (name "r-stfit")
    (version "0.99.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stfit" version))
       (sha256
        (base32 "0668nnsgkdd2v7bx06wnq8sc0asbi19p4752jm0hj2jziiibk0kn"))))
    (properties `((upstream-name . "stfit")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp
                             r-rcolorbrewer
                             r-rastervis
                             r-raster
                             r-matrix
                             r-foreach
                             r-fda
                             r-doparallel
                             r-abind))
    (home-page "https://cran.r-project.org/package=stfit")
    (synopsis "Spatio-Temporal Functional Imputation Tool")
    (description
     "This package provides a general spatiotemporal satellite image imputation method
based on sparse functional data analytic techniques.  The imputation method
applies and extends the Functional Principal Analysis by Conditional Estimation
(PACE).  The underlying idea for the proposed procedure is to impute a missing
pixel by borrowing information from temporally and spatially contiguous pixels
based on the best linear unbiased prediction.")
    (license license:gpl3)))

(define-public r-stevethemes
  (package
    (name "r-stevethemes")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stevethemes" version))
       (sha256
        (base32 "06bv1s3yjw2jiqc3izlr4pj7ws3wfq7s33fr1b614cb6hbj6pvan"))))
    (properties `((upstream-name . "stevethemes")))
    (build-system r-build-system)
    (propagated-inputs (list r-systemfonts r-rlang r-ggplot2))
    (home-page "http://svmiller.com/stevethemes/")
    (synopsis "Steve's 'ggplot2' Themes and Related Theme Elements")
    (description
     "This is a compilation of my preferred themes and related theme elements for
ggplot2'.  I believe these themes and theme elements are aesthetically pleasing,
both for pedagogical instruction and for the presentation of applied statistical
research to a wide audience.  These themes imply routine use of easily
obtained/free fonts, simple forms of which are included in this package.")
    (license license:expat)))

(define-public r-stevetemplates
  (package
    (name "r-stevetemplates")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stevetemplates" version))
       (sha256
        (base32 "1q9wj132y5ms7wdsxv90c6hawni5b2vynsxnym3rzr5wmif5a7v0"))))
    (properties `((upstream-name . "stevetemplates")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmarkdown))
    (home-page "https://cran.r-project.org/package=stevetemplates")
    (synopsis "Steve's R Markdown Templates")
    (description
     "These are my collection of R Markdown templates, mostly for compilation to PDF.
These are useful for all things academic and professional, if you are using R
Markdown for things like your CV or your articles and manuscripts.")
    (license license:gpl2)))

(define-public r-stevemisc
  (package
    (name "r-stevemisc")
    (version "1.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stevemisc" version))
       (sha256
        (base32 "1xbhmz3yh7qjchq07khd8qa61654x3chj3srlyd8c15in0dxch5z"))))
    (properties `((upstream-name . "stevemisc")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rmarkdown
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-lme4
                             r-labelled
                             r-httr
                             r-ggplot2
                             r-forcats
                             r-dplyr
                             r-arm))
    (home-page "https://cran.r-project.org/package=stevemisc")
    (synopsis "Steve's Miscellaneous Functions")
    (description
     "These are miscellaneous functions that I find useful for my research and
teaching.  The contents include themes for plots, functions for simulating
quantities of interest from regression models, functions for simulating various
forms of fake data for instructional/research purposes, and many more.  All
told, the functions provided here are broadly useful for data organization, data
presentation, data recoding, and data simulation.")
    (license license:gpl2+)))

(define-public r-stevedore
  (package
    (name "r-stevedore")
    (version "0.9.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stevedore" version))
       (sha256
        (base32 "0mx4l7yiicvpri5qdw9czlvr27a9753a6z98s8k19lbz58nim1wr"))))
    (properties `((upstream-name . "stevedore")))
    (build-system r-build-system)
    (inputs (list docker))
    (propagated-inputs (list r-yaml r-jsonlite r-curl r-crayon))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/richfitz/stevedore")
    (synopsis "Docker Client")
    (description
     "Work with containers over the Docker API. Rather than using system calls to
interact with a docker client, using the API directly means that we can receive
richer information from docker.  The interface in the package is automatically
generated using the @code{OpenAPI} (a.k.a., swagger') specification, and all
return values are checked in order to make them type stable.")
    (license license:expat)))

(define-public r-stevedata
  (package
    (name "r-stevedata")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stevedata" version))
       (sha256
        (base32 "0ggfgf3g240r69g4wnhyxvzp46by9b79hzhhpbs1rmh2pzd6zj96"))))
    (properties `((upstream-name . "stevedata")))
    (build-system r-build-system)
    (home-page "http://svmiller.com/stevedata/")
    (synopsis
     "Steve's Toy Data for Teaching About a Variety of Methodological, Social, and Political Topics")
    (description
     "This is a collection of various kinds of data with broad uses for teaching.  My
students, and academics like me who teach the same topics I teach, should find
this useful if their teaching workflow is also built around the R programming
language.  The applications are multiple but mostly cluster on topics of
statistical methodology, international relations, and political economy.")
    (license license:gpl2)))

(define-public r-stereomorph
  (package
    (name "r-stereomorph")
    (version "1.6.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StereoMorph" version))
       (sha256
        (base32 "0qrbl86ba83j4r6j2phap5n6ryi729gbypn95h0vbgrv752nayqp"))))
    (properties `((upstream-name . "StereoMorph")))
    (build-system r-build-system)
    (propagated-inputs (list r-tiff
                             r-svgviewr
                             r-shiny
                             r-rjson
                             r-rcpp
                             r-png
                             r-mass
                             r-jpeg
                             r-bezier))
    (home-page "https://aaronolsen.github.io/software/stereomorph.html")
    (synopsis "Stereo Camera Calibration and Reconstruction")
    (description
     "This package provides functions for the collection of 3D points and curves using
a stereo camera setup.")
    (license license:cc-by-sa4.0)))

(define-public r-stepwisetest
  (package
    (name "r-stepwisetest")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StepwiseTest" version))
       (sha256
        (base32 "1fdm4s9l6grgd45r98ybbsh40rnmnn16c0id6lv28cpmssi0iphi"))))
    (properties `((upstream-name . "StepwiseTest")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=StepwiseTest")
    (synopsis
     "Multiple Testing Method to Control Generalized Family-Wise Error Rate and False Discovery Proportion")
    (description
     "Collection of stepwise procedures to conduct multiple hypotheses testing.  The
details of the stepwise algorithm can be found in Romano and Wolf (2007)
<DOI:10.1214/009053606000001622> and Hsu, Kuan, and Yen (2014)
<DOI:10.1093/jjfinec/nbu014>.")
    (license license:gpl2+)))

(define-public r-stepsplitreg
  (package
    (name "r-stepsplitreg")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stepSplitReg" version))
       (sha256
        (base32 "04ks599bb3rbyc81kyakj6nmj7i8fvkb1pild0gd3i6j84j70071"))))
    (properties `((upstream-name . "stepSplitReg")))
    (build-system r-build-system)
    (propagated-inputs (list r-splitglm r-rcpparmadillo r-rcpp r-nnls))
    (home-page "https://cran.r-project.org/package=stepSplitReg")
    (synopsis "Stepwise Split Regularized Regression")
    (description
     "This package provides functions to perform stepwise split regularized
regression.  The approach first uses a stepwise algorithm to split the variables
into the models with a goodness of fit criterion, and then regularization is
applied to each model.  The weights of the models in the ensemble are determined
based on a criterion selected by the user.")
    (license license:gpl2+)))

(define-public r-stepsignalmargilike
  (package
    (name "r-stepsignalmargilike")
    (version "2.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StepSignalMargiLike" version))
       (sha256
        (base32 "0j85lvs2bljfhf482r31sq3xgk8l73hcw22wgxxp8z8nla38cwf9"))))
    (properties `((upstream-name . "StepSignalMargiLike")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=StepSignalMargiLike")
    (synopsis "Step-Wise Signal Extraction via Marginal Likelihood")
    (description
     "This package provides function to estimate multiple change points using marginal
likelihood method.  See the Manual file in data folder for a detailed
description of all functions, and a walk through tutorial.  For more information
of the method, please see Du, Kao and Kou (2016)
<doi:10.1080/01621459.2015.1006365>.")
    (license license:gpl2+)))

(define-public r-steps
  (package
    (name "r-steps")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "steps" version))
       (sha256
        (base32 "0yvqz5jhzwqkm3gphb2mmlq1ip6sr8g6c09hirs827j12dj63ixf"))))
    (properties `((upstream-name . "steps")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridislite
                             r-rcpp
                             r-rastervis
                             r-raster
                             r-memuse
                             r-future-apply
                             r-future))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/steps-dev/steps")
    (synopsis "Spatially- and Temporally-Explicit Population Simulator")
    (description
     "Software to simulate population change across space and time.  Visintin et al.
(2020) <doi:10.1111/2041-210X.13354>.")
    (license license:gpl2+)))

(define-public r-steprf
  (package
    (name "r-steprf")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "steprf" version))
       (sha256
        (base32 "1wscxnglxi61agmfn1zmhh75c7h5394r8d8khr02p22a234npqqi"))))
    (properties `((upstream-name . "steprf")))
    (build-system r-build-system)
    (propagated-inputs (list r-spm2 r-spm r-randomforest r-psy))
    (home-page "https://cran.r-project.org/package=steprf")
    (synopsis "Stepwise Predictive Variable Selection for Random Forest")
    (description
     "An introduction to several novel predictive variable selection methods for
random forest.  They are based on various variable importance methods (i.e.,
averaged variable importance (AVI), and knowledge informed AVI (i.e., KIAVI, and
KIAVI2)) and predictive accuracy in stepwise algorithms.  For details of the
variable selection methods, please see: Li, J., Siwabessy, J., Huang, Z. and
Nichol, S. (2019) <doi:10.3390/geosciences9040180>.  Li, J., Alvarez, B.,
Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F.,
Nichol, S. (2017). <DOI: 10.13140/RG.2.2.27686.22085>.")
    (license license:gpl2+)))

(define-public r-stepreg
  (package
    (name "r-stepreg")
    (version "1.5.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StepReg" version))
       (sha256
        (base32 "0rhqds2rxrllpbwdifyni4f16lk16fyy0fgzgvq2sj4q7x62i565"))))
    (properties `((upstream-name . "StepReg")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-survival
                             r-summarytools
                             r-stringr
                             r-shinythemes
                             r-shinyjs
                             r-shinycssloaders
                             r-shiny
                             r-rmarkdown
                             r-purrr
                             r-mass
                             r-ggrepel
                             r-ggplot2
                             r-ggcorrplot
                             r-flextable
                             r-dt
                             r-dplyr
                             r-cowplot))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=StepReg")
    (synopsis "Stepwise Regression Analysis")
    (description
     "The stepwise regression analysis is a statistical technique used to identify a
subset of predictor variables essential for constructing predictive models.
This package performs stepwise regression analysis across various regression
models such as linear, logistic, Cox proportional hazards, Poisson, Gamma, and
negative binomial regression.  It incorporates diverse stepwise regression
algorithms like forward selection, backward elimination, and bidirectional
elimination alongside the best subset method.  Additionally, it offers a wide
range of selection criteria, including Akaike Information Criterion (AIC), Sawa
Bayesian Information Criterion (BIC), and Significance Levels (SL).  We
validated the output accuracy of @code{StepReg} using public datasets within the
SAS software environment.  To facilitate efficient model comparison and
selection, @code{StepReg} allows for multiple strategies and selection metrics
to be executed in a single function call.  Moreover, @code{StepReg} integrates a
Shiny application for interactive regression analysis, broadening its
accessibility.")
    (license license:expat)))

(define-public r-stepr
  (package
    (name "r-stepr")
    (version "2.1-10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stepR" version))
       (sha256
        (base32 "08v9blxqyha4phk90f09vdq8vh9czprfvsiwk06l8am01cspdj4j"))))
    (properties `((upstream-name . "stepR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-r-cache r-lowpassfilter r-digest))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=stepR")
    (synopsis "Multiscale Change-Point Inference")
    (description
     "Allows fitting of step-functions to univariate serial data where neither the
number of jumps nor their positions is known by implementing the multiscale
regression estimators SMUCE, simulataneous multiscale changepoint estimator, (K.
Frick, A. Munk and H. Sieling, 2014) <doi:10.1111/rssb.12047> and HSMUCE,
heterogeneous SMUCE, (F. Pein, H. Sieling and A. Munk, 2017)
<doi:10.1111/rssb.12202>.  In addition, confidence intervals for the
change-point locations and bands for the unknown signal can be obtained.")
    (license license:gpl3)))

(define-public r-stepplr
  (package
    (name "r-stepplr")
    (version "0.93")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stepPlr" version))
       (sha256
        (base32 "1i54nyz8z5vq3mzfh6h2vd1q0hsdazc4mhrj9ad0zdvn0qnz61lv"))))
    (properties `((upstream-name . "stepPlr")))
    (build-system r-build-system)
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=stepPlr")
    (synopsis
     "L2 Penalized Logistic Regression with Stepwise Variable Selection")
    (description
     "L2 penalized logistic regression for both continuous and discrete predictors,
with forward stagewise/forward stepwise variable selection procedure.")
    (license license:gpl2+)))

(define-public r-steppenal
  (package
    (name "r-steppenal")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stepPenal" version))
       (sha256
        (base32 "08gizl6c606ibbv7x2rdvfw37rghkrprwszha79yngkzpajw8gql"))))
    (properties `((upstream-name . "stepPenal")))
    (build-system r-build-system)
    (propagated-inputs (list r-proc r-mvtnorm r-glmnet r-dfoptim r-caret))
    (home-page "https://cran.r-project.org/package=stepPenal")
    (synopsis "Stepwise Forward Variable Selection in Penalized Regression")
    (description
     "Model Selection Based on Combined Penalties.  This package implements a stepwise
forward variable selection algorithm based on a penalized likelihood criterion
that combines the L0 with L2 or L1 norms.")
    (license license:gpl2)))

(define-public r-steppedpower
  (package
    (name "r-steppedpower")
    (version "0.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SteppedPower" version))
       (sha256
        (base32 "0dxl88gfwi6wb9k4qmqhb9hqgvjpzbvwfpiqadzjwg1h6yg487y5"))))
    (properties `((upstream-name . "SteppedPower")))
    (build-system r-build-system)
    (propagated-inputs (list r-rfast r-plotly r-matrix))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SteppedPower")
    (synopsis "Power Calculation for Stepped Wedge Designs")
    (description
     "This package provides tools for power and sample size calculation as well as
design diagnostics for longitudinal mixed model settings, with a focus on
stepped wedge designs.  All calculations are oracle estimates i.e.  assume
random effect variances to be known (or guessed) in advance.  The method is
introduced in Hussey and Hughes (2007) <doi:10.1016/j.cct.2006.05.007>,
extensions are discussed in Li et al. (2020) <doi:10.1177/0962280220932962>.")
    (license license:expat)))

(define-public r-stepp
  (package
    (name "r-stepp")
    (version "3.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stepp" version))
       (sha256
        (base32 "0kdjp3kxvyjy8fyrzisiv9fjx53dpsg69ksd08lnngvzw31rv5yy"))))
    (properties `((upstream-name . "stepp")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-scales r-rstudioapi r-car))
    (native-inputs (list gfortran))
    (home-page "https://www.r-project.org")
    (synopsis "Subpopulation Treatment Effect Pattern Plot (STEPP)")
    (description
     "This package provides a method to explore the treatment-covariate interactions
in survival or generalized linear model (GLM) for continuous, binomial and count
data arising from two or more treatment arms of a clinical trial.  A permutation
distribution approach to inference is implemented, based on permuting the
covariate values within each treatment group.")
    (license license:gpl2+)))

(define-public r-stepmixr
  (package
    (name "r-stepmixr")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stepmixr" version))
       (sha256
        (base32 "0yh0dbv14bdzwlz3fwl5ibmmg2nzb6prfdwribavfndx8zxbwgn7"))))
    (properties `((upstream-name . "stepmixr")))
    (build-system r-build-system)
    (propagated-inputs (list r-reticulate))
    (home-page "https://github.com/Labo-Lacourse/StepMixr")
    (synopsis "Interface to 'Python' Package 'StepMix'")
    (description
     "This is an interface for the Python package @code{StepMix}'.  It is a Python
package following the scikit-learn API for model-based clustering and
generalized mixture modeling (latent class/profile analysis) of continuous and
categorical data. @code{StepMix} handles missing values through Full Information
Maximum Likelihood (FIML) and provides multiple stepwise
Expectation-Maximization (EM) estimation methods based on pseudolikelihood
theory.  Additional features include support for covariates and distal outcomes,
various simulation utilities, and non-parametric bootstrapping, which allows
inference in semi-supervised and unsupervised settings.")
    (license license:gpl2)))

(define-public r-stepjglm
  (package
    (name "r-stepjglm")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stepjglm" version))
       (sha256
        (base32 "0xsb0wg7ka4ska2jkzaic7kqdzj2fkhpf5sxr3a116f2najj9ax2"))))
    (properties `((upstream-name . "stepjglm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsq))
    (home-page "https://cran.r-project.org/package=stepjglm")
    (synopsis "Variable Selection for Joint Modeling of Mean and Dispersion")
    (description
     "This package provides a Package for selecting variables for the joint modeling
of mean and dispersion (including models for mixture experiments) based on
hypothesis testing and the quality of model's fit.  In each iteration of the
selection process, a criterion for checking the goodness of fit is used as a
filter for choosing the terms that will be evaluated by a hypothesis test.
Pinto & Pereira (2021) <@code{arXiv:2109.07978>}.")
    (license license:gpl3)))

(define-public r-stepgwr
  (package
    (name "r-stepgwr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StepGWR" version))
       (sha256
        (base32 "0sqgixmxla1sv3jkjhby0gggzs3iyg1cwq267y9hymzx2gsafry5"))))
    (properties `((upstream-name . "StepGWR")))
    (build-system r-build-system)
    (propagated-inputs (list r-qpdf r-numbers r-mass))
    (home-page "https://cran.r-project.org/package=StepGWR")
    (synopsis
     "Hybrid Spatial Model for Prediction and Capturing Spatial Variation in the Data")
    (description
     "It is a hybrid spatial model that combines the variable selection capabilities
of stepwise regression methods with the predictive power of the Geographically
Weighted Regression(GWR) model.The developed hybrid model follows a two-step
approach where the stepwise variable selection method is applied first to
identify the subset of predictors that have the most significant impact on the
response variable, and then a GWR model is fitted using those selected variables
for spatial prediction at test or unknown locations.  For method details,see
Leung, Y., Mei, C. L. and Zhang, W. X. (2000).<DOI:10.1068/a3162>.This hybrid
spatial model aims to improve the accuracy and interpretability of GWR
predictions by selecting a subset of relevant variables through a stepwise
selection process.This approach is particularly useful for modeling spatially
varying relationships and improving the accuracy of spatial predictions.")
    (license license:gpl2+)))

(define-public r-stepgbm
  (package
    (name "r-stepgbm")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stepgbm" version))
       (sha256
        (base32 "1wcc4m9alphacdf9df5z29hz881625czxpyq93kzwc0nhnvz2gp0"))))
    (properties `((upstream-name . "stepgbm")))
    (build-system r-build-system)
    (propagated-inputs (list r-steprf r-spm))
    (home-page "https://cran.r-project.org/package=stepgbm")
    (synopsis
     "Stepwise Variable Selection for Generalized Boosted Regression Modeling")
    (description
     "An introduction to a couple of novel predictive variable selection methods for
generalised boosted regression modeling (gbm).  They are based on various
variable influence methods (i.e., relative variable influence (RVI) and
knowledge informed RVI (i.e., KIRVI, and KIRVI2)) that adopted similar ideas as
AVI, KIAVI and KIAVI2 in the steprf package, and also based on predictive
accuracy in stepwise algorithms.  For details of the variable selection methods,
please see: Li, J., Siwabessy, J., Huang, Z. and Nichol, S. (2019)
<doi:10.3390/geosciences9040180>.  Li, J., Alvarez, B., Siwabessy, J., Tran, M.,
Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). <DOI:
10.13140/RG.2.2.27686.22085>.")
    (license license:gpl2+)))

(define-public r-stepdownfdp
  (package
    (name "r-stepdownfdp")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stepdownfdp" version))
       (sha256
        (base32 "1yqzxn4d52ih03f0mpvb9dw9pn9jfbgnyay1pfhh112zml5h6i3m"))))
    (properties `((upstream-name . "stepdownfdp")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma))
    (home-page "https://github.com/uni-Arya/stepdownfdp")
    (synopsis "Step-Down Procedure to Control the False Discovery Proportion")
    (description
     "This package provides a step-down procedure for controlling the False Discovery
Proportion (FDP) in a competition-based setup, implementing Dong et al. (2020)
<@code{arXiv:2011.11939>}.  Such setups include target-decoy competition (TDC)
in computational mass spectrometry and the knockoff construction in linear
regression.")
    (license license:expat)))

(define-public r-stepcount
  (package
    (name "r-stepcount")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stepcount" version))
       (sha256
        (base32 "1isa817lqkb5a1rl2kb7nm6gbfp6c2kqk399mrw4j2ysbhcjgiq7"))))
    (properties `((upstream-name . "stepcount")))
    (build-system r-build-system)
    (propagated-inputs (list r-reticulate
                             r-readr
                             r-magrittr
                             r-lubridate
                             r-curl
                             r-assertthat))
    (home-page "https://cran.r-project.org/package=stepcount")
    (synopsis "Estimate Step Counts from 'Accelerometry' Data")
    (description
     "Interfaces the stepcount Python module
<https://github.com/@code{OxWearables/stepcount>} to estimate step counts and
other activities from accelerometry data.")
    (license license:expat)))

(define-public r-stepcam
  (package
    (name "r-stepcam")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "STEPCAM" version))
       (sha256
        (base32 "03crbc7hag8w333j9c7k0q7zy7xmfid4lq773p74r55jmac5xpjf"))))
    (properties `((upstream-name . "STEPCAM")))
    (build-system r-build-system)
    (propagated-inputs (list r-vcd
                             r-mass
                             r-gtools
                             r-geometry
                             r-fd
                             r-ape
                             r-ade4))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/thijsjanzen/STEPCAM")
    (synopsis "ABC-SMC Inference of STEPCAM")
    (description
     "Collection of model estimation, and model plotting functions related to the
STEPCAM family of community assembly models.  STEPCAM is a STEPwise Community
Assembly Model that infers the relative contribution of Dispersal Assembly,
Habitat Filtering and Limiting Similarity from a dataset consisting of the
combination of trait and abundance data.  See also
<http://onlinelibrary.wiley.com/wol1/doi/10.1890/14-0454.1/abstract> for more
information.")
    (license license:gpl2)))

(define-public r-stepbeta
  (package
    (name "r-stepbeta")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StepBeta" version))
       (sha256
        (base32 "1wr0f4c64hkhq8kjv6vf8laz5pmlyc0ajgd8007q8jris4jydzs6"))))
    (properties `((upstream-name . "StepBeta")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-glue r-combinat r-betareg r-aod))
    (home-page "https://cran.r-project.org/package=StepBeta")
    (synopsis
     "Stepwise Procedure for Beta, Beta-Binomial and Negative Binomial Regression Models")
    (description
     "Starting from a Regression Model, it provides a stepwise procedure to select the
linear predictor.")
    (license license:gpl3)))

(define-public r-stenr
  (package
    (name "r-stenr")
    (version "0.6.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stenR" version))
       (sha256
        (base32 "19vn7123wys1jqrm4y8f6b4kvwygi1ql6ady2d5jz93vf29ihdzj"))))
    (properties `((upstream-name . "stenR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-r6
                             r-moments
                             r-dplyr
                             r-data-table
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://statismike.github.io/stenR/")
    (synopsis "Standardization of Raw Discrete Questionnaire Scores")
    (description
     "An user-friendly framework to preprocess raw item scores of questionnaires into
factors or scores and standardize them.  Standardization can be made either by
their normalization in representative sample, or by import of premade scoring
table.")
    (license license:expat)))

(define-public r-stencilaschema
  (package
    (name "r-stencilaschema")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stencilaschema" version))
       (sha256
        (base32 "117vl2h78ndnb70s8mpd0bs1n6h1vizv4wzn6g4y9j12ma63nldb"))))
    (properties `((upstream-name . "stencilaschema")))
    (build-system r-build-system)
    (home-page "https://github.com/stencila/schema#readme")
    (synopsis "Bindings for Stencila Schema")
    (description
     "This package provides R bindings for the Stencila Schema
<https://schema.stenci.la>.  This package is primarily aimed at R developers
wanting to programmatically generate, or modify, executable documents.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-stempcens
  (package
    (name "r-stempcens")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StempCens" version))
       (sha256
        (base32 "0jphyikfggx0yj5f5k3fykf3fdv9msppymzpyin1m7xfmfi5cwia"))))
    (properties `((upstream-name . "StempCens")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmvtnorm
                             r-rdpack
                             r-rcpparmadillo
                             r-rcpp
                             r-mvtnorm
                             r-mcmcglmm
                             r-ggplot2
                             r-distances))
    (home-page "https://cran.r-project.org/package=StempCens")
    (synopsis
     "Spatio-Temporal Estimation and Prediction for Censored/Missing Responses")
    (description
     "It estimates the parameters of a censored or missing data in spatio-temporal
models using the SAEM algorithm (Delyon et al., 1999).  This algorithm is a
stochastic approximation of the widely used EM algorithm and an important tool
for models in which the E-step does not have an analytic form.  Besides the
expressions obtained to estimate the parameters to the proposed model, we
include the calculations for the observed information matrix using the method
developed by Louis (1982).  To examine the performance of the fitted model,
case-deletion measure are provided.")
    (license license:gpl2+)))

(define-public r-stemmatology
  (package
    (name "r-stemmatology")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stemmatology" version))
       (sha256
        (base32 "1l2y4jlszz1dnafdl2wqqfgdjis07i3gzfnp1v1af32pvjy4zqhw"))))
    (properties `((upstream-name . "stemmatology")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-igraph r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Jean-Baptiste-Camps/stemmatology")
    (synopsis "Stemmatological Analysis of Textual Traditions")
    (description
     "Explore and analyse the genealogy of textual or musical traditions, from their
variants, with various stemmatological methods, mainly the disagreement-based
algorithms suggested by Camps and Cafiero (2015)
<doi:10.1484/M.LECTIO-EB.5.102565>.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-stemanalysis
  (package
    (name "r-stemanalysis")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StemAnalysis" version))
       (sha256
        (base32 "0xp1scdmrq3q61mbpq3362wv2zgd86g1wf827p2fdnihvkmdvp21"))))
    (properties `((upstream-name . "StemAnalysis")))
    (build-system r-build-system)
    (propagated-inputs (list r-lmfor))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/forestscientist/StemAnalysis")
    (synopsis
     "Reconstructing Tree Growth and Carbon Accumulation with Stem Analysis Data")
    (description
     "Use stem analysis data to reconstructing tree growth and carbon accumulation.
Users can independently or in combination perform a number of standard tasks for
any tree species. (i) Age class determination. (ii) The cumulative growth, mean
annual increment, and current annual increment of diameter at breast height
(DBH) with bark, tree height, and stem volume with bark are estimated. (iii)
Tree biomass and carbon storage estimation from volume and allometric models are
calculated. (iv) Height-diameter relationship is fitted with nonlinear models,
if diameter at breast height (DBH) or tree height are available, which can be
used to retrieve tree height and diameter at breast height (DBH).
<https://github.com/forestscientist/@code{StemAnalysis>}.")
    (license license:expat)))

(define-public r-stellar
  (package
    (name "r-stellar")
    (version "0.3-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stellaR" version))
       (sha256
        (base32 "0spn25fbsfswx2rshzk34pqkicynyl256a4f7xrh51bhyh9qff44"))))
    (properties `((upstream-name . "stellaR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=stellaR")
    (synopsis
     "Evolutionary Tracks and Isochrones from Pisa Stellar Evolution Database")
    (description
     "Manages and display stellar tracks and isochrones from Pisa low-mass database.
Includes tools for isochrones construction and tracks interpolation.")
    (license license:gpl2+)))

(define-public r-stelfi
  (package
    (name "r-stelfi")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stelfi" version))
       (sha256
        (base32 "06sj1az5803xh1ikkawdrp0c8yc1kh8iqky6vazxr516hc3b0f9w"))))
    (properties `((upstream-name . "stelfi")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmb
                             r-sf
                             r-rcppeigen
                             r-matrix
                             r-gridextra
                             r-ggplot2
                             r-fmesher
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/cmjt/stelfi/")
    (synopsis
     "Hawkes and Log-Gaussian Cox Point Processes Using Template Model Builder")
    (description
     "Fit Hawkes and log-Gaussian Cox process models with extensions.  Introduced in
Hawkes (1971) <doi:10.2307/2334319> a Hawkes process is a self-exciting temporal
point process where the occurrence of an event immediately increases the chance
of another.  We extend this to consider self-inhibiting process and a
non-homogeneous background rate.  A log-Gaussian Cox process is a Poisson point
process where the log-intensity is given by a Gaussian random field.  We extend
this to a joint likelihood formulation fitting a marked log-Gaussian Cox model.
In addition, the package offers functionality to fit self-exciting
spatiotemporal point processes.  Models are fitted via maximum likelihood using
TMB (Template Model Builder).  Where included 1) random fields are assumed to be
Gaussian and are integrated over using the Laplace approximation and 2) a
stochastic partial differential equation model, introduced by Lindgren, Rue, and
LindstrÃ¶m. (2011) <doi:10.1111/j.1467-9868.2011.00777.x>, is defined for the
field(s).")
    (license license:gpl3+)))

(define-public r-steiniv
  (package
    (name "r-steiniv")
    (version "0.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SteinIV" version))
       (sha256
        (base32 "1bm4lc7g9h9jkb1dpzb84289bwxcywp0a8vylv6ipvhiqbqk5d95"))))
    (properties `((upstream-name . "SteinIV")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SteinIV")
    (synopsis
     "Semi-Parametric Stein-Like Estimator with Instrumental Variables")
    (description
     "Routines for computing different types of linear estimators, based on
instrumental variables (IVs), including the semi-parametric Stein-like (SPS)
estimator, originally introduced by Judge and Mittelhammer (2004)
<DOI:10.1198/016214504000000430>.")
    (license license:gpl2+)))

(define-public r-steepness
  (package
    (name "r-steepness")
    (version "0.3-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "steepness" version))
       (sha256
        (base32 "1lv2n4s8dj7axsmwmpfp4aw6v530r7437f44533pbx6p4n71hpzl"))))
    (properties `((upstream-name . "steepness")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=steepness")
    (synopsis "Testing Steepness of Dominance Hierarchies")
    (description
     "The steepness package computes steepness as a property of dominance hierarchies.
 Steepness is defined as the absolute slope of the straight line fitted to the
normalized David's scores.  The normalized David's scores can be obtained on the
basis of dyadic dominance indices corrected for chance or by means of
proportions of wins.  Given an observed sociomatrix, it computes hierarchy's
steepness and estimates statistical significance by means of a randomization
test.")
    (license license:gpl2+)))

(define-public r-steadyica
  (package
    (name "r-steadyica")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "steadyICA" version))
       (sha256
        (base32 "0mcalbsgajdpk45k9vpyavn079063hw4ihkw72n9wcy5nb0da14g"))))
    (properties `((upstream-name . "steadyICA")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-mass r-combinat r-clue))
    (home-page "https://cran.r-project.org/package=steadyICA")
    (synopsis
     "ICA and Tests of Independence via Multivariate Distance Covariance")
    (description
     "This package provides functions related to multivariate measures of independence
and ICA: -estimate independent components by minimizing distance covariance;
-conduct a test of mutual independence based on distance covariance; -estimate
independent components via infomax (a popular method but generally performs
poorer than mdcovica, @code{ProDenICA}, and/or @code{fastICA}, but is useful for
comparisons); -order indepedent components by skewness; -match independent
components from multiple estimates; -other functions useful in ICA.")
    (license license:gpl2+)))

(define-public r-stdvectors
  (package
    (name "r-stdvectors")
    (version "0.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stdvectors" version))
       (sha256
        (base32 "0gxylknr146qyg9aj22md076cdhrsz75dg25a1hx8525cm71nwd6"))))
    (properties `((upstream-name . "stdvectors")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://github.com/digEmAll/stdvectors")
    (synopsis "C++ Standard Library Vectors in R")
    (description
     "Allows the creation and manipulation of C++ std::vector's in R.")
    (license license:gpl2+)))

(define-public r-stdreg2
  (package
    (name "r-stdreg2")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stdReg2" version))
       (sha256
        (base32 "0vbiqm4cg4wnn94gl9f8616k6h6p92bn1myh4apak88c3vajhmyy"))))
    (properties `((upstream-name . "stdReg2")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-generics r-drgee r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://sachsmc.github.io/stdReg2/")
    (synopsis "Regression Standardization for Causal Inference")
    (description
     "This package contains more modern tools for causal inference using regression
standardization.  Four general classes of models are implemented; generalized
linear models, conditional generalized estimating equation models, Cox
proportional hazards models, and shared frailty gamma-Weibull models.
Methodological details are described in SjÃ¶lander, A. (2016)
<doi:10.1007/s10654-016-0157-3>.  Also includes functionality for doubly robust
estimation for generalized linear models in some special cases, and the ability
to implement custom models.")
    (license license:agpl3+)))

(define-public r-stdreg
  (package
    (name "r-stdreg")
    (version "3.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stdReg" version))
       (sha256
        (base32 "0x4awgbja70mdpbavgkg9mpnrxxc0qpsnl8i3rj9hs4vwbdkalr8"))))
    (properties `((upstream-name . "stdReg")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-numderiv r-drgee r-data-table))
    (home-page "https://cran.r-project.org/package=stdReg")
    (synopsis "Regression Standardization")
    (description
     "This package contains functionality for regression standardization.  Four
general classes of models are allowed; generalized linear models, conditional
generalized estimating equation models, Cox proportional hazards models and
shared frailty gamma-Weibull models.  Sjolander, A. (2016)
<doi:10.1007/s10654-016-0157-3>.")
    (license license:lgpl3+)))

(define-public r-stdmod
  (package
    (name "r-stdmod")
    (version "0.2.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stdmod" version))
       (sha256
        (base32 "1vr4r62hyikpj0kg4rzhbj3jp2aaqxk7khj0v1afwz28lsdxjp9k"))))
    (properties `((upstream-name . "stdmod")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-manymome r-lavaan r-ggplot2 r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://sfcheung.github.io/stdmod/")
    (synopsis "Standardized Moderation Effect and Its Confidence Interval")
    (description
     "This package provides functions for computing a standardized moderation effect
in moderated regression and forming its confidence interval by nonparametric
bootstrapping as proposed in Cheung, Cheung, Lau, Hui, and Vong (2022)
<doi:10.1037/hea0001188>.  Also includes simple-to-use functions for computing
conditional effects (unstandardized or standardized) and plotting moderation
effects.")
    (license license:gpl3)))

(define-public r-stddiff
  (package
    (name "r-stddiff")
    (version "3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stddiff" version))
       (sha256
        (base32 "1lirfxqiq59vhjkq1zv27ycybbs9n62c14klkkcf74j15a54cmbn"))))
    (properties `((upstream-name . "stddiff")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=stddiff")
    (synopsis
     "Calculate the Standardized Difference for Numeric, Binary and Category Variables")
    (description
     "This package contains three main functions including stddiff.numeric(),
stddiff.binary() and stddiff.category().  These are used to calculate the
standardized difference between two groups.  It is especially used to evaluate
the balance between two groups before and after propensity score matching.")
    (license license:gpl3)))

(define-public r-stcpr6
  (package
    (name "r-stcpr6")
    (version "0.9.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stcpR6" version))
       (sha256
        (base32 "0li5n6z56r5h1d43q3x43rpqjwm35m654z3qmkmj4n42pl9a39jw"))))
    (properties `((upstream-name . "stcpR6")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-r6))
    (home-page "https://github.com/shinjaehyeok/stcpR6")
    (synopsis
     "Sequential Test and Change-Point Detection Algorithms Based on E-Values / E-Detectors")
    (description
     "Algorithms of nonparametric sequential test and online change-point detection
for streams of univariate (sub-)Gaussian, binary, and bounded random variables,
introduced in following publications - Shin et al. (2024)
<doi:10.48550/@code{arXiv.2203.03532>}, Shin et al. (2021)
<doi:10.48550/@code{arXiv.2010.08082>}.")
    (license license:gpl3+)))

(define-public r-stcov
  (package
    (name "r-stcov")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stcov" version))
       (sha256
        (base32 "166w929sgd7nanw1zjhzwv50hd5vqhgsrgxnsga4dzrcvndalw70"))))
    (properties `((upstream-name . "stcov")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=stcov")
    (synopsis "Stein's Covariance Estimator")
    (description
     "Estimates a covariance matrix using Stein's isotonized covariance estimator, or
a related estimator suggested by Haff.")
    (license license:gpl2+)))

(define-public r-stcos
  (package
    (name "r-stcos")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stcos" version))
       (sha256
        (base32 "146s0jdd88pax9xsn2z56x8sfkjx8yr8sf59ma9zryq42ajyq43d"))))
    (properties `((upstream-name . "stcos")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-rcpparmadillo r-rcpp r-matrix r-dplyr))
    (home-page "https://github.com/holans/ST-COS")
    (synopsis "Space-Time Change of Support")
    (description
     "Spatio-temporal change of support (STCOS) methods are designed for statistical
inference on geographic and time domains which differ from those on which the
data were observed.  In particular, a parsimonious class of STCOS models
supporting Gaussian outcomes was introduced by Bradley, Wikle, and Holan
<doi:10.1002/sta4.94>.  The stcos package contains tools which facilitate use of
STCOS models.")
    (license license:expat)))

(define-public r-stbl
  (package
    (name "r-stbl")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stbl" version))
       (sha256
        (base32 "0hlc73i0qkxixw8z27sc0fj0i613x248i4bwk2zdrsby4zfhbxvq"))))
    (properties `((upstream-name . "stbl")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs r-rlang r-glue r-cli))
    (home-page "https://github.com/jonthegeek/stbl")
    (synopsis "Stabilize Function Arguments")
    (description
     "This package provides a set of consistent, opinionated functions to quickly
check function arguments, coerce them to the desired configuration, or deliver
informative error messages when that is not possible.")
    (license license:expat)))

(define-public r-stb
  (package
    (name "r-stb")
    (version "0.6.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "STB" version))
       (sha256
        (base32 "12r59wj5nxllkif5047a7gw7li9xj71zybw7isgdf3y6iyv4f9pw"))))
    (properties `((upstream-name . "STB")))
    (build-system r-build-system)
    (propagated-inputs (list r-vca r-matrix))
    (home-page "https://cran.r-project.org/package=STB")
    (synopsis "Simultaneous Tolerance Bounds")
    (description
     "This package provides an implementation of simultaneous tolerance bounds (STB),
useful for checking whether a numeric vector fits to a hypothetical
null-distribution or not.  Furthermore, there are functions for computing STB
(bands, intervals) for random variates of linear mixed models fitted with
package VCA'.  All kinds of, possibly transformed (studentized, standardized,
Pearson-type transformed) random variates (residuals, random effects), can be
assessed employing STB-methodology.")
    (license license:gpl3+)))

(define-public r-statvisual
  (package
    (name "r-statvisual")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statVisual" version))
       (sha256
        (base32 "1cc9p9g8lzn0lgh793r977yhqbk89l74m443gnznjlfml69bfb21"))))
    (properties `((upstream-name . "statVisual")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyverse
                             r-tibble
                             r-rpart-plot
                             r-rmarkdown
                             r-reshape2
                             r-rcolorbrewer
                             r-randomforest
                             r-proc
                             r-pheatmap
                             r-magrittr
                             r-limma
                             r-knitr
                             r-gridextra
                             r-glmnet
                             r-ggrepel
                             r-ggplot2
                             r-ggfortify
                             r-ggdendro
                             r-ggally
                             r-gbm
                             r-forestplot
                             r-factoextra
                             r-dplyr
                             r-biobase))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=statVisual")
    (synopsis "Statistical Visualization Tools")
    (description
     "Visualization functions in the applications of translational medicine (TM) and
biomarker (BM) development to compare groups by statistically visualizing data
and/or results of analyses, such as visualizing data by displaying in one figure
different groups histograms, boxplots, densities, scatter plots, error-bar
plots, or trajectory plots, by displaying scatter plots of top principal
components or dendrograms with data points colored based on group information,
or visualizing volcano plots to check the results of whole genome analyses for
gene differential expression.")
    (license license:gpl2+)))

(define-public r-statswalesr
  (package
    (name "r-statswalesr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statswalesr" version))
       (sha256
        (base32 "03h0gq8ag51my5z8mw78rhbkksfpa7af8my6lb468wrcfv69k0gl"))))
    (properties `((upstream-name . "statswalesr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-jsonlite r-httr r-dplyr r-curl))
    (home-page "https://cran.r-project.org/package=statswalesr")
    (synopsis "Easily Extract Data from 'StatsWales'")
    (description
     "Download data from @code{StatsWales} into R. Removes the need for the user to
write their own loops when parsing data from the @code{StatsWales} API. Provides
functions for datasets (<http://open.statswales.gov.wales/en-gb/dataset>) and
metadata (<http://open.statswales.gov.wales/en-gb/discover/metadata>) endpoints.")
    (license license:expat)))

(define-public r-statsr
  (package
    (name "r-statsr")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statsr" version))
       (sha256
        (base32 "138yjifz8q3dqcm3wbzn4sxdydwnqvwkz0h20r8ls3pijqh6fc2y"))))
    (properties `((upstream-name . "statsr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-shiny
                             r-rmarkdown
                             r-knitr
                             r-gridextra
                             r-ggplot2
                             r-dplyr
                             r-cubature
                             r-broom
                             r-bayesfactor))
    (home-page "https://github.com/StatsWithR/statsr")
    (synopsis
     "Companion Software for the Coursera Statistics with R Specialization")
    (description
     "Data and functions to support Bayesian and frequentist inference and decision
making for the Coursera Specialization \"Statistics with R\".  See
<https://github.com/@code{StatsWithR/statsr>} for more information.")
    (license license:expat)))

(define-public r-statsomat
  (package
    (name "r-statsomat")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Statsomat" version))
       (sha256
        (base32 "1lfgbbf1pcfbih5nfb6rbiq2h0hlxg1fzmcf4330yc48r998q4x6"))))
    (properties `((upstream-name . "Statsomat")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinydisconnect
                             r-shiny
                             r-rmarkdown
                             r-reshape2
                             r-readr
                             r-knitr
                             r-kableextra
                             r-gridextra
                             r-ggplot2
                             r-energy
                             r-ddoutlier
                             r-data-table
                             r-corrplot
                             r-car))
    (home-page "https://statsomat.com")
    (synopsis
     "Shiny Apps for Automated Data Analysis and Automated Interpretation")
    (description
     "Shiny apps for automated data analysis, annotated outputs and human-readable
interpretation in natural language.  Designed especially for learners and
applied researchers.  Currently available methods: EDA, EDA with Python,
Correlation Analysis, Principal Components Analysis, Confirmatory Factor
Analysis.")
    (license (license:fsdg-compatible "AGPL"))))

(define-public r-statsearchanalyticsr
  (package
    (name "r-statsearchanalyticsr")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statsearchanalyticsr" version))
       (sha256
        (base32 "0m2zv5sgv7w9yk6q7rwx8x2zy6xx5w0d6c9vynddr08pn4mdqjhs"))))
    (properties `((upstream-name . "statsearchanalyticsr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-purrr
                             r-lubridate
                             r-jsonlite
                             r-httr
                             r-glue))
    (home-page "https://searchdiscovery.github.io/statsearchanalyticsr/")
    (synopsis "An Interface for the 'STAT Search Analytics' 'API'")
    (description
     "Pull data from the STAT Search Analytics API
<https://help.getstat.com/knowledgebase/api-services/>.  It was developed by the
Search Discovery team to help analyze keyword ranking data.")
    (license license:expat)))

(define-public r-stats4teaching
  (package
    (name "r-stats4teaching")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stats4teaching" version))
       (sha256
        (base32 "1i2v97k6c75096hp23zpffmdbajdzzkqp508p9dvw841vpx2wxdn"))))
    (properties `((upstream-name . "stats4teaching")))
    (build-system r-build-system)
    (propagated-inputs (list r-rstatix
                             r-pwr
                             r-psych
                             r-nortest
                             r-mvn
                             r-mass
                             r-knitr
                             r-clustergeneration
                             r-car
                             r-asbio))
    (home-page "https://cran.r-project.org/package=stats4teaching")
    (synopsis "Simulate Pedagogical Statistical Data")
    (description
     "Univariate and multivariate normal data simulation.  They also supply a brief
summary of the analysis for each experiment/design: - Independent samples. -
One-way and two-way Anova. - Paired samples (T-Test & Regression). - Repeated
measures (Anova & Multiple Regression). - Clinical Assay.")
    (license license:gpl3)))

(define-public r-stats19
  (package
    (name "r-stats19")
    (version "3.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stats19" version))
       (sha256
        (base32 "1af7a2p1n1rxgx25qpkw27qmhy0gyiq2549vibdp8gfdybsf38g6"))))
    (properties `((upstream-name . "stats19")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-readr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ropensci/stats19")
    (synopsis "Work with Open Road Traffic Casualty Data from Great Britain")
    (description
     "This package provides tools to help download, process and analyse the UK road
collision data collected using the STATS19 form.  The datasets are provided as
CSV files with detailed road safety information about the circumstances of car
crashes and other incidents on the roads resulting in casualties in Great
Britain from 1979 to present.  Tables are available on colissions with the
circumstances (e.g. speed limit of road), information about vehicles involved
(e.g. type of vehicle), and casualties (e.g. age).  The statistics relate only
to events on public roads that were reported to the police, and subsequently
recorded, using the STATS19 collision reporting form.  See the Department for
Transport website
<https://www.data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-safety-data>
for more information on these datasets.  The package is described in a paper in
the Journal of Open Source Software (Lovelace et al.  2019)
<doi:10.21105/joss.01181>.  See Gilardi et al. (2022) <doi:10.1111/rssa.12823>,
Vidal-Tortosa et al. (2021) <doi:10.1016/j.jth.2021.101291>, and Tait et al.
(2023) <doi:10.1016/j.aap.2022.106895> for examples of how the data can be used
for methodological and empirical road safety research.")
    (license license:gpl3)))

(define-public r-statrank
  (package
    (name "r-statrank")
    (version "0.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StatRank" version))
       (sha256
        (base32 "14d8v3bp8vgksi6q0mxajwd9s8zi6lns3qwi1vcr5xp9rjp4n6iy"))))
    (properties `((upstream-name . "StatRank")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncdist r-plyr r-ggplot2))
    (home-page "https://cran.r-project.org/package=StatRank")
    (synopsis
     "Statistical Rank Aggregation: Inference, Evaluation, and Visualization")
    (description
     "This package provides a set of methods to implement Generalized Method of
Moments and Maximal Likelihood methods for Random Utility Models.  These methods
are meant to provide inference on rank comparison data.  These methods accept
full, partial, and pairwise rankings, and provides methods to break down full or
partial rankings into their pairwise components.  Please see Generalized
Method-of-Moments for Rank Aggregation from NIPS 2013 for a description of some
of our methods.")
    (license license:gpl2+)))

(define-public r-statquotes
  (package
    (name "r-statquotes")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statquotes" version))
       (sha256
        (base32 "0y805alr98zz306jjnikggdd02c3yrp5izzwhh457bsb85i22yri"))))
    (properties `((upstream-name . "statquotes")))
    (build-system r-build-system)
    (propagated-inputs (list r-wordcloud r-tidytext r-stringr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/friendly/statquotes/")
    (synopsis "Quotes on Statistics, Data Visualization and Science")
    (description
     "Generates a random quotation from a database of quotes on topics in statistics,
data visualization and science.  Other functions allow searching the quotes
database by key term tags, or authors or creating a word cloud.  The output is
designed to be suitable for use at the console, in Rmarkdown and @code{LaTeX}.")
    (license license:gpl2+)))

(define-public r-statpsych
  (package
    (name "r-statpsych")
    (version "1.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statpsych" version))
       (sha256
        (base32 "1kxgwzg6qwkrlih0rwllmm31c9yyxp6w051wslb4q695piq4las2"))))
    (properties `((upstream-name . "statpsych")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-mnonr r-mathjaxr))
    (home-page "https://cran.r-project.org/package=statpsych")
    (synopsis "Statistical Methods for Psychologists")
    (description
     "This package implements confidence interval and sample size methods that are
especially useful in psychological research.  The methods can be applied in
1-group, 2-group, paired-samples, and multiple-group designs and to a variety of
parameters including means, medians, proportions, slopes, standardized mean
differences, standardized linear contrasts of means, plus several measures of
correlation and association.  The confidence intervals and sample size functions
are applicable to single parameters as well as differences, ratios, and linear
contrasts of parameters.  The sample size functions can be used to approximate
the sample size needed to estimate a parameter or function of parameters with
desired confidence interval precision or to perform a variety of hypothesis
tests (directional two-sided, equivalence, superiority, noninferiority) with
desired power.  For details see: Statistical Methods for Psychologists, Volumes
1 â 4, <https://dgbonett.sites.ucsc.edu/>.")
    (license license:gpl3)))

(define-public r-statprograms
  (package
    (name "r-statprograms")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statprograms" version))
       (sha256
        (base32 "0m7px7fmpz2zzlfdi7rbllcjvcjp8iy94f6fa1w4xinh7vz3y6vq"))))
    (properties `((upstream-name . "statprograms")))
    (build-system r-build-system)
    (home-page "http://brettklamer.com/work/statprograms/")
    (synopsis "Graduate Statistics Program Datasets")
    (description
     "This package provides a small collection of data on graduate statistics programs
from the United States.")
    (license license:expat)))

(define-public r-statpermeco
  (package
    (name "r-statpermeco")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StatPerMeCo" version))
       (sha256
        (base32 "1nm6brlj6h7jx8nqagrfk294ay3pfmkiizabwlb87qfnsfwchpk3"))))
    (properties `((upstream-name . "StatPerMeCo")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=StatPerMeCo")
    (synopsis
     "Statistical Performance Measures to Evaluate Covariance Matrix Estimates")
    (description
     "Statistical performance measures used in the econometric literature to evaluate
conditional covariance/correlation matrix estimates (MSE, MAE, Euclidean
distance, Frobenius distance, Stein distance, asymmetric loss function,
eigenvalue loss function and the loss function defined in Eq. (4.6) of Engle et
al. (2016) <doi:10.2139/ssrn.2814555>).  Additionally, compute Eq. (3.1) and
(4.2) of Li et al. (2016) <doi:10.1080/07350015.2015.1092975> to compare the
factor loading matrix.  The statistical performance measures implemented have
been previously used in, for instance, Laurent et al. (2012)
<doi:10.1002/jae.1248>, Amendola et al. (2015) <doi:10.1002/for.2322> and Becker
et al. (2015) <doi:10.1016/j.ijforecast.2013.11.007>.")
    (license license:gpl2+)))

(define-public r-statnipokladna
  (package
    (name "r-statnipokladna")
    (version "0.7.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statnipokladna" version))
       (sha256
        (base32 "0hdpyqq6qa01kvwa2ml8virm6h75xwlck2xx3madh9jkgijnysfa"))))
    (properties `((upstream-name . "statnipokladna")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-stringi
                             r-rlang
                             r-readr
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-lifecycle
                             r-jsonlite
                             r-httr
                             r-dplyr
                             r-curl
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/petrbouchal/statnipokladna")
    (synopsis "Use Data from the Czech Public Finance Database")
    (description
     "Get programmatic access to data from the Czech public budgeting and accounting
database, StÃ¡tnÃ­ pokladna <https://monitor.statnipokladna.cz/>.")
    (license license:expat)))

(define-public r-statnetweb
  (package
    (name "r-statnetweb")
    (version "0.5.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statnetWeb" version))
       (sha256
        (base32 "12qwx0gnrmb449rz5a2qhds6rparfpw8ak4n0cxk9dmy6d47903l"))))
    (properties `((upstream-name . "statnetWeb")))
    (build-system r-build-system)
    (propagated-inputs (list r-sna
                             r-shiny
                             r-rcolorbrewer
                             r-network
                             r-latticeextra
                             r-lattice
                             r-ergm))
    (home-page "https://statnet.org")
    (synopsis "Shiny App for Network Modeling with 'statnet'")
    (description
     "This package provides a graphical user interface for cross-sectional network
modeling with the statnet software suite <https://github.com/statnet>.")
    (license license:gpl3)))

(define-public r-statnet
  (package
    (name "r-statnet")
    (version "2019.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statnet" version))
       (sha256
        (base32 "17dj6vjnjyqrfhzjq1lys5b29j942kd15zffb69jidni3slf20q9"))))
    (properties `((upstream-name . "statnet")))
    (build-system r-build-system)
    (propagated-inputs (list r-tsna
                             r-tergm
                             r-statnet-common
                             r-sna
                             r-networkdynamic
                             r-network
                             r-ergm-count
                             r-ergm))
    (home-page "http://statnet.org")
    (synopsis "Software Tools for the Statistical Analysis of Network Data")
    (description
     "Statnet is a collection of packages for statistical network analysis that are
designed to work together because they share common data representations and API
design.  They provide an integrated set of tools for the representation,
visualization, analysis, and simulation of many different forms of network data.
 This package is designed to make it easy to install and load the key statnet
packages in a single step.  Learn more about statnet at
<http://www.statnet.org>.  Tutorials for many packages can be found at
<https://github.com/statnet/Workshops/wiki>.  For an introduction to functions
in this package, type help(package='statnet').")
    (license (license:fsdg-compatible "GPL-3 + file LICENSE"))))

(define-public r-statmatch
  (package
    (name "r-statmatch")
    (version "1.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StatMatch" version))
       (sha256
        (base32 "0kwmxrhmcd74gm6nnqqnqa3jy60050a113l5hspjix8q7s99ybw5"))))
    (properties `((upstream-name . "StatMatch")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey r-proxy r-lpsolve r-ggplot2 r-dplyr))
    (home-page "https://github.com/marcellodo/StatMatch")
    (synopsis "Statistical Matching or Data Fusion")
    (description
     "Integration of two data sources referred to the same target population which
share a number of variables.  Some functions can also be used to impute missing
values in data sets through hot deck imputation methods.  Methods to perform
statistical matching when dealing with data from complex sample surveys are
available too.")
    (license license:gpl2+)))

(define-public r-statisfactory
  (package
    (name "r-statisfactory")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statisfactory" version))
       (sha256
        (base32 "0i5irapzdmp8dfjjam504n47xbdmzqhy7y31ah6az3w9kr4bv7j0"))))
    (properties `((upstream-name . "statisfactory")))
    (build-system r-build-system)
    (propagated-inputs (list r-omnibus))
    (home-page "https://github.com/adamlilith/statisfactory")
    (synopsis "Statistical and Geometrical Tools")
    (description
     "This package provides a collection of statistical and geometrical tools
including the aligned rank transform (ART; Higgins et al.  1990
<doi:10.4148/2475-7772.1443>; Peterson 2002 <doi:10.22237/jmasm/1020255240>;
Wobbrock et al.  2011 <doi:10.1145/1978942.1978963>), 2-D histograms and
histograms with overlapping bins, a function for making all possible formulae
within a set of constraints, amongst others.")
    (license license:gpl3+)))

(define-public r-stationary
  (package
    (name "r-stationary")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stationaRy" version))
       (sha256
        (base32 "1620fdlybn3hr96yngnp802imr9b7n90835lyyd86r2wn6psdzvl"))))
    (properties `((upstream-name . "stationaRy")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-readr
                             r-progress
                             r-magrittr
                             r-lutz
                             r-lubridate
                             r-dplyr
                             r-downloader))
    (home-page "https://github.com/rich-iannone/stationaRy")
    (synopsis "Detailed Meteorological Data from Stations All Over the World")
    (description
     "Acquire hourly meteorological data from stations located all over the world.
There is a wealth of data available, with historic weather data accessible from
nearly 30,000 stations.  The available data is automatically downloaded from a
data repository and processed into a tibble for the exact range of years
requested.  A relative humidity approximation is provided using the
August-Roche-Magnus formula, which was adapted from Alduchov and Eskridge (1996)
<doi:10.1175%2F1520-0450%281996%29035%3C0601%3AIMFAOS%3E2.0.CO%3B2>.")
    (license license:expat)))

(define-public r-staticryptr
  (package
    (name "r-staticryptr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "staticryptR" version))
       (sha256
        (base32 "1ki3kpjvwbradgr3nip8cxgasxcys1qyryh0gq9kmx9f0dbql5y2"))))
    (properties `((upstream-name . "staticryptR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=staticryptR")
    (synopsis "Encrypt HTML Files Using 'staticrypt'")
    (description
     "This package provides a convenient interface to the staticrypt by Robin Moisson
<https://github.com/robinmoisson/staticrypt>---'Node.js package for adding a
password protection layer to static HTML pages.  This package can be integrated
into the post-render process of quarto documents to secure them with a password.")
    (license license:expat)))

(define-public r-statgraph
  (package
    (name "r-statgraph")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statGraph" version))
       (sha256
        (base32 "0342hxbf4irhkr7i4ggq63ac66i97y4fcixm1jvbwbdahss6f38y"))))
    (properties `((upstream-name . "statGraph")))
    (build-system r-build-system)
    (propagated-inputs (list r-rarpack
                             r-mvtnorm
                             r-mass
                             r-igraph
                             r-foreach
                             r-doparallel
                             r-cluster))
    (home-page "https://www.ime.usp.br/~fujita/software.html")
    (synopsis "Statistical Methods for Graphs")
    (description
     "This package contains statistical methods to analyze graphs, such as graph
parameter estimation, model selection based on the Graph Information Criterion,
statistical tests to discriminate two or more populations of graphs, correlation
between graphs, and clustering of graphs.  References: Takahashi et al. (2012)
<doi:10.1371/journal.pone.0049949>, Fujita et al. (2017)
<doi:10.3389/fnins.2017.00066>, Fujita et al. (2017)
<doi:10.1016/j.csda.2016.11.016>, Fujita et al. (2019)
<doi:10.1093/comnet/cnz028>.")
    (license license:gpl3+)))

(define-public r-statgensta
  (package
    (name "r-statgensta")
    (version "1.0.14")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statgenSTA" version))
       (sha256
        (base32 "0mj314ikqrhjjs2dcri0da8sbmar7acnh2rnpx99ylk32i6l6lrl"))))
    (properties `((upstream-name . "statgenSTA")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-xtable
                             r-spats
                             r-scales
                             r-rlang
                             r-qtl
                             r-maps
                             r-mapproj
                             r-lme4
                             r-knitr
                             r-gridextra
                             r-ggrepel
                             r-ggplot2
                             r-emmeans))
    (native-inputs (list r-knitr))
    (home-page "https://biometris.github.io/statgenSTA/index.html")
    (synopsis "Single Trial Analysis (STA) of Field Trials")
    (description
     "Phenotypic analysis of field trials using mixed models with and without spatial
components.  One of a series of statistical genetic packages for streamlining
the analysis of typical plant breeding experiments developed by Biometris.  Some
functions have been created to be used in conjunction with the R package asreml
for the ASReml software, which can be obtained upon purchase from VSN
international (<https://vsni.co.uk/software/asreml-r/>).")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-statgenqtlxt
  (package
    (name "r-statgenqtlxt")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statgenQTLxT" version))
       (sha256
        (base32 "1lq8v49zf9l6gaifdwc0qpx8kknnhqw2jg19v2vr7da1xqd7wfdi"))))
    (properties `((upstream-name . "statgenQTLxT")))
    (build-system r-build-system)
    (propagated-inputs (list r-statgengwas
                             r-sommer
                             r-rcpparmadillo
                             r-rcpp
                             r-foreach
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=statgenQTLxT")
    (synopsis "Multi-Trait and Multi-Trial Genome Wide Association Studies")
    (description
     "Fast multi-trait and multi-trail Genome Wide Association Studies (GWAS)
following the method described in Zhou and Stephens. (2014),
<doi:10.1038/nmeth.2848>.  One of a series of statistical genetic packages for
streamlining the analysis of typical plant breeding experiments developed by
Biometris.")
    (license license:gpl3)))

(define-public r-statgenmpp
  (package
    (name "r-statgenmpp")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statgenMPP" version))
       (sha256
        (base32 "1qpf9gic9671hr6zyj56sqwx9wl51qr6gj84rfssdla718h766im"))))
    (properties `((upstream-name . "statgenMPP")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringi
                             r-statgenibd
                             r-statgengwas
                             r-spam
                             r-scales
                             r-matrix
                             r-lmmsolver
                             r-gridextra
                             r-ggplot2
                             r-foreach
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=statgenMPP")
    (synopsis "QTL Mapping for Multi Parent Populations")
    (description
     "For Multi Parent Populations (MPP) Identity By Descend (IBD) probabilities are
computed using Hidden Markov Models.  These probabilities are then used in a
mixed model approach for QTL Mapping as described in Li et al.
(<doi:10.1007/s00122-021-03919-7>).")
    (license license:gpl3+)))

(define-public r-statgenibd
  (package
    (name "r-statgenibd")
    (version "1.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statgenIBD" version))
       (sha256
        (base32 "1dw0riwycd59hcidnxczy61xib8if8m2hv6dni2qd6n2i99vfbpc"))))
    (properties `((upstream-name . "statgenIBD")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringi
                             r-statgengwas
                             r-rlang
                             r-rcpparmadillo
                             r-rcpp
                             r-matrix
                             r-ggplot2
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://biometris.github.io/statgenIBD/index.html")
    (synopsis "Calculation of IBD Probabilities")
    (description
     "For biparental, three and four-way crosses Identity by Descent (IBD)
probabilities can be calculated using Hidden Markov Models and inheritance
vectors following Lander and Green (<https://www.jstor.org/stable/29713>) and
Huang (<doi:10.1073/pnas.1100465108>).  One of a series of statistical genetic
packages for streamlining the analysis of typical plant breeding experiments
developed by Biometris.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-statgenhtp
  (package
    (name "r-statgenhtp")
    (version "1.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statgenHTP" version))
       (sha256
        (base32 "0ia3xw8cnh4hyx31w2hzgqx2mp9fipk59rq6pvc8n33r3byzph9p"))))
    (properties `((upstream-name . "statgenHTP")))
    (build-system r-build-system)
    (propagated-inputs (list r-spats
                             r-spam
                             r-scales
                             r-rlang
                             r-reshape2
                             r-matrix
                             r-lubridate
                             r-locfit
                             r-lmmsolver
                             r-gridextra
                             r-ggplot2
                             r-ggnewscale
                             r-ggforce
                             r-factoextra
                             r-animation))
    (native-inputs (list r-knitr))
    (home-page "https://biometris.github.io/statgenHTP/index.html")
    (synopsis "High Throughput Phenotyping (HTP) Data Analysis")
    (description
     "Phenotypic analysis of data coming from high throughput phenotyping (HTP)
platforms, including different types of outlier detection, spatial analysis, and
parameter estimation.  The package is being developed within the EPPN2020
project (<https://eppn2020.plant-phenotyping.eu/>).  Some functions have been
created to be used in conjunction with the R package asreml for the ASReml
software, which can be obtained upon purchase from VSN international
(<https://vsni.co.uk/software/asreml-r/>).")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-statgengxe
  (package
    (name "r-statgengxe")
    (version "1.0.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statgenGxE" version))
       (sha256
        (base32 "0p2z8qf0gjznkcp99dzrdjkbv9433h62iwcj3h01dpx2f6v0f6zb"))))
    (properties `((upstream-name . "statgenGxE")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-xtable
                             r-statgensta
                             r-rlang
                             r-lme4
                             r-knitr
                             r-gridextra
                             r-ggplot2
                             r-emmeans))
    (native-inputs (list r-knitr))
    (home-page "https://biometris.github.io/statgenGxE/index.html")
    (synopsis "Genotype by Environment (GxE) Analysis")
    (description
     "Analysis of multi environment data of plant breeding experiments following the
analyses described in Malosetti, Ribaut, and van Eeuwijk (2013),
<doi:10.3389/fphys.2013.00044>.  One of a series of statistical genetic packages
for streamlining the analysis of typical plant breeding experiments developed by
Biometris.  Some functions have been created to be used in conjunction with the
R package asreml for the ASReml software, which can be obtained upon purchase
from VSN international (<https://vsni.co.uk/software/asreml-r/>).")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-statgengwas
  (package
    (name "r-statgengwas")
    (version "1.0.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statgenGWAS" version))
       (sha256
        (base32 "0isjzb83vwml654b1azgvnwiijpihdk24b3jamls976is359qvrv"))))
    (properties `((upstream-name . "statgenGWAS")))
    (build-system r-build-system)
    (propagated-inputs (list r-sommer r-rcpparmadillo r-rcpp r-ggplot2
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://biometris.github.io/statgenGWAS/index.html")
    (synopsis "Genome Wide Association Studies")
    (description
     "Fast single trait Genome Wide Association Studies (GWAS) following the method
described in Kang et al. (2010), <doi:10.1038/ng.548>.  One of a series of
statistical genetic packages for streamlining the analysis of typical plant
breeding experiments developed by Biometris.")
    (license license:gpl3)))

(define-public r-statespacer
  (package
    (name "r-statespacer")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statespacer" version))
       (sha256
        (base32 "18bir894yrbff5h5pbqr342xckb78zzb24x2yrv9ffhrl5dvyk5d"))))
    (properties `((upstream-name . "statespacer")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-rcpparmadillo r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://DylanB95.github.io/statespacer/")
    (synopsis "State Space Modelling in 'R'")
    (description
     "This package provides a tool that makes estimating models in state space form a
breeze.  See \"Time Series Analysis by State Space Methods\" by Durbin and Koopman
(2012, ISBN: 978-0-19-964117-8) for details about the algorithms implemented.")
    (license license:expat)))

(define-public r-states
  (package
    (name "r-states")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "states" version))
       (sha256
        (base32 "1ffaj7rzqnwzki4hr5f90hz3racaigmk8qbzanxbbbfsy881avmr"))))
    (properties `((upstream-name . "states")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-lifecycle r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/andybega/states")
    (synopsis "Create Panels of Independent States")
    (description
     "Create panel data consisting of independent states from 1816 to the present.
The package includes the Gleditsch & Ward (G&W) and Correlates of War (COW)
lists of independent states, as well as helper functions for working with state
panel data and standardizing other data sources to create
country-year/month/etc.  data.")
    (license license:expat)))

(define-public r-statelevelforest
  (package
    (name "r-statelevelforest")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StateLevelForest" version))
       (sha256
        (base32 "1y4y8idp6vq1108bs95h1xd2r8gja3ap56r31hnbi3ckyrhw46al"))))
    (properties `((upstream-name . "StateLevelForest")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=StateLevelForest")
    (synopsis "Historical State-Level Forest Cover Data in the United States")
    (description
     "This package provides a unique dataset of historical forest cover across all
states in the United States, spanning from 1907 to 2017, along with 1630 as a
reference year.  This dataset is important for understanding environmental
changes and land use trends over time.  It includes functionality for easy
access of the data.")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-statebins
  (package
    (name "r-statebins")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statebins" version))
       (sha256
        (base32 "1v67da939qg672aqni2i37w70pgasgx6r55b4fjjc3zzviq8gipx"))))
    (properties `((upstream-name . "statebins")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-ggplot2))
    (home-page "https://gitlab.com/hrbrmstr/statebins")
    (synopsis "Create United States Uniform Cartogram Heatmaps")
    (description
     "The cartogram heatmaps generated by the included methods are an alternative to
choropleth maps for the United States and are based on work by the Washington
Post graphics department in their report on \"The states most threatened by
trade\"
(<http://www.washingtonpost.com/wp-srv/special/business/states-most-threatened-by-trade/>).
\"State bins\" preserve as much of the geographic placement of the states as
possible but have the look and feel of a traditional heatmap.  Functions are
provided that allow for use of a binned, discrete scale, a continuous scale or
manually specified colors depending on what is needed for the underlying data.")
    (license license:expat)))

(define-public r-statdataml
  (package
    (name "r-statdataml")
    (version "1.0-27")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StatDataML" version))
       (sha256
        (base32 "168bydradzprvvyfchzq4g3nc6f8kpgd5165i02nhj1irwxa6jh8"))))
    (properties `((upstream-name . "StatDataML")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml))
    (home-page "https://cran.r-project.org/package=StatDataML")
    (synopsis "Read and Write StatDataML Files")
    (description
     "Support for reading and writing files in @code{StatDataML---an} XML-based data
exchange format.")
    (license license:gpl2)))

(define-public r-statda
  (package
    (name "r-statda")
    (version "1.7.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StatDA" version))
       (sha256
        (base32 "0jp0qd96z2z2w7lqkyy0csz45fk0774wjf57idqbla3n21jbn4q9"))))
    (properties `((upstream-name . "StatDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-sp
                             r-sgeostat
                             r-robustbase
                             r-rgl
                             r-mgcv
                             r-mba
                             r-mass
                             r-geor
                             r-e1071
                             r-cluster))
    (home-page "http://cstat.tuwien.ac.at/filz/")
    (synopsis "Statistical Analysis for Environmental Data")
    (description
     "Statistical analysis methods for environmental data are implemented.  There is a
particular focus on robust methods, and on methods for compositional data.  In
addition, larger data sets from geochemistry are provided.  The statistical
methods are described in Reimann, Filzmoser, Garrett, Dutter (2008,
ISBN:978-0-470-98581-6).")
    (license license:gpl3+)))

(define-public r-statconfr
  (package
    (name "r-statconfr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statConfR" version))
       (sha256
        (base32 "0yzg2259r8w8b1kkrrj6494p9iyj7mk0zvxbwmgi8hbclrfqyw2n"))))
    (properties `((upstream-name . "statConfR")))
    (build-system r-build-system)
    (propagated-inputs (list r-plyr))
    (home-page "https://github.com/ManuelRausch/StatConfR")
    (synopsis "Models of Decision Confidence and Metacognition")
    (description
     "This package provides fitting functions and other tools for decision confidence
and metacognition researchers, including meta-d'/d', often considered to be the
gold standard to measure metacognitive efficiency.  Also allows to fit several
static models of decision making and confidence to test the assumptions
underlying meta-d'/d and which may serve as an alternative when the assumptions
of meta-d'/d do not hold.  See also Rausch et al. (2023)
<doi:10.1037/met0000634>.")
    (license license:gpl3+)))

(define-public r-statcomp
  (package
    (name "r-statcomp")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statcomp" version))
       (sha256
        (base32 "0793k14rxiwq98zlrbsmbbkjn5z2ny5c90pia7cx3s77a3wdc96l"))))
    (properties `((upstream-name . "statcomp")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-matrix))
    (home-page "https://cran.r-project.org/package=statcomp")
    (synopsis
     "Statistical Complexity and Information Measures for Time Series Analysis")
    (description
     "An implementation of local and global statistical complexity measures (aka
Information Theory Quantifiers, ITQ) for time series analysis based on ordinal
statistics (Bandt and Pompe (2002) <DOI:10.1103/@code{PhysRevLett.88.174102>}).
Several distance measures that operate on ordinal pattern distributions,
auxiliary functions for ordinal pattern analysis, and generating functions for
stochastic and deterministic-chaotic processes for ITQ testing are provided.")
    (license license:gpl2)))

(define-public r-statcodelists
  (package
    (name "r-statcodelists")
    (version "0.9.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statcodelists" version))
       (sha256
        (base32 "0hn7fqsghfnly325b58m5q0ba9hvi1rc0lb4dwp2xm5lynjmsbs9"))))
    (properties `((upstream-name . "statcodelists")))
    (build-system r-build-system)
    (home-page "https://statcodelists.dataobservatory.eu")
    (synopsis "Use Standardized Statistical Codelists")
    (description
     "The goal of statcodelists is to promote the reuse and exchange of statistical
information and related metadata with making the internationally standardized
SDMX code lists available for the R user.  SDMX has been published as an ISO
International Standard (ISO 17369).  The metadata definitions, including the
codelists are updated regularly according to the standard.  The authoritative
version of the code lists made available in this package is
<https://sdmx.org/?page_id=3215/>.")
    (license license:cc0)))

(define-public r-statcanr
  (package
    (name "r-statcanr")
    (version "0.2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statcanR" version))
       (sha256
        (base32 "1hnf3zjlpfp86lii9sz8m2dvzbj9bs320ffdr81q2jsla71xybaa"))))
    (properties `((upstream-name . "statcanR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-reshape2
                             r-readr
                             r-qs
                             r-qpdf
                             r-httr
                             r-ggplot2
                             r-dt
                             r-dplyr
                             r-data-table
                             r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/warint/statcanR/")
    (synopsis "Client for Statistics Canada's Open Economic Data")
    (description
     "An easy connection with R to Statistics Canada's Web Data Service.  Open
economic data (formerly known as CANSIM tables, now identified by Product IDs
(PID)) are accessible as a data frame, directly in the user's R environment.
Warin, Le Duc (2019) <doi:10.6084/m9.figshare.10544735>.")
    (license license:expat)))

(define-public r-statbasics
  (package
    (name "r-statbasics")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statBasics" version))
       (sha256
        (base32 "1hhwg2190bndjgmxmv37ihqdg665s9zn1h1smwshs59wk0r7bzjx"))))
    (properties `((upstream-name . "statBasics")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-stringr))
    (home-page "https://cran.r-project.org/package=statBasics")
    (synopsis "Basic Functions to Statistical Methods Course")
    (description
     "Basic statistical methods with some modifications for the course Statistical
Methods at Federal University of Bahia (Brazil).  All methods in this packages
are explained in the text book of Montgomery and Runger (2010) <ISBN:
978-1-119-74635-5>.")
    (license license:expat)))

(define-public r-statar
  (package
    (name "r-statar")
    (version "0.7.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "statar" version))
       (sha256
        (base32 "0fjlmzndcm88dlqf6kmd1ssy0ll2g7vap6cai954lh37zbbbb674"))))
    (properties `((upstream-name . "statar")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-stringr
                             r-rlang
                             r-matrixstats
                             r-lazyeval
                             r-ggplot2
                             r-dplyr
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/matthieugomez/statar")
    (synopsis "Tools Inspired by 'Stata' to Manipulate Tabular Data")
    (description
     "This package provides a set of tools inspired by Stata to explore data.frames
('summarize', tabulate', xtile', pctile', binscatter', elapsed quarters/month,
lead/lag).")
    (license license:gpl2)))

(define-public r-statamarkdown
  (package
    (name "r-statamarkdown")
    (version "0.9.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Statamarkdown" version))
       (sha256
        (base32 "1ir2qh492q0rn6rwnmvj2cxqsssmsd5hm9vlyg0r7dk22grh19z0"))))
    (properties `((upstream-name . "Statamarkdown")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-xfun r-knitr))
    (home-page "https://cran.r-project.org/package=Statamarkdown")
    (synopsis "'Stata' Markdown")
    (description "Settings and functions to extend the knitr Stata engine.")
    (license license:expat)))

(define-public r-stat2data
  (package
    (name "r-stat2data")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Stat2Data" version))
       (sha256
        (base32 "1fpp3b4k7x915a9wkpyj4dvvqp0wz7c3lpbh154vrxrdsr712z0k"))))
    (properties `((upstream-name . "Stat2Data")))
    (build-system r-build-system)
    (home-page "https://github.com/statmanrobin/Stat2Data")
    (synopsis "Datasets for Stat2")
    (description
     "Datasets for the textbook Stat2: Modeling with Regression and ANOVA (second
edition).  The package also includes data for the first edition, Stat2: Building
Models for a World of Data and a few functions for plotting diagnostics.")
    (license license:gpl3)))

(define-public r-stat2
  (package
    (name "r-stat2")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "STAT2" version))
       (sha256
        (base32 "09i6vzp5zmi84kdwafb5cbqvja32mdpj8bnp8gnc2wyqwy3a8cqv"))))
    (properties `((upstream-name . "STAT2")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny
                             r-rpivottable
                             r-rmarkdown
                             r-psycho
                             r-psych
                             r-hmisc
                             r-dplyr
                             r-corrgram))
    (home-page "https://cran.r-project.org/package=STAT2")
    (synopsis
     "Interactive Document for Working with Basic Statistical Analysis")
    (description
     "An interactive document on the topic of basic statistical analysis using
rmarkdown and shiny packages.  Runtime examples are provided in the package
function as well as at
<https://jarvisatharva.shinyapps.io/@code{StatisticsPrimer/>}.")
    (license license:gpl2)))

(define-public r-stat
  (package
    (name "r-stat")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "STAT" version))
       (sha256
        (base32 "0x0qpnlcb52k3x8jwiljsamx81hdcjagfpapv63z054bvig0jvgv"))))
    (properties `((upstream-name . "STAT")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny
                             r-rpivottable
                             r-rmarkdown
                             r-psycho
                             r-psych
                             r-hmisc
                             r-dplyr
                             r-corrgram))
    (home-page "https://cran.r-project.org/package=STAT")
    (synopsis
     "Interactive Document for Working with Basic Statistical Analysis")
    (description
     "An interactive document on the topic of basic statistical analysis using
rmarkdown and shiny packages.  Runtime examples are provided in the package
function as well as at
<https://jarvisatharva.shinyapps.io/@code{StatisticsPrimer/>}.")
    (license license:gpl2)))

(define-public r-starwarsdb
  (package
    (name "r-starwarsdb")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "starwarsdb" version))
       (sha256
        (base32 "00n9qqw4lbfk698qgvw8i42sy3dv5i5fiiqma3zks4rr9yspkldq"))))
    (properties `((upstream-name . "starwarsdb")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-magrittr r-duckdb r-dbi))
    (home-page "https://github.com/gadenbuie/starwarsdb")
    (synopsis
     "Relational Data from the 'Star Wars' API for Learning and Teaching")
    (description
     "This package provides data about the Star Wars movie franchise in a set of
relational tables or as a complete @code{DuckDB} database.  All data was
collected from the open source Star Wars API <https://swapi.dev/>.")
    (license license:expat)))

(define-public r-starvz
  (package
    (name "r-starvz")
    (version "0.8.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "starvz" version))
       (sha256
        (base32 "0rgrbnsn1cx6255scqyjrywbq2bn3mpn7aq60p31k6f80dd850lg"))))
    (properties `((upstream-name . "starvz")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-zoo
                             r-yaml
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-readr
                             r-rcpp
                             r-rcolorbrewer
                             r-purrr
                             r-patchwork
                             r-magrittr
                             r-lpsolve
                             r-gtools
                             r-ggplot2
                             r-dplyr
                             r-data-tree
                             r-bh))
    (home-page "https://github.com/schnorr/starvz")
    (synopsis "R-Based Visualization Techniques for Task-Based Applications")
    (description
     "Performance analysis workflow that combines the power of the R language (and the
tidyverse realm) and many auxiliary tools to provide a consistent, flexible,
extensible, fast, and versatile framework for the performance analysis of
task-based applications that run on top of the @code{StarPU} runtime (with its
MPI (Message Passing Interface) layer for multi-node support).  Its goal is to
provide a fruitful prototypical environment to conduct performance analysis
hypothesis-checking for task-based applications that run on heterogeneous
(multi-GPU, multi-core) multi-node HPC (High-performance computing) platforms.")
    (license license:gpl3)))

(define-public r-starvars
  (package
    (name "r-starvars")
    (version "1.1.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "starvars" version))
       (sha256
        (base32 "1mq31g05ahpsmda2zrmkg1dlaqqk4c9hm9fjzhgrsxlg4j3klbbf"))))
    (properties `((upstream-name . "starvars")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xts
                             r-vars
                             r-quantmod
                             r-optimparallel
                             r-matrixcalc
                             r-mass
                             r-lessr
                             r-ks
                             r-foreach
                             r-dosnow))
    (home-page "https://github.com/andbucci/starvars")
    (synopsis
     "Vector Logistic Smooth Transition Models Estimation and Prediction")
    (description
     "Allows the user to estimate a vector logistic smooth transition autoregressive
model via maximum log-likelihood or nonlinear least squares.  It further permits
to test for linearity in the multivariate framework against a vector logistic
smooth transition autoregressive model with a single transition variable.  The
estimation method is discussed in Terasvirta and Yang (2014,
<doi:10.1108/S0731-9053(2013)0000031008>).  Also, realized covariances can be
constructed from stock market prices or returns, as explained in Andersen et al.
(2001, <doi:10.1016/S0304-405X(01)00055-1>).")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-startupmsg
  (package
    (name "r-startupmsg")
    (version "0.9.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "startupmsg" version))
       (sha256
        (base32 "17h57636d0c1wcidw5ivqqf4wpyy402i24rj0vl5yszj99szgy29"))))
    (properties `((upstream-name . "startupmsg")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=startupmsg")
    (synopsis "Utilities for Start-Up Messages")
    (description
     "This package provides utilities to create or suppress start-up messages.")
    (license license:lgpl3)))

(define-public r-startup
  (package
    (name "r-startup")
    (version "0.22.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "startup" version))
       (sha256
        (base32 "04hifi5cck1vzhacmy9kg7k1f278lvvsrj6p0d1n61fiiv3fqscm"))))
    (properties `((upstream-name . "startup")))
    (build-system r-build-system)
    (home-page "https://henrikbengtsson.github.io/startup/")
    (synopsis "Friendly R Startup Configuration")
    (description
     "Adds support for R startup configuration via .Renviron.d and .Rprofile.d
directories in addition to .Renviron and .Rprofile files.  This makes it
possible to keep private / secret environment variables separate from other
environment variables.  It also makes it easier to share specific startup
settings by simply copying a file to a directory.")
    (license license:lgpl2.1+)))

(define-public r-starts
  (package
    (name "r-starts")
    (version "1.3-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "STARTS" version))
       (sha256
        (base32 "17zyi8hdaxaifk9nan5fcl76b0r8gwgxam6ksnc246kw8lay16a2"))))
    (properties `((upstream-name . "STARTS")))
    (build-system r-build-system)
    (propagated-inputs (list r-sirt r-rcpparmadillo r-rcpp r-lam r-cdm))
    (home-page "https://github.com/alexanderrobitzsch/STARTS")
    (synopsis "Functions for the STARTS Model")
    (description
     "This package contains functions for estimating the STARTS model of Kenny and
Zautra (1995, 2001) <DOI:10.1037/0022-006X.63.1.52>, <DOI:10.1037/10409-008>.
Penalized maximum likelihood estimation and Markov Chain Monte Carlo estimation
are also provided, see Luedtke, Robitzsch and Wagner (2018)
<DOI:10.1037/met0000155>.")
    (license license:gpl2+)))

(define-public r-startr
  (package
    (name "r-startr")
    (version "2.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "startR" version))
       (sha256
        (base32 "1p1yrwcmzzln5qjpsdsz3a8a3d6dlq5fvibx0zq3njmvbffwsh24"))))
    (properties `((upstream-name . "startR")))
    (build-system r-build-system)
    (propagated-inputs (list r-s2dv
                             r-pcict
                             r-multiapply
                             r-future
                             r-easyncdf
                             r-climprojdiags
                             r-bigmemory
                             r-abind))
    (home-page "https://earth.bsc.es/gitlab/es/startR/")
    (synopsis "Automatically Retrieve Multidimensional Distributed Data Sets")
    (description
     "Tool to automatically fetch, transform and arrange subsets of multi- dimensional
data sets (collections of files) stored in local and/or remote file systems or
servers, using multicore capabilities where possible.  The tool provides an
interface to perceive a collection of data sets as a single large
multidimensional data array, and enables the user to request for automatic
retrieval, processing and arrangement of subsets of the large array.  Wrapper
functions to add support for custom file formats can be plugged in/out, making
the tool suitable for any research field where large multidimensional data sets
are involved.")
    (license license:gpl3)))

(define-public r-starter
  (package
    (name "r-starter")
    (version "0.1.16")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "starter" version))
       (sha256
        (base32 "12yxsvqharj15ynh1m4p37bvkxpp9xfzxaqrcy680jjyzd7aiwpf"))))
    (properties `((upstream-name . "starter")))
    (build-system r-build-system)
    (propagated-inputs (list r-rstudioapi
                             r-rprojroot
                             r-rlang
                             r-renv
                             r-r-utils
                             r-glue
                             r-gert
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ddsjoberg/starter")
    (synopsis "Starter Kit for New Projects")
    (description
     "Get started with new projects by dropping a skeleton of a new project into a new
or existing directory, initialise git repositories, and create reproducible
environments with the renv package.  The package allows for dynamically named
files, folders, file content, as well as the functionality to drop individual
template files into existing projects.")
    (license license:agpl3+)))

(define-public r-startdesign
  (package
    (name "r-startdesign")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "STARTdesign" version))
       (sha256
        (base32 "1q61l3v8ishzax3ppy0pz4ky4l46472l6j36hjlsa8cl01ik1zjf"))))
    (properties `((upstream-name . "STARTdesign")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=STARTdesign")
    (synopsis
     "Single to Double Arm Transition Design for Phase II Clinical Trials")
    (description
     "The package is used for calibrating the design parameters for single-to-double
arm transition design proposed by Shi and Yin (2017).  The calibration is
performed via numerical enumeration to find the optimal design that satisfies
the constraints on the type I and II error rates.")
    (license license:gpl2+)))

(define-public r-starstileserver
  (package
    (name "r-starstileserver")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "starsTileServer" version))
       (sha256
        (base32 "04z3xaijhgn5g10vvx19lya6v17ypcq00w4jlgkap3fafw61pqhn"))))
    (properties `((upstream-name . "starsTileServer")))
    (build-system r-build-system)
    (propagated-inputs (list r-units
                             r-stars
                             r-sf
                             r-rlang
                             r-r6
                             r-png
                             r-plumber
                             r-leaflet
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://bartk.gitlab.io/starsTileServer")
    (synopsis "Dynamic Tile Server for R")
    (description
     "Makes it possible to serve map tiles for web maps (e.g. leaflet) based on a
function or a stars object without having to render them in advance.  This
enables parallelization of the rendering, separating the data source and
visualization location and to provide web services.")
    (license license:gpl3)))

(define-public r-starsextra
  (package
    (name "r-starsextra")
    (version "0.2.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "starsExtra" version))
       (sha256
        (base32 "0r3nqdgwx0nz1q9nm9nfc1awdbm3n52pqrc5hln6dw23wbpvzfd3"))))
    (properties `((upstream-name . "starsExtra")))
    (build-system r-build-system)
    (propagated-inputs (list r-units r-stars r-sf r-nngeo r-mgcv))
    (native-inputs (list r-knitr))
    (home-page "https://michaeldorman.github.io/starsExtra/")
    (synopsis "Miscellaneous Functions for Working with 'stars' Rasters")
    (description
     "Miscellaneous functions for working with stars objects, mainly single-band
rasters.  Currently includes functions for: (1) focal filtering, (2) detrending
of Digital Elevation Models, (3) calculating flow length, (4) calculating the
Convergence Index, (5) calculating topographic aspect and topographic slope.")
    (license license:expat)))

(define-public r-starschemar
  (package
    (name "r-starschemar")
    (version "1.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "starschemar" version))
       (sha256
        (base32 "11wbsxn5rvppinhhs12vma5xjva1ii440mqvnl5vjj23p0ldynmb"))))
    (properties `((upstream-name . "starschemar")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-snakecase
                             r-rlang
                             r-purrr
                             r-generics
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://josesamos.github.io/starschemar/")
    (synopsis "Obtaining Stars from Flat Tables")
    (description
     "Data in multidimensional systems is obtained from operational systems and is
transformed to adapt it to the new structure.  Frequently, the operations to be
performed aim to transform a flat table into a star schema.  Transformations can
be carried out using professional extract, transform and load tools or tools
intended for data transformation for end users.  With the tools mentioned, this
transformation can be carried out, but it requires a lot of work.  The main
objective of this package is to define transformations that allow obtaining
stars from flat tables easily.  In addition, it includes basic data cleaning,
dimension enrichment, incremental data refresh and query operations, adapted to
this context.")
    (license license:expat)))

(define-public r-starry
  (package
    (name "r-starry")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "starry" version))
       (sha256
        (base32 "18dn18y5vs4fhj3jzm2zmvyx2wqakkrkppmggizz60vrarnaqp8i"))))
    (properties `((upstream-name . "starry")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-shinywidgets
                             r-shinyjs
                             r-shiny
                             r-magrittr
                             r-ggplot2
                             r-dplyr
                             r-corrr
                             r-car
                             r-bslib))
    (home-page "https://joe-chelladurai.github.io/starry/")
    (synopsis "Explore Data with Plots and Tables")
    (description
     "This package provides modular functions and applications for quickly generating
plots and tables.  Each modular function opens a graphical user interface
providing the user with options to create and customise plots and tables.")
    (license license:expat)))

(define-public r-starnet
  (package
    (name "r-starnet")
    (version "0.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "starnet" version))
       (sha256
        (base32 "1rzssz1gj1bsfshk4a89vk7lllmq23334a8rahvivqkm1jvpxwnj"))))
    (properties `((upstream-name . "starnet")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-matrix r-glmnet r-cornet))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rauschenberger/starnet")
    (synopsis "Stacked Elastic Net")
    (description
     "This package implements stacked elastic net regression (Rauschenberger 2021,
<doi:10.1093/bioinformatics/btaa535>).  The elastic net generalises ridge and
lasso regularisation (Zou 2005, <doi:10.1111/j.1467-9868.2005.00503.x>).
Instead of fixing or tuning the mixing parameter alpha, we combine multiple
alpha by stacked generalisation (Wolpert 1992
<doi:10.1016/S0893-6080(05)80023-1>).")
    (license license:gpl3)))

(define-public r-starma
  (package
    (name "r-starma")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "starma" version))
       (sha256
        (base32 "07r0kyabhgbm2v39fcrw0qhxcxj9a9cb45g9chzcnn9qmvramcwx"))))
    (properties `((upstream-name . "starma")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-rcpparmadillo r-rcpp r-ggplot2))
    (home-page "https://cran.r-project.org/package=starma")
    (synopsis
     "Modelling Space Time AutoRegressive Moving Average (STARMA) Processes")
    (description
     "Statistical functions to identify, estimate and diagnose a Space-Time
@code{AutoRegressive} Moving Average (STARMA) model.")
    (license license:gpl2)))

(define-public r-starm
  (package
    (name "r-starm")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "starm" version))
       (sha256
        (base32 "0y580c6inmfix6agwd5nr340l3hjk2xxs95s468j5pghpjpb8rmv"))))
    (properties `((upstream-name . "starm")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=starm")
    (synopsis "Spatio-Temporal Autologistic Regression Model")
    (description
     "Estimates the coefficients of the two-time centered autologistic regression
model based on Gegout-Petit A., Guerin-Dubrana L., Li S. \"A new centered
spatio-temporal autologistic regression model.  Application to local spread of
plant diseases.\" 2019. <@code{arXiv:1811.06782>}, using a grid of binary
variables to estimate the spread of a disease on the grid over the years.")
    (license license:gpl3)))

(define-public r-stargazer
  (package
    (name "r-stargazer")
    (version "5.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stargazer" version))
       (sha256
        (base32 "0ra284jxjb1n6sd3114zf5l5pjrb7m7j073k8bhnrx8wl549p3i0"))))
    (properties `((upstream-name . "stargazer")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=stargazer")
    (synopsis "Well-Formatted Regression and Summary Statistics Tables")
    (description
     "This package produces @code{LaTeX} code, HTML/CSS code and ASCII text for
well-formatted tables that hold regression analysis results from several models
side-by-side, as well as summary statistics.")
    (license license:gpl2+)))

(define-public r-stareg
  (package
    (name "r-stareg")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "STAREG" version))
       (sha256
        (base32 "1skxwk817p4r7svnfrgv8bmkaz06ndqfxnypp9wdmndrgivrmg54"))))
    (properties `((upstream-name . "STAREG")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-qvalue))
    (home-page "https://cran.r-project.org/package=STAREG")
    (synopsis
     "An Empirical Bayes Approach for Replicability Analysis Across Two Studies")
    (description
     "This package provides a robust and powerful empirical Bayesian approach is
developed for replicability analysis of two large-scale experimental studies.
The method controls the false discovery rate by using the joint local false
discovery rate based on the replicability null as the test statistic.  An EM
algorithm combined with a shape constraint nonparametric method is used to
estimate unknown parameters and functions. [Li, Y. et al., (2023),
<https://www.biorxiv.org/content/10.1101/2023.05.30.542607v1>].")
    (license license:gpl3)))

(define-public r-stardom
  (package
    (name "r-stardom")
    (version "1.1.28")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "staRdom" version))
       (sha256
        (base32 "1nihk9fyng0skzi2g0rgl85c3ka92bmr1jaiq4l9mi81g823bk3b"))))
    (properties `((upstream-name . "staRdom")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-viridislite
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-readr
                             r-r-matlab
                             r-pracma
                             r-multiway
                             r-mba
                             r-matrixstats
                             r-gtools
                             r-ggplot2
                             r-ggally
                             r-foreach
                             r-eemr
                             r-drc
                             r-dplyr
                             r-doparallel
                             r-data-table
                             r-cdom))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=staRdom")
    (synopsis "PARAFAC Analysis of EEMs from DOM")
    (description
     "This is a user-friendly way to run a parallel factor (PARAFAC) analysis
(Harshman, 1971) <doi:10.1121/1.1977523> on excitation emission matrix (EEM)
data from dissolved organic matter (DOM) samples (Murphy et al., 2013)
<doi:10.1039/c3ay41160e>.  The analysis includes profound methods for model
validation.  Some additional functions allow the calculation of absorbance slope
parameters and create beautiful plots.")
    (license (license:fsdg-compatible "AGPL"))))

(define-public r-staplr
  (package
    (name "r-staplr")
    (version "3.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "staplr" version))
       (sha256
        (base32 "1wqknnrbcwcmw0kvyydwrgs0yz5cixpgxglva377lzqlbw8ll7wn"))))
    (properties `((upstream-name . "staplr")))
    (build-system r-build-system)
    (inputs (list openjdk))
    (propagated-inputs (list r-xml
                             r-stringr
                             r-rjava
                             r-purrr
                             r-pdftools
                             r-glue
                             r-fs
                             r-assertthat))
    (home-page "https://cran.r-project.org/package=staplr")
    (synopsis "Toolkit for PDF Files")
    (description
     "This package provides functions to manipulate PDF files: fill out PDF forms;
merge multiple PDF files into one; remove selected pages from a file; rename
multiple files in a directory; rotate entire pdf document; rotate selected pages
of a pdf file; Select pages from a file; splits single input PDF document into
individual pages; splits single input PDF document into parts from given points.")
    (license license:gpl3)))

(define-public r-stapler
  (package
    (name "r-stapler")
    (version "0.7.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stapler" version))
       (sha256
        (base32 "1bzhahn72fzm2bjpis76hphv8whnkp2jq8bm1qldadhng4z1nsak"))))
    (properties `((upstream-name . "stapler")))
    (build-system r-build-system)
    (propagated-inputs (list r-rnifti r-matrixstats))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/muschellij2/stapler")
    (synopsis "Simultaneous Truth and Performance Level Estimation")
    (description
     "An implementation of Simultaneous Truth and Performance Level Estimation
(STAPLE) <doi:10.1109/TMI.2004.828354>.  This method is used when there are
multiple raters for an object, typically an image, and this method fuses these
ratings into one rating.  It uses an expectation-maximization method to estimate
this rating and the individual specificity/sensitivity for each rater.")
    (license license:gpl3)))

(define-public r-stanmomo
  (package
    (name "r-stanmomo")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StanMoMo" version))
       (sha256
        (base32 "0hdh5c5zigmqs3v3ryk1h91w1a2mlqqjwbq51528w159frnd9mrl"))))
    (properties `((upstream-name . "StanMoMo")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyverse
                             r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-loo
                             r-latex2exp
                             r-httr
                             r-ggplot2
                             r-dplyr
                             r-bridgesampling
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/kabarigou/StanMoMo")
    (synopsis "Bayesian Mortality Modelling with 'Stan'")
    (description
     "Implementation of popular mortality models using the rstan package, which
provides the R interface to the Stan C++ library for Bayesian estimation.  The
package supports well-known models proposed in the actuarial and demographic
literature including the Lee-Carter (1992) <doi:10.1080/01621459.1992.10475265>
and the Cairns-Blake-Dowd (2006) <doi:10.1111/j.1539-6975.2006.00195.x> models.
By a simple call, the user inputs deaths and exposures and the package outputs
the MCMC simulations for each parameter, the log likelihoods and predictions.
Moreover, the package includes tools for model selection and Bayesian model
averaging by leave future-out validation.")
    (license license:gpl3)))

(define-public r-standby
  (package
    (name "r-standby")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "standby" version))
       (sha256
        (base32 "05kn866ibk0a0ncbsmfb31nhj7cfdidd8smvnpyrvw9rjs8k37hv"))))
    (properties `((upstream-name . "standby")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-shiny r-htmltools))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://standby.rsquaredacademy.com/")
    (synopsis "Alerts, Notifications and Loading Screen in 'Shiny'")
    (description
     "Easily create alerts, notifications, modals, info tips and loading screens in
Shiny'.  Includes several options to customize alerts and notifications by
including text, icons, images and buttons.  When wrapped around a Shiny output,
loading screen is automatically displayed while the output is being
recalculated.")
    (license license:gpl3+)))

(define-public r-standartox
  (package
    (name "r-standartox")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "standartox" version))
       (sha256
        (base32 "03g07yfw1l0j2xqjl74pvidlrrv2ia2kmhb94asj5d5xq7hr1004"))))
    (properties `((upstream-name . "standartox")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-httr r-fst r-data-table))
    (home-page "https://github.com/andschar/standartox")
    (synopsis "Ecotoxicological Information from the Standartox Database")
    (description
     "The <http://standartox.uni-landau.de> database offers cleaned, harmonized and
aggregated ecotoxicological test data, which can be used for assessing effects
and risks of chemical concentrations found in the environment.")
    (license license:expat)))

(define-public r-standardlastprofile
  (package
    (name "r-standardlastprofile")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "standardlastprofile" version))
       (sha256
        (base32 "0hj4hb2x7j1mfhkr6ccxjl4zkvhy3h0lvs71vzrz4mja3453bs5x"))))
    (properties `((upstream-name . "standardlastprofile")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/flrd/standardlastprofile")
    (synopsis "Data Package for BDEW Standard Load Profiles in Electricity")
    (description
     "Data on standard load profiles from the German Association of Energy and Water
Industries (BDEW Bundesverband der Energie- und Wasserwirtschaft e.V.) in a tidy
format.  The data and methodology are described in VDEW (1999), \"ReprÃ¤sentative
VDEW-Lastprofile\",
<https://www.bdew.de/media/documents/1999_Repraesentative-VDEW-Lastprofile.pdf>.
 The package also offers an interface for generating a standard load profile
over a user-defined period.  For the algorithm, see VDEW (2000), \"Anwendung der
ReprÃ¤sentativen VDEW-Lastprofile step-by-step\",
<https://www.bdew.de/media/documents/2000131_Anwendung-repraesentativen_Lastprofile-Step-by-step.pdf>.")
    (license license:cc0)))

(define-public r-standardize
  (package
    (name "r-standardize")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "standardize" version))
       (sha256
        (base32 "008nz9vf08qxwdsn373yzpmsw5lhbm6mpm6dlwyys58wq0bdvh28"))))
    (properties `((upstream-name . "standardize")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-mass r-lme4))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/CDEager/standardize")
    (synopsis "Tools for Standardizing Variables for Regression in R")
    (description
     "This package provides tools which allow regression variables to be placed on
similar scales, offering computational benefits as well as easing interpretation
of regression output.")
    (license license:gpl3+)))

(define-public r-stand
  (package
    (name "r-stand")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "STAND" version))
       (sha256
        (base32 "07wrpmvk0jjlghvrb37xyai48vgzj0fby8y09qdxsxdlgwqg1f3s"))))
    (properties `((upstream-name . "STAND")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "http://www.csm.ornl.gov/esh/statoed/")
    (synopsis "Statistical Analysis of Non-Detects")
    (description
     "This package provides functions for the analysis of occupational and
environmental data with non-detects.  Maximum likelihood (ML) methods for
censored log-normal data and non-parametric methods based on the product limit
estimate (PLE) for left censored data are used to calculate all of the
statistics recommended by the American Industrial Hygiene Association (AIHA) for
the complete data case.  Functions for the analysis of complete samples using
exact methods are also provided for the lognormal model.  Revised from
2007-11-05 survfit~1'.")
    (license license:gpl2+)))

(define-public r-stan4bart
  (package
    (name "r-stan4bart")
    (version "0.0-10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stan4bart" version))
       (sha256
        (base32 "0gf89r4wj1ahkzbc6hhbsf5xmnz092y9rb6qwfysf9ix5a6g281a"))))
    (properties `((upstream-name . "stan4bart")))
    (build-system r-build-system)
    (inputs (list tbb))
    (propagated-inputs (list r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-matrix
                             r-dbarts
                             r-bh))
    (home-page "https://github.com/vdorie/stan4bart")
    (synopsis
     "Bayesian Additive Regression Trees with Stan-Sampled Parametric Extensions")
    (description
     "Fits semiparametric linear and multilevel models with non-parametric additive
Bayesian additive regression tree (BART; Chipman, George, and @code{McCulloch}
(2010) <doi:10.1214/09-AOAS285>) components and Stan (Stan Development Team
(2021) <https://mc-stan.org/>) sampled parametric ones.  Multilevel models can
be expressed using lme4 syntax (Bates, Maechler, Bolker, and Walker (2015)
<doi:10.18637/jss.v067.i01>).")
    (license license:gpl3+)))

(define-public r-stampr
  (package
    (name "r-stampr")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stampr" version))
       (sha256
        (base32 "15jzf67db9yx4rw1c849v50fcjch8f37h37ly57nxp057zj2b1d2"))))
    (properties `((upstream-name . "stampr")))
    (build-system r-build-system)
    (propagated-inputs (list r-spdep
                             r-sf
                             r-rlang
                             r-lwgeom
                             r-geosphere
                             r-dplyr))
    (home-page "https://github.com/jedalong/stampr")
    (synopsis "Spatial Temporal Analysis of Moving Polygons")
    (description
     "Perform spatial temporal analysis of moving polygons; a longstanding analysis
problem in Geographic Information Systems.  Facilitates directional analysis,
distance analysis, and some other simple functionality for examining
spatial-temporal patterns of moving polygons.")
    (license license:gpl3)))

(define-public r-stampp
  (package
    (name "r-stampp")
    (version "1.6.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StAMPP" version))
       (sha256
        (base32 "0yqvy4yw9ccsbpjm6z1b9lh3435minfl7acnc1ha0zgiw8bvs2n2"))))
    (properties `((upstream-name . "StAMPP")))
    (build-system r-build-system)
    (propagated-inputs (list r-pegas r-foreach r-doparallel r-adegenet))
    (home-page "https://github.com/lpembleton/StAMPP")
    (synopsis "Statistical Analysis of Mixed Ploidy Populations")
    (description
     "Allows users to calculate pairwise Nei's Genetic Distances (Nei 1972), pairwise
Fixation Indexes (Fst) (Weir & Cockerham 1984) and also Genomic Relationship
matrixes following Yang et al. (2010) in mixed and single ploidy populations.
Bootstrapping across loci is implemented during Fst calculation to generate
confidence intervals and p-values around pairwise Fst values. @code{StAMPP}
utilises SNP genotype data of any ploidy level (with the ability to handle
missing data) and is coded to utilise multithreading where available to allow
efficient analysis of large datasets. @code{StAMPP} is able to handle genotype
data from genlight objects allowing integration with other packages such
adegenet.  Please refer to LW Pembleton, NOI Cogan & JW Forster, 2013, Molecular
Ecology Resources, 13(5), 946-952. <doi:10.1111/1755-0998.12129> for the
appropriate citation and user manual.  Thank you in advance.")
    (license license:gpl3)))

(define-public r-stakeholderanalysis
  (package
    (name "r-stakeholderanalysis")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StakeholderAnalysis" version))
       (sha256
        (base32 "164mah8h8izxaqp8hc43l6mlnf95pydkcx2laqrlqr9b0bybadxb"))))
    (properties `((upstream-name . "StakeholderAnalysis")))
    (build-system r-build-system)
    (home-page "https://www.r-project.org")
    (synopsis "Measuring Stakeholder Influence")
    (description
     "Proposes an original instrument for measuring stakeholder influence on the
development of an infrastructure project that is carried through by a
municipality, drawing on stakeholder classifications (Mitchell, Agle, & Wood,
1997) and input-output modelling (Hester & Adams, 2013).  Mitchell R., Agle
B.R., & Wood D.J. <doi:10.2307/259247> Hester, P.T., & Adams, K.M. (2013)
<doi:10.1016/j.procs.2013.09.282>.")
    (license license:gpl2+)))

(define-public r-staggered
  (package
    (name "r-staggered")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "staggered" version))
       (sha256
        (base32 "1v19sf51rfl58irr4six6j7pj61i46mbpwczx9p3z0cs37r92kyj"))))
    (properties `((upstream-name . "staggered")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen
                             r-rcpp
                             r-purrr
                             r-mass
                             r-magrittr
                             r-data-table))
    (home-page "https://cran.r-project.org/package=staggered")
    (synopsis "Efficient Estimation Under Staggered Treatment Timing")
    (description
     "Efficiently estimates treatment effects in settings with randomized staggered
rollouts, using tools proposed by Roth and Sant'Anna (2021)
<doi:10.48550/@code{arXiv.2102.01291>}.")
    (license license:gpl2)))

(define-public r-stagepop
  (package
    (name "r-stagepop")
    (version "1.1-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stagePop" version))
       (sha256
        (base32 "0yaxdq0p47qkjwjrsj4vb8rif9ip2k0vjnvz1k87h1mcslkfzygs"))))
    (properties `((upstream-name . "stagePop")))
    (build-system r-build-system)
    (propagated-inputs (list r-pbsddesolve r-desolve))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/HelenKettle/StagePop")
    (synopsis
     "Modelling the Population Dynamics of a Stage-Structured Species in Continuous Time")
    (description
     "This package provides facilities to implement and run population models of
stage-structured species...")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-stagedtrees
  (package
    (name "r-stagedtrees")
    (version "2.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stagedtrees" version))
       (sha256
        (base32 "1xvik7wk8kccdrnzxxqw3vga4498bm2k50bz4hmfx4xrrq3bapwg"))))
    (properties `((upstream-name . "stagedtrees")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-matrixstats r-cli))
    (home-page "https://github.com/stagedtrees/stagedtrees")
    (synopsis "Staged Event Trees")
    (description
     "This package creates and fits staged event tree probability models, which are
probabilistic graphical models capable of representing asymmetric conditional
independence statements for categorical variables.  Includes functions to
create, plot and fit staged event trees from data, as well as many efficient
structure learning algorithms.  References: Carli F, Leonelli M, Riccomagno E,
Varando G (2022). <doi: 10.18637/jss.v102.i06>.  Collazo R. A., GÃ¶rgen C. and
Smith J. Q. (2018, ISBN:9781498729604).  GÃ¶rgen C., Bigatti A., Riccomagno E.
and Smith J. Q. (2018) <@code{arXiv:1705.09457>}.  Thwaites P. A., Smith, J. Q.
(2017) <@code{arXiv:1510.00186>}.  Barclay L. M., Hutton J. L. and Smith J. Q.
(2013) <doi:10.1016/j.ijar.2013.05.006>.  Smith J. Q. and Anderson P. E. (2008)
<doi:10.1016/j.artint.2007.05.004>.")
    (license license:expat)))

(define-public r-stacomirtools
  (package
    (name "r-stacomirtools")
    (version "0.6.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stacomirtools" version))
       (sha256
        (base32 "1hw1k0mjddw46j3aagry2mx43wak03kkx4zzxplgcafwks5bp82y"))))
    (properties `((upstream-name . "stacomirtools")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable r-rpostgres r-rodbc r-pool r-dbi))
    (home-page "https://cran.r-project.org/package=stacomirtools")
    (synopsis "Connection Class for Package stacomiR")
    (description
     "S4 class wrappers for the ODBC and Pool DBI connection, also provides some
utilities to paste small datasets to clipboard, rename columns.  It is used by
the package @code{stacomiR} for connections to the database.  Development
versions of @code{stacomiR} are available in R-forge.")
    (license license:gpl2+)))

(define-public r-stacomir
  (package
    (name "r-stacomir")
    (version "0.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stacomiR" version))
       (sha256
        (base32 "0ik0804j8fvqmpj0xyzmjdsykw3672a5vyb4dhiss7dc1g4la20f"))))
    (properties `((upstream-name . "stacomiR")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-withr
                             r-stringr
                             r-stacomirtools
                             r-scales
                             r-rpostgres
                             r-rlang
                             r-reshape2
                             r-rcolorbrewer
                             r-pool
                             r-mgcv
                             r-magrittr
                             r-lubridate
                             r-lattice
                             r-intervals
                             r-hmisc
                             r-ggplot2
                             r-dplyr
                             r-dbi))
    (native-inputs (list r-knitr))
    (home-page "https://forgemia.inra.fr/stacomi/stacomir")
    (synopsis "Fish Migration Monitoring")
    (description
     "Graphical outputs and treatment for a database of fish pass monitoring.  It is a
part of the STACOMI open source project developed in France by the French Office
for Biodiversity institute to centralize data obtained by fish pass monitoring.
This version is available in French and English.  See
<http://stacomir.r-forge.r-project.org/> for more information on STACOMI'.")
    (license license:gpl2+)))

(define-public r-stacks
  (package
    (name "r-stacks")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stacks" version))
       (sha256
        (base32 "1bhfdnyf4w5r5bmfnrcwwy9h6qdimklbcr0b50vwmkj4jcyin48a"))))
    (properties `((upstream-name . "stacks")))
    (build-system r-build-system)
    (propagated-inputs (list r-workflows
                             r-vctrs
                             r-tune
                             r-tidyr
                             r-tibble
                             r-rsample
                             r-rlang
                             r-recipes
                             r-purrr
                             r-parsnip
                             r-glue
                             r-glmnet
                             r-ggplot2
                             r-generics
                             r-future
                             r-foreach
                             r-dplyr
                             r-dofuture
                             r-cli
                             r-butcher))
    (native-inputs (list r-knitr))
    (home-page "https://stacks.tidymodels.org/")
    (synopsis "Tidy Model Stacking")
    (description
     "Model stacking is an ensemble technique that involves training a model to
combine the outputs of many diverse statistical models, and has been shown to
improve predictive performance in a variety of settings.  stacks implements a
grammar for tidymodels'-aligned model stacking.")
    (license license:expat)))

(define-public r-stacking
  (package
    (name "r-stacking")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stacking" version))
       (sha256
        (base32 "0clxlkzz4dmri2ykk9aj9mfixpi2k0jhn2jw6zf6yyq1w70vscr7"))))
    (properties `((upstream-name . "stacking")))
    (build-system r-build-system)
    (propagated-inputs (list r-caret))
    (home-page "https://cran.r-project.org/package=stacking")
    (synopsis "Building Predictive Models with Stacking")
    (description
     "Building predictive models with stacking which is a type of ensemble learning.
Learners can be specified from those implemented in caret'.  For more
information of the package, see Nukui and Onogi (2023)
<doi:10.1101/2023.06.06.543970>.  Packages caret, parallel, snow, and packages
for base and meta learners should be installed.")
    (license license:expat)))

(define-public r-stackimpute
  (package
    (name "r-stackimpute")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StackImpute" version))
       (sha256
        (base32 "07b83bql9ch3arxzv94mllb61d4sv20lvpixlpzi7k8cjdddqizd"))))
    (properties `((upstream-name . "StackImpute")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-sandwich
                             r-mice
                             r-mass
                             r-magrittr
                             r-dplyr
                             r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=StackImpute")
    (synopsis "Tools for Analysis of Stacked Multiple Imputations")
    (description
     "This package provides methods for inference using stacked multiple imputations
augmented with weights.  The vignette provides example R code for implementation
in general multiple imputation settings.  For additional details about the
estimation algorithm, we refer the reader to Beesley, Lauren J and Taylor,
Jeremy M G (2020) âA stacked approach for chained equations multiple
imputation incorporating the substantive modelâ <doi:10.1111/biom.13372>, and
Beesley, Lauren J and Taylor, Jeremy M G (2021) âAccounting for not-at-random
missingness through imputation stackingâ <@code{arXiv:2101.07954>}.")
    (license license:gpl2)))

(define-public r-stackgbm
  (package
    (name "r-stackgbm")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stackgbm" version))
       (sha256
        (base32 "05fvp2d96n82dsldlip47lc146a094al6i7dyq5zg3ika14mq6hw"))))
    (properties `((upstream-name . "stackgbm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-progress r-proc))
    (native-inputs (list r-knitr))
    (home-page "https://nanx.me/stackgbm/")
    (synopsis "Stacked Gradient Boosting Machines")
    (description
     "This package provides a minimalist implementation of model stacking by Wolpert
(1992) <doi:10.1016/S0893-6080(05)80023-1> for boosted tree models.  A classic,
two-layer stacking model is implemented, where the first layer generates
features using gradient boosting trees, and the second layer employs a logistic
regression model that uses these features as inputs.  Utilities for training the
base models and parameters tuning are provided, allowing users to experiment
with different ensemble configurations easily.  It aims to provide a simple and
efficient way to combine multiple gradient boosting models to improve predictive
model performance and robustness.")
    (license license:expat)))

(define-public r-staccuracy
  (package
    (name "r-staccuracy")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "staccuracy" version))
       (sha256
        (base32 "0ghf7d53dm205jbf7nyw7n77k1cgsq83mr43wbdjj0h3sj3grzps"))))
    (properties `((upstream-name . "staccuracy")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-purrr r-dplyr r-cli))
    (home-page "https://github.com/tripartio/staccuracy")
    (synopsis "Standardized Accuracy and Other Model Performance Metrics")
    (description
     "Standardized accuracy (staccuracy) is framework for expressing accuracy scores
such that 50% represents a reference level of performance and 100% is perfect
prediction.  The staccuracy package provides tools for creating staccuracy
functions as well as some recommended staccuracy measures.  It also provides
functions for some classic performance metrics such as mean absolute error
(MAE), root mean squared error (RMSE), and area under the receiver operating
characteristic curve (AUCROC), as well as their winsorized versions when
applicable.")
    (license license:expat)))

(define-public r-stablespec
  (package
    (name "r-stablespec")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stablespec" version))
       (sha256
        (base32 "0m143cq0baj2lyakhr31mqk2lg78jzpdlpv2bhwz6bzzip7mqi22"))))
    (properties `((upstream-name . "stablespec")))
    (build-system r-build-system)
    (propagated-inputs (list r-sem
                             r-rgraphviz
                             r-polycor
                             r-nsga2r
                             r-matrixcalc
                             r-graph
                             r-ggm
                             r-foreach))
    (home-page "https://github.com/rahmarid/stablespec")
    (synopsis "Stable Specification Search in Structural Equation Models")
    (description
     "An exploratory and heuristic approach for specification search in Structural
Equation Modeling.  The basic idea is to subsample the original data and then
search for optimal models on each subset.  Optimality is defined through two
objectives: model fit and parsimony.  As these objectives are conflicting, we
apply a multi-objective optimization methods, specifically NSGA-II, to obtain
optimal models for the whole range of model complexities.  From these optimal
models, we consider only the relevant model specifications (structures), i.e.,
those that are both stable (occur frequently) and parsimonious and use those to
infer a causal model.")
    (license license:expat)))

(define-public r-stablelearner
  (package
    (name "r-stablelearner")
    (version "0.1-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stablelearner" version))
       (sha256
        (base32 "0a1fsy9hf63c1yfp4dallfsiknzsnhjl91yzqsgvni3gyx0vjykm"))))
    (properties `((upstream-name . "stablelearner")))
    (build-system r-build-system)
    (propagated-inputs (list r-ranger
                             r-randomforest
                             r-partykit
                             r-party
                             r-mass
                             r-e1071))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=stablelearner")
    (synopsis "Stability Assessment of Statistical Learning Methods")
    (description
     "Graphical and computational methods that can be used to assess the stability of
results from supervised statistical learning.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-stablegr
  (package
    (name "r-stablegr")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stableGR" version))
       (sha256
        (base32 "0aj7vhn8js2hmj2w6py0h79k0rfrjfb4wyr6y13lz796lgk06mnn"))))
    (properties `((upstream-name . "stableGR")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-mcmcse))
    (home-page "https://cran.r-project.org/package=stableGR")
    (synopsis "Stable Gelman-Rubin Diagnostic for Markov Chain Monte Carlo")
    (description
     "Practitioners of Bayesian statistics often use Markov chain Monte Carlo (MCMC)
samplers to sample from a posterior distribution.  This package determines
whether the MCMC sample is large enough to yield reliable estimates of the
target distribution.  In particular, this calculates a Gelman-Rubin convergence
diagnostic using stable and consistent estimators of Monte Carlo variance.
Additionally, this uses the connection between an MCMC sample's effective sample
size and the Gelman-Rubin diagnostic to produce a threshold for terminating MCMC
simulation.  Finally, this informs the user whether enough samples have been
collected and (if necessary) estimates the number of samples needed for a
desired level of accuracy.  The theory underlying these methods can be found in
\"Revisiting the Gelman-Rubin Diagnostic\" by Vats and Knudson (2018)
<@code{arXiv:1812:09384>}.")
    (license license:gpl3)))

(define-public r-stableestim
  (package
    (name "r-stableestim")
    (version "2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StableEstim" version))
       (sha256
        (base32 "0z8ia3zqkidnpg2zl055msc0dmhbsq73h8c43y0vfmhdzqszcx9d"))))
    (properties `((upstream-name . "StableEstim")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-testthat
                             r-stabledist
                             r-rdpack
                             r-numderiv
                             r-matrix
                             r-mass
                             r-fbasics))
    (home-page "https://github.com/GeoBosh/StableEstim")
    (synopsis
     "Estimate the Four Parameters of Stable Laws using Different Methods")
    (description
     "Estimate the four parameters of stable laws using maximum likelihood method,
generalised method of moments with finite and continuum number of points,
iterative Koutrouvelis regression and Kogon-@code{McCulloch} method.  The
asymptotic properties of the estimators (covariance matrix, confidence
intervals) are also provided.")
    (license license:gpl2+)))

(define-public r-stabilo
  (package
    (name "r-stabilo")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stabilo" version))
       (sha256
        (base32 "0w67y0prqzaf4m135dldvd65p4xgx5cxknbydxfq8f6c968i2hrm"))))
    (properties `((upstream-name . "stabilo")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma r-ggplot2))
    (home-page "https://cran.r-project.org/package=stabilo")
    (synopsis "Stabilometric Signal Quantification")
    (description
     "This package provides functions for stabilometric signal quantification.  The
input is a data frame containing the x, y coordinates of the center-of-pressure
displacement.  Jose Magalhaes de Oliveira (2017) <doi:10.3758/s13428-016-0706-4>
\"Statokinesigram normalization method\"; T E Prieto, J B Myklebust, R G Hoffmann,
E G Lovett, B M Myklebust (1996) <doi:10.1109/10.532130> \"Measures of postural
steadiness: Differences between healthy young and elderly adults\"; L F Oliveira
et al (1996) <doi:10.1088/0967-3334/17/4/008> \"Calculation of area of
stabilometric signals using principal component analisys\".")
    (license license:gpl3)))

(define-public r-stabilizedregression
  (package
    (name "r-stabilizedregression")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "StabilizedRegression" version))
       (sha256
        (base32 "02snfdx34k086craacfss1rnhldd81pdql4rf02w96jb1xadrq87"))))
    (properties `((upstream-name . "StabilizedRegression")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6
                             r-mass
                             r-glmnet
                             r-ggrepel
                             r-ggplot2
                             r-corpcor))
    (home-page "https://cran.r-project.org/package=StabilizedRegression")
    (synopsis "Stabilizing Regression and Variable Selection")
    (description
     "This package contains an implementation of @code{StabilizedRegression}', a
regression framework for heterogeneous data introduced in Pfister et al. (2021)
<@code{arXiv:1911.01850>}.  The procedure uses averaging to estimate a
regression of a set of predictors X on a response variable Y by enforcing
stability with respect to a given environment variable.  The resulting
regression leads to a variable selection procedure which allows to distinguish
between stable and unstable predictors.  The package further implements a
visualization technique which illustrates the trade-off between stability and
predictiveness of individual predictors.")
    (license license:gpl3)))

(define-public r-stability
  (package
    (name "r-stability")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stability" version))
       (sha256
        (base32 "1a44pn3dyxz39jxnb286p2vrq6rrmhw6snmsn80c53wnga9fihda"))))
    (properties `((upstream-name . "stability")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-scales
                             r-rlang
                             r-reshape2
                             r-matrixstats
                             r-magrittr
                             r-lme4
                             r-ggplot2
                             r-ggfortify
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=stability")
    (synopsis
     "Stability Analysis of Genotype by Environment Interaction (GEI)")
    (description
     "This package provides functionalities for performing stability analysis of
genotype by environment interaction (GEI) to identify superior and stable
genotypes across diverse environments.  It implements Eberhart and Russellâs
ANOVA method (1966)(<doi:10.2135/cropsci1966.0011183X000600010011x>), Finlay and
Wilkinsonâs Joint Linear Regression method (1963) (<doi:10.1071/AR9630742>),
Wrickeâs Ecovalence (1962, 1964), Shuklaâs stability variance parameter
(1972) (<doi:10.1038/hdy.1972.87>), Kangâs simultaneous selection for high
yield and stability (1991) (<doi:10.2134/agronj1991.00021962008300010037x>),
Additive Main Effects and Multiplicative Interaction (AMMI) method and Genotype
plus Genotypes by Environment (GGE) Interaction methods.")
    (license license:gpl2)))

(define-public r-stabiliser
  (package
    (name "r-stabiliser")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "stabiliser" version))
       (sha256
        (base32 "1zyqmj8s8x0h8dji50r3yhn3n3838vci81gm02p971px8x5wq9y8"))))
    (properties `((upstream-name . "stabiliser")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rsample
                             r-recipes
                             r-purrr
                             r-ncvreg
                             r-matrixstats
                             r-lmertest
                             r-lme4
                             r-knitr
                             r-hmisc
                             r-glmnet
                             r-ggplot2
                             r-expss
                             r-dplyr
                             r-caret
                             r-broom
                             r-bigstep))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=stabiliser")
    (synopsis "Stabilising Variable Selection")
    (description
     "This package provides a stable approach to variable selection through stability
selection and the use of a permutation-based objective stability threshold.
Lima et al (2021) <doi:10.1038/s41598-020-79317-8>, Meinshausen and Buhlmann
(2010) <doi:10.1111/j.1467-9868.2010.00740.x>.")
    (license license:expat)))

(define-public r-sta
  (package
    (name "r-sta")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sta" version))
       (sha256
        (base32 "00kv2gd0ar2j992cn7j516y3w9nx83ylxp35xrqg2n43z4kgsacs"))))
    (properties `((upstream-name . "sta")))
    (build-system r-build-system)
    (propagated-inputs (list r-trend
                             r-rcolorbrewer
                             r-raster
                             r-mapview
                             r-geots
                             r-foreach
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=sta")
    (synopsis "Seasonal Trend Analysis for Time Series Imagery in R")
    (description
     "Efficiently estimate shape parameters of periodic time series imagery with which
a statistical seasonal trend analysis (STA) is subsequently performed.  STA
output can be exported in conventional raster formats.  Methods to visualize STA
output are also implemented as well as the calculation of additional basic
statistics.  STA is based on (R. Eastman, F. Sangermano, B. Ghimire, H. Zhu, H.
Chen, N. Neeti, Y. Cai, E. Machado and S. Crema, 2009)
<doi:10.1080/01431160902755338>.")
    (license license:gpl2+)))

(define-public r-st
  (package
    (name "r-st")
    (version "1.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "st" version))
       (sha256
        (base32 "0j29k9fg8659yw1jwmcakiic51rin1dj1fmvpapy2wmz4c3pr0fp"))))
    (properties `((upstream-name . "st")))
    (build-system r-build-system)
    (propagated-inputs (list r-sda r-fdrtool r-corpcor))
    (home-page "https://strimmerlab.github.io/software/st/")
    (synopsis "Shrinkage t Statistic and Correlation-Adjusted t-Score")
    (description
     "This package implements the \"shrinkage t\" statistic introduced in Opgen-Rhein
and Strimmer (2007) <DOI:10.2202/1544-6115.1252> and a shrinkage estimate of the
\"correlation-adjusted t-score\" (CAT score) described in Zuber and Strimmer
(2009) <DOI:10.1093/bioinformatics/btp460>.  It also offers a convenient
interface to a number of other regularized t-statistics commonly employed in
high-dimensional case-control studies.")
    (license license:gpl3+)))

(define-public r-ssym
  (package
    (name "r-ssym")
    (version "1.5.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssym" version))
       (sha256
        (base32 "06qmzlsk232d3vwzb7hjrbrfw0jn6br55v1nldjij3nl75c52xw7"))))
    (properties `((upstream-name . "ssym")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-sandwich
                             r-numderiv
                             r-normalp
                             r-gigrvg
                             r-formula))
    (home-page "https://cran.r-project.org/package=ssym")
    (synopsis "Fitting Semi-Parametric log-Symmetric Regression Models")
    (description
     "Set of tools to fit a semi-parametric regression model suitable for analysis of
data sets in which the response variable is continuous, strictly positive,
asymmetric and possibly, censored.  Under this setup, both the median and the
skewness of the response variable distribution are explicitly modeled by using
semi-parametric functions, whose non-parametric components may be approximated
by natural cubic splines or P-splines.  Supported distributions for the model
error include log-normal, log-Student-t, log-power-exponential, log-hyperbolic,
log-contaminated-normal, log-slash, Birnbaum-Saunders and Birnbaum-Saunders-t
distributions.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-ssw
  (package
    (name "r-ssw")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssw" version))
       (sha256
        (base32 "0593w6zq59770r3hm7wbsbc71sw0hc9mzz1dmlpdnzgkgb6x7lqh"))))
    (properties `((upstream-name . "ssw")))
    (build-system r-build-system)
    (inputs (list python))
    (propagated-inputs (list r-reticulate))
    (native-inputs (list r-knitr))
    (home-page "https://nanx.me/ssw-r/")
    (synopsis
     "Striped Smith-Waterman Algorithm for Sequence Alignment using SIMD")
    (description
     "This package provides an R interface for SSW (Striped Smith-Waterman) via its
Python binding ssw-py'.  SSW is a fast C and C++ implementation of the
Smith-Waterman algorithm for pairwise sequence alignment using
Single-Instruction-Multiple-Data (SIMD) instructions.  SSW enhances the standard
algorithm by efficiently returning alignment information and suboptimal
alignment scores.  The core SSW library offers performance improvements for
various bioinformatics tasks, including protein database searches, short-read
alignments, primary and split-read mapping, structural variant detection, and
read-overlap graph generation.  These features make SSW particularly useful for
genomic applications.  Zhao et al. (2013) <doi:10.1371/journal.pone.0082138>
developed the original C and C++ implementation.")
    (license license:expat)))

(define-public r-ssvs
  (package
    (name "r-ssvs")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SSVS" version))
       (sha256
        (base32 "002k10b3s4f84jiq7yjazmf7jz02z5l4g9ma0cyrdglbhhn3pbrd"))))
    (properties `((upstream-name . "SSVS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-ggplot2 r-checkmate r-boomspikeslab
                             r-bayestestr))
    (home-page "https://github.com/sabainter/SSVS")
    (synopsis "Functions for Stochastic Search Variable Selection (SSVS)")
    (description
     "This package provides functions for performing stochastic search variable
selection (SSVS) for binary and continuous outcomes and visualizing the results.
 SSVS is a Bayesian variable selection method used to estimate the probability
that individual predictors should be included in a regression model.  Using MCMC
estimation, the method samples thousands of regression models in order to
characterize the model uncertainty regarding both the predictor set and the
regression parameters.  For details see Bainter, @code{McCauley}, Wager, and
Losin (2020) Improving practices for selecting a subset of important predictors
in psychology: An application to predicting pain, Advances in Methods and
Practices in Psychological Science 3(1), 66-80 <DOI:10.1177/2515245919885617>.")
    (license license:gpl3)))

(define-public r-sstvars
  (package
    (name "r-sstvars")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sstvars" version))
       (sha256
        (base32 "16ans039cx5kj0l1l8108jfplgz4mq5bbqhixks2dqmlsn5qn34m"))))
    (properties `((upstream-name . "sstvars")))
    (build-system r-build-system)
    (inputs (list lapack))
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-pbapply))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/saviviro/sstvars")
    (synopsis
     "Toolkit for Reduced Form and Structural Smooth Transition Vector Autoregressive Models")
    (description
     "Maximum likelihood estimation of smooth transition vector autoregressive models
with various types of transition weight functions, conditional distributions,
and identification methods.  Constrained estimation with various types of
constraints is available.  Residual based model diagnostics, forecasting,
simulations, and calculation of impulse response functions, generalized impulse
response functions, and generalized forecast error variance decompositions.  See
Heather Anderson, Farshid Vahid (1998) <doi:10.1016/S0304-4076(97)00076-6>,
Helmut LÃ¼tkepohl, Aleksei NetÅ¡unajev (2017) <doi:10.1016/j.jedc.2017.09.001>,
Markku Lanne, Savi Virolainen (2024) <doi:10.48550/@code{arXiv.2403.14216>},
Savi Virolainen (2024) <doi:10.48550/@code{arXiv.2404.19707>}.")
    (license license:gpl3)))

(define-public r-sstack
  (package
    (name "r-sstack")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Sstack" version))
       (sha256
        (base32 "137vsas2kw3l37c141g51sgx0j6z8ys6hbxjmsdymz1fxsjr4adg"))))
    (properties `((upstream-name . "Sstack")))
    (build-system r-build-system)
    (propagated-inputs (list r-randomforest r-foreach r-dplyr r-doparallel))
    (home-page "https://cran.r-project.org/package=Sstack")
    (synopsis
     "Bootstrap Stacking of Random Forest Models for Heterogeneous Data")
    (description
     "Generates and predicts a set of linearly stacked Random Forest models using
bootstrap sampling.  Individual datasets may be heterogeneous (not all samples
have full sets of features).  Contains support for parallelization but the user
should register their cores before running.  This is an extension of the method
found in Matlock (2018) <doi:10.1186/s12859-018-2060-2>.")
    (license license:gpl3)))

(define-public r-sssimple
  (package
    (name "r-sssimple")
    (version "0.6.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SSsimple" version))
       (sha256
        (base32 "1nhwr9kvlx899wsgzp47ykq3awp7p54zcvsdmjjh9ch4qcm9q2sm"))))
    (properties `((upstream-name . "SSsimple")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://cran.r-project.org/package=SSsimple")
    (synopsis "State Space Models")
    (description "Simulate, solve state space models.")
    (license license:gpl2+)))

(define-public r-sssc
  (package
    (name "r-sssc")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sssc" version))
       (sha256
        (base32 "00m5zfd3jnbxa8x76v4pfyl4qdfq90n8rqx4hngany0ka1z5g7m6"))))
    (properties `((upstream-name . "sssc")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam r-ggplot2 r-e1071 r-changepoint))
    (home-page "https://cran.r-project.org/package=sssc")
    (synopsis "Same Species Sample Contamination Detection")
    (description
     "Imports Variant Calling Format file into R. It can detect whether a sample
contains contaminant from the same species.  In the first stage of the approach,
a change-point detection method is used to identify copy number variations for
filtering.  Next, features are extracted from the data for a support vector
machine model.  For log-likelihood calculation, the deviation parameter is
estimated by maximum likelihood method.  Using a radial basis function kernel
support vector machine, the contamination of a sample can be detected.")
    (license license:gpl2)))

(define-public r-sss
  (package
    (name "r-sss")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sss" version))
       (sha256
        (base32 "1v2y3mykbh09573jdadl7rjdv1dy7f86pc98sdvkfz2g1ivwnnc3"))))
    (properties `((upstream-name . "sss")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "http://andrie.github.io/sss/")
    (synopsis
     "Import Files in the Triple-s (Standard Survey Structure) Format")
    (description
     "This package provides tools to import survey files in the .sss (triple-s)
format.  The package provides the function read.sss() that reads the .asc (or
.csv') and .sss files of a triple-s survey data file.  See also
<https://triple-s.org/>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-ssrtcalc
  (package
    (name "r-ssrtcalc")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SSRTcalc" version))
       (sha256
        (base32 "0m8qc1rzx3rag69m0jvanx524d49smkgnmj99fl54frzqh0ji1ss"))))
    (properties `((upstream-name . "SSRTcalc")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SSRTcalc")
    (synopsis "Easy SSRT Calculation")
    (description
     "This is a collection of functions to calculate stop-signal reaction time (SSRT).
 Includes functions for both \"integration\" and \"mean\" methods; both fixed and
adaptive stop-signal delays are supported (see appropriate functions).
Calculation is based on Verbruggen et al. (2019)
<doi:10.7554/@code{eLife.46323.001>} and Verbruggen et al. (2013)
<doi:10.1177/0956797612457390>.")
    (license license:gpl3)))

(define-public r-ssrn
  (package
    (name "r-ssrn")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssrn" version))
       (sha256
        (base32 "1s78wiyn6ff6f55cp5knhram6m61cbckdnzm9q6imrjwngjci6cx"))))
    (properties `((upstream-name . "ssrn")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-dplyr))
    (home-page "https://github.com/uribo/ssrn")
    (synopsis "Scan Statistics for Railway Network")
    (description
     "Implement the algorithm provided in scan for estimating the transmission route
on railway network using passenger volume.  It is a generalization of the scan
statistic approach for railway network to identify the hot railway route for
transmitting infectious diseases.")
    (license license:expat)))

(define-public r-ssrmst
  (package
    (name "r-ssrmst")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SSRMST" version))
       (sha256
        (base32 "15s6vcmk8a27yw3rlfkajby6qkizp4sfiyi63m7hc86svd15x4wg"))))
    (properties `((upstream-name . "SSRMST")))
    (build-system r-build-system)
    (propagated-inputs (list r-survrm2 r-survival))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SSRMST")
    (synopsis "Sample Size Calculation using Restricted Mean Survival Time")
    (description
     "Calculates the power and sample size based on the difference in Restricted Mean
Survival Time.")
    (license license:gpl2)))

(define-public r-ssrm-logmer
  (package
    (name "r-ssrm-logmer")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssrm.logmer" version))
       (sha256
        (base32 "1i9rrsixl3swd04w1gf9gf1r2wb9nkvwhc8ld4bx4zxzm1031f67"))))
    (properties `((upstream-name . "ssrm.logmer")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod r-sfsmisc))
    (home-page "https://cran.r-project.org/package=ssrm.logmer")
    (synopsis
     "Sample Size Determination for Longitudinal Designs with Binary Outcome")
    (description
     "This package provides the necessary sample size for a longitudinal study with
binary outcome in order to attain a pre-specified power while strictly
maintaining the Type I error rate.  Kapur K, Bhaumik R, Tang XC, Hur K, Reda DJ,
Bhaumik D (2014) <doi:10.1002/sim.6203>.")
    (license license:gpl2)))

(define-public r-ssrat
  (package
    (name "r-ssrat")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SSrat" version))
       (sha256
        (base32 "1mmwla7yqqldv0s5hqljq2k2qljdfhq97m5128gvf3iy7n2yjzgj"))))
    (properties `((upstream-name . "SSrat")))
    (build-system r-build-system)
    (propagated-inputs (list r-sna r-plyr))
    (home-page "https://cran.r-project.org/package=SSrat")
    (synopsis
     "Two-Dimensional Sociometric Status Determination with Rating Scales")
    (description
     "This package provides a set of functions for two-dimensional sociometric status
determination with rating scales.  For each person assessed, SSrat computes
probability distributions of the total scores for `Sympathy (S), `Antipathy (A),
social `Preference (P) and social `Impact (I), and applies a set of criteria for
sociometric status categorization.")
    (license license:gpl2)))

(define-public r-ssra
  (package
    (name "r-ssra")
    (version "0.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SSRA" version))
       (sha256
        (base32 "1xsjwwy1lpm5aqjs1cym34c73k3n6j7z9q3nh2xha57ai4g0drlw"))))
    (properties `((upstream-name . "SSRA")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-shape))
    (home-page "https://cran.r-project.org/package=SSRA")
    (synopsis "Sakai Sequential Relation Analysis")
    (description
     "Takea Semantic Structure Analysis (TSSA) and Sakai Sequential Relation Analysis
(SSRA) for polytomous items.  Package includes functions for generating a
sequential relation table and a treegram to visualize the sequential relations
between pairs of items.")
    (license license:gpl3)))

(define-public r-ssr
  (package
    (name "r-ssr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssr" version))
       (sha256
        (base32 "0l2f8lsmwv2f4y708g32jy2s4jj0j686830qp4a5ailyrdbbz1ix"))))
    (properties `((upstream-name . "ssr")))
    (build-system r-build-system)
    (propagated-inputs (list r-e1071 r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/enriquegit/ssr")
    (synopsis "Semi-Supervised Regression Methods")
    (description
     "An implementation of semi-supervised regression methods including self-learning
and co-training by committee based on Hady, M. F. A., Schwenker, F., & Palm, G.
(2009) <doi:10.1007/978-3-642-04274-4_13>.  Users can define which set of
regressors to use as base models from the caret package, other packages, or
custom functions.")
    (license license:gpl3)))

(define-public r-sspse
  (package
    (name "r-sspse")
    (version "1.1.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sspse" version))
       (sha256
        (base32 "1qm6gy689yy1hfffzhvw5y984cvx0hsqjh6nh97fni2hidwwxpqn"))))
    (properties `((upstream-name . "sspse")))
    (build-system r-build-system)
    (propagated-inputs (list r-scam r-rds r-kernsmooth r-coda))
    (home-page "https://hpmrg.org")
    (synopsis
     "Estimating Hidden Population Size using Respondent Driven Sampling Data")
    (description
     "Estimate the size of a networked population based on respondent-driven sampling
data.  The package is part of the \"RDS Analyst\" suite of packages for the
analysis of respondent-driven sampling data.  See Handcock, Gile and Mar (2014)
<doi:10.1214/14-EJS923>, Handcock, Gile and Mar (2015) <doi:10.1111/biom.12255>,
Kim and Handcock (2021) <doi:10.1093/jssam/smz055>, and @code{McLaughlin}, et.
al. (2023) <doi:10.1214/23-AOAS1807>.")
    (license (license:fsdg-compatible "GPL-3 + file LICENSE"))))

(define-public r-ssplots
  (package
    (name "r-ssplots")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SSplots" version))
       (sha256
        (base32 "15qh8yak1v8wc5mp5by7cfbqswlbhmkazsf050p6ysvm0ypq3jyd"))))
    (properties `((upstream-name . "SSplots")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-reshape2 r-ggplot2))
    (home-page "https://cran.r-project.org/package=SSplots")
    (synopsis "Stock Status Plots (SSPs)")
    (description
     "Pauly et al. (2008)
<http://legacy.seaaroundus.s3.amazonaws.com/doc/Researcher+Publications/dpauly/PDF/2008/Books%26Chapters/@code{FisheriesInLargeMarineEcosystems.pdf>}
created (and coined the name) Stock Status Plots for a UNEP compendium on Large
Marine Ecosystems(LMEs, Sherman and Hempel
(2009)<https://marineinfo.org/imis?module=ref&refid=142061&printversion=1&@code{dropIMIStitle=1>}).
 Stock status plots are bivariate graphs summarizing the status (e.g.,
developing, fully exploited, overexploited, etc.), through time, of the
multispecies fisheries of a fished area or ecosystem.  This package contains
three functions to generate stock status plots viz., SSplots_pauly() (as per the
criteria proposed by Pauly et al.,2008), SSplots_kleisner() (as per the criteria
proposed by Kleisner and Pauly (2011)
<http://www.ecomarres.com/downloads/regional.pdf> and Kleisner et al. (2013)
<doi:10.1111/j.1467-2979.2012.00469.x>)and SSplots_EPI() (as per the criteria
proposed by Jayasankar et al.,2021 <https://eprints.cmfri.org.in/11364/>).")
    (license license:gpl2+)))

(define-public r-ssp
  (package
    (name "r-ssp")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SSP" version))
       (sha256
        (base32 "0ccg81rsjki1fcqvj6lgmmmy5yw1gxy8khajl1i80qyi4v7ny9hk"))))
    (properties `((upstream-name . "SSP")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan r-sampling r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/edlinguerra/SSP")
    (synopsis "Simulated Sampling Procedure for Community Ecology")
    (description
     "Simulation-based sampling protocol (SSP) is an R package design to estimate
sampling effort in studies of ecological communities based on the definition of
pseudo-multivariate standard error (@code{MultSE}) (Anderson & Santana-Garcon,
2015) <doi:10.1111/ele.12385> and simulation of ecological data.  The
theoretical background is described in Guerra-Castro et al. (2020)
<doi:10.1101/2020.03.19.996991>.")
    (license license:gpl2)))

(define-public r-ssosvm
  (package
    (name "r-ssosvm")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SSOSVM" version))
       (sha256
        (base32 "11wlalpw6yhvv95xr8vvgxgl4jafc2ghzsk5wqyv71ahxarmrlss"))))
    (properties `((upstream-name . "SSOSVM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mvtnorm r-mass))
    (home-page "https://cran.r-project.org/package=SSOSVM")
    (synopsis "Stream Suitable Online Support Vector Machines")
    (description
     "Soft-margin support vector machines (SVMs) are a common class of classification
models.  The training of SVMs usually requires that the data be available all at
once in a single batch, however the Stochastic majorization-minimization (SMM)
algorithm framework allows for the training of SVMs on streamed data instead
Nguyen, Jones & @code{McLachlan(2018)<doi:10.1007/s42081-018-0001-y>}.  This
package utilizes the SMM framework to provide functions for training SVMs with
hinge loss, squared-hinge loss, and logistic loss.")
    (license license:gpl3)))

(define-public r-ssnbler
  (package
    (name "r-ssnbler")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SSNbler" version))
       (sha256
        (base32 "0h5cvl32hkdw39ky00r528qsab2zqa1922adgx1fjnqwswqvqy2i"))))
    (properties `((upstream-name . "SSNbler")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-ssn2
                             r-sf
                             r-rsqlite
                             r-pdist
                             r-igraph
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/pet221/SSNbler")
    (synopsis "Assemble 'SSN' Objects")
    (description
     "Import, create and assemble data needed to fit spatial-statistical
stream-network models using the SSN2 package for R'.  Streams, observations, and
prediction locations are represented as simple features and specific tools
provided to define topological relationships between features; calculate the
hydrologic distances (with flow-direction preserved) and the spatial additive
function used to weight converging stream segments; and export the topological,
spatial, and attribute information to an `SSN` (spatial stream network) object,
which can be efficiently stored, accessed and analysed in R'.  A detailed
description of methods used to calculate and format the spatial data can be
found in Peterson, E.E. and Ver Hoef, J.M., (2014) <doi:10.18637/jss.v056.i02>.")
    (license license:gpl3+)))

(define-public r-ssnbayes
  (package
    (name "r-ssnbayes")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SSNbayes" version))
       (sha256
        (base32 "1smfd7mas19y8n4jpywphck54crxn15lnc8dpi9s5il50icr2hls"))))
    (properties `((upstream-name . "SSNbayes")))
    (build-system r-build-system)
    (propagated-inputs (list r-ssn2 r-sf r-rstan r-plyr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/EdgarSantos-Fernandez/SSNbayes")
    (synopsis "Bayesian Spatio-Temporal Analysis in Stream Networks")
    (description
     "Fits Bayesian spatio-temporal models and makes predictions on stream networks
using the approach by Santos-Fernandez, Edgar, et al. (2022).\"Bayesian
spatio-temporal models for stream networks\". <@code{arXiv:2103.03538>}.  In
these models, spatial dependence is captured using stream distance and flow
connectivity, while temporal autocorrelation is modelled using vector
autoregression methods.")
    (license license:gpl2)))

(define-public r-ssn2
  (package
    (name "r-ssn2")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SSN2" version))
       (sha256
        (base32 "0yzhg5pvmm7bpv8c2qy345iv4r3s2liaxwmz87p9fscv01wx7m3i"))))
    (properties `((upstream-name . "SSN2")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-tibble
                             r-spmodel
                             r-sf
                             r-rsqlite
                             r-matrix
                             r-generics))
    (native-inputs (list r-knitr))
    (home-page "https://usepa.github.io/SSN2/")
    (synopsis "Spatial Modeling on Stream Networks")
    (description
     "Spatial statistical modeling and prediction for data on stream networks,
including models based on in-stream distance (Ver Hoef, J.M. and Peterson, E.E.,
(2010) <DOI:10.1198/jasa.2009.ap08248>.) Models are created using moving average
constructions.  Spatial linear models, including explanatory variables, can be
fit with (restricted) maximum likelihood.  Mapping and other graphical functions
are included.")
    (license license:gpl3)))

(define-public r-ssmutpa
  (package
    (name "r-ssmutpa")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssMutPA" version))
       (sha256
        (base32 "1gdjw84n10ii2crdwv6yr0y4x72b2rkknigzfm8jvpwkvslprxsk"))))
    (properties `((upstream-name . "ssMutPA")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rcolorbrewer
                             r-pheatmap
                             r-nbclust
                             r-matrix
                             r-maftools
                             r-kernlab
                             r-igraph
                             r-ggridges
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ssMutPA")
    (synopsis "Single-Sample Mutation-Based Pathway Analysis")
    (description
     "This package provides a systematic bioinformatics tool to perform single-sample
mutation-based pathway analysis by integrating somatic mutation data with the
Protein-Protein Interaction (PPI) network.  In this method, we use local and
global weighted strategies to evaluate the effects of network genes from
mutations according to the network topology and then calculate the
mutation-based pathway enrichment score (@code{ssMutPES}) to reflect the
accumulated effect of mutations of each pathway.  Subsequently, the
@code{ssMutPES} profiles are used for unsupervised spectral clustering to
identify cancer subtypes.")
    (license license:gpl2+)))

(define-public r-ssmsn
  (package
    (name "r-ssmsn")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssmsn" version))
       (sha256
        (base32 "0fzqdkqhj5hh8pzcwh7i0rglgdq22a5mriyis8r1vhlkdc7n5m39"))))
    (properties `((upstream-name . "ssmsn")))
    (build-system r-build-system)
    (propagated-inputs (list r-mcmcpack))
    (home-page "https://cran.r-project.org/package=ssmsn")
    (synopsis "Scale-Shape Mixtures of Skew-Normal Distributions")
    (description
     "It provides the density and random number generator for the Scale-Shape Mixtures
of Skew-Normal Distributions proposed by Jamalizadeh and Lin (2016)
<doi:10.1007/s00180-016-0691-1>.")
    (license license:gpl2+)))

(define-public r-ssmrob
  (package
    (name "r-ssmrob")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssmrob" version))
       (sha256
        (base32 "1xzhk8ni6375p2nfnl39x51ny2wi53a09fkr95nrdbxhkcm5flvz"))))
    (properties `((upstream-name . "ssmrob")))
    (build-system r-build-system)
    (propagated-inputs (list r-sampleselection r-robustbase r-mass))
    (home-page "https://cran.r-project.org/package=ssmrob")
    (synopsis "Robust Estimation and Inference in Sample Selection Models")
    (description
     "Package provides a set of tools for robust estimation and inference for models
with sample selectivity and endogenous treatment model.  For details, see
Zhelonkin and Ronchetti (2021) <doi:10.18637/jss.v099.i04>.")
    (license license:gpl2)))

(define-public r-ssmrcd
  (package
    (name "r-ssmrcd")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssMRCD" version))
       (sha256
        (base32 "056yk3bahjc2vd8nxg9anjzb3jz61ymgrp0wcv1381ci2qymzrvc"))))
    (properties `((upstream-name . "ssMRCD")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-rrcov
                             r-rootsolve
                             r-robustbase
                             r-reshape2
                             r-plot3d
                             r-matrix
                             r-ggplot2
                             r-foreach
                             r-expm
                             r-dplyr
                             r-doparallel
                             r-desctools
                             r-dbscan
                             r-car))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ssMRCD")
    (synopsis "Spatially Smoothed MRCD Estimator")
    (description
     "Estimation of the Spatially Smoothed Minimum Regularized Determinant
(@code{ssMRCD}) estimator and its usage in an @code{ssMRCD-based} outlier
detection method as described in Puchhammer and Filzmoser (2023)
<doi:10.1080/10618600.2023.2277875> and for sparse robust PCA for multi-source
data described in Puchhammer, Wilms and Filzmoser (2024)
<doi:10.48550/@code{arXiv.2407.16299>}.  Included are also complementary
visualization and parameter tuning tools.")
    (license license:gpl3)))

(define-public r-ssmousetrack
  (package
    (name "r-ssmousetrack")
    (version "1.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssMousetrack" version))
       (sha256
        (base32 "014b1ylynn3l3k24p4zr001ys32ajrxxdpk3jwl0lmfm7r63as58"))))
    (properties `((upstream-name . "ssMousetrack")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-ggplot2
                             r-dtw
                             r-cowplot
                             r-circstats
                             r-bh))
    (home-page "https://cran.r-project.org/package=ssMousetrack")
    (synopsis
     "Bayesian State-Space Modeling of Mouse-Tracking Experiments via Stan")
    (description
     "Estimates previously compiled state-space modeling for mouse-tracking
experiments using the rstan package, which provides the R interface to the Stan
C++ library for Bayesian estimation.")
    (license license:gpl3+)))

(define-public r-ssmodels
  (package
    (name "r-ssmodels")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssmodels" version))
       (sha256
        (base32 "19w3vr4zz23xbb4mr4zwqwj024i10hgnpm0cf47qh5c1zkjcwdib"))))
    (properties `((upstream-name . "ssmodels")))
    (build-system r-build-system)
    (propagated-inputs (list r-sn r-rdpack r-pracma r-numderiv r-misctools))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ssmodels")
    (synopsis "Sample Selection Models")
    (description
     "In order to facilitate the adjustment of the sample selection models existing in
the literature, we created the ssmodels package.  Our package allows the
adjustment of the classic Heckman model (Heckman (1976), Heckman (1979)
<doi:10.2307/1912352>), and the estimation of the parameters of this model via
the maximum likelihood method and two-step method, in addition to the adjustment
of the Heckman-t models, introduced in the literature by Marchenko and Genton
(2012) <doi:10.1080/01621459.2012.656011> and the Heckman-Skew model introduced
in the literature by Ogundimu and Hutton (2016) <doi:10.1111/sjos.12171>.  We
also implemented functions to adjust the generalized version of the Heckman
model, introduced by Bastos, Barreto-Souza, and Genton (2021)
<doi:10.5705/ss.202021.0068>, that allows the inclusion of covariables to the
dispersion and correlation parameters and a function to adjust the Heckman-BS
model introduced by Bastos and Barreto-Souza (2020)
<doi:10.1080/02664763.2020.1780570> that uses the Birnbaum-Saunders distribution
as a joint distribution of the selection and primary regression variables.")
    (license license:gpl2+)))

(define-public r-ssmn
  (package
    (name "r-ssmn")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssmn" version))
       (sha256
        (base32 "1rab9yjb05g5rphqlhwvdwmsyqlv1f4m0rqxfv8c5pbh170nbwbc"))))
    (properties `((upstream-name . "ssmn")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncdist r-sn r-moments r-mnormt))
    (home-page "https://cran.r-project.org/package=ssmn")
    (synopsis "Skew Scale Mixtures of Normal Distributions")
    (description
     "This package performs the EM algorithm for regression models using Skew Scale
Mixtures of Normal Distributions.")
    (license license:gpl2+)))

(define-public r-ssm
  (package
    (name "r-ssm")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SSM" version))
       (sha256
        (base32 "1h8yyzh5rn5jay70kyzvwirfndi049a5w28qigrjv5rxd7ml84l7"))))
    (properties `((upstream-name . "SSM")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/peterrobertcurtis/SSM")
    (synopsis "Fit and Analyze Smooth Supersaturated Models")
    (description
     "This package creates an S4 class \"SSM\" and defines functions for fitting smooth
supersaturated models, a polynomial model with spline-like behaviour.  Functions
are defined for the computation of Sobol indices for sensitivity analysis and
plotting the main effects using FANOVA methods.  It also implements the
estimation of the SSM metamodel error using a GP model with a variety of defined
correlation functions.")
    (license license:gpl3)))

(define-public r-sslr
  (package
    (name "r-sslr")
    (version "0.9.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SSLR" version))
       (sha256
        (base32 "0fv1jqri3gpzcfs429pgx4hjqmjrglb4g7nfqdihd2hjmq9k4sql"))))
    (properties `((upstream-name . "SSLR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rssl
                             r-rlang
                             r-rcpparmadillo
                             r-rcpp
                             r-rann
                             r-purrr
                             r-proxy
                             r-plyr
                             r-parsnip
                             r-magrittr
                             r-generics
                             r-foreach
                             r-dplyr
                             r-conclust))
    (native-inputs (list r-knitr))
    (home-page "https://dicits.ugr.es/software/SSLR/")
    (synopsis
     "Semi-Supervised Classification, Regression and Clustering Methods")
    (description
     "Providing a collection of techniques for semi-supervised classification,
regression and clustering.  In semi-supervised problem, both labeled and
unlabeled data are used to train a classifier.  The package includes a
collection of semi-supervised learning techniques: self-training, co-training,
democratic, decision tree, random forest, S3VM ...  etc, with a fairly intuitive
interface that is easy to use.")
    (license license:gpl3)))

(define-public r-ssizerna
  (package
    (name "r-ssizerna")
    (version "1.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssizeRNA" version))
       (sha256
        (base32 "1qw10yswzpg28fhvr6rsq0gh7xivfapw64gxc0hakb1nc31jf8ky"))))
    (properties `((upstream-name . "ssizeRNA")))
    (build-system r-build-system)
    (propagated-inputs (list r-ssize-fdr
                             r-qvalue
                             r-mass
                             r-limma
                             r-edger
                             r-biobase))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ssizeRNA")
    (synopsis "Sample Size Calculation for RNA-Seq Experimental Design")
    (description
     "We propose a procedure for sample size calculation while controlling false
discovery rate for RNA-seq experimental design.  Our procedure depends on the
Voom method proposed for RNA-seq data analysis by Law et al. (2014)
<DOI:10.1186/gb-2014-15-2-r29> and the sample size calculation method proposed
for microarray experiments by Liu and Hwang (2007)
<DOI:10.1093/bioinformatics/btl664>.  We develop a set of functions that
calculates appropriate sample sizes for two-sample t-test for RNA-seq
experiments with fixed or varied set of parameters.  The outputs also contain a
plot of power versus sample size, a table of power at different sample sizes,
and a table of critical test values at different sample sizes.  To install this
package, please use source(\"http://bioconductor.org/@code{biocLite.R}\");
@code{biocLite(\"ssizeRNA}\")'.  For R version 3.5 or greater, please use
if(!@code{requireNamespace(\"BiocManager}\", quietly =
TRUE)){install.packages(\"@code{BiocManager}\")};
@code{BiocManager::install(\"ssizeRNA}\")'.")
    (license license:gpl2+)))

(define-public r-ssize-fdr
  (package
    (name "r-ssize-fdr")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssize.fdr" version))
       (sha256
        (base32 "1yirk6sl72d99sznvr5hm1sn4766sfrw66hhffb9g88b19lfpprd"))))
    (properties `((upstream-name . "ssize.fdr")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=ssize.fdr")
    (synopsis "Sample Size Calculations for Microarray Experiments")
    (description
     "This package provides functions that calculate appropriate sample sizes for
one-sample t-tests, two-sample t-tests, and F-tests for microarray experiments
based on desired power while controlling for false discovery rates.  For all
tests, the standard deviations (variances) among genes can be assumed fixed or
random.  This is also true for effect sizes among genes in one-sample and two
sample experiments.  Functions also output a chart of power versus sample size,
a table of power at different sample sizes, and a table of critical test values
at different sample sizes.")
    (license license:gpl3)))

(define-public r-ssimparser
  (package
    (name "r-ssimparser")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssimparser" version))
       (sha256
        (base32 "01c9kbvdrkchf4rad9hysyflpkgdj294lbk94fc5pkc49fqznmrx"))))
    (properties `((upstream-name . "ssimparser")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-stringr r-magrittr r-dplyr r-airportr))
    (home-page "https://cran.r-project.org/package=ssimparser")
    (synopsis "Standard Schedules Information Parser")
    (description
     "Parse Standard Schedules Information file (types 2 and 3) into a Data Frame.
Can also expand schedules into flights.")
    (license license:expat)))

(define-public r-ssimmap
  (package
    (name "r-ssimmap")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SSIMmap" version))
       (sha256
        (base32 "1ydwwxisb8wwxb2n5xvz6474n4rwl57xpi4fa0b6jggkzxx113ig"))))
    (properties `((upstream-name . "SSIMmap")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-sf
                             r-scales
                             r-knitr
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Hailyee-Ha/SSIMmap")
    (synopsis "The Structural Similarity Index Measure for Maps")
    (description
     "Extends the classical SSIM method proposed by Wang', Bovik', Sheikh', and
Simoncelli'(2004) <doi:10.1109/TIP.2003.819861>.  for irregular lattice-based
maps and raster images.  The geographical SSIM method incorporates
well-developed geographically weighted summary statistics'('Brunsdon',
Fotheringham and Charlton 2002) <doi:10.1016/S0198-9715(01)00009-6> with an
adaptive bandwidth kernel function for irregular lattice-based maps.")
    (license license:expat)))

(define-public r-ssifs
  (package
    (name "r-ssifs")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssifs" version))
       (sha256
        (base32 "17q1p4cgdwmn3vlhkc4p49j1222wd8d6dcp7n5ngvqcfjn3lrarh"))))
    (properties `((upstream-name . "ssifs")))
    (build-system r-build-system)
    (propagated-inputs (list r-revecor
                             r-rdpack
                             r-r2jags
                             r-plyr
                             r-netmeta
                             r-igraph
                             r-gtools
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/georgiosseitidis/ssifs")
    (synopsis "Stochastic Search Inconsistency Factor Selection")
    (description
     "Evaluating the consistency assumption of Network Meta-Analysis both globally and
locally in the Bayesian framework.  Inconsistencies are located by applying
Bayesian variable selection to the inconsistency factors.  The implementation of
the method is described by Seitidis et al. (2022) <@code{arXiv:2211.07258>}.")
    (license license:gpl3+)))

(define-public r-sship
  (package
    (name "r-sship")
    (version "0.9.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sship" version))
       (sha256
        (base32 "0h0cbbvqfmik2z0qp34gym81p01x01yndymczb8vpqnj0nfnmyhc"))))
    (properties `((upstream-name . "sship")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml r-rcurl r-openssl r-jsonlite r-httr))
    (home-page "https://github.com/Rapporteket/sship")
    (synopsis "Tool for Secure Shipment of Content")
    (description
     "Convenient tools for exchanging files securely from within R. By encrypting the
content safe passage of files (shipment) can be provided by common but insecure
carriers such as ftp and email.  Based on asymmetric cryptography no management
of shared secrets is needed to make a secure shipment as long as authentic
public keys are available.  Public keys used for secure shipments may also be
obtained from external providers as part of the overall process.  Transportation
of files will require that relevant services such as ftp and email servers are
available.")
    (license license:gpl3)))

(define-public r-sshaped
  (package
    (name "r-sshaped")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Sshaped" version))
       (sha256
        (base32 "0jy926ykyw7a2q0wabbvm2sm2g4d5y8wl6na4xjmf0ybr6rma458"))))
    (properties `((upstream-name . "Sshaped")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=Sshaped")
    (synopsis "Nonparametric, Tuning-Free Estimation of S-Shaped Functions")
    (description
     "Estimation of an S-shaped function and its corresponding inflection point via a
least squares approach.  A sequential mixed primal-dual based algorithm is
implemented for the fast computation.  Details can be found in Feng et al.
(2022) <doi:10.1111/rssb.12481>.")
    (license license:gpl2)))

(define-public r-sshaarp
  (package
    (name "r-sshaarp")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SSHAARP" version))
       (sha256
        (base32 "0x8dinhhfk4q1cck7ms78f4k23430c4wyvlh2qclq0c83acjj4y9"))))
    (properties `((upstream-name . "SSHAARP")))
    (build-system r-build-system)
    (inputs (list gmt ghostscript))
    (propagated-inputs (list r-stringr
                             r-gtools
                             r-gmt
                             r-filesstrings
                             r-dplyr
                             r-desctools
                             r-data-table
                             r-bigdawg))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SSHAARP")
    (synopsis "Searching Shared HLA Amino Acid Residue Prevalence")
    (description
     "Processes amino acid alignments produced by the IPD-IMGT/HLA (Immuno
Polymorphism-@code{ImMunoGeneTics/Human} Leukocyte Antigen) Database to identify
user-defined amino acid residue motifs shared across HLA alleles, and calculates
the frequencies of those motifs based on HLA allele frequency data.  SSHAARP
(Searching Shared HLA Amino Acid Residue Prevalence) uses Generic Mapping Tools
(GMT) software and the GMT R package to generate global frequency heat maps that
illustrate the distribution of each user-defined map around the globe.  SSHAARP
analyzes the allele frequency data described by Solberg et al. (2008)
<doi:10.1016/j.humimm.2008.05.001>, a global set of 497 population samples from
185 published datasets, representing 66,800 individuals total.")
    (license license:gpl3+)))

(define-public r-ssh
  (package
    (name "r-ssh")
    (version "0.9.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssh" version))
       (sha256
        (base32 "1gnkpb2xqh0ahqbhm1zb0wxwy3gdis32pwdgv5vf9sdcj4q35zik"))))
    (properties `((upstream-name . "ssh")))
    (build-system r-build-system)
    (inputs (list zlib openssl openssh))
    (propagated-inputs (list r-credentials r-askpass))
    (native-inputs (list pkg-config r-knitr))
    (home-page "https://cran.r-project.org/package=ssh")
    (synopsis "Secure Shell (SSH) Client for R")
    (description
     "Connect to a remote server over SSH to transfer files via SCP, setup a secure
tunnel, or run a command or script on the host while streaming stdout and stderr
directly to the client.")
    (license license:expat)))

(define-public r-ssgraph
  (package
    (name "r-ssgraph")
    (version "1.15")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssgraph" version))
       (sha256
        (base32 "1ya5pbdjsi34bqv8r89jc7pr7p0smq7bpwfllvzjrviq08r935nd"))))
    (properties `((upstream-name . "ssgraph")))
    (build-system r-build-system)
    (propagated-inputs (list r-bdgraph))
    (native-inputs (list r-knitr))
    (home-page "https://www.uva.nl/profile/a.mohammadi")
    (synopsis "Bayesian Graph Structure Learning using Spike-and-Slab Priors")
    (description
     "Bayesian estimation for undirected graphical models using spike-and-slab priors.
 The package handles continuous, discrete, and mixed data.")
    (license license:gpl2+)))

(define-public r-ssgl
  (package
    (name "r-ssgl")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SSGL" version))
       (sha256
        (base32 "1f65sc713bzi6nq7ay9jnz92bxh7lrzj6aj77dczkafac2vgmba2"))))
    (properties `((upstream-name . "SSGL")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma r-mass r-grpreg))
    (home-page "https://cran.r-project.org/package=SSGL")
    (synopsis
     "Spike-and-Slab Group Lasso for Group-Regularized Generalized Linear Models")
    (description
     "Fits group-regularized generalized linear models (GLMs) using the spike-and-slab
group lasso (SSGL) prior introduced by Bai et al. (2022)
<doi:10.1080/01621459.2020.1765784> and extended to GLMs by Bai (2023)
<@code{arXiv:2007.07021>}.  This package supports fitting the SSGL model for the
following GLMs with group sparsity: Gaussian linear regression, binary logistic
regression, Poisson regression, negative binomial regression, and gamma
regression.  Stand-alone functions for group-regularized negative binomial
regression and group-regularized gamma regression are also available, with the
option of employing the group lasso penalty of Yuan and Lin (2006)
<doi:10.1111/j.1467-9868.2005.00532.x>, the group minimax concave penalty (MCP)
of Breheny and Huang <doi:10.1007/s11222-013-9424-2>, or the group smoothly
clipped absolute deviation (SCAD) penalty of Breheny and Huang (2015)
<doi:10.1007/s11222-013-9424-2>.")
    (license license:gpl3)))

(define-public r-ssfit
  (package
    (name "r-ssfit")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssfit" version))
       (sha256
        (base32 "1xq3gb76hs1mypjn6zyhwki5qkw3bnj51ljm10khky607mq5qz6q"))))
    (properties `((upstream-name . "ssfit")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey))
    (home-page "https://cran.r-project.org/package=ssfit")
    (synopsis "Fitting of Parametric Models using Summary Statistics")
    (description
     "Fits complex parametric models using the method proposed by Cox and Kartsonaki
(2012) without likelihoods.")
    (license license:gpl2+)))

(define-public r-ssfa
  (package
    (name "r-ssfa")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssfa" version))
       (sha256
        (base32 "1ypfx6k2zbc5n779ikj6pa8kwc6bg3zn4m044v46qn20348ygg0a"))))
    (properties `((upstream-name . "ssfa")))
    (build-system r-build-system)
    (propagated-inputs (list r-spdep r-spatialreg r-sp r-maxlik r-matrix))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=ssfa")
    (synopsis "Spatial Stochastic Frontier Analysis")
    (description
     "Spatial Stochastic Frontier Analysis (SSFA) is an original method for
controlling the spatial heterogeneity in Stochastic Frontier Analysis (SFA)
models, for cross-sectional data, by splitting the inefficiency term into three
terms: the first one related to spatial peculiarities of the territory in which
each single unit operates, the second one related to the specific production
features and the third one representing the error term.")
    (license license:gpl3)))

(define-public r-ssev
  (package
    (name "r-ssev")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssev" version))
       (sha256
        (base32 "1iw07gw9vhg073s5kkxh5f1si6p998rc1i2zglnpk5c3pi3l55wp"))))
    (properties `((upstream-name . "ssev")))
    (build-system r-build-system)
    (propagated-inputs (list r-pwr r-mess))
    (home-page "https://cran.r-project.org/package=ssev")
    (synopsis "Sample Size Computation for Fixed N with Optimal Reward")
    (description
     "Computes the optimal sample size for various 2-group designs (e.g., when
comparing the means of two groups assuming equal variances, unequal variances,
or comparing proportions) when the aim is to maximize the rewards over the full
decision procedure of a) running a trial (with the computed sample size), and b)
subsequently administering the winning treatment to the remaining N-n units in
the population.  Sample sizes and expected rewards for standard t- and z- tests
are also provided.")
    (license license:gpl3)))

(define-public r-sseparser
  (package
    (name "r-sseparser")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SSEparser" version))
       (sha256
        (base32 "1qjvrs8cv2m4g2rjcdylwss641z4cchzsdymn0lmmdpfcnfys1vy"))))
    (properties `((upstream-name . "SSEparser")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rlang r-r6 r-purrr r-magrittr))
    (home-page "https://github.com/calderonsamuel/SSEparser")
    (synopsis "Parse Server-Sent Events")
    (description
     "Functionality to parse server-sent events with a high-level interface that can
be extended for custom applications.")
    (license license:expat)))

(define-public r-sse
  (package
    (name "r-sse")
    (version "0.7-17")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sse" version))
       (sha256
        (base32 "0aqpi70rgmh2kmf11z2cvyl8c9klv8jm81pcpkzjrw3pbpgkqizf"))))
    (properties `((upstream-name . "sse")))
    (build-system r-build-system)
    (propagated-inputs (list r-lattice))
    (home-page "http://r-forge.r-project.org/projects/power/")
    (synopsis "Sample Size Estimation")
    (description
     "This package provides functions to evaluate user-defined power functions for a
parameter range, and draws a sensitivity plot.  It also provides a resampling
procedure for semi-parametric sample size estimation and methods for adding
information to a Sweave report.")
    (license license:gpl3)))

(define-public r-ssdtools
  (package
    (name "r-ssdtools")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssdtools" version))
       (sha256
        (base32 "13lm4nf2k89f89dvcr5j0jgxp3hf2wdffvf6vbrx4xz5wkhs642s"))))
    (properties `((upstream-name . "ssdtools")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-universals
                             r-tmb
                             r-tibble
                             r-stringr
                             r-ssddata
                             r-scales
                             r-rlang
                             r-rcppeigen
                             r-rcpp
                             r-purrr
                             r-plyr
                             r-lifecycle
                             r-goftest
                             r-glue
                             r-ggplot2
                             r-generics
                             r-furrr
                             r-chk
                             r-abind))
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "https://github.com/bcgov/ssdtools")
    (synopsis "Species Sensitivity Distributions")
    (description
     "Species sensitivity distributions are cumulative probability distributions which
are fitted to toxicity concentrations for different species as described by
Posthuma et al.(2001) <isbn:9781566705783>.  The ssdtools package uses Maximum
Likelihood to fit distributions such as the gamma, log-logistic, log-normal and
log-normal log-normal mixture.  Multiple distributions can be averaged using
Akaike Information Criteria.  Confidence intervals on hazard concentrations and
proportions are produced by parametric bootstrapping.")
    (license (list license:asl2.0
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-ssdr
  (package
    (name "r-ssdr")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sSDR" version))
       (sha256
        (base32 "1fqsglqzsbzdvcdnwwf7mg6x20zfpw66x63r6avac3w1pz3n7kvx"))))
    (properties `((upstream-name . "sSDR")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-mass))
    (home-page "https://cran.r-project.org/package=sSDR")
    (synopsis
     "Tools Developed for Structured Sufficient Dimension Reduction (sSDR)")
    (description
     "This package performs structured OLS (@code{sOLS}) and structured SIR
(@code{sSIR}).")
    (license license:gpl2+)))

(define-public r-ssdm
  (package
    (name "r-ssdm")
    (version "0.2.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SSDM" version))
       (sha256
        (base32 "03w1rkzpkhhz251pcy57fmyq0jdf4cplv3ns0h7j76fs2v9pfn3x"))))
    (properties `((upstream-name . "SSDM")))
    (build-system r-build-system)
    (propagated-inputs (list r-spthin
                             r-shinydashboard
                             r-shiny
                             r-sf
                             r-sdm
                             r-scales
                             r-rpart
                             r-reshape2
                             r-raster
                             r-randomforest
                             r-poibin
                             r-nnet
                             r-mgcv
                             r-magrittr
                             r-leaflet
                             r-itertools
                             r-iterators
                             r-ggplot2
                             r-gbm
                             r-foreach
                             r-earth
                             r-e1071
                             r-doparallel
                             r-dismo))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/sylvainschmitt/SSDM")
    (synopsis "Stacked Species Distribution Modelling")
    (description
     "Allows to map species richness and endemism based on stacked species
distribution models (SSDM).  Individuals SDMs can be created using a single or
multiple algorithms (ensemble SDMs).  For each species, an SDM can yield a
habitat suitability map, a binary map, a between-algorithm variance map, and can
assess variable importance, algorithm accuracy, and between- algorithm
correlation.  Methods to stack individual SDMs include summing individual
probabilities and thresholding then summing.  Thresholding can be based on a
specific evaluation metric or by drawing repeatedly from a Bernoulli
distribution.  The SSDM package also provides a user-friendly interface.")
    (license (list license:gpl3+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-ssdgsa
  (package
    (name "r-ssdgsa")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssdGSA" version))
       (sha256
        (base32 "1167vfacvhkc9p7pv30snj24jz2x09fpfvad5qb3brw7lkr0japs"))))
    (properties `((upstream-name . "ssdGSA")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidyselect
                             r-tibble
                             r-stringr
                             r-purrr
                             r-org-hs-eg-db
                             r-gsva
                             r-dplyr
                             r-clusterprofiler))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ssdGSA")
    (synopsis "Single Sample Directional Gene Set Analysis")
    (description
     "This package provides a method that inherits the standard gene set variation
analysis (GSVA) method and also provides the option to use summary statistics
from any analysis (disease vs healthy, lesional side vs nonlesional side, etc..)
input to define the direction of gene sets used for directional gene set score
calculation for a given disease.  Note to use this package, GSVA(>= 1.52.1) is
needed to pre-installed.  Hanzelmann, S., Castelo, R., and Guinney, J. (2013)
<doi:10.1186/1471-2105-14-7>.")
    (license license:gpl2)))

(define-public r-ssdforr
  (package
    (name "r-ssdforr")
    (version "1.5.35")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SSDforR" version))
       (sha256
        (base32 "0x8wkck67n74wdx6my8hfv1iz9dyh88ai1ipjkljjsfjmcgfg23q"))))
    (properties `((upstream-name . "SSDforR")))
    (build-system r-build-system)
    (propagated-inputs (list r-ttr
                             r-singlecasees
                             r-psych
                             r-modifiedmk
                             r-metafor
                             r-mass
                             r-mad
                             r-kendall))
    (home-page "https://cran.r-project.org/package=SSDforR")
    (synopsis "Functions to Analyze Single System Data")
    (description
     "This package provides functions to visually and statistically analyze single
system data.")
    (license license:gpl2+)))

(define-public r-ssddata
  (package
    (name "r-ssddata")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssddata" version))
       (sha256
        (base32 "0njcnjcgdghwrbjcbiq4yfn7nq9h4dvylrqh57c6vx35yqc4g375"))))
    (properties `((upstream-name . "ssddata")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-dplyr r-chk))
    (home-page "https://cran.r-project.org/package=ssddata")
    (synopsis "Species Sensitivity Distribution Data")
    (description
     "Reference data sets of species sensitivities to compare the results of fitting
species sensitivity distributions using software such as ssdtools and Burrlioz'.
 It consists of 17 primary data sets from four different Australian and Canadian
organizations as well as five datasets from anonymous sources.  It also includes
a data set of the results of fitting various distributions using different
software.")
    (license license:asl2.0)))

(define-public r-ssd4mosaic
  (package
    (name "r-ssd4mosaic")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssd4mosaic" version))
       (sha256
        (base32 "1266wn3vwlajr57sdjqk5zylnm7vcss37r4qwa02iv9d98qmwmrw"))))
    (properties `((upstream-name . "ssd4mosaic")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinyjs
                             r-shinybusy
                             r-shiny
                             r-rmarkdown
                             r-rlang
                             r-rhandsontable
                             r-jsonlite
                             r-htmlwidgets
                             r-htmltools
                             r-golem
                             r-ggplot2
                             r-fitdistrplus
                             r-config
                             r-actuar))
    (native-inputs (list r-knitr))
    (home-page "https://gitlab.in2p3.fr/mosaic-software/mosaic-ssd")
    (synopsis "Web Application for the SSD Module of the MOSAIC Platform")
    (description
     "Web application using shiny for the SSD (Species Sensitivity Distribution)
module of the MOSAIC (MOdeling and @code{StAtistical} tools for
@code{ecotoxICology}) platform.  It estimates the Hazardous Concentration for x%
of the species (HCx) from toxicity values that can be censored and provides
various plotting options for a better understanding of the results.  See our
companion paper Kon Kam King et al. (2014)
<doi:10.48550/@code{arXiv.1311.5772>}.")
    (license license:expat)))

(define-public r-sscor
  (package
    (name "r-sscor")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sscor" version))
       (sha256
        (base32 "1kcrr90cxg6k4qkc3pidhkwf4dsnlgbxczxigr2afwpd9cpf104c"))))
    (properties `((upstream-name . "sscor")))
    (build-system r-build-system)
    (propagated-inputs (list r-robustbase r-pcapp r-mvtnorm))
    (home-page "https://cran.r-project.org/package=sscor")
    (synopsis
     "Robust Correlation Estimation and Testing Based on Spatial Signs")
    (description
     "This package provides the spatial sign correlation and the two-stage spatial
sign correlation as well as a one-sample test for the correlation coefficient.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-ssc
  (package
    (name "r-ssc")
    (version "2.1-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssc" version))
       (sha256
        (base32 "1lq71p8hbjfg4a52009810k5dmhak9ddk1dywvhy7v2gjmxqnadw"))))
    (properties `((upstream-name . "ssc")))
    (build-system r-build-system)
    (propagated-inputs (list r-proxy))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/mabelc/SSC")
    (synopsis "Semi-Supervised Classification Methods")
    (description
     "This package provides a collection of self-labeled techniques for
semi-supervised classification.  In semi-supervised classification, both labeled
and unlabeled data are used to train a classifier.  This learning paradigm has
obtained promising results, specifically in the presence of a reduced set of
labeled examples.  This package implements a collection of self-labeled
techniques to construct a classification model.  This family of techniques
enlarges the original labeled set using the most confident predictions to
classify unlabeled data.  The techniques implemented can be applied to
classification problems in several domains by the specification of a supervised
base classifier.  At low ratios of labeled data, it can be shown to perform
better than classical supervised classifiers.")
    (license license:gpl3+)))

(define-public r-ssbtools
  (package
    (name "r-ssbtools")
    (version "1.5.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SSBtools" version))
       (sha256
        (base32 "1vxdmy2kqpnlbpnkgry1i58vda1b0pc3y9p8ncr47gxdbfxk9yn2"))))
    (properties `((upstream-name . "SSBtools")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-matrix r-mass))
    (home-page "https://github.com/statisticsnorway/ssb-ssbtools")
    (synopsis "Statistics Norway's Miscellaneous Tools")
    (description
     "This package provides functions used by other packages from Statistics Norway
are gathered.  General data manipulation functions, algorithms for statistical
disclosure control (Langsrud, 2024) <doi:10.1007/978-3-031-69651-0_6> and
functions for hierarchical computations by sparse model matrices are included
(Langsrud, 2023) <doi:10.32614/RJ-2023-088>.")
    (license license:expat)))

(define-public r-ssanv
  (package
    (name "r-ssanv")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssanv" version))
       (sha256
        (base32 "17a4a5azxm5h2vxia16frcwdyd36phpfm7fi40q6mnnrwbpkzsjd"))))
    (properties `((upstream-name . "ssanv")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=ssanv")
    (synopsis
     "Sample Size Adjusted for Nonadherence or Variability of Input Parameters")
    (description
     "This package provides a set of functions to calculate sample size for two-sample
difference in means tests.  Does adjustments for either nonadherence or
variability that comes from using data to estimate parameters.")
    (license license:gpl3)))

(define-public r-ssabss
  (package
    (name "r-ssabss")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ssaBSS" version))
       (sha256
        (base32 "1sfhrp5y4hhnc417jq2vyk935iyicl58jbw4nwrj3rayh0ng7dbp"))))
    (properties `((upstream-name . "ssaBSS")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xts
                             r-tsbss
                             r-jade
                             r-ictest
                             r-ggplot2
                             r-bssprep))
    (home-page "https://cran.r-project.org/package=ssaBSS")
    (synopsis "Stationary Subspace Analysis")
    (description
     "Stationary subspace analysis (SSA) is a blind source separation (BSS) variant
where stationary components are separated from non-stationary components.
Several SSA methods for multivariate time series are provided here (Flumian et
al. (2021); Hara et al. (2010) <doi:10.1007/978-3-642-17537-4_52>) along with
functions to simulate time series with time-varying variance and autocovariance
(Patilea and Raissi(2014) <doi:10.1080/01621459.2014.884504>).")
    (license license:gpl2+)))

(define-public r-ss3sim
  (package
    (name "r-ss3sim")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ss3sim" version))
       (sha256
        (base32 "0s2yajlhi07fvqc0rr4msd9g2jrswb83dx55wsxnw925vikqa5kz"))))
    (properties `((upstream-name . "ss3sim")))
    (build-system r-build-system)
    (propagated-inputs (list r-r4ss r-gtools r-ggplot2 r-foreach r-bbmle))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ss3sim/ss3sim")
    (synopsis
     "Fisheries Stock Assessment Simulation Testing with Stock Synthesis")
    (description
     "Develops a framework for fisheries stock assessment simulation testing with
Stock Synthesis (SS) as described in Anderson et al. (2014)
<doi:10.1371/journal.pone.0092725>.")
    (license license:expat)))

(define-public r-srvyr
  (package
    (name "r-srvyr")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "srvyr" version))
       (sha256
        (base32 "0576jgvnyfxks1r6nc53npgmljvm3i1v389zvar25pyq9n77sdpc"))))
    (properties `((upstream-name . "srvyr")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidyselect
                             r-tidyr
                             r-tibble
                             r-survey
                             r-rlang
                             r-magrittr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "http://gdfe.co/srvyr/")
    (synopsis "'dplyr'-Like Syntax for Summary Statistics of Survey Data")
    (description
     "Use piping, verbs like group_by and summarize', and other dplyr inspired
syntactic style when calculating summary statistics on survey data using
functions from the survey package.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-srttools
  (package
    (name "r-srttools")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SRTtools" version))
       (sha256
        (base32 "1203i6nqclx0faxyvhdaapmfxy8h95jj83svxriqh1hpava5s1dy"))))
    (properties `((upstream-name . "SRTtools")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr))
    (home-page "https://github.com/ChiHangChen/SRTtools")
    (synopsis "Adjust Srt File to Get Better Experience when Watching Movie")
    (description
     "Srt file is a common subtitle format for videos, it contains subtitle and when
the subtitle showed.  This package is for align time of srt file, and also
change color, style and position of subtitle in videos, the srt file will be
read as a vector into R, and can be write into srt file after modified using
this package.")
    (license license:gpl2+)))

(define-public r-srtsim
  (package
    (name "r-srtsim")
    (version "0.99.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SRTsim" version))
       (sha256
        (base32 "0gfi3dng3pl2ki4g6nbwjxhq4zbj3j4yq95j4dyfzbqj8k4ah7lv"))))
    (properties `((upstream-name . "SRTsim")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-spatstat-random
                             r-spatstat-geom
                             r-sp
                             r-shinydashboard
                             r-shinybs
                             r-shiny
                             r-sf
                             r-s4vectors
                             r-plotly
                             r-pdist
                             r-morpho
                             r-matrixstats
                             r-matrix
                             r-mass
                             r-magrittr
                             r-magick
                             r-ggpubr
                             r-ggplot2
                             r-fnn
                             r-dt
                             r-dplyr
                             r-dashboardthemes
                             r-concaveman))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SRTsim")
    (synopsis "Simulator for Spatially Resolved Transcriptomics")
    (description
     "An independent, reproducible, and flexible Spatially Resolved Transcriptomics
(SRT) simulation framework that can be used to facilitate the development of SRT
analytical methods for a wide variety of SRT-specific analyses.  It utilizes
spatial localization information to simulate SRT expression count data in a
reproducible and scalable fashion.  Two major simulation schemes are implemented
in SRTsim': reference-based and reference-free.")
    (license license:gpl3+)))

(define-public r-srt
  (package
    (name "r-srt")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "srt" version))
       (sha256
        (base32 "0vb9hkz1nzmkv9rgcbc9yxns8ws8ymyggfsnnzjwpklx5y1sf2pl"))))
    (properties `((upstream-name . "srt")))
    (build-system r-build-system)
    (home-page "https://k5cents.github.io/srt/")
    (synopsis "Read Subtitle Files as Tabular Data")
    (description
     "Read @code{SubRip} <https://sourceforge.net/projects/subrip/> subtitle files as
data frames for easy text analysis or manipulation.  Easily shift numeric
timings and export subtitles back into valid @code{SubRip} timestamp format to
sync subtitles and audio.")
    (license license:gpl3)))

(define-public r-srs
  (package
    (name "r-srs")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SRS" version))
       (sha256
        (base32 "0nx1sj91xkwplcmflq9c21zndaa4m21m9iqax86v947k070q48ql"))))
    (properties `((upstream-name . "SRS")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan r-shinycssloaders r-shinybusy r-shiny
                             r-dt))
    (home-page "https://cran.r-project.org/package=SRS")
    (synopsis "Scaling with Ranked Subsampling")
    (description
     "Analysis of species count data in ecology often requires normalization to an
identical sample size.  Rarefying (random subsampling without replacement),
which is a popular method for normalization, has been widely criticized for its
poor reproducibility and potential distortion of the community structure.  In
the context of microbiome count data, researchers explicitly advised against the
use of rarefying.  An alternative to rarefying is scaling with ranked
subsampling (SRS).  SRS consists of two steps.  In the first step, the total
counts for all OTUs (operational taxonomic units) or species in each sample are
divided by a scaling factor chosen in such a way that the sum of the scaled
counts Cscaled equals Cmin.  In the second step, the non-integer Cscaled values
are converted into integers by an algorithm that we dub ranked subsampling.  The
Cscaled value for each OTU or species is split into the integer part Cint (Cint
= floor(Cscaled)) and the fractional part Cfrac (Cfrac = Cscaled - Cints).
Since the sum of Cint is smaller or equal to Cmin, additional delta C = Cmin -
the sum of Cint counts have to be added to the library to reach the total count
of Cmin.  This is achieved as follows.  OTUs are ranked in the descending order
of their Cfrac values.  Beginning with the OTU of the highest rank, single count
per OTU is added to the normalized library until the total number of added
counts reaches delta C and the sum of all counts in the normalized library
equals Cmin.  When the lowest Cfrag involved in picking delta C counts is shared
by several OTUs, the OTUs used for adding a single count to the library are
selected in the order of their Cint values.  This selection minimizes the effect
of normalization on the relative frequencies of OTUs.  OTUs with identical Cfrag
as well as Cint are sampled randomly without replacement.  See Beule & Karlovsky
(2020) <doi:10.7717/peerj.9593> for details.")
    (license license:cc-by-sa4.0)))

(define-public r-srp
  (package
    (name "r-srp")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "srp" version))
       (sha256
        (base32 "0xdzfvi7z78xmyxwd8fha3lr86jwmgjba1w8hhn4sv3v5gc9vlkh"))))
    (properties `((upstream-name . "srp")))
    (build-system r-build-system)
    (propagated-inputs (list r-mgcv r-fda))
    (home-page "https://cran.r-project.org/package=srp")
    (synopsis "Smooth-Rough Partitioning of the Regression Coefficients")
    (description
     "This package performs the change-point detection in regression coefficients of
linear model by partitioning the regression coefficients into two classes of
smoothness.  The change-point and the regression coefficients are jointly
estimated.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-srnagenetic
  (package
    (name "r-srnagenetic")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sRNAGenetic" version))
       (sha256
        (base32 "1cvk40rkbxvr8jhwfqkgdfy150mi56y976pjp2w34rnghry6ql8z"))))
    (properties `((upstream-name . "sRNAGenetic")))
    (build-system r-build-system)
    (propagated-inputs (list r-venndiagram
                             r-plyr
                             r-ggsci
                             r-ggplot2
                             r-futile-logger
                             r-deseq2))
    (home-page "https://cran.r-project.org/package=sRNAGenetic")
    (synopsis "Analysis of Small RNA Expression Changes in Hybrid Plants")
    (description
     "The most important function of the R package is the genetic effects analysis of
small RNA in hybrid plants via two methods, and at the same time, it provides
various forms of graph related to data characteristics and expression analysis.
In terms of two classification methods, one is the calculation of the additive
(a) and dominant (d), the other is the evaluation of expression level dominance
by comparing the total expression of the small RNA in progeny with the
expression level in the parent species.")
    (license license:gpl3+)))

(define-public r-srm
  (package
    (name "r-srm")
    (version "0.4-26")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "srm" version))
       (sha256
        (base32 "1cw0a0n1chnri6jmcav04qa52s8jakviwjpq1yx1x5z2ya978r26"))))
    (properties `((upstream-name . "srm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://github.com/alexanderrobitzsch/srm")
    (synopsis "Structural Equation Modeling for the Social Relations Model")
    (description
     "This package provides functionality for structural equation modeling for the
social relations model (Kenny & La Voie, 1984;
<doi:10.1016/S0065-2601(08)60144-6>; Warner, Kenny, & Soto, 1979,
<doi:10.1037/0022-3514.37.10.1742>).  Maximum likelihood estimation (Gill &
Swartz, 2001, <doi:10.2307/3316080>; Nestler, 2018,
<doi:10.3102/1076998617741106>) and least squares estimation is supported (Bond
& Malloy, 2018, <doi:10.1016/B978-0-12-811967-9.00014-X>).")
    (license license:gpl2+)))

(define-public r-srlts
  (package
    (name "r-srlts")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "srlTS" version))
       (sha256
        (base32 "1alqywbp36adgnak1s78mcwfg0gcg90a9412nypylsk6bfa7wgsv"))))
    (properties `((upstream-name . "srlTS")))
    (build-system r-build-system)
    (propagated-inputs (list r-yardstick r-rlang r-rcpproll r-ncvreg r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://petersonr.github.io/srlTS/")
    (synopsis "Sparsity-Ranked Lasso for Time Series")
    (description
     "An implementation of sparsity-ranked lasso for time series data.  This
methodology is especially useful for large time series with exogenous features
and/or complex seasonality.  Originally described in Peterson and Cavanaugh
(2022) <doi:10.1007/s10182-021-00431-7> in the context of variable selection
with interactions and/or polynomials, ranked sparsity is a philosophy with
methods useful for variable selection in the presence of prior informational
asymmetry.  This situation exists for time series data with complex seasonality,
as shown in Peterson and Cavanaugh (2023+)
<doi:10.48550/@code{arXiv.2211.01492>}, which also describes this package in
greater detail.  The Sparsity-Ranked Lasso (SRL) for Time Series implemented in
@code{srlTS} can fit large/complex/high-frequency time series quickly, even with
a high-dimensional exogenous feature set.  The SRL is considerably faster than
its competitors, while often producing more accurate predictions.  Also included
is a long hourly series of arrivals into the University of Iowa Emergency
Department with concurrent local temperature.")
    (license license:gpl3+)))

(define-public r-srlars
  (package
    (name "r-srlars")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "srlars" version))
       (sha256
        (base32 "1l98d24r746h0yh1g885slaq292d3xiy355ihkwdrhvvmirs7l0f"))))
    (properties `((upstream-name . "srlars")))
    (build-system r-build-system)
    (propagated-inputs (list r-glmnet r-cellwise))
    (home-page "https://cran.r-project.org/package=srlars")
    (synopsis "Split Robust Least Angle Regression")
    (description
     "This package provides functions to perform split robust least angle regression.
The approach first uses the least angle regression algorithm to split the
variables into the models of an ensemble and robust estimates of the correlation
between predictors.  An elastic net estimator is then applied to the selected
predictors in each model using the imputed data from the detect deviating cell
(DDC) method.")
    (license license:gpl2+)))

(define-public r-sregsurvey
  (package
    (name "r-sregsurvey")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sregsurvey" version))
       (sha256
        (base32 "1wpqsjhj4vl8gzma4i9y75srzwl8zmkj55lmrhni7xar4d81mnb4"))))
    (properties `((upstream-name . "sregsurvey")))
    (build-system r-build-system)
    (propagated-inputs (list r-teachingsampling
                             r-magrittr
                             r-gamlss-dist
                             r-gamlss
                             r-dplyr
                             r-caret))
    (home-page "https://cran.r-project.org/package=sregsurvey")
    (synopsis "Semiparametric Model-Assisted Estimation in Finite Populations")
    (description
     "It is a framework to fit semiparametric regression estimators for the total
parameter of a finite population when the interest variable is asymmetric
distributed.  The main references for this package are Sarndal C.E., Swensson
B., and Wretman J. (2003,ISBN: 978-0-387-40620-6, \"Model Assisted Survey
Sampling.\" Springer-Verlag) Cardozo C.A, Paula G.A. and Vanegas L.H. (2022)
\"Generalized log-gamma additive partial linear mdoels with P-spline smoothing\",
Statistical Papers.  Cardozo C.A and Alonso-Malaver C.E. (2022).
\"Semi-parametric model assisted estimation in finite populations.\" In
preparation.")
    (license license:gpl3)))

(define-public r-sreg
  (package
    (name "r-sreg")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sreg" version))
       (sha256
        (base32 "05rvx5frd9c7qz00wjk8qirsnj1hmqlpvmzvf6lwbhbx2abwzd1j"))))
    (properties `((upstream-name . "sreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-rlang r-extradistr r-dplyr r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jutrifonov/sreg")
    (synopsis "Stratified Randomized Experiments")
    (description
     "Estimate average treatment effects (ATEs) in stratified randomized experiments.
sreg is designed to accommodate scenarios with multiple treatments and
cluster-level treatment assignments, and accommodates optimal linear covariate
adjustment based on baseline observable characteristics.  sreg computes
estimators and standard errors based on Bugni, Canay, Shaikh (2018)
<doi:10.1080/01621459.2017.1375934>; Bugni, Canay, Shaikh, Tabord-Meehan (2024+)
<doi:10.48550/@code{arXiv.2204.08356>}; and Jiang, Linton, Tang, Zhang (2023+)
<doi:10.48550/@code{arXiv.2201.13004>}.")
    (license license:expat)))

(define-public r-srdpdata
  (package
    (name "r-srdpdata")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sRdpData" version))
       (sha256
        (base32 "0zflpc44q6bj9rr8yhv5h7ksgmxrsvv28vfpvqbn3yd46hn1fdj7"))))
    (properties `((upstream-name . "sRdpData")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-rlang r-magrittr r-dplyr))
    (home-page "https://github.com/hgoers/sRdpData")
    (synopsis "Strategies of Resistance Data Project")
    (description
     "This package provides you with easy, programmatic access to SRDP data.")
    (license license:expat)))

(define-public r-srda
  (package
    (name "r-srda")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sRDA" version))
       (sha256
        (base32 "0g11smn4adm3b79m9w7wzcragxggkf8rrrsgj2yzdhm1d7zzjwbk"))))
    (properties `((upstream-name . "sRDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-matrix r-foreach r-elasticnet
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=sRDA")
    (synopsis "Sparse Redundancy Analysis")
    (description
     "Sparse redundancy analysis for high dimensional (biomedical) data.  Directional
multivariate analysis to express the maximum variance in the predicted data set
by a linear combination of variables of the predictive data set.  Implemented in
a partial least squares framework, for more details see Csala et al. (2017)
<doi:10.1093/bioinformatics/btx374>.")
    (license license:expat)))

(define-public r-srcs
  (package
    (name "r-srcs")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SRCS" version))
       (sha256
        (base32 "13zf3cqs53w68f9zc1fkb9ql84rvzn7g1hbykqrbvss8hjaq8x1r"))))
    (properties `((upstream-name . "SRCS")))
    (build-system r-build-system)
    (native-inputs (list r-r-rsp))
    (home-page "http://decsai.ugr.es/~pjvi/r-packages.html")
    (synopsis
     "Statistical Ranking Color Scheme for Multiple Pairwise Comparisons")
    (description
     "Implementation of the SRCS method for a color-based visualization of the results
of multiple pairwise tests on a large number of problem configurations, proposed
in: I.G. del Amo, D.A. Pelta.  SRCS: a technique for comparing multiple
algorithms under several factors in dynamic optimization problems.  In: E. Alba,
A. Nakib, P. Siarry (Eds.), Metaheuristics for Dynamic Optimization.  Series:
Studies in Computational Intelligence 433, Springer, Berlin/Heidelberg, 2012.")
    (license license:lgpl3+)))

(define-public r-srcr
  (package
    (name "r-srcr")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "srcr" version))
       (sha256
        (base32 "0hmlvq8rwhdv8hh7i517ml46p1hvd2idby4xx7firin8ca1829n5"))))
    (properties `((upstream-name . "srcr")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-dplyr r-dbi))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/baileych/srcr")
    (synopsis "Simplify Connections to Database Sources")
    (description
     "Connecting to databases requires boilerplate code to specify connection
parameters and to set up sessions properly with the DBMS. This package provides
a simple tool to fill two purposes: abstracting connection details, including
secret credentials, out of your source code and managing configuration for
frequently-used database connections in a persistent and flexible way, while
minimizing requirements on the runtime environment.")
    (license license:artistic2.0)))

(define-public r-srcpkgs
  (package
    (name "r-srcpkgs")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "srcpkgs" version))
       (sha256
        (base32 "1j7yzm6hl98jy9jx203zkr65d2zivx3k6zhnk3fnpvvpki1ma61s"))))
    (properties `((upstream-name . "srcpkgs")))
    (build-system r-build-system)
    (propagated-inputs (list r-pkgload r-devtools r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/kforner/srcpkgs")
    (synopsis "R Source Packages Manager")
    (description
     "Manage a collection/library of R source packages.  Discover, document, load,
test source packages.  Enable to use those packages as if they were actually
installed.  Quickly reload only what is needed on source code change.  Run tests
and checks in parallel.")
    (license license:gpl3+)))

(define-public r-sra
  (package
    (name "r-sra")
    (version "0.1.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sra" version))
       (sha256
        (base32 "080pdhzsf1bawi9l1bbvzdk7s9733sdlx4cpcm3whf9ffdbg6ahz"))))
    (properties `((upstream-name . "sra")))
    (build-system r-build-system)
    (home-page "https://github.com/lerouzic/sra")
    (synopsis "Selection Response Analysis")
    (description
     "Artificial selection through selective breeding is an efficient way to induce
changes in traits of interest in experimental populations.  This package (sra)
provides a set of tools to analyse artificial-selection response datasets.  The
data typically feature for several generations the average value of a trait in a
population, the variance of the trait, the population size and the average value
of the parents that were chosen to breed.  Sra implements two families of models
aiming at describing the dynamics of the genetic architecture of the trait
during the selection response.  The first family relies on purely descriptive
(phenomenological) models, based on an autoregressive framework.  The second
family provides different mechanistic models, accounting e.g. for inbreeding,
mutations, genetic and environmental canalization, or epistasis.  The parameters
underlying the dynamics of the time series are estimated by maximum likelihood.
The sra package thus provides (i) a wrapper for the R functions mle() and
optim() aiming at fitting in a convenient way a predetermined set of models, and
(ii) some functions to plot and analyze the output of the models.")
    (license license:gpl2)))

(define-public r-sr
  (package
    (name "r-sr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sr" version))
       (sha256
        (base32 "0d8c7gsr48xxg7y09n0namzqp148bk4idz51d2d5xlmlzcyq74k8"))))
    (properties `((upstream-name . "sr")))
    (build-system r-build-system)
    (propagated-inputs (list r-vdiffr r-rann r-progress r-ggplot2 r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://smoothregression.com")
    (synopsis "Smooth Regression - The Gamma Test and Tools")
    (description
     "Finds causal connections in precision data, finds lags and embeddings in time
series, guides training of neural networks and other smooth models, evaluates
their performance, gives a mathematically grounded answer to the over-training
problem.  Smooth regression is based on the Gamma test, which measures
smoothness in a multivariate relationship.  Causal relations are smooth, noise
is not.  sr includes the Gamma test and search techniques that use it.
References: Evans & Jones (2002) <doi:10.1098/rspa.2002.1010>, AJ Jones (2004)
<doi:10.1007/s10287-003-0006-1>.")
    (license license:gpl3+)))

(define-public r-squid
  (package
    (name "r-squid")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "squid" version))
       (sha256
        (base32 "1zpm787ifzrhww32ghi57ii37p9dssq7q7gbai9iqass78z90y2j"))))
    (properties `((upstream-name . "squid")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinymatrix
                             r-shiny
                             r-plotly
                             r-mass
                             r-lme4
                             r-ggplot2
                             r-data-table
                             r-brms
                             r-arm))
    (home-page "https://github.com/squid-group/squid")
    (synopsis "Statistical Quantification of Individual Differences")
    (description
     "This package provides a simulation-based tool made to help researchers to become
familiar with multilevel variations, and to build up sampling designs for their
study.  This tool has two main objectives: First, it provides an educational
tool useful for students, teachers and researchers who want to learn to use
mixed-effects models.  Users can experience how the mixed-effects model
framework can be used to understand distinct biological phenomena by
interactively exploring simulated multilevel data.  Second, it offers research
opportunities to those who are already familiar with mixed-effects models, as it
enables the generation of data sets that users may download and use for a range
of simulation-based statistical analyses such as power and sensitivity analysis
of multilevel and multivariate data [Allegue, H., Araya-Ajoy, Y.G., Dingemanse,
N.J., Dochtermann N.A., Garamszegi, L.Z., Nakagawa, S., Reale, D., Schielzeth,
H. and Westneat, D.F. (2016) <doi: 10.1111/2041-210X.12659>].")
    (license license:expat)))

(define-public r-squeezy
  (package
    (name "r-squeezy")
    (version "1.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "squeezy" version))
       (sha256
        (base32 "1dykp6hz056f9ajx1sg5bl89v4qmn41lsp9p9zzd9yib6d6ryfsm"))))
    (properties `((upstream-name . "squeezy")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-multiridge r-matrix r-glmnet))
    (home-page "https://arxiv.org/abs/2101.03875")
    (synopsis "Group-Adaptive Elastic Net Penalised Generalised Linear Models")
    (description
     "Fit linear and logistic regression models penalised with group-adaptive elastic
net penalties.  The group penalties correspond to groups of covariates defined
by a co-data group set.  The method accommodates inclusion of unpenalised
covariates and overlapping groups.  See Van Nee et al. (2021)
<@code{arXiv:2101.03875>}.")
    (license license:gpl3+)))

(define-public r-squeakr
  (package
    (name "r-squeakr")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SqueakR" version))
       (sha256
        (base32 "09l6dj1xq2zxxy6jnh1dl4rlcbx06fvf0jp2nypv3497dn97jsrh"))))
    (properties `((upstream-name . "SqueakR")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinydashboard
                             r-shiny
                             r-rstatix
                             r-rlist
                             r-report
                             r-readxl
                             r-rcolorbrewer
                             r-plotly
                             r-mass
                             r-googlesheets4
                             r-ggpubr
                             r-ggplot2
                             r-gghighlight
                             r-ggeasy
                             r-ggcorrplot
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://osimon81.github.io/SqueakR/")
    (synopsis "An Experiment Interface for 'DeepSqueak' Bioacoustics Research")
    (description
     "Data processing and visualizations for rodent vocalizations exported from
@code{DeepSqueak}'.  These functions are compatible with the @code{SqueakR}
Shiny Dashboard, which can be used to visualize experimental results and
analyses.")
    (license license:expat)))

(define-public r-squat
  (package
    (name "r-squat")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "squat" version))
       (sha256
        (base32 "0a5vm1ldwysadf50jvq7p988gv9arszg2a1hppcfjvwq193ywy0g"))))
    (properties `((upstream-name . "squat")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-scales
                             r-roahd
                             r-rlang
                             r-rcppeigen
                             r-rcpparmadillo
                             r-rcpp
                             r-purrr
                             r-progressr
                             r-mfpca
                             r-ggrepel
                             r-ggplot2
                             r-furrr
                             r-fundata
                             r-fdacluster
                             r-dtw
                             r-dbscan
                             r-cli))
    (home-page "https://github.com/LMJL-Alea/squat")
    (synopsis "Statistics for Quaternion Temporal Data")
    (description
     "An implementation of statistical tools for the analysis of rotation-valued time
series and functional data.  It relies on pre-existing quaternion data structure
provided by the Eigen C++ library.")
    (license license:gpl3+)))

(define-public r-squash
  (package
    (name "r-squash")
    (version "1.0.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "squash" version))
       (sha256
        (base32 "1x66iamj5pir6l9aqx4x7xylxxjpjrw8vcix9rbhfd0y0y2iqf7z"))))
    (properties `((upstream-name . "squash")))
    (build-system r-build-system)
    (home-page "https://github.com/aroneklund/squash")
    (synopsis "Color-Based Plots for Multivariate Visualization")
    (description
     "This package provides functions for color-based visualization of multivariate
data, i.e.  colorgrams or heatmaps.  Lower-level functions map numeric values to
colors, display a matrix as an array of colors, and draw color keys.
Higher-level plotting functions generate a bivariate histogram, a dendrogram
aligned with a color-coded matrix, a triangular distance matrix, and more.")
    (license license:artistic2.0)))

(define-public r-squant
  (package
    (name "r-squant")
    (version "1.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "squant" version))
       (sha256
        (base32 "1bxy0xxrrckw0j44jgqwy2ganl84r9b4hdc2g56xg9c735cnz4xf"))))
    (properties `((upstream-name . "squant")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-glmnet r-ggplot2))
    (home-page "https://cran.r-project.org/package=squant")
    (synopsis "Subgroup Identification Based on Quantitative Objectives")
    (description
     "This package provides a subgroup identification method for precision medicine
based on quantitative objectives.  This method can handle continuous, binary and
survival endpoint for both prognostic and predictive case.  For the predictive
case, the method aims at identifying a subgroup for which treatment is better
than control by at least a pre-specified or auto-selected constant.  For the
prognostic case, the method aims at identifying a subgroup that is at least
better than a pre-specified/auto-selected constant.  The derived signature is a
linear combination of predictors, and the selected subgroup are subjects with
the signature > 0.  The false discover rate when no true subgroup exists is
controlled at a user-specified level.")
    (license license:gpl3)))

(define-public r-sqrl
  (package
    (name "r-sqrl")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SQRL" version))
       (sha256
        (base32 "05qyrdvgrkv6r88pgwin8llh7qfzkivnrwz5ba1cwkbqg3pvfjhv"))))
    (properties `((upstream-name . "SQRL")))
    (build-system r-build-system)
    (propagated-inputs (list r-rodbc))
    (home-page "https://cran.r-project.org/package=SQRL")
    (synopsis "Enhances Interaction with 'ODBC' Databases")
    (description
     "This package provides simple and powerful interfaces that facilitate interaction
with ODBC data sources.  Each data source gets its own unique and dedicated
interface, wrapped around RODBC'.  Communication settings are remembered between
queries, and are managed silently in the background.  The interfaces support
multi-statement SQL scripts, which can be parameterised via metaprogramming
structures and embedded R expressions.")
    (license license:gpl3)))

(define-public r-sqn
  (package
    (name "r-sqn")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SQN" version))
       (sha256
        (base32 "1p7h4id8xz6dh5nn2935pj5b82mcmpmm8mj32l5i4wgwnh1amphp"))))
    (properties `((upstream-name . "SQN")))
    (build-system r-build-system)
    (propagated-inputs (list r-nor1mix r-mclust))
    (home-page "https://cran.r-project.org/package=SQN")
    (synopsis "Subset Quantile Normalization")
    (description
     "Normalization based a subset of negative control probes as described in Subset
quantile normalization using negative control features'.  Wu Z, Aryee MJ, J
Comput Biol.  2010 Oct;17(10):1385-95 [PMID 20976876].")
    (license license:lgpl2.0+)))

(define-public r-sqmtools
  (package
    (name "r-sqmtools")
    (version "1.6.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SQMtools" version))
       (sha256
        (base32 "0c725f0ksafxhgwhdb44h72gcs352afxi0gb843snnqwn5jmmsgh"))))
    (properties `((upstream-name . "SQMtools")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-pathview r-ggplot2 r-data-table))
    (home-page "https://github.com/jtamames/SqueezeMeta")
    (synopsis "Analyze Results Generated by the 'SqueezeMeta' Pipeline")
    (description
     "@code{SqueezeMeta} is a versatile pipeline for the automated analysis of
metagenomics/metatranscriptomics data
(<https://github.com/jtamames/@code{SqueezeMeta>}).  This package provides
functions loading @code{SqueezeMeta} results into R, filtering them based on
different criteria, and visualizing the results using basic plots.  The
@code{SqueezeMeta} project (and any subsets of it generated by the different
filtering functions) is parsed into a single object, whose different components
(e.g. tables with the taxonomic or functional composition across samples,
contig/gene abundance profiles) can be easily analyzed using other R packages
such as vegan or DESeq2'.  The methods in this package are further described in
Puente-SÃ¡nchez et al., (2020) <doi:10.1186/s12859-020-03703-2>.")
    (license license:gpl3)))

(define-public r-sqltargets
  (package
    (name "r-sqltargets")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sqltargets" version))
       (sha256
        (base32 "1wncxgjcaxn8dqc78vpqmp0hv7hvmyhp8i32k0992wrmvnghh1l3"))))
    (properties `((upstream-name . "sqltargets")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-targets
                             r-tarchetypes
                             r-stringr
                             r-rlang
                             r-readr
                             r-purrr
                             r-jinjar
                             r-glue
                             r-fs
                             r-dbi
                             r-cli))
    (home-page "https://github.com/daranzolin/sqltargets")
    (synopsis "'Targets' Extension for 'SQL' Queries")
    (description
     "This package provides an extension for SQL queries as separate file within
targets pipelines.  The shorthand creates two targets, the query file and the
query result.")
    (license license:expat)))

(define-public r-sqlstrings
  (package
    (name "r-sqlstrings")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sqlstrings" version))
       (sha256
        (base32 "147496898m4rqa63p72axw0g0a208pcy22mdhn0s7zv0j50lilc4"))))
    (properties `((upstream-name . "sqlstrings")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-readr r-fs))
    (home-page "https://cran.r-project.org/package=sqlstrings")
    (synopsis "Map 'SQL' Code to R Lists")
    (description
     "This package provides a helper function, to bulk read SQL code from separate
files and load it into an R list, where the list elements contain the individual
statements and queries as strings.  This works by annotating the SQL code with a
name comment, which also will be the name of the list element.")
    (license license:expat)))

(define-public r-sqlscore
  (package
    (name "r-sqlscore")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sqlscore" version))
       (sha256
        (base32 "1wvjiy7g0rxfy10vssdrv5li5sbvrin9wjy0ai0nxk503kw7g74k"))))
    (properties `((upstream-name . "sqlscore")))
    (build-system r-build-system)
    (propagated-inputs (list r-dbplyr))
    (home-page "https://github.com/wwbrannon/sqlscore/")
    (synopsis "Utilities for Generating SQL Queries from Model Objects")
    (description
     "This package provides utilities for generating SQL queries (particularly CREATE
TABLE statements) from R model objects.  The most important use case is
generating SQL to score a generalized linear model or related model represented
as an R object, in which case the package handles parsing formula operators and
including the model's response function.")
    (license license:expat)))

(define-public r-sqlrender
  (package
    (name "r-sqlrender")
    (version "1.19.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SqlRender" version))
       (sha256
        (base32 "1mq3v5ab8k6drnsfhlc0n01iif315jd4h84vcs0chaq5p4mq5zgb"))))
    (properties `((upstream-name . "SqlRender")))
    (build-system r-build-system)
    (inputs (list openjdk))
    (propagated-inputs (list r-rlang r-rjava r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://ohdsi.github.io/SqlRender/")
    (synopsis "Rendering Parameterized SQL and Translation to Dialects")
    (description
     "This package provides a rendering tool for parameterized SQL that also
translates into different SQL dialects.  These dialects include Microsoft SQL
Server', Oracle', @code{PostgreSql}', Amazon @code{RedShift}', Apache Impala',
IBM Netezza', Google @code{BigQuery}', Microsoft PDW', Snowflake', Azure Synapse
Analytics Dedicated', Apache Spark', SQLite', and @code{InterSystems} IRIS'.")
    (license license:asl2.0)))

(define-public r-sqlparser
  (package
    (name "r-sqlparser")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sqlparseR" version))
       (sha256
        (base32 "0zcna9y61v1qklpnhrwgv1djg4hyx3k5an32caapszi8qm9nq3x8"))))
    (properties `((upstream-name . "sqlparseR")))
    (build-system r-build-system)
    (propagated-inputs (list r-reticulate))
    (home-page "https://cran.r-project.org/package=sqlparseR")
    (synopsis
     "Wrapper for 'Python' Module 'sqlparse': Parse, Split, and Format 'SQL'")
    (description
     "Wrapper for the non-validating SQL parser Python module sqlparse
<https://github.com/andialbrecht/sqlparse>.  It allows parsing, splitting, and
formatting SQL statements.")
    (license license:gpl3)))

(define-public r-sqlove
  (package
    (name "r-sqlove")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SQLove" version))
       (sha256
        (base32 "0k746spnhi07rdw8lbblsxgabqlzr3h368svxhs7987r333wlsyh"))))
    (properties `((upstream-name . "SQLove")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjdbc r-readr r-dbi))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SQLove")
    (synopsis "Execute 'SQL' Scripts in 'R' Containing Multiple Queries")
    (description
     "The nature of working with structured query language ('SQL') scripts efficiently
often requires the creation of temporary tables and there are few clean and
simple R SQL execution approaches that allow you to complete this kind of work
with the R environment.  This package seeks to give SQL implementations in R a
little love by deploying functions that allow you to deploy complex SQL scripts
within a typical R workflow.")
    (license license:expat)))

(define-public r-sqliteutils
  (package
    (name "r-sqliteutils")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sqliteutils" version))
       (sha256
        (base32 "0v7d52m9yvl5iadqnscgrcvniwhk7i7fmzd3gw23a0q4qxjs8iy9"))))
    (properties `((upstream-name . "sqliteutils")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsqlite r-magrittr r-dplyr r-dbplyr r-dbi))
    (home-page "https://cran.r-project.org/package=sqliteutils")
    (synopsis "Utility Functions for 'SQLite'")
    (description
     "This package provides a tool for working with SQLite databases.  SQLite has some
idiosyncrasies and limitations that impose some hurdles to the R developer who
is using this database as a repository.  For instance, SQLite doesn't have a
date type and sqliteutils has some functions to deal with that.")
    (license license:expat)))

(define-public r-sqliter
  (package
    (name "r-sqliter")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sqliter" version))
       (sha256
        (base32 "17jjljq60szz0m8p2wc5l56659aap7an5gknc848dp89ycjgj3zx"))))
    (properties `((upstream-name . "sqliter")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rsqlite r-functional r-dbi))
    (home-page "https://github.com/wilsonfreitas/sqliter/")
    (synopsis "Connection wrapper to SQLite databases")
    (description
     "sqliter helps users, mainly data munging practioneers, to organize their sql
calls in a clean structure.  It simplifies the process of extracting and
transforming data into useful formats.")
    (license license:expat)))

(define-public r-sqlhelpers
  (package
    (name "r-sqlhelpers")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sqlHelpers" version))
       (sha256
        (base32 "1r46sak65ipfnflmd7w7ac6p62ihx7l9lpi8cjrx3c5xb6gaaxqx"))))
    (properties `((upstream-name . "sqlHelpers")))
    (build-system r-build-system)
    (propagated-inputs (list r-toolbox r-stringi r-odbc r-dbi r-data-table))
    (home-page "https://cran.r-project.org/package=sqlHelpers")
    (synopsis "Collection of 'SQL' Utilities for 'T-SQL' and 'Postgresql'")
    (description
     "Includes functions for interacting with common meta data fields, writing insert
statements, calling functions, and more for T-SQL and Postgresql'.")
    (license license:gpl3+)))

(define-public r-sqlhelper
  (package
    (name "r-sqlhelper")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sqlhelper" version))
       (sha256
        (base32 "0170nghfy4cc7yhccfcj9sijzi87ly8j0lzn2sl3xmkdldpa2172"))))
    (properties `((upstream-name . "sqlhelper")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-sf
                             r-rlang
                             r-rappdirs
                             r-purrr
                             r-pool
                             r-glue
                             r-dbi))
    (native-inputs (list r-knitr))
    (home-page "https://majerr.github.io/sqlhelper/dev/")
    (synopsis "Easier 'SQL' Integration")
    (description
     "Execute files of SQL and manage database connections.  SQL statements and
queries may be interpolated with string literals.  Execution of individual
statements and queries may be controlled with keywords.  Multiple connections
may be defined with YAML and accessed by name.")
    (license license:gpl3+)))

(define-public r-sqlcaser
  (package
    (name "r-sqlcaser")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sqlcaser" version))
       (sha256
        (base32 "0bnrgrmirypzcpyyi3ayi9k6b4phh5cjaa4ldizhsk14d8bbhw40"))))
    (properties `((upstream-name . "sqlcaser")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=sqlcaser")
    (synopsis "'SQL' Case Statement Generator")
    (description
     "Includes built-in methods for generating long SQL CASE statements, and other SQL
statements that may otherwise be arduous to construct by hand.  The generated
statement can easily be concatenated to string literals to form queries to
SQL'-like databases, such as when using the RODBC package.  The current methods
include casewhen() for building CASE statements, inlist() for building IN
statements, and updatetable() for building UPDATE statements.")
    (license license:expat)))

(define-public r-sql
  (package
    (name "r-sql")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SQL" version))
       (sha256
        (base32 "1l949crb4fz13yxbr9gag7lfqs9ak3ab3i25g20mjrz6iix99hrd"))))
    (properties `((upstream-name . "SQL")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-duckdb r-dbi r-arrow))
    (home-page "https://cran.r-project.org/package=SQL")
    (synopsis "Executes 'SQL' Statements")
    (description
     "Runs SQL statements on in-memory data frames within a temporary in-memory duckdb
data base.")
    (license license:gpl2+)))

(define-public r-sqi
  (package
    (name "r-sqi")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SQI" version))
       (sha256
        (base32 "1mb1vzc8r52zr1rp4dakmsdayv47pqsbissljskl9c0nkhlah1kq"))))
    (properties `((upstream-name . "SQI")))
    (build-system r-build-system)
    (propagated-inputs (list r-readxl r-olsrr r-matrixstats r-factominer
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=SQI")
    (synopsis "Soil Quality Index")
    (description
     "The overall performance of soil ecosystem services and productivity greatly
relies on soil health, making it a crucial indicator.  The evaluation of soil
physical, chemical, and biological parameters is necessary to determine the
overall soil quality index.  In our package, three commonly used methods,
including linear scoring, regression-based, and principal component-based soil
quality indexing, are employed to calculate the soil quality index.  This
package has been developed using concept of Bastida et al. (2008) and Doran and
Parkin (1994) <doi:10.1016/j.geoderma.2008.08.007>
<doi:10.2136/sssaspecpub35.c1>.")
    (license license:gpl3)))

(define-public r-spyvsspy
  (package
    (name "r-spyvsspy")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPYvsSPY" version))
       (sha256
        (base32 "1hxc73wwrzqh1r1xr9yd4hy4ma24virg4iv978lfyc9f9n56znp5"))))
    (properties `((upstream-name . "SPYvsSPY")))
    (build-system r-build-system)
    (home-page "https://github.com/shabbychef/SPYvsSPY")
    (synopsis "Spy vs. Spy Data")
    (description
     "Data on the Spy vs. Spy comic strip of Mad magazine, created and written by
Antonio Prohias.")
    (license license:lgpl3)))

(define-public r-sputnik
  (package
    (name "r-sputnik")
    (version "1.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPUTNIK" version))
       (sha256
        (base32 "1jp1gprib1ppwnsgr7457b48ljlb70s2rzccq660d5lrngs8afca"))))
    (properties `((upstream-name . "SPUTNIK")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-spatstat-geom
                             r-spatstat-explore
                             r-reshape
                             r-irlba
                             r-infotheo
                             r-imager
                             r-ggplot2
                             r-foreach
                             r-edger
                             r-e1071
                             r-dosnow))
    (home-page "https://github.com/paoloinglese/SPUTNIK")
    (synopsis
     "Spatially Automatic Denoising for Imaging Mass Spectrometry Toolkit")
    (description
     "Set of tools for peak filtering of mass spectrometry imaging data based on
spatial distribution of signal.  Given a region-of-interest, representing the
spatial region where the informative signal is expected to be localized, a
series of filters determine which peak signals are characterized by an
implausible spatial distribution.  The filters reduce the dataset dimension and
increase its information vs noise ratio, improving the quality of the
unsupervised analysis results, reducing data dimension and simplifying the
chemical interpretation.  The methods are described in Inglese P. et al (2019)
<doi:10.1093/bioinformatics/bty622>.")
    (license license:gpl3+)))

(define-public r-spurs
  (package
    (name "r-spurs")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spuRs" version))
       (sha256
        (base32 "00c0hnyrcbkbj269czxbfgmgi88y0vxzm79sxclllkzr9rdd1p53"))))
    (properties `((upstream-name . "spuRs")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-lattice))
    (home-page "https://cran.r-project.org/package=spuRs")
    (synopsis
     "Functions and Datasets for \"Introduction to Scientific Programming and Simulation Using R\"")
    (description
     "This package provides functions and datasets from Jones, O.D., R. Maillardet,
and A.P. Robinson.  2014.  An Introduction to Scientific Programming and
Simulation, Using R. 2nd Ed.  Chapman And Hall/CRC.")
    (license license:gpl3)))

(define-public r-spup
  (package
    (name "r-spup")
    (version "1.4-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spup" version))
       (sha256
        (base32 "045bvl2nzr78yrzp07718m5929wxmj0g4rd535f4qdaj95m137dm"))))
    (properties `((upstream-name . "spup")))
    (build-system r-build-system)
    (propagated-inputs (list r-whisker
                             r-raster
                             r-purrr
                             r-mvtnorm
                             r-magrittr
                             r-gstat))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=spup")
    (synopsis "Spatial Uncertainty Propagation Analysis")
    (description
     "Uncertainty propagation analysis in spatial environmental modelling following
methodology described in Heuvelink et al. (2007) <doi:10.1080/13658810601063951>
and Brown and Heuvelink (2007) <doi:10.1016/j.cageo.2006.06.015>.  The package
provides functions for examining the uncertainty propagation starting from input
data and model parameters, via the environmental model onto model outputs.  The
functions include uncertainty model specification, stochastic simulation and
propagation of uncertainty using Monte Carlo (MC) techniques.  Uncertain
variables are described by probability distributions.  Both numerical and
categorical data types are handled.  Spatial auto-correlation within an
attribute and cross-correlation between attributes is accommodated for.  The MC
realizations may be used as input to the environmental models called from R, or
externally.")
    (license license:gpl3+)))

(define-public r-sptotal
  (package
    (name "r-sptotal")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sptotal" version))
       (sha256
        (base32 "09wkcc1lbc9d0m19ncqcb3xxgl30lyii6r5nqb9rbjqyf2jl2iak"))))
    (properties `((upstream-name . "sptotal")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis r-ggplot2))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://highamm.github.io/sptotal/index.html")
    (synopsis "Predicting Totals and Weighted Sums from Spatial Data")
    (description
     "This package performs predictions of totals and weighted sums, or finite
population block kriging, on spatial data using the methods in Ver Hoef (2008)
<doi:10.1007/s10651-007-0035-y>.  The primary outputs are an estimate of the
total, mean, or weighted sum in the region, an estimated prediction variance,
and a plot of the predicted and observed values.  This is useful primarily to
users with ecological data that are counts or densities measured on some sites
in a finite area of interest.  Spatial prediction for the total count or average
density in the entire region can then be done using the functions in this
package.")
    (license license:gpl2)))

(define-public r-sptimer
  (package
    (name "r-sptimer")
    (version "3.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spTimer" version))
       (sha256
        (base32 "0xibbwsvnfs43q58bbld23mbpzhby2qcwavhr30k4yd5fxzfpzbr"))))
    (properties `((upstream-name . "spTimer")))
    (build-system r-build-system)
    (propagated-inputs (list r-spacetime r-sp r-extradistr r-coda))
    (home-page "https://cran.r-project.org/package=spTimer")
    (synopsis "Spatio-Temporal Bayesian Modelling")
    (description
     "Fits, spatially predicts and temporally forecasts large amounts of space-time
data using [1] Bayesian Gaussian Process (GP) Models, [2] Bayesian
Auto-Regressive (AR) Models, and [3] Bayesian Gaussian Predictive Processes
(GPP) based AR Models for spatio-temporal big-n problems.  Bakar and Sahu (2015)
<doi:10.18637/jss.v063.i15>.")
    (license license:gpl2+)))

(define-public r-spthin
  (package
    (name "r-spthin")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spThin" version))
       (sha256
        (base32 "0j10j1cbbwp3baqm3b59rxlnd643mkx1mirlxpnr1ad2g7xpm69f"))))
    (properties `((upstream-name . "spThin")))
    (build-system r-build-system)
    (propagated-inputs (list r-spam r-knitr r-fields))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=spThin")
    (synopsis
     "Functions for Spatial Thinning of Species Occurrence Records for Use in Ecological Models")
    (description
     "This package provides a set of functions that can be used to spatially thin
species occurrence data.  The resulting thinned data can be used in ecological
modeling, such as ecological niche modeling.")
    (license license:gpl3)))

(define-public r-spte2m
  (package
    (name "r-spte2m")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpTe2M" version))
       (sha256
        (base32 "1xykmbgwwnvl7c9mf0aam3s55xmq2pxdy5wkkz9nzj1c74m0bxqf"))))
    (properties `((upstream-name . "SpTe2M")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmarkdown
                             r-mass
                             r-maps
                             r-mapproj
                             r-knitr
                             r-glmnet
                             r-ggplot2))
    (native-inputs (list r-rmarkdown r-knitr gfortran))
    (home-page "https://cran.r-project.org/package=SpTe2M")
    (synopsis "Nonparametric Modeling and Monitoring of Spatio-Temporal Data")
    (description
     "Spatio-temporal data have become increasingly popular in many research fields.
Such data often have complex structures that are difficult to describe and
estimate.  This package provides reliable tools for modeling complicated
spatio-temporal data.  It also includes tools of online process monitoring to
detect possible change-points in a spatio-temporal process over time.  More
specifically, the package implements the spatio-temporal mean estimation
procedure described in Yang and Qiu (2018) <doi:10.1002/sim.7622>, the
spatio-temporal covariance estimation procedure discussed in Yang and Qiu (2019)
<doi:10.1002/sim.8315>, the three-step method for the joint estimation of
spatio-temporal mean and covariance functions suggested by Yang and Qiu (2022)
<doi:10.1007/s10463-021-00787-2>, the spatio-temporal disease surveillance
method discussed in Qiu and Yang (2021) <doi:10.1002/sim.9150> that can
accommodate the covariate effect, the spatial-LASSO-based process monitoring
method proposed by Qiu and Yang (2023) <doi:10.1080/00224065.2022.2081104>, and
the online spatio-temporal disease surveillance method described in Yang and Qiu
(2020) <doi:10.1080/24725854.2019.1696496>.")
    (license license:gpl3+)))

(define-public r-sptdyn
  (package
    (name "r-sptdyn")
    (version "2.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spTDyn" version))
       (sha256
        (base32 "05f9ginar5xc3aiyvy2dlad4bs62x2z492h096g77pj2wbba8ysh"))))
    (properties `((upstream-name . "spTDyn")))
    (build-system r-build-system)
    (propagated-inputs (list r-sptimer r-spacetime r-sp r-coda))
    (home-page "https://cran.r-project.org/package=spTDyn")
    (synopsis "Spatially Varying and Spatio-Temporal Dynamic Linear Models")
    (description
     "Fits, spatially predicts, and temporally forecasts space-time data using
Gaussian Process (GP): (1) spatially varying coefficient process models and (2)
spatio-temporal dynamic linear models.  Bakar et al., (2016).  Bakar et al.,
(2015).")
    (license license:gpl2+)))

(define-public r-spsutil
  (package
    (name "r-spsutil")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spsUtil" version))
       (sha256
        (base32 "1hhiz2bmgrvpadag1d4ljynk7mhwknaslja9hcmf665l2x3hzdl6"))))
    (properties `((upstream-name . "spsUtil")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-r6
                             r-magrittr
                             r-httr
                             r-glue
                             r-crayon
                             r-assertthat))
    (home-page "https://github.com/lz100/spsUtil")
    (synopsis "'systemPipeShiny' Utility Functions")
    (description
     "The @code{systemPipeShiny} (SPS) framework comes with many useful utility
functions.  However, installing the whole framework is heavy and takes some
time.  If you like only a few useful utility functions from SPS, install this
package is enough.")
    (license license:gpl3+)))

(define-public r-spsurvey
  (package
    (name "r-spsurvey")
    (version "5.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spsurvey" version))
       (sha256
        (base32 "13h25mhb45s2k73a5709zlxfxylxqls0avan6d8pcp0iyrmpxjak"))))
    (properties `((upstream-name . "spsurvey")))
    (build-system r-build-system)
    (propagated-inputs (list r-units
                             r-survey
                             r-sf
                             r-sampling
                             r-mass
                             r-lme4
                             r-deldir
                             r-crossdes
                             r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://usepa.github.io/spsurvey/")
    (synopsis "Spatial Sampling Design and Analysis")
    (description
     "This package provides a design-based approach to statistical inference, with a
focus on spatial data.  Spatially balanced samples are selected using the
Generalized Random Tessellation Stratified (GRTS) algorithm.  The GRTS algorithm
can be applied to finite resources (point geometries) and infinite resources
(linear / linestring and areal / polygon geometries) and flexibly accommodates a
diverse set of sampling design features, including stratification, unequal
inclusion probabilities, proportional (to size) inclusion probabilities, legacy
(historical) sites, a minimum distance between sites, and two options for
replacement sites (reverse hierarchical order and nearest neighbor).  Data are
analyzed using a wide range of analysis functions that perform categorical
variable analysis, continuous variable analysis, attributable risk analysis,
risk difference analysis, relative risk analysis, change analysis, and trend
analysis.  spsurvey can also be used to summarize objects, visualize objects,
select samples that are not spatially balanced, select panel samples, measure
the amount of spatial balance in a sample, adjust design weights, and more.  For
additional details, see Dumelle et al. (2023) <doi:10.18637/jss.v105.i03>.")
    (license license:gpl3+)))

(define-public r-spsurv
  (package
    (name "r-spsurv")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spsurv" version))
       (sha256
        (base32 "11jlbzvzxv3d1d0nwpbii46ddvphglrxv25rw7pvxbybcx2i7k6x"))))
    (properties `((upstream-name . "spsurv")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppeigen
                             r-rcpp
                             r-mass
                             r-loo
                             r-coda
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=spsurv")
    (synopsis "Bernstein Polynomial Based Semiparametric Survival Analysis")
    (description
     "This package provides a set of reliable routines to ease semiparametric survival
regression modeling based on Bernstein polynomials.  spsurv includes
proportional hazards, proportional odds and accelerated failure time frameworks
for right-censored data.  RV Panaro (2020) <@code{arXiv:2003.10548>}.")
    (license license:gpl3)))

(define-public r-spsur
  (package
    (name "r-spsur")
    (version "1.0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spsur" version))
       (sha256
        (base32 "0ar1b48r2hi50rvzkr432lpcc4c9r72hxkz74gip5gf12i7mx2ci"))))
    (properties `((upstream-name . "spsur")))
    (build-system r-build-system)
    (propagated-inputs (list r-sphet
                             r-spdep
                             r-spatialreg
                             r-sparsemvn
                             r-rlang
                             r-rdpack
                             r-numderiv
                             r-minqa
                             r-matrix
                             r-mass
                             r-gridextra
                             r-gmodels
                             r-ggplot2
                             r-formula))
    (native-inputs (list r-knitr))
    (home-page "https://CRAN.R-project.org/package=spsur")
    (synopsis "Spatial Seemingly Unrelated Regression Models")
    (description
     "This package provides a collection of functions to test and estimate Seemingly
Unrelated Regression (usually called SUR) models, with spatial structure, by
maximum likelihood and three-stage least squares.  The package estimates the
most common spatial specifications, that is, SUR with Spatial Lag of X
regressors (called SUR-SLX), SUR with Spatial Lag Model (called SUR-SLM), SUR
with Spatial Error Model (called SUR-SEM), SUR with Spatial Durbin Model (called
SUR-SDM), SUR with Spatial Durbin Error Model (called SUR-SDEM), SUR with
Spatial Autoregressive terms and Spatial Autoregressive Disturbances (called
SUR-SARAR), SUR-SARAR with Spatial Lag of X regressors (called SUR-GNM) and SUR
with Spatially Independent Model (called SUR-SIM).  The methodology of these
models can be found in next references Minguez, R., Lopez, F.A., and Mur, J.
(2022) <doi:10.18637/jss.v104.i11> Mur, J., Lopez, F.A., and Herrera, M. (2010)
<doi:10.1080/17421772.2010.516443> Lopez, F.A., Mur, J., and Angulo, A. (2014)
<doi:10.1007/s00168-014-0624-2>.")
    (license license:gpl3)))

(define-public r-spstack
  (package
    (name "r-spstack")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spStack" version))
       (sha256
        (base32 "11ikxhm7iqb3pk6q2nw2mym23znvlxqfzs9b1569hh33znki2p7m"))))
    (properties `((upstream-name . "spStack")))
    (build-system r-build-system)
    (propagated-inputs (list r-rstudioapi
                             r-mba
                             r-ggplot2
                             r-future-apply
                             r-future
                             r-cvxr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/SPan-18/spStack-dev")
    (synopsis "Bayesian Geostatistics Using Predictive Stacking")
    (description
     "Fits Bayesian hierarchical spatial process models for point-referenced Gaussian,
Poisson, binomial, and binary data using stacking of predictive densities.  It
involves sampling from analytically available posterior distributions
conditional upon some candidate values of the spatial process parameters and,
subsequently assimilate inference from these individual posterior distributions
using Bayesian predictive stacking.  Our algorithm is highly parallelizable and
hence, much faster than traditional Markov chain Monte Carlo algorithms while
delivering competitive predictive performance.  See Zhang, Tang, and Banerjee
(2024) <doi:10.48550/@code{arXiv.2304.12414>}, and, Pan, Zhang, Bradley, and
Banerjee (2024) <doi:10.48550/@code{arXiv.2406.04655>} for details.")
    (license license:gpl3)))

(define-public r-spsp
  (package
    (name "r-spsp")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPSP" version))
       (sha256
        (base32 "0gf5cw2fnssl198ccpvnma4bcqi33whl6ihc1s9i2lw6xg4qvb33"))))
    (properties `((upstream-name . "SPSP")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-ncvreg r-matrix r-lars r-glmnet))
    (home-page "https://xiaorui.site/SPSP/")
    (synopsis "Selection by Partitioning the Solution Paths")
    (description
     "An implementation of the feature Selection procedure by Partitioning the entire
Solution Paths (namely SPSP) to identify the relevant features rather than using
a single tuning parameter.  By utilizing the entire solution paths, this
procedure can obtain better selection accuracy than the commonly used approach
of selecting only one tuning parameter based on existing criteria,
cross-validation (CV), generalized CV, AIC, BIC, and extended BIC (Liu, Y., &
Wang, P. (2018) <doi:10.1214/18-EJS1434>).  It is more stable and accurate (low
false positive and false negative rates) than other variable selection
approaches.  In addition, it can be flexibly coupled with the solution paths of
Lasso, adaptive Lasso, ridge regression, and other penalized estimators.")
    (license license:gpl2+)))

(define-public r-spsl
  (package
    (name "r-spsl")
    (version "0.1-9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPSL" version))
       (sha256
        (base32 "0kf6mmbvavid3dw2l4fkclk669wi13wkyykhwn5272d6qnhlhhca"))))
    (properties `((upstream-name . "SPSL")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SPSL")
    (synopsis "Site Percolation on Square Lattices (SPSL)")
    (description
     "This package provides basic functionality for labeling iso- & anisotropic
percolation clusters on 2D & 3D square lattices with various lattice sizes,
occupation probabilities, von Neumann & Moore (1,d)-neighborhoods, and random
variables weighting the percolation lattice sites.")
    (license license:gpl3)))

(define-public r-spsh
  (package
    (name "r-spsh")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spsh" version))
       (sha256
        (base32 "1rmsgp0mrb4153v5f5xby7r3izvgz1a0vi63g5p9q5jd6z0gd2i1"))))
    (properties `((upstream-name . "spsh")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack
                             r-pracma
                             r-lhs
                             r-hypergeo
                             r-fme
                             r-deoptim))
    (home-page "https://cran.r-project.org/package=spsh")
    (synopsis
     "Estimation and Prediction of Parameters of Various Soil Hydraulic Property Models")
    (description
     "Estimates model parameters of soil hydraulic property functions by inverting
measured data.  A wide range of hydraulic models, weighting schemes, global
optimization algorithms, Markov chain Monte Carlo samplers, and extended
statistical analyses of results are provided.  Prediction of soil hydraulic
property model parameters and common soil properties using pedotransfer
functions is facilitated.  Parameter estimation is based on identically and
independentally distributed (weighted) model residuals, and simple model
selection criteria (Hoege, M., Woehling, T., and Nowak, W. (2018)
<doi:10.1002/2017WR021902>) can be calculated.  The included models are the van
Genuchten-Mualem in its unimodal, bimodal and trimodal form, the the Kosugi 2
parametric-Mualem model, and the Fredlund-Xing model.  All models can be
extended to account for non-capillary water storage and conductivity (Weber,
T.K.D., Durner, W., Streck, T. and Diamantopoulos, E. (2019)
<doi:10.1029/2018WR024584>.  The isothermal vapour conductivity (Saito, H.,
Simunek, J. and Mohanty, B.P. (2006) <doi:10.2136/vzj2006.0007>) is calculated
based on volumetric air space and a selection of different tortuosity models:
(Grable, A.R., Siemer, E.G. (1968)
<doi:10.2136/sssaj1968.03615995003200020011x>, Lai, S.H., Tiedje J.M., Erickson,
E. (1976) <doi:10.2136/sssaj1976.03615995004000010006x>, Moldrup, P., Olesen,
T., Rolston, D.E., and Yamaguchi, T. (1997)
<doi:10.1097/00010694-199709000-00004>, Moldrup, P., Olesen, T., Yoshikawa, S.,
Komatsu, T., and Rolston, D.E. (2004) <doi:10.2136/sssaj2004.7500>, Moldrup, P.,
Olesen, T., Yoshikawa, S., Komatsu, T., and Rolston, D.E. (2005)
<doi:10.1097/01.ss.0000196768.44165.1f>, Millington, R.J., Quirk, J.P. (1961)
<doi:10.1039/TF9615701200>, Penman, H.L. (1940) <doi:10.1017/S0021859600048164>,
and Xu, X, Nieber, J.L. Gupta, S.C. (1992)
<doi:10.2136/sssaj1992.03615995005600060014x>).")
    (license license:gpl2+)))

(define-public r-spselect
  (package
    (name "r-spselect")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spselect" version))
       (sha256
        (base32 "02aiml4dh40n6yl52ypramn4847ykdi4y5lhsmchnnp4nnnqycsn"))))
    (properties `((upstream-name . "spselect")))
    (build-system r-build-system)
    (propagated-inputs (list r-tester r-pracma r-magic))
    (home-page "https://cran.r-project.org/package=spselect")
    (synopsis "Selecting Spatial Scale of Covariates in Regression Models")
    (description
     "Fits spatial scale (SS) forward stepwise regression, SS incremental forward
stagewise regression, SS least angle regression (LARS), and SS lasso models.
All area-level covariates are considered at all available scales to enter a
model, but the SS algorithms are constrained to select each area-level covariate
at a single spatial scale.")
    (license license:gpl2+)))

(define-public r-spscomps
  (package
    (name "r-spscomps")
    (version "0.3.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spsComps" version))
       (sha256
        (base32 "1yhd2iw9z614mcb2lnq43x5ixfpc5lbdm5avgawp726c3fvflky8"))))
    (properties `((upstream-name . "spsComps")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-shinytoastr
                             r-shinyace
                             r-shiny
                             r-r6
                             r-magrittr
                             r-htmltools
                             r-glue
                             r-crayon
                             r-assertthat))
    (home-page "https://github.com/lz100/spsComps")
    (synopsis "'systemPipeShiny' UI and Server Components")
    (description
     "The @code{systemPipeShiny} (SPS) framework comes with many UI and server
components.  However, installing the whole framework is heavy and takes some
time.  If you would like to use UI and server components from SPS in your own
Shiny apps, do not hesitate to try this package.")
    (license license:gpl3+)))

(define-public r-sps
  (package
    (name "r-sps")
    (version "0.5.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sps" version))
       (sha256
        (base32 "0rrg4908sydsnxxw9d87y2js6gyg9kf04y50p6j4jb0ig766xl0z"))))
    (properties `((upstream-name . "sps")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://marberts.github.io/sps/")
    (synopsis "Sequential Poisson Sampling")
    (description
     "Sequential Poisson sampling is a variation of Poisson sampling for drawing
probability-proportional-to-size samples with a given number of units, and is
commonly used for price-index surveys.  This package gives functions to draw
stratified sequential Poisson samples according to the method by Ohlsson (1998,
ISSN:0282-423X), as well as other order sample designs by RosÃ©n (1997,
<doi:10.1016/S0378-3758(96)00186-3>), and generate appropriate bootstrap
replicate weights according to the generalized bootstrap method by Beaumont and
Patak (2012, <doi:10.1111/j.1751-5823.2011.00166.x>).")
    (license license:expat)))

(define-public r-sprtt
  (package
    (name "r-sprtt")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sprtt" version))
       (sha256
        (base32 "0nllzisyf6ykamdi9y9hfscfa3hkmfaz7ddg9l25sqnawjvwrncr"))))
    (properties `((upstream-name . "sprtt")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr
                             r-mbess
                             r-lifecycle
                             r-glue
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://meikesteinhilber.github.io/sprtt/")
    (synopsis "Sequential Probability Ratio Tests Toolbox")
    (description
     "It is a toolbox for Sequential Probability Ratio Tests (SPRT), Wald (1945)
<doi:10.2134/agronj1947.00021962003900070011x>.  SPRTs are applied to the data
during the sampling process, ideally after each observation.  At any stage, the
test will return a decision to either continue sampling or terminate and accept
one of the specified hypotheses.  The seq_ttest() function performs one-sample,
two-sample, and paired t-tests for testing one- and two-sided hypotheses
(Schnuerch & Erdfelder (2019) <doi:10.1037/met0000234>).  The seq_anova()
function allows to perform a sequential one-way fixed effects ANOVA (Steinhilber
et al. (2023) <doi:10.31234/osf.io/m64ne>).  Learn more about the package by
using vignettes \"@code{browseVignettes(package} = \"sprtt\")\" or go to the website
<https://meikesteinhilber.github.io/sprtt/>.")
    (license license:agpl3+)))

(define-public r-sprintr
  (package
    (name "r-sprintr")
    (version "0.9.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sprintr" version))
       (sha256
        (base32 "0nnzjn7zrcgcmk3rs0hn56jcjrzrn5z0kc77bv4zzvbz5vr8gbwj"))))
    (properties `((upstream-name . "sprintr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-glmnet))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=sprintr")
    (synopsis "Sparse Reluctant Interaction Modeling")
    (description
     "An implementation of a computationally efficient method to fit large-scale
interaction models based on the reluctant interaction selection principle.  The
method and its properties are described in greater depth in Yu, G., Bien, J.,
and Tibshirani, R.J. (2019) \"Reluctant interaction modeling\", which is available
at <@code{arXiv:1907.08414>}.")
    (license license:gpl3)))

(define-public r-springsteen
  (package
    (name "r-springsteen")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spRingsteen" version))
       (sha256
        (base32 "13mdbsbq51sp34xp04lgc4klwb3chnnkvh5r99qfapbbr0vra8vl"))))
    (properties `((upstream-name . "spRingsteen")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-devtools))
    (home-page "https://github.com/obrienjoey/spRingsteen")
    (synopsis "All Things Data and Springsteen")
    (description
     "An R data package containing setlists from all Bruce Springsteen concerts over
1973-2021.  Also includes all his song details such as lyrics and albums.  Data
extracted from: <http://brucebase.wikidot.com/>.")
    (license license:cc0)))

(define-public r-springpheno
  (package
    (name "r-springpheno")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "springpheno" version))
       (sha256
        (base32 "16pa56k1pbazv3vx5lhzc8n3pazrfh1q4bi7116bbvsdgpp9vs8g"))))
    (properties `((upstream-name . "springpheno")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=springpheno")
    (synopsis "Spring Phenological Indices")
    (description
     "Computes the extended spring indices (SI-x) and false spring exposure indices
(FSEI).  The SI-x indices are standard indices used for analysis in spring
phenology studies.  In addition, the FSEI is also from research on the
climatology of false springs and adjusted to include an early and late false
spring exposure index.  The indices include the first leaf index, first bloom
index, and false spring exposure indices, along with all calculations for all
functions needed to calculate each index.  The main function returns all
indices, but each function can also be run separately.  Allstadt et al. (2015)
<doi: 10.1088/1748-9326/10/10/104008> Ault et al. (2015) <doi:
10.1016/j.cageo.2015.06.015> Peterson and Abatzoglou (2014) <doi:
10.1002/2014GL059266> Schwarz et al. (2006) <doi:
10.1111/j.1365-2486.2005.01097.x> Schwarz et al. (2013) <doi: 10.1002/joc.3625>.")
    (license (license:fsdg-compatible "Unlimited"))))

(define-public r-springer
  (package
    (name "r-springer")
    (version "0.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "springer" version))
       (sha256
        (base32 "0kb11yn9cl48kmni452knfyibi88gn1jc98m3cdbpqlfhflj4gm5"))))
    (properties `((upstream-name . "springer")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mass))
    (home-page "https://github.com/feizhoustat/springer")
    (synopsis
     "Sparse Group Variable Selection for Gene-Environment Interactions in the Longitudinal Study")
    (description
     "Recently, regularized variable selection has emerged as a powerful tool to
identify and dissect gene-environment interactions.  Nevertheless, in
longitudinal studies with high dimensional genetic factors, regularization
methods for GÃE interactions have not been systematically developed.  In this
package, we provide the implementation of sparse group variable selection, based
on both the quadratic inference function (QIF) and generalized estimating
equation (GEE), to accommodate the bi-level selection for longitudinal GÃE
studies with high dimensional genomic features.  Alternative methods conducting
only the group or individual level selection have also been included.  The core
modules of the package have been developed in C++.")
    (license license:gpl2)))

(define-public r-sprex
  (package
    (name "r-sprex")
    (version "1.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sprex" version))
       (sha256
        (base32 "14idml4mipd4wyza7hqf49ww4dparmwaps35hxm8jg5h0w1pgh7j"))))
    (properties `((upstream-name . "sprex")))
    (build-system r-build-system)
    (propagated-inputs (list r-swfscmisc))
    (home-page "https://cran.r-project.org/package=sprex")
    (synopsis "Calculate Species Richness and Extrapolation Metrics")
    (description
     "Calculate species richness functions for rarefaction and extrapolation.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-spreval
  (package
    (name "r-spreval")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spreval" version))
       (sha256
        (base32 "1zapfld0d6yyxqvijkb243zqin9xwqhmnznd2nnd4cp0rq3106zf"))))
    (properties `((upstream-name . "spreval")))
    (build-system r-build-system)
    (propagated-inputs (list r-timedate r-interp))
    (native-inputs (list r-knitr))
    (home-page "https://glgrabow.github.io/spreval/")
    (synopsis "Evaluation of Sprinkler Irrigation Uniformity and Efficiency")
    (description
     "Processing and analysis of field collected or simulated sprinkler system catch
data (depths) to characterize irrigation uniformity and efficiency using
standard and other measures.  Standard measures include the Christiansen
coefficient of uniformity (CU) as found in Christiansen, J.E.(1942,
ISBN:0138779295, \"Irrigation by Sprinkling\"); and distribution uniformity (DU),
potential efficiency of the low quarter (PELQ), and application efficiency of
the low quarter (AELQ) that are implementations of measures of the same notation
in Keller, J. and Merriam, J.L. (1978) \"Farm Irrigation System Evaluation: A
Guide for Management\" <https://pdf.usaid.gov/pdf_docs/PNAAG745.pdf>.
spreval::DU.lh is similar to spreval::DU but is the distribution uniformity of
the low half instead of low quarter as in DU. spreval::PELQT is a version of
spreval::PELQ adapted for traveling systems instead of lateral move or solid-set
sprinkler systems.  The function spreval::eff is analogous to the method used to
compute application efficiency for furrow irrigation presented in Walker, W. and
Skogerboe, G.V. (1987,ISBN:0138779295, \"Surface Irrigation: Theory and
Practice\"),that uses piecewise integration of infiltrated depth compared against
soil-moisture deficit (SMD), when the argument \"target\" is set equal to SMD. The
other functions contained in the package provide graphical representation of
sprinkler system uniformity, and other standard univariate parametric and
non-parametric statistical measures as applied to sprinkler system catch depths.
 A sample data set of field test data spreval::catchcan (catch depths) is
provided and is used in examples and vignettes.  Agricultural systems
emphasized, but this package can be used for landscape irrigation evaluation,
and a landscape (turf) vignette is included as an example application.")
    (license license:gpl3)))

(define-public r-spreg
  (package
    (name "r-spreg")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPreg" version))
       (sha256
        (base32 "1702i86qcbnqyi7r5wc2ciyk3l46329kmnrz91jzqq85z8cwgak1"))))
    (properties `((upstream-name . "SPreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-ucminf r-sn))
    (home-page "https://cran.r-project.org/package=SPreg")
    (synopsis "Bias Reduction in the Skew-Probit Model for a Binary Response")
    (description
     "This package provides a function for the estimation of parameters in a binary
regression with the skew-probit link function.  Naive MLE, Jeffrey type of prior
and Cauchy prior type of penalization are implemented, as described in
@code{DongHyuk} Lee and Samiran Sinha (2019+)
<doi:10.1080/00949655.2019.1590579>.")
    (license license:gpl3)))

(define-public r-spreda
  (package
    (name "r-spreda")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPREDA" version))
       (sha256
        (base32 "0fn4p5apjq8zgd3zms2b88ga5wbsv86y59ac5x06w75rf071xglv"))))
    (properties `((upstream-name . "SPREDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-nlme))
    (home-page "https://cran.r-project.org/package=SPREDA")
    (synopsis "Statistical Package for Reliability Data Analysis")
    (description
     "The Statistical Package for REliability Data Analysis (SPREDA) implements
recently-developed statistical methods for the analysis of reliability data.
Modern technological developments, such as sensors and smart chips, allow us to
dynamically track product/system usage as well as other environmental variables,
such as temperature and humidity.  We refer to these variables as dynamic
covariates.  The package contains functions for the analysis of time-to-event
data with dynamic covariates and degradation data with dynamic covariates.  The
package also contains functions that can be used for analyzing time-to-event
data with right censoring, and with left truncation and right censoring.
Financial support from NSF and @code{DuPont} are acknowledged.")
    (license license:gpl2)))

(define-public r-spreadr
  (package
    (name "r-spreadr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spreadr" version))
       (sha256
        (base32 "180dvr39i1glc0jllsnnglwrxh133q20v7px2fmdi7229pr175id"))))
    (properties `((upstream-name . "spreadr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp
                             r-matrix
                             r-igraph
                             r-ggplot2
                             r-extrafont
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=spreadr")
    (synopsis "Simulating Spreading Activation in a Network")
    (description
     "The notion of spreading activation is a prevalent metaphor in the cognitive
sciences.  This package provides the tools for cognitive scientists and
psychologists to conduct computer simulations that implement spreading
activation in a network representation.  The algorithmic method implemented in
spreadr subroutines follows the approach described in Vitevitch, Ercal, and
Adagarla (2011, Frontiers), who viewed activation as a fixed cognitive resource
that could spread among nodes that were connected to each other via edges or
connections (i.e., a network).  See Vitevitch, M. S., Ercal, G., & Adagarla, B.
(2011).  Simulating retrieval from a highly clustered network: Implications for
spoken word recognition.  Frontiers in Psychology, 2, 369.
<doi:10.3389/fpsyg.2011.00369> and Siew, C. S. Q. (2019).  spreadr: A R package
to simulate spreading activation in a network.  Behavior Research Methods, 51,
910-929. <doi: 10.3758/s13428-018-1186-5>.")
    (license license:gpl3)))

(define-public r-spray
  (package
    (name "r-spray")
    (version "1.0-26")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spray" version))
       (sha256
        (base32 "05vq5hy2k5c1pa9nwnklyqwaxxwbhck9wsc5lngkk0xdy35sfqsg"))))
    (properties `((upstream-name . "spray")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rcpp r-partitions r-magic r-disordr))
    (home-page "https://github.com/RobinHankin/spray")
    (synopsis "Sparse Arrays and Multivariate Polynomials")
    (description
     "Sparse arrays interpreted as multivariate polynomials.  Uses @code{disordR}
discipline (Hankin, 2022, <doi:10.48550/ARXIV.2210.03856>).  To cite the package
in publications please use Hankin (2022) <doi:10.48550/ARXIV.2210.10848>.")
    (license license:gpl2+)))

(define-public r-sppop
  (package
    (name "r-sppop")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpPOP" version))
       (sha256
        (base32 "10s11sd8afridrkdg6q6wxqin0c214dl692sjz5f4ypv3irmwy04"))))
    (properties `((upstream-name . "SpPOP")))
    (build-system r-build-system)
    (propagated-inputs (list r-qpdf r-numbers))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SpPOP")
    (synopsis
     "Generation of Spatial Population under Different Levels of Relationships among Variables")
    (description
     "The developed package can be used to generate a spatial population for different
levels of relationships among the dependent and auxiliary variables along with
spatially varying model parameters.  A spatial layout is designed as a
[0,k-1]x[0,k-1] square region on which observations are collected at (k x k)
lattice points with a unit distance between any two neighbouring points along
the horizontal and vertical axes.  For method details see Chao, Liu., Chuanhua,
Wei.  and Yunan, Su. (2018).<doi:10.1080/10485252.2018.1499907>.  The generated
spatial population can be utilized in Geographically Weighted Regression model
based analysis for studying the spatially varying relationships among the
variables.  Furthermore, various statistical analysis can be performed on this
spatially generated data.")
    (license license:gpl2+)))

(define-public r-sppcomb
  (package
    (name "r-sppcomb")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPPcomb" version))
       (sha256
        (base32 "1kn3pi3c7g7waill1grm382ixbvnc7z8pbgzqd94hxqzgkivhid7"))))
    (properties `((upstream-name . "SPPcomb")))
    (build-system r-build-system)
    (propagated-inputs (list r-nleqslv))
    (home-page "https://cran.r-project.org/package=SPPcomb")
    (synopsis "Combining Different Spatial Datasets in Cancer Risk Estimation")
    (description
     "We propose a novel two-step procedure to combine epidemiological data obtained
from diverse sources with the aim to quantify risk factors affecting the
probability that an individual develops certain disease such as cancer.  See Hui
Huang, Xiaomei Ma, Rasmus Waagepetersen, Theodore R. Holford, Rong Wang, Harvey
Risch, Lloyd Mueller & Yongtao Guan (2014) A New Estimation Approach for
Combining Epidemiological Data From Multiple Sources, Journal of the American
Statistical Association, 109:505, 11-23, <doi:10.1080/01621459.2013.870904>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-spouse
  (package
    (name "r-spouse")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPOUSE" version))
       (sha256
        (base32 "10p3x3862dss779jfxw1kxhs3g94zpjpvnlww1gsw1c5hxzvmqb5"))))
    (properties `((upstream-name . "SPOUSE")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SPOUSE")
    (synopsis "Scatter Plots Over-Viewed Using Summary Ellipses")
    (description
     "Summary ellipses superimposed on a scatter plot contain all bi-variate summary
statistics for regression analysis.  Furthermore, the outer ellipse flags
potential outliers.  Multiple groups can be compared in terms of centers and
spreads as illustrated in the examples.")
    (license license:gpl3)))

(define-public r-spotoroo
  (package
    (name "r-spotoroo")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spotoroo" version))
       (sha256
        (base32 "004pjlad31xkxvl9bm4ga33ikmph93blb0qd1rpvs8s0lgi7v6pn"))))
    (properties `((upstream-name . "spotoroo")))
    (build-system r-build-system)
    (propagated-inputs (list r-progress
                             r-patchwork
                             r-ggrepel
                             r-ggplot2
                             r-ggextra
                             r-ggbeeswarm
                             r-geodist
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://tengmcing.github.io/spotoroo/")
    (synopsis "Spatiotemporal Clustering of Satellite Hot Spot Data")
    (description
     "An algorithm to cluster satellite hot spot data spatially and temporally.")
    (license license:expat)))

(define-public r-spotidy
  (package
    (name "r-spotidy")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spotidy" version))
       (sha256
        (base32 "0hg8d3piiwp4q3257qsv5l41zc3c5a8mbq595ci63id8fc2q2pkd"))))
    (properties `((upstream-name . "spotidy")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr r-magrittr r-httr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=spotidy")
    (synopsis
     "Providing Convenience Functions to Connect R with the Spotify API")
    (description
     "Providing convenience functions to connect R with the Spotify application
programming interface ('API').  At first it aims to help setting up the OAuth2.0
Authentication flow.  The default output of the get_*() functions is tidy, but
optionally the functions could return the raw response from the API as well.
The search_*() and get_*() functions can be combined.  See the vignette for more
information and examples and the official Spotify for Developers website
<https://developer.spotify.com/documentation/web-api/> for information about the
Web API'.")
    (license license:expat)))

(define-public r-sportyr
  (package
    (name "r-sportyr")
    (version "2.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sportyR" version))
       (sha256
        (base32 "1br7wbxr488pknqq3ik6w86rrkx5cn61jrm2b8az27zh8k362dv6"))))
    (properties `((upstream-name . "sportyR")))
    (build-system r-build-system)
    (inputs (list pandoc pandoc))
    (propagated-inputs (list r-rlang r-glue r-ggplot2 r-ggfittext))
    (native-inputs (list r-knitr))
    (home-page "https://sportyr.sportsdataverse.org/")
    (synopsis
     "Plot Scaled 'ggplot' Representations of Sports Playing Surfaces")
    (description
     "Create scaled ggplot representations of playing surfaces.  Playing surfaces are
drawn pursuant to rule-book specifications.  This package should be used as a
baseline plot for displaying any type of tracking data.")
    (license license:gpl3+)))

(define-public r-sportstour
  (package
    (name "r-sportstour")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SportsTour" version))
       (sha256
        (base32 "06d66yv0b7c3mg1bcnrlg576njxgri107ni0vnl27hzf46fyidwa"))))
    (properties `((upstream-name . "SportsTour")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SportsTour")
    (synopsis
     "Display Tournament Fixtures using Knock Out and Round Robin Techniques")
    (description
     "Use of Knock Out and Round Robin Techniques in preparing tournament fixtures as
discussed in the Book Health and Physical Education by Dr. V K
Sharma'(2018,ISBN:978-93-5272-134-4).")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-sportscausal
  (package
    (name "r-sportscausal")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPORTSCausal" version))
       (sha256
        (base32 "1ww1wi3kflwj1f5slfv0mfi7gbaj7y8rk1m3n15szpxb9zm8grks"))))
    (properties `((upstream-name . "SPORTSCausal")))
    (build-system r-build-system)
    (propagated-inputs (list r-keras r-causalimpact))
    (home-page "https://cran.r-project.org/package=SPORTSCausal")
    (synopsis "Spillover Time Series Causal Inference")
    (description
     "This package provides a time series causal inference model for Randomized
Controlled Trial (RCT) under spillover effect.  SPORTSCausal (Spillover Time
Series Causal Inference) separates treatment effect and spillover effect from
given responses of experiment group and control group by predicting the response
without treatment.  It reports both effects by fitting the Bayesian Structural
Time Series (BSTS) model based on @code{CausalImpact}', as described in
Brodersen et al. (2015) <doi:10.1214/14-AOAS788>.")
    (license license:gpl2)))

(define-public r-sport
  (package
    (name "r-sport")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sport" version))
       (sha256
        (base32 "1vifh5x3rqnj0y7lzxhxa8jiiq9gza7r55kajcdib0hr65amwssk"))))
    (properties `((upstream-name . "sport")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-ggplot2 r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/gogonzo/sport")
    (synopsis "Sequential Pairwise Online Rating Techniques")
    (description
     "Calculates ratings for two-player or multi-player challenges.  Methods included
in package such as are able to estimate ratings (players strengths) and their
evolution in time, also able to predict output of challenge.  Algorithms are
based on Bayesian Approximation Method, and they don't involve any matrix
inversions nor likelihood estimation.  Parameters are updated sequentially, and
computation doesn't require any additional RAM to make estimation feasible.
Additionally, base of the package is written in C++ what makes sport computation
even faster.  Methods used in the package refer to Mark E. Glickman (1999)
<http://www.glicko.net/research/glicko.pdf>; Mark E. Glickman (2001)
<doi:10.1080/02664760120059219>; Ruby C. Weng, Chih-Jen Lin (2011)
<https://www.jmlr.org/papers/volume12/weng11a/weng11a.pdf>; W. Penny, Stephen J.
Roberts (1999) <doi:10.1109/IJCNN.1999.832603>.")
    (license license:gpl2)))

(define-public r-spork
  (package
    (name "r-spork")
    (version "0.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spork" version))
       (sha256
        (base32 "1qigb3m98rrsz2ggf23dxcr79zmri8safdkly7iwzm14lysjzl7p"))))
    (properties `((upstream-name . "spork")))
    (build-system r-build-system)
    (propagated-inputs (list r-png r-latexpdf r-kableextra r-ggplot2))
    (home-page "https://cran.r-project.org/package=spork")
    (synopsis "Generalized Label Formatting")
    (description
     "The spork syntax describes label formatting concisely, supporting mixed nesting
of subscripts and superscripts to arbitrary depth.  It intends to be easy to
read and write in plain text, and easy to convert to equivalent presentations in
plotmath', latex', and html'.  Greek symbols and a multiplication symbol are
explicitly supported.  See ?as_spork and ?as_previews.")
    (license license:gpl3)))

(define-public r-spooky
  (package
    (name "r-spooky")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spooky" version))
       (sha256
        (base32 "18qlm1dvspw2n7xanrxvflgvdppva3pxrs6m56cia2klv95rn1xn"))))
    (properties `((upstream-name . "spooky")))
    (build-system r-build-system)
    (propagated-inputs (list r-tictoc
                             r-scales
                             r-readr
                             r-purrr
                             r-philentropy
                             r-moments
                             r-modeest
                             r-lubridate
                             r-imputets
                             r-greybox
                             r-ggplot2
                             r-fastdummies
                             r-fancova
                             r-entropy))
    (home-page "https://rpubs.com/giancarlo_vercellino/spooky")
    (synopsis
     "Time Feature Extrapolation Using Spectral Analysis and Jack-Knife Resampling")
    (description
     "Proposes application of spectral analysis and jack-knife resampling for
multivariate sequence forecasting.  The application allows for a fast random
search in a compact space of hyper-parameters composed by Sequence Length and
Jack-Knife Leave-N-Out.")
    (license license:gpl3)))

(define-public r-spongecake
  (package
    (name "r-spongecake")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spongecake" version))
       (sha256
        (base32 "13fxizbfjvqkhkmxk5bwv2fj2xyjkbxrxicpdgwpg0wsk4dhj3s8"))))
    (properties `((upstream-name . "spongecake")))
    (build-system r-build-system)
    (inputs (list ffmpeg))
    (propagated-inputs (list r-plyr r-magrittr r-jpeg r-ggplot2))
    (home-page "https://github.com/ThinkRstat/spongecake")
    (synopsis "Transform a Movie into a Synthetic Picture")
    (description
     "Transform a Movie into a Synthetic Picture.  A frame every 10 seconds is
summarized into one colour, then every generated colors are stacked together.")
    (license license:gpl3)))

(define-public r-spongebob
  (package
    (name "r-spongebob")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spongebob" version))
       (sha256
        (base32 "1ar5173fwx1am5rgjlnczfla6b49g0azl0h9vn89ipw7f71rr5j7"))))
    (properties `((upstream-name . "spongebob")))
    (build-system r-build-system)
    (home-page "https://github.com/jayqi/spongebob")
    (synopsis "SpongeBob-Case Converter : spOngEboB-CASe CoNVertER")
    (description
     "Convert text (and text in R objects) to Mocking @code{SpongeBob} case
<https://knowyourmeme.com/memes/mocking-spongebob> and show them off in fun
ways. @code{CoNVErT} @code{TexT} (@code{AnD} @code{TeXt} In r @code{ObJeCtS}) To
MO@code{CkINg} @code{SpoNgebOb} C@code{AsE}
<https://knowyourmeme.com/memes/mocking-spongebob> @code{aND} @code{shOw}
@code{tHem} OFf IN Fun @code{WayS}.")
    (license license:bsd-3)))

(define-public r-spoiler
  (package
    (name "r-spoiler")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spoiler" version))
       (sha256
        (base32 "105shgv62x3qjw4477qfi79h9bdih4n8hg6wn842dyfq940wkr5b"))))
    (properties `((upstream-name . "spoiler")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-shiny r-htmltools))
    (native-inputs (list esbuild))
    (home-page "https://github.com/etiennebacher/spoiler")
    (synopsis
     "Blur 'HTML' Elements in 'Shiny' Applications Using 'Spoiler-Alert.js'")
    (description
     "It can be useful to temporarily hide some text or other HTML elements in Shiny
applications.  Building on Spoiler-Alert.js', it is possible to select the
elements to hide at startup, to partially reveal them by hovering them, and to
completely show them when clicking on them.")
    (license license:expat)))

(define-public r-spoccupancy
  (package
    (name "r-spoccupancy")
    (version "0.7.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spOccupancy" version))
       (sha256
        (base32 "0gl6z2dch254nmyd19w7z3jmivadzvv27ymsbn1rk08qm19bf69g"))))
    (properties `((upstream-name . "spOccupancy")))
    (build-system r-build-system)
    (propagated-inputs (list r-spabundance
                             r-rann
                             r-lme4
                             r-foreach
                             r-doparallel
                             r-coda
                             r-abind))
    (home-page "https://www.jeffdoser.com/files/spoccupancy-web")
    (synopsis
     "Single-Species, Multi-Species, and Integrated Spatial Occupancy Models")
    (description
     "Fits single-species, multi-species, and integrated non-spatial and spatial
occupancy models using Markov Chain Monte Carlo (MCMC).  Models are fit using
Polya-Gamma data augmentation detailed in Polson, Scott, and Windle (2013)
<doi:10.1080/01621459.2013.829001>.  Spatial models are fit using either
Gaussian processes or Nearest Neighbor Gaussian Processes (NNGP) for large
spatial datasets.  Details on NNGP models are given in Datta, Banerjee, Finley,
and Gelfand (2016) <doi:10.1080/01621459.2015.1044091> and Finley, Datta, and
Banerjee (2022) <doi:10.18637/jss.v103.i05>.  Provides functionality for data
integration of multiple single-species occupancy data sets using a joint
likelihood framework.  Details on data integration are given in Miller,
Pacifici, Sanderlin, and Reich (2019) <doi:10.1111/2041-210X.13110>.  Details on
single-species and multi-species models are found in @code{MacKenzie}, Nichols,
Lachman, Droege, Royle, and Langtimm (2002)
<doi:10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2> and Dorazio and Royle
<doi:10.1198/016214505000000015>, respectively.")
    (license license:gpl3+)))

(define-public r-spocc
  (package
    (name "r-spocc")
    (version "1.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spocc" version))
       (sha256
        (base32 "0ldr7n889jch6q4b4y2ligd3z5g4z4jdf5rk1cig86x9m5wdw57f"))))
    (properties `((upstream-name . "spocc")))
    (build-system r-build-system)
    (propagated-inputs (list r-wk
                             r-whisker
                             r-tibble
                             r-s2
                             r-rvertnet
                             r-ridigbio
                             r-rgbif
                             r-rebird
                             r-lubridate
                             r-jsonlite
                             r-data-table
                             r-crul))
    (home-page "https://github.com/ropensci/spocc")
    (synopsis "Interface to Species Occurrence Data Sources")
    (description
     "This package provides a programmatic interface to many species occurrence data
sources, including Global Biodiversity Information Facility ('GBIF'),
@code{iNaturalist}', @code{eBird}', Integrated Digitized Biocollections
('@code{iDigBio}'), @code{VertNet}', Ocean Biogeographic Information System
('OBIS'), and Atlas of Living Australia ('ALA').  Includes functionality for
retrieving species occurrence data, and combining those data.")
    (license license:expat)))

(define-public r-spnngp
  (package
    (name "r-spnngp")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spNNGP" version))
       (sha256
        (base32 "1qanar1b3avlw8q18yisl4yjwbvzmz8kir8q21rnjdx612wmgsll"))))
    (properties `((upstream-name . "spNNGP")))
    (build-system r-build-system)
    (propagated-inputs (list r-rann r-formula r-coda))
    (home-page "https://www.finley-lab.com/")
    (synopsis
     "Spatial Regression Models for Large Datasets using Nearest Neighbor Gaussian Processes")
    (description
     "Fits univariate Bayesian spatial regression models for large datasets using
Nearest Neighbor Gaussian Processes (NNGP) detailed in Finley, Datta, Banerjee
(2022) <doi:10.18637/jss.v103.i05>, Finley, Datta, Cook, Morton, Andersen, and
Banerjee (2019) <doi:10.1080/10618600.2018.1537924>, and Datta, Banerjee,
Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091>.")
    (license license:gpl2+)))

(define-public r-spnn
  (package
    (name "r-spnn")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spnn" version))
       (sha256
        (base32 "1yj89j7nynfyw1ikm6q5kdwdgiiwxx0aa6kgnri5wfnb4l0356l7"))))
    (properties `((upstream-name . "spnn")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mass))
    (home-page "https://cran.r-project.org/package=spnn")
    (synopsis "Scale Invariant Probabilistic Neural Networks")
    (description
     "Scale invariant version of the original PNN proposed by Specht (1990)
<doi:10.1016/0893-6080(90)90049-q> with the added functionality of allowing for
smoothing along multiple dimensions while accounting for covariances within the
data set.  It is written in the R statistical programming language.  Given a
data set with categorical variables, we use this algorithm to estimate the
probabilities of a new observation vector belonging to a specific category.
This type of neural network provides the benefits of fast training time relative
to backpropagation and statistical generalization with only a small set of known
observations.")
    (license license:gpl2+)))

(define-public r-spnmf
  (package
    (name "r-spnmf")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpNMF" version))
       (sha256
        (base32 "1xybxx47i3ww5d7chwl38xc48fbsclgyxcki8h85c6dkm49dxy8i"))))
    (properties `((upstream-name . "SpNMF")))
    (build-system r-build-system)
    (propagated-inputs (list r-nmf))
    (home-page "https://cran.r-project.org/package=SpNMF")
    (synopsis "Supervised NMF")
    (description
     "Non-negative Matrix Factorization(NMF) is a powerful tool for identifying the
key features of microbial communities and a dimension-reduction method.  When we
are interested in the differences between the structures of two groups of
communities, supervised NMF(Yun Cai, Hong Gu and Tobby Kenney
(2017),<doi:10.1186/s40168-017-0323-1>) provides a better way to do this, while
retaining all the advantages of NMF -- such as interpretability, and being based
on a simple biological intuition.")
    (license license:gpl3)))

(define-public r-spnetwork
  (package
    (name "r-spnetwork")
    (version "0.4.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spNetwork" version))
       (sha256
        (base32 "18g885w54d5hx9pwbqwz28gs723h9wpnfhr4a0yl37j5i3rasccx"))))
    (properties `((upstream-name . "spNetwork")))
    (build-system r-build-system)
    (propagated-inputs (list r-spdep
                             r-sfheaders
                             r-sf
                             r-rdpack
                             r-rcppprogress
                             r-rcpparmadillo
                             r-rcpp
                             r-progressr
                             r-igraph
                             r-ggplot2
                             r-future-apply
                             r-dbscan
                             r-data-table
                             r-cubature
                             r-bh
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://jeremygelb.github.io/spNetwork/")
    (synopsis "Spatial Analysis on Network")
    (description
     "Perform spatial analysis on network.  Implement several methods for spatial
analysis on network: Network Kernel Density estimation, building of spatial
matrices based on network distance ('listw objects from spdep package), K
functions estimation for point pattern analysis on network, k nearest neighbours
on network, reachable area calculation, and graph generation References: Okabe
et al (2019) <doi:10.1080/13658810802475491>; Okabe et al (2012,
ISBN:978-0470770818);Baddeley et al (2015, ISBN:9781482210200).")
    (license license:gpl2)))

(define-public r-spnaf
  (package
    (name "r-spnaf")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spnaf" version))
       (sha256
        (base32 "1l9p56f6yg10drag7mphk183kzz6s4b2ghfn3dlifgq76yqi7zfl"))))
    (properties `((upstream-name . "spnaf")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-spdep
                             r-sf
                             r-rlang
                             r-magrittr
                             r-dplyr
                             r-deldir))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=spnaf")
    (synopsis "Spatial Network Autocorrelation for Flow Data")
    (description
     "Identify statistically significant flow clusters using the local spatial network
autocorrelation statistic G_ij* proposed by Berglund and KarlstrÃ¶m (1999)
<doi:10.1007/s101090050013>.  The metric, an extended statistic of Getis/Ord G
('Getis and Ord 1992) <doi:10.1111/j.1538-4632.1992.tb00261.x>, detects a group
of flows having similar traits in terms of directionality.  You provide OD data
and the associated polygon to get results with several parameters, some of which
are defined by spdep package.")
    (license license:expat)))

(define-public r-spmoran
  (package
    (name "r-spmoran")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spmoran" version))
       (sha256
        (base32 "04w6k7lkrnfxspxg42dqgrkbg6a0427lb0xrll80r329irkk9rqp"))))
    (properties `((upstream-name . "spmoran")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-spdep
                             r-sf
                             r-rcolorbrewer
                             r-rarpack
                             r-matrix
                             r-ggplot2
                             r-foreach
                             r-fnn
                             r-fields
                             r-doparallel))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/dmuraka/spmoran")
    (synopsis
     "Fast Spatial and Spatio-Temporal Regression using Moran Eigenvectors")
    (description
     "This package provides a collection of functions for estimating spatial and
spatio-temporal regression models.  Moran eigenvectors are used as spatial basis
functions to efficiently approximate spatially dependent Gaussian processes
(i.e., random effects eigenvector spatial filtering; see Murakami and Griffith
2015 <doi: 10.1007/s10109-015-0213-7>).  The implemented models include linear
regression with residual spatial dependence, spatially/spatio-temporally varying
coefficient models (Murakami et al., 2017, 2024;
<doi:10.1016/j.spasta.2016.12.001>,<doi:10.48550/@code{arXiv.2410.07229>}),
spatially filtered unconditional quantile regression (Murakami and Seya, 2019
<doi:10.1002/env.2556>), Gaussian and non-Gaussian spatial mixed models through
compositionally-warping (Murakami et al.  2021,
<doi:10.1016/j.spasta.2021.100520>).")
    (license license:gpl2+)))

(define-public r-spmodel
  (package
    (name "r-spmodel")
    (version "0.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spmodel" version))
       (sha256
        (base32 "1kywpczgj1iiml7h1c3l7x9yc557iwnfypjqq8fvh2p7sl8c68cx"))))
    (properties `((upstream-name . "spmodel")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-sf r-matrix r-generics))
    (native-inputs (list r-knitr))
    (home-page "https://usepa.github.io/spmodel/")
    (synopsis "Spatial Statistical Modeling and Prediction")
    (description
     "Fit, summarize, and predict for a variety of spatial statistical models applied
to point-referenced and areal (lattice) data.  Parameters are estimated using
various methods.  Additional modeling features include anisotropy, non-spatial
random effects, partition factors, big data approaches, and more.  Model-fit
statistics are used to summarize, visualize, and compare models.  Predictions at
unobserved locations are readily obtainable.  For additional details, see
Dumelle et al. (2023) <doi:10.1371/journal.pone.0282524>.")
    (license license:gpl3)))

(define-public r-spmlficmcm
  (package
    (name "r-spmlficmcm")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPmlficmcm" version))
       (sha256
        (base32 "1acs3560a7h6xx286m40abr9b7i5qihn6wni8flj0biahmsszzx6"))))
    (properties `((upstream-name . "SPmlficmcm")))
    (build-system r-build-system)
    (propagated-inputs (list r-nleqslv))
    (home-page "https://cran.r-project.org/package=SPmlficmcm")
    (synopsis
     "Semiparametric Maximum Likelihood Method for Interactions Gene-Environment in Case-Mother Control-Mother Designs")
    (description
     "This package implements the method of general semiparametric maximum likelihood
estimation for logistic models in case-mother control-mother designs.")
    (license license:gpl2)))

(define-public r-spmc
  (package
    (name "r-spmc")
    (version "0.3.15")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spMC" version))
       (sha256
        (base32 "1fv6769xz44xc50ad00df7bacqbwy6xnqm2072zjgkl1dbskfjma"))))
    (properties `((upstream-name . "spMC")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=spMC")
    (synopsis "Continuous-Lag Spatial Markov Chains")
    (description
     "This package provides a set of functions is provided for 1) the stratum lengths
analysis along a chosen direction, 2) fast estimation of continuous lag spatial
Markov chains model parameters and probability computing (also for large data
sets), 3) transition probability maps and transiograms drawing, 4) simulation
methods for categorical random fields.  More details on the methodology are
discussed in Sartore (2013) <doi:10.32614/RJ-2013-022> and Sartore et al. (2016)
<doi:10.1016/j.cageo.2016.06.001>.")
    (license license:gpl2+)))

(define-public r-spmaps
  (package
    (name "r-spmaps")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spMaps" version))
       (sha256
        (base32 "1v5gvnyfj04rxraqngrmpkpq3874y422hr6yirj52iksb8ry0kgh"))))
    (properties `((upstream-name . "spMaps")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp r-sf))
    (home-page "https://github.com/rte-antares-rpackage/spMaps")
    (synopsis "Europe SpatialPolygonsDataFrame Builder")
    (description
     "Build custom Europe @code{SpatialPolygonsDataFrame}, if you don't know what is a
@code{SpatialPolygonsDataFrame} see @code{SpatialPolygons}() in sp', by example
for @code{mapLayout}() in @code{antaresViz}'.  Antares is a powerful software
developed by RTE to simulate and study electric power systems (more information
about Antares here: <https://antares-simulator.org/>).")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-spm2
  (package
    (name "r-spm2")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spm2" version))
       (sha256
        (base32 "1fac131zg5z7jxbkjy6745b4pggpmkjyl9iyq15yr7yzfs77yz62"))))
    (properties `((upstream-name . "spm2")))
    (build-system r-build-system)
    (propagated-inputs (list r-spm
                             r-sp
                             r-randomforest
                             r-nlme
                             r-gstat
                             r-glmnet
                             r-gbm
                             r-fields
                             r-e1071))
    (home-page "https://cran.r-project.org/package=spm2")
    (synopsis "Spatial Predictive Modeling")
    (description
     "An updated and extended version of spm package, by introducing some further
novel functions for modern statistical methods (i.e., generalised linear models,
glmnet, generalised least squares), thin plate splines, support vector machine,
kriging methods (i.e., simple kriging, universal kriging, block kriging, kriging
with an external drift), and novel hybrid methods (228 hybrids plus numerous
variants) of modern statistical methods or machine learning methods with
mathematical and/or univariate geostatistical methods for spatial predictive
modelling.  For each method, two functions are provided, with one function for
assessing the predictive errors and accuracy of the method based on
cross-validation, and the other for generating spatial predictions.  It also
contains a couple of functions for data preparation and predictive accuracy
assessment.")
    (license license:gpl2+)))

(define-public r-spm
  (package
    (name "r-spm")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spm" version))
       (sha256
        (base32 "0slpy3cmv5l2gbgvy3yfdhzm73427bl39shadpmf3jkkbflcczsn"))))
    (properties `((upstream-name . "spm")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-ranger
                             r-randomforest
                             r-psy
                             r-gstat
                             r-gbm
                             r-biomod2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=spm")
    (synopsis "Spatial Predictive Modeling")
    (description
     "Introduction to some novel accurate hybrid methods of geostatistical and machine
learning methods for spatial predictive modelling.  It contains two commonly
used geostatistical methods, two machine learning methods, four hybrid methods
and two averaging methods.  For each method, two functions are provided.  One
function is for assessing the predictive errors and accuracy of the method based
on cross-validation.  The other one is for generating spatial predictions using
the method.  For details please see: Li, J., Potter, A., Huang, Z., Daniell, J.
J. and Heap, A. (2010)
<https:www.ga.gov.au/metadata-gateway/metadata/record/gcat_71407> Li, J., Heap,
A. D., Potter, A., Huang, Z. and Daniell, J. (2011)
<doi:10.1016/j.csr.2011.05.015> Li, J., Heap, A. D., Potter, A. and Daniell, J.
(2011) <doi:10.1016/j.envsoft.2011.07.004> Li, J., Potter, A., Huang, Z. and
Heap, A. (2012) <https:www.ga.gov.au/metadata-gateway/metadata/record/74030>.")
    (license license:gpl2+)))

(define-public r-splutil
  (package
    (name "r-splutil")
    (version "2022.6.20")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "splutil" version))
       (sha256
        (base32 "02hic08ckjg7iyjcyjncrkvjnpbpxk0v8g1yxy2s1vaw0mmfj4cz"))))
    (properties `((upstream-name . "splutil")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr r-ggplot2 r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://docs.sykdomspulsen.no/splutil/")
    (synopsis "Utility Functions for Common Base-R Problems Relating to Lists")
    (description
     "Utility functions that help with common base-R problems relating to lists.
Lists in base-R are very flexible.  This package provides functions to quickly
and easily characterize types of lists.  That is, to identify if all elements in
a list are null, data.frames, lists, or fully named lists.  Other functionality
is provided for the handling of lists, such as the easy splitting of lists into
equally sized groups, and the unnesting of data.frames within fully named lists.")
    (license license:expat)))

(define-public r-splustimeseries
  (package
    (name "r-splustimeseries")
    (version "1.5.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "splusTimeSeries" version))
       (sha256
        (base32 "1q2ldyfz27dam033gki3f8chp87qy8f0s985hn762fv2cpi5p02m"))))
    (properties `((upstream-name . "splusTimeSeries")))
    (build-system r-build-system)
    (propagated-inputs (list r-splustimedate))
    (home-page "https://github.com/spkaluzny/splusTimeSeries")
    (synopsis "Time Series from 'S-PLUS'")
    (description
     "This package provides a collection of classes and methods for working with
indexed rectangular data.  The index values can be calendar (@code{timeSeries}
class) or numeric (@code{signalSeries} class).  Methods are included for
aggregation, alignment, merging, and summaries.  The code was originally
available in S-PLUS'.")
    (license license:bsd-3)))

(define-public r-splustimedate
  (package
    (name "r-splustimedate")
    (version "2.5.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "splusTimeDate" version))
       (sha256
        (base32 "1avas42g0bm40vzj9fag6p5wzw5cd43vv9xx2ycgc48579lfyh7v"))))
    (properties `((upstream-name . "splusTimeDate")))
    (build-system r-build-system)
    (home-page "https://github.com/spkaluzny/splusTimeDate")
    (synopsis "Times and Dates from 'S-PLUS'")
    (description
     "This package provides a collection of classes and methods for working with times
and dates.  The code was originally available in S-PLUS'.")
    (license license:bsd-3)))

(define-public r-spls
  (package
    (name "r-spls")
    (version "2.2-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spls" version))
       (sha256
        (base32 "0bmb0ai5z80njhypd342i711x0bdkwcvlyn374lyyzj8h3d97mmv"))))
    (properties `((upstream-name . "spls")))
    (build-system r-build-system)
    (propagated-inputs (list r-pls r-nnet r-mass))
    (home-page "https://cran.r-project.org/package=spls")
    (synopsis
     "Sparse Partial Least Squares (SPLS) Regression and Classification")
    (description
     "This package provides functions for fitting a sparse partial least squares
(SPLS) regression and classification (Chun and Keles (2010)
<doi:10.1111/j.1467-9868.2009.00723.x>).")
    (license license:gpl2+)))

(define-public r-splot
  (package
    (name "r-splot")
    (version "0.5.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "splot" version))
       (sha256
        (base32 "14x2fsf41izlb99pxypgy3f4yaj33j04a68nan0f9545fjl78v4x"))))
    (properties `((upstream-name . "splot")))
    (build-system r-build-system)
    (home-page "https://miserman.github.io/splot/")
    (synopsis "Simplified Plotting for Data Exploration")
    (description
     "Automates common plotting tasks to ease data exploration.  Makes density plots
(potentially overlaid on histograms), scatter plots with prediction lines, or
bar or line plots with error bars.  For each type, y, or x and y variables can
be plotted at levels of other variables, all with minimal specification.")
    (license license:gpl2+)))

(define-public r-splmm
  (package
    (name "r-splmm")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "splmm" version))
       (sha256
        (base32 "1qnzm1mnx5i0q1a5534s5n6fwgs95fdknc6k5s65z9jqd33n39c4"))))
    (properties `((upstream-name . "splmm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-progress
                             r-plot3d
                             r-penalized
                             r-misctools
                             r-mass
                             r-gridextra
                             r-ggplot2
                             r-emulator))
    (home-page "https://cran.r-project.org/package=splmm")
    (synopsis "Simultaneous Penalized Linear Mixed Effects Models")
    (description
     "This package contains functions that fit linear mixed-effects models for
high-dimensional data (p>>n) with penalty for both the fixed effects and random
effects for variable selection.  The details of the algorithm can be found in
Luoying Yang @code{PhD} thesis (Yang and Wu 2020).  The algorithm implementation
is based on the R package lmmlasso'.  Reference: Yang L, Wu TT (2020).
Model-Based Clustering of Longitudinal Data in High-Dimensionality.  Unpublished
thesis.")
    (license license:gpl3)))

(define-public r-splm
  (package
    (name "r-splm")
    (version "1.6-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "splm" version))
       (sha256
        (base32 "0zwyl8pyx5ldr1rwqlkmzzbjc7r4dp261lsvw4b37ydm0sdhsizk"))))
    (properties `((upstream-name . "splm")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-spdep
                             r-spatialreg
                             r-spam
                             r-plm
                             r-nlme
                             r-maxlik
                             r-matrix
                             r-mass
                             r-bdsmatrix))
    (home-page "https://cran.r-project.org/package=splm")
    (synopsis "Econometric Models for Spatial Panel Data")
    (description
     "ML and GM estimation and diagnostic testing of econometric models for spatial
panel data.")
    (license license:gpl2)))

(define-public r-splittools
  (package
    (name "r-splittools")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "splitTools" version))
       (sha256
        (base32 "1qm9rippq5726vgsdcim4xvssv5r00d46zjzbcg06vy71klgrc6p"))))
    (properties `((upstream-name . "splitTools")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mayer79/splitTools")
    (synopsis "Tools for Data Splitting")
    (description
     "Fast, lightweight toolkit for data splitting.  Data sets can be partitioned into
disjoint groups (e.g. into training, validation, and test) or into (repeated)
k-folds for subsequent cross-validation.  Besides basic splits, the package
supports stratified, grouped as well as blocked splitting.  Furthermore,
cross-validation folds for time series data can be created.  See e.g. Hastie et
al. (2001) <doi:10.1007/978-0-387-84858-7> for the basic background on data
partitioning and cross-validation.")
    (license license:gpl2+)))

(define-public r-splitsplitplot
  (package
    (name "r-splitsplitplot")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SplitSplitPlot" version))
       (sha256
        (base32 "0k10is14z27gndqmg0sr9mgvpymayi9z2vv5fwqmvfvxg8y7j4hl"))))
    (properties `((upstream-name . "SplitSplitPlot")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SplitSplitPlot")
    (synopsis
     "Analysis of Split-Split-Plot Experiments (Analise De Experimentos Em Parcela Subsubdividida)")
    (description
     "This package performs analysis of split-split plot experiments in both
completely randomized and randomized complete block designs.  With the results,
you can obtain ANOVA, mean tests, and regression analysis (Este pacote faz a
analise de experimentos em parcela subsubdivididas no delineamento inteiramente
casualizado e delineamento em blocos casualizados.  Com resultados e possÃ­vel
obter a ANOVA, testes de medias e anÃ¡lise de regressao)
<https://www.expstat.com/pacotes-do-r>.")
    (license license:gpl3)))

(define-public r-splitsoftening
  (package
    (name "r-splitsoftening")
    (version "2.1-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SplitSoftening" version))
       (sha256
        (base32 "1fgcqax81zpknw642fm9dx1f6dpdgbcf3va0m1bhj6kl98yq7dqd"))))
    (properties `((upstream-name . "SplitSoftening")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SplitSoftening")
    (synopsis "Softening Splits in Decision Trees")
    (description
     "Allows to produce and use classification trees with soft (probability) splits,
as described in: DvoÅÃ¡k, J. (2019), <doi:10.1007/s00180-019-00867-1>.")
    (license license:gpl2+)))

(define-public r-splitselect
  (package
    (name "r-splitselect")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "splitSelect" version))
       (sha256
        (base32 "03gziy1hhm35hw8q2w9w6d4x2zci5q2wds0mkzihvfm0wkshi4fw"))))
    (properties `((upstream-name . "splitSelect")))
    (build-system r-build-system)
    (propagated-inputs (list r-multicool r-glmnet r-foreach r-doparallel))
    (home-page "https://cran.r-project.org/package=splitSelect")
    (synopsis "Best Split Selection Modeling for Low-Dimensional Data")
    (description
     "This package provides functions to generate or sample from all possible splits
of features or variables into a number of specified groups.  Also computes the
best split selection estimator (for low-dimensional data) as defined in
Christidis, Van Aelst and Zamar (2019) <@code{arXiv:1812.05678>}.")
    (license license:gpl2+)))

(define-public r-splitreg
  (package
    (name "r-splitreg")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SplitReg" version))
       (sha256
        (base32 "1s012qldrsq8rpfpzf7kyd7631b65zai269x53xcaqn75r5ssh3f"))))
    (properties `((upstream-name . "SplitReg")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=SplitReg")
    (synopsis "Split Regularized Regression")
    (description
     "This package provides functions for computing split regularized estimators
defined in Christidis, Lakshmanan, Smucler and Zamar (2019)
<@code{arXiv:1712.03561>}.  The approach fits linear regression models that
split the set of covariates into groups.  The optimal split of the variables
into groups and the regularized estimation of the regression coefficients are
performed by minimizing an objective function that encourages sparsity within
each group and diversity among them.  The estimated coefficients are then pooled
together to form the final fit.")
    (license license:gpl2+)))

(define-public r-splitknockoff
  (package
    (name "r-splitknockoff")
    (version "2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SplitKnockoff" version))
       (sha256
        (base32 "13s40w98rm5bfd58272wm927nwsngydwag9pdpajhrz2lkbagnkz"))))
    (properties `((upstream-name . "SplitKnockoff")))
    (build-system r-build-system)
    (propagated-inputs (list r-rspectra
                             r-mvtnorm
                             r-matrix
                             r-mass
                             r-latex2exp
                             r-glmnet
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SplitKnockoff")
    (synopsis "Split Knockoffs for Structural Sparsity")
    (description
     "Split Knockoff is a data adaptive variable selection framework for controlling
the (directional) false discovery rate (FDR) in structural sparsity, where
variable selection on linear transformation of parameters is of concern.  This
proposed scheme relaxes the linear subspace constraint to its neighborhood,
often known as variable splitting in optimization.  Simulation experiments can
be reproduced following the Vignette.  Split Knockoffs is first defined in Cao
et al. (2021) <doi:10.48550/@code{arXiv.2103.16159>}.")
    (license license:expat)))

(define-public r-splithalfr
  (package
    (name "r-splithalfr")
    (version "2.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "splithalfr" version))
       (sha256
        (base32 "0zy7p2cvxbq2p24vyi55hl4qcj42igfbsapmx3xh15rpcdhzsmm0"))))
    (properties `((upstream-name . "splithalfr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-rlang r-psych r-dplyr r-bcaboot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/tpronk/splithalfr")
    (synopsis "Estimate Split-Half Reliabilities")
    (description
     "Estimates split-half reliabilities for scoring algorithms of cognitive tasks and
questionnaires.  The splithalfr supports researcher-provided scoring algorithms,
with six vignettes illustrating how on included datasets.  The package provides
four splitting methods (first-second, odd-even, permutated, Monte Carlo), the
option to stratify splits by task design, a number of reliability coefficients,
and the option to sub-sample data.")
    (license license:gpl3)))

(define-public r-splithalf
  (package
    (name "r-splithalf")
    (version "0.8.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "splithalf" version))
       (sha256
        (base32 "0lq1a07kh2jdvg68p2jzhjcidwp6r83qbkggqdv0rmdj4x47lj5s"))))
    (properties `((upstream-name . "splithalf")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-robustbase
                             r-rcpp
                             r-psych
                             r-plyr
                             r-patchwork
                             r-lme4
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sdparsons/splithalf")
    (synopsis "Calculate Task Split Half Reliability Estimates")
    (description
     "Estimate the internal consistency of your tasks with a permutation based
split-half reliability approach.  Unofficial release name: \"I eat stickers all
the time, dude!\".")
    (license license:gpl3)))

(define-public r-splitglm
  (package
    (name "r-splitglm")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SplitGLM" version))
       (sha256
        (base32 "0i9fs09bch3qsf6b6sws82hd5rzhhfcf17f30b5w7b2rr0sc9dxa"))))
    (properties `((upstream-name . "SplitGLM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=SplitGLM")
    (synopsis "Split Generalized Linear Models")
    (description
     "This package provides functions to compute split generalized linear models.  The
approach fits generalized linear models that split the covariates into groups.
The optimal split of the variables into groups and the regularized estimation of
the coefficients are performed by minimizing an objective function that
encourages sparsity within each group and diversity among them.  Example
applications can be found in Christidis et al. (2021) <@code{arXiv:2102.08591>}.")
    (license license:gpl2+)))

(define-public r-splitfngr
  (package
    (name "r-splitfngr")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "splitfngr" version))
       (sha256
        (base32 "0cf9y68m72j890a732m0n784nr12ydljr6s8i80f0in6q8lmw8ls"))))
    (properties `((upstream-name . "splitfngr")))
    (build-system r-build-system)
    (propagated-inputs (list r-lbfgs))
    (home-page "https://cran.r-project.org/package=splitfngr")
    (synopsis "Combined Evaluation and Split Access of Functions")
    (description
     "Some R functions, such as optim(), require a function its gradient passed as
separate arguments.  When these are expensive to calculate it may be much faster
to calculate the function (fn) and gradient (gr) together since they often share
many calculations (chain rule).  This package allows the user to pass in a
single function that returns both the function and gradient, then splits (hence
splitfngr') them so the results can be accessed separately.  The functions
provided allow this to be done with any number of functions/values, not just for
functions and gradients.")
    (license license:gpl3)))

(define-public r-split
  (package
    (name "r-split")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPlit" version))
       (sha256
        (base32 "0n9458sj5sw4k9qd11gvmqmjf9hy1hh0bwx1jzm6r901l1wbp7xi"))))
    (properties `((upstream-name . "SPlit")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=SPlit")
    (synopsis "Split a Dataset for Training and Testing")
    (description
     "Procedure to optimally split a dataset for training and testing.  SPlit is based
on the method of support points, which is independent of modeling methods.
Please see Joseph and Vakayil (2021) <doi:10.1080/00401706.2021.1921037> for
details.  This work is supported by U.S. National Science Foundation grant
DMREF-1921873.")
    (license license:gpl2+)))

(define-public r-splinets
  (package
    (name "r-splinets")
    (version "1.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Splinets" version))
       (sha256
        (base32 "11wg4ddr2nscjnqlfvpphskbhmyglnq110v5lngvp8sszw84dwkc"))))
    (properties `((upstream-name . "Splinets")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=Splinets")
    (synopsis
     "Functional Data Analysis using Splines and Orthogonal Spline Bases")
    (description
     "Splines are efficiently represented through their Taylor expansion at the knots.
 The representation accounts for the support sets and is thus suitable for
sparse functional data.  Two cases of boundary conditions are considered:
zero-boundary or periodic-boundary for all derivatives except the last.  The
periodical splines are represented graphically using polar coordinates.  The
B-splines and orthogonal bases of splines that reside on small total support are
implemented.  The orthogonal bases are referred to as splinets and are utilized
for functional data analysis.  Random spline generator is implemented as well as
all fundamental algebraic and calculus operations on splines.  The optimal, in
the least square sense, functional fit by splinets to data consisting of sampled
values of functions as well as splines build over another set of knots is
obtained and used for functional data analysis. <@code{arXiv:2102.00733>},
<doi:10.1016/j.cam.2022.114444>, <@code{arXiv:2302.07552>}.")
    (license license:gpl2+)))

(define-public r-splinetree
  (package
    (name "r-splinetree")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "splinetree" version))
       (sha256
        (base32 "0gqb3kwca42crrx6fj42ryl5pkxvankhp1chdlvi5lgg8mzmqsnl"))))
    (properties `((upstream-name . "splinetree")))
    (build-system r-build-system)
    (propagated-inputs (list r-treeclust
                             r-rpart
                             r-nlme
                             r-mosaic
                             r-mclust
                             r-ggplot2))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/anna-neufeld/splinetree")
    (synopsis "Longitudinal Regression Trees and Forests")
    (description
     "Builds regression trees and random forests for longitudinal or functional data
using a spline projection method.  Implements and extends the work of Yu and
Lambert (1999) <doi:10.1080/10618600.1999.10474847>.  This method allows trees
and forests to be built while considering either level and shape or only shape
of response trajectories.")
    (license license:expat)))

(define-public r-splines2
  (package
    (name "r-splines2")
    (version "0.5.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "splines2" version))
       (sha256
        (base32 "1p0xl3k33b539j6fifxaack956z3p4yfvhglcpvral315p8pnzn2"))))
    (properties `((upstream-name . "splines2")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://wwenjie.org/splines2")
    (synopsis "Regression Spline Functions and Classes")
    (description
     "Constructs basis functions of B-splines, M-splines, I-splines, convex splines
(C-splines), periodic splines, natural cubic splines, generalized Bernstein
polynomials, their derivatives, and integrals (except C-splines) by closed-form
recursive formulas.  It also contains a C++ head-only library integrated with
Rcpp.  See Wang and Yan (2021) <doi:10.6339/21-JDS1020> for details.")
    (license license:gpl3+)))

(define-public r-splice
  (package
    (name "r-splice")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPLICE" version))
       (sha256
        (base32 "1vzacbgb2z6b7cbcn6l5zjminl8gr5irzva0l8shq7g8cqggrvwm"))))
    (properties `((upstream-name . "SPLICE")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-synthetic r-lifecycle))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/agi-lab/SPLICE")
    (synopsis
     "Synthetic Paid Loss and Incurred Cost Experience (SPLICE) Simulator")
    (description
     "An extension to the individual claim simulator called @code{SynthETIC} (on
CRAN), to simulate the evolution of case estimates of incurred losses through
the lifetime of an insurance claim.  The transactional simulation output now
comprises key dates, and both claim payments and revisions of estimated incurred
losses.  An initial set of test parameters, designed to mirror the experience of
a real insurance portfolio, were set up and applied by default to generate a
realistic test data set of incurred histories (see vignette).  However, the
distributional assumptions used to generate this data set can be easily modified
by users to match their experiences.  Reference: Avanzi B, Taylor G, Wang M
(2021) \"SPLICE: A Synthetic Paid Loss and Incurred Cost Experience Simulator\"
<@code{arXiv:2109.04058>}.")
    (license license:gpl3)))

(define-public r-spldv
  (package
    (name "r-spldv")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spldv" version))
       (sha256
        (base32 "10bvx4z2b0kl2dl91dvr611cfp28dvch41vj9z1k1qlw03ybqpr8"))))
    (properties `((upstream-name . "spldv")))
    (build-system r-build-system)
    (propagated-inputs (list r-sphet
                             r-spatialreg
                             r-numderiv
                             r-memisc
                             r-maxlik
                             r-matrix
                             r-mass
                             r-formula
                             r-car))
    (home-page "https://github.com/gpiras/spldv")
    (synopsis "Spatial Models for Limited Dependent Variables")
    (description
     "The current version of this package estimates spatial autoregressive models for
binary dependent variables using GMM estimators <doi:10.18637/jss.v107.i08>.  It
supports one-step (Pinkse and Slade, 1998) <doi:10.1016/S0304-4076(97)00097-3>
and two-step GMM estimator along with the linearized GMM estimator proposed by
Klier and @code{McMillen} (2008) <doi:10.1198/073500107000000188>.  It also
allows for either Probit or Logit model and compute the average marginal
effects.  All these models are presented in Sarrias and Piras (2023)
<doi:10.1016/j.jocm.2023.100432>.")
    (license license:gpl2+)))

(define-public r-splash
  (package
    (name "r-splash")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "splash" version))
       (sha256
        (base32 "1iviwf7dxlrbfzjigl8wd8q3i0jkdnb7mr8zy6zg0w81p4416q42"))))
    (properties `((upstream-name . "splash")))
    (build-system r-build-system)
    (home-page "https://github.com/villegar/splash/")
    (synopsis "Simple Process-Led Algorithms for Simulating Habitats")
    (description
     "This program calculates bioclimatic indices and fluxes (radiation,
evapotranspiration, soil moisture) for use in studies of ecosystem function,
species distribution, and vegetation dynamics under changing climate scenarios.
Predictions are based on a minimum of required inputs: latitude, precipitation,
air temperature, and cloudiness.  Davis et al. (2017)
<doi:10.5194/gmd-10-689-2017>.")
    (license license:gpl3)))

(define-public r-splancs
  (package
    (name "r-splancs")
    (version "2.01-45")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "splancs" version))
       (sha256
        (base32 "1lvn9gm2n7ic4cxz255jls33kkbblrdcka87v99apskz3pbg3k4b"))))
    (properties `((upstream-name . "splancs")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp))
    (native-inputs (list gfortran))
    (home-page "https://www.maths.lancs.ac.uk/~rowlings/Splancs/")
    (synopsis "Spatial and Space-Time Point Pattern Analysis")
    (description
     "The Splancs package was written as an enhancement to S-Plus for display and
analysis of spatial point pattern data; it has been ported to R and is in
\"maintenance mode\".")
    (license license:gpl2+)))

(define-public r-spiro
  (package
    (name "r-spiro")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spiro" version))
       (sha256
        (base32 "08v290c62x915624hydzns6qdbfxh08zsbgffkgd89inn2bwj55g"))))
    (properties `((upstream-name . "spiro")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-signal
                             r-readxl
                             r-knitr
                             r-ggplot2
                             r-digest
                             r-cowplot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ropensci/spiro")
    (synopsis "Manage Data from Cardiopulmonary Exercise Testing")
    (description
     "Import, process, summarize and visualize raw data from metabolic carts.  See
Robergs, Dwyer, and Astorino (2010) <doi:10.2165/11319670-000000000-00000> for
more details on data processing.")
    (license license:expat)))

(define-public r-spiritr
  (package
    (name "r-spiritr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spiritR" version))
       (sha256
        (base32 "1fjr63rl9hwqxzgm4lkb9v74cxxpi4lrlcgjixdm7zgccm8ihdvh"))))
    (properties `((upstream-name . "spiritR")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-magrittr r-httr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/awconway/spiritR")
    (synopsis "Template for Clinical Trial Protocol")
    (description
     "This package contains an R Markdown template for a clinical trial protocol
adhering to the SPIRIT statement.  The SPIRIT (Standard Protocol Items for
Interventional Trials) statement outlines recommendations for a minimum set of
elements to be addressed in a clinical trial protocol.  Also contains functions
to create a xml document from the template and upload it to
clinicaltrials.gov<https://www.clinicaltrials.gov/> for trial registration.")
    (license license:expat)))

(define-public r-spiralize
  (package
    (name "r-spiralize")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spiralize" version))
       (sha256
        (base32 "07ckzg2fhjjn5p1q7jpxlpj8ls5rzcafy3krjvr6a51hidlfan56"))))
    (properties `((upstream-name . "spiralize")))
    (build-system r-build-system)
    (propagated-inputs (list r-lubridate r-globaloptions r-getoptlong
                             r-complexheatmap r-circlize))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jokergoo/spiralize")
    (synopsis "Visualize Data on Spirals")
    (description
     "It visualizes data along an Archimedean spiral
<https://en.wikipedia.org/wiki/Archimedean_spiral>, makes so-called spiral graph
or spiral chart.  It has two major advantages for visualization: 1.  It is able
to visualize data with very long axis with high resolution.  2.  It is efficient
for time series data to reveal periodic patterns.")
    (license license:expat)))

(define-public r-spinyreg
  (package
    (name "r-spinyreg")
    (version "0.1-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spinyReg" version))
       (sha256
        (base32 "0kbg7rncrrl5xdsaw9vj909x97mfp77mjnvghczplmnwmmanyn72"))))
    (properties `((upstream-name . "spinyReg")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=spinyReg")
    (synopsis "Sparse Generative Model and Its EM Algorithm")
    (description
     "This package implements a generative model that uses a spike-and-slab like prior
distribution obtained by multiplying a deterministic binary vector.  Such a
model allows an EM algorithm, optimizing a type-II log-likelihood.")
    (license license:gpl2+)))

(define-public r-spinner
  (package
    (name "r-spinner")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spinner" version))
       (sha256
        (base32 "18fmcqqarfkixx4060paq236dc4fg8mbj9ngfz3khljb59lyh379"))))
    (properties `((upstream-name . "spinner")))
    (build-system r-build-system)
    (propagated-inputs (list r-torch
                             r-tictoc
                             r-rlist
                             r-readr
                             r-purrr
                             r-lubridate
                             r-igraph
                             r-ggthemes
                             r-ggplot2
                             r-fastdummies
                             r-entropy
                             r-abind))
    (home-page "https://rpubs.com/giancarlo_vercellino/spinner")
    (synopsis "An Implementation of Graph Net Architecture Based on 'torch'")
    (description
     "Proposes a torch implementation of Graph Net architecture allowing different
options for message passing and feature embedding.")
    (license license:gpl3)))

(define-public r-spinifex
  (package
    (name "r-spinifex")
    (version "0.3.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spinifex" version))
       (sha256
        (base32 "1hw7kc4shmclx8i4794w8mlhl7awfrz0nr2k33nbk4pzdy0c02hk"))))
    (properties `((upstream-name . "spinifex")))
    (build-system r-build-system)
    (propagated-inputs (list r-tourr
                             r-shiny
                             r-rdimtools
                             r-plotly
                             r-magrittr
                             r-ggplot2
                             r-gganimate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/nspyrison/spinifex/")
    (synopsis
     "Manual Tours, Manual Control of Dynamic Projections of Numeric Multivariate Data")
    (description
     "Data visualization tours animates linear projection of multivariate data as its
basis (ie.  orientation) changes.  The spinifex packages generates paths for
manual tours by manipulating the contribution of a single variable at a time
Cook & Buja (1997) <doi:10.1080/10618600.1997.10474754>.  Other types of tours,
such as grand (random walk) and guided (optimizing some objective function) are
available in the tourr package Wickham et al. <doi:10.18637/jss.v040.i02>.
spinifex builds on tourr and can render tours with gganimate and plotly
graphics, and allows for exporting as an .html widget and as an .gif,
respectively.  This work is fully discussed in Spyrison & Cook (2020)
<doi:10.32614/RJ-2020-027>.")
    (license license:expat)))

(define-public r-spind
  (package
    (name "r-spind")
    (version "2.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spind" version))
       (sha256
        (base32 "1yasqy086h4dv348krisc024mic0dvdsncqys95l85924djlfipp"))))
    (properties `((upstream-name . "spind")))
    (build-system r-build-system)
    (propagated-inputs (list r-waveslim
                             r-stringr
                             r-splancs
                             r-rlang
                             r-rje
                             r-rcolorbrewer
                             r-mass
                             r-lattice
                             r-ggplot2
                             r-geepack
                             r-gee))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/levisc8/spind")
    (synopsis "Spatial Methods and Indices")
    (description
     "This package provides functions for spatial methods based on generalized
estimating equations (GEE) and wavelet-revised methods (WRM), functions for
scaling by wavelet multiresolution regression (WMRR), conducting multi-model
inference, and stepwise model selection.  Further, contains functions for
spatially corrected model accuracy measures.")
    (license license:gpl3)))

(define-public r-spinbayes
  (package
    (name "r-spinbayes")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spinBayes" version))
       (sha256
        (base32 "1p3gfxw1jnnj22s43facsna1ivazfj3x431f1ckv8qycdv61h31d"))))
    (properties `((upstream-name . "spinBayes")))
    (build-system r-build-system)
    (propagated-inputs (list r-testthat r-rcpparmadillo r-rcpp r-glmnet
                             r-ggplot2))
    (home-page "https://github.com/jrhub/spinBayes")
    (synopsis
     "Semi-Parametric Gene-Environment Interaction via Bayesian Variable Selection")
    (description
     "Many complex diseases are known to be affected by the interactions between
genetic variants and environmental exposures beyond the main genetic and
environmental effects.  Existing Bayesian methods for gene-environment (GÃE)
interaction studies are challenged by the high-dimensional nature of the study
and the complexity of environmental influences.  We have developed a novel and
powerful semi-parametric Bayesian variable selection method that can accommodate
linear and nonlinear GÃE interactions simultaneously (Ren et al. (2020)
<doi:10.1002/sim.8434>).  Furthermore, the proposed method can conduct
structural identification by distinguishing nonlinear interactions from main
effects only case within Bayesian framework.  Spike-and-slab priors are
incorporated on both individual and group level to shrink coefficients
corresponding to irrelevant main and interaction effects to zero exactly.  The
Markov chain Monte Carlo algorithms of the proposed and alternative methods are
efficiently implemented in C++.")
    (license license:gpl2)))

(define-public r-spinar
  (package
    (name "r-spinar")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spINAR" version))
       (sha256
        (base32 "02y0y6xzckzhygs7fwvnd3mgw8ay78wg850d46v4q6vxpb144wjy"))))
    (properties `((upstream-name . "spINAR")))
    (build-system r-build-system)
    (propagated-inputs (list r-progress r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MFaymon/spINAR")
    (synopsis "(Semi)Parametric Estimation and Bootstrapping of INAR Models")
    (description
     "Semiparametric and parametric estimation of INAR models including a finite
sample refinement (Faymonville et al. (2022) <doi:10.1007/s10260-022-00655-0>)
for the semiparametric setting introduced in Drost et al. (2009)
<doi:10.1111/j.1467-9868.2008.00687.x>, different procedures to bootstrap INAR
data (Jentsch, C. and WeiÃ, C.H. (2017) <doi:10.3150/18-BEJ1057>) and flexible
simulation of INAR data.")
    (license license:gpl3+)))

(define-public r-spina
  (package
    (name "r-spina")
    (version "4.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPINA" version))
       (sha256
        (base32 "0x7y150iq072qqci2niqk7j8pvhcylb7kdvwambg1r1gi3ffycm9"))))
    (properties `((upstream-name . "SPINA")))
    (build-system r-build-system)
    (home-page "http://spina.sf.net/")
    (synopsis "Structure Parameter Inference Approach")
    (description
     "Calculates constant structure parameters of endocrine homeostatic systems from
equilibrium hormone concentrations.  Methods and equations have been described
in Dietrich et al. (2012) <doi:10.1155/2012/351864> and Dietrich et al. (2016)
<doi:10.3389/fendo.2016.00057>.")
    (license license:bsd-3)))

(define-public r-spillover
  (package
    (name "r-spillover")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Spillover" version))
       (sha256
        (base32 "1f1x8d6cpgp797l65kp4rqzi2yxjmlhqih8vbj89q2g4rmvyrbaf"))))
    (properties `((upstream-name . "Spillover")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-vars
                             r-tidyr
                             r-ggplot2
                             r-fastsom
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=Spillover")
    (synopsis "Spillover/Connectedness Index Based on VAR Modelling")
    (description
     "This package provides a user-friendly tool for estimating both total and
directional connectedness spillovers based on Diebold and Yilmaz (2009, 2012).
It also provides the user with rolling estimation for total and net indices.
User can find both orthogonalized and generalized versions for each kind of
measures.  See Diebold and Yilmaz (2009, 2012) find them at
<doi:10.1111/j.1468-0297.2008.02208.x> and
<doi:10.1016/j.ijforecast.2011.02.006>.")
    (license license:gpl2)))

(define-public r-spikeslabgam
  (package
    (name "r-spikeslabgam")
    (version "1.1-19")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spikeSlabGAM" version))
       (sha256
        (base32 "0qj16s4vmg058wzjd1bi1dc32kbj1gh56696hkqmpb87cgbwmff2"))))
    (properties `((upstream-name . "spikeSlabGAM")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-reshape
                             r-r2winbugs
                             r-mvtnorm
                             r-mcmcpack
                             r-mass
                             r-interp
                             r-gridextra
                             r-ggplot2
                             r-coda
                             r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/fabian-s/spikeSlabGAM")
    (synopsis
     "Bayesian Variable Selection and Model Choice for Generalized Additive Mixed Models")
    (description
     "Bayesian variable selection, model choice, and regularized estimation for
(spatial) generalized additive mixed regression models via stochastic search
variable selection with spike-and-slab priors.")
    (license license:expat)))

(define-public r-spikeslab
  (package
    (name "r-spikeslab")
    (version "1.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spikeslab" version))
       (sha256
        (base32 "1slpfps7xdvi3f3q334lilp9m9gi3n8dl00x1j8yyia6mnz0cimk"))))
    (properties `((upstream-name . "spikeslab")))
    (build-system r-build-system)
    (propagated-inputs (list r-randomforest r-lars))
    (home-page "https://ishwaran.org/")
    (synopsis
     "Prediction and Variable Selection Using Spike and Slab Regression")
    (description
     "Spike and slab for prediction and variable selection in linear regression
models.  Uses a generalized elastic net for variable selection.")
    (license license:gpl3+)))

(define-public r-spikes
  (package
    (name "r-spikes")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spikes" version))
       (sha256
        (base32 "080sna0sgii61kai5sirs6cijnvk1012djwfw705di9y2lvk1hqk"))))
    (properties `((upstream-name . "spikes")))
    (build-system r-build-system)
    (propagated-inputs (list r-emdbook))
    (home-page "https://cran.r-project.org/package=spikes")
    (synopsis
     "Detecting Election Fraud from Irregularities in Vote-Share Distributions")
    (description
     "Applies re-sampled kernel density method to detect vote fraud.  It estimates the
proportion of coarse vote-shares in the observed data relative to the null
hypothesis of no fraud.")
    (license license:gpl2+)))

(define-public r-spiga
  (package
    (name "r-spiga")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPIGA" version))
       (sha256
        (base32 "0yd06x5rh1h1s4v9apj43c90rkz8m5kssbny7y5mnw5mr2acds9b"))))
    (properties `((upstream-name . "SPIGA")))
    (build-system r-build-system)
    (propagated-inputs (list r-ga))
    (home-page "https://cran.r-project.org/package=SPIGA")
    (synopsis
     "Compute SPI Index using the Methods Genetic Algorithm and Maximum Likelihood")
    (description
     "Calculate the Standardized Precipitation Index (SPI) for monitoring drought,
using Artificial Intelligence techniques (SPIGA) and traditional numerical
technique Maximum Likelihood (SPIML).  For more information see:
http://drought.unl.edu/monitoringtools/downloadablespiprogram.aspx.")
    (license license:gpl2)))

(define-public r-spidr
  (package
    (name "r-spidr")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spidR" version))
       (sha256
        (base32 "09n0fz9iqc3n4c72h9hyc0ybzaqzy4l1rypvaig5kl98kx5nng1i"))))
    (properties `((upstream-name . "spidR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rworldxtra r-rworldmap r-rgbif r-jsonlite
                             r-httr))
    (home-page "https://cran.r-project.org/package=spidR")
    (synopsis "Spider Knowledge Online")
    (description
     "Allows the user to connect with the World Spider Catalogue (WSC;
<https://wsc.nmbe.ch/>) and the World Spider Trait (WST;
<https://spidertraits.sci.muni.cz/>) databases.  Also performs several basic
functions such as checking names validity, retrieving coordinate data from the
Global Biodiversity Information Facility (GBIF; <https://www.gbif.org/>), and
mapping.")
    (license license:gpl3)))

(define-public r-spiderbar
  (package
    (name "r-spiderbar")
    (version "0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spiderbar" version))
       (sha256
        (base32 "0yhxm3ns9xymm6msklpm1x0ahcq7gkfp1w92aafk2r3pip1g3ds5"))))
    (properties `((upstream-name . "spiderbar")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://github.com/hrbrmstr/spiderbar")
    (synopsis "Parse and Test Robots Exclusion Protocol Files and Rules")
    (description
     "The Robots Exclusion Protocol <https://www.robotstxt.org/orig.html> documents a
set of standards for allowing or excluding robot/spider crawling of different
areas of site content.  Tools are provided which wrap The rep-cpp
<https://github.com/seomoz/rep-cpp> C++ library for processing these robots.txt
files.")
    (license license:expat)))

(define-public r-spider
  (package
    (name "r-spider")
    (version "1.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spider" version))
       (sha256
        (base32 "1y034v42jxjl3vj87iz5kaizq7fwppmn56z7yyvrrmk5ps4nqr1j"))))
    (properties `((upstream-name . "spider")))
    (build-system r-build-system)
    (propagated-inputs (list r-pegas r-ape))
    (home-page "https://cran.r-project.org/package=spider")
    (synopsis "Species Identity and Evolution in R")
    (description
     "Analysis of species limits and DNA barcoding data.  Included are functions for
generating important summary statistics from DNA barcode data, assessing
specimen identification efficacy, testing and optimizing divergence threshold
limits, assessment of diagnostic nucleotides, and calculation of the probability
of reciprocal monophyly.  Additionally, a sliding window function offers
opportunities to analyse information across a gene, often used for marker design
in degraded DNA studies.  Further information on the package has been published
in Brown et al (2012) <doi:10.1111/j.1755-0998.2011.03108.x>.")
    (license license:expat)))

(define-public r-spicefp
  (package
    (name "r-spicefp")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpiceFP" version))
       (sha256
        (base32 "1vhsi9g925jc95589wp3cjlj8psvggkf1cws2awy94zi0z6lkiri"))))
    (properties `((upstream-name . "SpiceFP")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-purrr
                             r-matrix
                             r-genlasso
                             r-foreach
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=SpiceFP")
    (synopsis
     "Sparse Method to Identify Joint Effects of Functional Predictors")
    (description
     "This package provides a set of functions allowing to implement the
@code{SpiceFP} approach which is iterative.  It involves transformation of
functional predictors into several candidate explanatory matrices (based on
contingency tables), to which relative edge matrices with contiguity constraints
are associated.  Generalized Fused Lasso regression are performed in order to
identify the best candidate matrix, the best class intervals and related
coefficients at each iteration.  The approach is stopped when the maximal number
of iterations is reached or when retained coefficients are zeros.  Supplementary
functions allow to get coefficients of any candidate matrix or mean of
coefficients of many candidates.  The methods in this package are describing in
Girault Gnanguenon Guesse, Patrice Loisel, BÃ©nedicte Fontez, Thierry Simonneau,
Nadine Hilgert (2021) \"An exploratory penalized regression to identify combined
effects of functional variables -Application to agri-environmental issues\"
<https://hal.archives-ouvertes.fr/hal-03298977>.")
    (license license:gpl3)))

(define-public r-sphunif
  (package
    (name "r-sphunif")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sphunif" version))
       (sha256
        (base32 "125w91cz60j3lkx8dm74c8gh67k5j2k8mypx7rihym9rsdibdric"))))
    (properties `((upstream-name . "sphunif")))
    (build-system r-build-system)
    (propagated-inputs (list r-rotasym
                             r-rcpparmadillo
                             r-rcpp
                             r-gsl
                             r-future
                             r-foreach
                             r-dorng
                             r-dofuture))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/egarpor/sphunif")
    (synopsis "Uniformity Tests on the Circle, Sphere, and Hypersphere")
    (description
     "Implementation of uniformity tests on the circle and (hyper)sphere.  The main
function of the package is unif_test(), which conveniently collects more than 35
tests for assessing uniformity on S^{p-1} = {x in R^p : ||x|| = 1}, p >= 2.  The
test statistics are implemented in the unif_stat() function, which allows
computing several statistics for different samples within a single call, thus
facilitating Monte Carlo experiments.  Furthermore, the unif_stat_MC() function
allows parallelizing them in a simple way.  The asymptotic null distributions of
the statistics are available through the function unif_stat_distr().  The core
of sphunif is coded in C++ by relying on the Rcpp package.  The package also
provides several novel datasets and gives the replicability for the data
applications/simulations in GarcÃ­a-PortuguÃ©s et al. (2021)
<doi:10.1007/978-3-030-69944-4_12>, GarcÃ­a-PortuguÃ©s et al. (2023)
<doi:10.3150/21-BEJ1454>, GarcÃ­a-PortuguÃ©s et al. (2024)
<doi:10.48550/@code{arXiv.2108.09874>}, and FernÃ¡ndez-de-Marcos and
GarcÃ­a-PortuguÃ©s (2024) <doi:10.48550/@code{arXiv.2405.13531>}.")
    (license license:gpl3)))

(define-public r-sphet
  (package
    (name "r-sphet")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sphet" version))
       (sha256
        (base32 "1qr4n4kbmc58qmr8niqq3mnwv18x9q2f24c1kg67asf7n3pnmy77"))))
    (properties `((upstream-name . "sphet")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-spdep
                             r-spdata
                             r-spatialreg
                             r-sp
                             r-sf
                             r-nlme
                             r-mvtnorm
                             r-matrix
                             r-coda))
    (home-page "https://github.com/gpiras/sphet")
    (synopsis
     "Estimation of Spatial Autoregressive Models with and without Heteroskedastic Innovations")
    (description
     "This package provides functions for fitting Cliff-Ord-type spatial
autoregressive models with and without heteroskedastic innovations using
Generalized Method of Moments estimation are provided.  Some support is
available for fitting spatial HAC models, and for fitting with non-spatial
endogeneous variables using instrumental variables.")
    (license license:gpl2)))

(define-public r-sphericalcubature
  (package
    (name "r-sphericalcubature")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SphericalCubature" version))
       (sha256
        (base32 "03j9f9v3zrykrd4lc74i19s3xyc0z3l2kg3056qg591sxw64h7vh"))))
    (properties `((upstream-name . "SphericalCubature")))
    (build-system r-build-system)
    (propagated-inputs (list r-simplicialcubature r-mvmesh r-cubature r-abind))
    (home-page "https://cran.r-project.org/package=SphericalCubature")
    (synopsis
     "Numerical Integration over Spheres and Balls in n-Dimensions; Multivariate Polar Coordinates")
    (description
     "This package provides several methods to integrate functions over the unit
sphere and ball in n-dimensional Euclidean space.  Routines for converting
to/from multivariate polar/spherical coordinates are also provided.")
    (license license:gpl2+)))

(define-public r-spheretessellation
  (package
    (name "r-spheretessellation")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sphereTessellation" version))
       (sha256
        (base32 "1b8wxbh44qd0rznjln3icp9k4jy598rva9kwcgzscpjm5j86r97w"))))
    (properties `((upstream-name . "sphereTessellation")))
    (build-system r-build-system)
    (inputs (list mpfr gmp))
    (propagated-inputs (list r-rgl
                             r-rcppeigen
                             r-rcppcgal
                             r-rcpp
                             r-polychrome
                             r-colorsgen
                             r-bh))
    (native-inputs (list pkg-config))
    (home-page "https://github.com/stla/sphereTessellation")
    (synopsis "Delaunay and VoronoÃ¯ Tessellations on the Sphere")
    (description
     "This package performs Delaunay and VoronoÃ¯ tessellations on spheres and
provides some functions to plot them.  The algorithms are mainly performed by
the C++ library CGAL (<https://www.cgal.org/>).")
    (license license:gpl3)))

(define-public r-spheresmooth
  (package
    (name "r-spheresmooth")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spheresmooth" version))
       (sha256
        (base32 "1xf1cg3y7dvx274ccmia97lj278ghpx2p1ayqrfxxl1cnw4p53rv"))))
    (properties `((upstream-name . "spheresmooth")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=spheresmooth")
    (synopsis "Piecewise Geodesic Smoothing for Spherical Data")
    (description
     "Fitting a smooth path to a given set of noisy spherical data observed at known
time points.  It implements a piecewise geodesic curve fitting method on the
unit sphere based on a velocity-based penalization scheme.  The proposed
approach is implemented using the Riemannian block coordinate descent algorithm.
 To understand the method and algorithm, one can refer to Bak, K. Y., Shin, J.
K., & Koo, J. Y. (2023) <doi:10.1080/02664763.2022.2054962> for the case of
order 1.  Additionally, this package includes various functions necessary for
handling spherical data.")
    (license license:gpl2+)))

(define-public r-sphereplot
  (package
    (name "r-sphereplot")
    (version "1.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sphereplot" version))
       (sha256
        (base32 "1jxjd66hcyqdizzikxsncswswj7c1rprigz1xii82rkcms8iygzm"))))
    (properties `((upstream-name . "sphereplot")))
    (build-system r-build-system)
    (propagated-inputs (list r-rgl))
    (home-page "https://cran.r-project.org/package=sphereplot")
    (synopsis "Spherical Plotting")
    (description
     "Various functions for creating spherical coordinate system plots via extensions
to rgl.")
    (license license:gpl2)))

(define-public r-spherepc
  (package
    (name "r-spherepc")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spherepc" version))
       (sha256
        (base32 "0y05ylyxzawgjlivaxs513mfjjfmw6fm98b83sq76n8yw56pr9ny"))))
    (properties `((upstream-name . "spherepc")))
    (build-system r-build-system)
    (propagated-inputs (list r-sphereplot r-rgl r-geosphere))
    (home-page "https://cran.r-project.org/package=spherepc")
    (synopsis "Spherical Principal Curves")
    (description
     "Fitting dimension reduction methods to data lying on two-dimensional sphere.
This package provides principal geodesic analysis, principal circle, principal
curves proposed by Hauberg, and spherical principal curves.  Moreover, it offers
the method of locally defined principal geodesics which is underway.  The
detailed procedures are described in Lee, J., Kim, J.-H. and Oh, H.-S. (2021)
<doi:10.1109/TPAMI.2020.3025327>.  Also see Kim, J.-H., Lee, J. and Oh, H.-S.
(2020) <@code{arXiv:2003.02578>}.")
    (license license:gpl3+)))

(define-public r-sphereoptimize
  (package
    (name "r-sphereoptimize")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SphereOptimize" version))
       (sha256
        (base32 "1b01jpj0sagi3hwfvgd346wvrcypv5b3hh0xfdd8qqnwjgkq8ixi"))))
    (properties `((upstream-name . "SphereOptimize")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SphereOptimize")
    (synopsis "Optimization on a Unit Sphere")
    (description
     "This package provides a simple tool for numerical optimization on the unit
sphere.  This is achieved by combining the spherical coordinating system with
L-BFGS-B optimization.  This algorithm is implemented in Kolkiewicz, A., Rice,
G., & Xie, Y. (2020) <doi:10.1016/j.jspi.2020.07.001>.")
    (license license:gpl3)))

(define-public r-spheredata
  (package
    (name "r-spheredata")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spheredata" version))
       (sha256
        (base32 "1wy90kv7r3q7sxca8w6c66kxxqhnp9ylsz16wafdsxm64003m1b7"))))
    (properties `((upstream-name . "spheredata")))
    (build-system r-build-system)
    (home-page "https://github.com/santosoph/spheredata")
    (synopsis
     "Students' Performance Dataset in Physics Education Research (SPHERE)")
    (description
     "This package provides a multidimensional dataset of students performance
assessment in high school physics.  The SPHERE dataset was collected from 497
students in four public high schools specifically measuring their conceptual
understanding, scientific ability, and attitude toward physics [see Santoso et
al. (2024) <doi:10.17632/88d7m2fv7p.1>].  The data collection was conducted
using some research based assessments established by the physics education
research community.  They include the Force Concept Inventory, the Force and
Motion Conceptual Evaluation, the Rotational and Rolling Motion Conceptual
Survey, the Fluid Mechanics Concept Inventory, the Mechanical Waves Conceptual
Survey, the Thermal Concept Evaluation, the Survey of Thermodynamic Processes
and First and Second Laws, the Scientific Abilities Assessment Rubrics, and the
Colorado Learning Attitudes about Science Survey.  Students attributes related
to gender, age, socioeconomic status, domicile, literacy, physics identity, and
test results administered using teachers developed items are also reported in
this dataset.")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-spgwr
  (package
    (name "r-spgwr")
    (version "0.6-37")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spgwr" version))
       (sha256
        (base32 "1lxl4byihwih0iban0ypsh7p7vzbprjzl4xv62181dbsjm5xhmyk"))))
    (properties `((upstream-name . "spgwr")))
    (build-system r-build-system)
    (propagated-inputs (list r-spdata r-sp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rsbivand/spgwr/")
    (synopsis "Geographically Weighted Regression")
    (description
     "This package provides functions for computing geographically weighted
regressions are provided, based on work by Chris Brunsdon, Martin Charlton and
Stewart Fotheringham.")
    (license license:gpl2+)))

(define-public r-spgs
  (package
    (name "r-spgs")
    (version "1.0-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spgs" version))
       (sha256
        (base32 "0ln0fhxiavzwp5qlz5akk5896dyznwbz23f816vkjnxmw7pi4p70"))))
    (properties `((upstream-name . "spgs")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=spgs")
    (synopsis "Statistical Patterns in Genomic Sequences")
    (description
     "This package provides a collection of statistical hypothesis tests and other
techniques for identifying certain spatial relationships/phenomena in DNA
sequences.  In particular, it provides tests and graphical methods for
determining whether or not DNA sequences comply with Chargaff's second parity
rule or exhibit purine-pyrimidine parity.  In addition, there are functions for
efficiently simulating discrete state space Markov chains and testing arbitrary
symbolic sequences of symbols for the presence of first-order Markovianness.
Also, it has functions for counting words/k-mers (and cylinder patterns) in
arbitrary symbolic sequences.  Functions which take a DNA sequence as input can
handle sequences stored as @code{SeqFastadna} objects from the seqinr package.")
    (license license:gpl2+)))

(define-public r-spgarch
  (package
    (name "r-spgarch")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spGARCH" version))
       (sha256
        (base32 "1wrmk2ybgysvibfy8vvp7pyqda5apqp0jxjfp3hlb416fza52966"))))
    (properties `((upstream-name . "spGARCH")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm
                             r-spdep
                             r-rsolnp
                             r-rcppeigen
                             r-rcpp
                             r-nleqslv
                             r-matrix))
    (home-page "https://cran.r-project.org/package=spGARCH")
    (synopsis "Spatial ARCH and GARCH Models (spGARCH)")
    (description
     "This package provides a collection of functions to deal with spatial and
spatiotemporal autoregressive conditional heteroscedasticity (spatial ARCH and
GARCH models) by Otto, Schmid, Garthoff (2018, Spatial Statistics)
<@code{arXiv:1609.00711>}: simulation of spatial ARCH-type processes
(@code{spARCH}, exponential @code{spARCH}, complex @code{spARCH});
quasi-maximum-likelihood estimation of the parameters of @code{spARCH} models
and spatial autoregressive models with @code{spARCH} disturbances, diagnostic
checks, visualizations.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-spfw
  (package
    (name "r-spfw")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spFW" version))
       (sha256
        (base32 "1d00a01mnwc7lcnqgz2dfglhwqwhh9vm4yd904zq3s26zv4qapyf"))))
    (properties `((upstream-name . "spFW")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=spFW")
    (synopsis "Hierarchical Spatial Finlay-Wilkinson Model")
    (description
     "Estimation and Prediction Functions Using Bayesian Hierarchical Spatial
Finlay-Wilkinson Model for Analysis of Multi-Environment Field Trials.")
    (license license:gpl3)))

(define-public r-spfsr
  (package
    (name "r-spfsr")
    (version "2.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spFSR" version))
       (sha256
        (base32 "1d4hdbgy21cq6hl5r8ig9mydiab4wr2df9wbrlhyy90a4x7gxfyi"))))
    (properties `((upstream-name . "spFSR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tictoc
                             r-ranger
                             r-mlr3pipelines
                             r-mlr3learners
                             r-mlr3
                             r-lgr
                             r-ggplot2
                             r-future))
    (home-page "https://www.featureranking.com/")
    (synopsis
     "Feature Selection and Ranking via Simultaneous Perturbation Stochastic Approximation")
    (description
     "An implementation of feature selection, weighting and ranking via simultaneous
perturbation stochastic approximation (SPSA).  The SPSA-FSR algorithm searches
for a locally optimal set of features that yield the best predictive performance
using some error measures such as mean squared error (for regression problems)
and accuracy rate (for classification problems).")
    (license license:gpl3)))

(define-public r-spfilter
  (package
    (name "r-spfilter")
    (version "1.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spfilteR" version))
       (sha256
        (base32 "1s11d88y4jwzd85lyircgz9j67mwbqld1jicynxb7wayk8akfyf4"))))
    (properties `((upstream-name . "spfilteR")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sjuhl/spfilteR")
    (synopsis
     "Semiparametric Spatial Filtering with Eigenvectors in (Generalized) Linear Models")
    (description
     "This package provides tools to decompose (transformed) spatial connectivity
matrices and perform supervised or unsupervised semiparametric spatial filtering
in a regression framework.  The package supports unsupervised spatial filtering
in standard linear as well as some generalized linear regression models.")
    (license license:gpl3)))

(define-public r-spfda
  (package
    (name "r-spfda")
    (version "0.9.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spfda" version))
       (sha256
        (base32 "094j68gyy5yw7d2mn3n864h6vf8nd76x9ypfzc1liggnw7qs0fdl"))))
    (properties `((upstream-name . "spfda")))
    (build-system r-build-system)
    (propagated-inputs (list r-mathjaxr))
    (home-page "https://github.com/dipterix/spfda")
    (synopsis "Function-on-Scalar Regression with Group-Bridge Penalty")
    (description
     "This package implements a group-bridge penalized function-on-scalar regression
model proposed by Wang et al. (2020) <@code{arXiv:2006.10163>}, to
simultaneously estimate functional coefficient and recover the local sparsity.")
    (license license:expat)))

(define-public r-spfa
  (package
    (name "r-spfa")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spfa" version))
       (sha256
        (base32 "0xkq5679whrvxnbbcbari533jpima27gcyqbszvkg0z95q956ndj"))))
    (properties `((upstream-name . "spfa")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=spfa")
    (synopsis "Semi-Parametric Factor Analysis")
    (description
     "Estimation, scoring, and plotting functions for the semi-parametric factor model
proposed by Liu & Wang (2022) <doi:10.1007/s11336-021-09832-8> and Liu & Wang
(2023) <@code{arXiv:2303.10079>}.  Both the conditional densities of observed
responses given the latent factors and the joint density of latent factors are
estimated non-parametrically.  Functional parameters are approximated by
smoothing splines, whose coefficients are estimated by penalized maximum
likelihood using an expectation-maximization (EM) algorithm.  E- and M-steps can
be parallelized on multi-thread computing platforms that support @code{OpenMP}'.
 Both continuous and unordered categorical response variables are supported.")
    (license license:expat)))

(define-public r-spex
  (package
    (name "r-spex")
    (version "0.7.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spex" version))
       (sha256
        (base32 "0lsmh1sywd4si0v10aix44hybwph3gan8w8cxz956dcgkz60p41v"))))
    (properties `((upstream-name . "spex")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp r-reproj r-raster r-quadmesh r-crsmeta))
    (home-page "https://mdsumner.github.io/spex/")
    (synopsis "Spatial Extent Tools")
    (description
     "This package provides functions to produce a fully fledged geo-spatial object
extent as a @code{SpatialPolygonsDataFrame}'.  Also included are functions to
generate polygons from raster data using quadmesh techniques, a round number
buffered extent, and general spatial-extent and raster-like extent helpers
missing from the originating packages.  Some latitude-based tools for polar maps
are included.")
    (license license:gpl3)))

(define-public r-spev
  (package
    (name "r-spev")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPEV" version))
       (sha256
        (base32 "0gvbxmc0y8x2wf2lfgncwjhqy8gp8lk3mwmbcbhj8by0krglwipb"))))
    (properties `((upstream-name . "SPEV")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SPEV")
    (synopsis "Unsmoothed and Smoothed Penalized PCA using Nesterov Smoothing")
    (description
     "We provide functionality to implement penalized PCA with an option to smooth the
objective function using Nesterov smoothing.  Two functions are available to
compute a user-specified number of eigenvectors.  The function
unsmoothed_penalized_EV() computes a penalized PCA without smoothing and has
three parameters (the input matrix, the Lasso penalty, and the number of desired
eigenvectors).  The function smoothed_penalized_EV() computes a smoothed
penalized PCA using the same parameters and additionally requires the
specification of a smoothing parameter.  Both functions return a matrix having
the desired eigenvectors as columns.")
    (license license:gpl2+)))

(define-public r-spetestnp
  (package
    (name "r-spetestnp")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpeTestNP" version))
       (sha256
        (base32 "1767xhnfdq9qm626j8jr0l6vnr7kkljsyarz3y318y33ja3rx71j"))))
    (properties `((upstream-name . "SpeTestNP")))
    (build-system r-build-system)
    (propagated-inputs (list r-foreach r-doparallel))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/HippolyteBoucher/SpeTestNP")
    (synopsis "Non-Parametric Tests of Parametric Specifications")
    (description
     "This package performs non-parametric tests of parametric specifications.  Five
tests are available.  Specific bandwidth and kernel methods can be chosen along
with many other options.  Allows parallel computing to quickly compute p-values
based on the bootstrap.  Methods implemented in the package are H.J. Bierens
(1982) <doi:10.1016/0304-4076(82)90105-1>, J.C. Escanciano (2006)
<doi:10.1017/S0266466606060506>, P.L. Gozalo (1997)
<doi:10.1016/S0304-4076(97)86571-2>, P. Lavergne and V. Patilea (2008)
<doi:10.1016/j.jeconom.2007.08.014>, P. Lavergne and V. Patilea (2012)
<doi:10.1198/jbes.2011.07152>, J.H. Stock and M.W. Watson (2006)
<doi:10.1111/j.1538-4616.2007.00014.x>, C.F.J. Wu (1986)
<doi:10.1214/aos/1176350142>, J. Yin, Z. Geng, R. Li, H. Wang (2010)
<https://www.jstor.org/stable/24309002> and J.X. Zheng (1996)
<doi:10.1016/0304-4076(95)01760-7>.")
    (license license:gpl2)))

(define-public r-sperrorest
  (package
    (name "r-sperrorest")
    (version "3.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sperrorest" version))
       (sha256
        (base32 "0hhigyv0p92gmxkchy4cim9mczsclahy5k6zkql5bjdky19zf72q"))))
    (properties `((upstream-name . "sperrorest")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rocr r-future-apply r-future r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://giscience-fsu.github.io/sperrorest/")
    (synopsis
     "Perform Spatial Error Estimation and Variable Importance Assessment")
    (description
     "This package implements spatial error estimation and permutation-based variable
importance measures for predictive models using spatial cross-validation and
spatial block bootstrap.")
    (license license:gpl3)))

(define-public r-sperich
  (package
    (name "r-sperich")
    (version "1.5-9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sperich" version))
       (sha256
        (base32 "0wanxma1iq49z0p0fywzfialg89c3qvfdhbdnskl59klyj36f5y7"))))
    (properties `((upstream-name . "sperich")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp r-raster r-foreach))
    (home-page "https://cran.r-project.org/package=sperich")
    (synopsis "Auxiliary Functions to Estimate Centers of Biodiversity")
    (description
     "This package provides some easy-to-use functions to interpolate species range
based on species occurrences and to estimate centers of biodiversity.")
    (license license:gpl2+)))

(define-public r-spemd
  (package
    (name "r-spemd")
    (version "0.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spemd" version))
       (sha256
        (base32 "1aw8dg2fm8qd95bgim2kjwp7ax4b2g78521llsxv7fhdj9hl7rgw"))))
    (properties `((upstream-name . "spemd")))
    (build-system r-build-system)
    (propagated-inputs (list r-spdep r-sp r-mba))
    (home-page "https://github.com/pierreroudier/spemd")
    (synopsis
     "Bi-Dimensional Implementation of the Empirical Mode Decomposition for Spatial Data")
    (description
     "This implementation of the Empirical Mode Decomposition (EMD) works in 2
dimensions simultaneously, and can be applied on spatial data.  It can handle
both gridded or un-gridded datasets.")
    (license license:gpl3)))

(define-public r-spellcheckr
  (package
    (name "r-spellcheckr")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spellcheckr" version))
       (sha256
        (base32 "09snc1ymzr4njnr3w502b84dqwjjd414higjxqi48jvh27br5k1l"))))
    (properties `((upstream-name . "spellcheckr")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-dplyr r-data-table))
    (home-page "https://cran.r-project.org/package=spellcheckr")
    (synopsis "Correct the Spelling of a Given Word in the English Language")
    (description
     "Corrects the spelling of a given word in English using a modification of Peter
Norvig's spell correct algorithm (see <http://norvig.com/spell-correct.html>)
which handles up to three edits.  The algorithm tries to find the spelling with
maximum probability of intended correction out of all possible candidate
corrections from the original word.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-spei
  (package
    (name "r-spei")
    (version "1.8.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPEI" version))
       (sha256
        (base32 "1xn7hdj5h68mvkb81pmwha665c6k57pa7k59bb84zhvxgdnczksn"))))
    (properties `((upstream-name . "SPEI")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-tlmoments
                             r-reshape
                             r-lubridate
                             r-lmomco
                             r-lmom
                             r-ggplot2
                             r-checkmate))
    (home-page "https://spei.csic.es")
    (synopsis
     "Calculation of the Standardized Precipitation-Evapotranspiration Index")
    (description
     "This package provides a set of functions for computing potential
evapotranspiration and several widely used drought indices including the
Standardized Precipitation-Evapotranspiration Index (SPEI).")
    (license license:gpl2)))

(define-public r-speff2trial
  (package
    (name "r-speff2trial")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "speff2trial" version))
       (sha256
        (base32 "18g4ap6m9li42amj1aa9dazp2sqz161zfijklmnphq63f2b5gczc"))))
    (properties `((upstream-name . "speff2trial")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-leaps))
    (home-page "https://github.com/mjuraska/speff2trial")
    (synopsis
     "Semiparametric Efficient Estimation for a Two-Sample Treatment Effect")
    (description
     "This package performs estimation and testing of the treatment effect in a
2-group randomized clinical trial with a quantitative, dichotomous, or
right-censored time-to-event endpoint.  The method improves efficiency by
leveraging baseline predictors of the endpoint.  The inverse probability
weighting technique of Robins, Rotnitzky, and Zhao (JASA, 1994) is used to
provide unbiased estimation when the endpoint is missing at random.")
    (license license:gpl2)))

(define-public r-spef
  (package
    (name "r-spef")
    (version "1.0.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spef" version))
       (sha256
        (base32 "115lmv2a21ws1mj22v1fbk72mqn3siqmw3frbg4nci96lnyr7qh6"))))
    (properties `((upstream-name . "spef")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-squarem
                             r-sm
                             r-plyr
                             r-nleqslv
                             r-ggplot2
                             r-bb))
    (home-page "http://github.com/stc04003/spef")
    (synopsis "Semiparametric Estimating Functions")
    (description
     "This package provides functions for fitting semiparametric regression models for
panel count survival data.  An overview of the package can be found in Wang and
Yan (2011) <doi:10.1016/j.cmpb.2010.10.005> and Chiou et al. (2018)
<doi:10.1111/insr.12271>.")
    (license license:gpl3+)))

(define-public r-speedytax
  (package
    (name "r-speedytax")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "speedytax" version))
       (sha256
        (base32 "0iqgn0mxw5ygnnjz1sbmpz1wqcx1gx5xpncbl93ra8k7qmidw7gk"))))
    (properties `((upstream-name . "speedytax")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rcpp
                             r-phyloseq
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=speedytax")
    (synopsis "Rapidly Import Classifier Results into 'phyloseq'")
    (description
     "Import classification results from the RDP Classifier (Ribosomal Database
Project), USEARCH sintax, vsearch sintax and the QIIME2 (Quantitative Insights
into Microbial Ecology) classifiers into phyloseq tax_table objects.")
    (license license:expat)))

(define-public r-speedycode
  (package
    (name "r-speedycode")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "speedycode" version))
       (sha256
        (base32 "0k9pnrmv5zmiyia1js3va2gx7nwbg2b35jl4r1lm2hwl3c901jfw"))))
    (properties `((upstream-name . "speedycode")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-purrr r-dplyr))
    (home-page "https://cran.r-project.org/package=speedycode")
    (synopsis
     "Automate Code for Adding Labels, Recoding and Renaming Variables, and Converting ASCII Files")
    (description
     "Label, recode, rename, and convert datasets and ASCII files more efficiently.
speedycode automates the code necessary for labeling variables with the labelled
package, recoding and renaming variables with dplyr syntax, and converting ASCII
files with the readroper package.  Most functions require only the name of the
dataset and the code will be automatically written.  Some convenience functions
useful for converting ASCII files are also included.")
    (license license:gpl3)))

(define-public r-speechbr
  (package
    (name "r-speechbr")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "speechbr" version))
       (sha256
        (base32 "0jf0zj64hm1zxbpcry1iff3nddf2wl315whzcbzj8936qfam92p5"))))
    (properties `((upstream-name . "speechbr")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rvest
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-janitor
                             r-httr
                             r-httptest
                             r-glue
                             r-dplyr
                             r-abjutils))
    (home-page "https://github.com/dcardosos/speechbr")
    (synopsis
     "Access the Speechs and Speaker's Informations of House of Representatives of Brazil")
    (description
     "Scrap speech text and speaker informations of speeches of House of
Representatives of Brazil, and transform in a cleaned tibble.")
    (license license:expat)))

(define-public r-speech
  (package
    (name "r-speech")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "speech" version))
       (sha256
        (base32 "1jxlk24vspsbk27p63kccadbf09vvb7xxx0wahj7908ygg7q5a1f"))))
    (properties `((upstream-name . "speech")))
    (build-system r-build-system)
    (propagated-inputs (list r-tm
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rvest
                             r-purrr
                             r-pdftools
                             r-magrittr
                             r-lubridate
                             r-dplyr))
    (home-page "https://github.com/Nicolas-Schmidt/speech")
    (synopsis "Legislative Speeches")
    (description
     "Converts the floor speeches of Uruguayan legislators, extracted from the
parliamentary minutes, to tidy data.frame where each observation is the
intervention of a single legislator.")
    (license license:gpl3)))

(define-public r-spedinstabr
  (package
    (name "r-spedinstabr")
    (version "2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPEDInstabR" version))
       (sha256
        (base32 "1365ridi0mf5391l3jsp9djfkpimlamqflqd0l5pja3hyxvci51l"))))
    (properties `((upstream-name . "SPEDInstabR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SPEDInstabR")
    (synopsis
     "Estimation of the Relative Importance of Factors Affecting Species Distribution Based on Stability Concept")
    (description
     "From output files obtained from the software @code{ModestR}', the relative
contribution of factors to explain species distribution is depicted using
several plots.  A global geographic raster file for each environmental variable
may be also obtained with the mean relative contribution, considering all
species present in each raster cell, of the factor to explain species
distribution.  Finally, for each variable it is also possible to compare the
frequencies of any variable obtained in the cells where the species is present
with the frequencies of the same variable in the cells of the extent.")
    (license license:gpl2+)))

(define-public r-spedecon
  (package
    (name "r-spedecon")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spedecon" version))
       (sha256
        (base32 "06vyyznf5xlwqb662plvnm7pjjwwsb6h745yr9izjam1zyavvq1c"))))
    (properties `((upstream-name . "spedecon")))
    (build-system r-build-system)
    (propagated-inputs (list r-quadprog))
    (home-page "https://www.davidjkent.org")
    (synopsis
     "Smoothness-Penalized Deconvolution for Density Estimation Under Measurement Error")
    (description
     "This package implements the Smoothness-Penalized Deconvolution method for
estimating a probability density under measurement error of Kent and Ruppert
(2023) <doi:10.1080/01621459.2023.2259028>.  The estimator is formed by
computing a histogram of the error-contaminated data, and then finding an
estimate that minimizes a reconstruction error plus a smoothness-inducing
penalty term.  The primary function, sped(), takes the data and error
distribution, and returns the estimator as a function.")
    (license license:gpl3)))

(define-public r-sped
  (package
    (name "r-sped")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sped" version))
       (sha256
        (base32 "0dxfr9fpwmnl0nq6szg5ph1mfl2qp28n4d2kkq092qyzbvk74rnp"))))
    (properties `((upstream-name . "sped")))
    (build-system r-build-system)
    (propagated-inputs (list r-pooh))
    (home-page "https://cran.r-project.org/package=sped")
    (synopsis "Multi-Gene Descent Probabilities")
    (description
     "Do multi-gene descent probabilities (Thompson, 1983,
<doi:10.1098/rspb.1983.0072>) and special cases thereof (Thompson, 1986,
<doi:10.1002/zoo.1430050210>) including inbreeding and kinship coefficients.
But does much more: probabilities of any set of genes descending from any other
set of genes.")
    (license license:gpl2+)))

(define-public r-spectrino
  (package
    (name "r-spectrino")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spectrino" version))
       (sha256
        (base32 "1fqm2n3iw6qqvhv3rbj9ikvn1k7aganacb5lwbywagwq4dx9d4qn"))))
    (properties `((upstream-name . "spectrino")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-httpuv))
    (home-page "http://www.spectrino.com")
    (synopsis
     "Spectra Viewer, Organizer, Data Preparation and Property Blocks")
    (description
     "Spectra viewer, organizer, data preparation and property blocks from within R or
stand-alone.  Binary (application) part is installed separately using
@code{spnInstallApp}() from spectrino package.")
    (license license:gpl2+)))

(define-public r-spectran
  (package
    (name "r-spectran")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Spectran" version))
       (sha256
        (base32 "03nqfsg0hia8v1i7npzxjin3zm4x7sihk389h7cky46vfd95g627"))))
    (properties `((upstream-name . "Spectran")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-webshot2
                             r-waiter
                             r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-spscomps
                             r-spacesxyz
                             r-shinywidgets
                             r-shinyjs
                             r-shinyfeedback
                             r-shinydashboard
                             r-shinyalert
                             r-shiny
                             r-scales
                             r-rlang
                             r-readr
                             r-purrr
                             r-png
                             r-patchwork
                             r-pagedown
                             r-openxlsx
                             r-magrittr
                             r-htmltools
                             r-gt
                             r-ggtext
                             r-ggridges
                             r-ggrepel
                             r-ggplot2
                             r-gghighlight
                             r-dplyr
                             r-cowplot
                             r-colorspec
                             r-chromote))
    (home-page "https://github.com/LiTGde/Spectran")
    (synopsis "Visual and Non-Visual Spectral Analysis of Light")
    (description
     "Analyse light spectra for visual and non-visual (often called melanopic) needs,
wrapped up in a Shiny App.  Spectran allows for the import of spectra in various
CSV forms but also provides a wide range of example spectra and even the
creation of own spectral power distributions.  The goal of the app is to provide
easy access and a visual overview of the spectral calculations underlying common
parameters used in the field.  It is thus ideal for educational purposes or the
creation of presentation ready graphs in lighting research and application.
Spectran uses equations and action spectra described in CIE S026 (2018)
<doi:10.25039/S026.2018>, DIN/TS 5031-100 (2021) <doi:10.31030/3287213>, and
ISO/CIE 23539 (2023) <doi:10.25039/IS0.CIE.23539.2023>.")
    (license license:expat)))

(define-public r-spectralr
  (package
    (name "r-spectralr")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spectralR" version))
       (sha256
        (base32 "1kz47gydsqa3g47m6z546sf5jnfscsly1v3dpj97ns0qf5sh57bp"))))
    (properties `((upstream-name . "spectralR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-sf
                             r-rlang
                             r-rgee
                             r-reshape2
                             r-ggplot2
                             r-geojsonio
                             r-dplyr))
    (home-page "https://github.com/olehprylutskyi/spectralR/")
    (synopsis
     "Obtain and Visualize Spectral Reflectance Data for Earth Surface Polygons")
    (description
     "This package provides tools for obtaining, processing, and visualizing spectral
reflectance data for the user-defined land or water surface classes for visual
exploring in which wavelength the classes differ.  Input should be a shapefile
with polygons of surface classes (it might be different habitat types, crops,
vegetation, etc.).  The Sentinel-2 L2A satellite mission optical bands pixel
data are obtained through the Google Earth Engine service
(<https://earthengine.google.com/>) and used as a source of spectral data.")
    (license license:gpl3)))

(define-public r-spectralmap
  (package
    (name "r-spectralmap")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpectralMap" version))
       (sha256
        (base32 "15689023k9jzg3s7bx5m97dmn00z876amqhxsxksy8n8wf09wr57"))))
    (properties `((upstream-name . "SpectralMap")))
    (build-system r-build-system)
    (propagated-inputs (list r-scatterplot3d r-fields))
    (home-page "https://cran.r-project.org/package=SpectralMap")
    (synopsis "Diffusion Map and Spectral Map")
    (description
     "This package implements the diffusion map method of dimensionality reduction and
spectral method of combining multiple diffusion maps, including creation of the
spectra and visualization of maps.")
    (license license:gpl2)))

(define-public r-spectralgraphtopology
  (package
    (name "r-spectralgraphtopology")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spectralGraphTopology" version))
       (sha256
        (base32 "09nsszm2f6q8kz84kn7ihhb627q186jwpvr6fm633l8app4ilf2x"))))
    (properties `((upstream-name . "spectralGraphTopology")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlist
                             r-rcppeigen
                             r-rcpparmadillo
                             r-rcpp
                             r-progress
                             r-matrix
                             r-mass))
    (native-inputs (list r-rmarkdown r-r-rsp r-knitr r-cvxr))
    (home-page "https://github.com/dppalomar/spectralGraphTopology")
    (synopsis "Learning Graphs from Data via Spectral Constraints")
    (description
     "In the era of big data and hyperconnectivity, learning high-dimensional
structures such as graphs from data has become a prominent task in machine
learning and has found applications in many fields such as finance, health care,
and networks. @code{spectralGraphTopology} is an open source, documented, and
well-tested R package for learning graphs from data.  It provides
implementations of state of the art algorithms such as Combinatorial Graph
Laplacian Learning (CGL), Spectral Graph Learning (SGL), Graph Estimation based
on Majorization-Minimization (GLE-MM), and Graph Estimation based on Alternating
Direction Method of Multipliers (GLE-ADMM).  In addition, graph learning has
been widely employed for clustering, where specific algorithms are available in
the literature.  To this end, we provide an implementation of the Constrained
Laplacian Rank (CLR) algorithm.")
    (license license:gpl3)))

(define-public r-spectralgp
  (package
    (name "r-spectralgp")
    (version "1.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spectralGP" version))
       (sha256
        (base32 "1jf09nsil4r90vdj7n1k6ma9dzzx3bwv0fa7svil9pxrd2zlbkbs"))))
    (properties `((upstream-name . "spectralGP")))
    (build-system r-build-system)
    (home-page "http://www.jstatsoft.org/v19/a2")
    (synopsis "Approximate Gaussian Processes Using the Fourier Basis")
    (description
     "Routines for creating, manipulating, and performing Bayesian inference about
Gaussian processes in one and two dimensions using the Fourier basis
approximation: simulation and plotting of processes, calculation of coefficient
variances, calculation of process density, coefficient proposals (for use in
MCMC).  It uses R environments to store GP objects as references/pointers.")
    (license license:gpl2+)))

(define-public r-spectralclmixed
  (package
    (name "r-spectralclmixed")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpectralClMixed" version))
       (sha256
        (base32 "10nx03kr35irvvink8kfx6gbv90wnlsx514r7921hrjvcjiqn82s"))))
    (properties `((upstream-name . "SpectralClMixed")))
    (build-system r-build-system)
    (propagated-inputs (list r-rspectra r-ggplot2 r-ggally r-cluster))
    (home-page "https://cran.r-project.org/package=SpectralClMixed")
    (synopsis "Spectral Clustering for Mixed Type Data")
    (description
     "This package performs cluster analysis of mixed-type data using Spectral
Clustering, see F. Mbuga and, C. Tortora (2022) <doi:10.3390/stats5010001>.")
    (license license:gpl2+)))

(define-public r-spectralanomaly
  (package
    (name "r-spectralanomaly")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spectralAnomaly" version))
       (sha256
        (base32 "0gfgnbl2awliw94xy7q269n5dzj2sh95wz470cynr1m035kp51s1"))))
    (properties `((upstream-name . "spectralAnomaly")))
    (build-system r-build-system)
    (home-page "https://al-obrien.github.io/spectralAnomaly/")
    (synopsis "Detect Anomalies Using the Spectral Residual Algorithm")
    (description
     "Apply the spectral residual algorithm to data, such as a time series, to detect
anomalies.  Anomaly scores can be used to determine outliers based upon a
threshold or fed into more sophisticated prediction models.  Methods are based
upon \"Time-Series Anomaly Detection Service at Microsoft\", Ren, H., Xu, B.,
Wang, Y., et al., (2019) <doi:10.48550/@code{arXiv.1906.03821>}.")
    (license license:expat)))

(define-public r-spectralanalysis
  (package
    (name "r-spectralanalysis")
    (version "4.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spectralAnalysis" version))
       (sha256
        (base32 "0b2f992p7vpzhzncrvpdkifbgymyiqxz66yk6md3j39wmx3ijkjw"))))
    (properties `((upstream-name . "spectralAnalysis")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-viridis
                             r-signal
                             r-rcolorbrewer
                             r-plyr
                             r-pls
                             r-plotly
                             r-nnls
                             r-nmf
                             r-magrittr
                             r-jsonlite
                             r-hnmf
                             r-ggplot2
                             r-dplyr
                             r-data-table
                             r-biocgenerics
                             r-baseline))
    (native-inputs (list r-knitr))
    (home-page "https://openanalytics.eu")
    (synopsis "Pre-Process, Visualize and Analyse Spectral Data")
    (description
     "Infrared, near-infrared and Raman spectroscopic data measured during chemical
reactions, provide structural fingerprints by which molecules can be identified
and quantified.  The application of these spectroscopic techniques as inline
process analytical tools (PAT), provides the pharmaceutical and chemical
industry with novel tools, allowing to monitor their chemical processes,
resulting in a better process understanding through insight in reaction rates,
mechanistics, stability, etc.  Data can be read into R via the generic
spc-format, which is generally supported by spectrometer vendor software.
Versatile pre-processing functions are available to perform baseline correction
by linking to the baseline package; noise reduction via the signal package; as
well as time alignment, normalization, differentiation, integration and
interpolation.  Implementation based on the S4 object system allows storing a
pre-processing pipeline as part of a spectral data object, and easily
transferring it to other datasets.  Interactive plotting tools are provided
based on the plotly package.  Non-negative matrix factorization (NMF) has been
implemented to perform multivariate analyses on individual spectral datasets or
on multiple datasets at once.  NMF provides a parts-based representation of the
spectral data in terms of spectral signatures of the chemical compounds and
their relative proportions.  See @code{hNMF'-package} for references on
available methods.  The functionality to read in spc-files was adapted from the
@code{hyperSpec} package.")
    (license license:gpl3)))

(define-public r-spectral
  (package
    (name "r-spectral")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spectral" version))
       (sha256
        (base32 "116gn41nvlw7jmr6qx9pl8hckg2pzgzx49kql52j5xqf39sgyx9p"))))
    (properties `((upstream-name . "spectral")))
    (build-system r-build-system)
    (propagated-inputs (list r-rhpcblasctl r-rasterimage r-pbapply r-lattice))
    (home-page "https://cran.r-project.org/package=spectral")
    (synopsis "Common Methods of Spectral Data Analysis")
    (description
     "On discrete data spectral analysis is performed by Fourier and Hilbert
transforms as well as with model based analysis called Lomb-Scargle method.
Fragmented and irregularly spaced data can be processed in almost all methods.
Both, FFT as well as LOMB methods take multivariate data and return standardized
PSD. For didactic reasons an analytical approach for deconvolution of noise
spectra and sampling function is provided.  A user friendly interface helps to
interpret the results.")
    (license license:gpl2)))

(define-public r-spectr
  (package
    (name "r-spectr")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spectr" version))
       (sha256
        (base32 "1jbadg9n42qrbw1v6hqrdp5pzy2nn1kvqi4xscxdxvc6c33i7zr4"))))
    (properties `((upstream-name . "spectr")))
    (build-system r-build-system)
    (propagated-inputs (list r-lomb r-foreach r-data-table))
    (home-page "https://spectr.hugheylab.org")
    (synopsis "Calculate the Periodogram of a Time-Course")
    (description
     "This package provides a consistent interface to use various methods to calculate
the periodogram and estimate the period of a rhythmic time-course.  Methods
include Lomb-Scargle, fast Fourier transform, and three versions of the
chi-square periodogram.  See Tackenberg and Hughey (2021)
<doi:10.1371/journal.pcbi.1008567>.")
    (license license:gpl2)))

(define-public r-spectator
  (package
    (name "r-spectator")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spectator" version))
       (sha256
        (base32 "07wawsmfh529973j3iwn7fbk547a38mqlmbrva2n4nvgm1k5qy9l"))))
    (properties `((upstream-name . "spectator")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-httr r-geojsonsf))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=spectator")
    (synopsis "Interface to the 'Spectator Earth' API")
    (description
     "This package provides interface to the Spectator Earth API
<https://api.spectator.earth/>, mainly for obtaining the acquisition plans and
satellite overpasses for Sentinel-1, Sentinel-2, Landsat-8 and Landsat-9
satellites.  Current position and trajectory can also be obtained for a much
larger set of satellites.  It is also possible to search the archive for
available images over the area of interest for a given (past) period, get the
URL links to download the whole image tiles, or alternatively to download the
image for just the area of interest based on selected spectral bands.")
    (license license:gpl3)))

(define-public r-spectacles
  (package
    (name "r-spectacles")
    (version "0.5-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spectacles" version))
       (sha256
        (base32 "10qc46kspnmc85w3a29jx1fj0jxpzxa9b5ygpszhjkz3w8q6s8i4"))))
    (properties `((upstream-name . "spectacles")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-signal
                             r-reshape2
                             r-plyr
                             r-ggplot2
                             r-epir
                             r-baseline))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/pierreroudier/spectacles/")
    (synopsis
     "Storing, Manipulating and Analysis Spectroscopy and Associated Data")
    (description
     "Stores and eases the manipulation of spectra and associated data, with dedicated
classes for spatial and soil-related data.")
    (license license:gpl3)))

(define-public r-specsverification
  (package
    (name "r-specsverification")
    (version "0.5-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpecsVerification" version))
       (sha256
        (base32 "1v76lilyj65f04881yw19v0qi7n08ysdnr7sc4hf5d8wnmvdh3v3"))))
    (properties `((upstream-name . "SpecsVerification")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=SpecsVerification")
    (synopsis
     "Forecast Verification Routines for Ensemble Forecasts of Weather and Climate")
    (description
     "This package provides a collection of forecast verification routines developed
for the SPECS FP7 project.  The emphasis is on comparative verification of
ensemble forecasts of weather and climate.")
    (license license:gpl2+)))

(define-public r-specs
  (package
    (name "r-specs")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "specs" version))
       (sha256
        (base32 "0x1s783b25x37f2a74fh0g2jc9ypbrfkm42j8yx67hmxlggsch8d"))))
    (properties `((upstream-name . "specs")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=specs")
    (synopsis "Single-Equation Penalized Error-Correction Selector (SPECS)")
    (description
     "Implementation of SPECS, your favourite Single-Equation Penalized
Error-Correction Selector developed in Smeekes and Wijler (2021)
<doi:10.1016/j.jeconom.2020.07.021>.  SPECS provides a fully automated
estimation procedure for large and potentially (co)integrated datasets.  The
dataset in levels is converted to a conditional error-correction model, either
by the user or by means of the functions included in this package, and various
specialised forms of penalized regression can be applied to the model.
Automated options for initializing and selecting a sequence of penalties, as
well as the construction of penalty weights via an initial estimator, are
available.  Moreover, the user may choose from a number of pre-specified
deterministic configurations to further simplify the model building process.")
    (license license:gpl2+)))

(define-public r-specr
  (package
    (name "r-specr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "specr" version))
       (sha256
        (base32 "1y7d1x9rps0xkm8jghpnva8mmzwqa0c93mk6rf2dyrbyw850ddax"))))
    (properties `((upstream-name . "specr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-purrr
                             r-parallelly
                             r-magrittr
                             r-lme4
                             r-lifecycle
                             r-igraph
                             r-glue
                             r-ggraph
                             r-ggplot2
                             r-future
                             r-furrr
                             r-dplyr
                             r-cowplot
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://masurp.github.io/specr/")
    (synopsis "Conducting and Visualizing Specification Curve Analyses")
    (description
     "This package provides utilities for conducting specification curve analyses
(Simonsohn, Simmons & Nelson (2020, <doi: 10.1038/s41562-020-0912-z>) or
multiverse analyses (Steegen, Tuerlinckx, Gelman & Vanpaemel, 2016, <doi:
10.1177/1745691616658637>) including functions to setup, run, evaluate, and plot
all specifications.")
    (license license:gpl3)))

(define-public r-specklestar
  (package
    (name "r-specklestar")
    (version "0.0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "specklestar" version))
       (sha256
        (base32 "04vm989yi31vqnf5rs6cdyikm4jzfd9njxh0ls3dvjrg19ipsnmn"))))
    (properties `((upstream-name . "specklestar")))
    (build-system r-build-system)
    (inputs (list fftw))
    (propagated-inputs (list r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://drastega.github.io/docs/specklestar_vignette.html")
    (synopsis "Reduction of Speckle Data from BTA 6-m Telescope")
    (description
     "This package provides a set of functions for obtaining positional parameters and
magnitude difference between components of binary and multiple stellar systems
from series of speckle images.")
    (license license:gpl2)))

(define-public r-speck
  (package
    (name "r-speck")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPECK" version))
       (sha256
        (base32 "0n67gqwd01klk8x3ds5ik11nwiga0138vdw3yhnrj1pp3pnpnq7k"))))
    (properties `((upstream-name . "SPECK")))
    (build-system r-build-system)
    (propagated-inputs (list r-seurat r-rsvd r-matrix r-magrittr
                             r-ckmeans-1d-dp))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SPECK")
    (synopsis
     "Receptor Abundance Estimation using Reduced Rank Reconstruction and Clustered Thresholding")
    (description
     "Surface Protein abundance Estimation using CKmeans-based clustered thresholding
('SPECK') is an unsupervised learning-based method that performs receptor
abundance estimation for single cell RNA-sequencing data based on reduced rank
reconstruction (RRR) and a clustered thresholding mechanism.  Seurat's
normalization method is described in: Hao et al., (2021)
<doi:10.1016/j.cell.2021.04.048>, Stuart et al., (2019)
<doi:10.1016/j.cell.2019.05.031>, Butler et al., (2018) <doi:10.1038/nbt.4096>
and Satija et al., (2015) <doi:10.1038/nbt.3192>.  Method for the RRR is further
detailed in: Erichson et al., (2019) <doi:10.18637/jss.v089.i11> and Halko et
al., (2009) <@code{arXiv:0909.4061>}.  Clustering method is outlined in: Song et
al., (2020) <doi:10.1093/bioinformatics/btaa613> and Wang et al., (2011)
<doi:10.32614/RJ-2011-015>.")
    (license license:gpl2+)))

(define-public r-specieschrom
  (package
    (name "r-specieschrom")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "specieschrom" version))
       (sha256
        (base32 "0h2hgh0amvv7in5hplh6x3k95bnjm6d24nzmc57yihcsqjndli9k"))))
    (properties `((upstream-name . "specieschrom")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-ggplot2 r-colorramps))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/loick-klpr/specieschrom")
    (synopsis "The Species Chromatogram")
    (description
     "This package provides a simple method to display and characterise the
multidimensional ecological niche of a species.  The method also estimates the
optimums and amplitudes along each niche dimension.  Give also an estimation of
the degree of niche overlapping between species.  See Kleparski and Beaugrand
(2022) <doi:10.1002/ece3.8830> for further details.")
    (license license:gpl3)))

(define-public r-species
  (package
    (name "r-species")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPECIES" version))
       (sha256
        (base32 "0fyl8794hfdlhghskdv4gif88rdppd4a2jm3dyfdlwdj5zv5l12w"))))
    (properties `((upstream-name . "SPECIES")))
    (build-system r-build-system)
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=SPECIES")
    (synopsis "Statistical Package for Species Richness Estimation")
    (description
     "Implementation of various methods in estimation of species richness or diversity
in Wang (2011)<doi:10.18637/jss.v040.i09>.")
    (license license:gpl2)))

(define-public r-spechelpers
  (package
    (name "r-spechelpers")
    (version "0.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpecHelpers" version))
       (sha256
        (base32 "1v3v717ah2fkx9225860dwppdf5m6nnnaaa4iwmj30rn17nqr4jh"))))
    (properties `((upstream-name . "SpecHelpers")))
    (build-system r-build-system)
    (propagated-inputs (list r-splancs r-gsubfn))
    (home-page "https://github.com/bryanhanson/SpecHelpers")
    (synopsis "Spectroscopy Related Utilities")
    (description
     "Utility functions for spectroscopy.  1.  Functions to simulate spectra for use
in teaching or testing.  2.  Functions to process files created by
@code{LoggerPro} and @code{SpectraSuite} software.")
    (license license:gpl3)))

(define-public r-specdetec
  (package
    (name "r-specdetec")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpecDetec" version))
       (sha256
        (base32 "1940pl4vm1kzszq0hwhqkwbk1xmrimjdf03acpdndy089mdg9avc"))))
    (properties `((upstream-name . "SpecDetec")))
    (build-system r-build-system)
    (propagated-inputs (list r-abind))
    (home-page "https://cran.r-project.org/package=SpecDetec")
    (synopsis "Change Points Detection with Spectral Clustering")
    (description
     "Calculate change point based on spectral clustering with the option to
automatically calculate the number of clusters if this information is not
available.")
    (license license:gpl3)))

(define-public r-speccurvier
  (package
    (name "r-speccurvier")
    (version "0.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "speccurvieR" version))
       (sha256
        (base32 "1iyylk1fvb4fk7wq81qkwd9wwnxd8v1rq3xhjgbf45k84f34iman"))))
    (properties `((upstream-name . "speccurvieR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-sandwich
                             r-pbapply
                             r-magrittr
                             r-lmtest
                             r-ggplot2
                             r-fixest
                             r-dplyr
                             r-combinat))
    (home-page "https://github.com/zaynesember/speccurvieR")
    (synopsis "Easy, Fast, and Pretty Specification Curve Analysis")
    (description
     "Making specification curve analysis easy, fast, and pretty.  It improves upon
existing offerings with additional features and tidyverse integration.  Users
can easily visualize and evaluate how their models behave under different
specifications with a high degree of customization.  For a description and
applications of specification curve analysis see Simonsohn, Simmons, and Nelson
(2020) <doi:10.1038/s41562-020-0912-z>.")
    (license license:expat)))

(define-public r-spec
  (package
    (name "r-spec")
    (version "0.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spec" version))
       (sha256
        (base32 "0jsl37m912pg3dcphs8l83ywj5rr9q0s9si57nkvz64skdkypcav"))))
    (properties `((upstream-name . "spec")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr r-encode r-csv))
    (home-page "https://cran.r-project.org/package=spec")
    (synopsis "Data Specification Format and Interface")
    (description
     "This package creates a data specification that describes the columns of a table
(data.frame).  Provides methods to read, write, and update the specification.
Checks whether a table matches its specification.  See
specification.data.frame(),read.spec(), write.spec(), as.csv.spec(),
respecify.character(), and %matches%.data.frame().")
    (license license:gpl3)))

(define-public r-spearmanci
  (package
    (name "r-spearmanci")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spearmanCI" version))
       (sha256
        (base32 "0dn5gbyvw5rqqqbag16ljfwm4pc8mzyjq3fb49g7zq3c6l16wj27"))))
    (properties `((upstream-name . "spearmanCI")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-emplik))
    (home-page "https://cran.r-project.org/package=spearmanCI")
    (synopsis
     "Jackknife Euclidean / Empirical Likelihood Inference for Spearman's Rho")
    (description
     "This package provides functions for conducting jackknife Euclidean / empirical
likelihood inference for Spearman's rho (de Carvalho and Marques (2012)
<doi:10.1080/10920277.2012.10597644>).")
    (license license:gpl3+)))

(define-public r-speakr
  (package
    (name "r-speakr")
    (version "3.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "speakr" version))
       (sha256
        (base32 "159ir49i2fdw5fcq93rqdhf5ij5s9sfr2ygdy2snbgnx3rgi1sfc"))))
    (properties `((upstream-name . "speakr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-readr
                             r-quarto
                             r-lifecycle
                             r-ggplot2
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/stefanocoretta/speakr")
    (synopsis "Wrapper for the Phonetic Software 'Praat'")
    (description
     "It allows running Praat scripts from R and it provides some wrappers for basic
plotting.  It also adds support for literate markdown tangling.  The package is
designed to bring reproducible phonetic research into R.")
    (license license:expat)))

(define-public r-speakeasyr
  (package
    (name "r-speakeasyr")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "speakeasyR" version))
       (sha256
        (base32 "0jrndx1cjxg7f0p7v5z7frpfi8bs6nxpdv21v8ld6n4ffl0vvrbq"))))
    (properties `((upstream-name . "speakeasyR")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-matrix))
    (native-inputs (list pkg-config r-knitr gfortran))
    (home-page "https://github.com/SpeakEasy-2/speakeasyR")
    (synopsis "Fast and Robust Multi-Scale Graph Clustering")
    (description
     "This package provides a graph community detection algorithm that aims to be
performant on large graphs and robust, returning consistent results across runs.
@code{SpeakEasy} 2 (SE2), the underlying algorithm, is described in Chris
Gaiteri, David R. Connell & Faraz A. Sultan et al. (2023)
<doi:10.1186/s13059-023-03062-0>.  The core algorithm is written in C',
providing speed and keeping the memory requirements low.  This implementation
can take advantage of multiple computing cores without increasing memory usage.
SE2 can detect community structure across scales, making it a good choice for
biological data, which often has hierarchical structure.  Graphs can be passed
to the algorithm as adjacency matrices using base R matrices, the Matrix
library, igraph graphs, or any data that can be coerced into a matrix.")
    (license license:gpl3+)))

(define-public r-spdynmod
  (package
    (name "r-spdynmod")
    (version "1.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spdynmod" version))
       (sha256
        (base32 "1zbh1p3lmil4vsrw7q6af3p5xqvhw2k9hx59kbzabnzj4gy8wjk3"))))
    (properties `((upstream-name . "spdynmod")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp r-raster r-desolve r-animation))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/javimarlop/spdynmod")
    (synopsis "Spatio-Dynamic Wetland Plant Communities Model")
    (description
     "This package provides a spatio-dynamic modelling package that focuses on three
characteristic wetland plant communities in a semiarid Mediterranean wetland in
response to hydrological pressures from the catchment.  The package includes the
data on watershed hydrological pressure and the initial raster maps of plant
communities but also allows for random initial distribution of plant
communities.  For more detailed info see: Martinez-Lopez et al. (2015)
<doi:10.1016/j.ecolmodel.2014.11.024>.")
    (license license:gpl2+)))

(define-public r-spduration
  (package
    (name "r-spduration")
    (version "0.17.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spduration" version))
       (sha256
        (base32 "1cgrpfljb43qay38qjhb2xccawh8llrp545bm5bv29k7h1nsa9cb"))))
    (properties `((upstream-name . "spduration")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-separationplot
                             r-rcpparmadillo
                             r-rcpp
                             r-mass
                             r-forecast
                             r-corpcor))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/andybega/spduration")
    (synopsis "Split-Population Duration (Cure) Regression")
    (description
     "An implementation of split-population duration regression models.  Unlike
regular duration models, split-population duration models are mixture models
that accommodate the presence of a sub-population that is not at risk for
failure, e.g. cancer patients who have been cured by treatment.  This package
implements Weibull and Loglogistic forms for the duration component, and focuses
on data with time-varying covariates.  These models were originally formulated
in Boag (1949) and Berkson and Gage (1952), and extended in Schmidt and Witte
(1989).")
    (license license:gpl3)))

(define-public r-spdownscale
  (package
    (name "r-spdownscale")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spdownscale" version))
       (sha256
        (base32 "0pl560km9629iwy13h7359ab7ixzs09hg986kvk0xbnc348srb86"))))
    (properties `((upstream-name . "spdownscale")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=spdownscale")
    (synopsis "Spatial Downscaling Using Bias Correction Approach")
    (description
     "Spatial downscaling of climate data (Global Circulation Models/Regional Climate
Models) using quantile-quantile bias correction technique.")
    (license license:gpl2)))

(define-public r-spdl
  (package
    (name "r-spdl")
    (version "0.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spdl" version))
       (sha256
        (base32 "185qzlwac7acq126xpvcd71nv25zgkrdr8m73gv2sn1zxj78hyid"))))
    (properties `((upstream-name . "spdl")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppspdlog))
    (home-page "https://github.com/eddelbuettel/spdl")
    (synopsis "Easier Use of 'RcppSpdlog' Functions via Wrapper")
    (description
     "Logging functions in @code{RcppSpdlog} provide access to the logging
functionality from the spdlog C++ library.  This package offers shorter
convenience wrappers for the R functions which match the C++ functions, namely
via, say, spdl::debug() at the debug level.  The actual formatting is done by
the fmt::format() function from the fmtlib library (that is also std::format()
in C++20 or later).")
    (license license:gpl2+)))

(define-public r-spdesign
  (package
    (name "r-spdesign")
    (version "0.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spdesign" version))
       (sha256
        (base32 "01lmbclp4g813zjdv3cb2bqh836q844cvn537vvgj6s9dl6sxivi"))))
    (properties `((upstream-name . "spdesign")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-randtoolbox
                             r-matrixstats
                             r-future
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://spdesign.edsandorf.me")
    (synopsis "Designing Stated Preference Experiments")
    (description
     "Contemporary software commonly used to design stated preference experiments are
expensive and the code is closed source.  This is a free software package with
an easy to use interface to make flexible stated preference experimental designs
using state-of-the-art methods.  For an overview of stated choice experimental
design theory, see e.g., Rose, J. M. & Bliemer, M. C. J. (2014) in Hess S. &
Daly.  A. <doi:10.4337/9781781003152>.  The package website can be accessed at
<https://spdesign.edsandorf.me>.  We acknowledge funding from the European
Unionâs Horizon 2020 research and innovation program under the Marie
Sklodowska-Curie grant INS@code{PiRE} (Grant agreement ID: 793163).")
    (license license:cc-by-sa4.0)))

(define-public r-spdates
  (package
    (name "r-spdates")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spDates" version))
       (sha256
        (base32 "0k6383hgrs4751qnwx03s85a9a0ldpxm7xanrnmg13plc2713kvm"))))
    (properties `((upstream-name . "spDates")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridislite
                             r-sp
                             r-smatr
                             r-rlang
                             r-rcarbon
                             r-raster
                             r-magrittr
                             r-gstat
                             r-ggplot2
                             r-dplyr
                             r-data-table))
    (home-page "https://cran.r-project.org/package=spDates")
    (synopsis "Analysis of Spatial Gradients in Radiocarbon Dates")
    (description
     "Inspired by space-time regressions often performed to assess the expansion of
the Neolithic from the Near East to Europe (Pinhasi et al.  2005
<doi:10.1371/journal.pbio.0030410>).  Test for significant correlations between
the (earliest) radiocarbon dates of archaeological sites and their respective
distances from a hypothetical center of origin.  Both ordinary least squares
(OLS) and reduced major axis (RMA) methods are supported (Russell et al.  2014
<doi:10.1371/journal.pone.0087854>).  It is also possible to iterate over many
sites to identify the most likely origin.")
    (license license:expat)))

(define-public r-spd
  (package
    (name "r-spd")
    (version "2.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spd" version))
       (sha256
        (base32 "00zxh4ri47b61jkcjf5idl9hhlfld6rhczsnhmjsax59884f2i8m"))))
    (properties `((upstream-name . "spd")))
    (build-system r-build-system)
    (propagated-inputs (list r-kernsmooth))
    (home-page "http://www.unstarched.net")
    (synopsis "Semi Parametric Distribution")
    (description
     "The Semi Parametric Piecewise Distribution blends the Generalized Pareto
Distribution for the tails with a kernel based interior.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-spcr
  (package
    (name "r-spcr")
    (version "2.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spcr" version))
       (sha256
        (base32 "09clpz0vqssagmfjcrs2qp5i61ppjr9nxn342l6gbdnbw91wc7f3"))))
    (properties `((upstream-name . "spcr")))
    (build-system r-build-system)
    (home-page "https://doi.org/10.1016/j.csda.2015.03.016")
    (synopsis "Sparse Principal Component Regression")
    (description
     "The sparse principal component regression is computed.  The regularization
parameters are optimized by cross-validation.")
    (license license:gpl2+)))

(define-public r-spcp
  (package
    (name "r-spcp")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spCP" version))
       (sha256
        (base32 "0rphd8zawr2r14dzwv77plk5xw020pkc26ir72qiqc1acin42xd1"))))
    (properties `((upstream-name . "spCP")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mvtnorm r-msm))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=spCP")
    (synopsis "Spatially Varying Change Points")
    (description
     "This package implements a spatially varying change point model with unique
intercepts, slopes, variance intercepts and slopes, and change points at each
location.  Inference is within the Bayesian setting using Markov chain Monte
Carlo (MCMC).  The response variable can be modeled as Gaussian (no nugget),
probit or Tobit link and the five spatially varying parameter are modeled
jointly using a multivariate conditional autoregressive (MCAR) prior.  The MCAR
is a unique process that allows for a dissimilarity metric to dictate the local
spatial dependencies.  Full details of the package can be found in the
accompanying vignette.  Furthermore, the details of the package can be found in
the corresponding paper on @code{arXiv} by Berchuck et al (2018): \"A spatially
varying change points model for monitoring glaucoma progression using visual
field data\", <@code{arXiv:1811.11038>}.")
    (license license:gpl2+)))

(define-public r-spcov
  (package
    (name "r-spcov")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spcov" version))
       (sha256
        (base32 "037pl4kh87b85v7q1nrxcm7vw5f3pr7m2b76424ic9sxa56wfdnp"))))
    (properties `((upstream-name . "spcov")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=spcov")
    (synopsis "Sparse Estimation of a Covariance Matrix")
    (description
     "This package provides a covariance estimator for multivariate normal data that
is sparse and positive definite.  Implements the majorize-minimize algorithm
described in Bien, J., and Tibshirani, R. (2011), \"Sparse Estimation of a
Covariance Matrix,\" Biometrika.  98(4).  807--820.")
    (license license:gpl2)))

(define-public r-spcosa
  (package
    (name "r-spcosa")
    (version "0.4-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spcosa" version))
       (sha256
        (base32 "1y3qbz9kslb5k7h62pk7g2lk69y9i625r2q0dychhj91fdqx30n4"))))
    (properties `((upstream-name . "spcosa")))
    (build-system r-build-system)
    (inputs (list openjdk))
    (propagated-inputs (list r-sp r-rjava r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://git.wur.nl/Walvo001/spcosa")
    (synopsis
     "Spatial Coverage Sampling and Random Sampling from Compact Geographical Strata")
    (description
     "Spatial coverage sampling and random sampling from compact geographical strata
created by k-means.  See Walvoort et al. (2010)
<doi:10.1016/j.cageo.2010.04.005> for details.")
    (license license:gpl3+)))

(define-public r-spcompute
  (package
    (name "r-spcompute")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPCompute" version))
       (sha256
        (base32 "15jjx5ryxv6cjiz144gsi69pgba5dvxavdv8glqykwfz6ch42lq0"))))
    (properties `((upstream-name . "SPCompute")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SPCompute")
    (synopsis "Compute Power or Sample Size for GWAS with Covariate Effect")
    (description
     "Fast computation of the required sample size or the achieved power, for GWAS
studies with different types of covariate effects and different types of
covariate-gene dependency structure.  For the detailed description of the
methodology, see Zhang (2022) \"Power and Sample Size Computation for Genetic
Association Studies of Binary Traits: Accounting for Covariate Effects\"
<@code{arXiv:2203.15641>}.")
    (license license:gpl3+)))

(define-public r-spcdanalyze
  (package
    (name "r-spcdanalyze")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPCDAnalyze" version))
       (sha256
        (base32 "0zaxlc4dw678s0v22ghv7mc4vqsgkcnmbxh7065w6qj9vm7pg5kz"))))
    (properties `((upstream-name . "SPCDAnalyze")))
    (build-system r-build-system)
    (propagated-inputs (list r-plyr r-nlme r-lme4))
    (home-page "https://cran.r-project.org/package=SPCDAnalyze")
    (synopsis
     "Design and Analyze Studies using the Sequential Parallel Comparison Design")
    (description
     "Programs to find the sample size or power of studies using the Sequential
Parallel Comparison Design (SPCD) and programs to analyze such studies.  This is
a clinical trial design where patients initially on placebo who did not respond
are re-randomized between placebo and active drug in a second phase and the
results of the two phases are pooled.  The method of analyzing binary data with
this design is described in Fava,Evins, Dorer and Schoenfeld(2003)
<doi:10.1159/000069738>, and the method of analyzing continuous data is
described in Chen, Yang, Hung and Wang (2011) <doi:10.1016/j.cct.2011.04.006>.")
    (license (license:fsdg-compatible "Unlimited"))))

(define-public r-spcavrp
  (package
    (name "r-spcavrp")
    (version "0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPCAvRP" version))
       (sha256
        (base32 "10bvr4lqnim40fihq495a4fy4f33pvfd7gsaqjj9bh6gy0z8y4mi"))))
    (properties `((upstream-name . "SPCAvRP")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://arxiv.org/abs/1712.05630")
    (synopsis
     "Sparse Principal Component Analysis via Random Projections (SPCAvRP)")
    (description
     "This package implements the SPC@code{AvRP} algorithm, developed and analysed in
\"Sparse principal component analysis via random projections\" Gataric, M., Wang,
T. and Samworth, R. J. (2018) <@code{arXiv:1712.05630>}.  The algorithm is based
on the aggregation of eigenvector information from carefully-selected random
projections of the sample covariance matrix.")
    (license license:gpl3)))

(define-public r-spcalda
  (package
    (name "r-spcalda")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPCALDA" version))
       (sha256
        (base32 "1bmp2zz0favmpyp0ap8a2r1mg1nlan7zg5cj75drdnfpqlsn5vgl"))))
    (properties `((upstream-name . "SPCALDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=SPCALDA")
    (synopsis "New Reduced-Rank Linear Discriminant Analysis Method")
    (description
     "This package provides a new reduced-rank LDA method which works for high
dimensional multi-class data.")
    (license license:gpl2)))

(define-public r-spcadjust
  (package
    (name "r-spcadjust")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spcadjust" version))
       (sha256
        (base32 "016i3zaaq800x4niz4fixa57nfj1m10sz5xskff4vq4v9fjn2sl0"))))
    (properties `((upstream-name . "spcadjust")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=spcadjust")
    (synopsis "Functions for Calibrating Control Charts")
    (description
     "Calibration of thresholds of control charts such as CUSUM charts based on past
data, taking estimation error into account.")
    (license license:gpl2+)))

(define-public r-spc4sts
  (package
    (name "r-spc4sts")
    (version "0.6.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spc4sts" version))
       (sha256
        (base32 "0lvsixjgdfgnr7y7kk2227bcs8sqy2mrpw50dwwgdjvm5q0sr7l7"))))
    (properties `((upstream-name . "spc4sts")))
    (build-system r-build-system)
    (propagated-inputs (list r-rpart r-gridextra))
    (home-page "https://cran.r-project.org/package=spc4sts")
    (synopsis "Statistical Process Control for Stochastic Textured Surfaces")
    (description
     "This package provides statistical process control tools for stochastic textured
surfaces.  The current version supports the following tools: (1) generic
modeling of stochastic textured surfaces. (2) local defect monitoring and
diagnostics in stochastic textured surfaces, which was proposed by Bui and Apley
(2018a) <doi:10.1080/00401706.2017.1302362>. (3) global change monitoring in the
nature of stochastic textured surfaces, which was proposed by Bui and Apley
(2018b) <doi:10.1080/00224065.2018.1507559>. (4) computation of dissimilarity
matrix of stochastic textured surface images, which was proposed by Bui and
Apley (2019b) <doi:10.1016/j.csda.2019.01.019>.")
    (license license:gpl2)))

(define-public r-spc
  (package
    (name "r-spc")
    (version "0.7.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spc" version))
       (sha256
        (base32 "0lp9ngjshkpww28vqsbazh1wila8p1v5x4xq04x58acwhf3cpjxx"))))
    (properties `((upstream-name . "spc")))
    (build-system r-build-system)
    (home-page "https://www.r-project.org")
    (synopsis
     "Statistical Process Control -- Calculation of ARL and Other Control Chart Performance Measures")
    (description
     "Evaluation of control charts by means of the zero-state, steady-state ARL
(Average Run Length) and RL quantiles.  Setting up control charts for given
in-control ARL. The control charts under consideration are one- and two-sided
EWMA, CUSUM, and Shiryaev-Roberts schemes for monitoring the mean or variance of
normally distributed independent data.  ARL calculation of the same set of
schemes under drift (in the mean) are added.  Eventually, all ARL measures for
the multivariate EWMA (MEWMA) are provided.")
    (license license:gpl2+)))

(define-public r-spbsampling
  (package
    (name "r-spbsampling")
    (version "1.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Spbsampling" version))
       (sha256
        (base32 "0p98l58qfc4ddimmqca77cr536q2pnrlcb3r344c7apnakidz7ng"))))
    (properties `((upstream-name . "Spbsampling")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=Spbsampling")
    (synopsis "Spatially Balanced Sampling")
    (description
     "Selection of spatially balanced samples.  In particular, the implemented
sampling designs allow to select probability samples well spread over the
population of interest, in any dimension and using any distance function (e.g.
Euclidean distance, Manhattan distance).  For more details, Pantalone F,
Benedetti R, and Piersimoni F (2022) <doi:10.18637/jss.v103.c02>, Benedetti R
and Piersimoni F (2017) <doi:10.1002/bimj.201600194>, and Benedetti R and
Piersimoni F (2017) <@code{arXiv:1710.09116>}.  The implementation has been done
in C++ through the use of Rcpp and @code{RcppArmadillo}'.")
    (license license:gpl3)))

(define-public r-spbfa
  (package
    (name "r-spbfa")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spBFA" version))
       (sha256
        (base32 "1bi3lhbx37zlj2x0nql51x5422zxc1gcbn8vqjf81badwf80fwsp"))))
    (properties `((upstream-name . "spBFA")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-pgdraw r-mvtnorm r-msm))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=spBFA")
    (synopsis "Spatial Bayesian Factor Analysis")
    (description
     "This package implements a spatial Bayesian non-parametric factor analysis model
with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC).
Spatial correlation is introduced in the columns of the factor loadings matrix
using a Bayesian non-parametric prior, the probit stick-breaking process.  Areal
spatial data is modeled using a conditional autoregressive (CAR) prior and
point-referenced spatial data is treated using a Gaussian process.  The response
variable can be modeled as Gaussian, probit, Tobit, or Binomial (using
Polya-Gamma augmentation).  Temporal correlation is introduced for the latent
factors through a hierarchical structure and can be specified as exponential or
first-order autoregressive.  Full details of the package can be found in the
accompanying vignette.  Furthermore, the details of the package can be found in
\"Bayesian Non-Parametric Factor Analysis for Longitudinal Spatial Surfaces\", by
Berchuck et al (2019), <@code{arXiv:1911.04337>}.  The paper is in press at the
journal Bayesian Analysis.")
    (license license:gpl2+)))

(define-public r-spbayessurv
  (package
    (name "r-spbayessurv")
    (version "1.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spBayesSurv" version))
       (sha256
        (base32 "0z91k88m5bh8ipicb43kknxshjr6dwr6whfkm28j49xwjrm4ydsy"))))
    (properties `((upstream-name . "spBayesSurv")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rcpparmadillo
                             r-rcpp
                             r-mass
                             r-fields
                             r-coda))
    (home-page "https://cran.r-project.org/package=spBayesSurv")
    (synopsis
     "Bayesian Modeling and Analysis of Spatially Correlated Survival Data")
    (description
     "This package provides several Bayesian survival models for spatial/non-spatial
survival data: proportional hazards (PH), accelerated failure time (AFT),
proportional odds (PO), and accelerated hazards (AH), a super model that
includes PH, AFT, PO and AH as special cases, Bayesian nonparametric
nonproportional hazards (LDDPM), generalized accelerated failure time (GAFT),
and spatially smoothed Polya tree density estimation.  The spatial dependence is
modeled via frailties under PH, AFT, PO, AH and GAFT, and via copulas under
LDDPM and PH. Model choice is carried out via the logarithm of the pseudo
marginal likelihood (LPML), the deviance information criterion (DIC), and the
Watanabe-Akaike information criterion (WAIC).  See Zhou, Hanson and Zhang (2020)
<doi:10.18637/jss.v092.i09>.")
    (license license:gpl2+)))

(define-public r-spbayes
  (package
    (name "r-spbayes")
    (version "0.4-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spBayes" version))
       (sha256
        (base32 "17jnbwafk5kky0zwjni9nvbcllxr5ww0jrrq5gzzddjrp1x1ybhd"))))
    (properties `((upstream-name . "spBayes")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp r-matrix r-magic r-formula r-coda))
    (home-page "https://www.finley-lab.com")
    (synopsis "Univariate and Multivariate Spatial-Temporal Modeling")
    (description
     "Fits univariate and multivariate spatio-temporal random effects models for
point-referenced data using Markov chain Monte Carlo (MCMC).  Details are given
in Finley, Banerjee, and Gelfand (2015) <doi:10.18637/jss.v063.i13> and Finley
and Banerjee <doi:10.1016/j.envsoft.2019.104608>.")
    (license license:gpl2+)))

(define-public r-spbal
  (package
    (name "r-spbal")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spbal" version))
       (sha256
        (base32 "0wh0d1zfb1368bzpg0cjy0wa0ms4x6xr9y6n773prr8r8vs6fp09"))))
    (properties `((upstream-name . "spbal")))
    (build-system r-build-system)
    (propagated-inputs (list r-units r-sf r-rcppthread r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=spbal")
    (synopsis "Spatially Balanced Sampling Algorithms")
    (description
     "Encapsulates a number of spatially balanced sampling algorithms, namely,
Balanced Acceptance Sampling (equal, unequal, seed point, panels), Halton frames
(for discretizing a continuous resource), Halton Iterative Partitioning (equal
probability) and Simple Random Sampling.  Robertson, B. L., Brown, J. A.,
@code{McDonald}, T. and Jaksons, P. (2013) <doi:10.1111/biom.12059>.  Robertson,
B. L., @code{McDonald}, T., Price, C. J. and Brown, J. A. (2017)
<doi:10.1016/j.spl.2017.05.004>.  Robertson, B. L., @code{McDonald}, T., Price,
C. J. and Brown, J. A. (2018) <doi:10.1007/s10651-018-0406-6>.  Robertson, B.
L., van Dam-Bates, P. and Gansell, O. (2021a) <doi:10.1007/s10651-020-00481-1>.")
    (license license:expat)))

(define-public r-spbabel
  (package
    (name "r-spbabel")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spbabel" version))
       (sha256
        (base32 "0y30v7p54mmy43w7wclhywwk1zih6h66hkgd7dc101yiqvnx4m84"))))
    (properties `((upstream-name . "spbabel")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-sp r-rlang r-pkgconfig r-dplyr))
    (home-page "https://mdsumner.github.io/spbabel/")
    (synopsis "Convert Spatial Data Using Tidy Tables")
    (description
     "This package provides tools to convert from specific formats to more general
forms of spatial data.  Using tables to store the actual entities present in
spatial data provides flexibility, and the functions here deliberately minimize
the level of interpretation applied, leaving that for specific applications.
Includes support for simple features, round-trip for Spatial classes and
long-form tables, analogous to ggplot2::fortify'.  There is also a more normal
form representation that decomposes simple features and their kin to tables of
objects, parts, and unique coordinates.")
    (license license:gpl3)))

(define-public r-spb
  (package
    (name "r-spb")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPB" version))
       (sha256
        (base32 "1j9x11wl86cqjdamcksha6db1fhcdina439nl3fl04aahj61zisb"))))
    (properties `((upstream-name . "SPB")))
    (build-system r-build-system)
    (propagated-inputs (list r-knitr r-crayon))
    (home-page "https://cran.r-project.org/package=SPB")
    (synopsis "Simple Progress Bars for Procedural Coding")
    (description
     "This package provides a simple progress bar to use for basic and advanced users
that suits all those who prefer procedural programming.  It is especially useful
for integration into markdown files thanks to the progress bar's customisable
appearance.")
    (license license:gpl3+)))

(define-public r-spatsurv
  (package
    (name "r-spatsurv")
    (version "2.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spatsurv" version))
       (sha256
        (base32 "17s88w8ikrazhjbilvwcw1i1r9b77ci7f69jgr8lni7dd7l0b0hy"))))
    (properties `((upstream-name . "spatsurv")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-stringr
                             r-spatstat-random
                             r-spatstat-geom
                             r-spatstat-explore
                             r-sp
                             r-sf
                             r-rcolorbrewer
                             r-raster
                             r-matrix
                             r-lubridate
                             r-iterators
                             r-fields))
    (home-page "https://cran.r-project.org/package=spatsurv")
    (synopsis
     "Bayesian Spatial Survival Analysis with Parametric Proportional Hazards Models")
    (description
     "Bayesian inference for parametric proportional hazards spatial survival models;
flexible spatial survival models.  See Benjamin M. Taylor, Barry S. Rowlingson
(2017) <doi:10.18637/jss.v077.i04>.")
    (license license:gpl3)))

(define-public r-spatstat-local
  (package
    (name "r-spatstat-local")
    (version "5.1-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spatstat.local" version))
       (sha256
        (base32 "1brbk98hibsb7nh71xb3nhxsj8bb6lzclg746xd62cn9y72dl0v0"))))
    (properties `((upstream-name . "spatstat.local")))
    (build-system r-build-system)
    (propagated-inputs (list r-tensor
                             r-spatstat-utils
                             r-spatstat-univar
                             r-spatstat-sparse
                             r-spatstat-random
                             r-spatstat-model
                             r-spatstat-geom
                             r-spatstat-explore
                             r-spatstat-data
                             r-spatstat))
    (home-page "https://cran.r-project.org/package=spatstat.local")
    (synopsis "Extension to 'spatstat' for Local Composite Likelihood")
    (description
     "Extension to the spatstat package, enabling the user to fit point process models
to point pattern data by local composite likelihood ('geographically weighted
regression').")
    (license license:gpl2+)))

(define-public r-spatstat-knet
  (package
    (name "r-spatstat-knet")
    (version "3.1-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spatstat.Knet" version))
       (sha256
        (base32 "1l5742m0sq94fl055anxdxl5fr3b4nv6l82kjrxfrpfnyv6kpkib"))))
    (properties `((upstream-name . "spatstat.Knet")))
    (build-system r-build-system)
    (propagated-inputs (list r-spatstat-utils
                             r-spatstat-univar
                             r-spatstat-sparse
                             r-spatstat-random
                             r-spatstat-model
                             r-spatstat-linnet
                             r-spatstat-geom
                             r-spatstat-explore
                             r-spatstat-data
                             r-spatstat
                             r-matrix))
    (home-page "https://cran.r-project.org/package=spatstat.Knet")
    (synopsis "Extension to 'spatstat' for Large Datasets on a Linear Network")
    (description
     "Extension to the spatstat family of packages, for analysing large datasets of
spatial points on a network.  The geometrically- corrected K function is
computed using a memory-efficient tree-based algorithm described by Rakshit,
Baddeley and Nair (2019).")
    (license license:gpl2+)))

(define-public r-spatstat-gui
  (package
    (name "r-spatstat-gui")
    (version "3.1-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spatstat.gui" version))
       (sha256
        (base32 "108ls75y8zhcygfra9zbd2c0gfpf6n0kl2d6jlixa712l3m6lx8l"))))
    (properties `((upstream-name . "spatstat.gui")))
    (build-system r-build-system)
    (propagated-inputs (list r-spatstat-utils
                             r-spatstat-univar
                             r-spatstat-random
                             r-spatstat-model
                             r-spatstat-linnet
                             r-spatstat-geom
                             r-spatstat-explore
                             r-spatstat-data
                             r-spatstat
                             r-rpanel))
    (home-page "https://cran.r-project.org/package=spatstat.gui")
    (synopsis "Interactive Graphics Functions for the 'spatstat' Package")
    (description
     "Extension to the spatstat package, containing interactive graphics capabilities.")
    (license license:gpl2+)))

(define-public r-spatsoc
  (package
    (name "r-spatsoc")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spatsoc" version))
       (sha256
        (base32 "0aj9kg9013m0j3nx5qr2mvwczskkk0wy19yx1pb33wnqvhv2w0n0"))))
    (properties `((upstream-name . "spatsoc")))
    (build-system r-build-system)
    (inputs (list sqlite proj geos gdal))
    (propagated-inputs (list r-units r-sf r-igraph r-data-table r-adehabitathr))
    (native-inputs (list r-knitr))
    (home-page "https://docs.ropensci.org/spatsoc/")
    (synopsis
     "Group Animal Relocation Data by Spatial and Temporal Relationship")
    (description
     "Detects spatial and temporal groups in GPS relocations (Robitaille et al. (2019)
<doi:10.1111/2041-210X.13215>).  It can be used to convert GPS relocations to
gambit-of-the-group format to build proximity-based social networks In addition,
the randomizations function provides data-stream randomization methods suitable
for GPS data.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-spats
  (package
    (name "r-spats")
    (version "1.0-19")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpATS" version))
       (sha256
        (base32 "06fyns1g1iapps0f3g2555rl2cval91icc1ydszh6zv0zhq7m0la"))))
    (properties `((upstream-name . "SpATS")))
    (build-system r-build-system)
    (propagated-inputs (list r-spam r-fields r-data-table))
    (home-page "https://cran.r-project.org/package=SpATS")
    (synopsis "Spatial Analysis of Field Trials with Splines")
    (description
     "Analysis of field trial experiments by modelling spatial trends using
two-dimensional Penalised spline (P-spline) models.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-spatpomp
  (package
    (name "r-spatpomp")
    (version "0.36.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spatPomp" version))
       (sha256
        (base32 "1dg7f65mk8mrspccb8kbssbswpx3dmp15q6mksdis8k0mk4br9qz"))))
    (properties `((upstream-name . "spatPomp")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-rlang
                             r-pomp
                             r-ggplot2
                             r-foreach
                             r-dplyr
                             r-abind))
    (home-page "https://github.com/kidusasfaw/spatPomp")
    (synopsis
     "Inference for Spatiotemporal Partially Observed Markov Processes")
    (description
     "Inference on panel data using spatiotemporal partially-observed Markov process
(@code{SpatPOMP}) models.  The @code{spatPomp} package extends pomp to include
algorithms taking advantage of the spatial structure in order to assist with
handling high dimensional processes.  See Asfaw et al. (2024)
<doi:10.48550/@code{arXiv.2101.01157>} for further description of the package.")
    (license license:gpl3)))

(define-public r-spatpca
  (package
    (name "r-spatpca")
    (version "1.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatPCA" version))
       (sha256
        (base32 "1lx429glr6273ssy1in192jx4iynqidkfmj9iibwiij3fk9y0sa5"))))
    (properties `((upstream-name . "SpatPCA")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppparallel r-rcpparmadillo r-rcpp r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/egpivo/SpatPCA")
    (synopsis "Regularized Principal Component Analysis for Spatial Data")
    (description
     "Provide regularized principal component analysis incorporating smoothness,
sparseness and orthogonality of eigen-functions by using the alternating
direction method of multipliers algorithm (Wang and Huang, 2017,
<DOI:10.1080/10618600.2016.1157483>).  The method can be applied to either
regularly or irregularly spaced data, including 1D, 2D, and 3D.")
    (license license:gpl3)))

(define-public r-spatopic
  (package
    (name "r-spatopic")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpaTopic" version))
       (sha256
        (base32 "19jyxav7mbhpfi85b99wk6lah81ra81r5vbj4l6xm75fli4g3qzm"))))
    (properties `((upstream-name . "SpaTopic")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf
                             r-rcppprogress
                             r-rcpparmadillo
                             r-rcpp
                             r-rann
                             r-iterators
                             r-foreach))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/xiyupeng/SpaTopic")
    (synopsis
     "Topic Inference to Identify Tissue Architecture in Multiplexed Images")
    (description
     "This package provides a novel spatial topic model to integrate both cell type
and spatial information to identify the complex spatial tissue architecture on
multiplexed tissue images without human intervention.  The Package implements a
collapsed Gibbs sampling algorithm for inference. @code{SpaTopic} is scalable to
large-scale image datasets without extracting neighborhood information for every
single cell.  For more details on the methodology, see
<https://xiyupeng.github.io/@code{SpaTopic/>}.")
    (license license:gpl3+)))

(define-public r-spatmca
  (package
    (name "r-spatmca")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatMCA" version))
       (sha256
        (base32 "051c6mdij04n7xc256qx0119gsg337w2xly3ykiy47nrd3svdvc3"))))
    (properties `((upstream-name . "SpatMCA")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-rcppparallel
                             r-rcpparmadillo
                             r-rcpp
                             r-mass
                             r-ggplot2))
    (home-page "https://github.com/egpivo/SpatMCA")
    (synopsis "Regularized Spatial Maximum Covariance Analysis")
    (description
     "Provide regularized maximum covariance analysis incorporating smoothness,
sparseness and orthogonality of couple patterns by using the alternating
direction method of multipliers algorithm.  The method can be applied to either
regularly or irregularly spaced data, including 1D, 2D, and 3D (Wang and Huang,
2017 <doi:10.1002/env.2481>).")
    (license license:gpl3)))

(define-public r-spatialwidget
  (package
    (name "r-spatialwidget")
    (version "0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spatialwidget" version))
       (sha256
        (base32 "01w0iwwjfbb8cxfs3l1vxlay8x6694d7c7ksrfrdd003h7dxwksj"))))
    (properties `((upstream-name . "spatialwidget")))
    (build-system r-build-system)
    (propagated-inputs (list r-sfheaders
                             r-rcpp
                             r-rapidjsonr
                             r-jsonify
                             r-interleave
                             r-geometries
                             r-geojsonsf
                             r-colourvalues
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page
     "https://symbolixau.github.io/spatialwidget/articles/spatialwidget.html")
    (synopsis "Formats Spatial Data for Use in Htmlwidgets")
    (description
     "Many packages use htmlwidgets <https://CRAN.R-project.org/package=htmlwidgets>
for interactive plotting of spatial data.  This package provides functions for
converting R objects, such as simple features, into structures suitable for use
in htmlwidgets mapping libraries.")
    (license license:expat)))

(define-public r-spatialwarnings
  (package
    (name "r-spatialwarnings")
    (version "3.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spatialwarnings" version))
       (sha256
        (base32 "0k3xzc5xrdmgx2ax85qn2gb8cchwj031x1n3b2hsajrfwl6cgwzi"))))
    (properties `((upstream-name . "spatialwarnings")))
    (build-system r-build-system)
    (propagated-inputs (list r-segmented
                             r-rcpparmadillo
                             r-rcpp
                             r-plyr
                             r-gsl
                             r-ggplot2
                             r-future-apply
                             r-future))
    (home-page "https://github.com/spatial-ews/spatialwarnings")
    (synopsis "Spatial Early Warning Signals of Ecosystem Degradation")
    (description
     "This package provides tools to compute and assess significance of early-warnings
signals (EWS) of ecosystem degradation on raster data sets.  EWS are spatial
metrics derived from raster data -- e.g. spatial autocorrelation -- that
increase before an ecosystem undergoes a non-linear transition (Genin et al.
(2018) <doi:10.1111/2041-210X.13058>).")
    (license license:expat)))

(define-public r-spatialvx
  (package
    (name "r-spatialvx")
    (version "1.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatialVx" version))
       (sha256
        (base32 "108mpxpk03gmak69fkd7snhls11hh31fwmva9kqh71rh5g3a47fw"))))
    (properties `((upstream-name . "SpatialVx")))
    (build-system r-build-system)
    (propagated-inputs (list r-waveslim
                             r-turboem
                             r-spatstat-model
                             r-spatstat-linnet
                             r-spatstat-geom
                             r-spatstat
                             r-smoothie
                             r-smatr
                             r-maps
                             r-fields
                             r-fastcluster
                             r-distillery
                             r-circstats
                             r-boot))
    (home-page "https://projects.ral.ucar.edu/icp/SpatialVx/")
    (synopsis "Spatial Forecast Verification")
    (description
     "Spatial forecast verification refers to verifying weather forecasts when the
verification set (forecast and observations) is on a spatial field, usually a
high-resolution gridded spatial field.  Most of the functions here require the
forecast and observed fields to be gridded and on the same grid.  For a thorough
review of most of the methods in this package, please see Gilleland et al.
(2009) <doi: 10.1175/2009WAF2222269.1> and for a tutorial on some of the main
functions available here, see Gilleland (2022) <doi: 10.5065/4px3-5a05>.")
    (license license:gpl2+)))

(define-public r-spatialvs
  (package
    (name "r-spatialvs")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatialVS" version))
       (sha256
        (base32 "1xghfiz9wmmx11ssms7zjy3ggkjmc6kkbdwp3v5wjwpwapafzdk3"))))
    (properties `((upstream-name . "SpatialVS")))
    (build-system r-build-system)
    (propagated-inputs (list r-nlme r-mass r-fields))
    (home-page "https://cran.r-project.org/package=SpatialVS")
    (synopsis "Spatial Variable Selection")
    (description
     "Perform variable selection for the spatial Poisson regression model under the
adaptive elastic net penalty.  Spatial count data with covariates is the input.
We use a spatial Poisson regression model to link the spatial counts and
covariates.  For maximization of the likelihood under adaptive elastic net
penalty, we implemented the penalized quasi-likelihood (PQL) and the approximate
penalized loglikelihood (APL) methods.  The proposed methods can automatically
select important covariates, while adjusting for possible spatial correlations
among the responses.  More details are available in Xie et al. (2018,
<@code{arXiv:1809.06418>}).  The package also contains the Lyme disease dataset,
which consists of the disease case data from 2006 to 2011, and demographic data
and land cover data in Virginia.  The Lyme disease case data were collected by
the Virginia Department of Health.  The demographic data (e.g., population
density, median income, and average age) are from the 2010 census.  Land cover
data were obtained from the Multi-Resolution Land Cover Consortium for 2006.")
    (license license:gpl2)))

(define-public r-spatialtools
  (package
    (name "r-spatialtools")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatialTools" version))
       (sha256
        (base32 "1ka3w1k4y66lcajm2kpyk05r4pgkay57k1n74r7v7yjpvb085i7k"))))
    (properties `((upstream-name . "SpatialTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-spbayes r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=SpatialTools")
    (synopsis "Tools for Spatial Data Analysis")
    (description
     "This package provides tools for spatial data analysis.  Emphasis on kriging.
Provides functions for prediction and simulation.  Intended to be relatively
straightforward, fast, and flexible.")
    (license license:gpl2+)))

(define-public r-spatialtime
  (package
    (name "r-spatialtime")
    (version "1.3.4-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spatialTIME" version))
       (sha256
        (base32 "0i5fcy2q53612kq1qjg2sp10lwsiva6nay0v2jl2mw28zx6p22s9"))))
    (properties `((upstream-name . "spatialTIME")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-spatstat-univar
                             r-spatstat-geom
                             r-spatstat-explore
                             r-scales
                             r-rcolorbrewer
                             r-purrr
                             r-pbmcapply
                             r-magrittr
                             r-ggplot2
                             r-future
                             r-furrr
                             r-dplyr
                             r-dixon
                             r-crayon))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/FridleyLab/spatialTIME")
    (synopsis "Spatial Analysis of Vectra Immunoflourescent Data")
    (description
     "Visualization and analysis of Vectra Immunoflourescent data.  Options for
calculating both the univariate and bivariate Ripley's K are included.
Calculations are performed using a permutation-based approach presented by
Wilson et al. <doi:10.1101/2021.04.27.21256104>.")
    (license license:expat)))

(define-public r-spatialsample
  (package
    (name "r-spatialsample")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spatialsample" version))
       (sha256
        (base32 "0ywcdk7fbaqbk016c3wa1f62c4h2y3s4l14g4v6vnsh1i6y32410"))))
    (properties `((upstream-name . "spatialsample")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-units
                             r-tidyselect
                             r-tibble
                             r-sf
                             r-rsample
                             r-rlang
                             r-purrr
                             r-glue
                             r-ggplot2
                             r-dplyr
                             r-cpp11))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/tidymodels/spatialsample")
    (synopsis "Spatial Resampling Infrastructure")
    (description
     "This package provides functions and classes for spatial resampling to use with
the rsample package, such as spatial cross-validation (Brenning, 2012)
<doi:10.1109/IGARSS.2012.6352393>.  The scope of rsample and spatialsample is to
provide the basic building blocks for creating and analyzing resamples of a
spatial data set, but neither package includes functions for modeling or
computing statistics.  The resampled spatial data sets created by spatialsample
do not contain much overhead in memory.")
    (license license:expat)))

(define-public r-spatialromle
  (package
    (name "r-spatialromle")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatialRoMLE" version))
       (sha256
        (base32 "1m80vcd27g11v0gxnjz6p4ghljpxdb8jpkns8ry5yzhfvcpf29jc"))))
    (properties `((upstream-name . "SpatialRoMLE")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SpatialRoMLE")
    (synopsis "Robust Maximum Likelihood Estimation for Spatial Error Model")
    (description
     "This package provides robust estimation for spatial error model to presence of
outliers in the residuals.  The classical estimation methods can be influenced
by the presence of outliers in the data.  We proposed a robust estimation
approach based on the robustified likelihood equations for spatial error model
(Vural Yildirim & Yeliz Mert Kantar (2020): Robust estimation approach for
spatial error model, Journal of Statistical Computation and Simulation,
<doi:10.1080/00949655.2020.1740223>).")
    (license license:gpl3)))

(define-public r-spatialrisk
  (package
    (name "r-spatialrisk")
    (version "0.7.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spatialrisk" version))
       (sha256
        (base32 "1r6dr4n3d0x1yb4vcbfq0aqdqmy0h4n73snl86wrk86agvi5rmwp"))))
    (properties `((upstream-name . "spatialrisk")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-units
                             r-tmap
                             r-sf
                             r-rcppprogress
                             r-rcpp
                             r-leaflet
                             r-leafgl
                             r-leafem
                             r-ggplot2
                             r-geohashtools
                             r-gensa
                             r-fs
                             r-dplyr
                             r-data-table
                             r-colourvalues
                             r-classint))
    (home-page "https://github.com/mharinga/spatialrisk")
    (synopsis "Calculating Spatial Risk")
    (description
     "This package provides methods for spatial risk calculations.  It offers an
efficient approach to determine the sum of all observations within a circle of a
certain radius.  This might be beneficial for insurers who are required (by a
recent European Commission regulation) to determine the maximum value of insured
fire risk policies of all buildings that are partly or fully located within a
circle of a radius of 200m.  See Church (1974) <doi:10.1007/BF01942293> for a
description of the problem.")
    (license license:gpl2+)))

(define-public r-spatialrf
  (package
    (name "r-spatialrf")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spatialRF" version))
       (sha256
        (base32 "1lbwk7ziv292kxkz6rpr44jij3q13ljmqaccq56x6c2l8wkxl583"))))
    (properties `((upstream-name . "spatialRF")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-tidyselect
                             r-tidyr
                             r-tibble
                             r-rlang
                             r-ranger
                             r-patchwork
                             r-magrittr
                             r-huxtable
                             r-ggplot2
                             r-foreach
                             r-dplyr
                             r-doparallel))
    (home-page "https://blasbenito.github.io/spatialRF/")
    (synopsis "Easy Spatial Modeling with Random Forest")
    (description
     "Automatic generation and selection of spatial predictors for spatial regression
with Random Forest.  Spatial predictors are surrogates of variables driving the
spatial structure of a response variable.  The package offers two methods to
generate spatial predictors from a distance matrix among training cases: 1)
Moran's Eigenvector Maps (MEMs; Dray, Legendre, and Peres-Neto 2006
<DOI:10.1016/j.ecolmodel.2006.02.015>): computed as the eigenvectors of a
weighted matrix of distances; 2) RFsp (Hengl et al. <DOI:10.7717/peerj.5518>):
columns of the distance matrix used as spatial predictors.  Spatial predictors
help minimize the spatial autocorrelation of the model residuals and facilitate
an honest assessment of the importance scores of the non-spatial predictors.
Additionally, functions to reduce multicollinearity, identify relevant variable
interactions, tune random forest hyperparameters, assess model transferability
via spatial cross-validation, and explore model results via partial dependence
curves and interaction surfaces are included in the package.  The modelling
functions are built around the highly efficient ranger package (Wright and
Ziegler 2017 <DOI:10.18637/jss.v077.i01>).")
    (license license:gpl3)))

(define-public r-spatialregimes
  (package
    (name "r-spatialregimes")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatialRegimes" version))
       (sha256
        (base32 "1sp9fs8kwv5gm8bx9z5blkwj3c4lmcqw5126myr09rzz5xjmzzrx"))))
    (properties `((upstream-name . "SpatialRegimes")))
    (build-system r-build-system)
    (propagated-inputs (list r-spdep r-spatialreg r-quantreg r-plm r-gwmodel))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=SpatialRegimes")
    (synopsis "Spatial Constrained Clusterwise Regression")
    (description
     "This package provides a collection of functions for estimating spatial regimes,
aggregations of neighboring spatial units that are homogeneous in functional
terms.  The term spatial regime, therefore, should not be understood as a
synonym for cluster.  More precisely, the term cluster does not presuppose any
functional relationship between the variables considered, while the term regime
is linked to a regressive relationship underlying the spatial process.")
    (license license:gpl2+)))

(define-public r-spatialreg
  (package
    (name "r-spatialreg")
    (version "1.3-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spatialreg" version))
       (sha256
        (base32 "1i00gclgkkkvkhnhrnvc8r7666glch9zi3qcqcijlrfk48psmzpf"))))
    (properties `((upstream-name . "spatialreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-spdep
                             r-spdata
                             r-sf
                             r-nlme
                             r-multcomp
                             r-matrix
                             r-mass
                             r-learnbayes
                             r-coda
                             r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/r-spatial/spatialreg/")
    (synopsis "Spatial Regression Analysis")
    (description
     "This package provides a collection of all the estimation functions for spatial
cross-sectional models (on lattice/areal data using spatial weights matrices)
contained up to now in spdep'.  These model fitting functions include maximum
likelihood methods for cross-sectional models proposed by Cliff and Ord (1973,
ISBN:0850860369) and (1981, ISBN:0850860814), fitting methods initially
described by Ord (1975) <doi:10.1080/01621459.1975.10480272>.  The models are
further described by Anselin (1988) <doi:10.1007/978-94-015-7799-1>.  Spatial
two stage least squares and spatial general method of moment models initially
proposed by Kelejian and Prucha (1998) <doi:10.1023/A:1007707430416> and (1999)
<doi:10.1111/1468-2354.00027> are provided.  Impact methods and MCMC fitting
methods proposed by @code{LeSage} and Pace (2009) <doi:10.1201/9781420064254>
are implemented for the family of cross-sectional spatial regression models.
Methods for fitting the log determinant term in maximum likelihood and MCMC
fitting are compared by Bivand et al. (2013) <doi:10.1111/gean.12008>, and model
fitting methods by Bivand and Piras (2015) <doi:10.18637/jss.v063.i18>; both of
these articles include extensive lists of references.  A recent review is
provided by Bivand', Millo and Piras (2021) <doi:10.3390/math9111276>.
spatialreg >= 1.1-* corresponded to spdep >= 1.1-1, in which the model fitting
functions were deprecated and passed through to spatialreg', but masked those in
spatialreg'.  From versions 1.2-*, the functions have been made defunct in
spdep'.")
    (license license:gpl2)))

(define-public r-spatialrdd
  (package
    (name "r-spatialrdd")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatialRDD" version))
       (sha256
        (base32 "169166gyrxv7lcah7v9v0r9pllsnsjf81j2yqz9jqfn027rc0lhx"))))
    (properties `((upstream-name . "SpatialRDD")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf
                             r-sandwich
                             r-rlang
                             r-rdrobust
                             r-magrittr
                             r-lmtest
                             r-ggplot2
                             r-dplyr
                             r-cowplot
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://axlehner.github.io/SpatialRDD/")
    (synopsis
     "Conduct Multiple Types of Geographic Regression Discontinuity Designs")
    (description
     "Spatial versions of Regression Discontinuity Designs (RDDs) are becoming
increasingly popular as tools for causal inference.  However, conducting
state-of-the-art analyses often involves tedious and time-consuming steps.  This
package offers comprehensive functionalities for executing all required spatial
and econometric tasks in a streamlined manner.  Moreover, it equips researchers
with tools for performing essential placebo and balancing checks
comprehensively.  The fact that researchers do not have to rely on APIs of
external GIS software ensures replicability and raises the standard for spatial
RDDs.")
    (license license:gpl3)))

(define-public r-spatialprobit
  (package
    (name "r-spatialprobit")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spatialprobit" version))
       (sha256
        (base32 "0smb4k366xf7yzxxbk85963vi0jrk9jz83v7b2l1djhk4gxp7qm4"))))
    (properties `((upstream-name . "spatialprobit")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmvtnorm r-spdep r-spatialreg r-mvtnorm
                             r-matrix))
    (home-page "https://www.r-project.org")
    (synopsis "Spatial Probit Models")
    (description
     "This package provides a collection of methods for the Bayesian estimation of
Spatial Probit, Spatial Ordered Probit and Spatial Tobit Models.  Original
implementations from the works of @code{LeSage} and Pace (2009, ISBN:
1420064258) were ported and adjusted for R, as described in Wilhelm and de Matos
(2013) <doi:10.32614/RJ-2013-013>.")
    (license license:gpl2+)))

(define-public r-spatialposition
  (package
    (name "r-spatialposition")
    (version "2.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatialPosition" version))
       (sha256
        (base32 "12xglqrw4mqdfshd325gicrd3gnrkiaj1i2p5mlpklhdciahv1pi"))))
    (properties `((upstream-name . "SpatialPosition")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp r-sf r-raster r-isoband))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/riatelab/SpatialPosition")
    (synopsis "Spatial Position Models")
    (description
     "Computes spatial position models: the potential model as defined by Stewart
(1941) <doi:10.1126/science.93.2404.89> and catchment areas as defined by Reilly
(1931) or Huff (1964) <doi:10.2307/1249154>.")
    (license license:gpl3)))

(define-public r-spatialpop
  (package
    (name "r-spatialpop")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatialPOP" version))
       (sha256
        (base32 "0pgd4injkimjp6pid42arhfbavx2i8crkdvqr35bjvf26p7hbvv3"))))
    (properties `((upstream-name . "SpatialPOP")))
    (build-system r-build-system)
    (propagated-inputs (list r-qpdf r-numbers r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SpatialPOP")
    (synopsis
     "Generation of Spatial Data with Spatially Varying Model Parameter")
    (description
     "This package provides a spatial population can be generated based on spatially
varying regression model under the assumption that observations are collected
from a uniform two-dimensional grid consist of (m * m) lattice points with unit
distance between any two neighbouring points.  For method details see Chao,
Liu., Chuanhua, Wei.  and Yunan, Su. (2018).<DOI:10.1080/10485252.2018.1499907>.
 This spatially generated data can be used to test different issues related to
the statistical analysis of spatial data.  This generated spatial data can be
utilized in geographically weighted regression analysis for studying the
spatially varying relationships among the variables.")
    (license license:gpl2+)))

(define-public r-spatialpack
  (package
    (name "r-spatialpack")
    (version "0.4-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatialPack" version))
       (sha256
        (base32 "1cz6i5ws2d3nyfds02xwjzi7yyrn8a1wz0h4kr8v3h5ldfbp1abf"))))
    (properties `((upstream-name . "SpatialPack")))
    (build-system r-build-system)
    (propagated-inputs (list r-fastmatrix))
    (native-inputs (list gfortran))
    (home-page "http://spatialpack.mat.utfsm.cl")
    (synopsis
     "Tools for Assessment the Association Between Two Spatial Processes")
    (description
     "This package provides tools to assess the association between two spatial
processes.  Currently, several methodologies are implemented: A modified t-test
to perform hypothesis testing about the independence between the processes, a
suitable nonparametric correlation coefficient, the codispersion coefficient,
and an F test for assessing the multiple correlation between one spatial process
and several others.  Functions for image processing and computing the spatial
association between images are also provided.  Functions contained in the
package are intended to accompany Vallejos, R., Osorio, F., Bevilacqua, M.
(2020).  Spatial Relationships Between Two Georeferenced Variables: With
Applications in R. Springer, Cham <doi:10.1007/978-3-030-56681-4>.")
    (license license:gpl3)))

(define-public r-spatialnp
  (package
    (name "r-spatialnp")
    (version "1.1-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatialNP" version))
       (sha256
        (base32 "0cxh1f4182mfihq6m82zh2lgafrsiqvgrfxbk66zkjrrbfn8gkcl"))))
    (properties `((upstream-name . "SpatialNP")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SpatialNP")
    (synopsis
     "Multivariate Nonparametric Methods Based on Spatial Signs and Ranks")
    (description
     "Test and estimates of location, tests of independence, tests of sphericity and
several estimates of shape all based on spatial signs, symmetrized signs, ranks
and signed ranks.  For details, see Oja and Randles (2004)
<doi:10.1214/088342304000000558> and Oja (2010) <doi:10.1007/978-1-4419-0468-3>.")
    (license license:gpl2)))

(define-public r-spatialml
  (package
    (name "r-spatialml")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatialML" version))
       (sha256
        (base32 "05jjv441knmqci66vgy0lsp17hv3fmkfn1ijci5phn39cx797zf5"))))
    (properties `((upstream-name . "SpatialML")))
    (build-system r-build-system)
    (propagated-inputs (list r-ranger r-randomforest r-caret))
    (home-page "https://stamatisgeoai.eu/")
    (synopsis "Spatial Machine Learning")
    (description
     "This package implements a spatial extension of the random forest algorithm
(Georganos et al. (2019) <doi:10.1080/10106049.2019.1595177>).  Allows for a
geographically weighted random forest regression including a function to find
the optical bandwidth. (Georganos and Kalogirou (2022)
<https://www.mdpi.com/2220-9964/11/9/471>).")
    (license license:gpl2+)))

(define-public r-spatialkwd
  (package
    (name "r-spatialkwd")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatialKWD" version))
       (sha256
        (base32 "0b5vfsmpl3zxcwsg7bj85kfbyv8a2bgfq72b3rk4r6ms7ngc5i16"))))
    (properties `((upstream-name . "SpatialKWD")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=SpatialKWD")
    (synopsis "Spatial KWD for Large Spatial Maps")
    (description
     "This package contains efficient implementations of Discrete Optimal Transport
algorithms for the computation of Kantorovich-Wasserstein distances between
pairs of large spatial maps (Bassetti, Gualandi, Veneroni (2020),
<doi:10.1137/19M1261195>).  All the algorithms are based on an ad-hoc
implementation of the Network Simplex algorithm.  The package has four main
helper functions: @code{compareOneToOne}() (to compare two spatial maps),
@code{compareOneToMany}() (to compare a reference map with a list of other
maps), @code{compareAll}() (to compute a matrix of distances between a list of
maps), and @code{focusArea}() (to compute the KWD distance within a focus area).
 In non-convex maps, the helper functions first build the convex-hull of the
input bins and pad the weights with zeros.")
    (license (license:fsdg-compatible "EUPL (>= 1.2)"))))

(define-public r-spatialkde
  (package
    (name "r-spatialkde")
    (version "0.8.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatialKDE" version))
       (sha256
        (base32 "15s72mjz3pqny5i2c6xg5k4dzwb6d00zg8gihik91f8j06fy9w6r"))))
    (properties `((upstream-name . "SpatialKDE")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-sf
                             r-rlang
                             r-raster
                             r-progress
                             r-magrittr
                             r-glue
                             r-dplyr
                             r-cpp11))
    (native-inputs (list r-knitr))
    (home-page "https://jancaha.github.io/SpatialKDE/index.html")
    (synopsis "Kernel Density Estimation for Spatial Data")
    (description
     "Calculate Kernel Density Estimation (KDE) for spatial data.  The algorithm is
inspired by the tool Heatmap from QGIS'.  The method is described by: Hart, T.,
Zandbergen, P. (2014) <doi:10.1108/PIJPSM-04-2013-0039>, Nelson, T. A., Boots,
B. (2008) <doi:10.1111/j.0906-7590.2008.05548.x>, Chainey, S., Tompson, L.,
Uhlig, S.(2008) <doi:10.1057/palgrave.sj.8350066>.")
    (license license:expat)))

(define-public r-spatialising
  (package
    (name "r-spatialising")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spatialising" version))
       (sha256
        (base32 "0q2gj16crhr1s92pazj6zbhr03xjp0pqwdc64qanpxmv4sbm65wq"))))
    (properties `((upstream-name . "spatialising")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-rcpp r-comat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Nowosad/spatialising")
    (synopsis "Ising Model for Spatial Data")
    (description
     "This package performs simulations of binary spatial raster data using the Ising
model (Ising (1925) <doi:10.1007/BF02980577>; Onsager (1944)
<doi:10.1103/@code{PhysRev.65.117>}).  It allows to set a few parameters that
represent internal and external pressures, and the number of simulations
(Stepinski and Nowosad (2023) <doi:10.1098/rsos.231005>).")
    (license license:expat)))

(define-public r-spatialgraph
  (package
    (name "r-spatialgraph")
    (version "1.0-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatialGraph" version))
       (sha256
        (base32 "1875vi3dm6jm7bfmc9ydn4aaygls0c0k5pk540qq2f3ywbfq10zd"))))
    (properties `((upstream-name . "SpatialGraph")))
    (build-system r-build-system)
    (propagated-inputs (list r-splancs
                             r-sp
                             r-shape
                             r-sf
                             r-pracma
                             r-igraph))
    (home-page "https://github.com/garciapintado/SpatialGraph")
    (synopsis "The SpatialGraph Class and Utilities")
    (description
     "Provision of the S4 @code{SpatialGraph} class built on top of objects provided
by igraph and sp packages, and associated utilities.  See the documentation of
the @code{SpatialGraph-class} within this package for further description.  An
example of how from a few points one can arrive to a @code{SpatialGraph} is
provided in the function sl2sg().")
    (license license:gpl2+)))

(define-public r-spatialgev
  (package
    (name "r-spatialgev")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatialGEV" version))
       (sha256
        (base32 "1wk55vllcsqclzb9cb81pf28gkwxpql8kycyxry6b2ic6c32xgp4"))))
    (properties `((upstream-name . "SpatialGEV")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmb r-rcppeigen r-mvtnorm r-matrix r-evd))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SpatialGEV")
    (synopsis "Fit Spatial Generalized Extreme Value Models")
    (description
     "Fit latent variable models with the GEV distribution as the data likelihood and
the GEV parameters following latent Gaussian processes.  The models in this
package are built using the template model builder TMB in R, which has the fast
ability to integrate out the latent variables using Laplace approximation.  This
package allows the users to choose in the fit function which GEV parameter(s) is
considered as a spatially varying random effect following a Gaussian process, so
the users can fit spatial GEV models with different complexities to their
dataset without having to write the models in TMB by themselves.  This package
also offers methods to sample from both fixed and random effects posteriors as
well as the posterior predictive distributions at different spatial locations.
Methods for fitting this class of models are described in Chen, Ramezan, and
Lysy (2024) <doi:10.48550/@code{arXiv.2110.07051>}.")
    (license license:gpl3)))

(define-public r-spatialfdar
  (package
    (name "r-spatialfdar")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatialfdaR" version))
       (sha256
        (base32 "14l3fvwrzyrldbk8d2p0syk9lmhn208yi20493qd41babsaaxll3"))))
    (properties `((upstream-name . "SpatialfdaR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmarkdown r-rgl r-knitr r-geometry r-fda))
    (native-inputs (list r-knitr))
    (home-page "http://www.functionaldata.org")
    (synopsis "Spatial Functional Data Analysis")
    (description
     "Finite element modeling (FEM) uses meshes of triangles to define surfaces.  A
surface within a triangle may be either linear or quadratic.  In the order one
case each node in the mesh is associated with a basis function and the basis is
called the order one finite element basis.  In the order two case each edge
mid-point is also associated with a basis function.  Functions are provided for
smoothing, density function estimation point evaluation and plotting results.
Two papers illustrating the finite element data analysis are Sangalli, L.M.,
Ramsay, J.O., Ramsay, T.O. (2013)<http://www.mox.polimi.it/~sangalli> and
Bernardi, M.S, Carey, M., Ramsay, J. O., Sangalli, L.
(2018)<http://www.mox.polimi.it/~sangalli>.  Modelling spatial anisotropy via
regression with partial differential regularization Journal of Multivariate
Analysis, 167, 15-30.")
    (license license:gpl2+)))

(define-public r-spatialepi
  (package
    (name "r-spatialepi")
    (version "1.2.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatialEpi" version))
       (sha256
        (base32 "0q14scafm31xansk5zkhk6hb5jqcmi519jsd4drwkfvvdk8hf93d"))))
    (properties `((upstream-name . "SpatialEpi")))
    (build-system r-build-system)
    (propagated-inputs (list r-spdep r-sp r-rcpparmadillo r-rcpp r-mass))
    (home-page "https://github.com/rudeboybert/SpatialEpi")
    (synopsis "Methods and Data for Spatial Epidemiology")
    (description
     "This package provides methods and data for cluster detection and disease
mapping.")
    (license license:gpl2)))

(define-public r-spatialeco
  (package
    (name "r-spatialeco")
    (version "2.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spatialEco" version))
       (sha256
        (base32 "0z5m78x72if7bbhfjb92xv2qg4i7vh5hq9w5djd615piin9qwfsk"))))
    (properties `((upstream-name . "spatialEco")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-sf))
    (home-page "https://github.com/jeffreyevans/spatialEco")
    (synopsis "Spatial Analysis and Modelling Utilities")
    (description
     "Utilities to support spatial data manipulation, query, sampling and modelling in
ecological applications.  Functions include models for species population
density, spatial smoothing, multivariate separability, point process model for
creating pseudo- absences and sub-sampling, Quadrant-based sampling and
analysis, auto-logistic modeling, sampling models, cluster optimization,
statistical exploratory tools and raster-based metrics.")
    (license license:gpl3)))

(define-public r-spatialddls
  (package
    (name "r-spatialddls")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatialDDLS" version))
       (sha256
        (base32 "1wnyy7wi7bqlm2wjmjw18b7fxyi0ngfry4rzdb4q0fl6x0lkl0hj"))))
    (properties `((upstream-name . "SpatialDDLS")))
    (build-system r-build-system)
    (inputs (list tensorflow python))
    (propagated-inputs (list r-zinbwave
                             r-tensorflow
                             r-summarizedexperiment
                             r-spatialexperiment
                             r-singlecellexperiment
                             r-scuttle
                             r-scran
                             r-s4vectors
                             r-rlang
                             r-reticulate
                             r-reshape2
                             r-pbapply
                             r-matrix
                             r-keras
                             r-gtools
                             r-grr
                             r-ggpubr
                             r-ggplot2
                             r-fnn
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://diegommcc.github.io/SpatialDDLS/")
    (synopsis
     "Deconvolution of Spatial Transcriptomics Data Based on Neural Networks")
    (description
     "Deconvolution of spatial transcriptomics data based on neural networks and
single-cell RNA-seq data. @code{SpatialDDLS} implements a workflow to create
neural network models able to make accurate estimates of cell composition of
spots from spatial transcriptomics data using deep learning and the meaningful
information provided by single-cell RNA-seq data.  See Torroja and Sanchez-Cabo
(2019) <doi:10.3389/fgene.2019.00978> and MaÃ±anes et al. (2024)
<doi:10.1093/bioinformatics/btae072> to get an overview of the method and see
some examples of its performance.")
    (license license:gpl3)))

(define-public r-spatialcovariance
  (package
    (name "r-spatialcovariance")
    (version "0.6-9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spatialCovariance" version))
       (sha256
        (base32 "1m86s9a059spp97y37dcirrgjshcqzpdj11cq92vji624w4nrhlb"))))
    (properties `((upstream-name . "spatialCovariance")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=spatialCovariance")
    (synopsis
     "Computation of Spatial Covariance Matrices for Data on Rectangles")
    (description
     "This package provides functions that compute the spatial covariance matrix for
the matern and power classes of spatial models, for data that arise on
rectangular units.  This code can also be used for the change of support problem
and for spatial data that arise on irregularly shaped regions like counties or
zipcodes by laying a fine grid of rectangles and aggregating the integrals in a
form of Riemann integration.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-spatialbss
  (package
    (name "r-spatialbss")
    (version "0.14-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatialBSS" version))
       (sha256
        (base32 "0z8k4wd5rlx3bmp4zmfvblkf4nrkkqqfcpv5wzncyhi2ffgnybvm"))))
    (properties `((upstream-name . "SpatialBSS")))
    (build-system r-build-system)
    (propagated-inputs (list r-spatialnp
                             r-sp
                             r-robustbase
                             r-rcpparmadillo
                             r-rcpp
                             r-jade
                             r-distances))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SpatialBSS")
    (synopsis "Blind Source Separation for Multivariate Spatial Data")
    (description
     "Blind source separation for multivariate spatial data based on
simultaneous/joint diagonalization of (robust) local covariance matrices.  This
package is an implementation of the methods described in Bachoc, Genton,
Nordhausen, Ruiz-Gazen and Virta (2020) <doi:10.1093/biomet/asz079>.")
    (license license:gpl2+)))

(define-public r-spatialacc
  (package
    (name "r-spatialacc")
    (version "0.1-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatialAcc" version))
       (sha256
        (base32 "1livfnkqm9d0aw1q1m944clyj850n5m1n32k99vwf4byl5d5vhnn"))))
    (properties `((upstream-name . "SpatialAcc")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp))
    (home-page "https://stamatisgeoai.eu/")
    (synopsis "Spatial Accessibility Measures")
    (description
     "This package provides a set of spatial accessibility measures from a set of
locations (demand) to another set of locations (supply).  It aims, among others,
to support research on spatial accessibility to health care facilities.
Includes the locations and some characteristics of major public hospitals in
Greece.")
    (license license:gpl2+)))

(define-public r-spaths
  (package
    (name "r-spaths")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spaths" version))
       (sha256
        (base32 "1637y3ws9ivjkc5k4r3cwza485ynyjya1wgk866g0h6v7rpfx7xq"))))
    (properties `((upstream-name . "spaths")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/cdueben/spaths")
    (synopsis "Shortest Paths Between Points in Grids")
    (description
     "Shortest paths between points in grids.  Optional barriers and custom transition
functions.  Applications regarding planet Earth, as well as generally spheres
and planes.  Optimized for computational performance, customizability, and user
friendliness.  Graph-theoretical implementation tailored to gridded data.
Currently focused on Dijkstra's (1959) <doi:10.1007/BF01386390> algorithm.
Future updates broaden the scope to other least cost path algorithms and to
centrality measures.")
    (license license:expat)))

(define-public r-spatgrid
  (package
    (name "r-spatgrid")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatGRID" version))
       (sha256
        (base32 "10388v6fzykgxd35mvknrz92mvgby2vnsyw6jb2xi94fj21ybkp9"))))
    (properties `((upstream-name . "SpatGRID")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp r-sf r-raster r-qpdf))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SpatGRID")
    (synopsis "Spatial Grid Generation from Longitude and Latitude List")
    (description
     "The developed function is designed for the generation of spatial grids based on
user-specified longitude and latitude coordinates.  The function first validates
the input longitude and latitude values, ensuring they fall within the
appropriate geographic ranges.  It then creates a polygon from the coordinates
and determines the appropriate Universal Transverse Mercator zone based on the
provided hemisphere and longitude values.  Subsequently, transforming the input
Shapefile to the Universal Transverse Mercator projection when necessary.
Finally, a spatial grid is generated with the specified interval and saved as a
Shapefile.  For method details see, Brus,D.J.(2022).<DOI:10.1201/9781003258940>.
 The function takes into account crucial parameters such as the hemisphere
(north or south), desired grid interval, and the output Shapefile path.  The
developed function is an efficient tool, simplifying the process of empty
spatial grid generation for applications such as, geo-statistical analysis,
digital soil mapping product generation, etc.  Whether for environmental
studies, urban planning, or any other geo-spatial analysis, this package caters
to the diverse needs of users working with spatial data, enhancing the
accessibility and ease of spatial data processing and visualization.")
    (license license:gpl2+)))

(define-public r-spatgraphs
  (package
    (name "r-spatgraphs")
    (version "3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spatgraphs" version))
       (sha256
        (base32 "1xiq4a8br46qk19k23kkb6l1b7ciw9lmmrvwfznwkamwps6dwlg8"))))
    (properties `((upstream-name . "spatgraphs")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-matrix))
    (home-page "https://cran.r-project.org/package=spatgraphs")
    (synopsis "Graph Edge Computations for Spatial Point Patterns")
    (description
     "Graphs (or networks) and graph component calculations for spatial locations in
1D, 2D, 3D etc.")
    (license license:gpl2+)))

(define-public r-spatgeom
  (package
    (name "r-spatgeom")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spatgeom" version))
       (sha256
        (base32 "1z6haqqzghji6703crs69nvqx93h19bdkw0frpmrcxa60fxm0kyr"))))
    (properties `((upstream-name . "spatgeom")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf
                             r-scales
                             r-purrr
                             r-lwgeom
                             r-ggplot2
                             r-dplyr
                             r-cowplot))
    (home-page "https://github.com/maikol-solis/spatgeom")
    (synopsis "Geometric Spatial Point Analysis")
    (description
     "The implementation to perform the geometric spatial point analysis developed in
HernÃ¡ndez & SolÃ­s (2022) <doi:10.1007/s00180-022-01244-1>.  It estimates the
geometric goodness-of-fit index for a set of variables against a response one
based on the sf package.  The package has methods to print and plot the results.")
    (license license:expat)))

(define-public r-spatgc
  (package
    (name "r-spatgc")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatGC" version))
       (sha256
        (base32 "1y7lxxg39rd3y0ba5rsg4majbgjj5ld22kkx1w3fg1qhj9xacmnc"))))
    (properties `((upstream-name . "SpatGC")))
    (build-system r-build-system)
    (propagated-inputs (list r-spdep r-sf r-mvtnorm))
    (home-page "https://github.com/mahsanst/SpatGC")
    (synopsis "Bayesian Modeling of Spatial Count Data")
    (description
     "This package provides a collection of functions for preparing data and fitting
Bayesian count spatial regression models, with a specific focus on the
Gamma-Count (GC) model.  The GC model is well-suited for modeling dispersed
count data, including under-dispersed or over-dispersed counts, or counts with
equivalent dispersion, using Integrated Nested Laplace Approximations (INLA).
The package includes functions for generating data from the GC model, as well as
spatially correlated versions of the model.  See Nadifar, Baghishani, Fallah
(2023) <doi:10.1007/s13253-023-00550-5>.")
    (license license:gpl2+)))

(define-public r-spatfd
  (package
    (name "r-spatfd")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatFD" version))
       (sha256
        (base32 "1v7j0fkd031kfgx2xvkd02kvzn3k5dp6s82jvcm7rdrd18z4hzkv"))))
    (properties `((upstream-name . "SpatFD")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-sp
                             r-sf
                             r-reshape
                             r-proxy
                             r-plotly
                             r-mass
                             r-gstat
                             r-ggplot2
                             r-geor
                             r-fda-usc
                             r-fda
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=SpatFD")
    (synopsis
     "Functional Geostatistics: Univariate and Multivariate Functional Spatial Prediction")
    (description
     "Performance of functional kriging, cokriging, optimal sampling and simulation
for spatial prediction of functional data.  The framework of spatial prediction,
optimal sampling and simulation are extended from scalar to functional data.
@code{SpatFD} is based on the Karhunen-LoÃ¨ve expansion that allows to represent
the observed functions in terms of its empirical functional principal
components.  Based on this approach, the functional auto-covariances and
cross-covariances required for spatial functional predictions and optimal
sampling, are completely determined by the sum of the spatial auto-covariances
and cross-covariances of the respective score components.  The package provides
new classes of data and functions for modeling spatial dependence structure
among curves.  The spatial prediction of curves at unsampled locations can be
carried out using two types of predictors, and both of them report, the
respective variances of the prediction error.  In addition, there is a function
for the determination of spatial locations sampling configuration that ensures
minimum variance of spatial functional prediction.  There are also two functions
for plotting predicted curves at each location and mapping the surface at each
time point, respectively.  References Bohorquez, M., Giraldo, R., and Mateu, J.
(2016) <doi:10.1007/s10260-015-0340-9>, Bohorquez, M., Giraldo, R., and Mateu,
J. (2016) <doi:10.1007/s00477-016-1266-y>, Bohorquez M., Giraldo R. and Mateu J.
(2021) <doi:10.1002/9781119387916>.")
    (license license:gpl3)))

(define-public r-spatentropy
  (package
    (name "r-spatentropy")
    (version "2.2-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpatEntropy" version))
       (sha256
        (base32 "0krcyymvwdcckiji1dlm9pyc2x82k5dfkvglfchv8w5kf6y2qmbh"))))
    (properties `((upstream-name . "SpatEntropy")))
    (build-system r-build-system)
    (propagated-inputs (list r-spatstat-random r-spatstat-geom r-spatstat))
    (home-page "https://cran.r-project.org/package=SpatEntropy")
    (synopsis "Spatial Entropy Measures")
    (description
     "The heterogeneity of spatial data presenting a finite number of categories can
be measured via computation of spatial entropy.  Functions are available for the
computation of the main entropy and spatial entropy measures in the literature.
They include the traditional version of Shannon's entropy (Shannon, 1948
<doi:10.1002/j.1538-7305.1948.tb01338.x>), Batty's spatial entropy (Batty, 1974
<doi:10.1111/j.1538-4632.1974.tb01014.x>), O'Neill's entropy (O'Neill et al.,
1998 <doi:10.1007/BF00162741>), Li and Reynolds contagion index (Li and
Reynolds, 1993 <doi:10.1007/BF00125347>), Karlstrom and Ceccato's entropy
(Karlstrom and Ceccato, 2002
<https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-61351>), Leibovici's entropy
(Leibovici, 2009 <doi:10.1007/978-3-642-03832-7_24>), Parresol and Edwards
entropy (Parresol and Edwards, 2014 <doi:10.3390/e16041842>) and Altieri's
entropy (Altieri et al., 2018, <doi:10.1007/s10651-017-0383-1>).  Full
references for all measures can be found under the topic @code{SpatEntropy}'.
The package is able to work with lattice and point data.  The updated version
works with the updated spatstat package (>= 3.0-2).")
    (license license:gpl3)))

(define-public r-spate
  (package
    (name "r-spate")
    (version "1.7.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spate" version))
       (sha256
        (base32 "114amd3rh9ns6pyh92c5n991904mfwd6vi5cljzm2w3rhyf9vjci"))))
    (properties `((upstream-name . "spate")))
    (build-system r-build-system)
    (inputs (list fftw))
    (propagated-inputs (list r-truncnorm r-mvtnorm))
    (native-inputs (list pkg-config))
    (home-page "https://cran.r-project.org/package=spate")
    (synopsis
     "Spatio-Temporal Modeling of Large Data Using a Spectral SPDE Approach")
    (description
     "Functionality for spatio-temporal modeling of large data sets is provided.  A
Gaussian process in space and time is defined through a stochastic partial
differential equation (SPDE).  The SPDE is solved in the spectral space, and
after discretizing in time and space, a linear Gaussian state space model is
obtained.  When doing inference, the main computational difficulty consists in
evaluating the likelihood and in sampling from the full conditional of the
spectral coefficients, or equivalently, the latent space-time process.  In
comparison to the traditional approach of using a spatio-temporal covariance
function, the spectral SPDE approach is computationally advantageous.  See
Sigrist, Kuensch, and Stahel (2015) <doi:10.1111/rssb.12061> for more
information on the methodology.  This package aims at providing tools for two
different modeling approaches.  First, the SPDE based spatio-temporal model can
be used as a component in a customized hierarchical Bayesian model (HBM).  The
functions of the package then provide parameterizations of the process part of
the model as well as computationally efficient algorithms needed for doing
inference with the HBM. Alternatively, the adaptive MCMC algorithm implemented
in the package can be used as an algorithm for doing inference without any
additional modeling.  The MCMC algorithm supports data that follow a Gaussian or
a censored distribution with point mass at zero.  Covariates can be included in
the model through a regression term.")
    (license license:gpl2)))

(define-public r-spass
  (package
    (name "r-spass")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spass" version))
       (sha256
        (base32 "1c148z0i3d185hnf54xmgppjrblv3b2s3sm0lmj78gvav04mj5s4"))))
    (properties `((upstream-name . "spass")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-mvtnorm r-multcomp r-mass r-geepack))
    (home-page "https://cran.r-project.org/package=spass")
    (synopsis "Study Planning and Adaptation of Sample Size")
    (description
     "Sample size estimation and blinded sample size reestimation in Adaptive Study
Design.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-spas
  (package
    (name "r-spas")
    (version "2024.1.31")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPAS" version))
       (sha256
        (base32 "06axnkq22jyc208ndvmiyp13vfr9sxg85rsdzdi6mm70rzgwba6c"))))
    (properties `((upstream-name . "SPAS")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmb
                             r-reshape2
                             r-rcppeigen
                             r-plyr
                             r-numderiv
                             r-msm
                             r-matrix
                             r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SPAS")
    (synopsis "Stratified-Petersen Analysis System")
    (description
     "The Stratified-Petersen Analysis System (SPAS) is designed to estimate abundance
in two-sample capture-recapture experiments where the capture and recaptures are
stratified.  This is a generalization of the simple Lincoln-Petersen estimator.
Strata may be defined in time or in space or both, and the s strata in which
marking takes place may differ from the t strata in which recoveries take place.
 When s=t, SPAS reduces to the method described by Darroch (1961)
<doi:10.2307/2332748>.  When s<t, SPAS implements the methods described in
Plante, Rivest, and Tremblay (1988) <doi:10.2307/2533994>.  Schwarz and Taylor
(1998) <doi:10.1139/f97-238> describe the use of SPAS in estimating return of
salmon stratified by time and geography.  A related package, BTSPAS, deals with
temporal stratification where a spline is used to model the distribution of the
population over time as it passes the second capture location.  This is the
R-version of the (now obsolete) standalone Windows program available at
<https://home.cs.umanitoba.ca/~popan/spas/spas_home.html>.")
    (license license:gpl2+)))

(define-public r-sparvaride
  (package
    (name "r-sparvaride")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparvaride" version))
       (sha256
        (base32 "17skm44cdv540jik8lv5y0dp6yzjgy775vjzq3dv1jv58nqjvps1"))))
    (properties `((upstream-name . "sparvaride")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://hdarjus.github.io/sparvaride/")
    (synopsis "Variance Identification in Sparse Factor Analysis")
    (description
     "This is an implementation of the algorithm described in Section 3 of Hosszejni
and FrÃ¼hwirth-Schnatter (2022) <doi:10.48550/@code{arXiv.2211.00671>}.  The
algorithm is used to verify that the counting rule CR(r,1) holds for the
sparsity pattern of the transpose of a factor loading matrix.  As detailed in
Section 2 of the same paper, if CR(r,1) holds, then the idiosyncratic variances
are generically identified.  If CR(r,1) does not hold, then we do not know
whether the idiosyncratic variances are identified or not.")
    (license license:gpl3+)))

(define-public r-spartaas
  (package
    (name "r-spartaas")
    (version "1.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPARTAAS" version))
       (sha256
        (base32 "0795n9z8z4mr0fc4mrdq8xgnn3jaqcqsdpfq0iyyl7zn3nl3bgca"))))
    (properties `((upstream-name . "SPARTAAS")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-shinywidgets
                             r-shinythemes
                             r-shinyjs
                             r-shinyjqui
                             r-shinydashboard
                             r-shinycssloaders
                             r-shiny
                             r-scatterd3
                             r-scales
                             r-rstudioapi
                             r-plotly
                             r-nor1mix
                             r-mass
                             r-lmtest
                             r-leaflet
                             r-ks
                             r-htmlwidgets
                             r-htmltools
                             r-ggplot2
                             r-ggdendro
                             r-fpc
                             r-foreign
                             r-fastcluster
                             r-factominer
                             r-explor
                             r-dplyr
                             r-colorspace
                             r-cluster
                             r-ape
                             r-ade4))
    (home-page "https://spartaas.gitpages.huma-num.fr/r-package/")
    (synopsis
     "Statistical Pattern Recognition and daTing using Archaeological Artefacts assemblageS")
    (description
     "Statistical pattern recognition and dating using archaeological artefacts
assemblages.  Package of statistical tools for archaeology.
hclustcompro()/perioclust(): Bellanger Lise, Coulon Arthur, Husi Philippe (2021,
ISBN:978-3-030-60103-4).  mapclust(): Bellanger Lise, Coulon Arthur, Husi
Philippe (2021) <doi:10.1016/j.jas.2021.105431>.  seriograph(): Desachy Bruno
(2004) <doi:10.3406/pica.2004.2396>.  cerardat(): Bellanger Lise, Husi Philippe
(2012) <doi:10.1016/j.jas.2011.06.031>.")
    (license license:gpl2+)))

(define-public r-sparta
  (package
    (name "r-sparta")
    (version "0.8.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparta" version))
       (sha256
        (base32 "109i8yhv8snhpccrhxy1k8xcy9jj8m3li2d7vaylvxbrlnc26d89"))))
    (properties `((upstream-name . "sparta")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mlindsk/sparta")
    (synopsis "Sparse Tables")
    (description "Fast Multiplication and Marginalization of Sparse Tables.")
    (license license:expat)))

(define-public r-sparsio
  (package
    (name "r-sparsio")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparsio" version))
       (sha256
        (base32 "181kzs9x3h497c7shfalbzrjz05bkj7mmjh9nyphxyi2jjrr6y1q"))))
    (properties `((upstream-name . "sparsio")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-matrix))
    (home-page "https://github.com/dselivanov/sparsio")
    (synopsis "I/O Operations with Sparse Matrices")
    (description
     "Fast SVMlight reader and writer.  SVMlight is most commonly used format for
storing sparse matrices (possibly with some target variable) on disk.  For
additional information about SVMlight format see
<http://svmlight.joachims.org/>.")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-sparsevfc
  (package
    (name "r-sparsevfc")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SparseVFC" version))
       (sha256
        (base32 "0d4394jg5fk4cgpds014xl3dhf7wlx5i99bnxpj5xcbvfdqzvwvl"))))
    (properties `((upstream-name . "SparseVFC")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr r-pdist))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Sciurus365/SparseVFC")
    (synopsis "Sparse Vector Field Consensus for Vector Field Learning")
    (description
     "The sparse vector field consensus (@code{SparseVFC}) algorithm (Ma et al., 2013
<doi:10.1016/j.patcog.2013.05.017>) for robust vector field learning.  Largely
translated from the Matlab functions in <https://github.com/jiayi-ma/VFC>.")
    (license license:gpl3+)))

(define-public r-sparsevctrs
  (package
    (name "r-sparsevctrs")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparsevctrs" version))
       (sha256
        (base32 "18w5d1ggyxvwm6rcf0bspsblf6s5m0snr4pf22y9sqfjb282vdz0"))))
    (properties `((upstream-name . "sparsevctrs")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs r-rlang r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/r-lib/sparsevctrs")
    (synopsis "Sparse Vectors for Use in Data Frames")
    (description
     "This package provides sparse vectors powered by ALTREP (Alternative
Representations for R Objects) that behave like regular vectors, and can thus be
used in data frames.  Also provides tools to convert between sparse matrices and
data frames with sparse columns and functions to interact with sparse vectors.")
    (license license:expat)))

(define-public r-sparsevb
  (package
    (name "r-sparsevb")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparsevb" version))
       (sha256
        (base32 "1cigq8hwymrrkiy3iyrxsjjbj228ahrd7mq9lqar4jlb925jzqx4"))))
    (properties `((upstream-name . "sparsevb")))
    (build-system r-build-system)
    (propagated-inputs (list r-selectiveinference r-rcppensmallen
                             r-rcpparmadillo r-rcpp r-glmnet))
    (home-page "https://cran.r-project.org/package=sparsevb")
    (synopsis
     "Spike-and-Slab Variational Bayes for Linear and Logistic Regression")
    (description
     "This package implements variational Bayesian algorithms to perform scalable
variable selection for sparse, high-dimensional linear and logistic regression
models.  Features include a novel prioritized updating scheme, which uses a
preliminary estimator of the variational means during initialization to generate
an updating order prioritizing large, more relevant, coefficients.  Sparsity is
induced via spike-and-slab priors with either Laplace or Gaussian slabs.  By
default, the heavier-tailed Laplace density is used.  Formal derivations of the
algorithms and asymptotic consistency results may be found in Kolyan Ray and
Botond Szabo (2020) <doi:10.1080/01621459.2020.1847121> and Kolyan Ray, Botond
Szabo, and Gabriel Clara (2020) <@code{arXiv:2010.11665>}.")
    (license license:gpl3+)))

(define-public r-sparsevar
  (package
    (name "r-sparsevar")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparsevar" version))
       (sha256
        (base32 "1kcy6bm04mccky529nryy5l001bv71hx304ysiabl4myakw0rwy7"))))
    (properties `((upstream-name . "sparsevar")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2
                             r-picasso
                             r-ncvreg
                             r-mvtnorm
                             r-matrix
                             r-glmnet
                             r-ggplot2
                             r-doparallel
                             r-corpcor))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/svazzole/sparsevar")
    (synopsis "Sparse VAR/VECM Models Estimation")
    (description
     "This package provides a wrapper for sparse VAR/VECM time series models
estimation using penalties like ENET (Elastic Net), SCAD (Smoothly Clipped
Absolute Deviation) and MCP (Minimax Concave Penalty).  Based on the work of
Sumanta Basu and George Michailidis <doi:10.1214/15-AOS1315>.")
    (license license:gpl2)))

(define-public r-sparsetscgm
  (package
    (name "r-sparsetscgm")
    (version "4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SparseTSCGM" version))
       (sha256
        (base32 "01lfb3j907244hgapb85kba7zdmgzhw452gri208qgrjzfv2x9yh"))))
    (properties `((upstream-name . "SparseTSCGM")))
    (build-system r-build-system)
    (propagated-inputs (list r-network
                             r-mvtnorm
                             r-mass
                             r-longitudinal
                             r-huge
                             r-glasso
                             r-abind))
    (home-page "https://cran.r-project.org/package=SparseTSCGM")
    (synopsis "Sparse Time Series Chain Graphical Models")
    (description
     "Computes sparse vector autoregressive 				coefficients and precision matrices
for time series 		chain graphical models.  Fentaw Abegaz and Ernst Wit
(2013)	<doi:10.1093/biostatistics/kxt005>.")
    (license license:gpl3+)))

(define-public r-sparsesvm
  (package
    (name "r-sparsesvm")
    (version "1.1-7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparseSVM" version))
       (sha256
        (base32 "1zyil9sc8bxs99n3bhfxhcd59ng0qvlcwk3ff2sbh4bykqlcxya8"))))
    (properties `((upstream-name . "sparseSVM")))
    (build-system r-build-system)
    (home-page "https://github.com/CY-dev/sparseSVM")
    (synopsis
     "Solution Paths of Sparse High-Dimensional Support Vector Machine with Lasso or Elastic-Net Regularization")
    (description
     "Offers a fast algorithm for fitting solution paths of sparse SVM models with
lasso or elastic-net regularization.  Reference: Congrui Yi and Jian Huang
(2017) <doi:10.1080/10618600.2016.1256816>.")
    (license license:gpl3)))

(define-public r-sparsestep
  (package
    (name "r-sparsestep")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparsestep" version))
       (sha256
        (base32 "1mvwjd3ks9nf63nmd13ncxrs12nrypw6vg4xpqympzc57p73qvmi"))))
    (properties `((upstream-name . "sparsestep")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (home-page "https://github.com/GjjvdBurg/SparseStep")
    (synopsis "SparseStep Regression")
    (description
     "This package implements the @code{SparseStep} model for solving regression
problems with a sparsity constraint on the parameters.  The @code{SparseStep}
regression model was proposed in Van den Burg, Groenen, and Alfons (2017)
<@code{arXiv:1701.06967>}.  In the model, a regularization term is added to the
regression problem which approximates the counting norm of the parameters.  By
iteratively improving the approximation a sparse solution to the regression
problem can be obtained.  In this package both the standard @code{SparseStep}
algorithm is implemented as well as a path algorithm which uses golden section
search to determine solutions with different values for the regularization
parameter.")
    (license license:gpl2+)))

(define-public r-sparsesem
  (package
    (name "r-sparsesem")
    (version "4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparseSEM" version))
       (sha256
        (base32 "15pgh5x01inyl8kxaqsz9hf7iv3splzarch89yz1nappv86c7khq"))))
    (properties `((upstream-name . "sparseSEM")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=sparseSEM")
    (synopsis
     "Elastic Net Penalized Maximum Likelihood for Structural Equation Models with Network GPT Framework")
    (description
     "This package provides elastic net penalized maximum likelihood estimator for
structural equation models (SEM).  The package implements `lasso` and `elastic
net` (l1/l2) penalized SEM and estimates the model parameters with an efficient
block coordinate ascent algorithm that maximizes the penalized likelihood of the
SEM. Hyperparameters are inferred from cross-validation (CV).  A Stability
Selection (STS) function is also available to provide accurate causal effect
selection.  The software achieves high accuracy performance through a `Network
Generative Pre-trained Transformer` (Network GPT) Framework with two steps: 1)
pre-trains the model to generate a complete (fully connected) graph; and 2) uses
the complete graph as the initial state to fit the `elastic net` penalized SEM.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-sparsereg
  (package
    (name "r-sparsereg")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparsereg" version))
       (sha256
        (base32 "1ipsang2fppmjx4h5ljgzx3c44z3lggc376ghrqqgmxb5ql8bcp3"))))
    (properties `((upstream-name . "sparsereg")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam
                             r-rcpparmadillo
                             r-rcpp
                             r-msm
                             r-mcmcpack
                             r-mass
                             r-gridextra
                             r-glmnet
                             r-gigrvg
                             r-ggplot2
                             r-coda))
    (home-page "https://cran.r-project.org/package=sparsereg")
    (synopsis
     "Sparse Bayesian Models for Regression, Subgroup Analysis, and Panel Data")
    (description
     "Sparse modeling provides a mean selecting a small number of non-zero effects
from a large possible number of candidate effects.  This package includes a
suite of methods for sparse modeling: estimation via EM or MCMC, approximate
confidence intervals with nominal coverage, and diagnostic and summary plots.
The method can implement sparse linear regression and sparse probit regression.
Beyond regression analyses, applications include subgroup analysis, particularly
for conjoint experiments, and panel data.  Future versions will include
extensions to models with truncated outcomes, propensity score, and instrumental
variable analysis.")
    (license license:gpl2+)))

(define-public r-sparser
  (package
    (name "r-sparser")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparseR" version))
       (sha256
        (base32 "166140mb4d63z59whg8c8478n5zx8jlmw7m4ji77ma9wws4pr6ww"))))
    (properties `((upstream-name . "sparseR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-recipes r-ncvreg r-magrittr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://petersonr.github.io/sparseR/")
    (synopsis
     "Variable Selection under Ranked Sparsity Principles for Interactions and Polynomials")
    (description
     "An implementation of ranked sparsity methods, including penalized regression
methods such as the sparsity-ranked lasso, its non-convex alternatives, and
elastic net, as well as the sparsity-ranked Bayesian Information Criterion.  As
described in Peterson and Cavanaugh (2022) <doi:10.1007/s10182-021-00431-7>,
ranked sparsity is a philosophy with methods primarily useful for variable
selection in the presence of prior informational asymmetry, which occurs in the
context of trying to perform variable selection in the presence of interactions
and/or polynomials.  Ultimately, this package attempts to facilitate dealing
with cumbersome interactions and polynomials while not avoiding them entirely.
Typically, models selected under ranked sparsity principles will also be more
transparent, having fewer falsely selected interactions and polynomials than
other methods.")
    (license license:gpl3)))

(define-public r-sparsepp
  (package
    (name "r-sparsepp")
    (version "1.22")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparsepp" version))
       (sha256
        (base32 "0wa1585pkbn3y11apyim4z1ixn300c1yyv9bf2qldb6cjfiv7699"))))
    (properties `((upstream-name . "sparsepp")))
    (build-system r-build-system)
    (home-page "https://github.com/greg7mdp/sparsepp")
    (synopsis "'Rcpp' Interface to 'sparsepp'")
    (description
     "This package provides interface to sparsepp - fast, memory efficient hash map.
It is derived from Google's excellent sparsehash implementation.  We believe
sparsepp provides an unparalleled combination of performance and memory usage,
and will outperform your compiler's unordered_map on both counts.  Only Google's
dense_hash_map is consistently faster, at the cost of much greater memory usage
(especially when the final size of the map is not known in advance).")
    (license license:bsd-3)))

(define-public r-sparsepca
  (package
    (name "r-sparsepca")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparsepca" version))
       (sha256
        (base32 "0wk2nm37p15gb8i4s45izzzdmwywr56kv4g0j3jd5s3slm224vv4"))))
    (properties `((upstream-name . "sparsepca")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsvd))
    (home-page "https://github.com/erichson/spca")
    (synopsis "Sparse Principal Component Analysis (SPCA)")
    (description
     "Sparse principal component analysis (SPCA) attempts to find sparse weight
vectors (loadings), i.e., a weight vector with only a few active (nonzero)
values.  This approach provides better interpretability for the principal
components in high-dimensional data settings.  This is, because the principal
components are formed as a linear combination of only a few of the original
variables.  This package provides efficient routines to compute SPCA.
Specifically, a variable projection solver is used to compute the sparse
solution.  In addition, a fast randomized accelerated SPCA routine and a robust
SPCA routine is provided.  Robust SPCA allows to capture grossly corrupted
entries in the data.  The methods are discussed in detail by N. Benjamin
Erichson et al. (2018) <@code{arXiv:1804.00341>}.")
    (license license:gpl3+)))

(define-public r-sparsenet
  (package
    (name "r-sparsenet")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparsenet" version))
       (sha256
        (base32 "1r3p14qbwkmb8hmgfh0ymzz52lqxgc9cnp8yh8y8la1sm3hxc92n"))))
    (properties `((upstream-name . "sparsenet")))
    (build-system r-build-system)
    (propagated-inputs (list r-shape r-matrix))
    (native-inputs (list gfortran))
    (home-page
     "https://hastie.su.domains/public/Papers/Sparsenet/Mazumder-SparseNetCoordinateDescent-2011.pdf")
    (synopsis "Fit Sparse Linear Regression Models via Nonconvex Optimization")
    (description
     "Efficient procedure for fitting regularization paths between L1 and L0, using
the MC+ penalty of Zhang, C.H. (2010)<doi:10.1214/09-AOS729>.  Implements the
methodology described in Mazumder, Friedman and Hastie (2011) <DOI:
10.1198/jasa.2011.tm09738>.  Sparsenet computes the regularization surface over
both the family parameter and the tuning parameter by coordinate descent.")
    (license license:gpl2)))

(define-public r-sparsemvn
  (package
    (name "r-sparsemvn")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparseMVN" version))
       (sha256
        (base32 "1vd97w4mrc6s9d95a43cpz90adhn2015as93j55hm5phpgj24v75"))))
    (properties `((upstream-name . "sparseMVN")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (native-inputs (list r-knitr))
    (home-page "https://braunm.github.io/sparseMVN/")
    (synopsis
     "Multivariate Normal Functions for Sparse Covariance and Precision Matrices")
    (description
     "Computes multivariate normal (MVN) densities, and samples from MVN
distributions, when the covariance or precision matrix is sparse.")
    (license (license:fsdg-compatible "MPL (>= 2.0)"))))

(define-public r-sparsemse
  (package
    (name "r-sparsemse")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SparseMSE" version))
       (sha256
        (base32 "1alpvj0ya3r64wwfdzz2aaxgaf6512glzl65p97606c2fwrm6db7"))))
    (properties `((upstream-name . "SparseMSE")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcapture r-lpsolve))
    (home-page "https://arxiv.org/abs/1902.05156")
    (synopsis "'Multiple Systems Estimation for Sparse Capture Data'")
    (description
     "This package implements the routines and algorithms developed and analysed in
\"Multiple Systems Estimation for Sparse Capture Data: Inferential Challenges
when there are Non-Overlapping Lists\" Chan, L, Silverman, B. W., Vincent, K
(2019) <@code{arXiv:1902.05156>}.  This package explicitly handles situations
where there are pairs of lists which have no observed individuals in common.  It
deals correctly with parameters whose estimated values can be considered as
being negative infinity.  It also addresses other possible issues of
non-existence and non-identifiability of maximum likelihood estimates.")
    (license license:gpl2+)))

(define-public r-sparsemodr
  (package
    (name "r-sparsemodr")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPARSEMODr" version))
       (sha256
        (base32 "0nshy1ppwsik7x1n0xp3wknm7fsl7c6y5523qnr41ijh18j165bq"))))
    (properties `((upstream-name . "SPARSEMODr")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-tidyverse
                             r-rcpp
                             r-lubridate
                             r-geosphere
                             r-future-apply
                             r-future
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/NAU-CCL/SPARSEMODr")
    (synopsis "SPAtial Resolution-SEnsitive Models of Outbreak Dynamics")
    (description
     "Implementation of spatially-explicit, stochastic disease models with
customizable time windows that describe how parameter values fluctuate during
outbreaks (e.g., in response to public health or conservation interventions).")
    (license license:gpl2+)))

(define-public r-sparsemdc
  (package
    (name "r-sparsemdc")
    (version "0.99.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SparseMDC" version))
       (sha256
        (base32 "0d3ashk3l9fjdbiyjhaxc05ayyscissrg7lwhnh46hv3fydbixlj"))))
    (properties `((upstream-name . "SparseMDC")))
    (build-system r-build-system)
    (propagated-inputs (list r-foreach r-dorng r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SparseMDC")
    (synopsis "Implementation of SparseMDC Algorithm")
    (description
     "This package implements the algorithm described in Barron, M., and Li, J. (Not
yet published).  This algorithm clusters samples from multiple ordered
populations, links the clusters across the conditions and identifies marker
genes for these changes.  The package was designed for @code{scRNA-Seq} data but
is also applicable to many other data types, just replace cells with samples and
genes with variables.  The package also contains functions for estimating the
parameters for @code{SparseMDC} as outlined in the paper.  We recommend that
users further select their marker genes using the magnitude of the cluster
centers.")
    (license license:gpl3)))

(define-public r-sparsematest
  (package
    (name "r-sparsematest")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparseMatEst" version))
       (sha256
        (base32 "06y061zl6id7cfw3cqzvwykzlijmala8hj61i04mgc491ngybimv"))))
    (properties `((upstream-name . "sparseMatEst")))
    (build-system r-build-system)
    (propagated-inputs (list r-glasso))
    (home-page "https://cran.r-project.org/package=sparseMatEst")
    (synopsis "Sparse Matrix Estimation and Inference")
    (description
     "The @code{sparseMatEst} package provides functions for estimating sparse
covariance and precision matrices with error control.  A false positive rate is
fixed corresponding to the probability of falsely including a matrix entry in
the support of the estimator.  It uses the binary search method outlined in
Kashlak and Kong (2019) <@code{arXiv:1705.02679>} and in Kashlak (2019)
<@code{arXiv:1903.10988>}.")
    (license license:gpl3)))

(define-public r-sparseltseigen
  (package
    (name "r-sparseltseigen")
    (version "0.2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparseLTSEigen" version))
       (sha256
        (base32 "08xhpm9hp9x9rmiys4fvh9y4z2xsdkzckzw3lq0vlzvjcaynvidq"))))
    (properties `((upstream-name . "sparseLTSEigen")))
    (build-system r-build-system)
    (propagated-inputs (list r-robusthd r-rcppeigen r-rcpp))
    (home-page "https://cran.r-project.org/package=sparseLTSEigen")
    (synopsis "RcppEigen back end for sparse least trimmed squares regression")
    (description
     "Use @code{RcppEigen} to fit least trimmed squares regression models with an L1
penalty in order to obtain sparse models.")
    (license license:gpl2+)))

(define-public r-sparselrmatrix
  (package
    (name "r-sparselrmatrix")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparseLRMatrix" version))
       (sha256
        (base32 "0b1qczhzbr67k5ayj7avmibyzvfg4jc67mb2m2lvifsf9wrqlqxb"))))
    (properties `((upstream-name . "sparseLRMatrix")))
    (build-system r-build-system)
    (propagated-inputs (list r-rspectra r-matrix))
    (home-page "https://rohelab.github.io/sparseLRMatrix/")
    (synopsis "Represent and Use Sparse + Low Rank Matrices")
    (description
     "This package provides an S4 class for representing and interacting with sparse
plus rank matrices.  At the moment the implementation is quite spare, but the
plan is eventually subclass Matrix objects.")
    (license license:expat)))

(define-public r-sparselpm
  (package
    (name "r-sparselpm")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SparseLPM" version))
       (sha256
        (base32 "1mpjf7yk2haa8hch315davb6nrhxhy0w2vgw2x4111rn4bskhd8m"))))
    (properties `((upstream-name . "SparseLPM")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan r-rcpparmadillo r-rcpp r-gtools))
    (home-page "https://cran.r-project.org/package=SparseLPM")
    (synopsis
     "The Sparse Latent Position Model for Nonnegative Interaction Data")
    (description
     "Models the nonnegative entries of a rectangular adjacency matrix using a sparse
latent position model, as illustrated in Rastelli, R. (2018) \"The Sparse Latent
Position Model for nonnegative weighted networks\" <@code{arXiv:1808.09262>}.")
    (license license:gpl3)))

(define-public r-sparseinv
  (package
    (name "r-sparseinv")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparseinv" version))
       (sha256
        (base32 "06inzix9m7b5g7c97lyvyl2g7fdr4iz60791hl2w9mah7nd6bfja"))))
    (properties `((upstream-name . "sparseinv")))
    (build-system r-build-system)
    (propagated-inputs (list r-spam r-rcpp r-matrix))
    (home-page "https://cran.r-project.org/package=sparseinv")
    (synopsis "Computation of the Sparse Inverse Subset")
    (description
     "This package creates a wrapper for the @code{SuiteSparse} routines that execute
the Takahashi equations.  These equations compute the elements of the inverse of
a sparse matrix at locations where the its Cholesky factor is structurally
non-zero.  The resulting matrix is known as a sparse inverse subset.  Some
helper functions are also implemented.  Support for spam matrices is currently
limited and will be implemented in the future.  See Rue and Martino (2007)
<doi:10.1016/j.jspi.2006.07.016> and Zammit-Mangion and Rougier (2018)
<doi:10.1016/j.csda.2018.02.001> for the application of these equations to
statistics.")
    (license (license:fsdg-compatible "GPL (>= 2.1)"))))

(define-public r-sparseindextracking
  (package
    (name "r-sparseindextracking")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparseIndexTracking" version))
       (sha256
        (base32 "0bgh29j22jm1yg2jp75xvv9jmqacb66vgv7ygf0xgnrwyrkxkm66"))))
    (properties `((upstream-name . "sparseIndexTracking")))
    (build-system r-build-system)
    (native-inputs (list r-rmarkdown r-r-rsp r-knitr))
    (home-page "https://CRAN.R-project.org/package=sparseIndexTracking")
    (synopsis "Design of Portfolio of Stocks to Track an Index")
    (description
     "Computation of sparse portfolios for financial index tracking, i.e., joint
selection of a subset of the assets that compose the index and computation of
their relative weights (capital allocation).  The level of sparsity of the
portfolios, i.e., the number of selected assets, is controlled through a
regularization parameter.  Different tracking measures are available, namely,
the empirical tracking error (ETE), downside risk (DR), Huber empirical tracking
error (HETE), and Huber downside risk (HDR).  See vignette for a detailed
documentation and comparison, with several illustrative examples.  The package
is based on the paper: K. Benidis, Y. Feng, and D. P. Palomar, \"Sparse
Portfolios for High-Dimensional Financial Index Tracking,\" IEEE Trans.  on
Signal Processing, vol.  66, no.  1, pp.  155-170, Jan.  2018.
<doi:10.1109/TSP.2017.2762286>.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-sparsehessianfd
  (package
    (name "r-sparsehessianfd")
    (version "0.3.3.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparseHessianFD" version))
       (sha256
        (base32 "1cdjdccbn9l64spgwrsf8vycy0g0bvm7v9y3wivam0drl7qc9df4"))))
    (properties `((upstream-name . "sparseHessianFD")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp r-matrix))
    (native-inputs (list r-knitr))
    (home-page "https://braunm.github.io/sparseHessianFD/")
    (synopsis "Numerical Estimation of Sparse Hessians")
    (description
     "Estimates Hessian of a scalar-valued function, and returns it in a sparse Matrix
format.  The sparsity pattern must be known in advance.  The algorithm is
especially efficient for hierarchical models with a large number of
heterogeneous units.  See Braun, M. (2017) <doi:10.18637/jss.v082.i10>.")
    (license (license:fsdg-compatible "MPL (== 2.0)"))))

(define-public r-sparsegrid
  (package
    (name "r-sparsegrid")
    (version "0.8.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SparseGrid" version))
       (sha256
        (base32 "057xbj2bhjm9i32kn39iscnqqdsvsmq0b8c92l8hnf9avf1sx10x"))))
    (properties `((upstream-name . "SparseGrid")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SparseGrid")
    (synopsis "Sparse grid integration in R")
    (description
     "@code{SparseGrid} is a package to create sparse grids for numerical integration,
based on code from www.sparse-grids.de.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-sparsegl
  (package
    (name "r-sparsegl")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparsegl" version))
       (sha256
        (base32 "1fp3csfs56nshw19yhr8nq77af9mbmpcncckifrx7spsl6ywl8w9"))))
    (properties `((upstream-name . "sparsegl")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rspectra
                             r-rlang
                             r-matrix
                             r-magrittr
                             r-ggplot2
                             r-dotcall64
                             r-cli))
    (native-inputs (list r-knitr gfortran))
    (home-page "https://github.com/dajmcdon/sparsegl")
    (synopsis "Sparse Group Lasso")
    (description
     "Efficient implementation of sparse group lasso with optional bound constraints
on the coefficients; see <doi:10.18637/jss.v110.i06>.  It supports the use of a
sparse design matrix as well as returning coefficient estimates in a sparse
matrix.  Furthermore, it correctly calculates the degrees of freedom to allow
for information criteria rather than cross-validation with very large data.
Finally, the interface to compiled code avoids unnecessary copies and allows for
the use of long integers.")
    (license license:expat)))

(define-public r-sparsefunclust
  (package
    (name "r-sparsefunclust")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SparseFunClust" version))
       (sha256
        (base32 "1p3spmgg7plkh7ppc80gqpnl8c29wfnaa89d5z71qij02lwrcxyg"))))
    (properties `((upstream-name . "SparseFunClust")))
    (build-system r-build-system)
    (propagated-inputs (list r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SparseFunClust")
    (synopsis "Sparse Functional Clustering")
    (description
     "This package provides a general framework for performing sparse functional
clustering as originally described in Floriello and Vitelli (2017)
<doi:10.1016/j.jmva.2016.10.008>, with the possibility of jointly handling data
misalignment (see Vitelli, 2019, <doi:10.48550/@code{arXiv.1912.00687>}).")
    (license license:gpl3+)))

(define-public r-sparseflmm
  (package
    (name "r-sparseflmm")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparseFLMM" version))
       (sha256
        (base32 "174lnhqig7m6fjfsj114ld059z9mjih3jzm5cq8iscxnhr9m11lr"))))
    (properties `((upstream-name . "sparseFLMM")))
    (build-system r-build-system)
    (propagated-inputs (list r-refund r-mgcv r-matrix r-mass r-data-table))
    (home-page "https://cran.r-project.org/package=sparseFLMM")
    (synopsis
     "Functional Linear Mixed Models for Irregularly or Sparsely Sampled Data")
    (description
     "Estimation of functional linear mixed models for irregularly or sparsely sampled
data based on functional principal component analysis.")
    (license license:gpl2)))

(define-public r-sparseeigen
  (package
    (name "r-sparseeigen")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparseEigen" version))
       (sha256
        (base32 "0vqcy61rarmp3liz8fr3vs3y40hkb3wfw89b5xsj1byaiap4772s"))))
    (properties `((upstream-name . "sparseEigen")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/dppalomar/sparseEigen")
    (synopsis "Computation of Sparse Eigenvectors of a Matrix")
    (description
     "Computation of sparse eigenvectors of a matrix (aka sparse PCA) with running
time 2-3 orders of magnitude lower than existing methods and better final
performance in terms of recovery of sparsity pattern and estimation of numerical
values.  Can handle covariance matrices as well as data matrices with real or
complex-valued entries.  Different levels of sparsity can be specified for each
individual ordered eigenvector and the method is robust in parameter selection.
See vignette for a detailed documentation and comparison, with several
illustrative examples.  The package is based on the paper: K. Benidis, Y. Sun,
P. Babu, and D. P. Palomar (2016). \"Orthogonal Sparse PCA and Covariance
Estimation via Procrustes Reformulation,\" IEEE Transactions on Signal Processing
<doi:10.1109/TSP.2016.2605073>.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-sparsediscrim
  (package
    (name "r-sparsediscrim")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparsediscrim" version))
       (sha256
        (base32 "1fzvki3az1y5bz6d7lygv3hjbzn17gq8g2s7xqmldlpq1v4dhjar"))))
    (properties `((upstream-name . "sparsediscrim")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-mvtnorm
                             r-ggplot2
                             r-dplyr
                             r-corpcor
                             r-bdsmatrix))
    (home-page "https://github.com/topepo/sparsediscrim")
    (synopsis "Sparse and Regularized Discriminant Analysis")
    (description
     "This package provides a collection of sparse and regularized discriminant
analysis methods intended for small-sample, high-dimensional data sets.  The
package features the High-Dimensional Regularized Discriminant Analysis
classifier from Ramey et al. (2017) <@code{arXiv:1602.01182>}.  Other
classifiers include those from Dudoit et al. (2002)
<doi:10.1198/016214502753479248>, Pang et al. (2009)
<doi:10.1111/j.1541-0420.2009.01200.x>, and Tong et al. (2012)
<doi:10.1093/bioinformatics/btr690>.")
    (license license:expat)))

(define-public r-sparsedfm
  (package
    (name "r-sparsedfm")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparseDFM" version))
       (sha256
        (base32 "0w4xn0hyk3l94b9nplbhlbzkjkh2fx4cyqyjjn5x6dpkf1wj9aaz"))))
    (properties `((upstream-name . "sparseDFM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-matrix r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=sparseDFM")
    (synopsis "Estimate Dynamic Factor Models with Sparse Loadings")
    (description
     "Implementation of various estimation methods for dynamic factor models (DFMs)
including principal components analysis (PCA) Stock and Watson (2002)
<doi:10.1198/016214502388618960>, 2Stage Giannone et al. (2008)
<doi:10.1016/j.jmoneco.2008.05.010>, expectation-maximisation (EM) Banbura and
Modugno (2014) <doi:10.1002/jae.2306>, and the novel EM-sparse approach for
sparse DFMs Mosley et al. (2023) <@code{arXiv:2303.11892>}.  Options to use
classic multivariate Kalman filter and smoother (KFS) equations from Shumway and
Stoffer (1982) <doi:10.1111/j.1467-9892.1982.tb00349.x> or fast univariate KFS
equations from Koopman and Durbin (2000) <doi:10.1111/1467-9892.00186>, and
options for independent and identically distributed (IID) white noise or
auto-regressive (AR(1)) idiosyncratic errors.  Algorithms coded in C++ and
linked to R via @code{RcppArmadillo}'.")
    (license license:gpl3+)))

(define-public r-sparsedc
  (package
    (name "r-sparsedc")
    (version "0.1.17")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SparseDC" version))
       (sha256
        (base32 "0gsfj8631s67a0r9qjjll4rbb57nzk5fwm5bbggvf0027b9hk0pp"))))
    (properties `((upstream-name . "SparseDC")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SparseDC")
    (synopsis "Implementation of SparseDC Algorithm")
    (description
     "This package implements the algorithm described in Barron, M., Zhang, S. and Li,
J. 2017, \"A sparse differential clustering algorithm for tracing cell type
changes via single-cell RNA-sequencing data\", Nucleic Acids Research, gkx1113,
<doi:10.1093/nar/gkx1113>.  This algorithm clusters samples from two different
populations, links the clusters across the conditions and identifies marker
genes for these changes.  The package was designed for @code{scRNA-Seq} data but
is also applicable to many other data types, just replace cells with samples and
genes with variables.  The package also contains functions for estimating the
parameters for @code{SparseDC} as outlined in the paper.  We recommend that
users further select their marker genes using the magnitude of the cluster
centers.")
    (license license:gpl3)))

(define-public r-sparsecov
  (package
    (name "r-sparsecov")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparseCov" version))
       (sha256
        (base32 "1zgqfjahlbbwimxcn7l0jn316xd2a65s8gy4pck0aksyfqh56kb1"))))
    (properties `((upstream-name . "sparseCov")))
    (build-system r-build-system)
    (propagated-inputs (list r-sparsemvn r-rfast r-mvnfast r-matrix))
    (home-page "https://github.com/chexjiang/sparseCov")
    (synopsis "Sparse Covariance Estimation Based on Thresholding")
    (description
     "This package provides a sparse covariance estimator based on different
thresholding operators.")
    (license license:gpl2+)))

(define-public r-sparsechol
  (package
    (name "r-sparsechol")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SparseChol" version))
       (sha256
        (base32 "1gva9665mlr8g14l7g6z591yhz2j6xpf56bhqhm6is3dbhl2ga24"))))
    (properties `((upstream-name . "SparseChol")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp r-matrix))
    (home-page "https://github.com/samuel-watson/SparseChol")
    (synopsis
     "Sparse Matrix C++ Classes Including Sparse Cholesky LDL Decomposition of Symmetric Matrices")
    (description
     "C++ classes for sparse matrix methods including implementation of sparse LDL
decomposition of symmetric matrices and solvers described by Timothy A. Davis
(2016) <https://fossies.org/linux/@code{SuiteSparse/LDL/Doc/ldl_userguide.pdf>}.
 Provides a set of C++ classes for basic sparse matrix specification and linear
algebra, and a class to implement sparse LDL decomposition and solvers.  See
<https://github.com/samuel-watson/@code{SparseChol>} for details.")
    (license license:gpl2+)))

(define-public r-sparsebiplots
  (package
    (name "r-sparsebiplots")
    (version "4.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SparseBiplots" version))
       (sha256
        (base32 "0jiyafhsddqa3llrp62lf8bgcvs3wp2pxc8ppaxx4zphkifw2yhr"))))
    (properties `((upstream-name . "SparseBiplots")))
    (build-system r-build-system)
    (propagated-inputs (list r-testthat
                             r-sparsepca
                             r-rlang
                             r-gtable
                             r-ggrepel
                             r-ggplot2))
    (home-page "https://github.com/mitzicubillamontilla/SparseBiplots")
    (synopsis
     "'HJ-Biplot' using Different Ways of Penalization Plotting with 'ggplot2'")
    (description
     "HJ-Biplot is a multivariate method that allow represent multivariate data on a
subspace of low dimension, in such a way that most of the variability of the
information is captured in a few dimensions.  This package implements three new
techniques and constructs in each case the HJ-Biplot', adapting restrictions to
reduce weights and / or produce zero weights in the dimensions, based on the
regularization theories.  It implements three methods of regularization: Ridge,
LASSO and Elastic Net.")
    (license license:gpl3+)))

(define-public r-sparsebc
  (package
    (name "r-sparsebc")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparseBC" version))
       (sha256
        (base32 "0a1siyi9kc805qji4alnw3c21spf4iw4wpsbfl50zvs52p8vl8w2"))))
    (properties `((upstream-name . "sparseBC")))
    (build-system r-build-system)
    (propagated-inputs (list r-glasso r-fields))
    (home-page "https://cran.r-project.org/package=sparseBC")
    (synopsis "Sparse Biclustering of Transposable Data")
    (description
     "This package implements the sparse biclustering proposal of Tan and Witten
(2014), Sparse biclustering of transposable data.  Journal of Computational and
Graphical Statistics 23(4):985-1008.")
    (license license:gpl2+)))

(define-public r-sparrpowr
  (package
    (name "r-sparrpowr")
    (version "0.2.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparrpowR" version))
       (sha256
        (base32 "0lzsm04m80l9h20xc1ifzw30fzbmg3xqspf1arzw5hk5081iid0q"))))
    (properties `((upstream-name . "sparrpowR")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-spatstat-random
                             r-spatstat-geom
                             r-sparr
                             r-lifecycle
                             r-iterators
                             r-future
                             r-foreach
                             r-fields
                             r-dorng
                             r-dofuture))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/machiela-lab/sparrpowR")
    (synopsis "Power Analysis to Detect Spatial Relative Risk Clusters")
    (description
     "Calculate the statistical power to detect clusters using kernel-based spatial
relative risk functions that are estimated using the sparr package.  Details
about the sparr package methods can be found in the tutorial: Davies et al.
(2018) <doi:10.1002/sim.7577>.  Details about kernel density estimation can be
found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>.  More information
about relative risk functions using kernel density estimation can be found in J.
F. Bithell (1991) <doi:10.1002/sim.4780101112>.")
    (license license:asl2.0)))

(define-public r-sparrafairness
  (package
    (name "r-sparrafairness")
    (version "0.0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPARRAfairness" version))
       (sha256
        (base32 "0nyixdddqq3cmzj00gm637dm64iwb63mgp34wvmway5cflqr8yck"))))
    (properties `((upstream-name . "SPARRAfairness")))
    (build-system r-build-system)
    (propagated-inputs (list r-ranger r-mvtnorm r-matrixstats r-cvauc))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SPARRAfairness")
    (synopsis
     "Analysis of Differential Behaviour of SPARRA Score Across Demographic Groups")
    (description
     "The SPARRA risk score (Scottish Patients At Risk of admission and Re-Admission)
estimates yearly risk of emergency hospital admission using electronic health
records on a monthly basis for most of the Scottish population.  This package
implements a suite of functions used to analyse the behaviour and performance of
the score, focusing particularly on differential performance over
demographically-defined groups.  It includes useful utility functions to plot
receiver-operator-characteristic, precision-recall and calibration curves, draw
stock human figures, estimate counterfactual quantities without the need to
re-compute risk scores, to simulate a semi-realistic dataset.")
    (license license:gpl3+)))

(define-public r-sparr
  (package
    (name "r-sparr")
    (version "2.3-15")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparr" version))
       (sha256
        (base32 "140gdk12dsl39k13g77iaqh0kzdi19ys3l00008fqn807mhcn2hz"))))
    (properties `((upstream-name . "sparr")))
    (build-system r-build-system)
    (propagated-inputs (list r-spatstat-utils
                             r-spatstat-univar
                             r-spatstat-random
                             r-spatstat-geom
                             r-spatstat-explore
                             r-spatstat
                             r-misc3d
                             r-foreach
                             r-doparallel))
    (home-page "https://tilmandavies.github.io/sparr/")
    (synopsis "Spatial and Spatiotemporal Relative Risk")
    (description
     "This package provides functions to estimate kernel-smoothed spatial and
spatio-temporal densities and relative risk functions, and perform subsequent
inference.  Methodological details can be found in the accompanying tutorial:
Davies et al. (2018) <DOI:10.1002/sim.7577>.")
    (license license:gpl2+)))

(define-public r-sparkxgb
  (package
    (name "r-sparkxgb")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparkxgb" version))
       (sha256
        (base32 "0rfj22ipp78nf6baa6rjzdphb6y7nrg07h5d3f2npdnirigihb3b"))))
    (properties `((upstream-name . "sparkxgb")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs r-sparklyr r-rlang r-magrittr r-fs))
    (home-page "https://cran.r-project.org/package=sparkxgb")
    (synopsis "Interface for 'XGBoost' on 'Apache Spark'")
    (description
     "This package provides a sparklyr <https://spark.posit.co/> extension that
provides an R interface for XGBoost <https://github.com/dmlc/xgboost> on Apache
Spark'.  XGBoost is an optimized distributed gradient boosting library.")
    (license license:asl2.0)))

(define-public r-sparkwarc
  (package
    (name "r-sparkwarc")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparkwarc" version))
       (sha256
        (base32 "0nzw6lgjrgqd1hj46jd52s8s3vdb7q2nc500jlgd0ydrr12zd123"))))
    (properties `((upstream-name . "sparkwarc")))
    (build-system r-build-system)
    (inputs (list zlib))
    (propagated-inputs (list r-sparklyr r-rcpp r-dbi))
    (home-page "https://cran.r-project.org/package=sparkwarc")
    (synopsis "Load WARC Files into Apache Spark")
    (description
     "Load WARC (Web ARChive) files into Apache Spark using sparklyr'.  This allows to
read files from the Common Crawl project <http://commoncrawl.org/>.")
    (license license:asl2.0)))

(define-public r-sparktf
  (package
    (name "r-sparktf")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparktf" version))
       (sha256
        (base32 "0jd018n7553q0ldv6whxbj2s38myig0m8zq4fbp8fsv34q5wnsx4"))))
    (properties `((upstream-name . "sparktf")))
    (build-system r-build-system)
    (inputs (list tensorflow))
    (propagated-inputs (list r-sparklyr))
    (home-page "https://cran.r-project.org/package=sparktf")
    (synopsis
     "Interface for 'TensorFlow' 'TFRecord' Files with 'Apache Spark'")
    (description
     "This package provides a sparklyr extension that enables reading and writing
@code{TensorFlow} TFRecord files via Apache Spark'.")
    (license license:asl2.0)))

(define-public r-sparktex
  (package
    (name "r-sparktex")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparktex" version))
       (sha256
        (base32 "0r6jnn9fj166pdhnjbsaqmfmnkq0qr1cjprihlnln9jad05mrkjx"))))
    (properties `((upstream-name . "sparktex")))
    (build-system r-build-system)
    (home-page "http://thomasleeper.com/software.html")
    (synopsis "Generate LaTeX sparklines in R")
    (description
     "Generate syntax for use with the sparklines package for @code{LaTeX}.")
    (license license:gpl2)))

(define-public r-sparklyr-nested
  (package
    (name "r-sparklyr-nested")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparklyr.nested" version))
       (sha256
        (base32 "185cq40958fgwrng14ifil1vwrni5nn6haxfknzarhn805hfkv7c"))))
    (properties `((upstream-name . "sparklyr.nested")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-sparklyr
                             r-rlang
                             r-purrr
                             r-listviewer
                             r-jsonlite
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=sparklyr.nested")
    (synopsis "'sparklyr' Extension for Nested Data")
    (description
     "This package provides a sparklyr extension adding the capability to work easily
with nested data.")
    (license (list license:asl2.0
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-sparklyr-flint
  (package
    (name "r-sparklyr-flint")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparklyr.flint" version))
       (sha256
        (base32 "0sszblvgxi8l8wlic5nrnm4pc9m0byyvayrja2476vnz0pqliv69"))))
    (properties `((upstream-name . "sparklyr.flint")))
    (build-system r-build-system)
    (propagated-inputs (list r-sparklyr r-rlang r-dplyr r-dbplyr))
    (native-inputs (list r-knitr))
    (home-page "<https://github.com/r-spark/sparklyr.flint>")
    (synopsis "Sparklyr Extension for 'Flint'")
    (description
     "This sparklyr extension makes Flint time series library functionalities
(<https://github.com/twosigma/flint>) easily accessible through R.")
    (license license:asl2.0)))

(define-public r-sparklyr
  (package
    (name "r-sparklyr")
    (version "1.8.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparklyr" version))
       (sha256
        (base32 "1yp3hwi1rhxx8sdhibcnjxsz1sd5jhfxn6cy9bphkwadrd19pn63"))))
    (properties `((upstream-name . "sparklyr")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-withr
                             r-vctrs
                             r-uuid
                             r-tidyselect
                             r-tidyr
                             r-rstudioapi
                             r-rlang
                             r-purrr
                             r-openssl
                             r-jsonlite
                             r-httr
                             r-glue
                             r-globals
                             r-generics
                             r-dplyr
                             r-dbplyr
                             r-dbi
                             r-config))
    (home-page "https://spark.posit.co/")
    (synopsis "R Interface to Apache Spark")
    (description
     "R interface to Apache Spark, a fast and general engine for big data processing,
see <https://spark.apache.org/>.  This package supports connecting to local and
remote Apache Spark clusters, provides a dplyr compatible back-end, and provides
an interface to Spark's built-in machine learning algorithms.")
    (license (list license:asl2.0
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-sparkline
  (package
    (name "r-sparkline")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparkline" version))
       (sha256
        (base32 "0lrr1lm7603di7x3mf53cp13d9ssjh9gmb43wa6z9yqapis2djm2"))))
    (properties `((upstream-name . "sparkline")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-htmlwidgets r-htmltools))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://cran.r-project.org/package=sparkline")
    (synopsis "'jQuery' Sparkline 'htmlwidget'")
    (description
     "Include interactive sparkline charts <http://omnipotent.net/jquery.sparkline> in
all R contexts with the convenience of htmlwidgets'.")
    (license license:expat)))

(define-public r-sparkhail
  (package
    (name "r-sparkhail")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparkhail" version))
       (sha256
        (base32 "1a9p9i7fs6l9rx5ha6akjjl7qhfak01xsjg28ysv0imikmwp6hy7"))))
    (properties `((upstream-name . "sparkhail")))
    (build-system r-build-system)
    (propagated-inputs (list r-sparklyr-nested r-sparklyr r-dplyr))
    (home-page "https://cran.r-project.org/package=sparkhail")
    (synopsis "'Sparklyr' Extension for 'Hail'")
    (description
     "Hail is an open-source, general-purpose, python based data analysis tool with
additional data types and methods for working with genomic data, see
<https://hail.is/>.  Hail is built to scale and has first-class support for
multi-dimensional structured data, like the genomic data in a genome-wide
association study (GWAS).  Hail is exposed as a python library, using primitives
for distributed queries and linear algebra implemented in scala', spark', and
increasingly C++'.  The sparkhail is an R extension using sparklyr package.  The
idea is to help R users to use hail functionalities with the well-know tidyverse
syntax, see <https://www.tidyverse.org/>.")
    (license (list license:asl2.0
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-sparkbq
  (package
    (name "r-sparkbq")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparkbq" version))
       (sha256
        (base32 "0gccm8q5a45k9zm8f90r7fhcmriq8d038p022k7cikbr6rqsrmyh"))))
    (properties `((upstream-name . "sparkbq")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-sparklyr))
    (home-page "http://www.mirai-solutions.com")
    (synopsis "Google 'BigQuery' Support for 'sparklyr'")
    (description
     "This package provides a sparklyr extension package providing an integration with
Google @code{BigQuery}'.  It supports direct import/export where records are
directly streamed from/to @code{BigQuery}'.  In addition, data may be
imported/exported via intermediate data extracts on Google Cloud Storage'.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-sparkavro
  (package
    (name "r-sparkavro")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparkavro" version))
       (sha256
        (base32 "0g6ac9msfbx8f8j8rj0hvlxjsn317zyam207q7i8kf0hshs1r58f"))))
    (properties `((upstream-name . "sparkavro")))
    (build-system r-build-system)
    (propagated-inputs (list r-sparklyr r-dplyr r-dbi))
    (home-page "https://cran.r-project.org/package=sparkavro")
    (synopsis "Load Avro file into 'Apache Spark'")
    (description
     "Load Avro Files into Apache Spark using sparklyr'.  This allows to read files
from Apache Avro <https://avro.apache.org/>.")
    (license (list license:asl2.0
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-spark-sas7bdat
  (package
    (name "r-spark-sas7bdat")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spark.sas7bdat" version))
       (sha256
        (base32 "1s1hd6hvjxnfb3w10v93pggijmm2h4rcsf7ckl34j2sw9bvv7zka"))))
    (properties `((upstream-name . "spark.sas7bdat")))
    (build-system r-build-system)
    (propagated-inputs (list r-sparklyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bnosac/spark.sas7bdat")
    (synopsis "Read in 'SAS' Data ('.sas7bdat' Files) into 'Apache Spark'")
    (description
     "Read in SAS Data ('.sas7bdat Files) into Apache Spark from R. Apache Spark is an
open source cluster computing framework available at <http://spark.apache.org>.
This R package uses the spark-sas7bdat Spark package
(<https://spark-packages.org/package/saurfang/spark-sas7bdat>) to import and
process SAS data in parallel using Spark'.  Hereby allowing to execute dplyr
statements in parallel on top of SAS data.")
    (license license:gpl3)))

(define-public r-sparcl
  (package
    (name "r-sparcl")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sparcl" version))
       (sha256
        (base32 "138krflvfdlx4hlh90mswds51i8aw2vv00gm5lgkfsvxj4w324jg"))))
    (properties `((upstream-name . "sparcl")))
    (build-system r-build-system)
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=sparcl")
    (synopsis
     "Perform Sparse Hierarchical Clustering and Sparse K-Means Clustering")
    (description
     "This package implements the sparse clustering methods of Witten and Tibshirani
(2010): \"A framework for feature selection in clustering\"; published in Journal
of the American Statistical Association 105(490): 713-726.")
    (license license:gpl2)))

(define-public r-spant
  (package
    (name "r-spant")
    (version "2.23.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spant" version))
       (sha256
        (base32 "0z0wlw937c1a1m3wc36gzhnzyl84344wij0rsyjgjvh7haax2biy"))))
    (properties `((upstream-name . "spant")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-signal
                             r-rniftyreg
                             r-rnifti
                             r-ptw
                             r-pracma
                             r-plyr
                             r-numderiv
                             r-nloptr
                             r-mmand
                             r-minpack-lm
                             r-jsonlite
                             r-irlba
                             r-fields
                             r-expm
                             r-abind))
    (native-inputs (list r-knitr gfortran))
    (home-page "https://martin3141.github.io/spant/")
    (synopsis "MR Spectroscopy Analysis Tools")
    (description
     "This package provides tools for reading, visualising and processing Magnetic
Resonance Spectroscopy data.  The package includes methods for spectral fitting:
Wilson (2021) <DOI:10.1002/mrm.28385> and spectral alignment: Wilson (2018)
<DOI:10.1002/mrm.27605>.")
    (license license:gpl3)))

(define-public r-spanova
  (package
    (name "r-spanova")
    (version "0.99.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spANOVA" version))
       (sha256
        (base32 "0y1kqqfrl9rrskyvmzdjw9zyjgyqmz8b3cfgbba49n41wx2aiw79"))))
    (properties `((upstream-name . "spANOVA")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-spdep
                             r-spatialreg
                             r-shinythemes
                             r-shinycssloaders
                             r-shinybs
                             r-shiny
                             r-scottknott
                             r-rmarkdown
                             r-mvtnorm
                             r-multcompview
                             r-multcomp
                             r-matrix
                             r-mass
                             r-knitr
                             r-gtools
                             r-geor
                             r-dt
                             r-car
                             r-ape))
    (home-page "https://cran.r-project.org/package=spANOVA")
    (synopsis
     "Analysis of Field Trials with Geostatistics & Spatial AR Models")
    (description
     "Perform analysis of variance when the experimental units are spatially
correlated.  There are two methods to deal with spatial dependence: Spatial
autoregressive models (see Rossoni, D. F., & Lima, R. R. (2019)
<doi:10.28951/rbb.v37i2.388>) and geostatistics (see Pontes, J. M., & Oliveira,
M. S. D. (2004) <doi:10.1590/S1413-70542004000100018>).  For both methods, there
are three multicomparison procedure available: Tukey, multivariate T, and
Scott-Knott.")
    (license license:gpl3)))

(define-public r-spanish
  (package
    (name "r-spanish")
    (version "0.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spanish" version))
       (sha256
        (base32 "15drc11lh0qxpww50p0smi8qxfbln82bx0yd74jqf71irhk5jxyp"))))
    (properties `((upstream-name . "spanish")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-magrittr))
    (home-page "https://ropenspain.github.io/spanish/")
    (synopsis
     "Translate Quantities from Strings to Integer and Back. Misc Functions on Spanish Data")
    (description
     "Character vector to numerical translation in Euros from Spanish spelled monetary
quantities.  Reverse translation from integer to Spanish.  Upper limit is up to
the millions range.  Geocoding via Cadastral web site.")
    (license license:gpl3)))

(define-public r-spamm
  (package
    (name "r-spamm")
    (version "4.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spaMM" version))
       (sha256
        (base32 "1nr44jg73gnbc3q1ip7zmzl1n5bggbcgjrdlh6fg81zp41ba3ydv"))))
    (properties `((upstream-name . "spaMM")))
    (build-system r-build-system)
    (propagated-inputs (list r-roi
                             r-rcppeigen
                             r-rcpp
                             r-proxy
                             r-pbapply
                             r-numderiv
                             r-nloptr
                             r-nlme
                             r-minqa
                             r-matrix
                             r-mass
                             r-gmp
                             r-geometry
                             r-crayon
                             r-boot
                             r-backports))
    (home-page "https://www.r-project.org")
    (synopsis "Mixed-Effect Models, with or without Spatial Random Effects")
    (description
     "Inference based on models with or without spatially-correlated random effects,
multivariate responses, or non-Gaussian random effects (e.g., Beta).  Variation
in residual variance (heteroscedasticity) can itself be represented by a
mixed-effect model.  Both classical geostatistical models (Rousset and Ferdy
2014 <doi:10.1111/ecog.00566>), and Markov random field models on irregular
grids (as considered in the INLA package, <https://www.r-inla.org>), can be
fitted, with distinct computational procedures exploiting the sparse matrix
representations for the latter case and other autoregressive models.  Laplace
approximations are used for likelihood or restricted likelihood.  Penalized
quasi-likelihood and other variants discussed in the h-likelihood literature
(Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) are also implemented.")
    (license (license:fsdg-compatible "CeCILL-2"))))

(define-public r-spam64
  (package
    (name "r-spam64")
    (version "2.10-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spam64" version))
       (sha256
        (base32 "0kb7jz12xb0i45b70x3p785md01s7wbwl8lgisq4i83jvlz016ln"))))
    (properties `((upstream-name . "spam64")))
    (build-system r-build-system)
    (native-inputs (list gfortran))
    (home-page "https://git.math.uzh.ch/reinhard.furrer/spam")
    (synopsis "64-Bit Extension of the SPArse Matrix R Package 'spam'")
    (description
     "This package provides the Fortran code of the R package spam with 64-bit
integers.  Loading this package together with the R package spam enables the
sparse matrix class spam to handle huge sparse matrices with more than 2^31-1
non-zero elements.  Documentation is provided in Gerber, Moesinger and Furrer
(2017) <doi:10.1016/j.cageo.2016.11.015>.")
    (license (list license:lgpl2.0 license:bsd-3))))

(define-public r-spagmix
  (package
    (name "r-spagmix")
    (version "0.4-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spagmix" version))
       (sha256
        (base32 "0s3y0xapqn67hjl42g9w6jkcip5x5fbv8fyq2angkvqahps1nrg4"))))
    (properties `((upstream-name . "spagmix")))
    (build-system r-build-system)
    (propagated-inputs (list r-spatstat-random
                             r-spatstat-geom
                             r-spatstat
                             r-sparr
                             r-mvtnorm
                             r-abind))
    (home-page "https://cran.r-project.org/package=spagmix")
    (synopsis
     "Artificial Spatial and Spatio-Temporal Densities on Bounded Windows")
    (description
     "Simple utilities to design and generate density functions on bounded regions in
space and space-time, and simulate independent, identically distributed data
therefrom.  See Davies & Lawson (2019) <doi:10.1080/00949655.2019.1575066> for
example.")
    (license license:gpl2+)))

(define-public r-spaero
  (package
    (name "r-spaero")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spaero" version))
       (sha256
        (base32 "0xigwi6w21y2fwicml0s2gylwnwp2qvrqskr0v8rd3bn01hg9ja3"))))
    (properties `((upstream-name . "spaero")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=spaero")
    (synopsis "Software for Project AERO")
    (description
     "This package implements methods for anticipating the emergence and eradication
of infectious diseases from surveillance time series.  Also provides support for
computational experiments testing the performance of such methods.")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-spades-tools
  (package
    (name "r-spades-tools")
    (version "2.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpaDES.tools" version))
       (sha256
        (base32 "1kxjwxjr8ncjygj94wpp8z2q58hrb0fyhnz3ajp1jlgpdz3jripi"))))
    (properties `((upstream-name . "SpaDES.tools")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-reproducible
                             r-rcpp
                             r-fpcompare
                             r-data-table
                             r-checkmate
                             r-backports))
    (home-page "https://spades-tools.predictiveecology.org")
    (synopsis
     "Additional Tools for Developing Spatially Explicit Discrete Event Simulation (SpaDES) Models")
    (description
     "This package provides GIS and map utilities, plus additional modeling tools for
developing cellular automata, dynamic raster models, and agent based models in
@code{SpaDES}'.  Included are various methods for spatial spreading, spatial
agents, GIS operations, random map generation, and others.  See
?@code{SpaDES.tools} for an categorized overview of these additional tools.  The
suggested package NLMR can be installed from the following repository:
(<https://@code{PredictiveEcology.r-universe.dev>}).")
    (license license:gpl3)))

(define-public r-spades-core
  (package
    (name "r-spades-core")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpaDES.core" version))
       (sha256
        (base32 "0avn2fvabwq8hfvpwkvrpmdq9692a8nx3km7343j217y7l4s7qdf"))))
    (properties `((upstream-name . "SpaDES.core")))
    (build-system r-build-system)
    (propagated-inputs (list r-whisker
                             r-terra
                             r-require
                             r-reproducible
                             r-quickplot
                             r-qs
                             r-lobstr
                             r-igraph
                             r-fs
                             r-data-table
                             r-cli))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://spades-core.predictiveecology.org/")
    (synopsis
     "Core Utilities for Developing and Running Spatially Explicit Discrete Event Models")
    (description
     "This package provides the core framework for a discrete event system to
implement a complete data-to-decisions, reproducible workflow.  The core
components facilitate the development of modular pieces, and enable the user to
include additional functionality by running user-built modules.  Includes
conditional scheduling, restart after interruption, packaging of reusable
modules, tools for developing arbitrary automated workflows, automated
interweaving of modules of different temporal resolution, and tools for
visualizing and understanding the within-project dependencies.  The suggested
package NLMR can be installed from the repository
(<https://@code{PredictiveEcology.r-universe.dev>}).")
    (license license:gpl3)))

(define-public r-spades
  (package
    (name "r-spades")
    (version "2.0.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpaDES" version))
       (sha256
        (base32 "0clanr9cwyjhpjv5wsfhm34rmpf5gjh1hq0pzh6p1yp63wnj13m8"))))
    (properties `((upstream-name . "SpaDES")))
    (build-system r-build-system)
    (propagated-inputs (list r-spades-tools r-spades-core r-reproducible
                             r-quickplot))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://spades.predictiveecology.org")
    (synopsis
     "Develop and Run Spatially Explicit Discrete Event Simulation Models")
    (description
     "Metapackage for implementing a variety of event-based models, with a focus on
spatially explicit models.  These include raster-based, event-based, and
agent-based models.  The core simulation components (provided by
@code{SpaDES.core}') are built upon a discrete event simulation (DES; see
Matloff (2011) ch 7.8.3 <https://nostarch.com/artofr.htm>) framework that
facilitates modularity, and easily enables the user to include additional
functionality by running user-built simulation modules (see also
@code{SpaDES.tools}').  Included are numerous tools to visualize rasters and
other maps (via @code{quickPlot}'), and caching methods for reproducible
simulations (via reproducible').  Tools for running simulation experiments are
provided by @code{SpaDES.experiment}'.  Additional functionality is provided by
the @code{SpaDES.addins} and @code{SpaDES.shiny} packages.")
    (license license:gpl3)))

(define-public r-spader
  (package
    (name "r-spader")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpadeR" version))
       (sha256
        (base32 "0iy2rkq4vvps1a73kqq37zpsyl4pvl3vh07dwvpfhvp7f8nxbx99"))))
    (properties `((upstream-name . "SpadeR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SpadeR")
    (synopsis "Species-Richness Prediction and Diversity Estimation with R")
    (description
     "Estimation of various biodiversity indices and related (dis)similarity measures
based on individual-based (abundance) data or sampling-unit-based (incidence)
data taken from one or multiple communities/assemblages.")
    (license license:gpl3+)))

(define-public r-spaddins
  (package
    (name "r-spaddins")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spAddins" version))
       (sha256
        (base32 "19wbcghcf0f9qvqy92jhsdcpmd9b4lkhxndvr71cyhzga539lh1n"))))
    (properties `((upstream-name . "spAddins")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rstudioapi r-purrr r-magrittr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/GegznaV/spAddins")
    (synopsis "Set of RStudio Addins")
    (description
     "This package provides a set of RStudio addins that are designed to be used in
combination with user-defined RStudio keyboard shortcuts.  These addins either:
1) insert text at a cursor position (e.g. insert operators %>%, <<-, %$%, etc.),
2) replace symbols in selected pieces of text (e.g., convert backslashes to
forward slashes which results in stings like \"c:\\data\\\" converted into
\"c:/data/\") or 3) enclose text with special symbols (e.g., converts \"bold\" into
\"**bold**\") which is convenient for editing R Markdown files.")
    (license license:expat)))

(define-public r-spadar
  (package
    (name "r-spadar")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SPADAR" version))
       (sha256
        (base32 "0xzhcy5nglwx9j2jkm3i9xj1iw1y9wbf6rwfjyhpiwwq6qn8mn5r"))))
    (properties `((upstream-name . "SPADAR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rceim r-mapproj))
    (home-page "https://cran.r-project.org/package=SPADAR")
    (synopsis "Spherical Projections of Astronomical Data")
    (description
     "This package provides easy to use functions to create all-sky grid plots of
widely used astronomical coordinate systems (equatorial, ecliptic, galactic) and
scatter plots of data on any of these systems including on-the-fly system
conversion.  It supports any type of spherical projection to the plane defined
by the mapproj package.")
    (license license:gpl3+)))

(define-public r-spacoap
  (package
    (name "r-spacoap")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpaCOAP" version))
       (sha256
        (base32 "1cj5avfsfzlb9jbyckpb2zjqkx9ny1msxcdcrcz7kr5njswyw55a"))))
    (properties `((upstream-name . "SpaCOAP")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-matrix
                             r-mass
                             r-laplacesdemon
                             r-irlba))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/feiyoung/SpaCOAP")
    (synopsis
     "High-Dimensional Spatial Covariate-Augmented Overdispersed Poisson Factor Model")
    (description
     "This package provides a spatial covariate-augmented overdispersed Poisson factor
model is proposed to perform efficient latent representation learning method for
high-dimensional large-scale spatial count data with additional covariates.")
    (license license:gpl3)))

(define-public r-spacetimebss
  (package
    (name "r-spacetimebss")
    (version "0.4-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpaceTimeBSS" version))
       (sha256
        (base32 "04gh8qcv949s9h94dydmj2bnipih8wnv6ml7iczqmjqich1i272m"))))
    (properties `((upstream-name . "SpaceTimeBSS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-matrix r-jade))
    (home-page "https://cran.r-project.org/package=SpaceTimeBSS")
    (synopsis "Blind Source Separation for Multivariate Spatio-Temporal Data")
    (description
     "Simultaneous/joint diagonalization of local autocovariance matrices to estimate
spatio-temporally uncorrelated random fields.")
    (license license:gpl2+)))

(define-public r-spacesxyz
  (package
    (name "r-spacesxyz")
    (version "1.3-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spacesXYZ" version))
       (sha256
        (base32 "10pmmfskgj1gfwfk5s44wdfk81l7kmga8xsirz49qnwy6xs148pv"))))
    (properties `((upstream-name . "spacesXYZ")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=spacesXYZ")
    (synopsis "CIE XYZ and some of Its Derived Color Spaces")
    (description
     "This package provides functions for converting among CIE XYZ, @code{xyY}, Lab,
and Luv.  Calculate Correlated Color Temperature (CCT) and the Planckian and
daylight loci.  The XYZs of some standard illuminants and some standard linear
chromatic adaptation transforms (CATs) are included.  Three standard color
difference metrics are included.")
    (license license:gpl3+)))

(define-public r-spacesrgb
  (package
    (name "r-spacesrgb")
    (version "1.5-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spacesRGB" version))
       (sha256
        (base32 "1v4az4qn965p0xds10ff7f4565j5n9pqc44501l6fi5j7h819hsc"))))
    (properties `((upstream-name . "spacesRGB")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=spacesRGB")
    (synopsis
     "Standard and User-Defined RGB Color Spaces, with Conversion Between RGB and CIE XYZ")
    (description
     "Standard RGB spaces included are @code{sRGB}, Adobe RGB, @code{ProPhoto} RGB,
BT.709, and others.  User-defined RGB spaces are also possible.  There is
partial support for ACES Color workflows.")
    (license license:gpl3+)))

(define-public r-spacenet
  (package
    (name "r-spacenet")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spaceNet" version))
       (sha256
        (base32 "1s2672icxs0qvdhl52xhllgvh1i9mjfrn5j3icyw6h61ljbnhy11"))))
    (properties `((upstream-name . "spaceNet")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-sna
                             r-rfast
                             r-rcpptn
                             r-permute
                             r-mclust
                             r-mass))
    (home-page "https://cran.r-project.org/package=spaceNet")
    (synopsis "Latent Space Models for Multidimensional Networks")
    (description
     "Latent space models for multivariate networks (multiplex) estimated via MCMC
algorithm.  See D Angelo et al. (2018) <@code{arXiv:1803.07166>} and D Angelo et
al. (2018) <@code{arXiv:1807.03874>}.")
    (license license:gpl2+)))

(define-public r-spacejamr
  (package
    (name "r-spacejamr")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spacejamr" version))
       (sha256
        (base32 "05rprsgnihhz3xhh7p9mkn4scpi318kvrkkrrw9j645d6bwfms5l"))))
    (properties `((upstream-name . "spacejamr")))
    (build-system r-build-system)
    (propagated-inputs (list r-spatstat-random
                             r-spatstat-geom
                             r-sf
                             r-magrittr
                             r-igraph
                             r-ggthemes
                             r-ggraph
                             r-ggplot2
                             r-dplyr
                             r-crsuggest))
    (home-page "https://github.com/dscolby/spacejamr")
    (synopsis "Simulate Spatial Bernoulli Networks")
    (description
     "Social network analysis is becoming commonplace in many social science
disciplines, but access to useful network data, especially among marginalized
populations, still remains a formidable challenge.  This package mitigates that
problem by providing tools to simulate spatial Bernoulli networks as proposed in
Carter T. Butts (2002, ISBN:978-0-493-72676-2), \"Spatial models of large-scale
interpersonal networks.\" Using this package, network analysts can simulate a
spatial point process or sequence with a given number of nodes inside a
geographical boundary and estimate the probability of a tie formation between
all node pairs.  When simulating a network, an analyst can choose between five
spatial interaction functions.  The package also enables quick comparison of
summary statistics for simulated networks and provides simple to use plotting
methods for its classes that return plots which can be further refined with the
ggplot2 package.")
    (license license:expat)))

(define-public r-spacci
  (package
    (name "r-spacci")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SpaCCI" version))
       (sha256
        (base32 "06crifqyjvnywqx7x9f3iazq3ilp2d756wjqmmavjykbzfhbi98j"))))
    (properties `((upstream-name . "SpaCCI")))
    (build-system r-build-system)
    (propagated-inputs (list r-seurat
                             r-reshape2
                             r-rcpparmadillo
                             r-rcpp
                             r-pheatmap
                             r-patchwork
                             r-nnls
                             r-matrix
                             r-ggrepel
                             r-ggplot2
                             r-dplyr
                             r-circlize))
    (home-page "https://cran.r-project.org/package=SpaCCI")
    (synopsis "Spatially Aware Cell-Cell Interaction Analysis")
    (description
     "This package provides tools for analyzing spatial cell-cell interactions based
on ligand-receptor pairs, including functions for local, regional, and global
analysis using spatial transcriptomics data.  Integrates with databases like
@code{CellChat} <http://www.cellchat.org/>, @code{CellPhoneDB}
<https://www.cellphonedb.org/>, Cellinker
<https://www.rna-society.org/cellinker/>, ICELLNET
<https://github.com/soumelis-lab/ICELLNET>, and @code{ConnectomeDB}
<https://humanconnectome.org/software/connectomedb/> to identify ligand-receptor
pairs, visualize interactions through heatmaps, chord diagrams, and infer
interactions on different spatial scales.")
    (license license:gpl2+)))

(define-public r-spabundance
  (package
    (name "r-spabundance")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spAbundance" version))
       (sha256
        (base32 "1705pq18bnycx06inbr29z7gcmn1381l44x56ncrmbdigj0i0lsk"))))
    (properties `((upstream-name . "spAbundance")))
    (build-system r-build-system)
    (propagated-inputs (list r-rann
                             r-lme4
                             r-foreach
                             r-doparallel
                             r-coda
                             r-abind))
    (home-page "https://cran.r-project.org/package=spAbundance")
    (synopsis
     "Univariate and Multivariate Spatial Modeling of Species Abundance")
    (description
     "Fits single-species (univariate) and multi-species (multivariate) non-spatial
and spatial abundance models in a Bayesian framework using Markov Chain Monte
Carlo (MCMC).  Spatial models are fit using Nearest Neighbor Gaussian Processes
(NNGPs).  Details on NNGP models are given in Datta, Banerjee, Finley, and
Gelfand (2016) <doi:10.1080/01621459.2015.1044091> and Finley, Datta, and
Banerjee (2022) <doi:10.18637/jss.v103.i05>.  Fits single-species and
multi-species spatial and non-spatial versions of generalized linear mixed
models (Gaussian, Poisson, Negative Binomial), N-mixture models (Royle 2004
<doi:10.1111/j.0006-341X.2004.00142.x>) and hierarchical distance sampling
models (Royle, Dawson, Bates (2004) <doi:10.1890/03-3127>).  Multi-species
spatial models are fit using a spatial factor modeling approach with NNGPs for
computational efficiency.")
    (license license:gpl3+)))

(define-public r-spaa
  (package
    (name "r-spaa")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "spaa" version))
       (sha256
        (base32 "163iipz1knxx1lzby9a3n1f014yqkf25z1wpwwy4gbx7sia499d5"))))
    (properties `((upstream-name . "spaa")))
    (build-system r-build-system)
    (home-page "https://github.com/helixcn/spaa")
    (synopsis "SPecies Association Analysis")
    (description
     "Miscellaneous functions for analysing species association and niche overlap.")
    (license license:gpl2)))

(define-public r-sp23design
  (package
    (name "r-sp23design")
    (version "0.9-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sp23design" version))
       (sha256
        (base32 "1ivsds210rpxj52labc9mf2ncm3vji983w0kj757jl1b308ql30s"))))
    (properties `((upstream-name . "sp23design")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mvtnorm))
    (home-page "https://cran.r-project.org/package=sp23design")
    (synopsis "Design and Simulation of Seamless Phase II-III Clinical Trials")
    (description
     "This package provides methods for generating, exploring and executing seamless
Phase II-III designs of Lai, Lavori and Shih using generalized likelihood ratio
statistics.  Includes pdf and source files that describe the entire R
implementation with the relevant mathematical details.")
    (license license:lgpl3)))

(define-public r-sp2000
  (package
    (name "r-sp2000")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SP2000" version))
       (sha256
        (base32 "01wrc7p36js20jv04zhphj2vq7qqfgxmrzic066wij0c0mnh0xn5"))))
    (properties `((upstream-name . "SP2000")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-xml
                             r-urltools
                             r-tibble
                             r-rlist
                             r-purrr
                             r-pbmcapply
                             r-jsonlite
                             r-dt))
    (home-page "https://otoliths.github.io/SP2000/")
    (synopsis "Catalogue of Life Toolkit")
    (description
     "This package provides a programmatic interface to <http://sp2000.org.cn>,
re-written based on an accompanying Species 2000 API. Access tables describing
catalogue of the Chinese known species of animals, plants, fungi,
micro-organisms, and more.  This package also supports access to catalogue of
life global <http://catalogueoflife.org>, China animal scientific database
<http://zoology.especies.cn> and catalogue of life Taiwan
<https://taibnet.sinica.edu.tw/home_eng.php>.  The development of SP2000 package
were supported by Biodiversity Survey and Assessment Project of the Ministry of
Ecology and Environment, China <2019HJ2096001006>,Yunnan University's \"Double
First Class\" Project <C176240405> and Yunnan University's Research Innovation
Fund for Graduate Students <2019227>.")
    (license license:artistic2.0)))

(define-public r-soyurt
  (package
    (name "r-soyurt")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SoyURT" version))
       (sha256
        (base32 "0l710ijqmqvpcsxaqia1g1bm5317qp1cc2nsf5ccz1vmgqxrxn1g"))))
    (properties `((upstream-name . "SoyURT")))
    (build-system r-build-system)
    (home-page "https://github.com/mdkrause/soyurt")
    (synopsis "USDA Northern Region Uniform Soybean Tests Dataset")
    (description
     "Data sets used by Krause et al. (2022) <doi:10.1101/2022.04.11.487885>.  It
comprises phenotypic records obtained from the USDA Northern Region Uniform
Soybean Tests from 1989 to 2019 for maturity groups II and III. In addition,
soil and weather variables are provided for the 591 observed environments
(combination of locations and years).")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-soynam
  (package
    (name "r-soynam")
    (version "1.6.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SoyNAM" version))
       (sha256
        (base32 "1ajsk51bd9aajlyv9q6yjmdm9xs9jf07j5m189m00mhl21xpzn0b"))))
    (properties `((upstream-name . "SoyNAM")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-rcppeigen r-rcpp r-nam r-lme4))
    (home-page "https://cran.r-project.org/package=SoyNAM")
    (synopsis "Soybean Nested Association Mapping Dataset")
    (description
     "Genomic and multi-environmental soybean data.  Soybean Nested Association
Mapping (@code{SoyNAM}) project dataset funded by the United Soybean Board
(USB).  BLUP function formats data for genome-wide prediction and association
analysis.")
    (license license:gpl3)))

(define-public r-sox
  (package
    (name "r-sox")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sox" version))
       (sha256
        (base32 "1phrs0mxycimzf7zz0g7bzkdkzf0fvqa86jwqnvmfyjjzqplpf2p"))))
    (properties `((upstream-name . "sox")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rcpp r-glmnet))
    (home-page "https://cran.r-project.org/package=sox")
    (synopsis "Structured Learning in Time-Dependent Cox Models")
    (description "Efficient procedures for fitting and cross-validating the
structurally-regularized time-dependent Cox models.")
    (license license:gpl3+)))

(define-public r-sovereign
  (package
    (name "r-sovereign")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sovereign" version))
       (sha256
        (base32 "1v5cchwia8v9h4fpb506v6nikp5k042axmryjmqrgimaly8dk9nv"))))
    (properties `((upstream-name . "sovereign")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xts
                             r-tidyr
                             r-strucchange
                             r-stringr
                             r-sandwich
                             r-randomforest
                             r-purrr
                             r-mclust
                             r-magrittr
                             r-lubridate
                             r-lmtest
                             r-gridextra
                             r-ggplot2
                             r-future
                             r-furrr
                             r-dplyr
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/tylerJPike/sovereign")
    (synopsis "State-Dependent Empirical Analysis")
    (description
     "This package provides a set of tools for state-dependent empirical analysis
through both VAR- and local projection-based state-dependent forecasts, impulse
response functions, historical decompositions, and forecast error variance
decompositions.")
    (license license:gpl3)))

(define-public r-southparkrshiny
  (package
    (name "r-southparkrshiny")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SouthParkRshiny" version))
       (sha256
        (base32 "0xzy66wa3712zpiarjjwfd93wdspkvnwk374gzabcrjw5jz16pi7"))))
    (properties `((upstream-name . "SouthParkRshiny")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinydashboard
                             r-shiny
                             r-knitr
                             r-kableextra
                             r-golem
                             r-ggpubr
                             r-ggplot2
                             r-config
                             r-bslib
                             r-box))
    (home-page "https://github.com/Amalan-ConStat/SouthParkRshiny")
    (synopsis "Data and 'Shiny' Application for the Show 'SouthPark'")
    (description
     "Ratings, votes, swear words and sentiments are analysed for the show
@code{SouthPark} through a Shiny application after web scraping from IMDB and
the website <https://southpark.fandom.com/wiki/South_Park_Archives>.")
    (license license:expat)))

(define-public r-soundshape
  (package
    (name "r-soundshape")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SoundShape" version))
       (sha256
        (base32 "12v0r42vcbmxznqlrsag59zdrkjsb86hxjn890j59n4bqnz7blr8"))))
    (properties `((upstream-name . "SoundShape")))
    (build-system r-build-system)
    (propagated-inputs (list r-tuner
                             r-stringr
                             r-seewave
                             r-reshape2
                             r-plot3d
                             r-geomorph
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/p-rocha/SoundShape")
    (synopsis "Sound Waves Onto Morphometric Data")
    (description
     "Implement a promising, and yet little explored protocol for bioacoustical
analysis, the eigensound method by @code{MacLeod}, Krieger and Jones (2013)
<doi:10.4404/hystrix-24.1-6299>.  Eigensound is a multidisciplinary method
focused on the direct comparison between stereotyped sounds from different
species. @code{SoundShape}', in turn, provide the tools required for anyone to
go from sound waves to Principal Components Analysis, using tools extracted from
traditional bioacoustics (i.e. @code{tuneR} and seewave packages), geometric
morphometrics (i.e.  geomorph package) and multivariate analysis (e.g. stats
package).  For more information, please see Rocha and Romano (2021) and check
@code{SoundShape} repository on @code{GitHub} for news and updates
<https://github.com/p-rocha/@code{SoundShape>}.")
    (license license:gpl3)))

(define-public r-soundgen
  (package
    (name "r-soundgen")
    (version "2.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "soundgen" version))
       (sha256
        (base32 "1mkn5qa4mxvsmjhkdcixd1wyr8l8jf04k88vvm317mv0lgcc45h2"))))
    (properties `((upstream-name . "soundgen")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-tuner
                             r-signal
                             r-shinyjs
                             r-shinybs
                             r-shiny
                             r-seewave
                             r-phontools
                             r-nonlineartseries
                             r-mvtnorm
                             r-foreach
                             r-dtw
                             r-doparallel
                             r-data-table))
    (home-page "http://cogsci.se/soundgen.html")
    (synopsis "Sound Synthesis and Acoustic Analysis")
    (description
     "This package performs parametric synthesis of sounds with harmonic and noise
components such as animal vocalizations or human voice.  Also offers tools for
audio manipulation and acoustic analysis, including pitch tracking, spectral
analysis, audio segmentation, pitch and formant shifting, etc.  Includes four
interactive web apps for synthesizing and annotating audio, manually correcting
pitch contours, and measuring formant frequencies.  Reference: Anikin (2019)
<doi:10.3758/s13428-018-1095-7>.")
    (license license:gpl2+)))

(define-public r-soundexbr
  (package
    (name "r-soundexbr")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SoundexBR" version))
       (sha256
        (base32 "0chc332v3wcz30v70yvdxhvcfdmvf4fj193cn00gl899xfxal89p"))))
    (properties `((upstream-name . "SoundexBR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SoundexBR")
    (synopsis "Phonetic-Coding for Portuguese")
    (description
     "The @code{SoundexBR} package provides an algorithm for decoding names into
phonetic codes, as pronounced in Portuguese.  The goal is for homophones to be
encoded to the same representation so that they can be matched despite minor
differences in spelling.  The algorithm mainly encodes consonants; a vowel will
not be encoded unless it is the first letter.  The soundex code resultant
consists of a four digits long string composed by one letter followed by three
numerical digits: the letter is the first letter of the name, and the digits
encode the remaining consonants.")
    (license license:gpl2+)))

(define-public r-soundecology
  (package
    (name "r-soundecology")
    (version "1.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "soundecology" version))
       (sha256
        (base32 "16h6gbdlyav7wbfisdv1f2zsqhr45liidgj7qqk8giwjxgan8q97"))))
    (properties `((upstream-name . "soundecology")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-tuner
                             r-seewave
                             r-pracma
                             r-oce
                             r-ineq))
    (native-inputs (list r-knitr))
    (home-page "http://ljvillanueva.github.io/soundecology/")
    (synopsis "Soundscape Ecology")
    (description
     "This package provides functions to calculate indices for soundscape ecology and
other ecology research that uses audio recordings.")
    (license license:gpl3)))

(define-public r-soundclass
  (package
    (name "r-soundclass")
    (version "0.0.9.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "soundClass" version))
       (sha256
        (base32 "0z697asv2kzs93b45ndbkxfdzq3fmi1f6aprvpxps4x354gg2pi6"))))
    (properties `((upstream-name . "soundClass")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-tuner
                             r-signal
                             r-shinyjs
                             r-shinyfiles
                             r-shinybs
                             r-shiny
                             r-seewave
                             r-rsqlite
                             r-magrittr
                             r-keras
                             r-htmltools
                             r-generics
                             r-dplyr
                             r-dbplyr
                             r-dbi))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=soundClass")
    (synopsis "Sound Classification Using Convolutional Neural Networks")
    (description
     "This package provides an all-in-one solution for automatic classification of
sound events using convolutional neural networks (CNN).  The main purpose is to
provide a sound classification workflow, from annotating sound events in
recordings to training and automating model usage in real-life situations.
Using the package requires a pre-compiled collection of recordings with sound
events of interest and it can be employed for: 1) Annotation: create a database
of annotated recordings, 2) Training: prepare train data from annotated
recordings and fit CNN models, 3) Classification: automate the use of the fitted
model for classifying new recordings.  By using automatic feature selection and
a user-friendly GUI for managing data and training/deploying models, this
package is intended to be used by a broad audience as it does not require
specific expertise in statistics, programming or sound analysis.  Please refer
to the vignette for further information.  Gibb, R., et al. (2019)
<doi:10.1111/2041-210X.13101> Mac Aodha, O., et al. (2018)
<doi:10.1371/journal.pcbi.1005995> Stowell, D., et al. (2019)
<doi:10.1111/2041-210X.13103> @code{LeCun}, Y., et al. (2012)
<doi:10.1007/978-3-642-35289-8_3>.")
    (license license:gpl3)))

(define-public r-sound
  (package
    (name "r-sound")
    (version "1.4.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sound" version))
       (sha256
        (base32 "14jk5lg73v3gvnyvam3bsbcayawprasvlvaxwqryh148brqgfcrd"))))
    (properties `((upstream-name . "sound")))
    (build-system r-build-system)
    (home-page "https://github.com/langenbergstefan/sound")
    (synopsis "Sound Interface for R")
    (description
     "Basic functions for dealing with wav files and sound samples.")
    (license license:gpl2+)))

(define-public r-sotu
  (package
    (name "r-sotu")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sotu" version))
       (sha256
        (base32 "0hx37as51q3rk7z9vp8awhkj7a87nwd2jlp1hvfkli2j6l07sk7d"))))
    (properties `((upstream-name . "sotu")))
    (build-system r-build-system)
    (home-page "https://github.com/statsmaths/sotu/")
    (synopsis "United States Presidential State of the Union Addresses")
    (description
     "The President of the United States is constitutionally obligated to provide a
report known as the State of the Union'.  The report summarizes the current
challenges facing the country and the president's upcoming legislative agenda.
While historically the State of the Union was often a written document, in
recent decades it has always taken the form of an oral address to a joint
session of the United States Congress.  This package provides the raw text from
every such address with the intention of being used for meaningful examples of
text analysis in R. The corpus is well suited to the task as it is historically
important, includes material intended to be read and material intended to be
spoken, and it falls in the public domain.  As the corpus spans over two
centuries it is also a good test of how well various methods hold up to the
idiosyncrasies of historical texts.  Associated data about each address, such as
the year, president, party, and format, are also included.")
    (license license:gpl2)))

(define-public r-sotkanet
  (package
    (name "r-sotkanet")
    (version "0.10.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sotkanet" version))
       (sha256
        (base32 "1384rsvhz39i8s8wvgik3jgv1h9yrdy7qmxpvmdqay6wjimmjz09"))))
    (properties `((upstream-name . "sotkanet")))
    (build-system r-build-system)
    (propagated-inputs (list r-refmanager
                             r-magrittr
                             r-lubridate
                             r-httr2
                             r-frictionless
                             r-digest
                             r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://ropengov.github.io/sotkanet/")
    (synopsis "Sotkanet Open Data Access and Analysis")
    (description
     "Access statistical information on welfare and health in Finland from the
Sotkanet open data portal <https://sotkanet.fi/sotkanet/fi/index>.")
    (license license:bsd-2)))

(define-public r-sos
  (package
    (name "r-sos")
    (version "2.1-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sos" version))
       (sha256
        (base32 "02wvdskc6fpb9yp83703xh332g69dhmvcfdjr47fcbfrzmvw1r0q"))))
    (properties `((upstream-name . "sos")))
    (build-system r-build-system)
    (propagated-inputs (list r-brew))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/sbgraves237/sos")
    (synopsis "Search Contributed R Packages, Sort by Package")
    (description "Search contributed R packages, sort by package.")
    (license license:gpl2+)))

(define-public r-sorvi
  (package
    (name "r-sorvi")
    (version "0.8.21")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sorvi" version))
       (sha256
        (base32 "0l8lznn2vnnyzq3i1hab7lcylmhkn9mpzrdymdmai4w2khg8r43b"))))
    (properties `((upstream-name . "sorvi")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tidyr
                             r-sf
                             r-rvest
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-gh
                             r-ggplot2
                             r-dplyr
                             r-dlstats
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ropengov/sorvi")
    (synopsis "Functions for Finnish Open Data")
    (description
     "Misc support functions for @code{rOpenGov} and open data downloads.")
    (license license:bsd-2)))

(define-public r-sortedeffects
  (package
    (name "r-sortedeffects")
    (version "1.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SortedEffects" version))
       (sha256
        (base32 "188qlgg3m8f07hbjim65yw3qy6200y0b54imkdqm8mq0f14nmi0f"))))
    (properties `((upstream-name . "SortedEffects")))
    (build-system r-build-system)
    (propagated-inputs (list r-sparsem r-quantreg r-pbapply r-hmisc r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/shuowencs/SortedEffects")
    (synopsis
     "Estimation and Inference Methods for Sorted Causal Effects and Classification Analysis")
    (description
     "This package implements the estimation and inference methods for sorted causal
effects and classification analysis as in Chernozhukov, Fernandez-Val and Luo
(2018) <doi:10.3982/ECTA14415>.")
    (license license:expat)))

(define-public r-sortable
  (package
    (name "r-sortable")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sortable" version))
       (sha256
        (base32 "1y6cljcjai2k2j7abzn07ab170gpp25zvl9gk755l4gwi7clgl2c"))))
    (properties `((upstream-name . "sortable")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny
                             r-rlang
                             r-learnr
                             r-jsonlite
                             r-htmlwidgets
                             r-htmltools
                             r-ellipsis
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://rstudio.github.io/sortable/")
    (synopsis "Drag-and-Drop in 'shiny' Apps with 'SortableJS'")
    (description
     "Enables drag-and-drop behaviour in Shiny apps, by exposing the functionality of
the @code{SortableJS} <https://sortablejs.github.io/Sortable/> @code{JavaScript}
library as an htmlwidget'.  You can use this in Shiny apps and widgets, learnr
tutorials as well as R Markdown.  In addition, provides a custom learnr question
type - question_rank() - that allows ranking questions with drag-and-drop.")
    (license license:expat)))

(define-public r-sorptionanalysis
  (package
    (name "r-sorptionanalysis")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SorptionAnalysis" version))
       (sha256
        (base32 "0drns1ajcga5z56hcgjxld1riwjn7vli8k1ma1xqifpy1qwy7ci6"))))
    (properties `((upstream-name . "SorptionAnalysis")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SorptionAnalysis")
    (synopsis "Static Adsorption Experiment Plotting and Analysis")
    (description
     "This package provides tools to efficiently analyze and visualize laboratory data
from aqueous static adsorption experiments.  The package provides functions to
plot Langmuir, Freundlich, and Temkin isotherms and functions to determine the
statistical conformity of data points to the Langmuir, Freundlich, and Temkin
adsorption models through statistical characterization of the isothermic least
squares regressions lines.  Scientific Reference: Dada, A.O, Olalekan, A.,
Olatunya, A. (2012) <doi:10.9790/5736-0313845>.")
    (license license:gpl2)))

(define-public r-sorocs
  (package
    (name "r-sorocs")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sorocs" version))
       (sha256
        (base32 "0gy23rldhnlz6jmf9rdzl4mmfkn5gjcavq2jfcz6dgjafl3v9vgl"))))
    (properties `((upstream-name . "sorocs")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-mcmcpack r-mass))
    (native-inputs (list r-knitr))
    (home-page "http://github.com/wzhang17/sorocs.git")
    (synopsis "Bayesian Semiparametric Approach to Correlated ROC Surfaces")
    (description
     "This package provides a Bayesian semiparametric Dirichlet process mixtures to
estimate correlated receiver operating characteristic (ROC) surfaces and the
associated volume under the surface (VUS) with stochastic order constraints.
The reference paper is:Zhen Chen, Beom Seuk Hwang, (2018) \"A Bayesian
semiparametric approach to correlated ROC surfaces with stochastic order
constraints\".  Biometrics, 75, 539-550. <doi:10.1111/biom.12997>.")
    (license license:gpl3)))

(define-public r-sorcering
  (package
    (name "r-sorcering")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sorcering" version))
       (sha256
        (base32 "1w0kwc7wbkdl41kcjgdc68hscklg10byzbk309r13s1101xn6g3v"))))
    (properties `((upstream-name . "sorcering")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-rcpparmadillo r-rcpp r-mathjaxr))
    (home-page "https://cran.r-project.org/package=sorcering")
    (synopsis
     "Soil Organic Carbon and CN Ratio Driven Nitrogen Modelling Framework")
    (description
     "Can be used to model the fate of soil organic carbon and soil organic nitrogen
and to calculate N mineralisation rates.  Provides a framework that numerically
solves differential equations of soil organic carbon models based on first-order
kinetics and extends these models to include the nitrogen component.  The name
sorcering is an acronym for Soil ORganic Carbon & CN Ratio @code{drIven}
Nitrogen @code{modellinG} framework'.")
    (license license:gpl2+)))

(define-public r-sor
  (package
    (name "r-sor")
    (version "0.23.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SOR" version))
       (sha256
        (base32 "1accs4bqy080nfmgkdg7bgamdrcwcn01y6nydvvq12w3v8asdvwh"))))
    (properties `((upstream-name . "SOR")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (home-page "https://cran.r-project.org/package=SOR")
    (synopsis "Estimation using Sequential Offsetted Regression")
    (description
     "Estimation for longitudinal data following outcome dependent sampling using the
sequential offsetted regression technique.  Includes support for binary, count,
and continuous data.  The first regression is a logistic regression, which uses
a known ratio (the probability of being sampled given that the
subject/observation was referred divided by the probability of being sampled
given that the subject/observation was no referred) as an offset to estimate the
probability of being referred given outcome and covariates.  The second
regression uses this estimated probability to calculate the mean population
response given covariates.")
    (license license:gpl3)))

(define-public r-soql
  (package
    (name "r-soql")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "soql" version))
       (sha256
        (base32 "12wvbxy9xx140b7mga4ma7hkg4q6kv549gkq15fnf4dn8q43x8nf"))))
    (properties `((upstream-name . "soql")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=soql")
    (synopsis "Helps Make Socrata Open Data API Calls")
    (description
     "Used to construct the URLs and parameters of Socrata Open Data API
<https://dev.socrata.com> calls, using the API's @code{SoQL} parameter format.
Has method-chained and sensical syntax.  Plays well with pipes.")
    (license license:expat)))

(define-public r-soptdmaea
  (package
    (name "r-soptdmaea")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "soptdmaeA" version))
       (sha256
        (base32 "03n1y82myq0rz14awjnx9nx1dz3dxqq58m13cw7a7ncap7fmw9r5"))))
    (properties `((upstream-name . "soptdmaeA")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-mass r-igraph))
    (home-page "https://cran.r-project.org/package=soptdmaeA")
    (synopsis
     "Sequential Optimal Designs for Two-Colour cDNA Microarray Experiments")
    (description
     "Computes sequential A-, MV-, D- and E-optimal or near-optimal block and
row-column designs for two-colour @code{cDNA} microarray experiments using the
linear fixed effects and mixed effects models where the interest is in a
comparison of all possible elementary treatment contrasts.  The package also
provides an optional method of using the graphical user interface (GUI) R
package tcltk to ensure that it is user friendly.")
    (license license:gpl2)))

(define-public r-sopie
  (package
    (name "r-sopie")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SOPIE" version))
       (sha256
        (base32 "1q6991l1wldywv685dv1pkd8ma8jlx4inqr6p1fw1xr7ymm8b1q2"))))
    (properties `((upstream-name . "SOPIE")))
    (build-system r-build-system)
    (propagated-inputs (list r-circular r-adgoftest))
    (home-page "https://cran.r-project.org/package=SOPIE")
    (synopsis
     "Non-Parametric Estimation of the Off-Pulse Interval of a Pulsar")
    (description
     "This package provides functions to non-parametrically estimate the off-pulse
interval of a source function originating from a pulsar.  The technique is based
on a sequential application of P-values obtained from goodness-of-fit tests for
the uniform distribution, such as the Kolmogorov-Smirnov, Cramer-von Mises,
Anderson-Darling and Rayleigh goodness-of-fit tests.")
    (license license:gpl3)))

(define-public r-sopc
  (package
    (name "r-sopc")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SOPC" version))
       (sha256
        (base32 "01gsszas6prl3gr2x36p3yqdjph5d0wzzbbxwq52gif8jkfmhzmr"))))
    (properties `((upstream-name . "SOPC")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr r-elasticnet))
    (home-page "https://cran.r-project.org/package=SOPC")
    (synopsis "The Sparse Online Principal Component Estimation Algorithm")
    (description
     "The sparse online principal component can not only process the online data set,
but also obtain a sparse solution of the online data set.  The philosophy of the
package is described in Guo G. (2022) <doi:10.1007/s00180-022-01270-z>.")
    (license license:expat)))

(define-public r-sop
  (package
    (name "r-sop")
    (version "1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SOP" version))
       (sha256
        (base32 "1c2mgdsd0ah0xcxzd8pncwvvh4ilp04dfblsjvw51vnm7lc2akxq"))))
    (properties `((upstream-name . "SOP")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=SOP")
    (synopsis "Generalised Additive P-Spline Regression Models Estimation")
    (description
     "Generalised additive P-spline regression models estimation using the separation
of overlapping precision matrices (SOP) method.  Estimation is based on the
equivalence between P-splines and linear mixed models, and variance/smoothing
parameters are estimated based on restricted maximum likelihood (REML).  The
package enables users to estimate P-spline models with overlapping penalties.
Based on the work described in Rodriguez-Alvarez et al. (2015)
<doi:10.1007/s11222-014-9464-2>; Rodriguez-Alvarez et al. (2019)
<doi:10.1007/s11222-018-9818-2>, and Eilers and Marx (1996)
<doi:10.1214/ss/1038425655>.")
    (license license:gpl2)))

(define-public r-sonify
  (package
    (name "r-sonify")
    (version "0.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sonify" version))
       (sha256
        (base32 "1wkqqgf8fhhaz1dmri2zzr8cdwmx3cgrxbprrz3yily5r1m17d15"))))
    (properties `((upstream-name . "sonify")))
    (build-system r-build-system)
    (propagated-inputs (list r-tuner))
    (home-page "https://cran.r-project.org/package=sonify")
    (synopsis "Data Sonification - Turning Data into Sound")
    (description
     "Sonification (or audification) is the process of representing data by sounds in
the audible range.  This package provides the R function sonify() that
transforms univariate data, sampled at regular or irregular intervals, into a
continuous sound with time-varying frequency.  The ups and downs in frequency
represent the ups and downs in the data.  Sonify provides a substitute for R's
plot function to simplify data analysis for the visually impaired.")
    (license license:gpl2+)))

(define-public r-sonicscrewdriver
  (package
    (name "r-sonicscrewdriver")
    (version "0.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sonicscrewdriver" version))
       (sha256
        (base32 "0z18hyicbh6naxq5hz5bzx61x7kr1j5jsk98j71m8dalch2wysbm"))))
    (properties `((upstream-name . "sonicscrewdriver")))
    (build-system r-build-system)
    (propagated-inputs (list r-tuner
                             r-suncalc
                             r-stringi
                             r-seewave
                             r-rdpack
                             r-mime
                             r-jsonlite
                             r-hms
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://sonicscrewdriver.ebaker.me.uk")
    (synopsis "Bioacoustic Analysis and Publication Tools")
    (description
     "This package provides tools for manipulating sound files for bioacoustic
analysis, and preparing analyses these for publication.  The package validates
that values are physically possible wherever feasible.")
    (license license:gpl3)))

(define-public r-soniclength
  (package
    (name "r-soniclength")
    (version "1.4.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sonicLength" version))
       (sha256
        (base32 "1wvdcjk641kcmflda0mibqr519qbqgb26mgwibjhm6pzgvwpy2dg"))))
    (properties `((upstream-name . "sonicLength")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sonicLength")
    (synopsis "Estimating Abundance of Clones from DNA Fragmentation Data")
    (description
     "Estimate the abundance of cell clones from the distribution of lengths of DNA
fragments (as created by sonication, whence `@code{sonicLength}').  The
algorithm in \"Estimating abundances of retroviral insertion sites from DNA
fragment length data\" by Berry CC, Gillet NA, Melamed A, Gormley N, Bangham CR,
Bushman FD. Bioinformatics; 2012 Mar 15;28(6):755-62 is implemented.  The
experimental setting and estimation details are described in detail there.
Briefly, integration of new DNA in a host genome (due to retroviral infection or
gene therapy) can be tracked using DNA sequencing, potentially allowing
characterization of the abundance of individual cell clones bearing distinct
integration sites.  The locations of integration sites can be determined by
fragmenting the host DNA (via sonication or fragmentase), breaking the newly
integrated DNA at a known sequence, amplifying the fragments containing both
host and integrated DNA, sequencing those amplicons, then mapping the host
sequences to positions on the reference genome.  The relative number of
fragments containing a given position in the host genome estimates the relative
abundance of cells hosting the corresponding integration site, but that number
is not available and the count of amplicons per fragment varies widely.
However, the expected number of distinct fragment lengths is a function of the
abundance of cells hosting an integration site at a given position and a certain
nuisance parameter.  The algorithm implicitly estimates that function to
estimate the relative abundance.")
    (license license:gpl2+)))

(define-public r-songevo
  (package
    (name "r-songevo")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SongEvo" version))
       (sha256
        (base32 "0mc5wlf5axbflys6g8b7xg0di3wwkli7q0a9fby6598fq9hbaqpd"))))
    (properties `((upstream-name . "SongEvo")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp r-lattice r-geosphere r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SongEvo")
    (synopsis "An Individual-Based Model of Bird Song Evolution")
    (description
     "Simulates the cultural evolution of quantitative traits of bird song.
@code{SongEvo} is an individual- (agent-) based model. @code{SongEvo} is
spatially-explicit and can be parameterized with, and tested against, measured
song data.  Functions are available for model implementation, sensitivity
analyses, parameter optimization, model validation, and hypothesis testing.")
    (license license:gpl3)))

(define-public r-sonar
  (package
    (name "r-sonar")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sonar" version))
       (sha256
        (base32 "1f0f5iqi3y9vnxyym14nm8dqxw0vcpnxmvvkx4sm612i4fclg1f9"))))
    (properties `((upstream-name . "sonar")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sonar")
    (synopsis "Fundamental Formulas for Sonar")
    (description
     "Formulas for calculating sound velocity, water pressure, depth, density,
absorption and sonar equations.")
    (license license:gpl3+)))

(define-public r-somspace
  (package
    (name "r-somspace")
    (version "1.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "somspace" version))
       (sha256
        (base32 "1268sj89cbkp2nm0i905s991a92w4aki93hxia2vqk6fdh200rdl"))))
    (properties `((upstream-name . "somspace")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-maps r-kohonen r-ggplot2
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=somspace")
    (synopsis "Spatial Analysis with Self-Organizing Maps")
    (description
     "Application of the Self-Organizing Maps technique for spatial classification of
time series.  The package uses spatial data, point or gridded, to create
clusters with similar characteristics.  The clusters can be further refined to a
smaller number of regions by hierarchical clustering and their spatial
dependencies can be presented as complex networks.  Thus, meaningful maps can be
created, representing the regional heterogeneity of a single variable.  More
information and an example of implementation can be found in Markonis and Strnad
(2020, <doi:10.1177/0959683620913924>).")
    (license license:gpl3)))

(define-public r-somnmr
  (package
    (name "r-somnmr")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SOMnmR" version))
       (sha256
        (base32 "16vd41x4sazjlqwp5fdxcxs8n8rkgjdg819yrbnjlhp54938laj3"))))
    (properties `((upstream-name . "SOMnmR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-quadprog
                             r-pracma
                             r-minpack-lm
                             r-intervalsurgeon
                             r-ggplot2
                             r-dplyr
                             r-data-table))
    (home-page "https://github.com/LuisCol8/SOMnmR/")
    (synopsis
     "Analysis of Soil Organic Matter using Nuclear Magnetic Resonance")
    (description
     "Integrates the 13C nuclear magnetic resonance spectra using different
integration ranges.  Output depends on the method chosen.  For the Molecular
Mixing Model, a measurement of the fitting quality is given by its R-factor.
For more details see: <doi:10.5281/zenodo.10137768>.")
    (license license:expat)))

(define-public r-sommer
  (package
    (name "r-sommer")
    (version "4.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sommer" version))
       (sha256
        (base32 "0lhbrv4m44zgd178xpm5h0qdn54ldfjz7c96mmhm41pz0zn97h5h"))))
    (properties `((upstream-name . "sommer")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppprogress
                             r-rcpparmadillo
                             r-rcpp
                             r-matrix
                             r-mass
                             r-crayon))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/covaruber/sommer")
    (synopsis "Solving Mixed Model Equations in R")
    (description
     "Structural multivariate-univariate linear mixed model solver for estimation of
multiple random effects with unknown variance-covariance structures (e.g.,
heterogeneous and unstructured) and known covariance among levels of random
effects (e.g., pedigree and genomic relationship matrices) (Covarrubias-Pazaran,
2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015
<doi:10.1016/j.ajhg.2014.12.006>; Jensen et al., 1997).  REML estimates can be
obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average
Information algorithms for the problems r x r (r being the number of records) or
using the Henderson-based average information algorithm for the problem c x c (c
being the number of coefficients to estimate).  Spatial models can also be
fitted using the two-dimensional spline functionality available.")
    (license license:gpl2+)))

(define-public r-sommd
  (package
    (name "r-sommd")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SOMMD" version))
       (sha256
        (base32 "1sx1qvd9hn4fxg7nrv5ra7z2rn1a27iicgpi0m29in097wf0xlm5"))))
    (properties `((upstream-name . "SOMMD")))
    (build-system r-build-system)
    (propagated-inputs (list r-kohonen r-igraph r-cluster r-bio3d r-abind))
    (home-page "https://cran.r-project.org/package=SOMMD")
    (synopsis
     "Self Organising Maps for the Analysis of Molecular Dynamics Data")
    (description
     "Processes data from Molecular Dynamics simulations using Self Organising Maps.
Features include the ability to read different input formats.  Trajectories can
be analysed to identify groups of important frames.  Output visualisation can be
generated for maps and pathways.  Methodological details can be found in Motta S
et al (2022) <doi:10.1021/acs.jctc.1c01163>.  I/O functions for xtc format files
were implemented using the xdrfile library available under open source license.
The relevant information can be found in inst/COPYRIGHT.")
    (license license:gpl3)))

(define-public r-somenv
  (package
    (name "r-somenv")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SOMEnv" version))
       (sha256
        (base32 "03g1c9ip0bmc5zhnzami3f525m9ic6v5gdhaxc8s7f1nxjf2fcdy"))))
    (properties `((upstream-name . "SOMEnv")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinycustomloader
                             r-shinycssloaders
                             r-shiny
                             r-rlist
                             r-plyr
                             r-openair
                             r-kohonen
                             r-dplyr
                             r-colourpicker))
    (home-page "https://github.com/SomEnv/somenv")
    (synopsis
     "SOM Algorithm for the Analysis of Multivariate Environmental Data")
    (description
     "Analysis of multivariate environmental high frequency data by Self-Organizing
Map and k-means clustering algorithms.  By means of the graphical user interface
it provides a comfortable way to elaborate by self-organizing map algorithm
rather big datasets (txt files up to 100 MB ) obtained by environmental
high-frequency monitoring by sensors/instruments.  The functions present in the
package are based on kohonen and openair packages implemented by functions
embedding Vesanto et al. (2001)
<http://www.cis.hut.fi/projects/somtoolbox/package/papers/techrep.pdf> heuristic
rules for map initialization parameters, k-means clustering algorithm and map
features visualization.  Cluster profiles visualization as well as graphs
dedicated to the visualization of time-dependent variables Licen et al. (2020)
<doi:10.4209/aaqr.2019.08.0414> are provided.")
    (license license:gpl3)))

(define-public r-somemtp
  (package
    (name "r-somemtp")
    (version "1.4.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "someMTP" version))
       (sha256
        (base32 "197ajnnp92qh807a55z0i9z8nw0vmf5vvr216nxjwa2l9kw8c84k"))))
    (properties `((upstream-name . "someMTP")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=someMTP")
    (synopsis "Some Multiple Testing Procedures")
    (description
     "It's a collection of functions for Multiplicity Correction and Multiple Testing.")
    (license license:gpl2+)))

(define-public r-sombrero
  (package
    (name "r-sombrero")
    (version "1.4-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SOMbrero" version))
       (sha256
        (base32 "15bwdw3awwqq6d4b8ja8lsvcyr2v37l5jc3ja3df3y0ziazdllpn"))))
    (properties `((upstream-name . "SOMbrero")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny
                             r-scatterplot3d
                             r-rlang
                             r-metr
                             r-markdown
                             r-interp
                             r-igraph
                             r-ggwordcloud
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "http://sombrero.clementine.wf/")
    (synopsis "SOM Bound to Realize Euclidean and Relational Outputs")
    (description
     "The stochastic (also called on-line) version of the Self-Organising Map (SOM)
algorithm is provided.  Different versions of the algorithm are implemented, for
numeric and relational data and for contingency tables as described,
respectively, in Kohonen (2001) <isbn:3-540-67921-9>, Olteanu & Villa-Vialaneix
(2005) <doi:10.1016/j.neucom.2013.11.047> and Cottrell et al (2004)
<doi:10.1016/j.neunet.2004.07.010>.  The package also contains many plotting
features (to help the user interpret the results), can handle (and impute)
missing values and is delivered with a graphical user interface based on shiny'.")
    (license license:gpl2+)))

(define-public r-somaticflags
  (package
    (name "r-somaticflags")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "somaticflags" version))
       (sha256
        (base32 "0b7g1i8qgrks6prdcmhdqpq9ivrlk47j8najq0baz02drrrf3fhm"))))
    (properties `((upstream-name . "somaticflags")))
    (build-system r-build-system)
    (home-page "https://github.com/CCICB/somaticflags")
    (synopsis "Database of Somatic Flags")
    (description
     "Database of genes which frequently sustain somatic mutations, but are unlikely
to drive cancer.")
    (license license:expat)))

(define-public r-somadataio
  (package
    (name "r-somadataio")
    (version "6.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SomaDataIO" version))
       (sha256
        (base32 "1r9hb804gjzqn0y91kyc4ays7x3pqxknsbz0b3gy2kkldkhb60c1"))))
    (properties `((upstream-name . "SomaDataIO")))
    (build-system r-build-system)
    (propagated-inputs (list r-usethis
                             r-tidyr
                             r-tibble
                             r-readxl
                             r-magrittr
                             r-lifecycle
                             r-dplyr
                             r-crayon
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://somalogic.github.io/SomaDataIO/")
    (synopsis "Input/Output 'SomaScan' Data")
    (description
     "Load and export @code{SomaScan} data via the @code{SomaLogic} Operating Co.,
Inc.  structured text file called an ADAT ('*.adat').  For file format see
<https://github.com/@code{SomaLogic/SomaLogic-Data/blob/master/README.md>}.  The
package also exports auxiliary functions for manipulating, wrangling, and
extracting relevant information from an ADAT object once in memory.")
    (license license:expat)))

(define-public r-soma
  (package
    (name "r-soma")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "soma" version))
       (sha256
        (base32 "05hm6yqh1d10kbgvy5j0cj2lvcnib4p9lmjcl32zxy1hazpagplq"))))
    (properties `((upstream-name . "soma")))
    (build-system r-build-system)
    (propagated-inputs (list r-reportr))
    (home-page "https://github.com/jonclayden/soma/")
    (synopsis
     "General-Purpose Optimisation with the Self-Organising Migrating Algorithm")
    (description
     "An R implementation of the Self-Organising Migrating Algorithm, a
general-purpose, stochastic optimisation algorithm.  The approach is similar to
that of genetic algorithms, although it is based on the idea of a series of
``migrations by a fixed set of individuals, rather than the development of
successive generations.  It can be applied to any cost-minimisation problem with
a bounded parameter space, and is robust to local minima.")
    (license license:gpl2)))

(define-public r-som-nn
  (package
    (name "r-som-nn")
    (version "1.4.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "som.nn" version))
       (sha256
        (base32 "1qirkm54yav7h5836l81rah07r4nm8iy7ky6pf7c2a7vfbwddiqk"))))
    (properties `((upstream-name . "som.nn")))
    (build-system r-build-system)
    (propagated-inputs (list r-som r-kohonen r-hexbin r-class))
    (home-page "https://cran.r-project.org/package=som.nn")
    (synopsis "Topological k-NN Classifier Based on Self-Organising Maps")
    (description
     "This package provides a topological version of k-NN: An abstract model is build
as 2-dimensional self-organising map.  Samples of unknown class are predicted by
mapping them on the SOM and analysing class membership of neurons in the
neighbourhood.")
    (license license:gpl3)))

(define-public r-solvesaphe
  (package
    (name "r-solvesaphe")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SolveSAPHE" version))
       (sha256
        (base32 "1r42h19l186jry2wxmrp9y1jdcwr7wx4an3nmryc4l33f48idbfr"))))
    (properties `((upstream-name . "SolveSAPHE")))
    (build-system r-build-system)
    (home-page "https://CRAN.R-project.org/package=SolveSAPHE")
    (synopsis "Solver Suite for Alkalinity-PH Equations")
    (description
     "Universal and robust algorithm for solving the total alkalinity-@code{pH}
equation presented in G. Munhoven (2013) <doi:10.5194/gmd-6-1367-2013> and G.
Munhoven (2021) <doi:10.5194/gmd-2020-447>.  The total alkalinity-@code{pH}
equation relates total alkalinity and @code{pH} for a given set of acid-base
concentrations in a given water sample, among which carbonic acid.  This package
is particularly useful in marine chemistry involving dissolved inorganic carbon.
 Original package in Fortran can be found at <doi:10.5281/zenodo.4328965>.")
    (license license:gpl2+)))

(define-public r-solvebio
  (package
    (name "r-solvebio")
    (version "2.15.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "solvebio" version))
       (sha256
        (base32 "0risi2s3m73hn7ip3x6gva6cr8dw9vmk237jncx9hcq07n5wr630"))))
    (properties `((upstream-name . "solvebio")))
    (build-system r-build-system)
    (propagated-inputs (list r-mime r-jsonlite r-httr))
    (home-page "https://github.com/solvebio/solvebio-r")
    (synopsis "The Official SolveBio API Client")
    (description
     "R language bindings for @code{SolveBio's} API. @code{SolveBio} is a biomedical
knowledge hub that enables life science organizations to collect and harmonize
the complex, disparate \"multi-omic\" data essential for today's R&D and BI needs.")
    (license license:expat)))

(define-public r-solrium
  (package
    (name "r-solrium")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "solrium" version))
       (sha256
        (base32 "16x4gjk0x6m9yjq9sk3kwbw8qm7w16vmbnfy5xa9zikw96cl3iky"))))
    (properties `((upstream-name . "solrium")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tibble
                             r-r6
                             r-plyr
                             r-jsonlite
                             r-dplyr
                             r-crul))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ropensci/solrium")
    (synopsis "General Purpose R Interface to 'Solr'")
    (description
     "This package provides a set of functions for querying and parsing data from Solr
(<https://solr.apache.org/>) endpoints (local and remote), including search,
faceting', highlighting', stats', and more like this'.  In addition, some
functionality is included for creating, deleting, and updating documents in a
Solr database'.")
    (license license:expat)))

(define-public r-solrad
  (package
    (name "r-solrad")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "solrad" version))
       (sha256
        (base32 "13ybllvmig1yqsy4md4kazs83hxb9lrlwy64yw1wwr1w6sxpbnp6"))))
    (properties `((upstream-name . "solrad")))
    (build-system r-build-system)
    (home-page "https://github.com/bnasr/solrad/")
    (synopsis
     "Calculating Solar Radiation and Related Variables Based on Location, Time and Topographical Conditions")
    (description
     "For surface energy models and estimation of solar positions and components with
varying topography, time and locations.  The functions calculate solar
top-of-atmosphere, open, diffuse and direct components, atmospheric
transmittance and diffuse factors, day length, sunrise and sunset, solar
azimuth, zenith, altitude, incidence, and hour angles, earth declination angle,
equation of time, and solar constant.  Details about the methods and equations
are explained in Seyednasrollah, Bijan, Mukesh Kumar, and Timothy E. Link.  On
the role of vegetation density on net snow cover radiation at the forest floor.
Journal of Geophysical Research: Atmospheres 118.15 (2013): 8359-8374,
<doi:10.1002/jgrd.50575>.")
    (license (list license:agpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-solitude
  (package
    (name "r-solitude")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "solitude" version))
       (sha256
        (base32 "1cqbcm72fq13lp0lazfngdylcasck1ffrj16ggxlryzps1181rhy"))))
    (properties `((upstream-name . "solitude")))
    (build-system r-build-system)
    (propagated-inputs (list r-ranger
                             r-r6
                             r-lgr
                             r-igraph
                             r-future-apply
                             r-data-table))
    (home-page "https://github.com/talegari/solitude")
    (synopsis "An Implementation of Isolation Forest")
    (description
     "Isolation forest is anomaly detection method introduced by the paper Isolation
based Anomaly Detection (Liu, Ting and Zhou <doi:10.1145/2133360.2133363>).")
    (license license:gpl3)))

(define-public r-solartime
  (package
    (name "r-solartime")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "solartime" version))
       (sha256
        (base32 "1lg7sc3vxsqqiq6a3qsl14gcc1fdi15h1mqcfjmq2j5xna6s5msx"))))
    (properties `((upstream-name . "solartime")))
    (build-system r-build-system)
    (propagated-inputs (list r-lubridate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bgctw/solartime")
    (synopsis
     "Utilities Dealing with Solar Time Such as Sun Position and Time of Sunrise")
    (description
     "Provide utilities to work with solar time, i.e.  where noon is exactly when sun
culminates.  Provides functions for computing sun position and times of sunrise
and sunset.")
    (license license:gpl3)))

(define-public r-solarpos
  (package
    (name "r-solarpos")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "solarPos" version))
       (sha256
        (base32 "0004da7vqpq14q5lhs8vyvjl99j8gzxd5wrlw5d1vwfns3ica0q5"))))
    (properties `((upstream-name . "solarPos")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=solarPos")
    (synopsis "Solar Position Algorithm for Solar Radiation Applications")
    (description "Calculation of solar zenith and azimuth angles.")
    (license license:gpl2)))

(define-public r-solar2
  (package
    (name "r-solar2")
    (version "0.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "solaR2" version))
       (sha256
        (base32 "0bs9pwskdi9pw2d1bkdb8gdsgvwwj4s9f8i2py8588qrjrrxn5wn"))))
    (properties `((upstream-name . "solaR2")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer r-latticeextra r-lattice
                             r-data-table))
    (home-page "https://solarization.github.io/solaR2/")
    (synopsis "Radiation and Photovoltaic Systems")
    (description
     "This package provides tools for calculating solar geometry, solar radiation on
horizontal and inclined planes, and simulating the performance of various
photovoltaic (PV) systems.  Supports daily and intradaily irradiation data,
enabling detailed analysis of grid-connected and water-pumping PV systems,
including shading effects and solar angle calculations.")
    (license license:gpl3)))

(define-public r-solar
  (package
    (name "r-solar")
    (version "0.46")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "solaR" version))
       (sha256
        (base32 "1hv9rj8kvqh5i77s6cls7hknz43scdsi5zjh04mjg0gynb265f4p"))))
    (properties `((upstream-name . "solaR")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-rcolorbrewer r-latticeextra r-lattice))
    (home-page "https://oscarperpinan.github.io/solar/")
    (synopsis "Radiation and Photovoltaic Systems")
    (description
     "Calculation methods of solar radiation and performance of photovoltaic systems
from daily and intradaily irradiation data sources.")
    (license license:gpl3)))

(define-public r-sokoban
  (package
    (name "r-sokoban")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sokoban" version))
       (sha256
        (base32 "07r2zv9rynvw06fpncrz3rsmkdy6z2pcvmm559bhs428kmnq2xib"))))
    (properties `((upstream-name . "sokoban")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sokoban")
    (synopsis "Sokoban Game")
    (description
     "Interactively play a game of sokoban ,which has nine game levels.Sokoban is a
type of transport puzzle, in which the player pushes boxes or crates around in a
warehouse, trying to get them to storage locations.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-sojourn-data
  (package
    (name "r-sojourn-data")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Sojourn.Data" version))
       (sha256
        (base32 "1flxiga9szhifndrhmf4blgms477gnnf4gd6wkxqgv3vgisc4q4i"))))
    (properties `((upstream-name . "Sojourn.Data")))
    (build-system r-build-system)
    (home-page "https://github.com/paulhibbing/Sojourn.Data")
    (synopsis "Supporting Objects for Sojourn Accelerometer Methods")
    (description
     "Stores objects (e.g. neural networks) that are needed for using Sojourn
accelerometer methods.  For more information, see Lyden K, Keadle S,
Staudenmayer J, & Freedson P (2014) <doi:10.1249/MSS.0b013e3182a42a2d>,
Ellingson LD, Schwabacher IJ, Kim Y, Welk GJ, & Cook DB (2016)
<doi:10.1249/MSS.0000000000000915>, and Hibbing PR, Ellingson LD, Dixon PM, &
Welk GJ (2018) <doi:10.1249/MSS.0000000000001486>.")
    (license license:gpl3)))

(define-public r-soilwater
  (package
    (name "r-soilwater")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "soilwater" version))
       (sha256
        (base32 "11gal7hj73j82qx65krmzpmf0gpm41ph8hw70df46dbf4w23vvjn"))))
    (properties `((upstream-name . "soilwater")))
    (build-system r-build-system)
    (home-page "https://github.com/ecor/soilwater")
    (synopsis
     "Implementation of Parametric Formulas for Soil Water Retention or Conductivity Curve")
    (description
     "It implements parametric formulas of soil water retention or conductivity curve.
 At the moment, only Van Genuchten (for soil water retention curve) and Mualem
(for hydraulic conductivity) were implemented.  See reference
(<http://en.wikipedia.org/wiki/Water_retention_curve>).")
    (license license:gpl2+)))

(define-public r-soiltexture
  (package
    (name "r-soiltexture")
    (version "1.5.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "soiltexture" version))
       (sha256
        (base32 "1jrmcb7n3jcvf3ybwf9rg6mmlvkwy960a365ny86746viiw3zaq1"))))
    (properties `((upstream-name . "soiltexture")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp r-mass))
    (home-page "https://github.com/julienmoeys/soiltexture")
    (synopsis
     "Functions for Soil Texture Plot, Classification and Transformation")
    (description
     "\"The Soil Texture Wizard\" is a set of R functions designed to produce texture
triangles (also called texture plots, texture diagrams, texture ternary plots),
classify and transform soil textures data.  These functions virtually allows to
plot any soil texture triangle (classification) into any triangle geometry
(isosceles, right-angled triangles, etc.).  This set of function is expected to
be useful to people using soil textures data from different soil texture
classification or different particle size systems.  Many (> 15) texture
triangles from all around the world are predefined in the package.  A simple
text based graphical user interface is provided: soiltexture_gui().")
    (license license:agpl3+)))

(define-public r-soiltesting
  (package
    (name "r-soiltesting")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SoilTesting" version))
       (sha256
        (base32 "0l808ic9irz7m8i3rcr65mqq5x4a3mk481c7f1ihca2rca43pm2j"))))
    (properties `((upstream-name . "SoilTesting")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SoilTesting")
    (synopsis "Organic Carbon and Plant Available Nutrient Contents in Soil")
    (description
     "Testing of soil for the contents of organic carbon, and available macro- and
micro-nutrients is a crucial part of soil fertility assessment.  This package
computes some routinely tested soil properties viz.  organic carbon (C), total
nitrogen (N), available N, mineral N, available phosphorus (P), available
potassium (K), available iron (Fe), available zinc (Zn), available manganese
(Mn), available copper (Cu), and available nickel (Ni) in soil based on
laboratory analysis data obtained by most commonly followed protocols.  Besides,
it can also draw standard curves based on absorption/emission vs. concentration
data, and give out unknown concentrations from absorption/emission readings.")
    (license license:gpl3+)))

(define-public r-soiltestcorr
  (package
    (name "r-soiltestcorr")
    (version "2.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "soiltestcorr" version))
       (sha256
        (base32 "1p1nimd7d0m2qb2rdj0pfrcw95rrch2vqkwhqbwsn2bxbgn942yj"))))
    (properties `((upstream-name . "soiltestcorr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-smatr
                             r-rlang
                             r-purrr
                             r-nlstools
                             r-nlraa
                             r-modelr
                             r-minpack-lm
                             r-ggpp
                             r-ggplot2
                             r-dplyr
                             r-data-table
                             r-aiccmodavg))
    (native-inputs (list r-knitr))
    (home-page "https://adriancorrendo.github.io/soiltestcorr/")
    (synopsis "Soil Test Correlation and Calibration")
    (description
     "This package provides a compilation of functions designed to assist users on the
correlation analysis of crop yield and soil test values.  Functions to estimate
crop response patterns to soil nutrient availability and critical soil test
values using various approaches such as: 1) the modified arcsine-log calibration
curve (Correndo et al. (2017) <doi:10.1071/CP16444>); 2) the graphical
Cate-Nelson quadrants analysis (Cate & Nelson (1965)), 3) the statistical
Cate-Nelson quadrants analysis (Cate & Nelson (1971)
<doi:10.2136/sssaj1971.03615995003500040048x>), 4) the linear-plateau regression
(Anderson & Nelson (1975) <doi:10.2307/2529422>), 5) the quadratic-plateau
regression (Bullock & Bullock (1994)
<doi:10.2134/agronj1994.00021962008600010033x>), and 6) the Mitscherlich-type
exponential regression (Melsted & Peck (1977) <doi:10.2134/asaspecpub29.c1>).
The package development stemmed from ongoing work with the Fertilizer
Recommendation Support Tool (FRST) and Feed the Future Innovation Lab for
Collaborative Research on Sustainable Intensification (SIIL) projects.")
    (license license:expat)))

(define-public r-soiltaxonomy
  (package
    (name "r-soiltaxonomy")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SoilTaxonomy" version))
       (sha256
        (base32 "19rm7gmp6ya1fq1plmajjp8w8hf5q0j0va5flvpj39i2by3w3xrc"))))
    (properties `((upstream-name . "SoilTaxonomy")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ncss-tech/SoilTaxonomy")
    (synopsis
     "System of Soil Classification for Making and Interpreting Soil Surveys")
    (description
     "Taxonomic dictionaries, formative element lists, and functions related to the
maintenance, development and application of U.S. Soil Taxonomy.  Data and
functionality are based on official U.S. Department of Agriculture sources
including the latest edition of the Keys to Soil Taxonomy.  Descriptions and
metadata are obtained from the National Soil Information System or Soil Survey
Geographic databases.  Other sources are referenced in the data documentation.
Provides tools for understanding and interacting with concepts in the U.S. Soil
Taxonomic System.  Most of the current utilities are for working with taxonomic
concepts at the \"higher\" taxonomic levels: Order, Suborder, Great Group, and
Subgroup.")
    (license license:gpl3+)))

(define-public r-soilsaltindex
  (package
    (name "r-soilsaltindex")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SoilSaltIndex" version))
       (sha256
        (base32 "1hkdhpk257rzk74x9iwmnnj2m1yahlgidslkvwip86qh9rdjdaf9"))))
    (properties `((upstream-name . "SoilSaltIndex")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp r-raster))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SoilSaltIndex")
    (synopsis "Soil Salinity Indices Generation using Satellite Data")
    (description
     "The developed function generates soil salinity indices using satellite data,
utilizing multiple spectral bands such as Blue, Green, Red, Near-Infrared (NIR),
and Shortwave Infrared (SWIR1, SWIR2).  It computes 24 different salinity
indices crucial for monitoring and analyzing salt-affected soils efficiently.
For more details see, Rani, et al. (2022). <DOI: 10.1007/s12517-022-09682-3>.
One of the key features of the developed function is its flexibility.  Users can
provide any combination of the required spectral bands, and the function will
automatically calculate only the relevant indices based on the available data.
This dynamic capability ensures that users can maximize the utility of their
data without the need for all spectral bands, making the package versatile and
user-friendly.  Outputs are provided as @code{GeoTIFF} file format, facilitating
easy integration with GIS workflows.")
    (license license:gpl2+)))

(define-public r-soilr
  (package
    (name "r-soilr")
    (version "1.2.107")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SoilR" version))
       (sha256
        (base32 "1j2p5rr8a62fsf77akksk8533ilcx8a7319wq4gjz680va8glp5h"))))
    (properties `((upstream-name . "SoilR")))
    (build-system r-build-system)
    (propagated-inputs (list r-sets
                             r-purrr
                             r-igraph
                             r-expm
                             r-desolve
                             r-assertthat))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=SoilR")
    (synopsis "Models of Soil Organic Matter Decomposition")
    (description
     "This package provides functions for modeling Soil Organic Matter decomposition
in terrestrial ecosystems with linear and nonlinear systems of differential
equations.  The package implements models according to the compartmental system
representation described in Sierra and others (2012)
<doi:10.5194/gmd-5-1045-2012> and Sierra and others (2014)
<doi:10.5194/gmd-7-1919-2014>.")
    (license license:gpl3)))

(define-public r-soilphysics
  (package
    (name "r-soilphysics")
    (version "5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "soilphysics" version))
       (sha256
        (base32 "1ijnqlb52c3vm6ydiginp0f82vkip0kbsvbd9yac3scrc5g1b5z3"))))
    (properties `((upstream-name . "soilphysics")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinydashboard
                             r-shiny
                             r-rhandsontable
                             r-mass
                             r-fields
                             r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://arsilva87.github.io/soilphysics/")
    (synopsis "Soil Physical Analysis")
    (description "Basic and model-based soil physical analyses.")
    (license license:gpl2+)))

(define-public r-soilmanager
  (package
    (name "r-soilmanager")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SoilManageR" version))
       (sha256
        (base32 "13qp3z09wgsfkgy8bzg79qzpy9hd0ir7hggcy5x0wl13988b5bg2"))))
    (properties `((upstream-name . "SoilManageR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-readxl
                             r-rdpack
                             r-magrittr
                             r-lubridate
                             r-ggthemes
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://gitlab.com/SoilManageR/")
    (synopsis
     "Calculate Soil Management Indicators for Agricultural Practice Assessment")
    (description
     "Calculate numerical agricultural soil management indicators from on a management
timeline of an arable field.  Currently, indicators for carbon (C) input into
the soil system, soil tillage intensity rating (STIR), number of soil cover and
living plant cover days, N fertilization and livestock intensity, and plant
diversity are implemented.  The functions can also be used independently of the
management timeline to calculate some indicators.  The package contains tables
with reference information for the functions, as well as a *.xlsx template to
collect the management data.")
    (license license:expat)))

(define-public r-soilhypfit
  (package
    (name "r-soilhypfit")
    (version "0.1-7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "soilhypfit" version))
       (sha256
        (base32 "1ssz9w9ibw8mban0fq50j4lgr8vmkrsn8v9cpkg0wl28z1hqbhz3"))))
    (properties `((upstream-name . "soilhypfit")))
    (build-system r-build-system)
    (inputs (list mpfr gmp))
    (propagated-inputs (list r-soilhyp
                             r-snowfall
                             r-rmpfr
                             r-quadprog
                             r-nloptr
                             r-mgcv))
    (home-page "https://cran.r-project.org/package=soilhypfit")
    (synopsis
     "Modelling of Soil Water Retention and Hydraulic Conductivity Data")
    (description
     "This package provides functions for efficiently estimating properties of the Van
Genuchten-Mualem model for soil hydraulic parameters from possibly sparse soil
water retention and hydraulic conductivity data by multi-response parameter
estimation methods (Stewart, W.E., Caracotsios, M. Soerensen, J.P. (1992)
\"Parameter estimation from multi-response data\" <doi:10.1002/aic.690380502>).
Parameter estimation is simplified by exploiting the fact that residual and
saturated water contents and saturated conductivity are conditionally linear
parameters (Bates, D. M. and Watts, D. G. (1988) \"Nonlinear Regression Analysis
and Its Applications\" <doi:10.1002/9780470316757>).  Estimated parameters are
optionally constrained by the evaporation characteristic length (Lehmann, P.,
Bickel, S., Wei, Z. and Or, D. (2020) \"Physical Constraints for Improved Soil
Hydraulic Parameter Estimation by Pedotransfer Functions\"
<doi:10.1029/2019WR025963>) to ensure that the estimated parameters are
physically valid.  Common S3 methods and further utility functions allow to
process, explore and visualise estimation results.")
    (license (list license:gpl2+ license:lgpl3))))

(define-public r-soilhyp
  (package
    (name "r-soilhyp")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SoilHyP" version))
       (sha256
        (base32 "0f4djzmbyz6x5av0d4ii972dw6295c4l3c7gbwp0yinpy3rgvh0q"))))
    (properties `((upstream-name . "SoilHyP")))
    (build-system r-build-system)
    (propagated-inputs (list r-lubridate r-data-table))
    (home-page "https://cran.r-project.org/package=SoilHyP")
    (synopsis "Soil Hydraulic Properties")
    (description
     "This package provides functions for (1) soil water retention (SWC) and
unsaturated hydraulic conductivity (Ku) (van Genuchten-Mualem (@code{vGM} or
@code{vG}) [1, 2], Peters-Durner-Iden (PDI) [3, 4, 5], Brooks and Corey (bc)
[8]), (2) fitting of parameter for SWC and/or Ku using Shuffled Complex
Evolution (SCE) optimisation and (3) calculation of soil hydraulic properties
(Ku and soil water contents) based on the simplified evaporation method (SEM)
[6, 7].  Main references: [1] van Genuchten (1980)
<doi:10.2136/sssaj1980.03615995004400050002x>, [2] Mualem (1976)
<doi:10.1029/WR012i003p00513>, [3] Peters (2013) <doi:10.1002/wrcr.20548>, [4]
Iden and Durner (2013) <doi:10.1002/2014WR015937>, [5] Peters (2014)
<doi:10.1002/2014WR015937>, [6] Wind G. P. (1966), [7] Peters and Durner (2008)
<doi:10.1016/j.jhydrol.2008.04.016> and [8] Brooks and Corey (1964).")
    (license license:gpl2+)))

(define-public r-soilfunctionality
  (package
    (name "r-soilfunctionality")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SoilFunctionality" version))
       (sha256
        (base32 "1r9ifr6ckbqlccrsxl5sph4s7hgkzfzyk3chiw421vvckpbdsl4s"))))
    (properties `((upstream-name . "SoilFunctionality")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SoilFunctionality")
    (synopsis "Soil Functionality Measurement")
    (description
     "Generally, soil functionality is characterized by its capability to sustain
microbial activity, nutritional element supply, structural stability and aid for
crop production.  Since soil functions can be linked to 80% of ecosystem
services, conservation of degraded land should strive to restore not only the
capacity of soil to sustain flora but also ecosystem provisions.  The primary
ecosystem services of soil are carbon sequestration, food or biomass production,
provision of microbial habitat, nutrient recycling.  However, the actual
magnitude of soil functions provided by agricultural land uses has never been
quantified.  Nutrient supply capacity (NSC) is a measure of nutrient dynamics in
restored land uses.  Carbon accumulation proficiency (CAP) is a measure of
ecosystem carbon sequestration.  Biological activity index (BAI) is the average
of responses of all enzyme activities in treated land over control/reference
land.  The CAP parameter investigates how land uses may affect carbon flows,
retention, and sequestration.  The CAP provides a signal for C cycles, flows,
and the systems relative operational supremacy.")
    (license license:gpl3)))

(define-public r-soilfoodwebs
  (package
    (name "r-soilfoodwebs")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "soilfoodwebs" version))
       (sha256
        (base32 "09nc2ywxk8kl0akdwxz5zddrnqljvqpixjaz2bjkpxy0xj0qd82l"))))
    (properties `((upstream-name . "soilfoodwebs")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rootsolve
                             r-quadprog
                             r-lpsolve
                             r-diagram
                             r-desolve))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=soilfoodwebs")
    (synopsis "Soil Food Web Analysis")
    (description
     "Analyzing soil food webs or any food web measured at equilibrium.  The package
calculates carbon and nitrogen fluxes and stability properties using methods
described by Hunt et al. (1987) <doi:10.1007/BF00260580>, de Ruiter et al.
(1995) <doi:10.1126/science.269.5228.1257>, Holtkamp et al. (2011)
<doi:10.1016/j.soilbio.2010.10.004>, and Buchkowski and Lindo (2021)
<doi:10.1111/1365-2435.13706>.  The package can also manipulate the structure of
the food web as well as simulate food webs away from equilibrium and run
decomposition experiments.")
    (license license:gpl3)))

(define-public r-soilfda
  (package
    (name "r-soilfda")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SoilFDA" version))
       (sha256
        (base32 "1zar9affa9cndr4m97fs7zhs9gz3y95k1xi0ci4m5hjqg00aiz7z"))))
    (properties `((upstream-name . "SoilFDA")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SoilFDA")
    (synopsis "Fractal Dimension Analysis of Soil Particle Size Distribution")
    (description
     "Function for the computation of fractal dimension based on mass of soil particle
size distribution by Tyler & Wheatcraft (1992)
<doi:10.2136/sssaj1992.03615995005600020005x>.  It also provides functions for
calculation of mean weight and geometric mean diameter of particle size
distribution by Perfect et al. (1992)
<doi:10.2136/sssaj1992.03615995005600050012x>.")
    (license license:gpl3+)))

(define-public r-soildb
  (package
    (name "r-soildb")
    (version "2.8.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "soilDB" version))
       (sha256
        (base32 "122vwi9149xd8cadm4lrbq1fdxvdxzh3vs23zdvcbsm4dmzqhr6k"))))
    (properties `((upstream-name . "soilDB")))
    (build-system r-build-system)
    (propagated-inputs (list r-dbi r-data-table r-curl r-aqp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ncss-tech/soilDB/")
    (synopsis "Soil Database Interface")
    (description
     "This package provides a collection of functions for reading soil data from U.S.
Department of Agriculture Natural Resources Conservation Service (USDA-NRCS) and
National Cooperative Soil Survey (NCSS) databases.")
    (license license:gpl3+)))

(define-public r-soilconservation
  (package
    (name "r-soilconservation")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SoilConservation" version))
       (sha256
        (base32 "17jwz338h2km43fddnp21hf4ri0gb0y2jiggm1236m5wl87livxx"))))
    (properties `((upstream-name . "SoilConservation")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SoilConservation")
    (synopsis "Soil and Water Conservation")
    (description
     "Includes four functions: RFactor_calc(), RFactor_est(), KFactor() and
@code{SoilLoss}().  The rainfall erosivity factors can be calculated or
estimated, and soil erodibility will be estimated by the equation extracted from
the monograph.  Soil loss will be estimated by the product of five factors
(rainfall erosivity, soil erodibility, length and steepness slope,
cover-management factor and support practice factor.  In the future, additional
functions can be included.  This efforts to advance research in soil and water
conservation, with fast and accurate results.")
    (license license:gpl3)))

(define-public r-soilchemistry
  (package
    (name "r-soilchemistry")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "soilchemistry" version))
       (sha256
        (base32 "1fkq5zpkcwh7ps2r79kc2pw7q2i4hjjnckx2f29l8064hswd9jc4"))))
    (properties `((upstream-name . "soilchemistry")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=soilchemistry")
    (synopsis
     "Computation of Properties Related to Soil Chemical Environment and Nutrient Availability")
    (description
     "Facilitates basic and equation-based analyses of some important soil properties
related to soil chemical environment and nutrient availability to plants.
Freundlich H (1907). <doi:10.1515/zpch-1907-5723>.  Datta SP, Bhadoria PBS
(1999).
<doi:10.1002%2F%28SICI%291522-2624%28199903%29162%3A2%3C183%3A%3AAID-JPLN183%3E3.0.CO%3B2-A>.\"Boron
adsorption and desorption in some acid soils of West Bengal, India\".  Langmuir I
(1918). <doi:10.1021/ja02242a004> \"The adsorption of gases on plane surfaces of
glass, mica, and platinum\".  Khasawneh FE (1971).
<doi:10.2136/sssaj1971.03615995003500030029x> \"Solution ion activity and plant
growth\".")
    (license license:gpl3+)))

(define-public r-soilassessment
  (package
    (name "r-soilassessment")
    (version "0.2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "soilassessment" version))
       (sha256
        (base32 "0cgyji88q3r6b57q32nk8xgxb4q29678h8zgh9ywxhr3mr40i0qv"))))
    (properties `((upstream-name . "soilassessment")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-sp
                             r-soiltexture
                             r-sf
                             r-raster
                             r-randomforest
                             r-png
                             r-nnet
                             r-hmisc
                             r-googledrive
                             r-fuzzyahp
                             r-e1071
                             r-desolve
                             r-caret))
    (home-page "https://cran.r-project.org/package=soilassessment")
    (synopsis
     "Assessment Models for Agriculture Soil Conditions and Crop Suitability")
    (description
     "Soil assessment builds information for improved decision in soil management.  It
analyzes soil conditions with regard to agriculture crop suitability
requirements [such as those given by FAO
<https://www.fao.org/land-water/databases-and-software/crop-information/en/>]
soil fertility classes, soil erosion, and soil salinity classification
[<doi:10.1002/ldr.4211>].  Suitability requirements are for crops grouped into
cereal crops, nuts, legumes, fruits, vegetables, industrial crops, and root
crops.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-soil
  (package
    (name "r-soil")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SOIL" version))
       (sha256
        (base32 "0dvfh84xjzald51kmdb546par2rbjdn3nys9y9w7cvafv2lc5yj9"))))
    (properties `((upstream-name . "SOIL")))
    (build-system r-build-system)
    (propagated-inputs (list r-ncvreg r-mass r-glmnet r-brglm2))
    (home-page "https://github.com/emeryyi/SOIL")
    (synopsis "Sparsity Oriented Importance Learning")
    (description
     "Sparsity Oriented Importance Learning (SOIL) provides a new variable importance
measure for high dimensional linear regression and logistic regression from a
sparse penalization perspective, by taking into account the variable selection
uncertainty via the use of a sensible model weighting.  The package is an
implementation of Ye, C., Yang, Y., and Yang, Y. (2017+).")
    (license license:gpl2)))

(define-public r-sohpie
  (package
    (name "r-sohpie")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SOHPIE" version))
       (sha256
        (base32 "034q69zc6wfcxbaj8l095xcwjlh1iq0rgvsvd2czahxzls2nqsn7"))))
    (properties `((upstream-name . "SOHPIE")))
    (build-system r-build-system)
    (propagated-inputs (list r-robustbase r-gtools r-fdrtool r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SOHPIE")
    (synopsis
     "Statistical Approach via Pseudo-Value Information and Estimation")
    (description
     "SOHPIE (pronounced as SOFIE) is a novel pseudo-value regression approach for
differential co-abundance network analysis of microbiome data, which can include
additional clinical covariate in the model.  The full methodological details can
be found in Ahn S and Datta S (2023) <@code{arXiv:2303.13702v1>}.")
    (license license:gpl3)))

(define-public r-softclustering
  (package
    (name "r-softclustering")
    (version "2.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SoftClustering" version))
       (sha256
        (base32 "0wnl3a0jnrhafkjj3yk0rij1ya0y4nxzix29mr5wpwgkhfyky8zy"))))
    (properties `((upstream-name . "SoftClustering")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SoftClustering")
    (synopsis "Soft Clustering Algorithms")
    (description
     "It contains soft clustering algorithms, in particular approaches derived from
rough set theory: Lingras & West original rough k-means, Peters refined rough
k-means, and PI rough k-means.  It also contains classic k-means and a
corresponding illustrative demo.")
    (license license:gpl2)))

(define-public r-softclassval
  (package
    (name "r-softclassval")
    (version "1.0-20160527")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "softclassval" version))
       (sha256
        (base32 "1f5qj5sajy3dg0mgss6f9w6v5a2prcypld9jcv457bd4n4a409kr"))))
    (properties `((upstream-name . "softclassval")))
    (build-system r-build-system)
    (propagated-inputs (list r-svunit r-arrayhelpers))
    (home-page "http://softclassval.r-forge.r-project.org/")
    (synopsis "Soft Classification Performance Measures")
    (description
     "An extension of sensitivity, specificity, positive and negative predictive value
to continuous predicted and reference memberships in [0, 1].")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-softbib
  (package
    (name "r-softbib")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "softbib" version))
       (sha256
        (base32 "0rhia0vx53lhmykf2rmnr9kbcvld35n2and1l3riqdfr6jrdg8pb"))))
    (properties `((upstream-name . "softbib")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmarkdown r-renv r-checkmate r-bibtex))
    (home-page "https://github.com/vincentarelbundock/softbib")
    (synopsis "Software Bibliographies for R Projects")
    (description
     "Detect libraries used in a project and automatically create software
bibliographies in PDF', Word', Rmarkdown', and @code{BibTeX} formats.")
    (license license:gpl3+)))

(define-public r-softbart
  (package
    (name "r-softbart")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SoftBart" version))
       (sha256
        (base32 "0s7d2gxh6qnpwzagfrwhacgd7w3hxx3xkxrhk6kqjykpckdwy091"))))
    (properties `((upstream-name . "SoftBart")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm
                             r-scales
                             r-rcpparmadillo
                             r-rcpp
                             r-progress
                             r-mass
                             r-glmnet
                             r-caret))
    (home-page "https://cran.r-project.org/package=SoftBart")
    (synopsis "Implements the SoftBart Algorithm")
    (description
     "This package implements the @code{SoftBart} model of described by Linero and
Yang (2018) <doi:10.1111/rssb.12293>, with the optional use of a
sparsity-inducing prior to allow for variable selection.  For usability, the
package maintains the same style as the @code{BayesTree} package.")
    (license license:gpl2+)))

(define-public r-sofia
  (package
    (name "r-sofia")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SOFIA" version))
       (sha256
        (base32 "1jlaggxa2nz2r5yb2xmf0dh5wmxfdk6xwn0l3nwsqh82vyxhp7l9"))))
    (properties `((upstream-name . "SOFIA")))
    (build-system r-build-system)
    (inputs (list circos))
    (propagated-inputs (list r-png))
    (home-page "http://cggl.horticulture.wisc.edu")
    (synopsis "Making Sophisticated and Aesthetical Figures in R")
    (description
     "Software that leverages the capabilities of Circos by manipulating data,
preparing configuration files, and running the Perl-native Circos directly from
the R environment with minimal user intervention.  Circos is a novel software
that addresses the challenges in visualizing genetic data by creating circular
ideograms composed of tracks of heatmaps, scatter plots, line plots, histograms,
links between common markers, glyphs, text, and etc.  Please see
<http://www.circos.ca>.")
    (license license:gpl3)))

(define-public r-sofi
  (package
    (name "r-sofi")
    (version "0.16.4.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Sofi" version))
       (sha256
        (base32 "0h9ir0xrwmsabfhwsr9hbpwabh1fsb3p51y8qcgm92iyvc6dl3mf"))))
    (properties `((upstream-name . "Sofi")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-sampling r-foreign))
    (home-page "http://www.sofi.uno/")
    (synopsis "Interfaz interactiva con fines didacticos")
    (description
     "Este paquete tiene la finalidad de ayudar a aprender de una forma interactiva,
teniendo ejemplos y la posibilidad de resolver nuevos al mismo tiempo.  Apuntes
de clase interactivos.")
    (license license:gpl2+)))

(define-public r-sofa
  (package
    (name "r-sofa")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sofa" version))
       (sha256
        (base32 "1w78f8pnx4vh6znpwwcy9k4j795zji4bv88mi6ihdv09ar7if7yv"))))
    (properties `((upstream-name . "sofa")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6 r-mime r-jsonlite r-crul))
    (native-inputs (list r-knitr))
    (home-page
     "https://github.com/ropensci/sofahttps://docs.ropensci.org/sofa")
    (synopsis "Connector to 'CouchDB'")
    (description
     "This package provides an interface to the @code{NoSQL} database @code{CouchDB}
(<http://couchdb.apache.org>).  Methods are provided for managing databases
within @code{CouchDB}', including creating/deleting/updating/transferring, and
managing documents within databases.  One can connect with a local
@code{CouchDB} instance, or a remote @code{CouchDB} databases such as Cloudant'.
 Documents can be inserted directly from vectors, lists, data.frames, and JSON'.
 Targeted at @code{CouchDB} v2 or greater.")
    (license license:expat)))

(define-public r-sodavis
  (package
    (name "r-sodavis")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sodavis" version))
       (sha256
        (base32 "1jf5sml51qxlpgv5b1rvyig4dm4ijw2hpnk85zf790jp22xm64jw"))))
    (properties `((upstream-name . "sodavis")))
    (build-system r-build-system)
    (propagated-inputs (list r-nnet r-mvtnorm r-mass))
    (home-page "https://cran.r-project.org/package=sodavis")
    (synopsis
     "SODA: Main and Interaction Effects Selection for Logistic Regression, Quadratic Discriminant and General Index Models")
    (description
     "Variable and interaction selection are essential to classification in
high-dimensional setting.  In this package, we provide the implementation of
SODA procedure, which is a forward-backward algorithm that selects both main and
interaction effects under logistic regression and quadratic discriminant
analysis.  We also provide an extension, S-SODA, for dealing with the variable
selection problem for semi-parametric models with continuous responses.")
    (license license:gpl2)))

(define-public r-socviz
  (package
    (name "r-socviz")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "socviz" version))
       (sha256
        (base32 "1294xwy26vshmaiw3pkx3f90j8g6fv64gygv88b7lnz6fhai1nq5"))))
    (properties `((upstream-name . "socviz")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-rlang r-magrittr r-fs r-dplyr))
    (home-page "http://kjhealy.github.io/socviz/")
    (synopsis "Utility Functions and Data Sets for Data Visualization")
    (description
     "Supporting materials for a course and book on data visualization.  It contains
utility functions for graphs and several sample data sets.  See Healy (2019)
<ISBN 978-0691181622>.")
    (license license:expat)))

(define-public r-sociome
  (package
    (name "r-sociome")
    (version "2.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sociome" version))
       (sha256
        (base32 "1fyimv1i83ck7hwb7nnbdk46fiasi4kb8h944dni73536mnhc57g"))))
    (properties `((upstream-name . "sociome")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tidycensus
                             r-stringr
                             r-rlang
                             r-purrr
                             r-psych
                             r-mice
                             r-magrittr
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=sociome")
    (synopsis
     "Operationalizing Social Determinants of Health Data for Researchers")
    (description
     "Accesses raw data via API and calculates social determinants of health measures
for user-specified locations in the US, returning them in tidyverse- and
sf-compatible data frames.")
    (license license:expat)))

(define-public r-socialrisk
  (package
    (name "r-socialrisk")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "socialrisk" version))
       (sha256
        (base32 "1v9hvrqwihfprs32pplxsv00myf0p5pa32601s8s826jss3fkx4w"))))
    (properties `((upstream-name . "socialrisk")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-stringr
                             r-rlang
                             r-magrittr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/WYATTBENSKEN/multimorbidity")
    (synopsis
     "Identifying Patient Social Risk from Administrative Health Care Data")
    (description
     "Social risks are increasingly becoming a critical component of health care
research.  One of the most common ways to identify social needs is by using
ICD-10-CM \"Z-codes.\" This package identifies social risks using varying
taxonomies of ICD-10-CM Z-codes from administrative health care data.  The
conceptual taxonomies come from: Centers for Medicare and Medicaid Services
(2021) <https://www.cms.gov/files/document/zcodes-infographic.pdf>, Reidhead
(2018) <https://web.mhanet.com/>, A Arons, S @code{DeSilvey}, C Fichtenberg, L
Gottlieb (2018)
<https://sirenetwork.ucsf.edu/tools-resources/resources/compendium-medical-terminology-codes-social-risk-factors>.")
    (license license:expat)))

(define-public r-socialranking
  (package
    (name "r-socialranking")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "socialranking" version))
       (sha256
        (base32 "0py0p2hbdfaf3phs94i97a955l6r7kxhc3ip2fg6r7glvgjrcrzn"))))
    (properties `((upstream-name . "socialranking")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-relations r-rdpack))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jassler/socialranking")
    (synopsis "Social Ranking Solutions for Power Relations on Coalitions")
    (description
     "The notion of power index has been widely used in literature to evaluate the
influence of individual players (e.g., voters, political parties, nations,
stockholders, etc.) involved in a collective decision situation like an
electoral system, a parliament, a council, a management board, etc., where
players may form coalitions.  Traditionally this ranking is determined through
numerical evaluation.  More often than not however only ordinal data between
coalitions is known.  The package socialranking offers a set of solutions to
rank players based on a transitive ranking between coalitions, including through
CP-Majority, ordinal Banzhaf or lexicographic excellence solution summarized by
Tahar Allouche, Bruno Escoffier, Stefano Moretti and Meltem ÃztÃ¼rk (2020,
<doi:10.24963/ijcai.2020/3>).")
    (license license:gpl3)))

(define-public r-socialmixr
  (package
    (name "r-socialmixr")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "socialmixr" version))
       (sha256
        (base32 "1drmcvkma3xym17mkpzswjxfcna1vn6r8ym7i21ws0m2whha1wwy"))))
    (properties `((upstream-name . "socialmixr")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-wpp2017
                             r-oai
                             r-memoise
                             r-lubridate
                             r-jsonlite
                             r-httr
                             r-data-table
                             r-curl
                             r-countrycode))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/epiforecasts/socialmixr")
    (synopsis "Social Mixing Matrices for Infectious Disease Modelling")
    (description
     "This package provides methods for sampling contact matrices from diary data for
use in infectious disease modelling, as discussed in Mossong et al. (2008)
<doi:10.1371/journal.pmed.0050074>.")
    (license license:expat)))

(define-public r-socialh
  (package
    (name "r-socialh")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "socialh" version))
       (sha256
        (base32 "134jdhnnjxgnc8k1ws7pni5g44imxblngf106rwp8ff5a2gmrf6c"))))
    (properties `((upstream-name . "socialh")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr r-ggplot2 r-dplyr r-data-table
                             r-circlize))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=socialh")
    (synopsis "Rank and Social Hierarchy for Gregarious Animals")
    (description
     "This package provides tools developed to facilitate the establishment of the
rank and social hierarchy for gregarious animals by the Si method developed by
Kondo & Hurnik (1990)<doi:10.1016/0168-1591(90)90125-W>.  It is also possible to
determine the number of agonistic interactions between two individuals,
sociometric and dyadics matrix from dataset obtained through electronic bins.
In addition, it is possible plotting the results using a bar plot, box plot, and
sociogram.")
    (license license:gpl3)))

(define-public r-social
  (package
    (name "r-social")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "social" version))
       (sha256
        (base32 "0ny81bhj80rlwwa6cgbkdz2rxz6bp0f3nvjr1sqxr4s8l8rnf4ic"))))
    (properties `((upstream-name . "social")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=social")
    (synopsis "Social Autocorrelation")
    (description
     "This package provides a set of functions to quantify and visualise social
autocorrelation.")
    (license license:gpl2+)))

(define-public r-soccer
  (package
    (name "r-soccer")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "socceR" version))
       (sha256
        (base32 "1vd0r3vixavg1br2q3kfn3nvl7dw854vkcfwcvyw411k84ivb13y"))))
    (properties `((upstream-name . "socceR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://github.com/ekstroem/socceR")
    (synopsis "Evaluating Sport Tournament Predictions")
    (description
     "This package provides functions for evaluating tournament predictions,
simulating results from individual soccer matches and tournaments.  See
<http://sandsynligvis.dk/2018/08/03/world-cup-prediction-winners/> for more
information.")
    (license license:gpl2+)))

(define-public r-soc-ca
  (package
    (name "r-soc-ca")
    (version "0.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "soc.ca" version))
       (sha256
        (base32 "16l0synym91b7djgzdcz43dnvw44fkyxky9gm37ixbxzq1czdp9w"))))
    (properties `((upstream-name . "soc.ca")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-shiny
                             r-rlang
                             r-reshape2
                             r-rcolorbrewer
                             r-purrr
                             r-matrix
                             r-magrittr
                             r-htmltable
                             r-gridextra
                             r-ggrepel
                             r-ggplot2
                             r-forcats
                             r-flextable
                             r-factominer
                             r-ellipse
                             r-dplyr))
    (home-page "https://github.com/Rsoc/soc.ca")
    (synopsis "Specific Correspondence Analysis for the Social Sciences")
    (description
     "Specific and class specific multiple correspondence analysis on survey-like
data.  Soc.ca is optimized to the needs of the social scientist and presents
easily interpretable results in near publication ready quality.")
    (license license:gpl3)))

(define-public r-sobolsequence
  (package
    (name "r-sobolsequence")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SobolSequence" version))
       (sha256
        (base32 "1vmp5jix3zvasvdirv8m88jc0cd8f34b4m1jvhs8g3v9lk8pxrrr"))))
    (properties `((upstream-name . "SobolSequence")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "http://web.maths.unsw.edu.au/~fkuo/sobol/")
    (synopsis "Sobol Sequences with Better Two-Dimensional Projections")
    (description
     "R implementation of S. Joe and F. Y. Kuo(2008) <DOI:10.1137/070709359>.  The
implementation is based on the data file new-joe-kuo-6.21201
<http://web.maths.unsw.edu.au/~fkuo/sobol/>.")
    (license license:bsd-3)))

(define-public r-sobolnp
  (package
    (name "r-sobolnp")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sobolnp" version))
       (sha256
        (base32 "00rmigjyd3fsmhrhbvjzrfsl4ir0xpfr4xbgjws6vkx2rmcqnq1s"))))
    (properties `((upstream-name . "sobolnp")))
    (build-system r-build-system)
    (propagated-inputs (list r-pbmcapply r-np r-minqa))
    (home-page "https://github.com/maikol-solis/sobolnp/")
    (synopsis "Nonparametric Sobol Estimator with Bootstrap Bandwidth")
    (description
     "Algorithm to estimate the Sobol indices using a non-parametric fit of the
regression curve.  The bandwidth is estimated using bootstrap to reduce the
finite-sample bias.  The package is based on the paper SolÃ­s, M. (2018)
<@code{arXiv:1803.03333>}.")
    (license license:expat)))

(define-public r-soas
  (package
    (name "r-soas")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SOAs" version))
       (sha256
        (base32 "1a3gwqyhw9fzb06xmyapxw0xyff1arb4sfsg3id7blkgxsmahd75"))))
    (properties `((upstream-name . "SOAs")))
    (build-system r-build-system)
    (propagated-inputs (list r-sfsmisc
                             r-partitions
                             r-lhs
                             r-igraph
                             r-frf2
                             r-doe-base
                             r-conf-design
                             r-combinat))
    (home-page "https://github.com/bertcarnell/SOAs")
    (synopsis "Creation of Stratum Orthogonal Arrays")
    (description
     "This package creates stratum orthogonal arrays (also known as strong orthogonal
arrays).  These are arrays with more levels per column than the typical
orthogonal array, and whose low order projections behave like orthogonal arrays,
when collapsing levels to coarser strata.  Details are described in Groemping
(2022) \"A unifying implementation of stratum (aka strong) orthogonal arrays\"
<http://www1.bht-berlin.de/FB_II/reports/Report-2022-002.pdf>.")
    (license license:gpl2+)))

(define-public r-soar
  (package
    (name "r-soar")
    (version "0.99-11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SOAR" version))
       (sha256
        (base32 "1n38gx5sxpkqfkk4y6vpp6g19b8bs5bisni9wn6311s0csizp86m"))))
    (properties `((upstream-name . "SOAR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SOAR")
    (synopsis "Memory management in R by delayed assignments")
    (description
     "Allows objects to be stored on disc and automatically recalled into memory, as
required, by delayed assignment.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-snvlfdr
  (package
    (name "r-snvlfdr")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SNVLFDR" version))
       (sha256
        (base32 "1nl414b1x5inp3bld5aipygz3d36x4z0jg3jirwyq9mdl9w3qbvw"))))
    (properties `((upstream-name . "SNVLFDR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SNVLFDR")
    (synopsis "Empirical Bayes Single Nucleotide Variant Calling")
    (description
     "Identifies single nucleotide variants in next-generation sequencing data by
estimating their local false discovery rates.  For more details, see
Karimnezhad, A. and Perkins, T. J. (2024) <doi:10.1038/s41598-024-51958-z>.")
    (license license:gpl3+)))

(define-public r-snvecr
  (package
    (name "r-snvecr")
    (version "3.9.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "snvecR" version))
       (sha256
        (base32 "1z383bbhbpz0hx3biwlh4dzzc0hmjqzka351xn572sb2pycxrgbb"))))
    (properties `((upstream-name . "snvecR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tibble
                             r-rlang
                             r-readr
                             r-purrr
                             r-glue
                             r-dplyr
                             r-desolve
                             r-cli
                             r-backports))
    (native-inputs (list r-knitr))
    (home-page "https://japhir.github.io/snvecR/")
    (synopsis "Calculate Earthâs Obliquity and Precession in the Past")
    (description
     "Easily calculate precession and obliquity from an orbital solution (defaults to
ZB18a from Zeebe and Lourens (2019) <doi:10.1126/science.aax0612>) and assumed
or reconstructed values for tidal dissipation (Td) and dynamical ellipticity
(Ed).  This is a translation and adaptation of the C'-code in the supplementary
material to Zeebe and Lourens (2022) <doi:10.1029/2021PA004349>, with further
details on the methodology described in Zeebe (2022)
<doi:10.3847/1538-3881/ac80f8>.  The name of the C'-routine is snvec', which
refers to the key units of computation: spin vector s and orbit normal vector n.")
    (license license:gpl3+)))

(define-public r-snsmart
  (package
    (name "r-snsmart")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "snSMART" version))
       (sha256
        (base32 "0sy8wvrjw1cbdlcdsjqk08k00v7bk1szkzv0a4xff56dis7z2hz3"))))
    (properties `((upstream-name . "snSMART")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncdist
                             r-tidyr
                             r-rjags
                             r-pracma
                             r-hdinterval
                             r-geepack
                             r-envstats
                             r-cubature
                             r-condmvnorm
                             r-bayestestr))
    (home-page "https://github.com/sidiwang/snSMART")
    (synopsis
     "Small N Sequential Multiple Assignment Randomized Trial Methods")
    (description
     "Consolidated data simulation, sample size calculation and analysis functions for
several @code{snSMART} (small sample sequential, multiple assignment, randomized
trial) designs under one library.  See Wei, B., Braun, T.M., Tamura, R.N. and
Kidwell, K.M. \"A Bayesian analysis of small n sequential multiple assignment
randomized trials (@code{snSMARTs}).\" (2018) Statistics in medicine, 37(26),
pp.3723-3732 <doi:10.1002/sim.7900>.")
    (license license:gpl2+)))

(define-public r-snsfdatasets
  (package
    (name "r-snsfdatasets")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SNSFdatasets" version))
       (sha256
        (base32 "1jam5j2s9imfrfdc3w80v4j0gnb09jkpq41lhrz636xw1wm2cids"))))
    (properties `((upstream-name . "SNSFdatasets")))
    (build-system r-build-system)
    (home-page "http://enricoschumann.net/R/packages/SNSFdatasets/")
    (synopsis
     "Download Datasets from the Swiss National Science Foundation (SNF, FNS, SNSF)")
    (description
     "Download and read datasets from the Swiss National Science Foundation (SNF, FNS,
SNSF; <https://snf.ch>).  The package is lightweight and without dependencies.
Downloaded data can optionally be cached, to avoid repeated downloads of the
same files.  There are also utilities for comparing different versions of
datasets, i.e.  to report added, removed and changed entries.")
    (license license:gpl3)))

(define-public r-snsequate
  (package
    (name "r-snsequate")
    (version "1.3-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SNSequate" version))
       (sha256
        (base32 "0k0g55jvm0ig6sgwkvsc63idf3vnbcw49a00l2f9rp8bqhglr0ip"))))
    (properties `((upstream-name . "SNSequate")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod
                             r-progress
                             r-plyr
                             r-moments
                             r-magic
                             r-knitr
                             r-equate
                             r-emdbook
                             r-ake))
    (home-page "https://www.mat.uc.cl/~jorge.gonzalez/")
    (synopsis
     "Standard and Nonstandard Statistical Models and Methods for Test Equating")
    (description
     "This package contains functions to perform various models and methods for test
equating (Kolen and Brennan, 2014 <doi:10.1007/978-1-4939-0317-7> ; Gonzalez and
Wiberg, 2017 <doi:10.1007/978-3-319-51824-4> ; von Davier et.  al, 2004
<doi:10.1007/b97446>).  It currently implements the traditional mean, linear and
equipercentile equating methods.  Both IRT observed-score and true-score
equating are also supported, as well as the mean-mean, mean-sigma, Haebara and
Stocking-Lord IRT linking methods.  It also supports newest methods such that
local equating, kernel equating (using Gaussian, logistic, Epanechnikov, uniform
and adaptive kernels) with presmoothing, and IRT parameter linking methods based
on asymmetric item characteristic functions.  Functions to obtain both standard
error of equating (SEE) and standard error of equating differences between two
equating functions (SEED) are also implemented for the kernel method of
equating.")
    (license license:gpl2+)))

(define-public r-snseg
  (package
    (name "r-snseg")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SNSeg" version))
       (sha256
        (base32 "0p2a8ilxg17a2idnbiaqlmlpbafadl8w1np4bxzbgkkjs0s0qijc"))))
    (properties `((upstream-name . "SNSeg")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-mvtnorm))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SNSeg")
    (synopsis
     "Self-Normalization(SN) Based Change-Point Estimation for Time Series")
    (description
     "Implementations self-normalization (SN) based algorithms for change-points
estimation in time series data.  This comprises nested local-window algorithms
for detecting changes in both univariate and multivariate time series developed
in Zhao, Jiang and Shao (2022) <doi:10.1111/rssb.12552>.")
    (license license:gpl3+)))

(define-public r-snschart
  (package
    (name "r-snschart")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SNSchart" version))
       (sha256
        (base32 "18rpg2zkqa6bxnbq3s0hlsg4wizz6iw3vckf5z66q3awxqycabg9"))))
    (properties `((upstream-name . "SNSchart")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SNSchart")
    (synopsis "Sequential Normal Scores in Statistical Process Management")
    (description
     "The methods discussed in this package are new non-parametric methods based on
sequential normal scores SNS (Conover et al (2017)
<doi:10.1080/07474946.2017.1360091>), designed for sequences of observations,
usually time series data, which may occur singly or in batches, and may be
univariate or multivariate.  These methods are designed to detect changes in the
process, which may occur as changes in location (mean or median), changes in
scale (standard deviation, or variance), or other changes of interest in the
distribution of the observations, over the time observed.  They usually apply to
large data sets, so computations need to be simple enough to be done in a
reasonable time on a computer, and easily updated as each new observation (or
batch of observations) becomes available.  Some examples and more detail in SNS
is presented in the work by Conover et al (2019) <@code{arXiv:1901.04443>}.")
    (license license:expat)))

(define-public r-sns
  (package
    (name "r-sns")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sns" version))
       (sha256
        (base32 "1zb0s89lp2049i7l919dpd7bd396y9s6v8913qz56fvsq95sbvcn"))))
    (properties `((upstream-name . "sns")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv r-mvtnorm r-coda))
    (home-page "https://cran.r-project.org/package=sns")
    (synopsis "Stochastic Newton Sampler (SNS)")
    (description
     "Stochastic Newton Sampler (SNS) is a Metropolis-Hastings-based, Markov Chain
Monte Carlo sampler for twice differentiable, log-concave probability density
functions (PDFs) where the proposal density function is a multivariate Gaussian
resulting from a second-order Taylor-series expansion of log-density around the
current point.  The mean of the Gaussian proposal is the full Newton-Raphson
step from the current point.  A Boolean flag allows for switching from SNS to
Newton-Raphson optimization (by choosing the mean of proposal function as next
point).  This can be used during burn-in to get close to the mode of the PDF
(which is unique due to concavity).  For high-dimensional densities, mixing can
be improved via state space partitioning strategy, in which SNS is applied to
disjoint subsets of state space, wrapped in a Gibbs cycle.  Numerical
differentiation is available when analytical expressions for gradient and
Hessian are not available.  Facilities for validation and numerical
differentiation of log-density are provided.  Note: Formerly available versions
of the @code{MfUSampler} can be obtained from the archive
<https://cran.r-project.org/src/contrib/Archive/@code{MfUSampler/>}.")
    (license license:gpl2+)))

(define-public r-snpsettest
  (package
    (name "r-snpsettest")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "snpsettest" version))
       (sha256
        (base32 "0djz39b59x3kd5albdwjsgvln1qbm9xfbbglbym87xc3924sbwqj"))))
    (properties `((upstream-name . "snpsettest")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-gaston r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/HimesGroup/snpsettest")
    (synopsis "Set-Based Association Test using GWAS Summary Statistics")
    (description
     "The goal of snpsettest is to provide simple tools that perform set-based
association tests (e.g., gene-based association tests) using GWAS (genome-wide
association study) summary statistics.  A set-based association test in this
package is based on the statistical model described in VEGAS (versatile
gene-based association study), which combines the effects of a set of SNPs
accounting for linkage disequilibrium between markers.  This package uses a
different approach from the original VEGAS implementation to compute set-level p
values more efficiently, as described in
<https://github.com/@code{HimesGroup/snpsettest/wiki/Statistical-test-in-snpsettest>}.")
    (license license:gpl3+)))

(define-public r-snpready
  (package
    (name "r-snpready")
    (version "0.9.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "snpReady" version))
       (sha256
        (base32 "1r96j8zh84dn7qh3zgl0p0v3a80hx2wd3c4jgjlr43hzl7yglpqr"))))
    (properties `((upstream-name . "snpReady")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rgl r-matrixcalc r-matrix r-impute))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=snpReady")
    (synopsis "Preparing Genotypic Datasets in Order to Run Genomic Analysis")
    (description
     "Three functions to clean, summarize and prepare genomic datasets to Genome
Selection and Genome Association analysis and to estimate population genetic
parameters.")
    (license license:gpl3)))

(define-public r-snpls
  (package
    (name "r-snpls")
    (version "1.0.27")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sNPLS" version))
       (sha256
        (base32 "0h05crdmwmzax4pwmyxb2z1dagyqb3kybpxc064y1kl8m84kcyvv"))))
    (properties `((upstream-name . "sNPLS")))
    (build-system r-build-system)
    (propagated-inputs (list r-pbapply
                             r-matrix
                             r-mass
                             r-ks
                             r-ggrepel
                             r-ggplot2
                             r-future-apply
                             r-future
                             r-clickr))
    (home-page "https://cran.r-project.org/package=sNPLS")
    (synopsis "NPLS Regression with L1 Penalization")
    (description
     "This package provides tools for performing variable selection in three-way data
using N-PLS in combination with L1 penalization, Selectivity Ratio and VIP
scores.  The N-PLS model (Rasmus Bro, 1996
<DOI:10.1002/(SICI)1099-128X(199601)10:1%3C47::AID-CEM400%3E3.0.CO;2-C>) is the
natural extension of PLS (Partial Least Squares) to N-way structures, and tries
to maximize the covariance between X and Y data arrays.  The package also adds
variable selection through L1 penalization, Selectivity Ratio and VIP scores.")
    (license license:gpl2+)))

(define-public r-snplist
  (package
    (name "r-snplist")
    (version "0.18.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "snplist" version))
       (sha256
        (base32 "1sa2s3br4agx920jpr250iih61p99v500nh0c1w99w0846g9908c"))))
    (properties `((upstream-name . "snplist")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsqlite r-rcpp r-r-utils r-dbi r-biomart))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=snplist")
    (synopsis "Tools to Create Gene Sets")
    (description
     "This package provides a set of functions to create SQL tables of gene and SNP
information and compose them into a SNP Set, for example to export to a PLINK
set.")
    (license license:gpl3)))

(define-public r-snplinkage
  (package
    (name "r-snplinkage")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "snplinkage" version))
       (sha256
        (base32 "1kfmpr81wc8ckz0mr093a7nk2x6xihss2ziwd7d7ykwl6pcl1w6k"))))
    (properties `((upstream-name . "snplinkage")))
    (build-system r-build-system)
    (propagated-inputs (list r-snprelate
                             r-reshape2
                             r-magrittr
                             r-knitr
                             r-gwastools
                             r-gtable
                             r-ggrepel
                             r-ggplot2
                             r-gdsfmt
                             r-data-table
                             r-cowplot
                             r-biomart))
    (native-inputs (list r-knitr))
    (home-page "https://gitlab.com/thomaschln/snplinkage")
    (synopsis
     "Single Nucleotide Polymorphisms Linkage Disequilibrium Visualizations")
    (description
     "Linkage disequilibrium visualizations of up to several hundreds of single
nucleotide polymorphisms (SNPs), annotated with chromosomic positions and gene
names.  Two types of plots are available for small numbers of SNPs (<40) and for
large numbers (tested up to 500).  Both can be extended by combining other
ggplots, e.g. association studies results, and functions enable to directly
visualize the effect of SNP selection methods, as minor allele frequency
filtering and @code{TagSNP} selection, with a second correlation heatmap.  The
SNPs correlations are computed on Genotype Data objects from the GWASTools
package using the SNPRelate package, and the plots are customizable ggplot2 and
gtable objects and are annotated using the @code{biomaRt} package.  Usage is
detailed in the vignette with example data and results from up to 500 SNPs of
1,200 scans are in Charlon T. (2019)
<doi:10.13097/archive-ouverte/unige:161795>.")
    (license license:gpl3)))

(define-public r-snpknock
  (package
    (name "r-snpknock")
    (version "0.8.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SNPknock" version))
       (sha256
        (base32 "121pdgvdffj61hw8x8m76na0cg5iw5gb586q05ikw5d959mmbzrs"))))
    (properties `((upstream-name . "SNPknock")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-rcppprogress r-rcpparmadillo r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://msesia.github.io/snpknock")
    (synopsis "Knockoffs for Hidden Markov Models and Genetic Data")
    (description
     "Generate knockoffs for genetic data and hidden Markov models.  For more
information, see the website below and the accompanying papers: \"Gene hunting
with hidden Markov model knockoffs\", Sesia et al., Biometrika, 2019,
(<doi:10.1093/biomet/asy033>). \"Multi-resolution localization of causal variants
across the genome\", Sesia et al., @code{bioRxiv}, 2019, (<doi:10.1101/631390>).")
    (license license:gpl3)))

(define-public r-snpfiltr
  (package
    (name "r-snpfiltr")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SNPfiltR" version))
       (sha256
        (base32 "01bm848v0jvf8v568ls8iglz7cjcikriaa7vpavz1smkkm1bs71d"))))
    (properties `((upstream-name . "SNPfiltR")))
    (build-system r-build-system)
    (propagated-inputs (list r-vcfr
                             r-rtsne
                             r-gridextra
                             r-ggridges
                             r-ggplot2
                             r-cluster
                             r-adegenet))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SNPfiltR")
    (synopsis "Interactively Filter SNP Datasets")
    (description
     "Is designed to interactively and reproducibly visualize and filter SNP
(single-nucleotide polymorphism) datasets.  This R-based implementation of SNP
and genotype filters facilitates an interactive and iterative SNP filtering
pipeline, which can be documented reproducibly via Rmarkdown.  SN@code{PfiltR}
contains functions for visualizing various quality and missing data metrics for
a SNP dataset, and then filtering the dataset based on user specified cutoffs.
All functions take @code{vcfR} objects as input, which can easily be generated
by reading standard vcf (variant call format) files into R using the R package
@code{vcfR} (Knaus and GrÃ¼nwald) (<doi:10.1111/1755-0998.12549>).  Each
SN@code{PfiltR} function can return a newly filtered @code{vcfR} object, which
can then be written to a local directory in standard vcf format using the
@code{vcfR} package, for downstream population genetic and phylogenetic
analyses.")
    (license license:expat)))

(define-public r-snpassoc
  (package
    (name "r-snpassoc")
    (version "2.1-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SNPassoc" version))
       (sha256
        (base32 "0ffsxlvdpd77sav5jjzv6vx5vwhh5gmw55ndzvf4299nnx5l2xka"))))
    (properties `((upstream-name . "SNPassoc")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-survival
                             r-poisbinom
                             r-plyr
                             r-mvtnorm
                             r-haplo-stats
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/isglobal-brge/SNPassoc")
    (synopsis "SNPs-Based Whole Genome Association Studies")
    (description
     "This package provides functions to perform most of the common analysis in genome
association studies are implemented.  These analyses include descriptive
statistics and exploratory analysis of missing values, calculation of
Hardy-Weinberg equilibrium, analysis of association based on generalized linear
models (either for quantitative or binary traits), and analysis of multiple SNPs
(haplotype and epistasis analysis).  Permutation test and related tests (sum
statistic and truncated product) are also implemented.  Max-statistic and
genetic risk-allele score exact distributions are also possible to be estimated.
 The methods are described in Gonzalez JR et al., 2007 <doi:
10.1093/bioinformatics/btm025>.")
    (license license:gpl2+)))

(define-public r-snpannotator
  (package
    (name "r-snpannotator")
    (version "0.2.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SNPannotator" version))
       (sha256
        (base32 "0g2k6ijsrqnwqj6llrfvsgzk1hgf96c0pd8rg5ry633jvjngf5v2"))))
    (properties `((upstream-name . "SNPannotator")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-rmarkdown
                             r-progress
                             r-openxlsx
                             r-kableextra
                             r-jsonlite
                             r-httr
                             r-ggplot2
                             r-foreach
                             r-doparallel
                             r-data-table))
    (home-page "https://cran.r-project.org/package=SNPannotator")
    (synopsis
     "Investigating the Functional Characteristics of Selected SNPs and Their Vicinity Genomic Region")
    (description
     "To investigate the functional characteristics of selected SNPs and their
vicinity genomic region.  Linked SNPs in moderate to high linkage disequilibrium
(e.g. r2>0.50) with the corresponding index SNPs will be selected for further
analysis.")
    (license license:gpl3)))

(define-public r-snpaimer
  (package
    (name "r-snpaimer")
    (version "2.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "snpAIMeR" version))
       (sha256
        (base32 "1b24x8aqfk909nzy3xzpvrcrfisnvh0p5v895yrsx8knm1cjh0x3"))))
    (properties `((upstream-name . "snpAIMeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-withr
                             r-tidyr
                             r-readr
                             r-magrittr
                             r-ggplot2
                             r-foreach
                             r-forcats
                             r-dplyr
                             r-doparallel
                             r-adegenet))
    (home-page "https://github.com/OksanaVe/snpAIMeR")
    (synopsis "Assess the Diagnostic Power of Genomic Marker Combinations")
    (description
     "Population genetics package for designing diagnostic panels.  Candidate markers,
marker combinations, and different panel sizes are assessed for how well they
can predict the source population of known samples.  Requires a genotype file of
candidate markers in STRUCTURE format.  Methods for population cross-validation
are described in Jombart (2008) <doi:10.1093/bioinformatics/btn129>.")
    (license license:expat)))

(define-public r-snowquery
  (package
    (name "r-snowquery")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "snowquery" version))
       (sha256
        (base32 "1dp4hwsrn7k6d3cvhqspi0g6p7m70rb4s3pk5yljnfx3fisig9pk"))))
    (properties `((upstream-name . "snowquery")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml r-rsqlite r-rpostgres r-reticulate r-dbi))
    (home-page "https://github.com/mermelstein/snowquery")
    (synopsis "Query 'Snowflake' Databases with 'SQL'")
    (description
     "This package provides a wrapper allowing SQL queries to be run on a Snowflake
instance directly from an R script, by using the snowflake-connector-python
package in the background.")
    (license license:gpl3+)))

(define-public r-snowft
  (package
    (name "r-snowft")
    (version "1.6-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "snowFT" version))
       (sha256
        (base32 "0y7ql23vv5qdx0pgblfybjwxc0rgvv7z868c6axgy78xrzm6qjd1"))))
    (properties `((upstream-name . "snowFT")))
    (build-system r-build-system)
    (propagated-inputs (list r-snow r-rlecuyer))
    (home-page "http://www.stat.washington.edu/hana/parallel/snowFT-doc.pdf")
    (synopsis "Fault Tolerant Simple Network of Workstations")
    (description
     "Extension of the snow package supporting fault tolerant and reproducible
applications, as well as supporting easy-to-use parallel programming - only one
function is needed.  Dynamic cluster size is also available.")
    (license license:gpl2+)))

(define-public r-snowflakes
  (package
    (name "r-snowflakes")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "snowflakes" version))
       (sha256
        (base32 "0adna9jfs0wv4dhyjczxhq7c0ldjfai07p1q9119avxyilv9x0vi"))))
    (properties `((upstream-name . "snowflakes")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=snowflakes")
    (synopsis "Random Snowflake Generator")
    (description
     "The function generates and plots random snowflakes.  Each snowflake is defined
by a given diameter, width of the crystal, color, and random seed.  Snowflakes
are plotted in such way that they always remain round, no matter what the aspect
ratio of the plot is.  Snowflakes can be created using transparent colors, which
creates a more interesting, somewhat realistic, image.  Images of the snowflakes
can be separately saved as svg files and used in websites as static or animated
images.")
    (license license:gpl2+)))

(define-public r-snowboot
  (package
    (name "r-snowboot")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "snowboot" version))
       (sha256
        (base32 "0jj3p0g4rwml8vmlwj10rfisxfjlipcrpvq5z134z4j35jp6ggv3"))))
    (properties `((upstream-name . "snowboot")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-rcpp r-igraph))
    (home-page "https://cran.r-project.org/package=snowboot")
    (synopsis "Bootstrap Methods for Network Inference")
    (description
     "This package provides functions for analysis of network objects, which are
imported or simulated by the package.  The non-parametric methods of analysis
center on snowball and bootstrap sampling for estimating functions of network
degree distribution.  For other parameters of interest, see, e.g., bootnet
package.")
    (license license:gpl3)))

(define-public r-snotelr
  (package
    (name "r-snotelr")
    (version "1.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "snotelr" version))
       (sha256
        (base32 "1972rnaxxhv3jns4y5chy3x2qqyh6fixay0irpn1yk70ka6r6jga"))))
    (properties `((upstream-name . "snotelr")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-rvest r-memoise r-httr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bluegreen-labs/snotelr")
    (synopsis "Calculate and Visualize 'SNOTEL' Snow Data and Seasonality")
    (description
     "Programmatic interface to the SNOTEL snow data
(<https://www.nrcs.usda.gov/programs-initiatives/sswsf-snow-survey-and-water-supply-forecasting-program>).
 Provides easy downloads of snow data into your R work space or a local
directory.  Additional post-processing routines to extract snow season indexes
are provided.")
    (license license:agpl3)))

(define-public r-snn
  (package
    (name "r-snn")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "snn" version))
       (sha256
        (base32 "0yywn3v1iz9xizwli3gmzprkx66b5a813mbp8hq2vsj8n4lfj8r5"))))
    (properties `((upstream-name . "snn")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=snn")
    (synopsis "Stabilized Nearest Neighbor Classifier")
    (description
     "Implement K-nearest neighbor classifier, weighted nearest neighbor classifier,
bagged nearest neighbor classifier, optimal weighted nearest neighbor classifier
and stabilized nearest neighbor classifier, and perform model selection via 5
fold cross-validation for them.  This package also provides functions for
computing the classification error and classification instability of a
classification procedure.")
    (license license:gpl3)))

(define-public r-snha
  (package
    (name "r-snha")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "snha" version))
       (sha256
        (base32 "1bksc9s23widrdvp1wfx6808b4cdwvmq3iqkd87pbcr16bvpvnwc"))))
    (properties `((upstream-name . "snha")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mittelmark/snha")
    (synopsis "Creating Correlation Networks using St. Nicolas House Analysis")
    (description
     "Create correlation networks using St.  Nicolas House Analysis ('SNHA').  The
package can be used for visualizing multivariate data similar to Principal
Component Analysis or Multidimensional Scaling using a ranking approach.  In
contrast to MDS and PCA', SNHA uses a network approach to explore interacting
variables.  For details see Hermanussen et.  al.  2021',
<doi:10.3390/ijerph18041741>.")
    (license license:expat)))

(define-public r-snfa
  (package
    (name "r-snfa")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "snfa" version))
       (sha256
        (base32 "01k2nw8b8mwsd07jijfqk3rj88l9m36c5jai7qma56fjxx23s3wb"))))
    (properties `((upstream-name . "snfa")))
    (build-system r-build-system)
    (propagated-inputs (list r-rootsolve
                             r-rdpack
                             r-quadprog
                             r-prodlim
                             r-ggplot2
                             r-abind))
    (home-page "https://cran.r-project.org/package=snfa")
    (synopsis "Smooth Non-Parametric Frontier Analysis")
    (description
     "Fitting of non-parametric production frontiers for use in efficiency analysis.
Methods are provided for both a smooth analogue of Data Envelopment Analysis
(DEA) and a non-parametric analogue of Stochastic Frontier Analysis (SFA).
Frontiers are constructed for multiple inputs and a single output using
constrained kernel smoothing as in Racine et al. (2009), which allow for the
imposition of monotonicity and concavity constraints on the estimated frontier.")
    (license license:gpl3)))

(define-public r-snem
  (package
    (name "r-snem")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "snem" version))
       (sha256
        (base32 "1r8l7cs1y82fh7bdfl18ilw4fria1cabghqddnfr34vfp85cndk9"))))
    (properties `((upstream-name . "snem")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://cran.r-project.org/package=snem")
    (synopsis
     "EM Algorithm for Multivariate Skew-Normal Distribution with Overparametrization")
    (description
     "Efficient estimation of multivariate skew-normal distribution in closed form.")
    (license license:gpl2+)))

(define-public r-snbdata
  (package
    (name "r-snbdata")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SNBdata" version))
       (sha256
        (base32 "1066bz4pg27pfg9cl1ykgzvyvdcrqqchcnsjka9yy8wx3fvp018p"))))
    (properties `((upstream-name . "SNBdata")))
    (build-system r-build-system)
    (home-page "http://enricoschumann.net/R/packages/SNBdata/")
    (synopsis "Download Data from the Swiss National Bank (SNB)")
    (description
     "Download data (tables and datasets) from the Swiss National Bank (SNB;
<https://www.snb.ch/en>), the Swiss central bank.  The package is lightweight
and comes with few dependencies; suggested packages are used only if data is to
be transformed into particular data structures, for instance into zoo objects.
Downloaded data can optionally be cached, to avoid repeated downloads of the
same files.")
    (license license:gpl3)))

(define-public r-snapshot
  (package
    (name "r-snapshot")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "snapshot" version))
       (sha256
        (base32 "0cif1ybxxjpyp3spnh98qpyw1i5sgi1jlafcbcldbqhsdzfz4q10"))))
    (properties `((upstream-name . "snapshot")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=snapshot")
    (synopsis
     "Gadget N-body cosmological simulation code snapshot I/O utilities")
    (description
     "This package provides functions for reading and writing Gadget N-body snapshots.
 The Gadget code is popular in astronomy for running N-body / hydrodynamical
cosmological and merger simulations.  To find out more about Gadget see the main
distribution page at www.mpa-garching.mpg.de/gadget/.")
    (license license:gpl2)))

(define-public r-snapkrig
  (package
    (name "r-snapkrig")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "snapKrig" version))
       (sha256
        (base32 "1rgfz1kxlgp3an4cd37mhjn47hkjms0c6m2469wcbcw0xqjii9ld"))))
    (properties `((upstream-name . "snapKrig")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/deankoch/snapKrig")
    (synopsis
     "Fast Kriging and Geostatistics on Grids with Kronecker Covariance")
    (description
     "Geostatistical modeling and kriging with gridded data using spatially separable
covariance functions (Kronecker covariances).  Kronecker products in these
models provide shortcuts for solving large matrix problems in likelihood and
conditional mean, making @code{snapKrig} computationally efficient with large
grids.  The package supplies its own S3 grid object class, and a host of methods
including plot, print, Ops, square bracket replace/assign, and more.  Our
computational methods are described in Koch, Lele, Lewis (2020)
<doi:10.7939/r3-g6qb-bq70>.")
    (license license:expat)))

(define-public r-snapchatadsr
  (package
    (name "r-snapchatadsr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "snapchatadsR" version))
       (sha256
        (base32 "0h1wiwycrafh1wzmdw8dm0zw1xpzvj7w0943kh2bx8wpp7ara2vy"))))
    (properties `((upstream-name . "snapchatadsR")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite))
    (native-inputs (list r-knitr))
    (home-page "https://windsor.ai/")
    (synopsis "Get Snapchat Ads Data via the 'Windsor.ai' API")
    (description
     "Collect your data on digital marketing campaigns from Snapchat Ads using the
Windsor.ai API <https://windsor.ai/api-fields/>.")
    (license license:gpl3)))

(define-public r-snap
  (package
    (name "r-snap")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "snap" version))
       (sha256
        (base32 "02bvs1r72y59p33a7yj1rqw75h08pvl794hdvvm15jzq1hy6j0ra"))))
    (properties `((upstream-name . "snap")))
    (build-system r-build-system)
    (propagated-inputs (list r-tictoc
                             r-tensorflow
                             r-stringr
                             r-reticulate
                             r-readr
                             r-purrr
                             r-keras
                             r-ggplot2
                             r-forcats
                             r-dplyr
                             r-dbscan
                             r-corelearn))
    (home-page "https://rpubs.com/giancarlo_vercellino/snap")
    (synopsis "Simple Neural Application")
    (description
     "This package provides a simple wrapper to easily design vanilla deep neural
networks using Tensorflow'/'Keras backend for regression, classification and
multi-label tasks, with some tweaks and tricks (skip shortcuts, embedding,
feature selection and anomaly detection).")
    (license license:gpl3)))

(define-public r-snakesandladdersanalysis
  (package
    (name "r-snakesandladdersanalysis")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SnakesAndLaddersAnalysis" version))
       (sha256
        (base32 "0h3664h6d32q201qfyv9y2gg4fhg3azdpwpmx4qfbc10hsc1ghl9"))))
    (properties `((upstream-name . "SnakesAndLaddersAnalysis")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SnakesAndLaddersAnalysis")
    (synopsis "Play and Analyse the Game of Snakes and Ladders")
    (description
     "Plays the game of Snakes and Ladders and has tools for analyses.  The tools
included allow you to find the average moves to win, frequency of each square,
importance of the snakes and the ladders, the most common square and the
plotting of the game played.")
    (license license:gpl2)))

(define-public r-snake
  (package
    (name "r-snake")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Snake" version))
       (sha256
        (base32 "10i1bqsva70lzavfqvc56ga70ddjfwf0hqpspj8fqwa0jw86jmbh"))))
    (properties `((upstream-name . "Snake")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=Snake")
    (synopsis "Game of Snake")
    (description
     "This package implements snake in R as a programming example, see
<https://en.wikipedia.org/wiki/Snake_(video_game_genre)>.")
    (license license:gpl3)))

(define-public r-snahelper
  (package
    (name "r-snahelper")
    (version "1.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "snahelper" version))
       (sha256
        (base32 "04l6ad4rj45kr7p98nbpsqbqppv2bgp71n6zph0aqm6b13hp0jyy"))))
    (properties `((upstream-name . "snahelper")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny
                             r-rstudioapi
                             r-miniui
                             r-igraph
                             r-graphlayouts
                             r-ggraph
                             r-ggplot2
                             r-formatr
                             r-dt
                             r-colourpicker))
    (home-page "https://github.com/schochastics/snahelper")
    (synopsis "'RStudio' Addin for Network Analysis and Visualization")
    (description
     "RStudio addin which provides a GUI to visualize and analyse networks.  After
finishing a session, the code to produce the plot is inserted in the current
script.  Alternatively, the function SN@code{AhelperGadget}() can be used
directly from the console.  Additional addins include the Netreader() for
reading network files, Netbuilder() to create small networks via point and
click, and the Componentlayouter() to layout networks with many components
manually.")
    (license license:expat)))

(define-public r-smvgraph
  (package
    (name "r-smvgraph")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smvgraph" version))
       (sha256
        (base32 "06pvnrhd3q913nxhk1icj11xkd1is3qi31b0kv6zbc0qkixn1ym7"))))
    (properties `((upstream-name . "smvgraph")))
    (build-system r-build-system)
    (propagated-inputs (list r-sortable
                             r-shinydashboard
                             r-shiny
                             r-mass
                             r-fmsb
                             r-desctools))
    (home-page "https://cran.r-project.org/package=smvgraph")
    (synopsis
     "Various Multivariate Graphics with Variable Choice in Shiny Apps")
    (description
     "Mosaic diagram, scatterplot matrix, Andrews curves, parallel coordinate diagram,
radar diagram, and Chernoff plots as a Shiny app, which allow the order of
variables to be changed interactively.  The apps are intended as teaching
examples.")
    (license license:gpl3)))

(define-public r-smvar
  (package
    (name "r-smvar")
    (version "1.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SMVar" version))
       (sha256
        (base32 "1mh15iggzbd7ip15pkwzmiy8p712cvvns5pgdyw1pvkcvbvkxsma"))))
    (properties `((upstream-name . "SMVar")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SMVar")
    (synopsis "Structural Model for Variances")
    (description
     "Implementation of the structural model for variances in order to detect
differentially expressed genes from gene expression data.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-smut
  (package
    (name "r-smut")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SMUT" version))
       (sha256
        (base32 "029ishj3jfq957bbkb2xw4jj0qpm4dasb8vpdik43pvd670mgq5s"))))
    (properties `((upstream-name . "SMUT")))
    (build-system r-build-system)
    (propagated-inputs (list r-skat r-rcppeigen r-rcpp r-mass))
    (home-page "https://cran.r-project.org/package=SMUT")
    (synopsis "Multi-SNP Mediation Intersection-Union Test")
    (description
     "Testing the mediation effect of multiple SNPs on an outcome through a mediator.")
    (license license:gpl2+)))

(define-public r-smtl
  (package
    (name "r-smtl")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sMTL" version))
       (sha256
        (base32 "03xq19vh4g3j3jbpad92m2rg933h9lcy4wrzkdi9y47v8h3fqyw1"))))
    (properties `((upstream-name . "sMTL")))
    (build-system r-build-system)
    (propagated-inputs (list r-juliaconnector r-juliacall r-glmnet r-dplyr
                             r-caret))
    (home-page "https://github.com/gloewing/sMTL")
    (synopsis "Sparse Multi-Task Learning")
    (description
     "This package implements L0-constrained Multi-Task Learning and domain
generalization algorithms.  The algorithms are coded in Julia allowing for fast
implementations of the coordinate descent and local combinatorial search
algorithms.  For more details, see a preprint of the paper: Loewinger et al.,
(2022) <@code{arXiv:2212.08697>}.")
    (license license:expat)))

(define-public r-smss
  (package
    (name "r-smss")
    (version "1.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smss" version))
       (sha256
        (base32 "04lgfdcvnzpnpplyl62fy7slyiy8wkqpjjrzmclgqis3c9zkkncp"))))
    (properties `((upstream-name . "smss")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=smss")
    (synopsis
     "Datasets for Agresti and Finlay's \"Statistical Methods for the Social Sciences\"")
    (description
     "Datasets used in \"Statistical Methods for the Social Sciences\" (SMSS) by Alan
Agresti and Barbara Finlay.")
    (license license:gpl3)))

(define-public r-smsroc
  (package
    (name "r-smsroc")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sMSROC" version))
       (sha256
        (base32 "1fnsaww9a1i4wlv0bfyr2cqr5nb4c07badg3y64ja1mnmc5xl3y6"))))
    (properties `((upstream-name . "sMSROC")))
    (build-system r-build-system)
    (propagated-inputs (list r-thregi
                             r-survival
                             r-rms
                             r-plotroc
                             r-icenreg
                             r-ggplot2
                             r-foreach
                             r-flextable))
    (home-page "https://cran.r-project.org/package=sMSROC")
    (synopsis "Assessment of Diagnostic and Prognostic Markers")
    (description
     "This package provides estimations of the Receiver Operating Characteristic (ROC)
curve and the Area Under the Curve (AUC) based on the two-stages mixed-subjects
ROC curve estimator (Diaz-Coto et al. (2020) <doi:10.1515/ijb-2019-0097> and
Diaz-Coto et al. (2020) <doi:10.1080/00949655.2020.1736071>).")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-sms
  (package
    (name "r-sms")
    (version "2.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sms" version))
       (sha256
        (base32 "0vr5jy8bxbczaqr9kg0fnanxhv9nj51yzgacrb63k33cs85p981m"))))
    (properties `((upstream-name . "sms")))
    (build-system r-build-system)
    (propagated-inputs (list r-iterators r-foreach r-doparallel))
    (home-page "https://cran.r-project.org/package=sms")
    (synopsis "Spatial Microsimulation")
    (description
     "Produce small area population estimates by fitting census data to survey data.")
    (license license:gpl3)))

(define-public r-smr
  (package
    (name "r-smr")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SMR" version))
       (sha256
        (base32 "0gg3al3qpcvw3k5zm4j36kbd2ar7pqgxxx0jngcg76j1sjhz9g9m"))))
    (properties `((upstream-name . "SMR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://bendeivide.github.io/SMR/")
    (synopsis "Externally Studentized Midrange Distribution")
    (description
     "Computes the studentized midrange distribution (pdf, cdf and quantile) and
generates random numbers.")
    (license license:gpl2+)))

(define-public r-smplot2
  (package
    (name "r-smplot2")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smplot2" version))
       (sha256
        (base32 "1aigy0md8xgv9yfr0391wpc9a36566m5avm71g48688hg26djrh7"))))
    (properties `((upstream-name . "smplot2")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-tibble
                             r-rlang
                             r-pwr
                             r-patchwork
                             r-hmisc
                             r-ggpubr
                             r-ggplot2
                             r-dplyr
                             r-cowplot))
    (home-page "https://smin95.github.io/dataviz/")
    (synopsis
     "Creating Standalone and Composite Plots in 'ggplot2' for Publications")
    (description
     "This package provides functions for creating and annotating a composite plot in
ggplot2'.  Offers background themes and shortcut plotting functions that produce
figures that are appropriate for the format of scientific journals.  Some
methods are described in Min and Zhou (2021) <doi:10.3389/fgene.2021.802894>.")
    (license license:gpl2)))

(define-public r-smpic
  (package
    (name "r-smpic")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smpic" version))
       (sha256
        (base32 "0ff2146gjcrc6nvrbf4779jh076abhb843zzlk2zvqir13qfap61"))))
    (properties `((upstream-name . "smpic")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-imager r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mikkelkrogsholm/smpic")
    (synopsis "Creates Images Sized for Social Media")
    (description
     "This package creates images that are the proper size for social media.
Beautiful plots, charts and graphs wither and die if they are not shared.
Social media is perfect for this but every platform has its own image
dimensions.  With smpic you can easily save your plots with the exact dimensions
needed for the different platforms.")
    (license license:expat)))

(define-public r-smovie
  (package
    (name "r-smovie")
    (version "1.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smovie" version))
       (sha256
        (base32 "0a2npwp8bwkqqwpdb99mm993na282qaj63xhwf1s8bwvia4q4npp"))))
    (properties `((upstream-name . "smovie")))
    (build-system r-build-system)
    (propagated-inputs (list r-rpanel))
    (native-inputs (list r-knitr))
    (home-page "https://paulnorthrop.github.io/smovie/")
    (synopsis "Some Movies to Illustrate Concepts in Statistics")
    (description
     "This package provides movies to help students to understand statistical
concepts.  The rpanel package <https://cran.r-project.org/package=rpanel> is
used to create interactive plots that move to illustrate key statistical ideas
and methods.  There are movies to: visualise probability distributions
(including user-supplied ones); illustrate sampling distributions of the sample
mean (central limit theorem), the median, the sample maximum (extremal types
theorem) and (the Fisher transformation of the) product moment correlation
coefficient; examine the influence of an individual observation in simple linear
regression; illustrate key concepts in statistical hypothesis testing.  Also
provided are dpqr functions for the distribution of the Fisher transformation of
the correlation coefficient under sampling from a bivariate normal distribution.")
    (license license:gpl2+)))

(define-public r-smotewb
  (package
    (name "r-smotewb")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SMOTEWB" version))
       (sha256
        (base32 "1vkkzp8vcr1pf010871wl5c6d9knryx2lvqcxn6nfy42ffbr5yx9"))))
    (properties `((upstream-name . "SMOTEWB")))
    (build-system r-build-system)
    (propagated-inputs (list r-rpart r-rfast r-rann r-fnn))
    (home-page "https://cran.r-project.org/package=SMOTEWB")
    (synopsis "Imbalanced Resampling using SMOTE with Boosting (SMOTEWB)")
    (description
     "This package provides the SMOTE with Boosting (SMOTEWB) algorithm.  See F.
SaÄlam, M. A. Cengiz (2022) <doi:10.1016/j.eswa.2022.117023>.  It is a
SMOTE-based resampling technique which creates synthetic data on the links
between nearest neighbors.  SMOTEWB uses boosting weights to determine where to
generate new samples and automatically decides the number of neighbors for eacg
sample.  It is robust to noise and outperforms most of the alternatives
according to Matthew Correlation Coefficient metric.  Alternative resampling
methods are also available in the package.")
    (license license:expat)))

(define-public r-smotefamily
  (package
    (name "r-smotefamily")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smotefamily" version))
       (sha256
        (base32 "05ck6c4pldqqspb7abjh8vabc3g0fp6ivfbd9y2q2rm031mil4p2"))))
    (properties `((upstream-name . "smotefamily")))
    (build-system r-build-system)
    (propagated-inputs (list r-igraph r-fnn r-dbscan))
    (home-page "https://cran.r-project.org/package=smotefamily")
    (synopsis
     "Collection of Oversampling Techniques for Class Imbalance Problem Based on SMOTE")
    (description
     "This package provides a collection of various oversampling techniques developed
from SMOTE is provided.  SMOTE is a oversampling technique which synthesizes a
new minority instance between a pair of one minority instance and one of its K
nearest neighbor.  Other techniques adopt this concept with other criteria in
order to generate balanced dataset for class imbalance problem.")
    (license license:gpl3+)))

(define-public r-smosr
  (package
    (name "r-smosr")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smosr" version))
       (sha256
        (base32 "1arj4skjqnwfk4yr26iwp1j9pjp75xk18hap0ff1wqpxmvajlmgj"))))
    (properties `((upstream-name . "smosr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-terra
                             r-rcurl
                             r-ncdf4
                             r-lubridate
                             r-fields))
    (home-page "https://github.com/tshestakova/smosr")
    (synopsis "Acquire and Explore BEC-SMOS L4 Soil Moisture Data in R")
    (description
     "This package provides functions that automate accessing, downloading and
exploring Soil Moisture and Ocean Salinity (SMOS) Level 4 (L4) data developed by
Barcelona Expert Center (BEC).  Particularly, it includes functions to search
for, acquire, extract, and plot BEC-SMOS L4 soil moisture data downscaled to ~1
km spatial resolution.  Note that SMOS is one of Earth Explorer Opportunity
missions by the European Space Agency (ESA).  More information about SMOS
products can be found at <https://earth.esa.int/eogateway/missions/smos/data>.")
    (license license:gpl3)))

(define-public r-smoots
  (package
    (name "r-smoots")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smoots" version))
       (sha256
        (base32 "1drlnh22gn6fdziplnja5w7gc5lyvpjq54aqqmwfh6h78ivcg0vs"))))
    (properties `((upstream-name . "smoots")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-progressr
                             r-progress
                             r-future-apply
                             r-future))
    (home-page "https://cran.r-project.org/package=smoots")
    (synopsis
     "Nonparametric Estimation of the Trend and Its Derivatives in TS")
    (description
     "The nonparametric trend and its derivatives in equidistant time series (TS) with
short-memory stationary errors can be estimated.  The estimation is conducted
via local polynomial regression using an automatically selected bandwidth
obtained by a built-in iterative plug-in algorithm or a bandwidth fixed by the
user.  A Nadaraya-Watson kernel smoother is also built-in as a comparison.  With
version 1.1.0, a linearity test for the trend function, forecasting methods and
backtesting approaches are implemented as well.  The smoothing methods of the
package are described in Feng, Y., Gries, T., and Fritz, M. (2020)
<doi:10.1080/10485252.2020.1759598>.")
    (license license:gpl3)))

(define-public r-smoothy
  (package
    (name "r-smoothy")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smoothy" version))
       (sha256
        (base32 "1wx13da490vgamjj2yih4xsaja51ga3hiqwkylvj23x3mnvyqkn8"))))
    (properties `((upstream-name . "smoothy")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-tidyr r-stringr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=smoothy")
    (synopsis
     "Automatic Estimation of the Most Likely Drug Combination using Smooth Algorithm")
    (description
     "This package provides a flexible moving average algorithm for modeling drug
exposure in pharmacoepidemiology studies as presented in the article: Ouchi, D.,
Giner-Soriano, M., GÃ³mez-Lumbreras, A., Vedia Urgell, C.,Torres, F., & Morros,
R. (2022). \"Automatic Estimation of the Most Likely Drug Combination in
Electronic Health Records Using the Smooth Algorithm : Development and
Validation Study.\" JMIR medical informatics, 10(11), e37976.
<doi:10.2196/37976>.")
    (license license:gpl3+)))

(define-public r-smoothwin
  (package
    (name "r-smoothwin")
    (version "3.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SmoothWin" version))
       (sha256
        (base32 "0zq2sq0w4rs3hrra24wgbbzv88d1hx6m8q8gmc5h6nbs1172hs66"))))
    (properties `((upstream-name . "SmoothWin")))
    (build-system r-build-system)
    (propagated-inputs (list r-rfast r-nlme))
    (home-page "http://hamedhaseli.webs.com")
    (synopsis "Soft Windowing on Linear Regression")
    (description
     "The main function in the package utilizes a windowing function in the form of an
exponential weighting function to linear models.  The bandwidth and sharpness of
the window are controlled by two parameters.  Then, a series of tests are used
to identify the right parameters of the window (see Hamed Haselimashhadi et al
(2019) <https://www.biorxiv.org/content/10.1101/656678v1>).")
    (license license:lgpl2.0+)))

(define-public r-smoothtensor
  (package
    (name "r-smoothtensor")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SmoothTensor" version))
       (sha256
        (base32 "19mbxsbp656i4qdkr2wplil542pxxb3xfznh3vv2nxi23phr01gy"))))
    (properties `((upstream-name . "SmoothTensor")))
    (build-system r-build-system)
    (propagated-inputs (list r-rtensor r-matrix))
    (home-page "https://arxiv.org/abs/2111.04681")
    (synopsis "Collection of Smooth Tensor Estimation Methods")
    (description
     "This package provides a list of methods for estimating a smooth tensor with an
unknown permutation.  It also contains several multi-variate functions for
generating permuted signal tensors and corresponding observed tensors.  For a
detailed introduction for the model and estimation techniques, see the paper by
Chanwoo Lee and Miaoyan Wang (2021) \"Smooth tensor estimation with unknown
permutations\" <@code{arXiv:2111.04681>}.")
    (license license:gpl3)))

(define-public r-smoothtail
  (package
    (name "r-smoothtail")
    (version "2.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smoothtail" version))
       (sha256
        (base32 "1sqkwniz9m03k23nba9ndmdm0g03mdshzm8risr6wz98jcqjilrk"))))
    (properties `((upstream-name . "smoothtail")))
    (build-system r-build-system)
    (propagated-inputs (list r-logcondens))
    (home-page "http://www.kasparrufibach.ch")
    (synopsis "Smooth Estimation of GPD Shape Parameter")
    (description
     "Given independent and identically distributed observations X(1), ..., X(n) from
a Generalized Pareto distribution with shape parameter gamma in [-1,0], offers
several estimates to compute estimates of gamma.  The estimates are based on the
principle of replacing the order statistics by quantiles of a distribution
function based on a log--concave density function.  This procedure is justified
by the fact that the GPD density is log--concave for gamma in [-1,0].")
    (license license:gpl2+)))

(define-public r-smoothsurv
  (package
    (name "r-smoothsurv")
    (version "2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smoothSurv" version))
       (sha256
        (base32 "1fw5ibcsr13n3ycs1rkcnd3x08qgxz31hzfra6hpjkzcavcxdlk4"))))
    (properties `((upstream-name . "smoothSurv")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "https://msekce.karlin.mff.cuni.cz/~komarek/")
    (synopsis "Survival Regression with Smoothed Error Distribution")
    (description
     "Contains, as a main contribution, a function to fit a regression model with
possibly right, left or interval censored observations and with the error
distribution expressed as a mixture of G-splines.  Core part of the computation
is done in compiled C++ written using the Scythe Statistical Library Version
0.3.")
    (license license:gpl2+)))

(define-public r-smoothroctime
  (package
    (name "r-smoothroctime")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smoothROCtime" version))
       (sha256
        (base32 "03iihjxb5xdaf74cm9cajqqjli754mdmv5v1y4hla9vv23017ca1"))))
    (properties `((upstream-name . "smoothROCtime")))
    (build-system r-build-system)
    (propagated-inputs (list r-ks))
    (home-page "https://cran.r-project.org/package=smoothROCtime")
    (synopsis "Smooth Time-Dependent ROC Curve Estimation")
    (description
     "Computes smooth estimations for the Cumulative/Dynamic and Incident/Dynamic ROC
curves, in presence of right censorship, based on the bivariate kernel density
estimation of the joint distribution function of the Marker and Time-to-event
variables.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-smoothr
  (package
    (name "r-smoothr")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smoothr" version))
       (sha256
        (base32 "1sf57ywx4836dcz8s6h20d0r68y41c11gjbg78bpai4k1401a2sq"))))
    (properties `((upstream-name . "smoothr")))
    (build-system r-build-system)
    (propagated-inputs (list r-units r-terra r-sf))
    (native-inputs (list r-knitr))
    (home-page "https://strimas.com/smoothr/")
    (synopsis "Smooth and Tidy Spatial Features")
    (description
     "This package provides tools for smoothing and tidying spatial features (i.e.
lines and polygons) to make them more aesthetically pleasing.  Smooth curves,
fill holes, and remove small fragments from lines and polygons.")
    (license license:gpl3)))

(define-public r-smoothmest
  (package
    (name "r-smoothmest")
    (version "0.1-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smoothmest" version))
       (sha256
        (base32 "1m8kiwv9nl3c7xggwspa9pridmpgsq3s1ivwrgsv2aaxddwg1wis"))))
    (properties `((upstream-name . "smoothmest")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://www.unibo.it/sitoweb/christian.hennig/en")
    (synopsis "Smoothed M-Estimators for 1-Dimensional Location")
    (description
     "Some M-estimators for 1-dimensional location (Bisquare, ML for the Cauchy
distribution, and the estimators from application of the smoothing principle
introduced in Hampel, Hennig and Ronchetti (2011) to the above, the Huber
M-estimator, and the median, main function is smoothm), and Pitman estimator.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-smoothie
  (package
    (name "r-smoothie")
    (version "1.0-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smoothie" version))
       (sha256
        (base32 "1ari4av4vsydaxg56vsfifc50kxsqy8xlxl7by32capc6pcjwvq8"))))
    (properties `((upstream-name . "smoothie")))
    (build-system r-build-system)
    (home-page "https://ral.ucar.edu/staff/ericg/")
    (synopsis "Two-Dimensional Field Smoothing")
    (description
     "Perform two-dimensional smoothing for spatial fields using FFT and the
convolution theorem (see Gilleland 2013, <doi:10.5065/D61834G2>).")
    (license license:gpl2+)))

(define-public r-smoothic
  (package
    (name "r-smoothic")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smoothic" version))
       (sha256
        (base32 "11id442cgnk6a0nd0c20rz5ccxlq3hhpsmvmlvg93jf4v6c2046z"))))
    (properties `((upstream-name . "smoothic")))
    (build-system r-build-system)
    (propagated-inputs (list r-toordinal
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-purrr
                             r-numderiv
                             r-mass
                             r-ggplot2
                             r-dplyr
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/meadhbh-oneill/smoothic")
    (synopsis "Variable Selection Using a Smooth Information Criterion")
    (description
     "Implementation of the SIC epsilon-telescope method, either using single or
distributional (multiparameter) regression.  Includes classical regression with
normally distributed errors and robust regression, where the errors are from the
Laplace distribution.  The \"smooth generalized normal distribution\" is used,
where the estimation of an additional shape parameter allows the user to move
smoothly between both types of regression.  See O'Neill and Burke (2022) \"Robust
Distributional Regression with Automatic Variable Selection\" for more details.
<@code{arXiv:2212.07317>}.  This package also contains the data analyses from
O'Neill and Burke (2023). \"Variable selection using a smooth information
criterion for distributional regression models\".
<doi:10.1007/s11222-023-10204-8>.")
    (license license:gpl3)))

(define-public r-smoothhr
  (package
    (name "r-smoothhr")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smoothHR" version))
       (sha256
        (base32 "0ljgk297sm201hzzjajddv39x78d90wn49sbzjrvia2d2hwz5s3j"))))
    (properties `((upstream-name . "smoothHR")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "https://github.com/arturstat/smoothHR")
    (synopsis "Smooth Hazard Ratio Curves Taking a Reference Value")
    (description
     "This package provides flexible hazard ratio curves allowing non-linear
relationships between continuous predictors and survival.  To better understand
the effects that each continuous covariate has on the outcome, results are
expressed in terms of hazard ratio curves, taking a specific covariate value as
reference.  Confidence bands for these curves are also derived.")
    (license license:gpl3)))

(define-public r-smoothhazard
  (package
    (name "r-smoothhazard")
    (version "2024.04.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SmoothHazard" version))
       (sha256
        (base32 "18nn6c94lr1l49z9wj5grxck72dk9rkppg0qh5qbgcdkfxww4096"))))
    (properties `((upstream-name . "SmoothHazard")))
    (build-system r-build-system)
    (propagated-inputs (list r-prodlim r-mvtnorm r-lava))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=SmoothHazard")
    (synopsis "Estimation of Smooth Hazard Models for Interval-Censored Data")
    (description
     "Estimation of two-state (survival) models and irreversible illness- death models
with possibly interval-censored, left-truncated and right-censored data.
Proportional intensities regression models can be specified to allow for
covariates effects separately for each transition.  We use either a parametric
approach with Weibull baseline intensities or a semi-parametric approach with
M-splines approximation of baseline intensities in order to obtain smooth
estimates of the hazard functions.  Parameter estimates are obtained by maximum
likelihood in the parametric approach and by penalized maximum likelihood in the
semi-parametric approach.")
    (license license:gpl2+)))

(define-public r-smoothedlasso
  (package
    (name "r-smoothedlasso")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smoothedLasso" version))
       (sha256
        (base32 "0007i40a12jgavgd95fbpiil1s331qd05ww6v19l49lbnlk3ldxg"))))
    (properties `((upstream-name . "smoothedLasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-matrix))
    (home-page "https://cran.r-project.org/package=smoothedLasso")
    (synopsis
     "Framework to Smooth L1 Penalized Regression Operators using Nesterov Smoothing")
    (description
     "We provide full functionality to smooth L1 penalized regression operators and to
compute regression estimates thereof.  For this, the objective function of a
user-specified regression operator is first smoothed using Nesterov smoothing
(see Y. Nesterov (2005) <doi:10.1007/s10107-004-0552-5>), resulting in a
modified objective function with explicit gradients everywhere.  The smoothed
objective function and its gradient are minimized via BFGS, and the obtained
minimizer is returned.  Using Nesterov smoothing, the smoothed objective
function can be made arbitrarily close to the original (unsmoothed) one.  In
particular, the Nesterov approach has the advantage that it comes with explicit
accuracy bounds, both on the L1/L2 difference of the unsmoothed to the smoothed
objective functions as well as on their respective minimizers (see G. Hahn, S.M.
Lutz, N. Laha, C. Lange (2020) <doi:10.1101/2020.09.17.301788>).  A progressive
smoothing approach is provided which iteratively smoothes the objective
function, resulting in more stable regression estimates.  A function to perform
cross validation for selection of the regularization parameter is provided.")
    (license license:gpl2+)))

(define-public r-smoothapc
  (package
    (name "r-smoothapc")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smoothAPC" version))
       (sha256
        (base32 "11f34qqdm8m0hmy9jf29q439slchvmqgdfrmak7vcdhfnjcylr5r"))))
    (properties `((upstream-name . "smoothAPC")))
    (build-system r-build-system)
    (propagated-inputs (list r-sparsem r-rgl r-quantreg r-lmtest r-colorspace))
    (home-page "https://bitbucket.org/alexanderdokumentov/smoothapcpackage")
    (synopsis
     "Smoothing of Two-Dimensional Demographic Data, Optionally Taking into Account Period and Cohort Effects")
    (description
     "The implemented method uses for smoothing bivariate thin plate splines,
bivariate lasso-type regularization, and allows for both period and cohort
effects.  Thus the mortality rates are modelled as the sum of four components: a
smooth bivariate function of age and time, smooth one-dimensional cohort
effects, smooth one-dimensional period effects and random errors.")
    (license license:gpl2+)))

(define-public r-smooth
  (package
    (name "r-smooth")
    (version "4.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smooth" version))
       (sha256
        (base32 "14yprj4kwqad9a4vhspxcgjiqzfya3x9hhmvcw8h4s6s6r2wjxiq"))))
    (properties `((upstream-name . "smooth")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xtable
                             r-statmod
                             r-rcpparmadillo
                             r-rcpp
                             r-pracma
                             r-nloptr
                             r-mass
                             r-greybox
                             r-generics))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/config-i1/smooth")
    (synopsis "Forecasting Using State Space Models")
    (description
     "This package provides functions implementing Single Source of Error state space
models for purposes of time series analysis and forecasting.  The package
includes ADAM (Svetunkov, 2023, <https://openforecast.org/adam/>), Exponential
Smoothing (Hyndman et al., 2008, <doi: 10.1007/978-3-540-71918-2>), SARIMA
(Svetunkov & Boylan, 2019 <doi: 10.1080/00207543.2019.1600764>), Complex
Exponential Smoothing (Svetunkov & Kourentzes, 2018, <doi:
10.13140/RG.2.2.24986.29123>), Simple Moving Average (Svetunkov & Petropoulos,
2018 <doi: 10.1080/00207543.2017.1380326>) and several simulation functions.  It
also allows dealing with intermittent demand based on the @code{iETS} framework
(Svetunkov & Boylan, 2019, <doi: 10.13140/RG.2.2.35897.06242>).")
    (license license:lgpl2.1)))

(define-public r-smoke
  (package
    (name "r-smoke")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smoke" version))
       (sha256
        (base32 "0kq64l5w5c86qnnqd3q5cm2n33dkfphpjgyxz30d2i0ayq4mjl0s"))))
    (properties `((upstream-name . "smoke")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack))
    (home-page "https://cran.r-project.org/package=smoke")
    (synopsis "Small Molecule Octet/BLI Kinetics Experiment")
    (description
     "Bio-Layer Interferometry (BLI) is a technology to determine the binding kinetics
between biomolecules.  BLI signals are small and noisy when small molecules are
investigated as ligands (analytes).  We develop this package to process and
analyze the BLI data acquired on Octet Red96 from Fortebio more accurately.  Sun
Q., Li X., et al (2020) <doi:10.1038/s41467-019-14238-3>.  In this new version,
we organize the BLI experiment data and analysis methods into a S4 class with
self-explaining structure.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-smof
  (package
    (name "r-smof")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smof" version))
       (sha256
        (base32 "1029vziss8is702d384fql78mpvl047xidrmn4qji31lnak6rkkk"))))
    (properties `((upstream-name . "smof")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=smof")
    (synopsis "Scoring Methodology for Ordered Factors")
    (description
     "Starting from a given object representing a fitted model (within a certain set
of model classes) whose linear predictor includes some ordered factor(s) among
the explanatory variables, a new model is constructed and fitted where each
named factor is replaced by a single numeric score, suitably chosen so that the
new variable produces a fit comparable with the standard methodology based on a
set of polynomial contrasts.  Two variants of the present approach have been
developed, one in each of the next references: Azzalini (2023)
<doi:10.1002/sta4.624>, (2024) <doi:10.48550/@code{arXiv.2406.15933>}.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-smncensreg
  (package
    (name "r-smncensreg")
    (version "3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SMNCensReg" version))
       (sha256
        (base32 "10nik348b6vcmwhj5scjvkjwi86fih3bqkgpv7qxxa8jx45ds52n"))))
    (properties `((upstream-name . "SMNCensReg")))
    (build-system r-build-system)
    (propagated-inputs (list r-performanceanalytics r-matrix))
    (home-page "https://cran.r-project.org/package=SMNCensReg")
    (synopsis
     "Fitting Univariate Censored Regression Model Under the Family of Scale Mixture of Normal Distributions")
    (description
     "Fit univariate right, left or interval censored regression model under the scale
mixture of normal distributions.")
    (license license:gpl3+)))

(define-public r-smmt
  (package
    (name "r-smmt")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SMMT" version))
       (sha256
        (base32 "02mm2f4cg8dbzdmpmdv8pf2jskk5kv0w99qzdjri00zwrcd6ckm9"))))
    (properties `((upstream-name . "SMMT")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-xml
                             r-tibble
                             r-rvest
                             r-dplyr
                             r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ValValetl/SMMT")
    (synopsis
     "The Swiss Municipal Data Merger Tool Maps Municipalities Over Time")
    (description
     "In Switzerland, the landscape of municipalities is changing rapidly mainly due
to mergers.  The Swiss Municipal Data Merger Tool automatically detects these
mutations and maps municipalities over time, i.e.  municipalities of an old
state to municipalities of a new state.  This functionality is helpful when
working with datasets that are based on different spatial references.  The
package's idea and use case is discussed in the following article:
<doi:10.1111/spsr.12487>.")
    (license license:gpl3)))

(define-public r-smmr
  (package
    (name "r-smmr")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smmR" version))
       (sha256
        (base32 "0b6w2k5j65jdngc871ch9ragr7qgwbkl4nhk3gqnbpvkr9m4x88f"))))
    (properties `((upstream-name . "smmR")))
    (build-system r-build-system)
    (propagated-inputs (list r-seqinr r-rcpparmadillo r-rcpp r-discreteweibull))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=smmR")
    (synopsis "Simulation, Estimation and Reliability of Semi-Markov Models")
    (description
     "This package performs parametric and non-parametric estimation and simulation
for multi-state discrete-time semi-Markov processes.  For the parametric
estimation, several discrete distributions are considered for the sojourn times:
Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial.  The
non-parametric estimation concerns the sojourn time distributions, where no
assumptions are done on the shape of distributions.  Moreover, the estimation
can be done on the basis of one or several sample paths, with or without
censoring at the beginning or/and at the end of the sample paths.  Reliability
indicators such as reliability, maintainability, availability, BMP-failure rate,
RG-failure rate, mean time to failure and mean time to repair are available as
well.  The implemented methods are described in Barbu, V.S., Limnios, N. (2008)
<doi:10.1007/978-0-387-73173-5>, Barbu, V.S., Limnios, N. (2008)
<doi:10.1080/10485250701261913> and Trevezas, S., Limnios, N. (2011)
<doi:10.1080/10485252.2011.555543>.  Estimation and simulation of discrete-time
k-th order Markov chains are also considered.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-smme
  (package
    (name "r-smme")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SMME" version))
       (sha256
        (base32 "12z02njv81d86275dv1l2f93y1i4cnq9lxk0yyaacgj3bnpdcm7h"))))
    (properties `((upstream-name . "SMME")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=SMME")
    (synopsis "Soft Maximin Estimation for Large Scale Heterogeneous Data")
    (description
     "Efficient procedure for solving the soft maximin problem for large scale
heterogeneous data, see Lund, Mogensen and Hansen (2022)
<doi:10.1111/sjos.12580>.  Currently Lasso and SCAD penalized estimation is
implemented.  Note this package subsumes and replaces the SMMA package.")
    (license license:expat)))

(define-public r-smma
  (package
    (name "r-smma")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SMMA" version))
       (sha256
        (base32 "1nwxp447m1igm7dlxw5kqw472f8470svwx9awjp2d84m66wplinr"))))
    (properties `((upstream-name . "SMMA")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=SMMA")
    (synopsis "Soft Maximin Estimation for Large Scale Array-Tensor Models")
    (description
     "Efficient design matrix free procedure for solving a soft maximin problem for
large scale array-tensor structured models, see Lund, Mogensen and Hansen (2019)
<@code{arXiv:1805.02407>}.  Currently Lasso and SCAD penalized estimation is
implemented.")
    (license license:gpl2+)))

(define-public r-smm
  (package
    (name "r-smm")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SMM" version))
       (sha256
        (base32 "1g1c7ssdcnzgv6b221ya1xrypg2ksahc7qj1042j8nlx5wg29cld"))))
    (properties `((upstream-name . "SMM")))
    (build-system r-build-system)
    (propagated-inputs (list r-seqinr r-discreteweibull))
    (native-inputs (list))
    (home-page "https://cran.r-project.org/package=SMM")
    (synopsis
     "Simulation and Estimation of Multi-State Discrete-Time Semi-Markov and Markov Models")
    (description
     "This package performs parametric and non-parametric estimation and simulation
for multi-state discrete-time semi-Markov processes.  For the parametric
estimation, several discrete distributions are considered for the sojourn times:
Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial.  The
non-parametric estimation concerns the sojourn time distributions, where no
assumptions are done on the shape of distributions.  Moreover, the estimation
can be done on the basis of one or several sample paths, with or without
censoring at the beginning or/and at the end of the sample paths.  The
implemented methods are described in Barbu, V.S., Limnios, N. (2008)
<doi:10.1007/978-0-387-73173-5>, Barbu, V.S., Limnios, N. (2008)
<doi:10.1080/10485250701261913> and Trevezas, S., Limnios, N. (2011)
<doi:10.1080/10485252.2011.555543>.  Estimation and simulation of discrete-time
k-th order Markov chains are also considered.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-smloutliers
  (package
    (name "r-smloutliers")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SMLoutliers" version))
       (sha256
        (base32 "10frs7wcyn368m7fvw2f1cyd0xqr6sv5jziixnyvr8q5fadyl2p0"))))
    (properties `((upstream-name . "SMLoutliers")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SMLoutliers")
    (synopsis
     "Outlier Detection Using Statistical and Machine Learning Methods")
    (description
     "Local Correlation Integral (LOCI) method for outlier identification is
implemented here.  The LOCI method developed here is invented in Breunig, et al.
(2000), see <doi:10.1145/342009.335388>.")
    (license license:gpl2)))

(define-public r-smlmkalman
  (package
    (name "r-smlmkalman")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smlmkalman" version))
       (sha256
        (base32 "1ns717rq0vipplh3l2ypinrscv3bjv86j68aw53ygs6g59h0v2vp"))))
    (properties `((upstream-name . "smlmkalman")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm r-spdep r-scales r-pracma))
    (home-page "https://cran.r-project.org/package=smlmkalman")
    (synopsis
     "Generation and Tracking of Super-Resolution Filamentous Datasets")
    (description
     "This package provides a pair of functions that allow for the generation and
tracking of coordinate data clouds without a time dimension, primarily for use
in super-resolution plant micro-tubule image segmentation.")
    (license license:gpl2)))

(define-public r-smleph
  (package
    (name "r-smleph")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smlePH" version))
       (sha256
        (base32 "0vkzgm5cjlwdxvrk4jjmj4jcwvpfs0kkc182iz0bkx6vp0ykmin7"))))
    (properties `((upstream-name . "smlePH")))
    (build-system r-build-system)
    (propagated-inputs (list r-splines2 r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/taehwa015/smlePH/")
    (synopsis
     "Sieve Maximum Full Likelihood Estimation for the Right-Censored Proportional Hazards Model")
    (description
     "Fitting the full likelihood proportional hazards model and extracting the
residuals.")
    (license license:gpl3+)))

(define-public r-smle
  (package
    (name "r-smle")
    (version "2.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SMLE" version))
       (sha256
        (base32 "1kfbvdimxrm8s5sizhb5r3mw2aiib75yxrcpz4drdaw5bd5mv8lf"))))
    (properties `((upstream-name . "SMLE")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvnfast r-matrixcalc r-glmnet))
    (home-page "https://cran.r-project.org/package=SMLE")
    (synopsis "Joint Feature Screening via Sparse MLE")
    (description
     "Feature screening is a powerful tool in processing ultrahigh dimensional data.
It attempts to screen out most irrelevant features in preparation for a more
elaborate analysis.  Xu and Chen (2014)<doi:10.1080/01621459.2013.879531>
proposed an effective screening method SMLE, which naturally incorporates the
joint effects among features in the screening process.  This package provides an
efficient implementation of SMLE-screening for high-dimensional linear,
logistic, and Poisson models.  The package also provides a function for
conducting accurate post-screening feature selection based on an iterative
hard-thresholding procedure and a user-specified selection criterion.")
    (license license:gpl3)))

(define-public r-smitidvisu
  (package
    (name "r-smitidvisu")
    (version "0.0.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SMITIDvisu" version))
       (sha256
        (base32 "143zz49rx7106v0a46dn1jssqxp60djf5rycfy38167zz7f9wx3y"))))
    (properties `((upstream-name . "SMITIDvisu")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-yaml r-rcpp r-magrittr r-jsonlite r-htmlwidgets))
    (native-inputs (list esbuild))
    (home-page "https://informatique-mia.inrae.fr/biosp/anr-smitid-project/")
    (synopsis
     "Visualize Data for Host and Viral Population from 'SMITIDstruct' using 'HTMLwidgets'")
    (description
     "Visualisation tools for SMITIDstruct package.  Allow to visualize host timeline,
transmission tree, index diversities and variant graph using HTMLwidgets'.  It
mainly using D3JS javascript framework.")
    (license (list license:gpl3+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-smitidstruct
  (package
    (name "r-smitidstruct")
    (version "0.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SMITIDstruct" version))
       (sha256
        (base32 "12ffxj96w3pi18l0scz5sd3dl37vd4vg2wcfykaxdhf8z142ha3k"))))
    (properties `((upstream-name . "SMITIDstruct")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-ggplot2 r-biostrings))
    (home-page "https://informatique-mia.inra.fr/biosp/anr-smitid-project")
    (synopsis
     "Data Structure and Manipulations Tool for Host and Viral Population")
    (description
     "Statistical Methods for Inferring Transmissions of Infectious Diseases from deep
sequencing data (SMITID).  It allow sequence-space-time host and viral
population data storage, indexation and querying.")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-smithwilsonyieldcurve
  (package
    (name "r-smithwilsonyieldcurve")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SmithWilsonYieldCurve" version))
       (sha256
        (base32 "0p24ay1y874qi4yvfa6b3xkf0m2gjwl8sl2dp59k0g20v0qzynzb"))))
    (properties `((upstream-name . "SmithWilsonYieldCurve")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SmithWilsonYieldCurve")
    (synopsis "Smith-Wilson Yield Curve Construction")
    (description
     "Constructs a yield curve by the Smith-Wilson method from a table of libor and
swap rates.  Now updated to take bond coupons and prices in the same table.")
    (license license:gpl3)))

(define-public r-smirnov
  (package
    (name "r-smirnov")
    (version "1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smirnov" version))
       (sha256
        (base32 "09mpb45wj8rfi6n6822h4c335xp2pl0xsyxgin1bkfw97yjcvrgk"))))
    (properties `((upstream-name . "smirnov")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=smirnov")
    (synopsis
     "Provides two taxonomic coefficients from E. S. Smirnov \"Taxonomic analysis\" (1969) book")
    (description
     "This tiny package contains one function smirnov() which calculates two scaled
taxonomic coefficients, Txy (coefficient of similarity) and Txx (coefficient of
originality).  These two characteristics may be used for the analysis of
similarities between any number of taxonomic groups, and also for assessing
uniqueness of giving taxon.  It is possible to use smirnov() output as a
distance measure: convert it to distance by \"as.dist(1 - smirnov(x))\".")
    (license license:gpl2+)))

(define-public r-smiles
  (package
    (name "r-smiles")
    (version "0.1-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smiles" version))
       (sha256
        (base32 "0bjdp4198069hmlcxy718idv50jbscl0l4ffl8aqvqh7iqmy14cz"))))
    (properties `((upstream-name . "smiles")))
    (build-system r-build-system)
    (propagated-inputs (list r-meta r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=smiles")
    (synopsis "Sequential Method in Leading Evidence Synthesis")
    (description
     "Trial sequential analysis emerges as an important method in data synthesis
realm.  It is necessary to integrate pooling methods and sequential analysis
coherently, as discussed in the Chapter by Thomas, J., Askie, L.M., Berlin,
J.A., Elliott, J.H., Ghersi, D., Simmonds, M., Takwoingi, Y., Tierney, J.F. and
Higgins, J.P. (2019). \"Prospective approaches to accumulating evidence\".  In
Cochrane Handbook for Systematic Reviews of Interventions (eds J.P.T. Higgins,
J. Thomas, J. Chandler, M. Cumpston, T. Li, M.J. Page and V.A. Welch).
<doi:10.1002/9781119536604.ch22>.")
    (license license:gpl3+)))

(define-public r-smile
  (package
    (name "r-smile")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smile" version))
       (sha256
        (base32 "1474jfk6vqcx3jv0mlf45l93bnn3f2qgm2j03rbjid4skg811618"))))
    (properties `((upstream-name . "smile")))
    (build-system r-build-system)
    (inputs (list proj geos gdal))
    (propagated-inputs (list r-sf
                             r-rcpparmadillo
                             r-rcpp
                             r-numderiv
                             r-mvtnorm
                             r-matrix))
    (native-inputs (list r-knitr))
    (home-page "https://lcgodoy.me/smile/")
    (synopsis "Spatial Misalignment: Interpolation, Linkage, and Estimation")
    (description
     "This package provides functions to estimate, predict and interpolate areal data.
 For estimation and prediction we assume areal data is an average of an
underlying continuous spatial process as in Moraga et al. (2017)
<doi:10.1016/j.spasta.2017.04.006>, Johnson et al. (2020)
<doi:10.1186/s12942-020-00200-w>, and Wilson and Wakefield (2020)
<doi:10.1093/biostatistics/kxy041>.  The interpolation methodology is (mostly)
based on Goodchild and Lam (1980, ISSN:01652273).")
    (license license:gpl3)))

(define-public r-smidm
  (package
    (name "r-smidm")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smidm" version))
       (sha256
        (base32 "1a26y70mb5qzl1zjaicvnb8i7z3r94qqzda2cpac776h1w35zy51"))))
    (properties `((upstream-name . "smidm")))
    (build-system r-build-system)
    (propagated-inputs (list r-extradistr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://gitlab.cc-asp.fraunhofer.de/ester/smidm")
    (synopsis "Statistical Modelling for Infectious Disease Management")
    (description
     "Statistical models for specific coronavirus disease 2019 use cases at German
local health authorities.  All models of Statistical modelling for infectious
disease management smidm are part of the decision support toolkit in the
@code{EsteR} project.  More information is published in Sonja JÃ¤ckle, Rieke
Alpers, Lisa KÃ¼hne, Jakob Schumacher, Benjamin Geisler, Max Westphal
\"'@code{EsteR} â A Digital Toolkit for COVID-19 Decision Support in Local
Health Authorities\" (2022) <doi:10.3233/SHTI220799> and Sonja JÃ¤ckle, Elias
RÃ¶ger, Volker Dicken, Benjamin Geisler, Jakob Schumacher, Max Westphal \"A
Statistical Model to Assess Risk for Supporting COVID-19 Quarantine Decisions\"
(2021) <doi:10.3390/ijerph18179166>.")
    (license license:bsd-3)))

(define-public r-smicd
  (package
    (name "r-smicd")
    (version "1.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smicd" version))
       (sha256
        (base32 "0lcdclp2bxs5g47a49xhwqgsl15czg2pym0d6z287wj4ccpxihxh"))))
    (properties `((upstream-name . "smicd")))
    (build-system r-build-system)
    (propagated-inputs (list r-weights
                             r-truncnorm
                             r-mvtnorm
                             r-lme4
                             r-laeken
                             r-ineq
                             r-hmisc
                             r-formula-tools))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=smicd")
    (synopsis "Statistical Methods for Interval-Censored Data")
    (description
     "This package provides functions that provide statistical methods for
interval-censored (grouped) data.  The package supports the estimation of linear
and linear mixed regression models with interval-censored dependent variables.
Parameter estimates are obtained by a stochastic expectation maximization
algorithm.  Furthermore, the package enables the direct (without covariates)
estimation of statistical indicators from interval-censored data via an
iterative kernel density algorithm.  Survey and Organisation for Economic
Co-operation and Development (OECD) weights can be included into the direct
estimation (see, Walter, P. (2019) <doi:10.17169/refubium-1621>).")
    (license license:gpl2)))

(define-public r-smfsb
  (package
    (name "r-smfsb")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smfsb" version))
       (sha256
        (base32 "092f9zcds183ya1j3qv2jjpi3rbpzzhb2wirlzpqk5bsfbyri93c"))))
    (properties `((upstream-name . "smfsb")))
    (build-system r-build-system)
    (propagated-inputs (list r-abind))
    (home-page "https://cran.r-project.org/package=smfsb")
    (synopsis "Stochastic Modelling for Systems Biology")
    (description
     "Code and data for modelling and simulation of stochastic kinetic biochemical
network models.  It contains the code and data associated with the second and
third editions of the book Stochastic Modelling for Systems Biology, published
by Chapman & Hall/CRC Press.")
    (license license:lgpl3)))

(define-public r-smfishhmrf
  (package
    (name "r-smfishhmrf")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smfishHmrf" version))
       (sha256
        (base32 "1k6mpyp11zv5rc3k98baymr903p2pf424nf9xng20q1li8s8y2ab"))))
    (properties `((upstream-name . "smfishHmrf")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-pracma r-fs))
    (home-page "https://bitbucket.org/qzhudfci/smfishhmrf-r/src/master/")
    (synopsis "Hidden Markov Random Field for Spatial Transcriptomic Data")
    (description
     "Discovery of spatial patterns with Hidden Markov Random Field.  This package is
designed for spatial transcriptomic data and single molecule fluorescent in situ
hybridization (FISH) data such as sequential fluorescence in situ hybridization
(@code{seqFISH}) and multiplexed error-robust fluorescence in situ hybridization
(MERFISH).  The methods implemented in this package are described in Zhu et al.
(2018) <doi:10.1038/nbt.4260>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-smfilter
  (package
    (name "r-smfilter")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SMFilter" version))
       (sha256
        (base32 "1islyqg9w08mvs2kf0ddmdlp885arzp0jy7mqvixjm4ayi5zfrri"))))
    (properties `((upstream-name . "SMFilter")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/yukai-yang/SMFilter")
    (synopsis
     "Filtering Algorithms for the State Space Models on the Stiefel Manifold")
    (description
     "This package provides the filtering algorithms for the state space models on the
Stiefel manifold as well as the corresponding sampling algorithms for uniform,
vector Langevin-Bingham and matrix Langevin-Bingham distributions on the Stiefel
manifold.")
    (license license:gpl3)))

(define-public r-smetlite
  (package
    (name "r-smetlite")
    (version "0.2.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smetlite" version))
       (sha256
        (base32 "1q7fqfdd6m7jv5waajmmsc7ql16lcyy6z2k41mz6gvdqz914gg16"))))
    (properties `((upstream-name . "smetlite")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-readr))
    (home-page "https://github.com/BaselDataScience/smetlite")
    (synopsis "Read and Write SMET Files")
    (description
     "Simple class to hold contents of a SMET file as specified in Bavay (2021)
<https://code.wsl.ch/snow-models/meteoio/-/blob/master/doc/SMET_specifications.pdf>.
 There numerical meteorological measurements are all based on MKS (SI) units and
timestamp is standardized to UTC time.")
    (license license:gpl3+)))

(define-public r-smerc
  (package
    (name "r-smerc")
    (version "1.8.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smerc" version))
       (sha256
        (base32 "181av1l1r327iszxrihilvfiqbc4m4496g94i5r9vhhamvgk8cwm"))))
    (properties `((upstream-name . "smerc")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppprogress r-rcpp r-pbapply))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=smerc")
    (synopsis "Statistical Methods for Regional Counts")
    (description
     "This package implements statistical methods for analyzing the counts of areal
data, with a focus on the detection of spatial clusters and clustering.  The
package has a heavy emphasis on spatial scan methods, which were first
introduced by Kulldorff and Nagarwalla (1995) <doi:10.1002/sim.4780140809> and
Kulldorff (1997) <doi:10.1080/03610929708831995>.")
    (license license:gpl2+)))

(define-public r-smdocker
  (package
    (name "r-smdocker")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smdocker" version))
       (sha256
        (base32 "13jkd81irk32m1w9djsgsgl79s8i9ynz10j7m8imqpk402zc7cbd"))))
    (properties `((upstream-name . "smdocker")))
    (build-system r-build-system)
    (propagated-inputs (list r-zip
                             r-uuid
                             r-paws-storage
                             r-paws-security-identity
                             r-paws-management
                             r-paws-machine-learning
                             r-paws-developer-tools
                             r-paws-compute
                             r-jsonlite))
    (home-page "https://github.com/DyfanJones/sm-docker")
    (synopsis
     "Build 'Docker Images' in 'Amazon SageMaker Studio' using 'Amazon Web Service CodeBuild'")
    (description
     "Allows users to easily build custom docker images <https://docs.docker.com/>
from Amazon Web Service Sagemaker <https://aws.amazon.com/sagemaker/> using
Amazon Web Service @code{CodeBuild} <https://aws.amazon.com/codebuild/>.")
    (license license:expat)))

(define-public r-smdic
  (package
    (name "r-smdic")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SMDIC" version))
       (sha256
        (base32 "1g1q079krf0a5aqbfgzv7kg1vv0r3q3m4kfh1fcvn0wj02drsfjx"))))
    (properties `((upstream-name . "SMDIC")))
    (build-system r-build-system)
    (propagated-inputs (list r-survminer
                             r-survival
                             r-samr
                             r-rcolorbrewer
                             r-preprocesscore
                             r-pracma
                             r-pheatmap
                             r-mass
                             r-maftools
                             r-gsva
                             r-e1071
                             r-backports))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SMDIC")
    (synopsis "Identification of Somatic Mutation-Driven Immune Cells")
    (description
     "This package provides a computing tool is developed to automated identify
somatic mutation-driven immune cells.  The operation modes including: i)
inferring the relative abundance matrix of tumor-infiltrating immune cells and
integrating it with a particular gene mutation status, ii) detecting
differential immune cells with respect to the gene mutation status and
converting the abundance matrix of significant differential immune cell into two
binary matrices (one for up-regulated and one for down-regulated), iii)
identifying somatic mutation-driven immune cells by comparing the gene mutation
status with each immune cell in the binary matrices across all samples, and iv)
visualization of immune cell abundance of samples in different mutation status..")
    (license license:gpl2+)))

(define-public r-smdi
  (package
    (name "r-smdi")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smdi" version))
       (sha256
        (base32 "17l5620pjwyfwl1p0h5zv264nllca3gzcnw94hbfvb79kyls9x2s"))))
    (properties `((upstream-name . "smdi")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-tableone
                             r-survival
                             r-stringr
                             r-randomforest
                             r-proc
                             r-naniar
                             r-mice
                             r-magrittr
                             r-lifecycle
                             r-hotelling
                             r-gt
                             r-glue
                             r-ggplot2
                             r-forcats
                             r-fastdummies
                             r-dplyr
                             r-caret
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://janickweberpals.gitlab-pages.partners.org/smdi/")
    (synopsis "Perform Structural Missing Data Investigations")
    (description
     "An easy to use implementation of routine structural missing data diagnostics
with functions to visualize the proportions of missing observations, investigate
missing data patterns and conduct various empirical missing data diagnostic
tests.  Reference: Weberpals J, Raman SR, Shaw PA, Lee H, Hammill BG, Toh S,
Connolly JG, Dandreo KJ, Tian F, Liu W, Li J, HernÃ¡ndez-MuÃ±oz JJ, Glynn RJ,
Desai RJ. smdi: an R package to perform structural missing data investigations
on partially observed confounders in real-world evidence studies.  JAMIA Open.
2024 Jan 31;7(1):ooae008. <doi:10.1093/jamiaopen/ooae008>.")
    (license license:gpl3+)))

(define-public r-smdata
  (package
    (name "r-smdata")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smdata" version))
       (sha256
        (base32 "1p26b46kg4hmhs2fl9h9vchar00llqv58ylx438bjgzz4f229qvr"))))
    (properties `((upstream-name . "smdata")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=smdata")
    (synopsis "Data to Accompany Smithson & Merkle, 2013")
    (description
     "This package contains data files to accompany Smithson & Merkle (2013),
Generalized Linear Models for Categorical and Continuous Limited Dependent
Variables.")
    (license license:gpl2)))

(define-public r-smd
  (package
    (name "r-smd")
    (version "0.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smd" version))
       (sha256
        (base32 "15npiffznxlvd8rih0z63vdxykhh8fcdsgbrjkjgnakd9fi5xad5"))))
    (properties `((upstream-name . "smd")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://bsaul.github.io/smd/")
    (synopsis "Compute Standardized Mean Differences")
    (description
     "Computes standardized mean differences and confidence intervals for multiple
data types based on Yang, D., & Dalton, J. E. (2012)
<https://support.sas.com/resources/papers/proceedings12/335-2012.pdf>.")
    (license license:expat)))

(define-public r-smcure
  (package
    (name "r-smcure")
    (version "2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smcure" version))
       (sha256
        (base32 "0377dj6wvny2y44phsf7177ivn70a55v316skwf02n03ghqx73k9"))))
    (properties `((upstream-name . "smcure")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "https://cran.r-project.org/package=smcure")
    (synopsis "Fit Semiparametric Mixture Cure Models")
    (description
     "An R-package for Estimating Semiparametric PH and AFT Mixture Cure Models.")
    (license license:gpl2)))

(define-public r-smcryptor
  (package
    (name "r-smcryptor")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smcryptoR" version))
       (sha256
        (base32 "05njdwx1ah1559k2fsvqg4wcm07k0570zxlnl4zscr042ixyg0sw"))))
    (properties `((upstream-name . "smcryptoR")))
    (build-system r-build-system)
    (inputs (list))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/zhuobie/smcryptoR")
    (synopsis "ShangMi(SM) Cryptographic Algorithms(SM2/SM3/SM4)")
    (description
     "Bindings to smcrypto <https://github.com/zhuobie/smcrypto>: a Rust
implementation of China's Standards of Encryption Algorithms, which is usually
called @code{ShangMi(SM}) algorithms.  It contains SM3 message digest algorithm,
SM2 asymmetric encryption algorithm and SM4 symmetric encryption algorithm.
Users can do message hash, encrypt/decrypt, sign/verify, key exchange and more.")
    (license license:expat)))

(define-public r-smcrm
  (package
    (name "r-smcrm")
    (version "0.0-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SMCRM" version))
       (sha256
        (base32 "1x06w00sdijhg5h1s61q4ym5wgk97pw9md6api7if2cxjv7h5zcy"))))
    (properties `((upstream-name . "SMCRM")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SMCRM")
    (synopsis
     "Data Sets for Statistical Methods in Customer Relationship Management by Kumar and Petersen (2012)")
    (description
     "Data Sets for Kumar and Petersen (2012).  Statistical Methods in Customer
Relationship Management, Wiley: New York.")
    (license license:gpl3)))

(define-public r-smcfcs
  (package
    (name "r-smcfcs")
    (version "1.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smcfcs" version))
       (sha256
        (base32 "0l658ynkf3lb1nckfrgysj19m6735fi2k48r7l5qaf6jq7f3l3bh"))))
    (properties `((upstream-name . "smcfcs")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam
                             r-survival
                             r-rlang
                             r-mass
                             r-checkmate
                             r-brglm2
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jwb133/smcfcs")
    (synopsis
     "Multiple Imputation of Covariates by Substantive Model Compatible Fully Conditional Specification")
    (description
     "This package implements multiple imputation of missing covariates by Substantive
Model Compatible Fully Conditional Specification.  This is a modification of the
popular FCS/chained equations multiple imputation approach, and allows
imputation of missing covariate values from models which are compatible with the
user specified substantive model.")
    (license license:gpl3)))

(define-public r-smccnet
  (package
    (name "r-smccnet")
    (version "2.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SmCCNet" version))
       (sha256
        (base32 "16kw1nk0y8v2dg4akv5biwznx6a0pv754453r3ymfxw7qjrknyqc"))))
    (properties `((upstream-name . "SmCCNet")))
    (build-system r-build-system)
    (propagated-inputs (list r-spls
                             r-rlist
                             r-purrr
                             r-proc
                             r-pracma
                             r-pbapply
                             r-matrix
                             r-magrittr
                             r-igraph
                             r-future
                             r-furrr
                             r-envstats))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/KechrisLab/SmCCNet")
    (synopsis "Sparse Multiple Canonical Correlation Network Analysis Tool")
    (description
     "This package provides a canonical correlation based framework (@code{SmCCNet})
designed for the construction of phenotype-specific multi-omics networks.  This
framework adeptly integrates single or multiple omics data types along with a
quantitative or binary phenotype of interest.  It offers a streamlined setup
process that can be tailored manually or configured automatically, ensuring a
flexible and user-friendly experience.")
    (license license:gpl3)))

(define-public r-smbinning
  (package
    (name "r-smbinning")
    (version "0.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smbinning" version))
       (sha256
        (base32 "1sr0nsqqzxdf402g9qx1ycigbh330idl9n2cyli4x4zxj1nqm3yv"))))
    (properties `((upstream-name . "smbinning")))
    (build-system r-build-system)
    (propagated-inputs (list r-sqldf r-partykit r-gsubfn r-formula))
    (home-page "https://cran.r-project.org/package=smbinning")
    (synopsis "Scoring Modeling and Optimal Binning")
    (description
     "This package provides a set of functions to build a scoring model from beginning
to end, leading the user to follow an efficient and organized development
process, reducing significantly the time spent on data exploration, variable
selection, feature engineering, binning and model selection among other
recurrent tasks.  The package also incorporates monotonic and customized
binning, scaling capabilities that transforms logistic coefficients into points
for a better business understanding and calculates and visualizes classic
performance metrics of a classification model.")
    (license license:gpl2+)))

(define-public r-smatr
  (package
    (name "r-smatr")
    (version "3.4-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smatr" version))
       (sha256
        (base32 "0qqqbg65flxh48sw7x90zvgzbpcfzb1811h0pz3zlhdw9a7khs9n"))))
    (properties `((upstream-name . "smatr")))
    (build-system r-build-system)
    (home-page "http://web.maths.unsw.edu.au/~dwarton")
    (synopsis "(Standardised) Major Axis Estimation and Testing Routines")
    (description
     "This package provides methods for fitting bivariate lines in allometry using the
major axis (MA) or standardised major axis (SMA), and for making inferences
about such lines.  The available methods of inference include confidence
intervals and one-sample tests for slope and elevation, testing for a common
slope or elevation amongst several allometric lines, constructing a confidence
interval for a common slope or elevation, and testing for no shift along a
common axis, amongst several samples.  See Warton et al.  2012
<doi:10.1111/j.2041-210X.2011.00153.x> for methods description.")
    (license license:gpl2)))

(define-public r-smartsva
  (package
    (name "r-smartsva")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SmartSVA" version))
       (sha256
        (base32 "10a8s2znsg8ywqkq9fsxiyqfsprrx33pqissazp2vmabs11mg4np"))))
    (properties `((upstream-name . "SmartSVA")))
    (build-system r-build-system)
    (propagated-inputs (list r-sva r-rspectra r-rcppeigen r-rcpp r-isva))
    (home-page "https://cran.r-project.org/package=SmartSVA")
    (synopsis "Fast and Robust Surrogate Variable Analysis")
    (description
     "Introduces a fast and efficient Surrogate Variable Analysis algorithm that
captures variation of unknown sources (batch effects) for high-dimensional data
sets.  The algorithm is built on the irwsva.build function of the sva package
and proposes a revision on it that achieves an order of magnitude faster running
time while trading no accuracy loss in return.")
    (license license:gpl3)))

(define-public r-smartsnp
  (package
    (name "r-smartsnp")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smartsnp" version))
       (sha256
        (base32 "1gzzhys6bja55iclkji254kri6r3iamiw7bv881mp615wi9r3hx2"))))
    (properties `((upstream-name . "smartsnp")))
    (build-system r-build-system)
    (propagated-inputs (list r-vroom
                             r-vegan
                             r-rspectra
                             r-rfast
                             r-rcpparmadillo
                             r-rcpp
                             r-foreach
                             r-data-table
                             r-bootsvd))
    (home-page "https://cran.r-project.org/package=smartsnp")
    (synopsis "Fast Multivariate Analyses of Big Genomic Data")
    (description
     "Fast computation of multivariate analyses of small (10s to 100s markers) to big
(1000s to 100000s) genotype data.  Runs Principal Component Analysis allowing
for centering, z-score standardization and scaling for genetic drift, projection
of ancient samples to modern genetic space and multivariate tests for
differences in group location (Permutation-Based Multivariate Analysis of
Variance) and dispersion (Permutation-Based Multivariate Analysis of
Dispersion).")
    (license license:expat)))

(define-public r-smartsizer
  (package
    (name "r-smartsizer")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smartsizer" version))
       (sha256
        (base32 "1jfhba8p5flz4v0m770kb3p2gw4vgzpxqb0ngl9fhw30jzkx6nwd"))))
    (properties `((upstream-name . "smartsizer")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=smartsizer")
    (synopsis "Power Analysis for a SMART Design")
    (description
     "This package provides a set of tools for determining the necessary sample size
in order to identify the optimal dynamic treatment regime in a sequential,
multiple assignment, randomized trial (SMART).  Utilizes multiple comparisons
with the best methodology to adjust for multiple comparisons.  Designed for an
arbitrary SMART design.  Please see Artman (2018)
<doi:10.1093/biostatistics/kxy064> for more details.")
    (license license:gpl3)))

(define-public r-smartsheetr
  (package
    (name "r-smartsheetr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smartsheetr" version))
       (sha256
        (base32 "1331fiapyvf6jbj6hgasascfy9alix5ylcy7nh99z1qcdl2asck7"))))
    (properties `((upstream-name . "smartsheetr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-memoise
                             r-jsonlite
                             r-httr
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=smartsheetr")
    (synopsis
     "Access and Write 'Smartsheet' Data using the 'Smartsheet' API 2.0")
    (description
     "Interact with the Smartsheet platform through the Smartsheet API 2.0.
<https://smartsheet.redoc.ly/>.  API is an acronym for application programming
interface; the Smartsheet API allows users to interact with Smartsheet sheets
directly within R.")
    (license license:expat)))

(define-public r-smartp
  (package
    (name "r-smartp")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SMARTp" version))
       (sha256
        (base32 "0gkhl9j4l67brfmln28m4q4hfi271jlg5aijnjwahlj8nsi6384y"))))
    (properties `((upstream-name . "SMARTp")))
    (build-system r-build-system)
    (propagated-inputs (list r-sn r-mvtnorm r-covr))
    (home-page "https://github.com/bandyopd/SMARTp")
    (synopsis
     "Sample Size for SMART Designs in Non-Surgical Periodontal Trials")
    (description
     "Sample size calculation to detect dynamic treatment regime (DTR) effects based
on change in clinical attachment level (CAL) outcomes from a non-surgical
chronic periodontitis treatments study.  The experiment is performed under a
Sequential Multiple Assignment Randomized Trial (SMART) design.  The clustered
tooth (sub-unit) level CAL outcomes are skewed, spatially-referenced, and
non-randomly missing.  The implemented algorithm is available in Xu et al.
(2019+) <@code{arXiv:1902.09386>}.")
    (license license:lgpl2.0+)))

(define-public r-smartmeteranalytics
  (package
    (name "r-smartmeteranalytics")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SmartMeterAnalytics" version))
       (sha256
        (base32 "0i8my7ngzdbay3cld8axhvbri0nh0dd1dmv2lbsgyx4adr5hb30f"))))
    (properties `((upstream-name . "SmartMeterAnalytics")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-stinepack r-plyr r-futile-logger r-fnn))
    (home-page "https://cran.r-project.org/package=SmartMeterAnalytics")
    (synopsis "Methods for Smart Meter Data Analysis")
    (description
     "This package provides methods for analysis of energy consumption data
(electricity, gas, water) at different data measurement intervals.  The package
provides feature extraction methods and algorithms to prepare data for data
mining and machine learning applications.  Deatiled descriptions of the methods
and their application can be found in Hopf (2019, ISBN:978-3-86309-669-4)
\"Predictive Analytics for Energy Efficiency and Energy Retailing\"
<doi:10.20378/irbo-54833> and Hopf et al. (2016) <doi:10.1007/s12525-018-0290-9>
\"Enhancing energy efficiency in the residential sector with smart meter data
analytics\".")
    (license license:expat)))

(define-public r-smartmap
  (package
    (name "r-smartmap")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smartmap" version))
       (sha256
        (base32 "0pxbjkkknng7xa8ihba69n1a2w9052fsbb0w8fq1y1qrqmmibwdi"))))
    (properties `((upstream-name . "smartmap")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-magrittr r-leaflet))
    (home-page "https://cran.r-project.org/package=smartmap")
    (synopsis "Smartly Create Maps from R Objects")
    (description
     "Preview spatial data as leaflet maps with minimal effort.  smartmap is optimized
for interactive use and distinguishes itself from similar packages because it
does not need real spatial ('sp or sf') objects an input; instead, it tries to
automatically coerce everything that looks like spatial data to sf objects or
leaflet maps.  It - for example - supports direct mapping of: a vector
containing a single coordinate pair, a two column matrix, a data.frame with
longitude and latitude columns, or the path or URL to a (possibly compressed)
shapefile'.")
    (license license:expat)))

(define-public r-smarterpoland
  (package
    (name "r-smarterpoland")
    (version "1.8.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SmarterPoland" version))
       (sha256
        (base32 "07nag50mzj7a4f5lprvvs6k7mppyzcwvkc3dj8b1il2k06q5ffh5"))))
    (properties `((upstream-name . "SmarterPoland")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjson r-jsonlite r-httr r-htmltools r-ggplot2))
    (home-page "https://cran.r-project.org/package=SmarterPoland")
    (synopsis
     "Tools for Accessing Various Datasets Developed by the Foundation SmarterPoland.pl")
    (description
     "This package provides tools for accessing and processing datasets prepared by
the Foundation @code{SmarterPoland.pl}.  Among all: access to API of Google
Maps, Central Statistical Office of Poland, @code{MojePanstwo}, Eurostat, WHO
and other sources.")
    (license license:gpl3)))

(define-public r-smarteda
  (package
    (name "r-smarteda")
    (version "0.3.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SmartEDA" version))
       (sha256
        (base32 "05m96kp1ylbxslf9y9n063r807wz3wxf6whqmysb7bgyjpsnbzlb"))))
    (properties `((upstream-name . "SmartEDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-sampling
                             r-rmarkdown
                             r-qpdf
                             r-islr
                             r-gridextra
                             r-ggplot2
                             r-ggally
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://daya6489.github.io/SmartEDA/")
    (synopsis "Summarize and Explore the Data")
    (description
     "Exploratory analysis on any input data describing the structure and the
relationships present in the data.  The package automatically select the
variable and does related descriptive statistics.  Analyzing information value,
weight of evidence, custom tables, summary statistics, graphical techniques will
be performed for both numeric and categorical predictors.")
    (license license:expat)))

(define-public r-smartdesign
  (package
    (name "r-smartdesign")
    (version "0.74")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smartDesign" version))
       (sha256
        (base32 "1sqbq5ramsfssjwwfsv4bxb7xvq1lf3w6h0hy4dkpdxa5c50lfr7"))))
    (properties `((upstream-name . "smartDesign")))
    (build-system r-build-system)
    (propagated-inputs (list r-knitr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=smartDesign")
    (synopsis "Sequential Multiple Assignment Randomized Trial Design")
    (description
     "SMART trial design, as described by He, J., @code{McClish}, D., Sabo, R. (2021)
<doi:10.1080/19466315.2021.1883472>, includes multiple stages of randomization,
where participants are randomized to an initial treatment in the first stage and
then subsequently re-randomized between treatments in the following stage.")
    (license license:gpl3+)))

(define-public r-smartbayesr
  (package
    (name "r-smartbayesr")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SMARTbayesR" version))
       (sha256
        (base32 "15s6vzxgbskjvgd91q0vb9qxmmsj391bjbw7s51j879cfqd16xg2"))))
    (properties `((upstream-name . "SMARTbayesR")))
    (build-system r-build-system)
    (propagated-inputs (list r-laplacesdemon))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SMARTbayesR")
    (synopsis
     "Bayesian Set of Best Dynamic Treatment Regimes and Sample Size in SMARTs for Binary Outcomes")
    (description
     "Permits determination of a set of optimal dynamic treatment regimes and sample
size for a SMART design in the Bayesian setting with binary outcomes.  Please
see Artman (2020) <@code{arXiv:2008.02341>}.")
    (license license:gpl3)))

(define-public r-smam
  (package
    (name "r-smam")
    (version "0.7.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smam" version))
       (sha256
        (base32 "0p7k4x27rbfmibifq9hn161v57zvlgw088inq2gnmm95sh6kqh1v"))))
    (properties `((upstream-name . "smam")))
    (build-system r-build-system)
    (inputs (list gsl))
    (propagated-inputs (list r-rcppparallel
                             r-rcppgsl
                             r-rcpp
                             r-numderiv
                             r-nloptr
                             r-matrix
                             r-foreach
                             r-envstats
                             r-dosnow
                             r-doparallel))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/ChaoranHu/smam")
    (synopsis "Statistical Modeling of Animal Movements")
    (description
     "Animal movement models including Moving-Resting Process with Embedded Brownian
Motion (Yan et al., 2014, <doi:10.1007/s10144-013-0428-8>; Pozdnyakov et al.,
2017, <doi:10.1007/s11009-017-9547-6>), Brownian Motion with Measurement Error
(Pozdnyakov et al., 2014, <doi:10.1890/13-0532.1>), Moving-Resting-Handling
Process with Embedded Brownian Motion (Pozdnyakov et al., 2020,
<doi:10.1007/s11009-020-09774-1>), Moving-Resting Process with Measurement Error
(Hu et al., 2021, <doi:10.1111/2041-210X.13694>), Moving-Moving Process with two
Embedded Brownian Motions.")
    (license license:gpl3+)))

(define-public r-smallstuff
  (package
    (name "r-smallstuff")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smallstuff" version))
       (sha256
        (base32 "17x73fcrvnw71bn52mg6lgpm9is99djy5avpnyqsffhqa8x6mzsv"))))
    (properties `((upstream-name . "smallstuff")))
    (build-system r-build-system)
    (propagated-inputs (list r-rocr
                             r-pryr
                             r-matrix
                             r-matlib
                             r-igraph
                             r-data-table
                             r-class))
    (home-page "https://cran.r-project.org/package=smallstuff")
    (synopsis "Dr. Small's Functions")
    (description
     "This package provides functions used in courses taught by Dr. Small at Drew
University.")
    (license license:gpl3)))

(define-public r-smallsets
  (package
    (name "r-smallsets")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smallsets" version))
       (sha256
        (base32 "13w7vdjg6pqq509r1jlmwi3z9qldq6damiy439dmm6wanmp9ybd6"))))
    (properties `((upstream-name . "smallsets")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmarkdown
                             r-reticulate
                             r-plotrix
                             r-patchwork
                             r-knitr
                             r-ggtext
                             r-ggplot2
                             r-flextable
                             r-colorspace
                             r-callr))
    (native-inputs (list r-knitr))
    (home-page "https://lydialucchesi.github.io/smallsets/")
    (synopsis "Visual Documentation for Data Preprocessing")
    (description
     "Data practitioners regularly use the R and Python programming languages to
prepare data for analyses.  Thus, they encode important data preprocessing
decisions in R and Python code.  The smallsets package subsequently decodes
these decisions into a Smallset Timeline, a static, compact visualisation of
data preprocessing decisions (Lucchesi et al. (2022)
<doi:10.1145/3531146.3533175>).  The visualisation consists of small data
snapshots of different preprocessing steps.  The smallsets package builds this
visualisation from a user's dataset and preprocessing code located in an R', R
Markdown', Python', or Jupyter Notebook file.  Users simply add structured
comments with snapshot instructions to the preprocessing code.  One optional
feature in smallsets requires installation of the Gurobi optimisation software
and gurobi R package, available from <https://www.gurobi.com>.  More information
regarding the optional feature and gurobi installation can be found in the
smallsets vignette.")
    (license license:gpl3+)))

(define-public r-smallcountrounding
  (package
    (name "r-smallcountrounding")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SmallCountRounding" version))
       (sha256
        (base32 "04h7mqjnnxjqi63x476fz5pm78y8ylk8x3h8mzlnfpq068gknzlc"))))
    (properties `((upstream-name . "SmallCountRounding")))
    (build-system r-build-system)
    (propagated-inputs (list r-ssbtools r-matrix))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/statisticsnorway/ssb-smallcountrounding")
    (synopsis "Small Count Rounding of Tabular Data")
    (description
     "This package provides a statistical disclosure control tool to protect frequency
tables in cases where small values are sensitive.  The function PLSrounding()
performs small count rounding of necessary inner cells so that all small
frequencies of cross-classifications to be published (publishable cells) are
rounded.  This is equivalent to changing micro data since frequencies of unique
combinations are changed.  Thus, additivity and consistency are guaranteed.  The
methodology is described in Langsrud and Heldal (2018)
<https://www.researchgate.net/publication/327768398_An_Algorithm_for_Small_Count_Rounding_of_Tabular_Data>.")
    (license license:expat)))

(define-public r-smallarea
  (package
    (name "r-smallarea")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smallarea" version))
       (sha256
        (base32 "0jcv0xbh8v4g6zxxs4yyd0divwzk9d2w7g01r4s65khxvy3av7yx"))))
    (properties `((upstream-name . "smallarea")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=smallarea")
    (synopsis "Fits a Fay Herriot Model")
    (description "Inference techniques for Fay Herriot Model.")
    (license license:gpl2+)))

(define-public r-smacpod
  (package
    (name "r-smacpod")
    (version "2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smacpod" version))
       (sha256
        (base32 "193mqxk17ygr518p2mvk9wnj3hnckxay9jz85ng00kz2kaykdpd6"))))
    (properties `((upstream-name . "smacpod")))
    (build-system r-build-system)
    (propagated-inputs (list r-spatstat-random
                             r-spatstat-geom
                             r-spatstat-explore
                             r-smerc
                             r-plotrix
                             r-pbapply
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=smacpod")
    (synopsis
     "Statistical Methods for the Analysis of Case-Control Point Data")
    (description
     "Statistical methods for analyzing case-control point data.  Methods include the
ratio of kernel densities, the difference in K Functions, the spatial scan
statistic, and q nearest neighbors of cases.")
    (license license:gpl2+)))

(define-public r-smacofx
  (package
    (name "r-smacofx")
    (version "1.6-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smacofx" version))
       (sha256
        (base32 "112457wqdyj4ld083ibzkfjww6xilyamcj7v6mal2gh1jxcf2r8k"))))
    (properties `((upstream-name . "smacofx")))
    (build-system r-build-system)
    (propagated-inputs (list r-weights
                             r-vegan
                             r-smacof
                             r-projectionbasedclustering
                             r-plotrix
                             r-minqa
                             r-mass))
    (home-page "https://r-forge.r-project.org/projects/stops/")
    (synopsis "Flexible Multidimensional Scaling and 'smacof' Extensions")
    (description
     "Flexible multidimensional scaling (MDS) methods and extensions to the package
smacof'.  This package contains various functions, wrappers, methods and classes
for fitting, plotting and displaying a large number of different flexible MDS
models (some as of yet unpublished).  These are: Torgerson scaling (Torgerson,
1958, ISBN:978-0471879459) with powers, Sammon mapping (Sammon, 1969,
<doi:10.1109/T-C.1969.222678>) with ratio and interval optimal scaling,
Multiscale MDS (Ramsay, 1977, <doi:10.1007/BF02294052>) with ratio and interval
optimal scaling, S-stress MDS (ALSCAL; Takane, Young & De Leeuw, 1977,
<doi:10.1007/BF02293745>) with ratio and interval optimal scaling, elastic
scaling (@code{McGee}, 1966, <doi:10.1111/j.2044-8317.1966.tb00367.x>) with
ratio and interval optimal scaling, r-stress MDS (De Leeuw, Groenen & Mair,
2016, <https://rpubs.com/deleeuw/142619>) with ratio, interval and non-metric
optimal scaling, power-stress MDS (POST-MDS; Buja & Swayne, 2002
<doi:10.1007/s00357-001-0031-0>) with ratio and interval optimal scaling,
restricted power-stress (Rusch, Mair & Hornik, 2021,
<doi:10.1080/10618600.2020.1869027>) with ratio and interval optimal scaling,
approximate power-stress with ratio optimal scaling (Rusch, Mair & Hornik, 2021,
<doi:10.1080/10618600.2020.1869027>), Box-Cox MDS (Chen & Buja, 2013,
<https://jmlr.org/papers/v14/chen13a.html>), local MDS (Chen & Buja, 2009,
<doi:10.1198/jasa.2009.0111>), curvilinear component analysis (Demartines &
Herault, 1997, <doi:10.1109/72.554199>) and curvilinear distance analysis (Lee,
Lendasse & Verleysen, 2004, <doi:10.1016/j.neucom.2004.01.007>).  There also are
experimental models (e.g., sparsified MDS and sparsified POST-MDS).  Some
functions are suitably flexible to allow any other sensible combination of
explicit power transformations for weights, distances and input proximities with
implicit ratio, interval or non-metric optimal scaling of the input proximities.
 Most functions use a Majorization-Minimization algorithm.  Currently the
methods are only available for one-mode data (symmetric dissimilarity matrices).")
    (license (list license:gpl2 license:gpl3))))

(define-public r-smacof
  (package
    (name "r-smacof")
    (version "2.1-7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smacof" version))
       (sha256
        (base32 "0fj9r6x3bn5kjb2v37prmba4brzjammcxrvswc0ixvx20r75m5ks"))))
    (properties `((upstream-name . "smacof")))
    (build-system r-build-system)
    (propagated-inputs (list r-wordcloud
                             r-weights
                             r-polynom
                             r-plotrix
                             r-nnls
                             r-mass
                             r-hmisc
                             r-foreach
                             r-ellipse
                             r-e1071
                             r-doparallel
                             r-colorspace))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=smacof")
    (synopsis "Multidimensional Scaling")
    (description
     "This package implements the following approaches for multidimensional scaling
(MDS) based on stress minimization using majorization (smacof):
ratio/interval/ordinal/spline MDS on symmetric dissimilarity matrices, MDS with
external constraints on the configuration, individual differences scaling
(idioscal, indscal), MDS with spherical restrictions, and
ratio/interval/ordinal/spline unfolding (circular restrictions,
row-conditional).  Various tools and extensions like jackknife MDS, bootstrap
MDS, permutation tests, MDS biplots, gravity models, unidimensional scaling,
drift vectors (asymmetric MDS), classical scaling, and Procrustes are
implemented as well.")
    (license license:gpl3)))

(define-public r-smaa
  (package
    (name "r-smaa")
    (version "0.3-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "smaa" version))
       (sha256
        (base32 "1lasa8179bp9fs49n26b41mw8vwkqnz5a051913rd246lmdjnyb0"))))
    (properties `((upstream-name . "smaa")))
    (build-system r-build-system)
    (home-page "https://github.com/gertvv/rsmaa")
    (synopsis "Stochastic Multi-Criteria Acceptability Analysis")
    (description
     "Implementation of the Stochastic Multi-Criteria Acceptability Analysis (SMAA)
family of Multiple Criteria Decision Analysis (MCDA) methods.  Tervonen, T. and
Figueira, J. R. (2008) <doi:10.1002/mcda.407>.")
    (license license:gpl3)))

(define-public r-slurmr
  (package
    (name "r-slurmr")
    (version "0.5-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "slurmR" version))
       (sha256
        (base32 "1akwdpv3235l9hrqrhlkd8m3l7dbrq6h2nq29jr6mimlp8jvk5g7"))))
    (properties `((upstream-name . "slurmR")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/USCbiostats/slurmR")
    (synopsis "Lightweight Wrapper for 'Slurm'")
    (description
     "Slurm', Simple Linux Utility for Resource Management
<https://slurm.schedmd.com/>, is a popular Linux based software used to schedule
jobs in HPC (High Performance Computing) clusters.  This R package provides a
specialized lightweight wrapper of Slurm with a syntax similar to that found in
the parallel R package.  The package also includes a method for creating socket
cluster objects spanning multiple nodes that can be used with the parallel
package.")
    (license license:expat)))

(define-public r-sltca
  (package
    (name "r-sltca")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SLTCA" version))
       (sha256
        (base32 "0pq87fbi79srgjblv40yyj3w1dzf115kfidp8gi564l8xf0qilqg"))))
    (properties `((upstream-name . "SLTCA")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam r-mvtnorm r-matrix r-geepack))
    (home-page "https://cran.r-project.org/package=SLTCA")
    (synopsis "Scalable and Robust Latent Trajectory Class Analysis")
    (description
     "Conduct latent trajectory class analysis with longitudinal data.  Our method
supports longitudinal continuous, binary and count data.  For more
methodological details, please refer to Hart, K.R., Fei, T. and Hanfelt, J.J.
(2020), Scalable and robust latent trajectory class analysis using artificial
likelihood.  Biometrics <doi:10.1111/biom.13366>.")
    (license license:gpl2+)))

(define-public r-slsedesign
  (package
    (name "r-slsedesign")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SLSEdesign" version))
       (sha256
        (base32 "09ng145z98xh92fddi76g7grwg77y08zyqa8z1hy4lx1z0x5i5g7"))))
    (properties `((upstream-name . "SLSEdesign")))
    (build-system r-build-system)
    (propagated-inputs (list r-cvxr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/chikuang/SLSEdesign")
    (synopsis
     "Optimal Regression Design under the Second-Order Least Squares Estimator")
    (description
     "With given inputs that include number of points, discrete design space, a
measure of skewness, models and parameter value, this package calculates the
objective value, optimal designs and plot the equivalence theory under A- and
D-optimal criteria under the second-order Least squares estimator.  This package
is based on the paper \"Properties of optimal regression designs under the
second-order least squares estimator\" by Chi-Kuang Yeh and Julie Zhou (2021)
<doi:10.1007/s00362-018-01076-6>.")
    (license license:gpl3)))

(define-public r-slr
  (package
    (name "r-slr")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "slr" version))
       (sha256
        (base32 "1pj23nxp5c12z37fssqc5vyyig9qsa7q4ziyygmj1gm7di313hdc"))))
    (properties `((upstream-name . "slr")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-ibd r-gmp))
    (home-page "https://cran.r-project.org/package=slr")
    (synopsis "Semi-Latin Rectangles")
    (description
     "This package provides a facility to generate balanced semi-Latin rectangles with
any cell size (preferably up to ten) with given number of treatments, see Uto,
N.P. and Bailey, R.A. (2020). \"Balanced Semi-Latin rectangles: properties,
existence and constructions for block size two\".  Journal of Statistical Theory
and Practice, 14(3), 1-11, <doi:10.1007/s42519-020-00118-3>.  It also provides
facility to generate partially balanced semi-Latin rectangles for cell size 2, 3
and 4 for any number of treatments.")
    (license license:gpl2+)))

(define-public r-slpreselection
  (package
    (name "r-slpreselection")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SLPresElection" version))
       (sha256
        (base32 "1i813q0m9h964nd61kwnvakci3n1bn06pvq9zvxb2clcch05qyfj"))))
    (properties `((upstream-name . "SLPresElection")))
    (build-system r-build-system)
    (home-page "https://github.com/Amalan-ConStat/SLPresElection")
    (synopsis "Presidential Election Data of \"Sri Lanka\" from 1982 to 2015")
    (description
     "Presidential Election data of \"Sri Lanka\"\" is stored in Pdf files, through Pdf
scraping they are converted into data-frames and stored in this R package.")
    (license license:expat)))

(define-public r-slp
  (package
    (name "r-slp")
    (version "1.0-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "slp" version))
       (sha256
        (base32 "0i9iv0s1wdp9cyzr8qixh6c7qvrj025vll13gyf8dfbidrp6hryl"))))
    (properties `((upstream-name . "slp")))
    (build-system r-build-system)
    (propagated-inputs (list r-mgcv))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=slp")
    (synopsis
     "Discrete Prolate Spheroidal (Slepian) Sequence Regression Smoothers")
    (description
     "Interface for creation of slp class smoother objects for use in Generalized
Additive Models (as implemented by packages gam and mgcv').")
    (license license:gpl2+)))

(define-public r-slowraker
  (package
    (name "r-slowraker")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "slowraker" version))
       (sha256
        (base32 "0glc3ldvbqkvzfvhs4s5mzx1kfz05px4k7rx5jfp9dm9d0gfpvsx"))))
    (properties `((upstream-name . "slowraker")))
    (build-system r-build-system)
    (inputs (list openjdk))
    (propagated-inputs (list r-snowballc r-opennlp r-nlp))
    (native-inputs (list r-knitr))
    (home-page "https://crew102.github.io/slowraker/index.html")
    (synopsis
     "Slow Version of the Rapid Automatic Keyword Extraction (RAKE) Algorithm")
    (description
     "This package provides a mostly pure-R implementation of the RAKE algorithm
(Rose, S., Engel, D., Cramer, N. and Cowley, W. (2010)
<doi:10.1002/9780470689646.ch1>), which can be used to extract keywords from
documents without any training data.")
    (license license:expat)))

(define-public r-slouch
  (package
    (name "r-slouch")
    (version "2.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "slouch" version))
       (sha256
        (base32 "129824gl4wqckmjddv0bnw8pjvbqvx4xkcb399dffgqgs07qvbcq"))))
    (properties `((upstream-name . "slouch")))
    (build-system r-build-system)
    (propagated-inputs (list r-memoise r-crayon r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/kopperud/slouch")
    (synopsis "Stochastic Linear Ornstein-Uhlenbeck Comparative Hypotheses")
    (description
     "An implementation of a phylogenetic comparative method.  It can fit univariate
among-species Ornstein-Uhlenbeck models of phenotypic trait evolution, where the
trait evolves towards a primary optimum.  The optimum can be modelled as a
single parameter, as multiple discrete regimes on the phylogenetic tree, and/or
with continuous covariates.  See also Hansen (1997) <doi:10.2307/2411186>,
Butler & King (2004) <doi:10.1086/426002>, Hansen et al. (2008)
<doi:10.1111/j.1558-5646.2008.00412.x>.")
    (license license:gpl2)))

(define-public r-slope
  (package
    (name "r-slope")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SLOPE" version))
       (sha256
        (base32 "1fwfk6caj3hhb1nc3msky3faakfkvilxqna8564zki5hnmbjny69"))))
    (properties `((upstream-name . "SLOPE")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-matrix r-ggplot2
                             r-foreach))
    (native-inputs (list r-knitr))
    (home-page "https://jolars.github.io/SLOPE/")
    (synopsis "Sorted L1 Penalized Estimation")
    (description
     "Efficient implementations for Sorted L-One Penalized Estimation (SLOPE):
generalized linear models regularized with the sorted L1-norm (Bogdan et al.
2015).  Supported models include ordinary least-squares regression, binomial
regression, multinomial regression, and Poisson regression.  Both dense and
sparse predictor matrices are supported.  In addition, the package features
predictor screening rules that enable fast and efficient solutions to
high-dimensional problems.")
    (license license:gpl3)))

(define-public r-slmodels
  (package
    (name "r-slmodels")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SLModels" version))
       (sha256
        (base32 "1c9r4kd2kfh2cn82kngzcqhdmxc4i7djp436s116gy9xs2gd2042"))))
    (properties `((upstream-name . "SLModels")))
    (build-system r-build-system)
    (propagated-inputs (list r-rocr))
    (home-page "https://cran.r-project.org/package=SLModels")
    (synopsis
     "Stepwise Linear Models for Binary Classification Problems under Youden Index Optimisation")
    (description
     "Stepwise models for the optimal linear combination of continuous variables in
binary classification problems under Youden Index optimisation.  Information on
the models implemented can be found at Aznar-Gimeno et al. (2021)
<doi:10.3390/math9192497>.")
    (license license:gpl3)))

(define-public r-slm
  (package
    (name "r-slm")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "slm" version))
       (sha256
        (base32 "1kzqipwmqdcnv04r69c6fq95kbjdbhk5qi1b1n06bdr7xnyzm56b"))))
    (properties `((upstream-name . "slm")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich r-ltsa r-expm r-capushe))
    (home-page "https://cran.r-project.org/package=slm")
    (synopsis "Stationary Linear Models")
    (description
     "This package provides statistical procedures for linear regression in the
general context where the errors are assumed to be correlated.  Different ways
to estimate the asymptotic covariance matrix of the least squares estimators are
available.  Starting from this estimation of the covariance matrix, the
confidence intervals and the usual tests on the parameters are modified.  The
functions of this package are very similar to those of lm': it contains methods
such as summary(), plot(), confint() and predict().  The slm package is
described in the paper by E. Caron, J. Dedecker and B. Michel (2019), \"Linear
regression with stationary errors: the R package slm\", @code{arXiv} preprint
<@code{arXiv:1906.06583>}.")
    (license license:gpl3)))

(define-public r-slippymath
  (package
    (name "r-slippymath")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "slippymath" version))
       (sha256
        (base32 "0dqnv089mcfxvpsc1px7mblim6m77n59xpcysz75jrvpc5q4lsrg"))))
    (properties `((upstream-name . "slippymath")))
    (build-system r-build-system)
    (propagated-inputs (list r-raster r-purrr r-png))
    (native-inputs (list r-knitr))
    (home-page "https://www.github.com/milesmcbain/slippymath")
    (synopsis "Slippy Map Tile Tools")
    (description
     "This package provides functions for performing common tasks when working with
slippy map tile service APIs e.g. Google maps, Open Street Map, Mapbox, Stamen,
among others.  Functionality includes converting from latitude and longitude to
tile numbers, determining tile bounding boxes, and compositing tiles to a
georeferenced raster image.")
    (license license:expat)))

(define-public r-slimrec
  (package
    (name "r-slimrec")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "slimrec" version))
       (sha256
        (base32 "1qzcvk7z8y11hwmybvppk1gf8di596zxy7qs22fclp6h6y18s377"))))
    (properties `((upstream-name . "slimrec")))
    (build-system r-build-system)
    (propagated-inputs (list r-pbapply r-matrix r-glmnet r-bigmemory
                             r-assertthat))
    (home-page "https://cran.r-project.org/package=slimrec")
    (synopsis
     "Sparse Linear Method to Predict Ratings and Top-N Recommendations")
    (description
     "Sparse Linear Method(SLIM) predicts ratings and top-n recommendations suited for
sparse implicit positive feedback systems.  SLIM is decomposed into multiple
elasticnet optimization problems which are solved in parallel over multiple
cores.  The package is based on \"SLIM: Sparse Linear Methods for Top-N
Recommender Systems\" by Xia Ning and George Karypis <doi:10.1109/ICDM.2011.134>.")
    (license license:gpl3)))

(define-public r-slim
  (package
    (name "r-slim")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "slim" version))
       (sha256
        (base32 "0x9bk6w6lmw15s85gw9g8d1wlb57kkvnmx1wsyasn93x9llkjva1"))))
    (properties `((upstream-name . "slim")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=slim")
    (synopsis "Singular Linear Models for Longitudinal Data")
    (description
     "Fits singular linear models to longitudinal data.  Singular linear models are
useful when the number, or timing, of longitudinal observations may be
informative about the observations themselves.  They are described in Farewell
(2010) <doi:10.1093/biomet/asp068>, and are extensions of the linear increments
model <doi:10.1111/j.1467-9876.2007.00590.x> to general longitudinal data.")
    (license license:gpl3)))

(define-public r-slidingwindows
  (package
    (name "r-slidingwindows")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SlidingWindows" version))
       (sha256
        (base32 "0hvxg4hrk3rb1wxhi71rgamxd8wcwys569137znfs0y8j6yvf0jj"))))
    (properties `((upstream-name . "SlidingWindows")))
    (build-system r-build-system)
    (propagated-inputs (list r-tsentropies r-performanceanalytics
                             r-nonlineartseries r-dcca))
    (home-page "https://github.com/efguedes/SlidingWindows")
    (synopsis "Methods for Time Series Analysis")
    (description
     "This package provides a collection of functions to perform Detrended Fluctuation
Analysis (DFA exponent), GUEDES et al. (2019) <doi:10.1016/j.physa.2019.04.132>
, Detrended cross-correlation coefficient (RHODCCA), GUEDES & ZEBENDE (2019)
<doi:10.1016/j.physa.2019.121286>, DMCA cross-correlation coefficient and
Detrended multiple cross-correlation coefficient (DMC), GUEDES & SILVA-FILHO &
ZEBENDE (2018) <doi:10.1016/j.physa.2021.125990>, both with sliding windows
approach.")
    (license license:gpl3)))

(define-public r-slideview
  (package
    (name "r-slideview")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "slideview" version))
       (sha256
        (base32 "031ahdqswl14sw0nxmhaam37g14cifb83dyp2zjz1mh6qpkzl2gs"))))
    (properties `((upstream-name . "slideview")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridislite r-raster r-lattice r-htmlwidgets
                             r-htmltools))
    (home-page "https://r-spatial.github.io/slideview/")
    (synopsis "Compare Raster Images Side by Side with a Slider")
    (description
     "Create a side-by-side view of raster(image)s with an interactive slider to
switch between regions of the images.  This can be especially useful for image
comparison of the same region at different time stamps.")
    (license license:expat)))

(define-public r-slide
  (package
    (name "r-slide")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SLIDE" version))
       (sha256
        (base32 "0x4fm29r7icg33k8gw3vqvxib8n9a1xwg484x9yffybips9ka22c"))))
    (properties `((upstream-name . "SLIDE")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SLIDE")
    (synopsis "Single Cell Linkage by Distance Estimation is SLIDE")
    (description
     "This statistical method uses the nearest neighbor algorithm to estimate absolute
distances between single cells based on a chosen constellation of surface
proteins, with these distances being a measure of the similarity between the two
cells being compared.  Based on Sen, N., Mukherjee, G., and Arvin, A.M. (2015)
<DOI:10.1016/j.ymeth.2015.07.008>.")
    (license license:gpl3)))

(define-public r-slickr
  (package
    (name "r-slickr")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "slickR" version))
       (sha256
        (base32 "01p72l3h8izg9pphjhkm83rgfsvlimf8aa5asa5lfgfbma60ivhb"))))
    (properties `((upstream-name . "slickR")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-xml2
                             r-lifecycle
                             r-htmlwidgets
                             r-htmltools
                             r-checkmate
                             r-base64enc))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://github.com/yonicd/slickR")
    (synopsis
     "Create Interactive Carousels with the 'JavaScript' 'Slick' Library")
    (description
     "Create and customize interactive carousels using the Slick @code{JavaScript}
library and the htmlwidgets package.  The carousels can contain plots produced
in R, images, iframes', videos and other htmlwidgets'.  These carousels can be
created directly from the R console, and viewed in the RStudio internal viewer,
in Shiny apps and R Markdown documents.")
    (license license:expat)))

(define-public r-slicedlhd
  (package
    (name "r-slicedlhd")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SlicedLHD" version))
       (sha256
        (base32 "17jv8ky2qn15l2ppx5xfnwzf15bhwd31s4ci8r05wlyvnmqqgbfh"))))
    (properties `((upstream-name . "SlicedLHD")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SlicedLHD")
    (synopsis "Sliced Latin Hypercube Designs")
    (description
     "This package provides a facility to generate sliced (orthogonal) Latin hypercube
designs with four and five slices.  For details about sliced and orthogonal
Latin hypercube designs, see Yang, J. F., Lin, C. D., Qian, P. Z., and Lin, D.
K. (2013). \"Construction of sliced orthogonal Latin hypercube designs\".
Statistica Sinica, 1117-1130, <doi:10.5705/ss.2012.037>.")
    (license license:gpl2+)))

(define-public r-slhd
  (package
    (name "r-slhd")
    (version "2.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SLHD" version))
       (sha256
        (base32 "0y3ilxd0phmks8zkmpgw7p5zrkwq4k95h976cwk58pavvhfwj9kb"))))
    (properties `((upstream-name . "SLHD")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SLHD")
    (synopsis "Maximin-Distance (Sliced) Latin Hypercube Designs")
    (description
     "Generate the optimal Latin Hypercube Designs (LHDs) for computer experiments
with quantitative factors and the optimal Sliced Latin Hypercube Designs (SLHDs)
for computer experiments with both quantitative and qualitative factors.
Details of the algorithm can be found in Ba, S., Brenneman, W. A. and Myers, W.
R. (2015), \"Optimal Sliced Latin Hypercube Designs,\" Technometrics.  Important
function in this package is \"@code{maximinSLHD}\".")
    (license license:lgpl2.1)))

(define-public r-slgf
  (package
    (name "r-slgf")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "slgf" version))
       (sha256
        (base32 "07gkl35hq3w42kw103gn9f15s0z4n49nq39ip1nzm3s6d1ad6in6"))))
    (properties `((upstream-name . "slgf")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-numderiv))
    (home-page "https://cran.r-project.org/package=slgf")
    (synopsis
     "Bayesian Model Selection with Suspected Latent Grouping Factors")
    (description
     "This package implements the Bayesian model selection method with suspected
latent grouping factor methodology of Metzger and Franck (2020),
<doi:10.1080/00401706.2020.1739561>.  SLGF detects latent heteroscedasticity or
group-based regression effects based on the levels of a user-specified
categorical predictor.")
    (license license:gpl2+)))

(define-public r-slfpca
  (package
    (name "r-slfpca")
    (version "3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SLFPCA" version))
       (sha256
        (base32 "1zib01240ds8q0cafp0dpz70jvjy322hnxg26f16h1lpmn55l0aj"))))
    (properties `((upstream-name . "SLFPCA")))
    (build-system r-build-system)
    (propagated-inputs (list r-psych r-fdapace r-fda))
    (home-page "https://cran.r-project.org/package=SLFPCA")
    (synopsis "Sparse Logistic Functional Principal Component Analysis")
    (description
     "Implementation for sparse logistic functional principal component analysis
(SLFPCA).  SLFPCA is specifically developed for functional binary data, and the
estimated eigenfunction can be strictly zero on some sub-intervals, which is
helpful for interpretation.  The crucial function of this package is SLFPCA().")
    (license license:gpl3+)))

(define-public r-slfm
  (package
    (name "r-slfm")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "slfm" version))
       (sha256
        (base32 "1xrla1c91x636vi55ajhaiz10293bppxylpm2jqrmfgs9gh2rbrn"))))
    (properties `((upstream-name . "slfm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-lattice r-coda))
    (home-page "https://cran.r-project.org/package=slfm")
    (synopsis
     "Fitting a Bayesian Sparse Latent Factor Model in Gene Expression Analysis")
    (description
     "Set of tools to find coherent patterns in gene expression (microarray) data
using a Bayesian Sparse Latent Factor Model (SLFM)
<DOI:10.1007/978-3-319-12454-4_15>.  Considerable effort has been put to build a
fast and memory efficient package, which makes this proposal an interesting and
computationally convenient alternative to study patterns of gene expressions
exhibited in matrices.  The package contains the implementation of two versions
of the model based on different mixture priors for the loadings: one relies on a
degenerate component at zero and the other uses a small variance normal
distribution for the spike part of the mixture.")
    (license license:gpl2)))

(define-public r-sleuth3
  (package
    (name "r-sleuth3")
    (version "1.0-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Sleuth3" version))
       (sha256
        (base32 "1llhf4hgyichzllzh74v63xw9zba25r0p87vfnhbhlf9q456mr6y"))))
    (properties `((upstream-name . "Sleuth3")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://r-forge.r-project.org/projects/sleuth2/")
    (synopsis
     "Data Sets from Ramsey and Schafer's \"Statistical Sleuth (3rd Ed)\"")
    (description
     "Data sets from Ramsey, F.L. and Schafer, D.W. (2013), \"The Statistical Sleuth: A
Course in Methods of Data Analysis (3rd ed)\", Cengage Learning.")
    (license license:gpl2+)))

(define-public r-sleuth2
  (package
    (name "r-sleuth2")
    (version "2.0-7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Sleuth2" version))
       (sha256
        (base32 "12dq3zknninigildvzn7cbs28r55pbp6cxn2ni1kwk9b8ycgwwpn"))))
    (properties `((upstream-name . "Sleuth2")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://r-forge.r-project.org/projects/sleuth2/")
    (synopsis
     "Data Sets from Ramsey and Schafer's \"Statistical Sleuth (2nd Ed)\"")
    (description
     "Data sets from Ramsey, F.L. and Schafer, D.W. (2002), \"The Statistical Sleuth: A
Course in Methods of Data Analysis (2nd ed)\", Duxbury.")
    (license license:gpl2+)))

(define-public r-slendr
  (package
    (name "r-slendr")
    (version "0.9.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "slendr" version))
       (sha256
        (base32 "1qqbi9rmby2v4q57sp6i64lk5h2srmriglc3m0f9isz34m0ivnzl"))))
    (properties `((upstream-name . "slendr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-shinywidgets
                             r-shiny
                             r-scales
                             r-reticulate
                             r-readr
                             r-purrr
                             r-png
                             r-magrittr
                             r-ijtiff
                             r-ggplot2
                             r-dplyr
                             r-digest
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bodkan/slendr")
    (synopsis "Simulation Framework for Spatiotemporal Population Genetics")
    (description
     "This package provides a framework for simulating spatially explicit genomic data
which leverages real cartographic information for programmatic and visual
encoding of spatiotemporal population dynamics on real geographic landscapes.
Population genetic models are then automatically executed by the S@code{LiM}
software by Haller et al. (2019) <doi:10.1093/molbev/msy228> behind the scenes,
using a custom built-in simulation S@code{LiM} script.  Additionally, fully
abstract spatial models not tied to a specific geographic location are
supported, and users can also simulate data from standard, non-spatial,
random-mating models.  These can be simulated either with the S@code{LiM}
built-in back-end script, or using an efficient coalescent population genetics
simulator msprime by Baumdicker et al. (2022) <doi:10.1093/genetics/iyab229>
with a custom-built Python script bundled with the R package.  Simulated genomic
data is saved in a tree-sequence format and can be loaded, manipulated, and
summarised using tree-sequence functionality via an R interface to the Python
module tskit by Kelleher et al. (2019) <doi:10.1038/s41588-019-0483-y>.
Complete model configuration, simulation and analysis pipelines can be therefore
constructed without a need to leave the R environment, eliminating friction
between disparate tools for population genetic simulations and data analysis.")
    (license license:expat)))

(define-public r-slemi
  (package
    (name "r-slemi")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SLEMI" version))
       (sha256
        (base32 "0fpnnzzflgahmiy1hypss215qbmq2wf35ps34r1izhvww5cj8f4r"))))
    (properties `((upstream-name . "SLEMI")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-reshape2
                             r-nnet
                             r-hmisc
                             r-gridextra
                             r-ggplot2
                             r-foreach
                             r-e1071
                             r-doparallel
                             r-corrplot
                             r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/TJetka/SLEMI")
    (synopsis "Statistical Learning Based Estimation of Mutual Information")
    (description
     "The implementation of the algorithm for estimation of mutual information and
channel capacity from experimental data by classification procedures (logistic
regression).  Technically, it allows to estimate information-theoretic measures
between finite-state input and multivariate, continuous output.  Method
described in Jetka et al. (2019) <doi:10.1371/journal.pcbi.1007132>.")
    (license license:gpl3+)))

(define-public r-sleev
  (package
    (name "r-sleev")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sleev" version))
       (sha256
        (base32 "0irc0k4asml5vfqwkv5rxw6xv1dj6sllravyggg49cdqfbhwcv3w"))))
    (properties `((upstream-name . "sleev")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpparmadillo r-rcpp))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=sleev")
    (synopsis "Semiparametric Likelihood Estimation with Errors in Variables")
    (description
     "Efficient regression analysis under general two-phase sampling, where Phase I
includes error-prone data and Phase II contains validated data on a subset.")
    (license license:gpl2+)))

(define-public r-sleepwalk
  (package
    (name "r-sleepwalk")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sleepwalk" version))
       (sha256
        (base32 "1y0945r9ksz8x6w5rx31sm9x2q2ydd9sc1fwa0paq5f11hygss5d"))))
    (properties `((upstream-name . "sleepwalk")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-scales
                             r-jsonlite
                             r-jrc
                             r-httpuv
                             r-ggplot2
                             r-cowplot))
    (native-inputs (list esbuild))
    (home-page "https://anders-biostat.github.io/sleepwalk/")
    (synopsis "Interactively Explore Dimension-Reduced Embeddings")
    (description
     "This package provides a tool to interactively explore the embeddings created by
dimension reduction methods such as Principal Components Analysis (PCA),
Multidimensional Scaling (MDS), T-distributed Stochastic Neighbour Embedding
(t-SNE), Uniform Manifold Approximation and Projection (UMAP) or any other.")
    (license license:gpl3)))

(define-public r-sleepr
  (package
    (name "r-sleepr")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sleepr" version))
       (sha256
        (base32 "1ka4pl6a09d1qga5svcirc5ywmzz9pcml09053745lz3p1v8fnn3"))))
    (properties `((upstream-name . "sleepr")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table r-behavr))
    (home-page "https://github.com/rethomics/sleepr")
    (synopsis "Analyse Activity and Sleep Behaviour")
    (description
     "Use behavioural variables to score activity and infer sleep from bouts of
immobility.  It is primarily designed to score sleep in fruit flies from
Drosophila Activity Monitor (@code{TriKinetics}) and Ethoscope data.  It
implements sleep scoring using the \"five-minute rule\" (Hendricks et al. (2000)
<DOI:10.1016/S0896-6273(00)80877-6>), activity classification for Ethoscopes
(Geissmann et al. (2017) <DOI:10.1371/journal.pbio.2003026>) and a new algorithm
to detect when animals are dead.")
    (license license:gpl3)))

(define-public r-sleeperapi
  (package
    (name "r-sleeperapi")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sleeperapi" version))
       (sha256
        (base32 "1lmpcpd407bz7fwb20c8m5vaxq7l7sgna7jmh6mjy579az0fzllj"))))
    (properties `((upstream-name . "sleeperapi")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-shinywidgets
                             r-shinycssloaders
                             r-shiny
                             r-scales
                             r-rlang
                             r-rcolorbrewer
                             r-purrr
                             r-plotly
                             r-jsonlite
                             r-httr
                             r-htmlwidgets
                             r-htmltools
                             r-dt
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=sleeperapi")
    (synopsis "Wrapper Functions Around 'Sleeper' (Fantasy Sports) API")
    (description
     "For those wishing to interact with the Sleeper (Fantasy Sports) API
(<https://docs.sleeper.com/>) without looking too much into its documentation
(found at <https://docs.sleeper.com/>), this package offers wrapper functions
around the available API calls to make it easier.")
    (license license:expat)))

(define-public r-sleepcycles
  (package
    (name "r-sleepcycles")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SleepCycles" version))
       (sha256
        (base32 "130r6wrsj4g725065zyxj0wzx1j10bh97288l4ckl4ka2kpawz8v"))))
    (properties `((upstream-name . "SleepCycles")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis r-stringr r-reshape2 r-plyr r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SleepCycles")
    (synopsis "Sleep Cycle Detection")
    (description
     "Sleep cycles are largely detected according to the originally proposed criteria
by Feinberg & Floyd (1979) <doi:10.1111/j.1469-8986.1979.tb02991.x> as described
in Blume & Cajochen (2021) <doi:10.1016/j.mex.2021.101318>.")
    (license license:gpl3)))

(define-public r-sleekts
  (package
    (name "r-sleekts")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sleekts" version))
       (sha256
        (base32 "0syk244xrsv8hz5sxm7wizk0kyn1nc6z4c63c8xn57fz130zj75k"))))
    (properties `((upstream-name . "sleekts")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sleekts")
    (synopsis "4253H, Twice Smoothing")
    (description
     "Compute Time series Resistant Smooth 4253H, twice smoothing method.")
    (license license:gpl3)))

(define-public r-sldassay
  (package
    (name "r-sldassay")
    (version "1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SLDAssay" version))
       (sha256
        (base32 "04ykj7s9kpvg5bmf7x16154kfpdy45b7y7hhyh31sxlscv7c5bbp"))))
    (properties `((upstream-name . "SLDAssay")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SLDAssay")
    (synopsis "Software for Analyzing Limiting Dilution Assays")
    (description
     "Calculates maximum likelihood estimate, exact and asymptotic confidence
intervals, and exact and asymptotic goodness of fit p-values for concentration
of infectious units from serial limiting dilution assays.  This package uses the
likelihood equation, exact goodness of fit p-values, and exact confidence
intervals described in Meyers et al. (1994)
<http://jcm.asm.org/content/32/3/732.full.pdf>.  This software is also
implemented as a web application through the Shiny R package
<https://iupm.shinyapps.io/sldassay/>.")
    (license license:gpl3)))

(define-public r-sld
  (package
    (name "r-sld")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sld" version))
       (sha256
        (base32 "1sx3wmpc89hjabj476dhhmsp0mxbfpqxr65y63jrqngx1c3zgbx1"))))
    (properties `((upstream-name . "sld")))
    (build-system r-build-system)
    (propagated-inputs (list r-lmom))
    (home-page "https://github.com/newystats/SLD/")
    (synopsis
     "Estimation and Use of the Quantile-Based Skew Logistic Distribution")
    (description
     "The skew logistic distribution is a quantile-defined generalisation of the
logistic distribution (van Staden and King 2015).  Provides random numbers,
quantiles, probabilities, densities and density quantiles for the distribution.
It provides Quantile-Quantile plots and method of L-Moments estimation
(including asymptotic standard errors) for the distribution.")
    (license license:gpl2+)))

(define-public r-slcm
  (package
    (name "r-slcm")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "slcm" version))
       (sha256
        (base32 "0avbgsf3hgvpamw6cbs9l7l8s8bc89sqyjskj7mhjbvxsjdqd416"))))
    (properties `((upstream-name . "slcm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://github.com/tmsalab/slcm")
    (synopsis "Sparse Latent Class Model for Cognitive Diagnosis")
    (description
     "Perform a Bayesian estimation of the exploratory Sparse Latent Class Model for
Binary Data described by Chen, Y., Culpepper, S. A., and Liang, F. (2020)
<doi:10.1007/s11336-019-09693-2>.")
    (license license:gpl2+)))

(define-public r-slcare
  (package
    (name "r-slcare")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SLCARE" version))
       (sha256
        (base32 "0ci04i231dgnknqaw2wwv9q7q6psazd537c2x3330kk6zydgass8"))))
    (properties `((upstream-name . "SLCARE")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rlang
                             r-rereg
                             r-reda
                             r-nnet
                             r-magrittr
                             r-ggplot2
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=SLCARE")
    (synopsis "Semiparametric Latent Class Analysis of Recurrent Events")
    (description
     "Efficient R package for latent class analysis of recurrent events, based on the
semiparametric multiplicative intensity model by Zhao et al. (2022)
<doi:10.1111/rssb.12499>.  SLCARE returns estimates for non-functional model
parameters along with the associated variance estimates and p-values.
Visualization tools are provided to depict the estimated functional model
parameters and related functional quantities of interest.  SLCARE also delivers
a model checking plot to help assess the adequacy of the fitted model.")
    (license license:gpl3+)))

(define-public r-slca
  (package
    (name "r-slca")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "slca" version))
       (sha256
        (base32 "0wllwz9cn9wvxwb4gd4g3blwhaplmfdxx06jki41ii64sq9cxbyq"))))
    (properties `((upstream-name . "slca")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-mass r-magrittr r-diagrammer))
    (home-page "https://cran.r-project.org/package=slca")
    (synopsis "Structural Modeling for Multiple Latent Class Variables")
    (description
     "This package provides comprehensive tools for the implementation of Structural
Latent Class Models (SLCM), including Latent Transition Analysis (LTA; Linda M.
Collins and Stephanie T. Lanza, 2009) <doi:10.1002/9780470567333>, Latent Class
Profile Analysis (LCPA; Hwan Chung et al., 2010)
<doi:10.1111/j.1467-985x.2010.00674.x>, and Joint Latent Class Analysis (JLCA;
Saebom Jeon et al., 2017) <doi:10.1080/10705511.2017.1340844>, and any other
extended models involving multiple latent class variables.")
    (license license:gpl3+)))

(define-public r-slbdd
  (package
    (name "r-slbdd")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SLBDD" version))
       (sha256
        (base32 "06v2id6rqdn9gcjmnpixdfxgh7wvqx9xpna6lnciqz974k8lblsb"))))
    (properties `((upstream-name . "SLBDD")))
    (build-system r-build-system)
    (propagated-inputs (list r-tsoutliers
                             r-tsclust
                             r-rnn
                             r-mts
                             r-matrixcalc
                             r-matrix
                             r-mass
                             r-imputets
                             r-gsarima
                             r-glmnet
                             r-forecast
                             r-fgarch
                             r-corpcor
                             r-cluster))
    (home-page "https://cran.r-project.org/package=SLBDD")
    (synopsis "Statistical Learning for Big Dependent Data")
    (description
     "Programs for analyzing large-scale time series data.  They include functions for
automatic specification and estimation of univariate time series, for clustering
time series, for multivariate outlier detections, for quantile plotting of many
time series, for dynamic factor models and for creating input data for deep
learning programs.  Examples of using the package can be found in the Wiley book
Statistical Learning with Big Dependent Data by Daniel PeÃ±a and Ruey S. Tsay
(2021).  ISBN 9781119417385.")
    (license license:gpl3)))

(define-public r-slasso
  (package
    (name "r-slasso")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "slasso" version))
       (sha256
        (base32 "0k83szk6jzvn5353p9ia2ipmjyl65ivx95rj62rq8dyjfrdyzs0d"))))
    (properties `((upstream-name . "slasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-plot3d
                             r-matrixstats
                             r-matrixcalc
                             r-mass
                             r-inline
                             r-fda-usc
                             r-fda
                             r-cxxfunplus))
    (home-page "https://github.com/unina-sfere/slasso")
    (synopsis
     "S-LASSO Estimator for the Function-on-Function Linear Regression")
    (description
     "This package implements the smooth LASSO estimator for the function-on-function
linear regression model described in Centofanti et al. (2020)
<@code{arXiv:2007.00529>}.")
    (license license:gpl3+)))

(define-public r-slapmeg
  (package
    (name "r-slapmeg")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SlaPMEG" version))
       (sha256
        (base32 "0ikhx14czcavfxppsaf0b6506l4gwhwk4p3wilr9zykw17s8ghzm"))))
    (properties `((upstream-name . "SlaPMEG")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2
                             r-mvtnorm
                             r-magic
                             r-lme4
                             r-lcmm
                             r-globaltest
                             r-ggplot2))
    (home-page "https://cran.r-project.org/package=SlaPMEG")
    (synopsis "Pathway Testing for Longitudinal Omics")
    (description
     "This package provides a self-contained hypothesis is tested for a given pathway
of longitudinal omics. @code{SlaPMEG} is a two-step procedure.  First, a shared
latent process mixed model is fitted over the longitudinal measures of omics in
a pathway.  This shared model allows deviation from the shared process at
subject level (a random intercept, slope, or both per subject) and also at omic
level (a random effect per omic).  These random effects summarize the
longitudinal trend of the observations which can be used to test for group
differences using Globaltest in the second step.  If the pathway is large or the
shared effect is small, the package fits a series of pairwise models and
estimates the shared random effects based on them.")
    (license license:gpl2+)))

(define-public r-slap
  (package
    (name "r-slap")
    (version "2024.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "slap" version))
       (sha256
        (base32 "1w1av896q4jfc1kv3mpisqra6s3cbcr7i7x8nm24hwq24bx0kdik"))))
    (properties `((upstream-name . "slap")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-cli))
    (home-page "https://github.com/tadascience/slap")
    (synopsis "Simplified Error Handling")
    (description
     "Alternative to using @code{withCallingHandlers}() in the simple case of catch
and rethrow.  The `%!%` operator evaluates the expression on its left hand side,
and if an error occurs, the right hand side is used to construct a new error
that embeds the original error.")
    (license license:expat)))

(define-public r-slackr
  (package
    (name "r-slackr")
    (version "3.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "slackr" version))
       (sha256
        (base32 "08yyl0s2rb7yzq9zd173avdxrl8z59dknsxbmgg4m94wf09xwy55"))))
    (properties `((upstream-name . "slackr")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-tibble
                             r-rlang
                             r-memoise
                             r-magrittr
                             r-jsonlite
                             r-httr
                             r-dplyr
                             r-cachem))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mrkaye97/slackr")
    (synopsis
     "Send Messages, Images, R Objects and Files to 'Slack' Channels/Users")
    (description
     "Slack <https://slack.com/> provides a service for teams to collaborate by
sharing messages, images, links, files and more.  Functions are provided that
make it possible to interact with the Slack platform API'.  When you need to
share information or data from R, rather than resort to copy/ paste in e-mails
or other services like Skype <https://www.skype.com/en/>, you can use this
package to send well-formatted output from multiple R objects and expressions to
all teammates at the same time with little effort.  You can also send images
from the current graphics device, R objects, and upload files.")
    (license license:expat)))

(define-public r-skyscaper
  (package
    (name "r-skyscaper")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "skyscapeR" version))
       (sha256
        (base32 "1x3s003wdmnf0h1k3gl6cvd35g516y7v2yzahr3wxk2np832kjli"))))
    (properties `((upstream-name . "skyscapeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-swephr
                             r-rootsolve
                             r-rcolorbrewer
                             r-png
                             r-plotrix
                             r-oce
                             r-numderiv
                             r-mess
                             r-httr
                             r-foreach
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=skyscapeR")
    (synopsis "Data Analysis and Visualization for Skyscape Archaeology")
    (description
     "Data reduction, visualization and statistical analysis of measurements of
orientation of archaeological structures, following Silva (2020)
<doi:10.1016/j.jas.2020.105138>.")
    (license license:gpl2+)))

(define-public r-skynet
  (package
    (name "r-skynet")
    (version "1.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "skynet" version))
       (sha256
        (base32 "08a5p3rk0qhsxawdxj5sfw07rdk56x6viy37w1b0hxqaxxxv239i"))))
    (properties `((upstream-name . "skynet")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rcurl
                             r-maps
                             r-leaflet
                             r-igraph
                             r-httr
                             r-ggrepel
                             r-ggplot2
                             r-geosphere
                             r-dplyr
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ropensci/skynet")
    (synopsis "Generates Networks from BTS Data")
    (description
     "This package provides a flexible tool that allows generating bespoke air
transport statistics for urban studies based on publicly available data from the
Bureau of Transport Statistics (BTS) in the United States
<https://www.transtats.bts.gov/databases.asp?Z1qr_VQ=E&Z1qr_Qr5p=N8vn6v10&f7owrp6_VQF=D>.")
    (license license:expat)))

(define-public r-skylight
  (package
    (name "r-skylight")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "skylight" version))
       (sha256
        (base32 "1yh3hk1z4c25pkb9gzyc16a68jd1za4h9z4l9xh5avkg68ixff5j"))))
    (properties `((upstream-name . "skylight")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bluegreen-labs/skylight")
    (synopsis "Simple Sky Illuminance Model")
    (description
     "This package provides a tool to calculate sky illuminance values (in lux) for
both sun and moon.  The model is a verbatim translation of the code by Janiczek
and @code{DeYoung} (1987) <https://archive.org/details/DTIC_ADA182110>.")
    (license license:agpl3)))

(define-public r-skpr
  (package
    (name "r-skpr")
    (version "1.7.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "skpr" version))
       (sha256
        (base32 "1g19pq73map54sypkarvyw191zgf4cbmr1hskr8amg75wvw8mg11"))))
    (properties `((upstream-name . "skpr")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-survival
                             r-scales
                             r-rcppeigen
                             r-rcpp
                             r-progressr
                             r-progress
                             r-magrittr
                             r-lmertest
                             r-lme4
                             r-iterators
                             r-future
                             r-foreach
                             r-dorng
                             r-doparallel
                             r-dofuture
                             r-car))
    (home-page "https://github.com/tylermorganwall/skpr")
    (synopsis
     "Design of Experiments Suite: Generate and Evaluate Optimal Designs")
    (description
     "Generates and evaluates D, I, A, Alias, E, T, and G optimal designs.  Supports
generation and evaluation of blocked and split/split-split/.../N-split plot
designs.  Includes parametric and Monte Carlo power evaluation functions, and
supports calculating power for censored responses.  Provides a framework to
evaluate power using functions provided in other packages or written by the
user.  Includes a Shiny graphical user interface that displays the underlying
code used to create and evaluate the design to improve ease-of-use and make
analyses more reproducible.  For details, see Morgan-Wall et al. (2021)
<doi:10.18637/jss.v099.i01>.")
    (license license:gpl3)))

(define-public r-sknn
  (package
    (name "r-sknn")
    (version "4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SKNN" version))
       (sha256
        (base32 "1iigrps3knwij2z87rbd60si7x19mrfi298l18imqkp8axydmyp2"))))
    (properties `((upstream-name . "SKNN")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SKNN")
    (synopsis "Super K-Nearest Neighbor (SKNN) Classification Algorithm")
    (description
     "It's a Super K-Nearest Neighbor classification method with using kernel density
to describe weight of the distance between a training observation and the
testing sample.")
    (license license:gpl2)))

(define-public r-skmeans
  (package
    (name "r-skmeans")
    (version "0.2-17")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "skmeans" version))
       (sha256
        (base32 "0686i48xpcis3m4352lhygff15j5dvd91f7q7dfkkbsnasl17ivw"))))
    (properties `((upstream-name . "skmeans")))
    (build-system r-build-system)
    (propagated-inputs (list r-slam r-cluster r-clue))
    (home-page "https://cran.r-project.org/package=skmeans")
    (synopsis "Spherical k-Means Clustering")
    (description
     "Algorithms to compute spherical k-means partitions.  Features several methods,
including a genetic and a fixed-point algorithm and an interface to the CLUTO
vcluster program.")
    (license license:gpl2)))

(define-public r-skm
  (package
    (name "r-skm")
    (version "0.1.5.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "skm" version))
       (sha256
        (base32 "06g3bdncq2r56d8k3dr87gqnibypbsps0gj4jxkw9q1sq1yaff3v"))))
    (properties `((upstream-name . "skm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppparallel
                             r-rcpparmadillo
                             r-rcpp
                             r-plyr
                             r-magrittr
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "http://github.com/gyang274/skm")
    (synopsis "Selective k-Means")
    (description
     "Algorithms for solving selective k-means problem, which is defined as finding k
rows in an m x n matrix such that the sum of each column minimal is minimized.
In the scenario when m == n and each cell value in matrix is a valid distance
metric, this is equivalent to a k-means problem.  The selective k-means extends
the k-means problem in the sense that it is possible to have m != n, often the
case m < n which implies the search is limited within a small subset of rows.
Also, the selective k-means extends the k-means problem in the sense that the
instance in row set can be instance not seen in the column set, e.g., select 2
from 3 internet service provider (row) for 5 houses (column) such that minimize
the overall cost (cell value) - overall cost is the sum of the column minimal of
the selected 2 service provider.")
    (license license:expat)))

(define-public r-sklarsomega
  (package
    (name "r-sklarsomega")
    (version "3.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sklarsomega" version))
       (sha256
        (base32 "027rx1x5hsn0qx7s6s2sqf13p49jbhi6g7w0fzfqf619p3fzkghg"))))
    (properties `((upstream-name . "sklarsomega")))
    (build-system r-build-system)
    (propagated-inputs (list r-spam
                             r-numderiv
                             r-mcmcse
                             r-matrix
                             r-laplacesdemon
                             r-hash
                             r-extradistr
                             r-dfoptim))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=sklarsomega")
    (synopsis "Measuring Agreement Using Sklar's Omega Coefficient")
    (description
     "This package provides tools for applying Sklar's Omega (Hughes, 2018)
<@code{arXiv:1803.02734>} methodology to nominal scores, ordinal scores,
percentages, counts, amounts (i.e., non-negative real numbers), and balances
(i.e., any real number).  The framework can accommodate any number of units, any
number of coders, and missingness; and can be used to measure agreement with a
gold standard, intra-coder agreement, and/or inter-coder agreement.  Frequentist
inference is supported for all levels of measurement.  Bayesian inference is
supported for continuous scores only.")
    (license license:gpl2+)))

(define-public r-skiptrack
  (package
    (name "r-skiptrack")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "skipTrack" version))
       (sha256
        (base32 "0s5w2jixm52pbpi2cmgf9s1d6prs49dz4kcrr135d0nbdx34ma2w"))))
    (properties `((upstream-name . "skipTrack")))
    (build-system r-build-system)
    (propagated-inputs (list r-optimg
                             r-mvtnorm
                             r-lifecycle
                             r-laplacesdemon
                             r-gridextra
                             r-glmnet
                             r-ggtext
                             r-ggplot2
                             r-genmcmcdiag
                             r-foreach
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/LukeDuttweiler/skipTrack")
    (synopsis
     "Bayesian Hierarchical Model that Controls for Non-Adherence in Mobile Menstrual Cycle Tracking")
    (description
     "This package implements a Bayesian hierarchical model designed to identify skips
in mobile menstrual cycle self-tracking on mobile apps.  Future developments
will allow for the inclusion of covariates affecting cycle mean and regularity,
as well as extra information regarding tracking non-adherence.  Main methods to
be outlined in a forthcoming paper, with alternative models from Li et al.
(2022) <doi:10.1093/jamia/ocab182>.")
    (license license:expat)))

(define-public r-skimr
  (package
    (name "r-skimr")
    (version "2.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "skimr" version))
       (sha256
        (base32 "11w32vfwm6pz4cvmm60na6pjbyjvw2xq489ij5xqzx0n3pma7wq6"))))
    (properties `((upstream-name . "skimr")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-repr
                             r-purrr
                             r-pillar
                             r-magrittr
                             r-knitr
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://docs.ropensci.org/skimr/")
    (synopsis "Compact and Flexible Summaries of Data")
    (description
     "This package provides a simple to use summary function that can be used with
pipes and displays nicely in the console.  The default summary statistics may be
modified by the user as can the default formatting.  Support for data frames and
vectors is included, and users can implement their own skim methods for specific
object types as described in a vignette.  Default summaries include support for
inline spark graphs.  Instructions for managing these on specific operating
systems are given in the \"Using skimr\" vignette and the README.")
    (license license:gpl3)))

(define-public r-skilljar
  (package
    (name "r-skilljar")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "skilljaR" version))
       (sha256
        (base32 "04x1hfg0kvbr5c1abl0kfh1x07kvgyzhxzd38j6blg16awcvfxva"))))
    (properties `((upstream-name . "skilljaR")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr r-magrittr r-jsonlite r-httr r-dplyr))
    (home-page "https://cran.r-project.org/package=skilljaR")
    (synopsis "Connect to Your 'Skilljar' Data")
    (description
     "This package provides functions that simplify calls to the Skilljar API. See
<https://api.skilljar.com/docs/> for documentation on the Skilljar API. This
package is not supported by Skilljar'.")
    (license license:cc0)))

(define-public r-skillings-mack
  (package
    (name "r-skillings-mack")
    (version "1.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Skillings.Mack" version))
       (sha256
        (base32 "0zxqiw87avw2rb2acj7mvpyfkf7iwnkshg73ib74y5ml9awmg2mw"))))
    (properties `((upstream-name . "Skillings.Mack")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrixcalc r-mass))
    (home-page "https://cran.r-project.org/package=Skillings.Mack")
    (synopsis
     "The Skillings-Mack Test Statistic for Block Designs with Missing Observations")
    (description
     "This package provides a generalization of the statistic used in Friedman's ANOVA
method and in Durbin's rank test.  This nonparametric statistical test is useful
for the data obtained from block designs with missing observations occurring
randomly.  A resulting p-value is based on the chi-squared distribution and
Monte Carlo method.")
    (license license:gpl2+)))

(define-public r-skfcpd
  (package
    (name "r-skfcpd")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SKFCPD" version))
       (sha256
        (base32 "115afbld15c8av179c023mvhzb9cmwc0mgv2s86vv3ldy6l2s39d"))))
    (properties `((upstream-name . "SKFCPD")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-reshape2
                             r-rcppeigen
                             r-rcpp
                             r-ggpubr
                             r-ggplot2
                             r-fastgasp))
    (home-page "https://cran.r-project.org/package=SKFCPD")
    (synopsis
     "Fast Online Changepoint Detection for Temporally Correlated Data")
    (description
     "Sequential Kalman filter for scalable online changepoint detection by temporally
correlated data.  It enables fast single and multiple change points with missing
values.  See the reference: Hanmo Li, Yuedong Wang, Mengyang Gu (2023),
<@code{arXiv:2310.18611>}.")
    (license license:gpl3+)))

(define-public r-skewt
  (package
    (name "r-skewt")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "skewt" version))
       (sha256
        (base32 "1jaldnqgjzky2vr290vzpzdwsa5s92l977v6k2yhpzmmw4k4x3l7"))))
    (properties `((upstream-name . "skewt")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=skewt")
    (synopsis "The Skewed Student-t Distribution")
    (description
     "Density, distribution function, quantile function and random generation for the
skewed t distribution of Fernandez and Steel.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-skewsamp
  (package
    (name "r-skewsamp")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "skewsamp" version))
       (sha256
        (base32 "1b41zjjnn7vphcrbvrvqx66jfd9fb8v22xmgql6wh26a2y81bman"))))
    (properties `((upstream-name . "skewsamp")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=skewsamp")
    (synopsis
     "Estimate Sample Sizes for Group Comparisons with Skewed Distributions")
    (description
     "Estimate necessary sample sizes for comparing the location of data from two
groups or categories when the distribution of the data is skewed.  The package
offers a non-parametric method for a Wilcoxon Mann-Whitney test of location
shift as well as methods for several generalized linear models, for instance,
Gamma regression.")
    (license license:expat)))

(define-public r-skewmlrm
  (package
    (name "r-skewmlrm")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "skewMLRM" version))
       (sha256
        (base32 "16gr0haag0pqw7k7nvfqlc6s5i92lkpd0ghkgvlwgql8jzw7zmlk"))))
    (properties `((upstream-name . "skewMLRM")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm
                             r-moments
                             r-matrixcalc
                             r-mass
                             r-foreach
                             r-doparallel
                             r-clustergeneration))
    (home-page "https://cran.r-project.org/package=skewMLRM")
    (synopsis
     "Estimation for Scale-Shape Mixtures of Skew-Normal Distributions")
    (description
     "Provide data generation and estimation tools for the multivariate scale mixtures
of normal presented in Lange and Sinsheimer (1993) <doi:10.2307/1390698>, the
multivariate scale mixtures of skew-normal presented in Zeller, Lachos and Vilca
(2011) <doi:10.1080/02664760903406504>, the multivariate skew scale mixtures of
normal presented in Louredo, Zeller and Ferreira (2021)
<doi:10.1007/s13571-021-00257-y> and the multivariate scale mixtures of
skew-normal-Cauchy presented in Kahrari et al. (2020)
<doi:10.1080/03610918.2020.1804582>.")
    (license license:gpl2+)))

(define-public r-skewlmm
  (package
    (name "r-skewlmm")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "skewlmm" version))
       (sha256
        (base32 "04jxf7bwh71r33s1s2xhiy0ipfaqz97ya86rr4m0f5kxnb942ggv"))))
    (properties `((upstream-name . "skewlmm")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncatednormal
                             r-relliptical
                             r-purrr
                             r-optimparallel
                             r-numderiv
                             r-nlme
                             r-mvtnorm
                             r-momtrunc
                             r-moments
                             r-matrixcalc
                             r-haven
                             r-ggrepel
                             r-ggplot2
                             r-future
                             r-furrr
                             r-dplyr))
    (home-page "https://github.com/fernandalschumacher/skewlmm")
    (synopsis "Scale Mixture of Skew-Normal Linear Mixed Models")
    (description
     "It fits scale mixture of skew-normal linear mixed models using an
expectationâmaximization (EM) type algorithm, including some possibilities for
modeling the within-subject dependence.  Details can be found in Schumacher,
Lachos and Matos (2021) <doi:10.1002/sim.8870>.")
    (license license:expat)))

(define-public r-skewhyperbolic
  (package
    (name "r-skewhyperbolic")
    (version "0.4-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SkewHyperbolic" version))
       (sha256
        (base32 "0zhnw9qksm91v9cnlriaqlgr2pd855v6z75xpyqc0ms1qa5m8jp6"))))
    (properties `((upstream-name . "SkewHyperbolic")))
    (build-system r-build-system)
    (propagated-inputs (list r-generalizedhyperbolic r-distributionutils))
    (home-page "https://r-forge.r-project.org/projects/rmetrics/")
    (synopsis "The Skew Hyperbolic Student t-Distribution")
    (description
     "This package provides functions are provided for the density function,
distribution function, quantiles and random number generation for the skew
hyperbolic t-distribution.  There are also functions that fit the distribution
to data.  There are functions for the mean, variance, skewness, kurtosis and
mode of a given distribution and to calculate moments of any order about any
centre.  To assess goodness of fit, there are functions to generate a Q-Q plot,
a P-P plot and a tail plot.")
    (license license:gpl2+)))

(define-public r-sketchy
  (package
    (name "r-sketchy")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sketchy" version))
       (sha256
        (base32 "090gsly8ih3k8c6vbs32ikwd5j5l906mwj3xfbpa1mr2pdpg7sxd"))))
    (properties `((upstream-name . "sketchy")))
    (build-system r-build-system)
    (propagated-inputs (list r-xaringanextra
                             r-urlchecker
                             r-stringr
                             r-stringi
                             r-rmarkdown
                             r-remotes
                             r-packrat
                             r-knitr
                             r-git2r
                             r-crayon
                             r-cli))
    (home-page "https://github.com/maRce10/sketchy")
    (synopsis "Create Custom Research Compendiums")
    (description
     "This package provides functions to create and manage research compendiums for
data analysis.  Research compendiums are a standard and intuitive folder
structure for organizing the digital materials of a research project, which can
significantly improve reproducibility.  The package offers several compendium
structure options that fit different research project as well as the ability of
duplicating the folder structure of existing projects or implementing custom
structures.  It also simplifies the use of version control.")
    (license license:gpl2+)))

(define-public r-sketching
  (package
    (name "r-sketching")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sketching" version))
       (sha256
        (base32 "0k2rdiawj84zhfik3qzqqzabm9sdjwnr008p0fhaaqpifbr0q5xj"))))
    (properties `((upstream-name . "sketching")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-phangorn r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sokbae/sketching/")
    (synopsis "Sketching of Data via Random Subspace Embeddings")
    (description
     "Construct sketches of data via random subspace embeddings.  For more details,
see the following papers.  Lee, S. and Ng, S. (2022). \"Least Squares Estimation
Using Sketched Data with Heteroskedastic Errors,\" Proceedings of the 39th
International Conference on Machine Learning (ICML22), 162:12498-12520.  Lee, S.
and Ng, S. (2020). \"An Econometric Perspective on Algorithmic Subsampling,\"
Annual Review of Economics, 12(1): 45â80.")
    (license license:gpl3)))

(define-public r-sketcher
  (package
    (name "r-sketcher")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sketcher" version))
       (sha256
        (base32 "011lx15whrdj5q5h33h9brkzbn2fnpm1nx9ip1v9xl5xfkbj7952"))))
    (properties `((upstream-name . "sketcher")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-readbitmap
                             r-png
                             r-magrittr
                             r-jpeg
                             r-imager
                             r-dplyr
                             r-downloader))
    (home-page "https://htsuda.net/sketcher/")
    (synopsis "Pencil Sketch Effect")
    (description
     "An implementation of image processing effects that convert a photo into a line
drawing image.  For details, please refer to Tsuda, H. (2020).  sketcher: An R
package for converting a photo into a sketch style image.
<doi:10.31234/osf.io/svmw5>.")
    (license license:expat)))

(define-public r-sketch
  (package
    (name "r-sketch")
    (version "1.1.20.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sketch" version))
       (sha256
        (base32 "03ssz4vwrcfcqs5lvnrkm7yqc8ir62kz3zp8aycgl88rw5qm9003"))))
    (properties `((upstream-name . "sketch")))
    (build-system r-build-system)
    (propagated-inputs (list r-v8
                             r-shiny
                             r-rstudioapi
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-jsonlite
                             r-htmltools
                             r-glue
                             r-base64enc))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=sketch")
    (synopsis "Interactive Sketches")
    (description
     "This package creates static / animated / interactive visualisations embeddable
in R Markdown documents.  It implements an R-to-@code{JavaScript} transpiler and
enables users to write @code{JavaScript} applications using the syntax of R.")
    (license license:asl2.0)))

(define-public r-skellam
  (package
    (name "r-skellam")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "skellam" version))
       (sha256
        (base32 "0hmms6709v7mzp6qgcxljjxq4nccqiwphn1nk3jh6mp2n0zvaqjd"))))
    (properties `((upstream-name . "skellam")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https//r-forge.r-project.org/projects/healthqueues/")
    (synopsis "Densities and Sampling for the Skellam Distribution")
    (description
     "This package provides functions for the Skellam distribution, including: density
(pmf), cdf, quantiles and regression.")
    (license license:gpl2+)))

(define-public r-skeletor
  (package
    (name "r-skeletor")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "skeletor" version))
       (sha256
        (base32 "1jfbfbkjx8mdwamsrkhvcnyn470in702vgcyw3g2dbgs84gl29q8"))))
    (properties `((upstream-name . "skeletor")))
    (build-system r-build-system)
    (home-page "https://github.com/nealrichardson/skeletor")
    (synopsis "An R Package Skeleton Generator")
    (description
     "This package provides a tool for bootstrapping new packages with useful
defaults, including a test suite outline that passes checks and helpers for
running tests, checking test coverage, building vignettes, and more.  Package
skeletons it creates are set up for pushing your package to @code{GitHub} and
using other hosted services for building and test automation.")
    (license license:expat)))

(define-public r-skedastic
  (package
    (name "r-skedastic")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "skedastic" version))
       (sha256
        (base32 "1ln0scwgvpif610jgwdl0rzlx41i8w2ri1vaq0kl11vg69xp5105"))))
    (properties `((upstream-name . "skedastic")))
    (build-system r-build-system)
    (propagated-inputs (list r-slam
                             r-roi-plugin-qpoases
                             r-roi
                             r-rfast
                             r-rdpack
                             r-quadprogxt
                             r-quadprog
                             r-pracma
                             r-osqp
                             r-mgcv
                             r-matrix
                             r-mass
                             r-inflection
                             r-compquadform
                             r-caret
                             r-broom
                             r-bazar))
    (home-page "https://github.com/tjfarrar/skedastic")
    (synopsis "Handling Heteroskedasticity in the Linear Regression Model")
    (description
     "This package implements numerous methods for testing for, modelling, and
correcting for heteroskedasticity in the classical linear regression model.  The
most novel contribution of the package is found in the functions that implement
the as-yet-unpublished auxiliary linear variance models and auxiliary nonlinear
variance models that are designed to estimate error variances in a
heteroskedastic linear regression model.  These models follow principles of
statistical learning described in Hastie (2009) <doi:10.1007/978-0-387-21606-5>.
 The nonlinear version of the model is estimated using quasi-likelihood methods
as described in Seber and Wild (2003, ISBN: 0-471-47135-6).  Bootstrap methods
for approximate confidence intervals for error variances are implemented as
described in Efron and Tibshirani (1993, ISBN: 978-1-4899-4541-9), including
also the expansion technique described in Hesterberg (2014)
<doi:10.1080/00031305.2015.1089789>.  The wild bootstrap employed here follows
the description in Davidson and Flachaire (2008)
<doi:10.1016/j.jeconom.2008.08.003>.  Tuning of hyper-parameters makes use of a
golden section search function that is modelled after the MATLAB function of
Zarnowiec (2022)
<https://www.mathworks.com/matlabcentral/fileexchange/25919-golden-section-method-algorithm>.
 A methodological description of the algorithm can be found in Fox (2021, ISBN:
978-1-003-00957-3).  There are 25 different functions that implement hypothesis
tests for heteroskedasticity.  These include a test based on Anscombe (1961)
<https://projecteuclid.org/euclid.bsmsp/1200512155>, Ramsey's (1969) BAMSET Test
<doi:10.1111/j.2517-6161.1969.tb00796.x>, the tests of Bickel (1978)
<doi:10.1214/aos/1176344124>, Breusch and Pagan (1979) <doi:10.2307/1911963>
with and without the modification proposed by Koenker (1981)
<doi:10.1016/0304-4076(81)90062-2>, Carapeto and Holt (2003)
<doi:10.1080/0266476022000018475>, Cook and Weisberg (1983)
<doi:10.1093/biomet/70.1.1> (including their graphical methods), Diblasi and
Bowman (1997) <doi:10.1016/S0167-7152(96)00115-0>, Dufour, Khalaf, Bernard, and
Genest (2004) <doi:10.1016/j.jeconom.2003.10.024>, Evans and King (1985)
<doi:10.1016/0304-4076(85)90085-5> and Evans and King (1988)
<doi:10.1016/0304-4076(88)90006-1>, Glejser (1969)
<doi:10.1080/01621459.1969.10500976> as formulated by Mittelhammer, Judge and
Miller (2000, ISBN: 0-521-62394-4), Godfrey and Orme (1999)
<doi:10.1080/07474939908800438>, Goldfeld and Quandt (1965)
<doi:10.1080/01621459.1965.10480811>, Harrison and @code{McCabe} (1979)
<doi:10.1080/01621459.1979.10482544>, Harvey (1976) <doi:10.2307/1913974>, Honda
(1989) <doi:10.1111/j.2517-6161.1989.tb01749.x>, Horn (1981)
<doi:10.1080/03610928108828074>, Li and Yao (2019)
<doi:10.1016/j.ecosta.2018.01.001> with and without the modification of Bai,
Pan, and Yin (2016) <doi:10.1007/s11749-017-0575-x>, Rackauskas and Zuokas
(2007) <doi:10.1007/s10986-007-0018-6>, Simonoff and Tsai (1994)
<doi:10.2307/2986026> with and without the modification of Ferrari, Cysneiros,
and Cribari-Neto (2004) <doi:10.1016/S0378-3758(03)00210-6>, Szroeter (1978)
<doi:10.2307/1913831>, Verbyla (1993) <doi:10.1111/j.2517-6161.1993.tb01918.x>,
White (1980) <doi:10.2307/1912934>, Wilcox and Keselman (2006)
<doi:10.1080/10629360500107923>, Yuce (2008)
<https://dergipark.org.tr/en/pub/iuekois/issue/8989/112070>, and Zhou, Song, and
Thompson (2015) <doi:10.1002/cjs.11252>.  Besides these heteroskedasticity
tests, there are supporting functions that compute the BLUS residuals of Theil
(1965) <doi:10.1080/01621459.1965.10480851>, the conditional two-sided p-values
of Kulinskaya (2008) <@code{arXiv:0810.2124v1>}, and probabilities for the
nonparametric trend statistic of Lehmann (1975, ISBN: 0-816-24996-1).  For
handling heteroskedasticity, in addition to the new auxiliary variance model
methods, there is a function to implement various existing
Heteroskedasticity-Consistent Covariance Matrix Estimators from the literature,
such as those of White (1980) <doi:10.2307/1912934>, @code{MacKinnon} and White
(1985) <doi:10.1016/0304-4076(85)90158-7>, Cribari-Neto (2004)
<doi:10.1016/S0167-9473(02)00366-3>, Cribari-Neto et al. (2007)
<doi:10.1080/03610920601126589>, Cribari-Neto and da Silva (2011)
<doi:10.1007/s10182-010-0141-2>, Aftab and Chang (2016)
<doi:10.18187/pjsor.v12i2.983>, and Li et al. (2017)
<doi:10.1080/00949655.2016.1198906>.")
    (license license:expat)))

(define-public r-skater
  (package
    (name "r-skater")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "skater" version))
       (sha256
        (base32 "1hw6hk2v6jxhv86dzjpqnakkrbp2k7gayg2zffpyd2z8l4mpgmxd"))))
    (properties `((upstream-name . "skater")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rlang
                             r-readr
                             r-purrr
                             r-magrittr
                             r-kinship2
                             r-dplyr
                             r-corrr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/signaturescience/skater")
    (synopsis "Utilities for SNP-Based Kinship Analysis")
    (description
     "Utilities for single nucleotide polymorphism (SNP) based kinship analysis
testing and evaluation.  The skater package contains functions for importing,
parsing, and analyzing pedigree data, performing relationship degree inference,
benchmarking relationship degree classification, and summarizing identity by
descent (IBD) segment data.  Package functions and methods are described in
Turner et al. (2021) \"skater: An R package for SNP-based Kinship Analysis,
Testing, and Evaluation\" <doi:10.1101/2021.07.21.453083>.")
    (license license:expat)))

(define-public r-sk4fga
  (package
    (name "r-sk4fga")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SK4FGA" version))
       (sha256
        (base32 "0jfs1x27ybd95qqxybwcf9bdn3yvhhp435g97yb13g6myc3c8sam"))))
    (properties `((upstream-name . "SK4FGA")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page
     "https://github.com/tobyhayward13/SCI118UOA_ForensicGlassAnalysis")
    (synopsis "Scott-Knott for Forensic Glass Analysis")
    (description
     "In forensics, it is common and effective practice to analyse glass fragments
from the scene and suspects to gain evidence of placing a suspect at the crime
scene.  This kind of analysis involves comparing the physical and chemical
attributes of glass fragments that exist on both the person and at the crime
scene, and assessing the significance in a likeness that they share.  The
package implements the Scott-Knott Modification 2 algorithm (SKM2) (Christopher
M. Triggs and James M. Curran and John S. Buckleton and Kevan A.J. Walsh (1997)
<doi:10.1016/S0379-0738(96)02037-3> \"The grouping problem in forensic glass
analysis: a divisive approach\", Forensic Science International, 85(1), 1--14)
for small sample glass fragment analysis using the refractive index (ri) of a
set of glass samples.  It also includes an experimental multivariate analog to
the Scott-Knott algorithm for similar analysis on glass samples with multiple
chemical concentration variables and multiple samples of the same item; testing
against the Hotellings T^2 distribution (J.M. Curran and C.M. Triggs and J.R.
Almirall and J.S. Buckleton and K.A.J. Walsh (1997)
<doi:10.1016/S1355-0306(97)72197-X> \"The interpretation of elemental composition
measurements from forensic glass evidence\", Science & Justice, 37(4), 241--244).")
    (license license:gpl2+)))

(define-public r-sjtable2df
  (package
    (name "r-sjtable2df")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sjtable2df" version))
       (sha256
        (base32 "1vb2wr122pfn6qald36mn1dkz6gygp81skl11halrwa7cvxwkl7z"))))
    (properties `((upstream-name . "sjtable2df")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-rvest
                             r-rlang
                             r-magrittr
                             r-kableextra
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/kapsner/sjtable2df")
    (synopsis "Convert 'sjPlot' HTML-Tables to R 'data.frame'")
    (description
     "This package provides a small set of helper functions to convert @code{sjPlot}
HTML-tables to R data.frame objects / knitr::kable-tables.")
    (license license:gpl3+)))

(define-public r-sjsdm
  (package
    (name "r-sjsdm")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sjSDM" version))
       (sha256
        (base32 "0jmagj162vdq9ikzd92fk61bq6jv3dm5w1bfpjbvzg55d3cqli44"))))
    (properties `((upstream-name . "sjSDM")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-rstudioapi
                             r-reticulate
                             r-qgam
                             r-mvtnorm
                             r-mgcv
                             r-metrics
                             r-mathjaxr
                             r-ggtern
                             r-ggplot2
                             r-crayon
                             r-cli
                             r-checkmate
                             r-beeswarm
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/TheoreticalEcology/s-jSDM/")
    (synopsis "Scalable Joint Species Distribution Modeling")
    (description
     "This package provides a scalable and fast method for estimating joint Species
Distribution Models (@code{jSDMs}) for big community data, including @code{eDNA}
data.  The package estimates a full (i.e.  non-latent) @code{jSDM} with
different response distributions (including the traditional multivariate probit
model).  The package allows to perform variation partitioning (VP) / ANOVA on
the fitted models to separate the contribution of environmental, spatial, and
biotic associations.  In addition, the total R-squared can be further
partitioned per species and site to reveal the internal metacommunity structure,
see Leibold et al., <doi:10.1111/oik.08618>.  The internal structure can then be
regressed against environmental and spatial distinctiveness, richness, and
traits to analyze metacommunity assembly processes.  The package includes
support for accounting for spatial autocorrelation and the option to fit
responses using deep neural networks instead of a standard linear predictor.  As
described in Pichler & Hartig (2021) <doi:10.1111/2041-210X.13687>, scalability
is achieved by using a Monte Carlo approximation of the joint likelihood
implemented via @code{PyTorch} and reticulate', which can be run on CPUs or
GPUs.")
    (license license:gpl3)))

(define-public r-sjdbc
  (package
    (name "r-sjdbc")
    (version "1.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sjdbc" version))
       (sha256
        (base32 "0cz5qc5lsipd3nlkzj8j03xmpzxp52c3q5av8pr7fm3vsdclawfk"))))
    (properties `((upstream-name . "sjdbc")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjava))
    (home-page "https://cran.r-project.org/package=sjdbc")
    (synopsis "JDBC Driver Interface")
    (description
     "This package provides a database-independent JDBC interface.")
    (license license:bsd-3)))

(define-public r-sizer
  (package
    (name "r-sizer")
    (version "0.1-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SiZer" version))
       (sha256
        (base32 "0hfhl2ihyxbg61knzij8i7ih1siap6dxfvbkwffxlaqpwrd8hgdn"))))
    (properties `((upstream-name . "SiZer")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-rlang r-ggplot2 r-dplyr r-boot))
    (home-page "https://github.com/dereksonderegger/SiZer")
    (synopsis "Significant Zero Crossings")
    (description
     "Calculates and plots the @code{SiZer} map for scatterplot data.  A @code{SiZer}
map is a way of examining when the p-th derivative of a scatterplot-smoother is
significantly negative, possibly zero or significantly positive across a range
of smoothing bandwidths.")
    (license license:gpl2+)))

(define-public r-sizemat
  (package
    (name "r-sizemat")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sizeMat" version))
       (sha256
        (base32 "1vbndadsn80ddrg471vp1qxf0gan38w6dkjk1p2npvfb47wdjj2h"))))
    (properties `((upstream-name . "sizeMat")))
    (build-system r-build-system)
    (propagated-inputs (list r-mcmcpack r-matrixstats r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=sizeMat")
    (synopsis "Estimate Size at Sexual Maturity")
    (description
     "Estimate morphometric and gonadal size at sexual maturity for organisms, usually
fish and invertebrates.  It includes methods for classification based on
relative growth (using principal components analysis, hierarchical clustering,
discriminant analysis), logistic regression (Frequentist or Bayes), parameters
estimation and some basic plots.")
    (license license:gpl2)))

(define-public r-sizeestimation
  (package
    (name "r-sizeestimation")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SizeEstimation" version))
       (sha256
        (base32 "1rz57y76hzp880511kzm7nhxf201n0dr7ccip6slrjz784dl7s27"))))
    (properties `((upstream-name . "SizeEstimation")))
    (build-system r-build-system)
    (propagated-inputs (list r-msm r-mcmcpack))
    (home-page "https://cran.r-project.org/package=SizeEstimation")
    (synopsis
     "Estimating the Sizes of Populations at Risk of HIV Infection from Multiple Data Sources Using a Bayesian Hierarchical Model")
    (description
     "This function develops an algorithm for presenting a Bayesian hierarchical model
for estimating the sizes of local and national drug injected populations in
Bangladesh.  The model incorporates multiple commonly used data sources
including mapping data, surveys, interventions, capture-recapture data,
estimates or guesstimates from organizations, and expert opinion.")
    (license license:gpl3)))

(define-public r-sixsigma
  (package
    (name "r-sixsigma")
    (version "0.11.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SixSigma" version))
       (sha256
        (base32 "1gb6giq9rsgsrwrk0m8r2ay4hi1kqb009rsak33wvmvqsavka8lf"))))
    (properties `((upstream-name . "SixSigma")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-testthat
                             r-scales
                             r-reshape2
                             r-nortest
                             r-lattice
                             r-ggplot2
                             r-e1071))
    (home-page "https://www.sixsigmawithr.com/")
    (synopsis "Six Sigma Tools for Quality Control and Improvement")
    (description
     "This package provides functions and utilities to perform Statistical Analyses in
the Six Sigma way.  Through the DMAIC cycle (Define, Measure, Analyze, Improve,
Control), you can manage several Quality Management studies: Gage R&R,
Capability Analysis, Control Charts, Loss Function Analysis, etc.  Data frames
used in the books \"Six Sigma with R\" [ISBN 978-1-4614-3652-2] and \"Quality
Control with R\" [ISBN 978-3-319-24046-6], are also included in the package.")
    (license license:gpl2+)))

(define-public r-sivs
  (package
    (name "r-sivs")
    (version "0.2.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sivs" version))
       (sha256
        (base32 "1mf881yy1f6s4f6bx4p5010f45pg09lww3npkkp1s11zpawgmdjq"))))
    (properties `((upstream-name . "sivs")))
    (build-system r-build-system)
    (propagated-inputs (list r-varhandle r-proc r-glmnet r-foreach
                             r-doparallel))
    (native-inputs (list r-markdown r-knitr))
    (home-page "https://github.com/mmahmoudian/sivs")
    (synopsis "Stable Iterative Variable Selection")
    (description
     "An iterative feature selection method (manuscript submitted) that internally
utilizes various Machine Learning methods that have embedded feature reduction
in order to shrink down the feature space into a small and yet robust set.")
    (license license:gpl3)))

(define-public r-sits
  (package
    (name "r-sits")
    (version "1.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sits" version))
       (sha256
        (base32 "0k0wb18zranqzjsczxjrn5xl9mprn3vzi8ds6hx7n3cr63crwp9w"))))
    (properties `((upstream-name . "sits")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-units
                             r-torch
                             r-tidyr
                             r-tibble
                             r-terra
                             r-sysfonts
                             r-slider
                             r-showtext
                             r-sf
                             r-rstac
                             r-rcpparmadillo
                             r-rcpp
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-gdalutilities
                             r-dplyr))
    (home-page "https://github.com/e-sensing/sits/")
    (synopsis
     "Satellite Image Time Series Analysis for Earth Observation Data Cubes")
    (description
     "An end-to-end toolkit for land use and land cover classification using big Earth
observation data, based on machine learning methods applied to satellite image
data cubes, as described in Simoes et al (2021) <doi:10.3390/rs13132428>.
Builds regular data cubes from collections in AWS, Microsoft Planetary Computer,
Brazil Data Cube, Copernicus Data Space Environment (CDSE), Digital Earth
Africa, Digital Earth Australia, NASA HLS using the Spatio-temporal Asset
Catalog (STAC) protocol (<https://stacspec.org/>) and the gdalcubes R package
developed by Appel and Pebesma (2019) <doi:10.3390/data4030092>.  Supports
visualization methods for images and time series and smoothing filters for
dealing with noisy time series.  Includes functions for quality assessment of
training samples using self-organized maps as presented by Santos et al (2021)
<doi:10.1016/j.isprsjprs.2021.04.014>.  Provides machine learning methods
including support vector machines, random forests, extreme gradient boosting,
multi-layer perceptrons, temporal convolutional neural networks proposed by
Pelletier et al (2019) <doi:10.3390/rs11050523>, and temporal attention encoders
by Garnot and Landrieu (2020) <doi:10.48550/@code{arXiv.2007.00586>}.  Supports
GPU processing of deep learning models using torch <https://torch.mlverse.org/>.
 Performs efficient classification of big Earth observation data cubes and
includes functions for post-classification smoothing based on Bayesian
inference, and methods for active learning and uncertainty assessment.  Supports
object-based time series analysis using package supercells
<https://jakubnowosad.com/supercells/>.  Enables best practices for estimating
area and assessing accuracy of land change as recommended by Olofsson et al
(2014) <doi:10.1016/j.rse.2014.02.015>.  Minimum recommended requirements: 16 GB
RAM and 4 CPU dual-core.")
    (license license:gpl2)))

(define-public r-sitreee
  (package
    (name "r-sitreee")
    (version "0.0-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sitreeE" version))
       (sha256
        (base32 "0jwxca8y0nn1aqx4c6gikgf31nb4dll543m0akd4jmqx6fc450kd"))))
    (properties `((upstream-name . "sitreeE")))
    (build-system r-build-system)
    (propagated-inputs (list r-sitree r-data-table))
    (home-page "https://cran.r-project.org/package=sitreeE")
    (synopsis "Sitree Extensions")
    (description
     "This package provides extensions for package sitree for allometric variables,
growth, mortality, recruitment, management, tree removal and external modifiers
functions.")
    (license license:gpl2+)))

(define-public r-sitree
  (package
    (name "r-sitree")
    (version "0.1-14")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sitree" version))
       (sha256
        (base32 "0b2dzws3n9q5ss8bna8prq0r0xg5ngqgkrwg92q51r9cv83vw1rf"))))
    (properties `((upstream-name . "sitree")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=sitree")
    (synopsis "Single Tree Simulator")
    (description "Framework to build an individual tree simulator.")
    (license license:gpl2+)))

(define-public r-sitools
  (package
    (name "r-sitools")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sitools" version))
       (sha256
        (base32 "0c0qnvsv06g6v7hxad96fkp9j641v8472mbphvaxa60k3xc7ackb"))))
    (properties `((upstream-name . "sitools")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sitools")
    (synopsis "Format a number to a string with SI prefix")
    (description
     "Format a number (or a list of numbers) to a string (or a list of strings) with
SI prefix.  Use SI prefixes as constants like (4 * milli)^2.")
    (license license:gpl3)))

(define-public r-sith
  (package
    (name "r-sith")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SITH" version))
       (sha256
        (base32 "1626nsbnz4fh49wymxm1cqb0nrfyxhmh9fmdxr62dzj5b53amknc"))))
    (properties `((upstream-name . "SITH")))
    (build-system r-build-system)
    (propagated-inputs (list r-scatterplot3d r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/phillipnicol/SITH")
    (synopsis "Spatial Model of Intra-Tumor Heterogeneity")
    (description
     "This package implements a three-dimensional stochastic model of cancer growth
and mutation similar to the one described in Waclaw et al. (2015)
<doi:10.1038/nature14971>.  Allows for interactive 3D visualizations of the
simulated tumor.  Provides a comprehensive summary of the spatial distribution
of mutants within the tumor.  Contains functions which create synthetic
sequencing datasets from the generated tumor.")
    (license license:gpl2+)))

(define-public r-siteymlgen
  (package
    (name "r-siteymlgen")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "siteymlgen" version))
       (sha256
        (base32 "1176fnf39ars5cdjvy5w2k6b92qv51hy86s71sl8ghr0lwdq9f59"))))
    (properties `((upstream-name . "siteymlgen")))
    (build-system r-build-system)
    (propagated-inputs (list r-ymlthis
                             r-yaml
                             r-stringr
                             r-rmarkdown
                             r-rlist
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-dplyr
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Acribbs/siteymlgen")
    (synopsis "Automatically Generate _site.yml File for 'R Markdown'")
    (description
     "The goal of siteymlgen is to make it easy to organise the building of your R
Markdown website.  The init() function placed within the first code chunk of the
index.Rmd file of an R project directory will initiate the generation of an
automatically written _site.yml file.  siteymlgen recommends a specific naming
convention for your R Markdown files.  This naming will ensure that your navbar
layout is ordered according to a hierarchy.")
    (license license:expat)))

(define-public r-sitepickr
  (package
    (name "r-sitepickr")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sitepickR" version))
       (sha256
        (base32 "1cqvf4d2jpav53v7h7vzknm8cbr5kqk1djqkhmjrwv2zjnx3fg97"))))
    (properties `((upstream-name . "sitepickR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-stringr
                             r-scales
                             r-sampling
                             r-reshape2
                             r-matchit
                             r-magrittr
                             r-ggplot2
                             r-dplyr
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=sitepickR")
    (synopsis "Two-Level Sample Selection with Optimal Site Replacement")
    (description
     "Carries out a two-level sample selection where the possibility of an initially
selected site not wanting to participate is anticipated, and the site is
optimally replaced.  The procedure aims to reduce bias (and/or loss of external
validity) with respect to the target population.  In selecting units and
sub-units, @code{sitepickR} uses the cube method developed by Deville & TillÃ©',
(2004) <http://www.math.helsinki.fi/msm/banocoss/Deville_Tille_2004.pdf> and
described in TillÃ© (2011)
<https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2011002/article/11609-eng.pdf?st=5-sx8Q8n>.
 The cube method is a probability sampling method that is designed to satisfy
criteria for balance between the sample and the population.  Recent research has
shown that this method performs well in simulations for studies of educational
programs (see Fay & Olsen (2021, under review).  To implement the cube method,
@code{sitepickR} uses the sampling R package
<https://cran.r-project.org/package=sampling>.  To implement statistical
matching, @code{sitepickR} uses the @code{MatchIt} R package
<https://cran.r-project.org/package=@code{MatchIt>}.")
    (license license:gpl3+)))

(define-public r-sit
  (package
    (name "r-sit")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SIT" version))
       (sha256
        (base32 "1hy5a74kp4xpmhw3cip8ccjbhdzfndnppmsk97zzxlblkl46kj3m"))))
    (properties `((upstream-name . "SIT")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://github.com/canyi-chen/SIT")
    (synopsis "Association Measurement Through Sliced Independence Test (SIT)")
    (description
     "Computes the sit coefficient between two vectors x and y, possibly all paired
coefficients for a matrix.  The reference for the methods implemented here is
Zhang, Yilin, Canyi Chen, and Liping Zhu.  2022. \"Sliced Independence Test.\"
Statistica Sinica. <doi:10.5705/ss.202021.0203>.  This package incorporates the
Galton peas example.")
    (license license:expat)))

(define-public r-sisvive
  (package
    (name "r-sisvive")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sisVIVE" version))
       (sha256
        (base32 "1vh53irxgk8ahw52cdqbbm89dvmzyf54izg4lm8a3v92k5p6nzwz"))))
    (properties `((upstream-name . "sisVIVE")))
    (build-system r-build-system)
    (propagated-inputs (list r-lars))
    (home-page "https://cran.r-project.org/package=sisVIVE")
    (synopsis "Some Invalid Some Valid Instrumental Variables Estimator")
    (description
     "Selects invalid instruments amongst a candidate of potentially bad instruments.
The algorithm selects potentially invalid instruments and provides an estimate
of the causal effect between exposure and outcome.")
    (license license:gpl2)))

(define-public r-sistmr
  (package
    (name "r-sistmr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sistmr" version))
       (sha256
        (base32 "08kajkb0ypbzcyxs9wv6mdz51r7zj01k2ahskidhlqww80rd407r"))))
    (properties `((upstream-name . "sistmr")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-rlang
                             r-ggrepel
                             r-ggplot2
                             r-ggbeeswarm
                             r-dplyr
                             r-blandaltmanleh))
    (home-page "https://cran.r-project.org/package=sistmr")
    (synopsis
     "Collection of Utility Function from the Inserm/Inria SISTM Team")
    (description
     "This package provides functions common to members of the SISTM team.")
    (license license:expat)))

(define-public r-sisti
  (package
    (name "r-sisti")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sisti" version))
       (sha256
        (base32 "020m38k1sndrhz42dg5w12x2gjyhxiissm07v0y4ps7wvqlp0hpa"))))
    (properties `((upstream-name . "sisti")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble))
    (home-page "https://rmagno.eu/sisti/")
    (synopsis "Real-Time PCR Data Sets by Sisti et al. (2010)")
    (description
     "This data package contains four datasets of quantitative PCR (@code{qPCR})
amplification curves that were used as supplementary data in the research
article by Sisti et al. (2010), <doi:10.1186/1471-2105-11-186>.  The primary
dataset comprises a ten-fold dilution series spanning copy numbers from 3.14 Ã
10^7 to 3.14 Ã 10^2, with twelve replicates per concentration.  These samples
are based on a @code{pGEM-T} Promega plasmid containing a 104 bp fragment of the
mitochondrial gene NADH dehydrogenase 1 (MT-ND1), amplified using the ND1/ND2
primer pair.  The remaining three datasets contain @code{qPCR} results in the
presence of specific PCR inhibitors: tannic acid, immunoglobulin G (@code{IgG}),
and quercetin, respectively, to assess their effects on the amplification
process.  These datasets are useful for researchers interested in PCR kinetics.
The original raw data file is available as Additional File 1:
<https://static-content.springer.com/esm/art%3A10.1186%2F1471-2105-11-186/@code{MediaObjects/12859_2009_3643_MOESM1_ESM.XLS>}.")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-sistec
  (package
    (name "r-sistec")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sistec" version))
       (sha256
        (base32 "0y43anin9vybiivf3jrxg19i9z4smmhhjvp52gci6icpn839kr8x"))))
    (properties `((upstream-name . "sistec")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-stringi
                             r-shiny
                             r-rlang
                             r-openxlsx
                             r-dplyr))
    (home-page "https://github.com/r-ifpe/sistec")
    (synopsis "Tools to Analyze 'Sistec' Datasets")
    (description
     "The Brazilian system for diploma registration and validation on technical and
superior courses are managing by Sistec platform, see
<https://sistec.mec.gov.br/>.  This package provides tools for Brazilian
institutions to update the student's registration and make data analysis about
their situation, retention and drop out.")
    (license license:gpl2+)))

(define-public r-sisireg
  (package
    (name "r-sisireg")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sisireg" version))
       (sha256
        (base32 "1jw1nl8l7kh5fa3hh7zh418brck6zayhhqm50nqhbghlmjs7y67i"))))
    (properties `((upstream-name . "sisireg")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-reticulate r-raster))
    (home-page "https://cran.r-project.org/package=sisireg")
    (synopsis "Sign-Simplicity-Regression-Solver")
    (description
     "Implementation of the SSR-Algorithm.  The Sign-Simplicity-Regression model is a
nonparametric statistical model which is based on residual signs and simplicity
assumptions on the regression function.  Goal is to calculate the most
parsimonious regression function satisfying the statistical adequacy
requirements.  Theory and functions are specified in Metzner (2020, ISBN:
979-8-68239-420-3, \"Trendbasierte Prognostik\") and Metzner (2021, ISBN:
979-8-59347-027-0, \"AdÃ¤quates Maschinelles Lernen\").")
    (license license:gpl2+)))

(define-public r-sisir
  (package
    (name "r-sisir")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SISIR" version))
       (sha256
        (base32 "01kgvk2m7wqn74nnihkqi7kps1fj7s6glx1why36jylllck3vbvb"))))
    (properties `((upstream-name . "SISIR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-rspectra
                             r-rlang
                             r-reshape2
                             r-rcolorbrewer
                             r-ranger
                             r-purrr
                             r-mixomics
                             r-matrix
                             r-magrittr
                             r-glmnet
                             r-ggplot2
                             r-foreach
                             r-expm
                             r-dplyr
                             r-doparallel
                             r-dendextend
                             r-corelearn
                             r-boruta
                             r-aricode
                             r-adjclust))
    (home-page "https://forgemia.inra.fr/sfcb/sisir")
    (synopsis "Select Intervals Suited for Functional Regression")
    (description
     "Interval fusion and selection procedures for regression with functional inputs.
Methods include a semiparametric approach based on Sliced Inverse Regression
(SIR), as described in <doi:10.1007/s11222-018-9806-6> (standard ridge and
sparse SIR are also included in the package) and a random forest based approach,
as described in <doi:10.1002/sam.11705>.")
    (license license:gpl2+)))

(define-public r-sisal
  (package
    (name "r-sisal")
    (version "0.48")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sisal" version))
       (sha256
        (base32 "1fh35ndra58dryw222ng7ys3gx1izyk5789k30lyd4n8wqy6sjjd"))))
    (properties `((upstream-name . "sisal")))
    (build-system r-build-system)
    (propagated-inputs (list r-r-methodss3
                             r-r-matlab
                             r-mgcv
                             r-lattice
                             r-digest
                             r-boot))
    (home-page "https://github.com/mvkorpel/sisal")
    (synopsis "Sequential Input Selection Algorithm")
    (description
     "This package implements the SISAL algorithm by Tikka and HollmÃ©n.  It is a
sequential backward selection algorithm which uses a linear model in a
cross-validation setting.  Starting from the full model, one variable at a time
is removed based on the regression coefficients.  From this set of models, a
parsimonious (sparse) model is found by choosing the model with the smallest
number of variables among those models where the validation error is smaller
than a threshold.  Also implements extensions which explore larger parts of the
search space and/or use ridge regression instead of ordinary least squares.")
    (license license:gpl2+)))

(define-public r-sis
  (package
    (name "r-sis")
    (version "0.8-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SIS" version))
       (sha256
        (base32 "1f5czwcgpmmn8habm24p3xh4w7jn2faais2x2gmaiq4dpjfnv6h8"))))
    (properties `((upstream-name . "SIS")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-ncvreg r-glmnet))
    (home-page "https://cran.r-project.org/package=SIS")
    (synopsis "Sure Independence Screening")
    (description
     "Variable selection techniques are essential tools for model selection and
estimation in high-dimensional statistical models.  Through this publicly
available package, we provide a unified environment to carry out variable
selection using iterative sure independence screening (SIS) (Fan and Lv
(2008)<doi:10.1111/j.1467-9868.2008.00674.x>) and all of its variants in
generalized linear models (Fan and Song (2009)<doi:10.1214/10-AOS798>) and the
Cox proportional hazards model (Fan, Feng and Wu
(2010)<doi:10.1214/10-IMSCOLL606>).")
    (license license:gpl2)))

(define-public r-sirus
  (package
    (name "r-sirus")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sirus" version))
       (sha256
        (base32 "0whjshqwy3dhim22rj358811s3h872d861j93zlhh32ivvwj2434"))))
    (properties `((upstream-name . "sirus")))
    (build-system r-build-system)
    (propagated-inputs (list r-rocr
                             r-rcppeigen
                             r-rcpp
                             r-matrix
                             r-glmnet
                             r-ggplot2))
    (home-page "https://gitlab.com/drti/sirus")
    (synopsis "Stable and Interpretable RUle Set")
    (description
     "This package provides a regression and classification algorithm based on random
forests, which takes the form of a short list of rules.  SIRUS combines the
simplicity of decision trees with a predictivity close to random forests.  The
core aggregation principle of random forests is kept, but instead of aggregating
predictions, SIRUS aggregates the forest structure: the most frequent nodes of
the forest are selected to form a stable rule ensemble model.  The algorithm is
fully described in the following articles: Benard C., Biau G., da Veiga S.,
Scornet E. (2021), Electron.  J. Statist., 15:427-505 <DOI:10.1214/20-EJS1792>
for classification, and Benard C., Biau G., da Veiga S., Scornet E. (2021),
AISTATS, PMLR 130:937-945 <http://proceedings.mlr.press/v130/benard21a>, for
regression.  This R package is a fork from the project ranger
(<https://github.com/imbs-hl/ranger>).")
    (license license:gpl3)))

(define-public r-sirthresholded
  (package
    (name "r-sirthresholded")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SIRthresholded" version))
       (sha256
        (base32 "19jslr3yy4m7i7xm3769dm43qh164w48hc36rdl6lwphydixn97m"))))
    (properties `((upstream-name . "SIRthresholded")))
    (build-system r-build-system)
    (propagated-inputs (list r-strucchange))
    (native-inputs (list r-knitr))
    (home-page "https://clement-w.github.io/SIRthresholded/")
    (synopsis "Sliced Inverse Regression with Thresholding")
    (description
     "This package implements a thresholded version of the Sliced Inverse Regression
method (Li, K. C. (1991) <doi:10.2307/2290563>), which allows to do variable
selection.")
    (license license:gpl2+)))

(define-public r-sirt
  (package
    (name "r-sirt")
    (version "4.1-15")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sirt" version))
       (sha256
        (base32 "0rihmkpnd6hdvb2qga387nw9hxz5ydhjbh9lgaf58nza7fwgqby1"))))
    (properties `((upstream-name . "sirt")))
    (build-system r-build-system)
    (propagated-inputs (list r-tam
                             r-rcpparmadillo
                             r-rcpp
                             r-pbv
                             r-pbapply
                             r-cdm))
    (home-page "https://github.com/alexanderrobitzsch/sirt")
    (synopsis "Supplementary Item Response Theory Models")
    (description
     "Supplementary functions for item response models aiming to complement existing R
packages.  The functionality includes among others multidimensional compensatory
and noncompensatory IRT models (Reckase, 2009, <doi:10.1007/978-0-387-89976-3>),
MCMC for hierarchical IRT models and testlet models (Fox, 2010,
<doi:10.1007/978-1-4419-0742-4>), NOHARM (@code{McDonald}, 1982,
<doi:10.1177/014662168200600402>), Rasch copula model (Braeken, 2011,
<doi:10.1007/s11336-010-9190-4>; Schroeders, Robitzsch & Schipolowski, 2014,
<doi:10.1111/jedm.12054>), faceted and hierarchical rater models
(@code{DeCarlo}, Kim & Johnson, 2011, <doi:10.1111/j.1745-3984.2011.00143.x>),
ordinal IRT model (ISOP; Scheiblechner, 1995, <doi:10.1007/BF02301417>), DETECT
statistic (Stout, Habing, Douglas & Kim, 1996,
<doi:10.1177/014662169602000403>), local structural equation modeling (LSEM;
Hildebrandt, Luedtke, Robitzsch, Sommer & Wilhelm, 2016,
<doi:10.1080/00273171.2016.1142856>).")
    (license license:gpl2+)))

(define-public r-sirmcmc
  (package
    (name "r-sirmcmc")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SIRmcmc" version))
       (sha256
        (base32 "0rn08h57gx458jrd9f5cahqsfp1gr3kb0kbxlxiv7khb6dns2lwm"))))
    (properties `((upstream-name . "SIRmcmc")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=SIRmcmc")
    (synopsis
     "Compartmental Susceptible-Infectious-Recovered (SIR) Model of Community and Household Infection")
    (description
     "We build an Susceptible-Infectious-Recovered (SIR) model where the rate of
infection is the sum of the household rate and the community rate.  We estimate
the posterior distribution of the parameters using the Metropolis algorithm.
Further details may be found in: F Scott Dahlgren, Ivo M Foppa, Melissa S
Stockwell, Celibell Y Vargas, Philip @code{LaRussa}, Carrie Reed (2021)
\"Household transmission of influenza A and B within a prospective cohort during
the 2013-2014 and 2014-2015 seasons\" <doi:10.1002/sim.9181>.")
    (license license:gpl2+)))

(define-public r-siren
  (package
    (name "r-siren")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "siren" version))
       (sha256
        (base32 "1w4a1dkhph0ai5faxvgmvf0phnpqapmp6wlax5r38ry5p11s0ljq"))))
    (properties `((upstream-name . "siren")))
    (build-system r-build-system)
    (propagated-inputs (list r-psych r-lavaan r-efa-mrfa))
    (home-page "https://cran.r-project.org/package=siren")
    (synopsis
     "Hybrid FA-CFA for Controlling Acquiescence in Restricted Factorial Solutions")
    (description
     "This package performs hybrid multi-stage factor analytic procedure for
controlling acquiescence in restricted solutions (Ferrando & Lorenzo-Seva, 2000
<https://www.uv.es/revispsi/articulos3.00/ferran7.pdf>).")
    (license license:gpl3)))

(define-public r-sire
  (package
    (name "r-sire")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SIRE" version))
       (sha256
        (base32 "0f624j087k7krg73bqn2qxdgzq66jyhvzz8n024vz3h4hwagxs5n"))))
    (properties `((upstream-name . "SIRE")))
    (build-system r-build-system)
    (propagated-inputs (list r-systemfit
                             r-stringr
                             r-rsolnp
                             r-psych
                             r-numderiv
                             r-matrixcalc
                             r-matrix
                             r-mass
                             r-magrittr
                             r-igraph
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=SIRE")
    (synopsis
     "Finding Feedback Effects in SEM and Testing for Their Significance")
    (description
     "This package provides two main functionalities.  1 - Given a system of
simultaneous equation, it decomposes the matrix of coefficients weighting the
endogenous variables into three submatrices: one includes the subset of
coefficients that have a causal nature in the model, two include the subset of
coefficients that have a interdependent nature in the model, either at
systematic level or induced by the correlation between error terms.  2 - Given a
decomposed model, it tests for the significance of the interdependent
relationships acting in the system, via Maximum likelihood and Wald test, which
can be built starting from the function output.  For theoretical reference see
Faliva (1992) <doi:10.1007/BF02589085> and Faliva and Zoia (1994)
<doi:10.1007/BF02589041>.")
    (license license:gpl3)))

(define-public r-sirad
  (package
    (name "r-sirad")
    (version "2.3-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sirad" version))
       (sha256
        (base32 "0vhmk2fmq4797gj6c8803zw1ipmdxfvdfgi2bygzxbn5bqd4da2n"))))
    (properties `((upstream-name . "sirad")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-raster))
    (home-page "http://sirad.r-forge.r-project.org/")
    (synopsis
     "Functions for Calculating Daily Solar Radiation and Evapotranspiration")
    (description
     "Calculating daily global solar radiation at horizontal surface using several
well-known models (i.e.  Angstrom-Prescott, Supit-Van Kappel, Hargreaves,
Bristow and Campbell, and Mahmood-Hubbard), and model calibration based on
ground-truth data, and (3) model auto-calibration.  The FAO Penmann-Monteith
equation to calculate evapotranspiration is also included.")
    (license license:gpl2)))

(define-public r-siqr
  (package
    (name "r-siqr")
    (version "0.8.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "siqr" version))
       (sha256
        (base32 "0yyb4abd7lghsb1qrw61zvbll3hs4dsldkvs3syrabwhxady8w43"))))
    (properties `((upstream-name . "siqr")))
    (build-system r-build-system)
    (propagated-inputs (list r-quantreg r-kernsmooth))
    (home-page "https://cran.r-project.org/package=siqr")
    (synopsis "An R Package for Single-Index Quantile Regression")
    (description
     "Single-Index Quantile Regression is effective in some scenarios.  We provides
functions that allow users to fit Single-Index Quantile Regression model.  It
also provides functions to do prediction, estimate standard errors of the
single-index coefficients via bootstrap, and visualize the estimated univariate
function.  Please see W., Y., Y. (2010) <doi:10.1016/j.jmva.2010.02.003> for
details.")
    (license license:gpl3)))

(define-public r-sipmg
  (package
    (name "r-sipmg")
    (version "1.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SIPmg" version))
       (sha256
        (base32 "1f3jirfnxxbrkala5kk0hkkm8gn2zciva6xy459vc80y058hr7z1"))))
    (properties `((upstream-name . "SIPmg")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-purrr
                             r-plyr
                             r-phyloseq
                             r-mass
                             r-magrittr
                             r-lazyeval
                             r-htssip
                             r-ggpubr
                             r-ggplot2
                             r-dplyr
                             r-deseq2
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://zielslab.github.io/SIPmg.github.io/")
    (synopsis "Statistical Analysis to Identify Isotope Incorporating MAGs")
    (description
     "Statistical analysis as part of a stable isotope probing (SIP) metagenomics
study to identify isotope incorporating taxa recovered as metagenome-assembled
genomes (MAGs).  Helpful reading and a vignette in bookdown format is provided
on the package site <https://zielslab.github.io/SIPmg.github.io/>.")
    (license license:gpl2)))

(define-public r-siplab
  (package
    (name "r-siplab")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "siplab" version))
       (sha256
        (base32 "149crdvw7sz7p4sz58n1ryvv3l0pm57wbvfd93xnljw7hwwq1dvk"))))
    (properties `((upstream-name . "siplab")))
    (build-system r-build-system)
    (propagated-inputs (list r-spatstat-geom r-spatstat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ogarciav/siplab/")
    (synopsis "Spatial Individual-Plant Modelling")
    (description
     "This package provides a platform for computing competition indices and
experimenting with spatially explicit individual-based vegetation models.")
    (license license:gpl2+)))

(define-public r-siphynetwork
  (package
    (name "r-siphynetwork")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SiPhyNetwork" version))
       (sha256
        (base32 "11p50ysznrrjjsiba1ivsbfpqg3zd5dqmkvj9p574acwhqj3dflp"))))
    (properties `((upstream-name . "SiPhyNetwork")))
    (build-system r-build-system)
    (propagated-inputs (list r-rstackdeque r-rcpp r-lifecycle r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SiPhyNetwork")
    (synopsis "Phylogenetic Simulator for Reticulate Evolution")
    (description
     "This package provides a simulator for reticulate evolution under a
birth-death-hybridization process.  Here the birth-death process is extended to
consider reticulate Evolution by allowing hybridization events to occur.  The
general purpose simulator allows the modeling of three different reticulate
patterns: lineage generative hybridization, lineage neutral hybridization, and
lineage degenerative hybridization.  Users can also specify hybridization events
to be dependent on a trait value or genetic distance.  We also extend some
phylogenetic tree utility and plotting functions for networks.  We allow two
different stopping conditions: simulated to a fixed time or number of taxa.
When simulating to a fixed number of taxa, the user can simulate under the
Generalized Sampling Approach that properly simulates phylogenies when assuming
a uniform prior on the root age.")
    (license license:gpl3)))

(define-public r-sipetool
  (package
    (name "r-sipetool")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SIPETool" version))
       (sha256
        (base32 "1w5sidiajrckps358xr526jfb4d0845p595qhr5i9b9q506n0klk"))))
    (properties `((upstream-name . "SIPETool")))
    (build-system r-build-system)
    (propagated-inputs (list r-convolutioner))
    (home-page "https://cran.r-project.org/package=SIPETool")
    (synopsis "SIFT-MS and CPET Data Processor")
    (description
     "Processor for selected ion flow tube mass spectrometer (SIFT-MS) output file
from breath analysis.  It allows the filtering of the SIFT output file (i.e.,
variation over time of the target analyte concentration) and the following
analysis for the determination of: maximum, average, and standard deviation
value of target concentration measured at each exhalation, and the respiratory
rate over the measurement.  Additionally, it is possible to align the SIFT-MS
data with other on-line techniques such as cardio pulmonary exercise test (CPET)
for a comprehensive characterization of breath samples.")
    (license license:gpl3)))

(define-public r-sipdibge
  (package
    (name "r-sipdibge")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SIPDIBGE" version))
       (sha256
        (base32 "1scrr08fpyv2qk8yqccwbvzslxp0cjv3939kkcs3v577l6ywb414"))))
    (properties `((upstream-name . "SIPDIBGE")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-rstudioapi
                             r-purrr
                             r-pnsibge
                             r-png
                             r-pndsibge
                             r-pnadcibge
                             r-covidibge
                             r-cli))
    (home-page "https://cran.r-project.org/package=SIPDIBGE")
    (synopsis "Collection of Household Survey Packages Conducted by IBGE")
    (description
     "This package provides access to packages developed for downloading, reading and
analyzing microdata from household surveys in Integrated System of Household
Surveys - SIPD conducted by Brazilian Institute of Geography and Statistics -
IBGE. More information can be obtained from the official website
<https://www.ibge.gov.br/>.")
    (license license:gpl3)))

(define-public r-sinrelef-ld
  (package
    (name "r-sinrelef-ld")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SINRELEF.LD" version))
       (sha256
        (base32 "15y8a56y3kjjxri1jjzb7wxpbs25bcxjnrzpabgj8rbrp3a4ghsq"))))
    (properties `((upstream-name . "SINRELEF.LD")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinyjs r-shinycssloaders r-shiny))
    (home-page "https://psico.fcep.urv.cat/utilitats/SINRELEF-LD/")
    (synopsis
     "Reliability and Relative Efficiency in Locally-Dependent Measures")
    (description
     "This package implements an approach aimed at assessing the accuracy and
effectiveness of raw scores obtained in scales that contain locally dependent
items.  The program uses as input the calibration (structural) item estimates
obtained from fitting extended unidimensional factor-analytic solutions in which
the existing local dependencies are included.  Measures of reliability (Omega)
and information are proposed at three levels: (a) total score, (b)
bivariate-doublet, and (c) item-by-item deletion, and are compared to those that
would be obtained if all the items had been locally independent.  All the
implemented procedures can be obtained from: (a) linear factor-analytic
solutions in which the item scores are treated as approximately continuous, and
(b) non-linear solutions in which the item scores are treated as
ordered-categorical.  A detailed guide can be obtained at the following url.")
    (license license:gpl3)))

(define-public r-sinib
  (package
    (name "r-sinib")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sinib" version))
       (sha256
        (base32 "08x2a5hn41vcsai3r36w1kgzka4ks53pkp6dxn90bsqh40ydb0db"))))
    (properties `((upstream-name . "sinib")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sinib")
    (synopsis "Sum of Independent Non-Identical Binomial Random Variables")
    (description
     "Density, distribution function, quantile function and random generation for the
sum of independent non-identical binomial distribution with parameters
\\code{size} and \\code{prob}.")
    (license license:gpl3)))

(define-public r-singr
  (package
    (name "r-singr")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "singR" version))
       (sha256
        (base32 "1xfmgavcm63imc5a57vk9x3yvdsf2y9599ipmimivdi6q0cxv7bc"))))
    (properties `((upstream-name . "singR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-mass
                             r-ictest
                             r-gam
                             r-clue))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=singR")
    (synopsis "Simultaneous Non-Gaussian Component Analysis")
    (description
     "Implementation of SING algorithm to extract joint and individual non-Gaussian
components from two datasets.  SING uses an objective function that maximizes
the skewness and kurtosis of latent components with a penalty to enhance the
similarity between subject scores.  Unlike other existing methods, SING does not
use PCA for dimension reduction, but rather uses non-Gaussianity, which can
improve feature extraction.  Benjamin B.Risk, Irina Gaynanova (2021)
<doi:10.1214/21-AOAS1466>.")
    (license license:expat)))

(define-public r-singlercapture
  (package
    (name "r-singlercapture")
    (version "0.2.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "singleRcapture" version))
       (sha256
        (base32 "18h10ms9xh97c4s9sa33bcvv341img73b9vh4ppgyk1lpissa1mb"))))
    (properties `((upstream-name . "singleRcapture")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich r-mathjaxr r-lamw r-foreach
                             r-doparallel))
    (home-page "https://github.com/ncn-foreigners/singleRcapture")
    (synopsis "Single-Source Capture-Recapture Models")
    (description
     "Implementation of single-source capture-recapture methods for population size
estimation using zero-truncated, zero-one truncated and zero-truncated
one-inflated Poisson, Geometric and Negative Binomial regression as well as
Zelterman's and Chao's regression.  Package includes point and interval
estimators for the population size with variances estimated using analytical or
bootstrap method.  Details can be found in: van der Heijden et all. (2003)
<doi:10.1191/1471082X03st057oa>, BÃ¶hning and van der Heijden (2019)
<doi:10.1214/18-AOAS1232>, BÃ¶hning et al. (2020) Capture-Recapture Methods for
the Social and Medical Sciences or BÃ¶hning and Friedl (2021)
<doi:10.1007/s10260-021-00556-8>.")
    (license license:expat)))

(define-public r-singlecellhaystack
  (package
    (name "r-singlecellhaystack")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "singleCellHaystack" version))
       (sha256
        (base32 "1kfkbfabv9j87rsy3nb3dmk6ckncvimrwax1p74vnszbz91a1zg7"))))
    (properties `((upstream-name . "singleCellHaystack")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-matrix r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://alexisvdb.github.io/singleCellHaystack/")
    (synopsis
     "Universal Differential Expression Prediction Tool for Single-Cell and Spatial Genomics Data")
    (description
     "One key exploratory analysis step in single-cell genomics data analysis is the
prediction of features with different activity levels.  For example, we want to
predict differentially expressed genes (DEGs) in single-cell RNA-seq data,
spatial DEGs in spatial transcriptomics data, or differentially accessible
regions (DARs) in single-cell ATAC-seq data. @code{singleCellHaystack} predicts
differentially active features in single cell omics datasets without relying on
the clustering of cells into arbitrary clusters. @code{singleCellHaystack} uses
Kullback-Leibler divergence to find features (e.g., genes, genomic regions, etc)
that are active in subsets of cells that are non-randomly positioned inside an
input space (such as 1D trajectories, 2D tissue sections, multi-dimensional
embeddings, etc).  For the theoretical background of @code{singleCellHaystack}
we refer to our original paper Vandenbon and Diez (Nature Communications, 2020)
<doi:10.1038/s41467-020-17900-3> and our update Vandenbon and Diez (Scientific
Reports, 2023) <doi:10.1038/s41598-023-38965-2>.")
    (license license:expat)))

(define-public r-singlecasees
  (package
    (name "r-singlecasees")
    (version "0.7.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SingleCaseES" version))
       (sha256
        (base32 "198rfvs45bqaaj91lcmasc98jximivksv6m1ik7wr49daifikddv"))))
    (properties `((upstream-name . "SingleCaseES")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect r-tidyr r-rlang r-purrr r-dplyr))
    (native-inputs (list r-rmarkdown r-knitr r-cleanrmd))
    (home-page "https://jepusto.github.io/SingleCaseES/")
    (synopsis "Calculator for Single-Case Effect Sizes")
    (description
     "This package provides R functions for calculating basic effect size indices for
single-case designs, including several non-overlap measures and parametric
effect size measures, and for estimating the gradual effects model developed by
Swan and Pustejovsky (2018) <DOI:10.1080/00273171.2018.1466681>.  Standard
errors and confidence intervals (based on the assumption that the outcome
measurements are mutually independent) are provided for the subset of effect
sizes indices with known sampling distributions.")
    (license license:gpl3)))

(define-public r-singcar
  (package
    (name "r-singcar")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "singcar" version))
       (sha256
        (base32 "0ixarjv8jxxpnc6imkk0gisjl0xv0bd9ah7y1sxdj2c8kb9d8k3n"))))
    (properties `((upstream-name . "singcar")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr r-mass r-cholwishart))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jorittmo/singcar")
    (synopsis "Comparing Single Cases to Small Samples")
    (description
     "When comparing single cases to control populations and no parameters are known
researchers and clinicians must estimate these with a control sample.  This is
often done when testing a case's abnormality on some variable or testing
abnormality of the discrepancy between two variables.  Appropriate frequentist
and Bayesian methods for doing this are here implemented, including tests
allowing for the inclusion of covariates.  These have been developed first and
foremost by John Crawford and Paul Garthwaite, e.g. in Crawford and Howell
(1998) <doi:10.1076/clin.12.4.482.7241>, Crawford and Garthwaite (2005)
<doi:10.1037/0894-4105.19.3.318>, Crawford and Garthwaite (2007)
<doi:10.1080/02643290701290146> and Crawford, Garthwaite and Ryan (2011)
<doi:10.1016/j.cortex.2011.02.017>.  The package is also equipped with power
calculators for each method.")
    (license license:expat)))

(define-public r-sinew
  (package
    (name "r-sinew")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sinew" version))
       (sha256
        (base32 "14ccm7i3zkjmj077b9ba2557n4v77k7646vylnrd22f20dzr52ba"))))
    (properties `((upstream-name . "sinew")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-stringi
                             r-sos
                             r-rstudioapi
                             r-rematch2
                             r-crayon
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/yonicd/sinew")
    (synopsis "Package Development Documentation and Namespace Management")
    (description
     "Manage package documentation and namespaces from the command line.
Programmatically attach namespaces in R and Rmd script, populates Roxygen2
skeletons with information scraped from within functions and populate the
Imports field of the DESCRIPTION file.")
    (license license:expat)))

(define-public r-sindyr
  (package
    (name "r-sindyr")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sindyr" version))
       (sha256
        (base32 "1jhy7p5f4hphvl05gibzqva2szzman62y2j5irxk86fmwj9kqajc"))))
    (properties `((upstream-name . "sindyr")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma r-matrixstats r-igraph r-arrangements))
    (home-page "https://cran.r-project.org/package=sindyr")
    (synopsis "Sparse Identification of Nonlinear Dynamics")
    (description
     "This implements the Brunton et al (2016; PNAS <doi:10.1073/pnas.1517384113>)
sparse identification algorithm for finding ordinary differential equations for
a measured system from raw data (SINDy).  The package includes a set of
additional tools for working with raw data, with an emphasis on cognitive
science applications (Dale and Bhat, 2018 <doi:10.1016/j.cogsys.2018.06.020>).
See <https://github.com/racdale/sindyr> for examples and updates.")
    (license license:gpl2+)))

(define-public r-sinar
  (package
    (name "r-sinar")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sinar" version))
       (sha256
        (base32 "0dywgqmvbrsygyvnln2mc5p6gaxvphr5byx7hkf0nkil54f2jzdf"))))
    (properties `((upstream-name . "sinar")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv r-mass))
    (home-page "https://cran.r-project.org/package=sinar")
    (synopsis
     "Conditional Least Squared (CLS) Method for the Model SINAR(1,1)")
    (description
     "Implementation of the Conditional Least Square (CLS) estimates and its
covariance matrix for the first-order spatial integer-valued autoregressive
model (SINAR(1,1)) proposed by Ghodsi (2012) <doi:10.1080/03610926.2011.560739>.")
    (license license:expat)))

(define-public r-sinaplot
  (package
    (name "r-sinaplot")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sinaplot" version))
       (sha256
        (base32 "0zfq6l8wjvdj7rndsfyh0a2xjlxmn2ij1q50hg3xjfpwrjxcsck8"))))
    (properties `((upstream-name . "sinaplot")))
    (build-system r-build-system)
    (propagated-inputs (list r-plyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=sinaplot")
    (synopsis
     "An Enhanced Chart for Simple and Truthful Representation of Single Observations over Multiple Classes")
    (description
     "The sinaplot is a data visualization chart suitable for plotting any single
variable in a multiclass data set.  It is an enhanced jitter strip chart, where
the width of the jitter is controlled by the density distribution of the data
within each class.")
    (license license:gpl2+)))

(define-public r-simvitd
  (package
    (name "r-simvitd")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimVitD" version))
       (sha256
        (base32 "1wz3b73pnccqsbg8wssah4gmwv7ajmhhca313r07sppgqskmm68r"))))
    (properties `((upstream-name . "SimVitD")))
    (build-system r-build-system)
    (propagated-inputs (list r-simpleboot))
    (home-page "https://cran.r-project.org/package=SimVitD")
    (synopsis "Simulation Tools for Planning Vitamin D Studies")
    (description
     "Simulation tools for planning Vitamin D studies.  Individual vitamin D status
profiles are simulated, modelling population heterogeneity in trial arms.
Exposures to infectious agents are generated, with infection depending on
vitamin D status.")
    (license license:gpl2+)))

(define-public r-simulmgf
  (package
    (name "r-simulmgf")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simulMGF" version))
       (sha256
        (base32 "0kycpaayf9rxc53jp3157zjvynpzhn278x1rnmwhvkhp9fmhnxka"))))
    (properties `((upstream-name . "simulMGF")))
    (build-system r-build-system)
    (home-page "https://github.com/mngar/simulMGF")
    (synopsis "Simulate SNP Matrix, Phenotype and Genotypic Effects")
    (description
     "Simulate genotypes in SNP (single nucleotide polymorphisms) Matrix as random
numbers from an uniform distribution, for diploid organisms (coded by 0, 1, 2),
Sikorska et al., (2013) <doi:10.1186/1471-2105-14-166>, or half-sib/full-sib SNP
matrix from real or simulated parents SNP data, assuming mendelian segregation.
Simulate phenotypic traits for real or simulated SNP data, controlled by a
specific number of quantitative trait loci and their effects, sampled from a
Normal or an Uniform distributions, assuming a pure additive model.  This is
useful for testing association and genomic prediction models or for educational
purposes.")
    (license license:expat)))

(define-public r-simule
  (package
    (name "r-simule")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simule" version))
       (sha256
        (base32 "0jzyk1r2jfs7qivmy9x44c5bagy55jpy3hf0xpl9xnk409c3y2wd"))))
    (properties `((upstream-name . "simule")))
    (build-system r-build-system)
    (propagated-inputs (list r-pcapp r-lpsolve r-igraph))
    (home-page "https://github.com/QData/SIMULE")
    (synopsis
     "Constrained L1 Minimization Approach for Estimating Multiple Sparse Gaussian or Nonparanormal Graphical Models")
    (description
     "This is an R implementation of a constrained l1 minimization approach for
estimating multiple Sparse Gaussian or Nonparanormal Graphical Models (SIMULE).
The SIMULE algorithm can be used to estimate multiple related precision
matrices.  For instance, it can identify context-specific gene networks from
multi-context gene expression datasets.  By performing data-driven network
inference from high-dimensional and heterogenous data sets, this tool can help
users effectively translate aggregated data into knowledge that take the form of
graphs among entities.  Please run demo(@code{simuleDemo}) to learn the basic
functions provided by this package.  For further details, please read the
original paper: Beilun Wang, Ritambhara Singh, Yanjun Qi (2017)
<DOI:10.1007/s10994-017-5635-7>.")
    (license license:gpl2)))

(define-public r-simulator
  (package
    (name "r-simulator")
    (version "0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simulator" version))
       (sha256
        (base32 "07mzgpfy43vpf58mrc7kcwvj49f27kr7w0fgf8vv80ls3bbb859i"))))
    (properties `((upstream-name . "simulator")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jacobbien/simulator")
    (synopsis "An Engine for Running Simulations")
    (description
     "This package provides a framework for performing simulations such as those
common in methodological statistics papers.  The design principles of this
package are described in greater depth in Bien, J. (2016) \"The simulator: An
Engine to Streamline Simulations,\" which is available at
<@code{arXiv:1607.00021>}.")
    (license license:gpl3)))

(define-public r-simulariatools
  (package
    (name "r-simulariatools")
    (version "2.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simulariatools" version))
       (sha256
        (base32 "0zzghs44yll5181mw5nqm49k4q01hzm88fqjq8fjfzza1gplrkf7"))))
    (properties `((upstream-name . "simulariatools")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-scales
                             r-reticulate
                             r-reshape2
                             r-lubridate
                             r-ggplot2))
    (home-page "https://www.simularia.it/simulariatools/")
    (synopsis "Simularia Tools for the Analysis of Air Pollution Data")
    (description
     "This package provides a set of tools developed at Simularia for Simularia, to
help preprocessing and post-processing of meteorological and air quality data.")
    (license license:gpl2+)))

(define-public r-simukde
  (package
    (name "r-simukde")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simukde" version))
       (sha256
        (base32 "0mpj8pfrlagc97v53idsiypjcpbaj07y6klanph5lkiwzsgbz8sv"))))
    (properties `((upstream-name . "simukde")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-mass r-ks))
    (home-page "https://github.com/galaamn/simukde")
    (synopsis "Simulation with Kernel Density Estimation")
    (description
     "Generates random values from a univariate and multivariate continuous
distribution by using kernel density estimation based on a sample.  Duong (2017)
<doi:10.18637/jss.v021.i07>, Christian P. Robert and George Casella (2010
ISBN:978-1-4419-1575-7) <doi:10.1007/978-1-4419-1576-4>.")
    (license (list license:gpl3+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-simuclustfactor
  (package
    (name "r-simuclustfactor")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simuclustfactor" version))
       (sha256
        (base32 "0yjcknqx44zjzk1ch8bnfbpf7dg34nwdhsmbnhyskzc67022g470"))))
    (properties `((upstream-name . "simuclustfactor")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack))
    (home-page "https://cran.r-project.org/package=simuclustfactor")
    (synopsis
     "Simultaneous Clustering and Factorial Decomposition of Three-Way Datasets")
    (description
     "This package implements two iterative techniques called T3Clus and 3Fkmeans,
aimed at simultaneously clustering objects and a factorial dimensionality
reduction of variables and occasions on three-mode datasets developed by Vichi
et al. (2007) <doi:10.1007/s00357-007-0006-x>.  Also, we provide a convex
combination of these two simultaneous procedures called CT3Clus and based on a
hyperparameter alpha (alpha in [0,1], with 3FKMeans for alpha=0 and T3Clus for
alpha= 1) also developed by Vichi et al. (2007) <doi:10.1007/s00357-007-0006-x>.
 Furthermore, we implemented the traditional tandem procedures of T3Clus
(TWCFTA) and 3FKMeans (TWFCTA) for sequential clustering-factorial decomposition
(TWCFTA), and vice-versa (TWFCTA) proposed by P. Arabie and L. Hubert (1996)
<doi:10.1007/978-3-642-79999-0_1>.")
    (license license:gpl3)))

(define-public r-simts
  (package
    (name "r-simts")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simts" version))
       (sha256
        (base32 "12v6djqij8w0vy25pndri4kng7zq7bncvbg9h28vg1m8sk3nlfc4"))))
    (properties `((upstream-name . "simts")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-scales
                             r-robcor
                             r-rcpparmadillo
                             r-rcpp
                             r-purrr
                             r-magrittr
                             r-dplyr
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/SMAC-Group/simts")
    (synopsis "Time Series Analysis Tools")
    (description
     "This package provides a system contains easy-to-use tools as a support for time
series analysis courses.  In particular, it incorporates a technique called
Generalized Method of Wavelet Moments (GMWM) as well as its robust
implementation for fast and robust parameter estimation of time series models
which is described, for example, in Guerrier et al. (2013) <doi:
10.1080/01621459.2013.799920>.  More details can also be found in the paper
linked to via the URL below.")
    (license (list license:agpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-simtrial
  (package
    (name "r-simtrial")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simtrial" version))
       (sha256
        (base32 "1gsd7ykqws7y5ggblbjjrg3yid9fj7samrnbk7fb4dnknvv8lgls"))))
    (properties `((upstream-name . "simtrial")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rlang
                             r-rcpp
                             r-mvtnorm
                             r-future
                             r-foreach
                             r-dofuture
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://merck.github.io/simtrial/")
    (synopsis "Clinical Trial Simulation")
    (description
     "This package provides some basic routines for simulating a clinical trial.  The
primary intent is to provide some tools to generate trial simulations for trials
with time to event outcomes.  Piecewise exponential failure rates and piecewise
constant enrollment rates are the underlying mechanism used to simulate a broad
range of scenarios such as those presented in Lin et al. (2020)
<doi:10.1080/19466315.2019.1697738>.  However, the basic generation of data is
done using pipes to allow maximum flexibility for users to meet different needs.")
    (license license:gpl3)))

(define-public r-simtrait
  (package
    (name "r-simtrait")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simtrait" version))
       (sha256
        (base32 "0zwas19q7lmmmznv44km1f9wj4vq34hl5zlakfs98lkp7f0z29nx"))))
    (properties `((upstream-name . "simtrait")))
    (build-system r-build-system)
    (propagated-inputs (list r-prroc))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/OchoaLab/simtrait")
    (synopsis "Simulate Complex Traits from Genotypes")
    (description
     "Simulate complex traits given a SNP genotype matrix and model parameters (the
desired heritability, number of causal loci, and either the true ancestral
allele frequencies used to generate the genotypes or the mean kinship for a real
dataset).  Emphasis on avoiding common biases due to the use of estimated allele
frequencies.  The code selects random loci to be causal, constructs coefficients
for these loci and random independent non-genetic effects, and can optionally
generate random group effects.  Traits can follow three models: random
coefficients, fixed effect sizes, and infinitesimal (multivariate normal).  GWAS
method benchmarking functions are also provided.  Described in Yao and Ochoa
(2022) <doi:10.1101/2022.03.25.485885>.")
    (license license:gpl3)))

(define-public r-simtool
  (package
    (name "r-simtool")
    (version "1.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simTool" version))
       (sha256
        (base32 "0q093dlfj23zq3w4schn1ycff79vgzr2s3wgbz2qz4s3b7fddk70"))))
    (properties `((upstream-name . "simTool")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs r-tidyr r-tibble r-purrr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MarselScheer/simTool")
    (synopsis
     "Conduct Simulation Studies with a Minimal Amount of Source Code")
    (description
     "Tool for statistical simulations that have two components.  One component
generates the data and the other one analyzes the data.  The main aims of the
package are the reduction of the administrative source code (mainly loops and
management code for the results) and a simple applicability of the package that
allows the user to quickly learn how to work with it.  Parallel computing is
also supported.  Finally, convenient functions are provided to summarize the
simulation results.")
    (license license:gpl3)))

(define-public r-simtimevar
  (package
    (name "r-simtimevar")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimTimeVar" version))
       (sha256
        (base32 "1x0lkgqwx1vz1gbyvy0hx20n638n30j0dny1iz5vs0cvviq3cdv8"))))
    (properties `((upstream-name . "SimTimeVar")))
    (build-system r-build-system)
    (propagated-inputs (list r-psych
                             r-plyr
                             r-mvtnorm
                             r-misctools
                             r-metafor
                             r-icc
                             r-corpcor
                             r-car))
    (home-page "https://cran.r-project.org/package=SimTimeVar")
    (synopsis
     "Simulate Longitudinal Dataset with Time-Varying Correlated Covariates")
    (description
     "Flexibly simulates a dataset with time-varying covariates with user-specified
exchangeable correlation structures across and within clusters.  Covariates can
be normal or binary and can be static within a cluster or time-varying.
Time-varying normal variables can optionally have linear trajectories within
each cluster.  See ?make_one_dataset for the main wrapper function.  See
Montez-Rath et al. <@code{arXiv:1709.10074>} for methodological details.")
    (license license:gpl2)))

(define-public r-simtimer
  (package
    (name "r-simtimer")
    (version "4.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simtimer" version))
       (sha256
        (base32 "1c5017xmh4767d75k4rs1pssyn7p5w41bcsjjmwk4c9g6w1jgx1w"))))
    (properties `((upstream-name . "simtimer")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "http://github.com/ims-fhs/simtimer")
    (synopsis "Datetimes as Integers for Discrete-Event Simulations")
    (description
     "Handles datetimes as integers for the usage inside Discrete-Event Simulations
(DES).  The conversion is made using the internally generic function
as.numeric() of the base package.  DES is described in Simulation Modeling and
Analysis by Averill Law and David Kelton (1999) <doi:10.2307/2288169>.")
    (license license:gpl3)))

(define-public r-simtargetcov
  (package
    (name "r-simtargetcov")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simTargetCov" version))
       (sha256
        (base32 "0gdqqwg9i2hga6l4s5x7wbbsm20b3sgwrci1g6hc04x1h9aawiw2"))))
    (properties `((upstream-name . "simTargetCov")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=simTargetCov")
    (synopsis
     "Data Transformation or Simulation with Empirical Covariance Matrix")
    (description
     "Transforms or simulates data with a target empirical covariance matrix supplied
by the user.  The method to obtain the data with the target empirical covariance
matrix is described in Section 5.1 of Christidis, Van Aelst and Zamar (2019)
<@code{arXiv:1812.05678>}.")
    (license license:gpl2+)))

(define-public r-simsurvnmarker
  (package
    (name "r-simsurvnmarker")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimSurvNMarker" version))
       (sha256
        (base32 "0ngr6cbhg3ccsm0v5mf3871pzxzmdprdap3kljfi8w6rrah830fx"))))
    (properties `((upstream-name . "SimSurvNMarker")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/boennecd/SimSurvNMarker")
    (synopsis "Simulate Survival Time and Markers")
    (description
     "This package provides functions to simulate from joint survival and marker
models.  The user can specific all basis functions of time, random or
deterministic covariates, random or deterministic left-truncation and
right-censoring times, and model parameters.")
    (license license:gpl2)))

(define-public r-simsurvey
  (package
    (name "r-simsurvey")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimSurvey" version))
       (sha256
        (base32 "0as8cifaymnd9rccynyhs4pfs27z0rhz18pbl0am0xvp3y7avbs0"))))
    (properties `((upstream-name . "SimSurvey")))
    (build-system r-build-system)
    (propagated-inputs (list r-stars
                             r-sf
                             r-rlang
                             r-progress
                             r-plotly
                             r-magrittr
                             r-lifecycle
                             r-foreach
                             r-doparallel
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://paulregular.github.io/SimSurvey/")
    (synopsis "Test Surveys by Simulating Spatially-Correlated Populations")
    (description
     "Simulate age-structured populations that vary in space and time and explore the
efficacy of a range of built-in or user-defined sampling protocols to reproduce
the population parameters of the known population. (See Regular et al. (2020)
<doi:10.1371/journal.pone.0232822> for more details).")
    (license license:gpl3)))

(define-public r-simsurv
  (package
    (name "r-simsurv")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simsurv" version))
       (sha256
        (base32 "16b2a3br107hy2fc0vann9wf5h9kb5j76baa37g1sfc0h19kg6kr"))))
    (properties `((upstream-name . "simsurv")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=simsurv")
    (synopsis "Simulate Survival Data")
    (description
     "Simulate survival times from standard parametric survival distributions
(exponential, Weibull, Gompertz), 2-component mixture distributions, or a
user-defined hazard, log hazard, cumulative hazard, or log cumulative hazard
function.  Baseline covariates can be included under a proportional hazards
assumption.  Time dependent effects (i.e.  non-proportional hazards) can be
included by interacting covariates with linear time or a user-defined function
of time.  Clustered event times are also accommodated.  The 2-component mixture
distributions can allow for a variety of flexible baseline hazard functions
reflecting those seen in practice.  If the user wishes to provide a user-defined
hazard or log hazard function then this is possible, and the resulting
cumulative hazard function does not need to have a closed-form solution.  For
details see the supporting paper <doi:10.18637/jss.v097.i03>.  Note that this
package is modelled on the survsim package available in the Stata software (see
Crowther and Lambert (2012)
<https://www.stata-journal.com/sjpdf.html?articlenum=st0275> or Crowther and
Lambert (2013) <doi:10.1002/sim.5823>).")
    (license (list license:gpl3+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-simstudy
  (package
    (name "r-simstudy")
    (version "0.8.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simstudy" version))
       (sha256
        (base32 "1qj0f5c40z1dhxm6wfrl83nd4h1s90qy7aa1ka3ziz8bpkpxkcnw"))))
    (properties `((upstream-name . "simstudy")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp
                             r-pbv
                             r-mvnfast
                             r-glue
                             r-fastglm
                             r-data-table
                             r-backports))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/kgoldfeld/simstudy")
    (synopsis "Simulation of Study Data")
    (description
     "Simulates data sets in order to explore modeling techniques or better understand
data generating processes.  The user specifies a set of relationships between
covariates, and generates data based on these specifications.  The final data
sets can represent data from randomized control trials, repeated measure
(longitudinal) designs, and cluster randomized trials.  Missingness can be
generated using various mechanisms (MCAR, MAR, NMAR).")
    (license license:gpl3)))

(define-public r-simstatespace
  (package
    (name "r-simstatespace")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simStateSpace" version))
       (sha256
        (base32 "072dyrzxpmp6i565162bpriviy47d59f3ssvkhpdpvs1d0g11j2a"))))
    (properties `((upstream-name . "simStateSpace")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://github.com/jeksterslab/simStateSpace")
    (synopsis "Simulate Data from State Space Models")
    (description
     "This package provides a streamlined and user-friendly framework for simulating
data in state space models, particularly when the number of subjects/units (n)
exceeds one, a scenario commonly encountered in social and behavioral sciences.
For an introduction to state space models in social and behavioral sciences,
refer to Chow, Ho, Hamaker, and Dolan (2010) <doi:10.1080/10705511003661553>.")
    (license license:gpl3+)))

(define-public r-simstandard
  (package
    (name "r-simstandard")
    (version "0.6.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simstandard" version))
       (sha256
        (base32 "1l1vny3csrr7krfpn25rzysmwjfv6mclf51y3mq02cimcwq9fzcy"))))
    (properties `((upstream-name . "simstandard")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-rlang
                             r-purrr
                             r-mvtnorm
                             r-magrittr
                             r-lavaan))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/wjschne/simstandard")
    (synopsis "Generate Standardized Data")
    (description
     "This package creates simulated data from structural equation models with
standardized loading.  Data generation methods are described in Schneider (2013)
<doi:10.1177/0734282913478046>.")
    (license license:cc0)))

(define-public r-simsst
  (package
    (name "r-simsst")
    (version "0.0.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimSST" version))
       (sha256
        (base32 "1c3bkcvq4767yabw7ifv3akhlavscgxwx5rp18c23icjd3pk9avx"))))
    (properties `((upstream-name . "SimSST")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-gamlss-dist r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SimSST")
    (synopsis "Simulated Stop Signal Task Data")
    (description
     "Stop signal task data of go and stop trials is generated per participant.  The
simulation process is based on the generally non-independent horse race model
and fixed stop signal delay or tracking method.  Each of go and stop process is
assumed having exponentially modified Gaussian(@code{ExG}) or Shifted Wald (SW)
distributions.  The output data can be converted to BEESTS software input data
enabling researchers to test and evaluate various brain stopping processes
manifested by @code{ExG} or SW distributional parameters of interest.  Methods
are described in: Soltanifar M (2020) <https://hdl.handle.net/1807/101208>,
Matzke D, Love J, Wiecki TV, Brown SD, Logan GD and Wagenmakers E-J (2013)
<doi:10.3389/fpsyg.2013.00918>, Logan GD, Van Zandt T, Verbruggen F, Wagenmakers
EJ. (2014) <doi:10.1037/a0035230>.")
    (license license:gpl3)))

(define-public r-simsl
  (package
    (name "r-simsl")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simsl" version))
       (sha256
        (base32 "12dc9lcy11g1yynycvcqm62npq0v13bg6fnykb5m9ijwga4x51il"))))
    (properties `((upstream-name . "simsl")))
    (build-system r-build-system)
    (propagated-inputs (list r-mgcv))
    (home-page "https://cran.r-project.org/package=simsl")
    (synopsis "Single-Index Models with a Surface-Link")
    (description
     "An implementation of a single-index regression for optimizing individualized
dose rules from an observational study.  To model interaction effects between
baseline covariates and a treatment variable defined on a continuum, we employ
two-dimensional penalized spline regression on an index-treatment domain, where
the index is defined as a linear combination of the covariates (a single-index).
 An unspecified main effect for the covariates is allowed, which can also be
modeled through a parametric model.  A unique contribution of this work is in
the parsimonious single-index parametrization specifically defined for the
interaction effect term.  We refer to Park, Petkova, Tarpey, and Ogden (2020)
<doi:10.1111/biom.13320> (for the case of a discrete treatment) and Park,
Petkova, Tarpey, and Ogden (2021) \"A single-index model with a surface-link for
optimizing individualized dose rules\" <@code{arXiv:2006.00267v2>} for detail of
the method.  The model can take a member of the exponential family as a response
variable and can also take an ordinal categorical response.  The main function
of this package is simsl().")
    (license license:gpl3)))

(define-public r-simseq
  (package
    (name "r-simseq")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimSeq" version))
       (sha256
        (base32 "068gg484w07qb4wajik2s3z79xfj0jg5l4pz69267dxi5kzd9fas"))))
    (properties `((upstream-name . "SimSeq")))
    (build-system r-build-system)
    (propagated-inputs (list r-fdrtool))
    (home-page "https://cran.r-project.org/package=SimSeq")
    (synopsis "Nonparametric Simulation of RNA-Seq Data")
    (description
     "RNA sequencing analysis methods are often derived by relying on hypothetical
parametric models for read counts that are not likely to be precisely satisfied
in practice.  Methods are often tested by analyzing data that have been
simulated according to the assumed model.  This testing strategy can result in
an overly optimistic view of the performance of an RNA-seq analysis method.  We
develop a data-based simulation algorithm for RNA-seq data.  The vector of read
counts simulated for a given experimental unit has a joint distribution that
closely matches the distribution of a source RNA-seq dataset provided by the
user.  Users control the proportion of genes simulated to be differentially
expressed (DE) and can provide a vector of weights to control the distribution
of effect sizes.  The algorithm requires a matrix of RNA-seq read counts with
large sample sizes in at least two treatment groups.  Many datasets are
available that fit this standard.")
    (license license:gpl2+)))

(define-public r-simsem
  (package
    (name "r-simsem")
    (version "0.5-16")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simsem" version))
       (sha256
        (base32 "0scjb308pb8ivy5blbk1wj9d1yxcqknhvh95a3728crfq2xcqnh0"))))
    (properties `((upstream-name . "simsem")))
    (build-system r-build-system)
    (propagated-inputs (list r-lavaan))
    (home-page "https://simsem.org/")
    (synopsis "SIMulated Structural Equation Modeling")
    (description
     "This package provides an easy framework for Monte Carlo simulation in structural
equation modeling, which can be used for various purposes, such as such as model
fit evaluation, power analysis, or missing data handling and planning.")
    (license license:gpl2+)))

(define-public r-simsalapar
  (package
    (name "r-simsalapar")
    (version "1.0-12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simsalapar" version))
       (sha256
        (base32 "0aac4mjx4pdfhy52jvr3v2d8hp20qglzfhrvqmrvvn5pcn2qmlh4"))))
    (properties `((upstream-name . "simsalapar")))
    (build-system r-build-system)
    (propagated-inputs (list r-sfsmisc r-gridbase r-colorspace))
    (home-page "https://cran.r-project.org/package=simsalapar")
    (synopsis "Tools for Simulation Studies in Parallel")
    (description
     "This package provides tools for setting up (\"design\"), conducting, and
evaluating large-scale simulation studies with graphics and tables, including
parallel computations.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-sims
  (package
    (name "r-sims")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sims" version))
       (sha256
        (base32 "0iqqzmgxlldiqbl5dc4yfc6g6p4ayw3mzsy3gdrpb4yxmharhfrk"))))
    (properties `((upstream-name . "sims")))
    (build-system r-build-system)
    (propagated-inputs (list r-yesno r-nlist r-future-apply r-chk))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/poissonconsulting/sims")
    (synopsis "Simulate Data from R or 'JAGS' Code")
    (description
     "Generates data from R or JAGS code for use in simulation studies.  The data are
returned as an nlist::nlists object and/or saved to file as individual .rds
files.  Parallelization is implemented using the future package.  Progress is
reported using the progressr package.")
    (license license:expat)))

(define-public r-simriv
  (package
    (name "r-simriv")
    (version "1.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SiMRiv" version))
       (sha256
        (base32 "069cjkwbzp7c62zgj939h5nbfmpcxkfx1sbk4np1hgzjfv3ybz86"))))
    (properties `((upstream-name . "SiMRiv")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-mco))
    (home-page "https://www.r-project.org")
    (synopsis
     "Simulating Multistate Movements in River/Heterogeneous Landscapes")
    (description
     "This package provides functions to generate and analyze spatially-explicit
individual-based multistate movements in rivers, heterogeneous and homogeneous
spaces.  This is done by incorporating landscape bias on local behaviour, based
on resistance rasters.  Although originally conceived and designed to simulate
trajectories of species constrained to linear habitats/dendritic ecological
networks (e.g. river networks), the simulation algorithm is built to be highly
flexible and can be applied to any (aquatic, semi-aquatic or terrestrial)
organism, independently on the landscape in which it moves.  Thus, the user will
be able to use the package to simulate movements either in homogeneous
landscapes, heterogeneous landscapes (e.g. semi-aquatic animal moving mainly
along rivers but also using the matrix), or even in highly contrasted landscapes
(e.g. fish in a river network).  The algorithm and its input parameters are the
same for all cases, so that results are comparable.  Simulated trajectories can
then be used as mechanistic null models (Potts & Lewis 2014,
<DOI:10.1098/rspb.2014.0231>) to test a variety of Movement Ecology hypotheses
(Nathan et al.  2008, <DOI:10.1073/pnas.0800375105>), including landscape
effects (e.g. resources, infrastructures) on animal movement and species site
fidelity, or for predictive purposes (e.g. road mortality risk,
dispersal/connectivity).  The package should be relevant to explore a broad
spectrum of ecological phenomena, such as those at the interface of animal
behaviour, management, landscape and movement ecology, disease and invasive
species spread, and population dynamics.")
    (license license:gpl2+)))

(define-public r-simrestore
  (package
    (name "r-simrestore")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simRestore" version))
       (sha256
        (base32 "1psw58k5i7vj7a49cski28dxwryi6qk167w2v003ja209pdpbfx8"))))
    (properties `((upstream-name . "simRestore")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-subplex r-shiny r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=simRestore")
    (synopsis
     "Simulate the Effect of Management Policies on Restoration Efforts")
    (description
     "Simulation methods to study the effect of management policies on efforts to
restore populations back to their original genetic composition.  Allows for
single-scenario simulation and for optimization of specific chosen scenarios.
Further information can be found in Hernandez, Janzen and Lavretsky (2023)
<doi:10.1111/1755-0998.13892>.")
    (license license:gpl2+)))

(define-public r-simrel
  (package
    (name "r-simrel")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simrel" version))
       (sha256
        (base32 "1knn8kw9m232agm4b3b0fc132frv5i7k8gxvmrcb1pnz62gsmg04"))))
    (properties `((upstream-name . "simrel")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-testthat
                             r-shiny
                             r-sfsmisc
                             r-scales
                             r-rstudioapi
                             r-rlang
                             r-reshape2
                             r-purrr
                             r-miniui
                             r-magrittr
                             r-jsonlite
                             r-gridextra
                             r-ggplot2
                             r-frf2))
    (native-inputs (list r-knitr))
    (home-page "https://simulatr.github.io/simrel/")
    (synopsis "Simulation of Multivariate Linear Model Data")
    (description
     "Researchers have been using simulated data from a multivariate linear model to
compare and evaluate different methods, ideas and models.  Additionally,
teachers and educators have been using a simulation tool to demonstrate and
teach various statistical and machine learning concepts.  This package helps
users to simulate linear model data with a wide range of properties by tuning
few parameters such as relevant latent components.  In addition, a shiny app as
an RStudio gadget gives users a simple interface for using the simulation
function.  See more on: SÃ¦bÃ¸, S., AlmÃ¸y, T., Helland, I.S. (2015)
<doi:10.1016/j.chemolab.2015.05.012> and Rimal, R., AlmÃ¸y, T., SÃ¦bÃ¸, S.
(2018) <doi:10.1016/j.chemolab.2018.02.009>.")
    (license license:gpl3)))

(define-public r-simreg
  (package
    (name "r-simreg")
    (version "3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimReg" version))
       (sha256
        (base32 "07wkrfv65mbddbnsv0cbdn7m1ahfk2z11dv6r6ia97vz9yz0k03j"))))
    (properties `((upstream-name . "SimReg")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-ontologysimilarity r-ontologyplot
                             r-ontologyindex))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SimReg")
    (synopsis "Similarity Regression")
    (description
     "Similarity regression, evaluating the probability of association between sets of
ontological terms and binary response vector.  A no-association model is
compared with one in which the log odds of a true response is linked to the
semantic similarity between terms and a latent characteristic ontological
profile - Phenotype Similarity Regression for Identifying the Genetic
Determinants of Rare Diseases', Greene et al 2016
<doi:10.1016/j.ajhg.2016.01.008>.")
    (license license:gpl2+)))

(define-public r-simrec
  (package
    (name "r-simrec")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simrec" version))
       (sha256
        (base32 "1rpsvp7l24ayqc0bbcjsivnqxbrf8491mk6i9ndhyna6hcn9lzzg"))))
    (properties `((upstream-name . "simrec")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/federicomarini/simrec")
    (synopsis
     "Simulation of Recurrent Event Data for Non-Constant Baseline Hazard")
    (description
     "Simulation of recurrent event data for non-constant baseline hazard in the total
time model with risk-free intervals and possibly a competing event.  Possibility
to cut the data to an interim data set.  Data can be plotted.  Details about the
method can be found in Jahn-Eimermacher, A. et al. (2015)
<doi:10.1186/s12874-015-0005-2>.")
    (license license:gpl2)))

(define-public r-simputation
  (package
    (name "r-simputation")
    (version "0.2.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simputation" version))
       (sha256
        (base32 "02l7j2s74mjd4vgly4nnf2w7as9m3m6qifa292r29iaw12l0zj81"))))
    (properties `((upstream-name . "simputation")))
    (build-system r-build-system)
    (propagated-inputs (list r-vim
                             r-rpart
                             r-randomforest
                             r-norm
                             r-missforest
                             r-mass
                             r-gower
                             r-glmnet))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/markvanderloo/simputation")
    (synopsis "Simple Imputation")
    (description
     "Easy to use interfaces to a number of imputation methods that fit in the
not-a-pipe operator of the magrittr package.")
    (license license:gpl3)))

(define-public r-simpr
  (package
    (name "r-simpr")
    (version "0.2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simpr" version))
       (sha256
        (base32 "0r85hphhkk624p671r3vp74j0v6y9qj2pwpm5d1nv81pdcdwk0zi"))))
    (properties `((upstream-name . "simpr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-lifecycle
                             r-generics
                             r-furrr
                             r-dplyr
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://statisfactions.github.io/simpr/")
    (synopsis "Flexible 'Tidyverse'-Friendly Simulations")
    (description
     "This package provides a general, tidyverse'-friendly framework for simulation
studies, design analysis, and power analysis.  Specify data generation, define
varying parameters, generate data, fit models, and tidy model results in a
single pipeline, without needing loops or custom functions.")
    (license license:gpl2)))

(define-public r-simpop
  (package
    (name "r-simpop")
    (version "2.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simPop" version))
       (sha256
        (base32 "0rgaalkaxxh92yryzd39qnpahzi7d2bqnnsgx4i5rai2vrj1hqfi"))))
    (properties `((upstream-name . "simPop")))
    (build-system r-build-system)
    (propagated-inputs (list r-xgboost
                             r-wrswor
                             r-vim
                             r-vcd
                             r-rcpparmadillo
                             r-rcpp
                             r-ranger
                             r-partykit
                             r-nnet
                             r-matrixstats
                             r-mass
                             r-lattice
                             r-foreach
                             r-fitdistrplus
                             r-envstats
                             r-e1071
                             r-doparallel
                             r-data-table
                             r-colorspace))
    (home-page "https://github.com/statistikat/simPop")
    (synopsis "Simulation of Complex Synthetic Data Information")
    (description
     "This package provides tools and methods to simulate populations for surveys
based on auxiliary data.  The tools include model-based methods, calibration and
combinatorial optimization algorithms, see Templ, Kowarik and Meindl (2017)
<doi:10.18637/jss.v079.i10>) and Templ (2017) <doi:10.1007/978-3-319-50272-4>.
The package was developed with support of the International Household Survey
Network, DFID Trust Fund TF011722 and funds from the World bank.")
    (license license:gpl2+)))

(define-public r-simplybee
  (package
    (name "r-simplybee")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SIMplyBee" version))
       (sha256
        (base32 "05vdrzddq57jzh2dyddf2yi09k6fddkgl83qd8c0fb14zqn9ix76"))))
    (properties `((upstream-name . "SIMplyBee")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-rann
                             r-r6
                             r-extradistr
                             r-bh
                             r-alphasimr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/HighlanderLab/SIMplyBee")
    (synopsis
     "'AlphaSimR' Extension for Simulating Honeybee Populations and Breeding Programmes")
    (description
     "An extension of the @code{AlphaSimR} package
(<https://cran.r-project.org/package=@code{AlphaSimR>}) for stochastic
simulations of honeybee populations and breeding programmes.  SI@code{MplyBee}
enables simulation of individual bees that form a colony, which includes a
queen, fathers (drones the queen mated with), virgin queens, workers, and
drones.  Multiple colony can be merged into a population of colonies, such as an
apiary or a whole country of colonies.  Functions enable operations on castes,
colony, or colonies, to ease R scripting of whole populations.  All
@code{AlphaSimR} functionality with respect to genomes and genetic and phenotype
values is available and further extended for honeybees, including
haplo-diploidy, complementary sex determiner locus, colony events (swarming,
supersedure, etc.), and colony phenotype values.")
    (license license:expat)))

(define-public r-simplyagree
  (package
    (name "r-simplyagree")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimplyAgree" version))
       (sha256
        (base32 "10n63rn7kl4k1q7k3jz4f3macmn8y662jy41vdspgdwf0iqvf5in"))))
    (properties `((upstream-name . "SimplyAgree")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-stringr
                             r-quantreg
                             r-purrr
                             r-patchwork
                             r-nlme
                             r-matrix
                             r-mass
                             r-magrittr
                             r-lme4
                             r-lifecycle
                             r-jmvcore
                             r-insight
                             r-ggplot2
                             r-emmeans
                             r-dplyr
                             r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://aaroncaldwell.us/SimplyAgree/")
    (synopsis "Flexible and Robust Agreement and Reliability Analyses")
    (description
     "Reliability and agreement analyses often have limited software support.
Therefore, this package was created to make agreement and reliability analyses
easier for the average researcher.  The functions within this package include
simple tests of agreement, agreement analysis for nested and replicate data, and
provide robust analyses of reliability.  In addition, this package contains a
set of functions to help when planning studies looking to assess measurement
agreement.")
    (license license:gpl3+)))

(define-public r-simplifynet
  (package
    (name "r-simplifynet")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simplifyNet" version))
       (sha256
        (base32 "04iv1alkd85lwwyf8k6iwsj9fbxl6smsjm2b34z91za70n0rsw5q"))))
    (properties `((upstream-name . "simplifyNet")))
    (build-system r-build-system)
    (propagated-inputs (list r-sanic r-matrix r-igraph r-dplyr))
    (home-page "https://cran.r-project.org/package=simplifyNet")
    (synopsis "Network Sparsification")
    (description
     "Network sparsification with a variety of novel and known network sparsification
techniques.  All network sparsification techniques reduce the number of edges,
not the number of nodes.  Network sparsification is sometimes referred to as
network dimensionality reduction.  This package is based on the work of
Spielman, D., Srivastava, N. (2009)<@code{arXiv:0803.0929>}.  Koutis I., Levin,
A., Peng, R. (2013)<@code{arXiv:1209.5821>}.  Toivonen, H., Mahler, S., Zhou, F.
(2010)<doi:10.1007>.  Foti, N., Hughes, J., Rockmore, D. (2011)<doi:10.1371>.")
    (license license:gpl3+)))

(define-public r-simplicialcubature
  (package
    (name "r-simplicialcubature")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimplicialCubature" version))
       (sha256
        (base32 "06dpbawn650lzybd5j4xkygbg5s2nqfz6b4g0skm4z7lsb2d9dhb"))))
    (properties `((upstream-name . "SimplicialCubature")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SimplicialCubature")
    (synopsis "Integration of Functions Over Simplices")
    (description
     "This package provides methods to integrate functions over m-dimensional
simplices in n-dimensional Euclidean space.  There are exact methods for
polynomials and adaptive methods for integrating an arbitrary function.")
    (license license:gpl2+)))

(define-public r-simplextree
  (package
    (name "r-simplextree")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simplextree" version))
       (sha256
        (base32 "18jvzcz4fdsi17j7qdqpjvx6zygvlcvpziikb8qxskaib5180l08"))))
    (properties `((upstream-name . "simplextree")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-magrittr))
    (home-page "https://github.com/peekxc/simplextree")
    (synopsis "Provides Tools for Working with General Simplicial Complexes")
    (description
     "This package provides an interface to a Simplex Tree data structure, which is a
data structure aimed at enabling efficient manipulation of simplicial complexes
of any dimension.  The Simplex Tree data structure was originally introduced by
Jean-Daniel Boissonnat and ClÃ©ment Maria (2014)
<doi:10.1007/s00453-014-9887-3>.")
    (license license:expat)))

(define-public r-simplexreg
  (package
    (name "r-simplexreg")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simplexreg" version))
       (sha256
        (base32 "1zkh00xbddhgz0qn0a5pj12n0hpx4f5kihpfj71x92pmxpzglcxh"))))
    (properties `((upstream-name . "simplexreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-plotrix r-formula))
    (home-page "https://cran.r-project.org/package=simplexreg")
    (synopsis
     "Regression Analysis of Proportional Data Using Simplex Distribution")
    (description
     "Simplex density, distribution, quantile functions as well as random variable
generation of the simplex distribution are given.  Regression analysis of
proportional data using various kinds of simplex models is available.  In
addition, GEE method can be applied to longitudinal data to model the
correlation.  Residual analysis is also involved.  Some subroutines are written
in C with GNU Scientific Library (GSL) so as to facilitate the computation.")
    (license license:gpl2)))

(define-public r-simpletex
  (package
    (name "r-simpletex")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simpletex" version))
       (sha256
        (base32 "13hcb3k5car0q5yqb9wqccdzgrqxvjbbzn8c9rcrx23wgwpr226l"))))
    (properties `((upstream-name . "simpletex")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-httr r-glue r-digest))
    (native-inputs (list r-knitr))
    (home-page "https://gitlab.com/chuxinyuan/simpletex")
    (synopsis "Mathematical Formulas and Character Recognition")
    (description
     "By calling the @code{SimpleTex} <https://simpletex.cn/> open API implements text
and mathematical formula recognition on the image, and the output formula can be
used directly with Markdown and @code{LaTeX}'.")
    (license license:expat)))

(define-public r-simplesetup
  (package
    (name "r-simplesetup")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simpleSetup" version))
       (sha256
        (base32 "0rbzmixk76b3lgi9m7ijhcl6dha7wbbxfgwdhnp2y338zny6c6rm"))))
    (properties `((upstream-name . "simpleSetup")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=simpleSetup")
    (synopsis "Set Up R Source Code Files for Use on Multiple Machines")
    (description
     "When working across multiple machines and, similarly for reproducible research,
it can be time consuming to ensure that you have all of the needed packages
installed and loaded and that the correct working directory is set.
@code{simpleSetup} provides simple functions for making these tasks more
straightforward.")
    (license license:gpl3+)))

(define-public r-simplercache
  (package
    (name "r-simplercache")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simpleRCache" version))
       (sha256
        (base32 "0rjlvq7ch90bjr8wyglhq1px0widdaxrgxfv9d0a5lm1gj5jggan"))))
    (properties `((upstream-name . "simpleRCache")))
    (build-system r-build-system)
    (propagated-inputs (list r-digest))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=simpleRCache")
    (synopsis "Simple R Cache")
    (description
     "Simple result caching in R based on R.cache.  The global environment is not
considered when caching results simplifying moving files between multiple
instances of R. Relies on more base functions than R.cache (e.g. cached results
are saved using @code{saveRDS}() and @code{readRDS}()).")
    (license license:lgpl2.0+)))

(define-public r-simplephenotypes
  (package
    (name "r-simplephenotypes")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simplePHENOTYPES" version))
       (sha256
        (base32 "0f5mzlx6vcvq6cvyz4v9yr1k2acgnxizg8clsxdm6hpi4maizrpk"))))
    (properties `((upstream-name . "simplePHENOTYPES")))
    (build-system r-build-system)
    (propagated-inputs (list r-snprelate r-mvtnorm r-gdsfmt r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/samuelbfernandes/simplePHENOTYPES")
    (synopsis "Simulation of Pleiotropic, Linked and Epistatic Phenotypes")
    (description
     "The number of studies involving correlated traits and the availability of tools
to handle this type of data has increased considerably in the last decade.  With
such a demand, we need tools for testing hypotheses related to single and
multi-trait (correlated) phenotypes based on many genetic settings.  Thus, we
implemented various options for simulation of pleiotropy and Linkage
Disequilibrium under additive, dominance and epistatic models.  The simulation
currently takes a marker data set as an input and then uses it for simulating
multiple traits as described in Fernandes and Lipka (2020)
<doi:10.1186/s12859-020-03804-y>.")
    (license license:expat)))

(define-public r-simpleneural
  (package
    (name "r-simpleneural")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simpleNeural" version))
       (sha256
        (base32 "01q04807qws870x1qhvlr3bp0dawy8q4bh00psakhln9j9dl1ljm"))))
    (properties `((upstream-name . "simpleNeural")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=simpleNeural")
    (synopsis "An Easy to Use Multilayer Perceptron")
    (description
     "Trains neural networks (multilayer perceptrons with one hidden layer) for bi- or
multi-class classification.")
    (license license:expat)))

(define-public r-simplemlp
  (package
    (name "r-simplemlp")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simpleMLP" version))
       (sha256
        (base32 "134h217d3ipzpxgj5fh04pkajqmxdnnlr53ykk27vi86dqk4087p"))))
    (properties `((upstream-name . "simpleMLP")))
    (build-system r-build-system)
    (propagated-inputs (list r-readr r-ggplot2))
    (home-page "https://cran.r-project.org/package=simpleMLP")
    (synopsis "Simple Multilayer Perceptron Neural Network")
    (description
     "Create and train a multilayer perceptron, a type of feedforward, fully connected
neural network.  Features 2 @code{ReLU} hidden layers.  Learn more about about
the activation functions and backpropagation used by this network in Goodfellow
et al. (2016, ISBN: 9780262035613) \"Deep Learning\".")
    (license license:expat)))

(define-public r-simplemh
  (package
    (name "r-simplemh")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simpleMH" version))
       (sha256
        (base32 "0yxc509qmxrcg4kvpkfy1nz8q3k1kvhhwyf93zg3alqf2nnjs50i"))))
    (properties `((upstream-name . "simpleMH")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Bisaloo/simpleMH")
    (synopsis "Simple Metropolis-Hastings MCMC Algorithm")
    (description
     "This package provides a very bare-bones interface to use the Metropolis-Hastings
Monte Carlo Markov Chain algorithm.  It is suitable for teaching and testing
purposes.")
    (license license:gpl3)))

(define-public r-simplegraphdb
  (package
    (name "r-simplegraphdb")
    (version "2021.03.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simplegraphdb" version))
       (sha256
        (base32 "0573hrv23nimmf3d8dgdynii79h84a3mfr4xxk3cggxhsxpfmb2i"))))
    (properties `((upstream-name . "simplegraphdb")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsqlite r-rjson r-dbi))
    (home-page "https://github.com/mikeasilva/simplegraphdb")
    (synopsis "Simple Graph Database")
    (description
     "This is a graph database in SQLite'.  It is inspired by Denis Papathanasiou's
Python simple-graph project on @code{GitHub}'.")
    (license license:expat)))

(define-public r-simplegraph
  (package
    (name "r-simplegraph")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simplegraph" version))
       (sha256
        (base32 "1aps8657clf20fy5qqgvpfq2f8x9w3dqw9z4c3nqhq09b5k6mgfl"))))
    (properties `((upstream-name . "simplegraph")))
    (build-system r-build-system)
    (home-page "https://github.com/gaborcsardi/simplegraph")
    (synopsis "Simple Graph Data Types and Basic Algorithms")
    (description
     "Simple classic graph algorithms for simple graph classes.  Graphs may possess
vertex and edge attributes.  simplegraph has no dependencies and it is written
entirely in R, so it is easy to install.")
    (license license:expat)))

(define-public r-simplefdr
  (package
    (name "r-simplefdr")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simpleFDR" version))
       (sha256
        (base32 "09w7l8pb6kibzifwyqcgshlxjq2gn6s642i78939q00q0m9qvdbk"))))
    (properties `((upstream-name . "simpleFDR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-dplyr))
    (home-page "https://cran.r-project.org/package=simpleFDR")
    (synopsis "Simple False Discovery Rate Calculation")
    (description
     "Using the adjustment method from Benjamini & Hochberg (1995)
<doi:10.1111/j.2517-6161.1995.tb02031.x>, this package determines which
variables are significant under repeated testing with a given dataframe of p
values and an user defined \"q\" threshold.  It then returns the original
dataframe along with a significance column where an asterisk denotes a
significant p value after FDR calculation, and NA denotes all other p values.
This package uses the Benjamini & Hochberg method specifically as described in
Lee, S., & Lee, D. K. (2018) <doi:10.4097/kja.d.18.00242>.")
    (license license:expat)))

(define-public r-simplecolors
  (package
    (name "r-simplecolors")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simplecolors" version))
       (sha256
        (base32 "1rckbcc2clxxj61q7z7d1d72q96fd9gvcl8hh8586ffcj5chvmgf"))))
    (properties `((upstream-name . "simplecolors")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-magrittr
                             r-ggplot2
                             r-forcats
                             r-dplyr
                             r-colorspace))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rjake/simplecolors")
    (synopsis "Access Color Names Using a Standardized Nomenclature")
    (description
     "This package provides a curated set of colors that are called using a
standardized syntax: saturation + hue + lightness.  For example, \"brightblue4\"
and \"mutedred2\".  Functions exists to return individual colors by name or to
build palettes across or within hues.  Most functions allow you to visualize the
palettes in addition to returning the desired hex codes.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-simplecache
  (package
    (name "r-simplecache")
    (version "0.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simpleCache" version))
       (sha256
        (base32 "0zayq4n8cc0xgszckjvvf55ygy8hwywv161pp8pqi2k4z91rq4lz"))))
    (properties `((upstream-name . "simpleCache")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/databio/simpleCache")
    (synopsis "Simply Caching R Objects")
    (description
     "This package provides intuitive functions for caching R objects, encouraging
reproducible, restartable, and distributed R analysis.  The user selects a
location to store caches, and then provides nothing more than a cache name and
instructions (R code) for how to produce the R object.  Also provides some
advanced options like environment assignments, recreating or reloading caches,
and cluster compute bindings (using the batchtools package) making it flexible
enough for use in large-scale data analysis projects.")
    (license license:bsd-2)))

(define-public r-simpleboot
  (package
    (name "r-simpleboot")
    (version "1.1-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simpleboot" version))
       (sha256
        (base32 "18pqsjkpl7szwfinc842q6s6vjlsr0gkkqr1l6174ibryqvlf2fx"))))
    (properties `((upstream-name . "simpleboot")))
    (build-system r-build-system)
    (propagated-inputs (list r-boot))
    (home-page "https://github.com/rdpeng/simpleboot")
    (synopsis "Simple Bootstrap Routines")
    (description "Simple bootstrap routines.")
    (license license:gpl2+)))

(define-public r-simple-regression
  (package
    (name "r-simple-regression")
    (version "0.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SIMPLE.REGRESSION" version))
       (sha256
        (base32 "0drcf4f1ypg667rkbfd4gcmnijbzx9j9qirvja0w2cqhv48mzj9g"))))
    (properties `((upstream-name . "SIMPLE.REGRESSION")))
    (build-system r-build-system)
    (propagated-inputs (list r-nlme r-mass))
    (home-page "https://cran.r-project.org/package=SIMPLE.REGRESSION")
    (synopsis "OLS, Moderated, Logistic, and Count Regressions Made Simple")
    (description
     "This package provides SPSS- and SAS-like output for least squares multiple
regression, logistic regression, and Poisson regression.  Detailed output is
also provided for OLS moderated regression, interaction plots, and
Johnson-Neyman regions of significance.  The output includes standardized
coefficients, partial and semi-partial correlations, collinearity diagnostics,
plots of residuals, and detailed information about simple slopes for
interactions.  There are numerous options for model plots, including plots of
interactions for both lm and lme models.")
    (license license:gpl2+)))

(define-public r-simplanonym
  (package
    (name "r-simplanonym")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simplanonym" version))
       (sha256
        (base32 "1zwc73qbf0xx0ca1pgk3hqdmf9b6ihlwflb2fq31lhk3ngka8kc6"))))
    (properties `((upstream-name . "simplanonym")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect r-forcats r-dplyr))
    (home-page "https://github.com/dkgaraujo/simplanonym")
    (synopsis "Consistent Anonymisation Across Datasets")
    (description
     "This package provides a simple function that anonymises a list of variables in a
consistent way: anonymised factors are not recycled and the same original levels
receive the same anonymised factor even if located in different datasets.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-simplace
  (package
    (name "r-simplace")
    (version "5.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simplace" version))
       (sha256
        (base32 "1249z5fh7z3n25bazkn9jky9j50gmi3l3mavxd53fc11jry038l2"))))
    (properties `((upstream-name . "simplace")))
    (build-system r-build-system)
    (inputs (list openjdk))
    (propagated-inputs (list r-rjava))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/gk-crop/simplace_rpkg/")
    (synopsis "Interface to Use the Modelling Framework 'SIMPLACE'")
    (description
     "Interface to interact with the modelling framework SIMPLACE and to parse the
results of simulations.")
    (license license:gpl2)))

(define-public r-simphony
  (package
    (name "r-simphony")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simphony" version))
       (sha256
        (base32 "0cpp6gga0p80yd7ir1qsgnv236sp35xybp7i0h104yzhnwgcqnkb"))))
    (properties `((upstream-name . "simphony")))
    (build-system r-build-system)
    (propagated-inputs (list r-foreach r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://simphony.hugheylab.org")
    (synopsis "Simulating Large-Scale, Rhythmic Data")
    (description
     "This package provides a tool for simulating rhythmic data: transcriptome data
using Gaussian or negative binomial distributions, and behavioral activity data
using Bernoulli or Poisson distributions.  See Singer et al. (2019)
<doi:10.7717/peerj.6985>.")
    (license license:gpl2)))

(define-public r-simph
  (package
    (name "r-simph")
    (version "1.3.13")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simPH" version))
       (sha256
        (base32 "086060f78f1dmpa6cyhx160blchckplw04gs6997f1hwzswci2l2"))))
    (properties `((upstream-name . "simPH")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-stringr
                             r-quadprog
                             r-mgcv
                             r-mass
                             r-lazyeval
                             r-gridextra
                             r-ggplot2
                             r-dplyr
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://CRAN.R-project.org/package=simPH")
    (synopsis
     "Simulate and Plot Estimates from Cox Proportional Hazards Models")
    (description
     "Simulates and plots quantities of interest (relative hazards, first differences,
and hazard ratios) for linear coefficients, multiplicative interactions,
polynomials, penalised splines, and non-proportional hazards, as well as
stratified survival curves from Cox Proportional Hazard models.  It also
simulates and plots marginal effects for multiplicative interactions.  Methods
described in Gandrud (2015) <doi:10.18637/jss.v065.i03>.")
    (license license:gpl3)))

(define-public r-simodels
  (package
    (name "r-simodels")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simodels" version))
       (sha256
        (base32 "0jbfkd2db38z1h0lww7ql5vizr1pniripa83ivzs00mvaj9xc2q6"))))
    (properties `((upstream-name . "simodels")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-rlang r-od r-geodist r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/robinlovelace/simodels")
    (synopsis "Flexible Framework for Developing Spatial Interaction Models")
    (description
     "Develop spatial interaction models (SIMs).  SIMs predict the amount of
interaction, for example number of trips per day, between geographic entities
representing trip origins and destinations.  Contains functions for creating
origin-destination datasets from geographic input datasets and calculating
movement between origin-destination pairs with constrained,
production-constrained, and attraction-constrained models (Wilson 1979)
<doi:10.1068/a030001>.")
    (license license:agpl3+)))

(define-public r-simode
  (package
    (name "r-simode")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simode" version))
       (sha256
        (base32 "13irkbjnzmz1mgs4rl7laix216xk4dwg6c1lvd3cjm64mx5bbxif"))))
    (properties `((upstream-name . "simode")))
    (build-system r-build-system)
    (propagated-inputs (list r-quadprog r-pracma r-ncvreg r-glmnet r-desolve))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=simode")
    (synopsis
     "Statistical Inference for Systems of Ordinary Differential Equations using Separable Integral-Matching")
    (description
     "This package implements statistical inference for systems of ordinary
differential equations, that uses the integral-matching criterion and takes
advantage of the separability of parameters, in order to obtain initial
parameter estimates for nonlinear least squares optimization.  Dattner & Yaari
(2018) <@code{arXiv:1807.04202>}.  Dattner et al. (2017)
<doi:10.1098/rsif.2016.0525>.  Dattner & Klaassen (2015)
<doi:10.1214/15-EJS1053>.")
    (license license:gpl2+)))

(define-public r-simnph
  (package
    (name "r-simnph")
    (version "0.5.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimNPH" version))
       (sha256
        (base32 "05qp6cnih319p88vrppwvxh595a7vawkc7508xzmzhvrjbn3p7jn"))))
    (properties `((upstream-name . "SimNPH")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-survival
                             r-stringr
                             r-simdesign
                             r-rlang
                             r-purrr
                             r-nphrct
                             r-nph
                             r-minipch
                             r-dplyr
                             r-car))
    (native-inputs (list r-r-rsp))
    (home-page "https://simnph.github.io/SimNPH/")
    (synopsis "Simulate Non-Proportional Hazards")
    (description
     "This package provides a toolkit for simulation studies concerning time-to-event
endpoints with non-proportional hazards. @code{SimNPH} encompasses functions for
simulating time-to-event data in various scenarios, simulating different trial
designs like fixed-followup, event-driven, and group sequential designs.  The
package provides functions to calculate the true values of common summary
statistics for the implemented scenarios and offers common analysis methods for
time-to-event data.  Helper functions for running simulations with the
@code{SimDesign} package and for aggregating and presenting the results are also
included.  Results of the conducted simulation study are available as preprint:
\"A neutral comparison of statistical methods for time-to-event analyses under
non-proportional hazards\", Klinglmueller et al. (2023)
<doi:10.48550/ARXIV.2310.05622>.")
    (license (license:fsdg-compatible "BSL-1.0"))))

(define-public r-simmulticorrdata
  (package
    (name "r-simmulticorrdata")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimMultiCorrData" version))
       (sha256
        (base32 "0brszbqxf40y65xp96c5hp7hhvz3gv0xlg93r5ik8qdh0bn0y1sw"))))
    (properties `((upstream-name . "SimMultiCorrData")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam
                             r-triangle
                             r-psych
                             r-nleqslv
                             r-matrix
                             r-ggplot2
                             r-genord
                             r-bb))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/AFialkowski/SimMultiCorrData")
    (synopsis "Simulation of Correlated Data with Multiple Variable Types")
    (description
     "Generate continuous (normal or non-normal), binary, ordinal, and count (Poisson
or Negative Binomial) variables with a specified correlation matrix.  It can
also produce a single continuous variable.  This package can be used to simulate
data sets that mimic real-world situations (i.e.  clinical or genetic data sets,
plasmodes).  All variables are generated from standard normal variables with an
imposed intermediate correlation matrix.  Continuous variables are simulated by
specifying mean, variance, skewness, standardized kurtosis, and fifth and sixth
standardized cumulants using either Fleishman's third-order
(<DOI:10.1007/BF02293811>) or Headrick's fifth-order
(<DOI:10.1016/S0167-9473(02)00072-5>) polynomial transformation.  Binary and
ordinal variables are simulated using a modification of the ordsample() function
from @code{GenOrd}'.  Count variables are simulated using the inverse cdf
method.  There are two simulation pathways which differ primarily according to
the calculation of the intermediate correlation matrix.  In Correlation Method
1, the intercorrelations involving count variables are determined using a
simulation based, logarithmic correlation correction (adapting Yahav and
Shmueli's 2012 method, <DOI:10.1002/asmb.901>).  In Correlation Method 2, the
count variables are treated as ordinal (adapting Barbiero and Ferrari's 2015
modification of @code{GenOrd}, <DOI:10.1002/asmb.2072>).  There is an optional
error loop that corrects the final correlation matrix to be within a
user-specified precision value of the target matrix.  The package also includes
functions to calculate standardized cumulants for theoretical distributions or
from real data sets, check if a target correlation matrix is within the possible
correlation bounds (given the distributions of the simulated variables),
summarize results (numerically or graphically), to verify valid power method
pdfs, and to calculate lower standardized kurtosis bounds.")
    (license license:gpl2)))

(define-public r-simmsm
  (package
    (name "r-simmsm")
    (version "1.1.42")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simMSM" version))
       (sha256
        (base32 "0nxs3n64mh1sbnbmqbj7hm8d43z0cja8da9yhsygl60r1jyl98xd"))))
    (properties `((upstream-name . "simMSM")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mvna))
    (home-page "https://cran.r-project.org/package=simMSM")
    (synopsis "Simulation of Event Histories for Multi-State Models")
    (description
     "Simulation of event histories with possibly non-linear baseline hazard rate
functions, non-linear (time-varying) covariate effect functions, and
dependencies on the past of the history.  Random generation of event histories
is performed using inversion sampling on the cumulative all-cause hazard rate
functions.")
    (license license:gpl2+)))

(define-public r-simms
  (package
    (name "r-simms")
    (version "1.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SIMMS" version))
       (sha256
        (base32 "1pdbgqndnhgyfcwgr5ijnnmn64cwlalwbanb2grkm7h80bpql3lw"))))
    (properties `((upstream-name . "SIMMS")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-randomforestsrc
                             r-mass
                             r-glmnet
                             r-foreach
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SIMMS")
    (synopsis "Subnetwork Integration for Multi-Modal Signatures")
    (description
     "Algorithms to create prognostic biomarkers using biological genesets or
networks.")
    (license license:gpl2)))

(define-public r-simmr
  (package
    (name "r-simmr")
    (version "0.5.1.217")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simmr" version))
       (sha256
        (base32 "15b24mmmljajfp5yrgfbnk9wm53vrky9sza3hsl4i77pvikdpf9q"))))
    (properties `((upstream-name . "simmr")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-reshape2
                             r-rcppdist
                             r-rcpparmadillo
                             r-rcpp
                             r-r2jags
                             r-ggplot2
                             r-ggally
                             r-compositions
                             r-checkmate
                             r-boot
                             r-bayesplot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/andrewcparnell/simmr")
    (synopsis "Stable Isotope Mixing Model")
    (description
     "Fits Stable Isotope Mixing Models (SIMMs) and is meant as a longer term
replacement to the previous widely-used package SIAR. SIMMs are used to infer
dietary proportions of organisms consuming various food sources from
observations on the stable isotope values taken from the organisms tissue
samples.  However SIMMs can also be used in other scenarios, such as in sediment
mixing or the composition of fatty acids.  The main functions are simmr_load()
and simmr_mcmc().  The two vignettes contain a quick start and a full listing of
all the features.  The methods used are detailed in the papers Parnell et al
2010 <doi:10.1371/journal.pone.0009672>, and Parnell et al 2013
<doi:10.1002/env.2221>.")
    (license license:gpl2+)))

(define-public r-simmp
  (package
    (name "r-simmp")
    (version "0.17.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simMP" version))
       (sha256
        (base32 "1pzg79dd08ds3pfzxnk9h95gaibicv00mnm0jb4i8v5is4gzrdhr"))))
    (properties `((upstream-name . "simMP")))
    (build-system r-build-system)
    (propagated-inputs (list r-xvector
                             r-iranges
                             r-genomicranges
                             r-genomeinfodb
                             r-foreach
                             r-doparallel
                             r-bsgenome
                             r-biostrings))
    (home-page "https://cran.r-project.org/package=simMP")
    (synopsis
     "Simulate Somatic Mutations in Cancer Genomes from Mutational Processes")
    (description
     "Simulates somatic single base substitutions carried in cancer genomes.  By only
providing a human reference genome, substitutions that result from mutational
processes operative in every cancer genome can be generated.")
    (license license:gpl2)))

(define-public r-simml
  (package
    (name "r-simml")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simml" version))
       (sha256
        (base32 "18qrpxf4fsqirfxj4ldkmdyc6zp2px2bcad9j50ihb3r2a33ny3x"))))
    (properties `((upstream-name . "simml")))
    (build-system r-build-system)
    (propagated-inputs (list r-mgcv))
    (home-page "https://cran.r-project.org/package=simml")
    (synopsis "Single-Index Models with Multiple-Links")
    (description
     "This package provides a major challenge in estimating treatment decision rules
from a randomized clinical trial dataset with covariates measured at baseline
lies in detecting relatively small treatment effect modification-related
variability (i.e., the treatment-by-covariates interaction effects on treatment
outcomes) against a relatively large non-treatment-related variability (i.e.,
the main effects of covariates on treatment outcomes).  The class of
Single-Index Models with Multiple-Links is a novel single-index model
specifically designed to estimate a single-index (a linear combination) of the
covariates associated with the treatment effect modification-related
variability, while allowing a nonlinear association with the treatment outcomes
via flexible link functions.  The models provide a flexible regression approach
to developing treatment decision rules based on patients data measured at
baseline.  We refer to Park, Petkova, Tarpey, and Ogden (2020)
<doi:10.1016/j.jspi.2019.05.008> and Park, Petkova, Tarpey, and Ogden (2020)
<doi:10.1111/biom.13320> (that allows an unspecified X main effect) for detail
of the method.  The main function of this package is simml().")
    (license license:gpl3)))

(define-public r-simmetric
  (package
    (name "r-simmetric")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simMetric" version))
       (sha256
        (base32 "09jpjlvmvb9z2v71n8cb14b0zfi78k609c7xvs78rghdiswqfv23"))))
    (properties `((upstream-name . "simMetric")))
    (build-system r-build-system)
    (propagated-inputs (list r-dplyr r-assertthat))
    (home-page "https://cran.r-project.org/package=simMetric")
    (synopsis
     "Metrics (with Uncertainty) for Simulation Studies that Evaluate Statistical Methods")
    (description
     "Allows users to quickly apply individual or multiple metrics to evaluate Monte
Carlo simulation studies.")
    (license license:expat)))

(define-public r-simmer-plot
  (package
    (name "r-simmer-plot")
    (version "0.1.18")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simmer.plot" version))
       (sha256
        (base32 "0wxn6g27i4fk4xkl9l8qbsqg61jr7hz5n974gfld38cigp1f62n9"))))
    (properties `((upstream-name . "simmer.plot")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-simmer
                             r-scales
                             r-ggplot2
                             r-dplyr
                             r-diagrammer))
    (native-inputs (list r-knitr))
    (home-page "https://r-simmer.org")
    (synopsis "Plotting Methods for 'simmer'")
    (description
     "This package provides a set of plotting methods for simmer trajectories and
simulations.")
    (license license:expat)))

(define-public r-simmer-bricks
  (package
    (name "r-simmer-bricks")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simmer.bricks" version))
       (sha256
        (base32 "1a3rm3fmr4ysack47vhmyxdg45pvplhz07bc5fsx17m08hhlyjhv"))))
    (properties `((upstream-name . "simmer.bricks")))
    (build-system r-build-system)
    (propagated-inputs (list r-simmer))
    (native-inputs (list r-knitr))
    (home-page "https://r-simmer.org")
    (synopsis "Helper Methods for 'simmer' Trajectories")
    (description
     "This package provides wrappers for common activity patterns in simmer
trajectories.")
    (license license:expat)))

(define-public r-simmer
  (package
    (name "r-simmer")
    (version "4.4.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simmer" version))
       (sha256
        (base32 "0lj9jrbs3p9ya8xyxvbwpxhkfxvvd1zaphqsvjxyin2l5a1waydc"))))
    (properties `((upstream-name . "simmer")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-magrittr r-codetools))
    (native-inputs (list r-knitr))
    (home-page "https://r-simmer.org")
    (synopsis "Discrete-Event Simulation for R")
    (description
     "This package provides a process-oriented and trajectory-based Discrete-Event
Simulation (DES) package for R. It is designed as a generic yet powerful
framework.  The architecture encloses a robust and fast simulation core written
in C++ with automatic monitoring capabilities.  It provides a rich and flexible
R API that revolves around the concept of trajectory, a common path in the
simulation model for entities of the same type.  Documentation about simmer is
provided by several vignettes included in this package, via the paper by Ucar,
Smeets & Azcorra (2019, <doi:10.18637/jss.v090.i02>), and the paper by Ucar,
HernÃ¡ndez, Serrano & Azcorra (2018, <doi:10.1109/MCOM.2018.1700960>); see
citation(\"simmer\") for details.")
    (license license:gpl2+)))

(define-public r-simle
  (package
    (name "r-simle")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SIMle" version))
       (sha256
        (base32 "1lwi9dxv93f6fnc3j0lkl7kvf806z5f5s8flxzm2km3j5fl0bp2j"))))
    (properties `((upstream-name . "SIMle")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-sie2nts
                             r-rcurl
                             r-plotly
                             r-matrix
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SIMle")
    (synopsis "Estimation and Inference for General Time Series Regression")
    (description
     "We provide functions for estimation and inference of nonlinear and
non-stationary time series regression using the sieve methods and bootstrapping
procedure.")
    (license license:expat)))

(define-public r-simlandr
  (package
    (name "r-simlandr")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simlandr" version))
       (sha256
        (base32 "1bgpka5z23763d8vlxz1pymgypqj0wyab3kcf6rkhliliksqqqp0"))))
    (properties `((upstream-name . "simlandr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-rlang
                             r-purrr
                             r-progress
                             r-plotly
                             r-mass
                             r-magrittr
                             r-lifecycle
                             r-ks
                             r-htmlwidgets
                             r-ggplot2
                             r-gganimate
                             r-forcats
                             r-dplyr
                             r-digest
                             r-bigmemory))
    (home-page "https://sciurus365.github.io/simlandr/")
    (synopsis "Simulation-Based Landscape Construction for Dynamical Systems")
    (description
     "This package provides a toolbox for constructing potential landscapes for
dynamical systems using Monte Carlo simulation.  The method is based on the
potential landscape definition by Wang et al. (2008)
<doi:10.1073/pnas.0800579105> (also see Zhou & Li, 2016 <doi:10.1063/1.4943096>
for further mathematical discussions) and can be used for a large variety of
models.")
    (license license:gpl3+)))

(define-public r-simjoint
  (package
    (name "r-simjoint")
    (version "0.3.12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimJoint" version))
       (sha256
        (base32 "1vh0yj9d73mi2isw8wn2hgr7ycxadcifwzl57jgkqgnwc2vzlmsg"))))
    (properties `((upstream-name . "SimJoint")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=SimJoint")
    (synopsis "Simulate Joint Distribution")
    (description
     "Simulate multivariate correlated data given nonparametric marginals and their
joint structure characterized by a Pearson or Spearman correlation matrix.  The
simulator engages the problem from a purely computational perspective.  It
assumes no statistical models such as copulas or parametric distributions, and
can approximate the target correlations regardless of theoretical feasibility.
The algorithm integrates and advances the Iman-Conover (1982) approach
<doi:10.1080/03610918208812265> and the Ruscio-Kaczetow iteration (2008)
<doi:10.1080/00273170802285693>.  Package functions are carefully implemented in
C++ for squeezing computing speed, suitable for large input in a manycore
environment.  Precision of the approximation and computing speed both
substantially outperform various CRAN packages to date.  Benchmarks are detailed
in function examples.  A simple heuristic algorithm is additionally designed to
optimize the joint distribution in the post-simulation stage.  The heuristic
demonstrated good potential of achieving the same level of precision of
approximation without the enhanced Iman-Conover-Ruscio-Kaczetow.  The package
contains a copy of Permuted Congruential Generator.")
    (license license:gpl3)))

(define-public r-simits
  (package
    (name "r-simits")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simITS" version))
       (sha256
        (base32 "0b62lvknfqli6ki4w360b1ih9nsm6pinaqskgra6vl2nzsim3sal"))))
    (properties `((upstream-name . "simITS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=simITS")
    (synopsis "Analysis via Simulation of Interrupted Time Series (ITS) Data")
    (description
     "Uses simulation to create prediction intervals for post-policy outcomes in
interrupted time series (ITS) designs, following Miratrix (2020)
<@code{arXiv:2002.05746>}.  This package provides methods for fitting ITS models
with lagged outcomes and variables to account for temporal dependencies.  It
then conducts inference via simulation, simulating a set of plausible
counterfactual post-policy series to compare to the observed post-policy series.
 This package also provides methods to visualize such data, and also to
incorporate seasonality models and smoothing and aggregation/summarization.
This work partially funded by Arnold Ventures in collaboration with MDRC.")
    (license license:gpl3)))

(define-public r-simitation
  (package
    (name "r-simitation")
    (version "0.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simitation" version))
       (sha256
        (base32 "1f2d5niq42wyrqlxjfdfb744h5fy646fvlbi7xakkhvkwrah8x5f"))))
    (properties `((upstream-name . "simitation")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=simitation")
    (synopsis "Simplified Simulations")
    (description
     "This package provides tools for generating and analyzing simulation studies.
Users may easily specify all terms of a simulation study, often in a single line
of code.  Common univariate and bivariate methods, such as t tests, proportions
tests, and chi squared tests, are integrated.  Multivariate studies involving
linear or logistic regression may also be specified with symbolic inputs.  The
simulation studies generate data for n observations in each of B experiments.
Analyses of each experiment are integrated, and empirical results across the
experiments are also provided.")
    (license license:gpl3)))

(define-public r-simireff
  (package
    (name "r-simireff")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simIReff" version))
       (sha256
        (base32 "0yi43splda8chnh4bq5x6chbrl5c0y9sv1k2m31763da0czayall"))))
    (properties `((upstream-name . "simIReff")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm
                             r-rvinecopulib
                             r-np
                             r-mass
                             r-ks
                             r-extradistr
                             r-bde))
    (home-page "https://github.com/julian-urbano/simIReff/")
    (synopsis
     "Stochastic Simulation for Information Retrieval Evaluation: Effectiveness Scores")
    (description
     "This package provides tools for the stochastic simulation of effectiveness
scores to mitigate data-related limitations of Information Retrieval evaluation
research, as described in Urbano and Nagler (2018)
<doi:10.1145/3209978.3210043>.  These tools include: fitting, selection and
plotting distributions to model system effectiveness, transformation towards a
prespecified expected value, proxy to fitting of copula models based on these
distributions, and simulation of new evaluation data from these distributions
and copula models.")
    (license license:expat)))

(define-public r-siminf
  (package
    (name "r-siminf")
    (version "9.8.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimInf" version))
       (sha256
        (base32 "1lpx7749ld6h5gdlyrlxwp2zx7g9fxxb6s7r8p2pxpyvgrss2pqr"))))
    (properties `((upstream-name . "SimInf")))
    (build-system r-build-system)
    (inputs (list gsl))
    (propagated-inputs (list r-matrix r-mass r-digest))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/stewid/SimInf")
    (synopsis
     "Framework for Data-Driven Stochastic Disease Spread Simulations")
    (description
     "This package provides an efficient and very flexible framework to conduct
data-driven epidemiological modeling in realistic large scale disease spread
simulations.  The framework integrates infection dynamics in subpopulations as
continuous-time Markov chains using the Gillespie stochastic simulation
algorithm and incorporates available data such as births, deaths and movements
as scheduled events at predefined time-points.  Using C code for the numerical
solvers and @code{OpenMP} (if available) to divide work over multiple processors
ensures high performance when simulating a sample outcome.  One of our design
goals was to make the package extendable and enable usage of the numerical
solvers from other R extension packages in order to facilitate complex
epidemiological research.  The package contains template models and can be
extended with user-defined models.  For more details see the paper by Widgren,
Bauer, Eriksson and Engblom (2019) <doi:10.18637/jss.v091.i12>.  The package
also provides functionality to fit models to time series data using the
Approximate Bayesian Computation Sequential Monte Carlo ('ABC-SMC') algorithm of
Toni and others (2009) <doi:10.1098/rsif.2008.0172>.")
    (license license:gpl3)))

(define-public r-simile
  (package
    (name "r-simile")
    (version "1.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Simile" version))
       (sha256
        (base32 "1izyjp18m1inac3svkf59z3lddrv44m7pdkhisgkr987xs8gdch4"))))
    (properties `((upstream-name . "Simile")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=Simile")
    (synopsis "Interact with Simile Models")
    (description
     "Allows a Simile model saved as a compiled binary to be loaded, parameterized,
executed and interrogated.  This version works with Simile v5.97 on.")
    (license (license:fsdg-compatible "Unlimited"))))

(define-public r-similaritymeasures
  (package
    (name "r-similaritymeasures")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimilarityMeasures" version))
       (sha256
        (base32 "1w4klcln4hy9vcik9csg7b3b8kk4raxgckwfrhqg089d80xbqsxj"))))
    (properties `((upstream-name . "SimilarityMeasures")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SimilarityMeasures")
    (synopsis "Trajectory Similarity Measures")
    (description
     "This package provides functions to run and assist four different similarity
measures.  The similarity measures included are: longest common subsequence
(LCSS), Frechet distance, edit distance and dynamic time warping (DTW).  Each of
these similarity measures can be calculated from two n-dimensional trajectories,
both in matrix form.")
    (license license:gpl3)))

(define-public r-simidm
  (package
    (name "r-simidm")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simIDM" version))
       (sha256
        (base32 "0iha1daw4jklgqrskflpw5rgmxdhni72kv8bfzid1vb28m7dpydq"))))
    (properties `((upstream-name . "simIDM")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-parallelly
                             r-mstate
                             r-future
                             r-furrr
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/insightsengineering/simIDM/")
    (synopsis "Simulating Oncology Trials using an Illness-Death Model")
    (description
     "Based on the illness-death model a large number of clinical trials with oncology
endpoints progression-free survival (PFS) and overall survival (OS) can be
simulated, see Meller, Beyersmann and Rufibach (2019) <doi:10.1002/sim.8295>.
The simulation set-up allows for random and event-driven censoring, an arbitrary
number of treatment arms, staggered study entry and drop-out.  Exponentially,
Weibull and piecewise exponentially distributed survival times can be generated.
 The correlation between PFS and OS can be calculated.")
    (license license:asl2.0)))

(define-public r-simico
  (package
    (name "r-simico")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SIMICO" version))
       (sha256
        (base32 "0inyiv2ccz6amdwy94liln0lfs9f12nwq6l0wcqvj42idyjas4ky"))))
    (properties `((upstream-name . "SIMICO")))
    (build-system r-build-system)
    (propagated-inputs (list r-icskat r-fastghquad r-compquadform r-bindata))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SIMICO")
    (synopsis "Set-Based Inference for Multiple Interval-Censored Outcomes")
    (description
     "This package contains tests for association between a set of genetic variants
and multiple correlated outcomes that are interval censored.  Interval-censored
data arises when the exact time of the onset of an outcome of interest is
unknown but known to fall between two time points.")
    (license license:gpl3)))

(define-public r-simhelpers
  (package
    (name "r-simhelpers")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simhelpers" version))
       (sha256
        (base32 "0xxnzs98q4py1z19g7hamfgd3gfhg4fc63w1drzbfsg53kzgxk13"))))
    (properties `((upstream-name . "simhelpers")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-tidyr r-rstudioapi r-rdpack r-furrr))
    (native-inputs (list r-knitr))
    (home-page "https://meghapsimatrix.github.io/simhelpers/")
    (synopsis "Helper Functions for Simulation Studies")
    (description
     "Calculates performance criteria measures and associated Monte Carlo standard
errors for simulation results.  Includes functions to help run simulation
studies.  Our derivation and explanation of formulas and our general simulation
workflow is closely aligned with the approach described by Morris, White, and
Crowther (2019) <DOI:10.1002/sim.8086>.")
    (license license:gpl3)))

(define-public r-simhaz
  (package
    (name "r-simhaz")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimHaz" version))
       (sha256
        (base32 "04q4xyc1ki1zr3grm3khfg0kbykjy3j9qpg332l7pxp4j3wa3aw3"))))
    (properties `((upstream-name . "SimHaz")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "http://www.stat.berkeley.edu/~rabbee/research_webpage.htm")
    (synopsis
     "Simulated Survival and Hazard Analysis for Time-Dependent Exposure")
    (description
     "Generate power for the Cox proportional hazards model by simulating survival
events data with time dependent exposure status for subjects.  A dichotomous
exposure variable is considered with a single transition from unexposed to
exposed status during the subject's time on study.")
    (license license:gpl2+)))

(define-public r-simgof
  (package
    (name "r-simgof")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simgof" version))
       (sha256
        (base32 "05d7im59i193f266kqy4lr37dw3v20j9niizhj9w8z1qlfqfqsrk"))))
    (properties `((upstream-name . "simgof")))
    (build-system r-build-system)
    (propagated-inputs (list r-ddst))
    (home-page "https://cran.r-project.org/package=simgof")
    (synopsis "Simultaneous Goodness-of-Fits Tests")
    (description
     "Routine that allows the user to run several goodness-of-fit tests.  It also
combines the tests and returns a properly adjusted family-wise p value.  Details
can be found in <@code{arXiv:2007.04727>}.")
    (license license:gpl2)))

(define-public r-simglm
  (package
    (name "r-simglm")
    (version "0.8.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simglm" version))
       (sha256
        (base32 "0spvr8bmdqg97y1qklr8c6svgwsdwa5haar4sbsnvpnrq5fb2baw"))))
    (properties `((upstream-name . "simglm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-purrr r-future-apply r-dplyr r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/lebebr01/simglm")
    (synopsis "Simulate Models Based on the Generalized Linear Model")
    (description
     "Simulates regression models, including both simple regression and generalized
linear mixed models with up to three level of nesting.  Power simulations that
are flexible allowing the specification of missing data, unbalanced designs, and
different random error distributions are built into the package.")
    (license license:expat)))

(define-public r-simframe
  (package
    (name "r-simframe")
    (version "0.5.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simFrame" version))
       (sha256
        (base32 "1a2p4hx80rrrzs4gjscdsy1jbzpahkw0h2nlna4q8p75blgnzwpn"))))
    (properties `((upstream-name . "simFrame")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-lattice))
    (home-page "https://cran.r-project.org/package=simFrame")
    (synopsis "Simulation Framework")
    (description
     "This package provides a general framework for statistical simulation, which
allows researchers to make use of a wide range of simulation designs with
minimal programming effort.  The package provides functionality for drawing
samples from a distribution or a finite population, for adding outliers and
missing values, as well as for visualization of the simulation results.  It
follows a clear object-oriented design and supports parallel computing to
increase computational performance.")
    (license license:gpl2+)))

(define-public r-simfit
  (package
    (name "r-simfit")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simfit" version))
       (sha256
        (base32 "0wlkkpxvpxd17y2kgfwjyjlrwj48z27mhss8r7yp4410l0a2llgy"))))
    (properties `((upstream-name . "simfit")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr r-ggplot2))
    (home-page "https://cran.r-project.org/package=simfit")
    (synopsis "Test Model Fit with Simulation")
    (description
     "Simulates data from model objects (e.g., from lm(), glm()), and plots this along
with the original data to compare how well the simulated data matches the
original data to determine model fit.")
    (license license:gpl3)))

(define-public r-simfinapi
  (package
    (name "r-simfinapi")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simfinapi" version))
       (sha256
        (base32 "1i9zf17nn9xbyccmr8blrlybqakrlzrxxm961bgrsb2pzawn6f6j"))))
    (properties `((upstream-name . "simfinapi")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppsimdjson
                             r-memoise
                             r-lifecycle
                             r-httr2
                             r-data-table
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/matthiasgomolka/simfinapi")
    (synopsis "Accessing 'SimFin' Data")
    (description
     "Through simfinapi, you can intuitively access the @code{SimFin} Web-API
(<https://www.simfin.com/>) to make @code{SimFin} data easily available in R. To
obtain an @code{SimFin} API key (and thus to use this package), you need to
register at <https://app.simfin.com/login>.")
    (license license:gpl3)))

(define-public r-simfam
  (package
    (name "r-simfam")
    (version "1.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simfam" version))
       (sha256
        (base32 "0fvg9fwq6j8qraxxr4686i1vp7wv2z52qq61i14dh455i85picc2"))))
    (properties `((upstream-name . "simfam")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/OchoaLab/simfam")
    (synopsis "Simulate and Model Family Pedigrees with Structured Founders")
    (description
     "The focus is on simulating and modeling families with founders drawn from a
structured population (for example, with different ancestries or other
potentially non-family relatedness), in contrast to traditional pedigree
analysis that treats all founders as equally unrelated.  Main function simulates
a random pedigree for many generations, avoiding close relatives, pairing
closest individuals according to a 1D geography and their randomly-drawn sex,
and with variable children sizes to result in a target population size per
generation.  Auxiliary functions calculate kinship matrices, admixture matrices,
and draw random genotypes across arbitrary pedigree structures starting from the
corresponding founder values.  The code is built around the plink FAM table
format for pedigrees.  Described in Yao and Ochoa (2022)
<doi:10.1101/2022.03.25.485885>.")
    (license license:gpl3+)))

(define-public r-simexboost
  (package
    (name "r-simexboost")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SIMEXBoost" version))
       (sha256
        (base32 "1w7r4rw4zbfk3ks3iinsvv0fcmc15xlq8ybb6bhfafki298kingi"))))
    (properties `((upstream-name . "SIMEXBoost")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=SIMEXBoost")
    (synopsis "Boosting Method for High-Dimensional Error-Prone Data")
    (description
     "Implementation of the boosting procedure with the simulation and extrapolation
approach to address variable selection and estimation for high-dimensional data
subject to measurement error in predictors.  It can be used to address
generalized linear models (GLM) in Chen (2023) <doi: 10.1007/s11222-023-10209-3>
and the accelerated failure time (AFT) model in Chen and Qiu (2023) <doi:
10.1111/biom.13898>.  Some relevant references include Chen and Yi (2021)
<doi:10.1111/biom.13331> and Hastie, Tibshirani, and Friedman (2008,
ISBN:978-0387848570).")
    (license license:gpl2)))

(define-public r-simexam
  (package
    (name "r-simexam")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simExam" version))
       (sha256
        (base32 "10p3y67cli5is2gj23cjy8y7zpbnn2jbxmfiq01ls0hc1jind82w"))))
    (properties `((upstream-name . "simExam")))
    (build-system r-build-system)
    (propagated-inputs (list r-msm r-matrix))
    (home-page "https://cran.r-project.org/package=simExam")
    (synopsis "Generate Simulated Data for IRT-Enabled Exams")
    (description
     "Generates binary test data based on Item Response Theory using the two-parameter
logistic model (Lord, 1980 <doi:10.4324/9780203056615>).  Useful functions for
test equating are included, e.g. functions for generating internal and external
common items between test forms and a function to create a linkage plans between
those forms.  Ancillary functions for generating true item and person parameters
as well as for calculating the probability of a person correctly answering an
item are also included.")
    (license license:gpl3)))

(define-public r-simexaft
  (package
    (name "r-simexaft")
    (version "1.0.7.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simexaft" version))
       (sha256
        (base32 "0n3n2g07pnpcqhbrjf78lbvqvc136g7jxlx6q27vnk96kwizh3f1"))))
    (properties `((upstream-name . "simexaft")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mvtnorm))
    (home-page "https://cran.r-project.org/package=simexaft")
    (synopsis "simexaft")
    (description
     "Implement of the Simulation-Extrapolation (SIMEX) algorithm for the accelerated
failure time (AFT) with covariates subject to measurement error.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-simex
  (package
    (name "r-simex")
    (version "1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simex" version))
       (sha256
        (base32 "1849wayygyqv0fh1i2qva7ggh2yah2nn0sgbcy9pldxrjq8q9iw0"))))
    (properties `((upstream-name . "simex")))
    (build-system r-build-system)
    (home-page "http://wolfganglederer.github.io/simex/")
    (synopsis "SIMEX- And MCSIMEX-Algorithm for Measurement Error Models")
    (description
     "Implementation of the SIMEX-Algorithm by Cook & Stefanski (1994)
<doi:10.1080/01621459.1994.10476871> and MCSIMEX by KÃ¼chenhoff, Mwalili &
Lesaffre (2006) <doi:10.1111/j.1541-0420.2005.00396.x>.")
    (license license:gpl3)))

(define-public r-simeucartellaw
  (package
    (name "r-simeucartellaw")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimEUCartelLaw" version))
       (sha256
        (base32 "1n0dvhwqyirb6myc30ydvskb1k7hlx4nkqy15gpx87zl01vpn8fz"))))
    (properties `((upstream-name . "SimEUCartelLaw")))
    (build-system r-build-system)
    (propagated-inputs (list r-plot3d))
    (home-page "https://cran.r-project.org/package=SimEUCartelLaw")
    (synopsis "Simulation of Legal Exemption System for European Cartel Law")
    (description
     "Monte Carlo simulations of a game-theoretic model for the legal exemption system
of the European cartel law are implemented in order to estimate the (mean)
deterrent effect of this system.  The input and output parameters of the
simulated cartel opportunities can be visualized by three-dimensional
projections.  A description of the model is given in Moritz et al. (2018)
<doi:10.1515/bejeap-2017-0235>.")
    (license license:gpl2+)))

(define-public r-simet
  (package
    (name "r-simet")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simET" version))
       (sha256
        (base32 "0l3mk2n1ik9xg660js97n03gix5im2f42q2r7hs9gvhs0g2552kp"))))
    (properties `((upstream-name . "simET")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-rlang
                             r-plyr
                             r-magrittr
                             r-lubridate
                             r-ggpubr
                             r-ggpmisc
                             r-ggplot2
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=simET")
    (synopsis
     "Tools for Simulation of Evapotranspiration of Field Crops and Soil Water Balance")
    (description
     "Supports the calculation of meteorological characteristics in evapotranspiration
research and reference crop evapotranspiration, and offers three models to
simulate crop evapotranspiration and soil water balance in the field, including
single crop coefficient and dual crop coefficient, as well as the
Shuttleworth-Wallace model.  These calculations main refer to Allen et al.(1998,
ISBN:92-5-104219-5), Teh (2006, ISBN:1-58-112-998-X), and Liu et al.(2006)
<doi:10.1016/j.agwat.2006.01.018>.")
    (license license:gpl3+)))

(define-public r-simest
  (package
    (name "r-simest")
    (version "0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simest" version))
       (sha256
        (base32 "1wp08dkbshn5parq69d7iizp4g16qzpdim37kcg09pgsc3aghwaj"))))
    (properties `((upstream-name . "simest")))
    (build-system r-build-system)
    (propagated-inputs (list r-nnls r-cobs))
    (home-page "https://cran.r-project.org/package=simest")
    (synopsis "Constrained Single Index Model Estimation")
    (description
     "Estimation of function and index vector in single index model with and without
shape constraints including different smoothness conditions.")
    (license license:gpl2)))

(define-public r-simer
  (package
    (name "r-simer")
    (version "0.9.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simer" version))
       (sha256
        (base32 "00080aqr66qh86rp5yy7zmymy6rh1k0ihqm1y0jbfcqd51sch48i"))))
    (properties `((upstream-name . "simer")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppprogress
                             r-rcpparmadillo
                             r-rcpp
                             r-matrix
                             r-mass
                             r-jsonlite
                             r-bigmemory
                             r-bh))
    (home-page "https://github.com/xiaolei-lab/SIMER")
    (synopsis "Data Simulation for Life Science and Breeding")
    (description
     "Data simulator including genotype, phenotype, pedigree, selection and
reproduction in R. It simulates most of reproduction process of animals or
plants and provides data for GS (Genomic Selection), GWAS (Genome-Wide
Association Study), and Breeding.  For ADI model, please see Kao C and Zeng Z
(2002) <doi:10.1093/genetics/160.3.1243>.  For build.cov, please see B. D.
Ripley (1987) <ISBN:9780470009604>.")
    (license license:asl2.0)))

(define-public r-simengine
  (package
    (name "r-simengine")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimEngine" version))
       (sha256
        (base32 "0rcggzaz6vjf4vrnl9ashyl2lyn0zydypaj9bjhar5bq652xrwz5"))))
    (properties `((upstream-name . "SimEngine")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-pbapply
                             r-mass
                             r-magrittr
                             r-dplyr
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://avi-kenny.github.io/SimEngine/")
    (synopsis "Modular Framework for Statistical Simulations in R")
    (description
     "An open-source R package for structuring, maintaining, running, and debugging
statistical simulations on both local and cluster-based computing
environments.See full documentation at
<https://avi-kenny.github.io/@code{SimEngine/>}.")
    (license license:gpl3)))

(define-public r-simed
  (package
    (name "r-simed")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simEd" version))
       (sha256
        (base32 "1r4ahsfkrymhachdcjj7nvdiplxjla0jlsay3bi04n6rrl758rny"))))
    (properties `((upstream-name . "simEd")))
    (build-system r-build-system)
    (propagated-inputs (list r-shape r-rstream))
    (home-page "https://cran.r-project.org/package=simEd")
    (synopsis "Simulation Education")
    (description
     "This package contains various functions to be used for simulation education,
including simple Monte Carlo simulation functions, queueing simulation
functions, variate generation functions capable of producing independent streams
and antithetic variates, functions for illustrating random variate generation
for various discrete and continuous distributions, and functions to compute
time-persistent statistics.  Also contains functions for visualizing:
event-driven details of a single-server queue model; a Lehmer random number
generator; variate generation via acceptance-rejection; and of generating a
non-homogeneous Poisson process via thinning.  Also contains two queueing data
sets (one fabricated, one real-world) to facilitate input modeling.  More
details on the use of these functions can be found in Lawson and Leemis (2015)
<doi:10.1109/WSC.2017.8248124>, in Kudlay, Lawson, and Leemis (2020)
<doi:10.1109/WSC48552.2020.9384010>, and in Lawson and Leemis (2021)
<doi:10.1109/WSC52266.2021.9715299>.")
    (license license:expat)))

(define-public r-simecol
  (package
    (name "r-simecol")
    (version "0.8-14")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simecol" version))
       (sha256
        (base32 "0b0zx5wqvrfs4rpn6ngdf0c9d99wf9d0niaps41lslvlnpddvjw8"))))
    (properties `((upstream-name . "simecol")))
    (build-system r-build-system)
    (propagated-inputs (list r-minqa r-desolve))
    (home-page "http://www.simecol.de/")
    (synopsis "Simulation of Ecological (and Other) Dynamic Systems")
    (description
     "An object oriented framework to simulate ecological (and other) dynamic systems.
 It can be used for differential equations, individual-based (or agent-based)
and other models as well.  It supports structuring of simulation scenarios (to
avoid copy and paste) and aims to improve readability and re-usability of code.")
    (license license:gpl2+)))

(define-public r-simdnamixtures
  (package
    (name "r-simdnamixtures")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simDNAmixtures" version))
       (sha256
        (base32 "17865ihl8bv3nqhsqy3dpi82x4q1q5dzff6r3qygdnmh0ymhr7d2"))))
    (properties `((upstream-name . "simDNAmixtures")))
    (build-system r-build-system)
    (propagated-inputs (list r-readr r-pedtools r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=simDNAmixtures")
    (synopsis "Simulate Forensic DNA Mixtures")
    (description
     "Mixed DNA profiles can be sampled according to models for probabilistic
genotyping.  Peak height variability is modelled using a log normal distribution
or a gamma distribution.  Sample contributors may be related according to a
pedigree.")
    (license license:gpl3+)))

(define-public r-simdistr
  (package
    (name "r-simdistr")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simdistr" version))
       (sha256
        (base32 "16fsj42zr627i57q1q72l1nhin7g6rxq9hfy487hf1ywd6rvns18"))))
    (properties `((upstream-name . "simdistr")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=simdistr")
    (synopsis
     "Assessment of Data Trial Distributions According to the Carlisle-Stouffer Method")
    (description
     "Assessment of the distributions of baseline continuous and categorical variables
in randomised trials.  This method is based on the Carlisle-Stouffer method with
Monte Carlo simulations.  It calculates p-values for each trial baseline
variable, as well as combined p-values for each trial - these p-values measure
how compatible are distributions of trials baseline variables with random
sampling.  This package also allows for graphically plotting the cumulative
frequencies of computed p-values.  Please note that code was partly adapted from
Carlisle JB, Loadsman JA. (2017) <doi:10.1111/anae.13650>.")
    (license license:gpl2)))

(define-public r-simdissolution
  (package
    (name "r-simdissolution")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimDissolution" version))
       (sha256
        (base32 "1rdsxykh740h22ln635m0ysm00pdw94vp7qhgify4mcvc46c3632"))))
    (properties `((upstream-name . "SimDissolution")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-dplyr r-alabama))
    (home-page "https://cran.r-project.org/package=SimDissolution")
    (synopsis
     "Modeling and Assessing Similarity of Drug Dissolutions Profiles")
    (description
     "Implementation of a model-based bootstrap approach for testing whether two
formulations are similar.  The package provides a function for fitting a
pharmacokinetic model to time-concentration data and comparing the results for
all five candidate models regarding the Residual Sum of Squares (RSS).  The
candidate set contains a First order, Hixson-Crowell, Higuchi, Weibull and a
logistic model.  The assessment of similarity implemented in this package is
performed regarding the maximum deviation of the profiles.  See Moellenhoff et
al. (2018) <doi:10.1002/sim.7689> for details.")
    (license license:gpl2+)))

(define-public r-simdesign
  (package
    (name "r-simdesign")
    (version "2.17.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimDesign" version))
       (sha256
        (base32 "0anmnzxxw7mv9kykjf2kmgv6qwxkrg48g91q32r2ia4dw8n17782"))))
    (properties `((upstream-name . "SimDesign")))
    (build-system r-build-system)
    (propagated-inputs (list r-testthat
                             r-snow
                             r-sessioninfo
                             r-rpushbullet
                             r-r-utils
                             r-progressr
                             r-pbapply
                             r-parallelly
                             r-future-apply
                             r-future
                             r-dplyr
                             r-beepr))
    (native-inputs (list r-knitr))
    (home-page "http://philchalmers.github.io/SimDesign/")
    (synopsis "Structure for Organizing Monte Carlo Simulation Designs")
    (description
     "This package provides tools to safely and efficiently organize and execute Monte
Carlo simulation experiments in R. The package controls the structure and
back-end of Monte Carlo simulation experiments by utilizing a
generate-analyse-summarise workflow.  The workflow safeguards against common
simulation coding issues, such as automatically re-simulating non-convergent
results, prevents inadvertently overwriting simulation files, catches error and
warning messages during execution, implicitly supports parallel processing with
high-quality random number generation, and provides tools for managing
high-performance computing (HPC) array jobs submitted to schedulers such as
SLURM. For a pedagogical introduction to the package see Sigal and Chalmers
(2016) <doi:10.1080/10691898.2016.1246953>.  For a more in-depth overview of the
package and its design philosophy see Chalmers and Adkins (2020)
<doi:10.20982/tqmp.16.4.p248>.")
    (license license:gpl2+)))

(define-public r-simdd
  (package
    (name "r-simdd")
    (version "1.1-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simdd" version))
       (sha256
        (base32 "05ngayian2klvi8mirpy3pvzzk737wbld31d8v4338mflk5prnqq"))))
    (properties `((upstream-name . "simdd")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=simdd")
    (synopsis
     "Simulation of Fisher Bingham and Related Directional Distributions")
    (description
     "Simulation methods for the Fisher Bingham distribution on the unit sphere, the
matrix Bingham distribution on a Grassmann manifold, the matrix Fisher
distribution on SO(3), and the bivariate von Mises sine model on the torus.  The
methods use an acceptance/rejection simulation algorithm for the Bingham
distribution and are described fully by Kent, Ganeiber and Mardia (2018)
<doi:10.1080/10618600.2017.1390468>.  These methods supersede earlier MCMC
simulation methods and are more general than earlier simulation methods.  The
methods can be slower in specific situations where there are existing non-MCMC
simulation methods (see Section 8 of Kent, Ganeiber and Mardia (2018)
<doi:10.1080/10618600.2017.1390468> for further details).")
    (license license:gpl2)))

(define-public r-simdata
  (package
    (name "r-simdata")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simdata" version))
       (sha256
        (base32 "1yzxa2xfjclhwbzvzdivgd99h8n9cvb16ws074r5gw2xvd0ilf3m"))))
    (properties `((upstream-name . "simdata")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-matrix r-igraph))
    (native-inputs (list r-knitr))
    (home-page "https://matherealize.github.io/simdata/")
    (synopsis "Generate Simulated Datasets")
    (description
     "Generate simulated datasets from an initial underlying distribution and apply
transformations to obtain realistic data.  Implements the NORTA
(Normal-to-anything) approach from Cario and Nelson (1997) and other data
generating mechanisms.  Simple network visualization tools are provided to
facilitate communicating the simulation setup.")
    (license license:gpl3)))

(define-public r-simdag
  (package
    (name "r-simdag")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simDAG" version))
       (sha256
        (base32 "160q8f65na8kbl3069hrjapz8cb9f1kk5kxp7928756jkak7ifvq"))))
    (properties `((upstream-name . "simDAG")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-rfast r-igraph r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/RobinDenz1/simDAG")
    (synopsis "Simulate Data from a DAG and Associated Node Information")
    (description
     "Simulate complex data from a given directed acyclic graph and information about
each individual node.  Root nodes are simply sampled from the specified
distribution.  Child Nodes are simulated according to one of many implemented
regressions, such as logistic regression, linear regression, poisson regression
and more.  Also includes a comprehensive framework for discrete-time simulation,
which can generate even more complex longitudinal data.")
    (license license:gpl3+)))

(define-public r-simctest
  (package
    (name "r-simctest")
    (version "2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simctest" version))
       (sha256
        (base32 "0igwn82zfx6ajv42gpyx6969aldygldkpdc9ik6601511sscvf2a"))))
    (properties `((upstream-name . "simctest")))
    (build-system r-build-system)
    (home-page "http://www2.imperial.ac.uk/~agandy")
    (synopsis "Safe Implementation of Monte Carlo Tests")
    (description
     "Algorithms for the implementation and evaluation of Monte Carlo tests, as well
as for their use in multiple testing procedures.")
    (license license:gpl2+)))

(define-public r-simcross
  (package
    (name "r-simcross")
    (version "0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simcross" version))
       (sha256
        (base32 "01ch7h2y7ybggxrwg17yx5d7psjnmambzn5zwwi94gwhxmzp987b"))))
    (properties `((upstream-name . "simcross")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://kbroman.org/simcross/")
    (synopsis "Simulate Experimental Crosses")
    (description
     "Simulate and plot general experimental crosses.  The focus is on simulating
genotypes with an aim towards flexibility rather than speed.  Meiosis is
simulated following the Stahl model, in which chiasma locations are the
superposition of two processes: a proportion p coming from a process exhibiting
no interference, and the remainder coming from a process following the
chi-square model.")
    (license license:gpl3)))

(define-public r-simcorrmix
  (package
    (name "r-simcorrmix")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimCorrMix" version))
       (sha256
        (base32 "1mx8xkg1nbh4x4xr6m672zyg00s3lky2sy5mz7dfkw40vz2bwi53"))))
    (properties `((upstream-name . "SimCorrMix")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam
                             r-triangle
                             r-simmulticorrdata
                             r-nleqslv
                             r-mvtnorm
                             r-matrix
                             r-mass
                             r-ggplot2
                             r-bb))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/AFialkowski/SimCorrMix")
    (synopsis
     "Simulation of Correlated Data with Multiple Variable Types Including Continuous and Count Mixture Distributions")
    (description
     "Generate continuous (normal, non-normal, or mixture distributions), binary,
ordinal, and count (regular or zero-inflated, Poisson or Negative Binomial)
variables with a specified correlation matrix, or one continuous variable with a
mixture distribution.  This package can be used to simulate data sets that mimic
real-world clinical or genetic data sets (i.e., plasmodes, as in Vaughan et al.,
2009 <DOI:10.1016/j.csda.2008.02.032>).  The methods extend those found in the
@code{SimMultiCorrData} R package.  Standard normal variables with an imposed
intermediate correlation matrix are transformed to generate the desired
distributions.  Continuous variables are simulated using either Fleishman
(1978)'s third order <DOI:10.1007/BF02293811> or Headrick (2002)'s fifth order
<DOI:10.1016/S0167-9473(02)00072-5> polynomial transformation method (the power
method transformation, PMT).  Non-mixture distributions require the user to
specify mean, variance, skewness, standardized kurtosis, and standardized fifth
and sixth cumulants.  Mixture distributions require these inputs for the
component distributions plus the mixing probabilities.  Simulation occurs at the
component level for continuous mixture distributions.  The target correlation
matrix is specified in terms of correlations with components of continuous
mixture variables.  These components are transformed into the desired mixture
variables using random multinomial variables based on the mixing probabilities.
However, the package provides functions to approximate expected correlations
with continuous mixture variables given target correlations with the components.
 Binary and ordinal variables are simulated using a modification of ordsample()
in package @code{GenOrd}'.  Count variables are simulated using the inverse CDF
method.  There are two simulation pathways which calculate intermediate
correlations involving count variables differently.  Correlation Method 1 adapts
Yahav and Shmueli's 2012 method <DOI:10.1002/asmb.901> and performs best with
large count variable means and positive correlations or small means and negative
correlations.  Correlation Method 2 adapts Barbiero and Ferrari's 2015
modification of the @code{GenOrd} package <DOI:10.1002/asmb.2072> and performs
best under the opposite scenarios.  The optional error loop may be used to
improve the accuracy of the final correlation matrix.  The package also contains
functions to calculate the standardized cumulants of continuous mixture
distributions, check parameter inputs, calculate feasible correlation
boundaries, and summarize and plot simulated variables.")
    (license license:gpl2)))

(define-public r-simcormultres
  (package
    (name "r-simcormultres")
    (version "1.9.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimCorMultRes" version))
       (sha256
        (base32 "061gnxvhii0hhnxd00hx9cpjmamcdvh10mcwjp43s09f5j4d564y"))))
    (properties `((upstream-name . "SimCorMultRes")))
    (build-system r-build-system)
    (propagated-inputs (list r-evd))
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "https://github.com/AnestisTouloumis/SimCorMultRes")
    (synopsis "Simulates Correlated Multinomial Responses")
    (description
     "Simulates correlated multinomial responses conditional on a marginal model
specification.")
    (license license:gpl3)))

(define-public r-simcop
  (package
    (name "r-simcop")
    (version "0.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimCop" version))
       (sha256
        (base32 "1yrdy77a9h14v92c63ng8phi2ig73wy4xjjdb75322grc0bd3jq6"))))
    (properties `((upstream-name . "SimCop")))
    (build-system r-build-system)
    (propagated-inputs (list r-quadprog))
    (home-page "https://cran.r-project.org/package=SimCop")
    (synopsis "Simulate from Arbitrary Copulae")
    (description
     "This package provides a framework to generating random variates from arbitrary
multivariate copulae, while concentrating on (bivariate) extreme value copulae.
Particularly useful if the multivariate copulae are not available in closed
form.")
    (license license:gpl2+)))

(define-public r-simcomp
  (package
    (name "r-simcomp")
    (version "3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimComp" version))
       (sha256
        (base32 "04qkis20zk0z42bv4nznffqlpwby0y0ij27gj4sa0ha864pg0hi5"))))
    (properties `((upstream-name . "SimComp")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-multcomp r-mratios))
    (home-page "https://cran.r-project.org/package=SimComp")
    (synopsis "Simultaneous Comparisons for Multiple Endpoints")
    (description
     "Simultaneous tests and confidence intervals are provided for one-way
experimental designs with one or many normally distributed, primary response
variables (endpoints).  Differences (Hasler and Hothorn, 2011
<doi:10.2202/1557-4679.1258>) or ratios (Hasler and Hothorn, 2012
<doi:10.1080/19466315.2011.633868>) of means can be considered.  Various
contrasts can be chosen, unbalanced sample sizes are allowed as well as
heterogeneous variances (Hasler and Hothorn, 2008 <doi:10.1002/bimj.200710466>)
or covariance matrices (Hasler, 2014 <doi:10.1515/ijb-2012-0015>).")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-simcdm
  (package
    (name "r-simcdm")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simcdm" version))
       (sha256
        (base32 "1pwh1ial7naanqpxhlsn6ij2i35xnj8hwyhvipzc7csd5l5ss5z1"))))
    (properties `((upstream-name . "simcdm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://tmsalab.github.io/simcdm/")
    (synopsis "Simulate Cognitive Diagnostic Model ('CDM') Data")
    (description
     "This package provides efficient R and C++ routines to simulate cognitive
diagnostic model data for Deterministic Input, Noisy \"And\" Gate ('DINA') and
reduced Reparameterized Unified Model ('@code{rRUM}') from Culpepper and Hudson
(2017) <doi: 10.1177/0146621617707511>, Culpepper (2015)
<doi:10.3102/1076998615595403>, and de la Torre (2009)
<doi:10.3102/1076998607309474>.")
    (license license:gpl2+)))

(define-public r-simcausal
  (package
    (name "r-simcausal")
    (version "0.5.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simcausal" version))
       (sha256
        (base32 "1k9qq4xr4xznqzw668z49bw3nw4q1vb87v134dwr8b1zns5r5a4v"))))
    (properties `((upstream-name . "simcausal")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-r6
                             r-matrix
                             r-igraph
                             r-data-table
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/osofr/simcausal")
    (synopsis
     "Simulating Longitudinal Data with Causal Inference Applications")
    (description
     "This package provides a flexible tool for simulating complex longitudinal data
using structural equations, with emphasis on problems in causal inference.
Specify interventions and simulate from intervened data generating
distributions.  Define and evaluate treatment-specific means, the average
treatment effects and coefficients from working marginal structural models.
User interface designed to facilitate the conduct of transparent and
reproducible simulation studies, and allows concise expression of complex
functional dependencies for a large number of time-varying nodes.  See the
package vignette for more information, documentation and examples.")
    (license license:gpl2)))

(define-public r-simcat
  (package
    (name "r-simcat")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simCAT" version))
       (sha256
        (base32 "0p82v24b7y3fbbyk4mxva6kzg0pxyxrnp739npcspira9d4v0ypa"))))
    (properties `((upstream-name . "simCAT")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinycssloaders r-shiny r-mirtcat r-mirt
                             r-dplyr))
    (home-page "https://github.com/alexandrejaloto/simCAT")
    (synopsis "Implements Computerized Adaptive Testing Simulations")
    (description
     "Computerized Adaptive Testing simulations with dichotomous and polytomous items.
 Selects items with Maximum Fisher Information method or randomly, with or
without constraints (content balancing and item exposure control).  Evaluates
the simulation results in terms of precision, item exposure, and test length.
Inspired on Magis & Barrada (2017) <doi:10.18637/jss.v076.c01>.")
    (license license:expat)))

(define-public r-simboot
  (package
    (name "r-simboot")
    (version "0.2-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simboot" version))
       (sha256
        (base32 "1kw2sy13vsys2rs0ss7016y8k8ykcbp6v959ylbp86dl9bnh3vpr"))))
    (properties `((upstream-name . "simboot")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-boot))
    (home-page "https://github.com/shearer/simboot")
    (synopsis "Simultaneous Inference for Diversity Indices")
    (description
     "This package provides estimation of simultaneous bootstrap and asymptotic
confidence intervals for diversity indices, namely the Shannon and the Simpson
index.  Several pre--specified multiple comparison types are available to
choose.  Further user--defined contrast matrices are applicable.  In addition,
simboot estimates adjusted as well as unadjusted p--values for two of the three
proposed bootstrap methods.  Further simboot allows for comparing biological
diversities of two or more groups while simultaneously testing a user-defined
selection of Hill numbers of orders q, which are considered as appropriate and
useful indices for measuring diversity.")
    (license license:gpl2+)))

(define-public r-simbiid
  (package
    (name "r-simbiid")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SimBIID" version))
       (sha256
        (base32 "01gkkgxx5x9pkwsmpmf1rg4i0ls7fl6yv6pcg8gscr51bnxsrwpp"))))
    (properties `((upstream-name . "SimBIID")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rcppxptrutils
                             r-rcpparmadillo
                             r-rcpp
                             r-rcolorbrewer
                             r-purrr
                             r-mvtnorm
                             r-ggplot2
                             r-dplyr
                             r-coda))
    (home-page "https://github.com/tjmckinley/SimBIID")
    (synopsis
     "Simulation-Based Inference Methods for Infectious Disease Models")
    (description
     "This package provides some code to run simulations of state-space models, and
then use these in the Approximate Bayesian Computation Sequential Monte Carlo
(ABC-SMC) algorithm of Toni et al. (2009) <doi:10.1098/rsif.2008.0172> and a
bootstrap particle filter based particle Markov chain Monte Carlo (PMCMC)
algorithm (Andrieu et al., 2010 <doi:10.1111/j.1467-9868.2009.00736.x>).  Also
provides functions to plot and summarise the outputs.")
    (license license:gpl3+)))

(define-public r-simaerep
  (package
    (name "r-simaerep")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "simaerep" version))
       (sha256
        (base32 "0sjq6lrs9fawgwpyr8p97c36m9a6yw16ww247kw86qncnb1ya4vx"))))
    (properties `((upstream-name . "simaerep")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-rcolorbrewer
                             r-purrr
                             r-progressr
                             r-magrittr
                             r-knitr
                             r-ggplot2
                             r-furrr
                             r-forcats
                             r-dplyr
                             r-dbplyr
                             r-cowplot))
    (home-page "https://openpharma.github.io/simaerep/")
    (synopsis "Find Clinical Trial Sites Under-Reporting Adverse Events")
    (description
     "Monitoring of Adverse Event (AE) reporting in clinical trials is important for
patient safety.  Sites that are under-reporting AEs can be detected using
Bootstrap-based simulations that simulate overall AE reporting.  Based on the
simulation an AE under-reporting probability is assigned to each site in a given
trial (Koneswarakantha 2021 <doi:10.1007/s40264-020-01011-5>).")
    (license license:expat)))

(define-public r-sim2dpredictr
  (package
    (name "r-sim2dpredictr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sim2Dpredictr" version))
       (sha256
        (base32 "14v35ngwj27j2r4mszwjz1dnk4iydakk5qf4z0swwi931q1jiphc"))))
    (properties `((upstream-name . "sim2Dpredictr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-spam
                             r-rdpack
                             r-matrixcalc
                             r-mass
                             r-dplyr))
    (home-page "https://github.com/jmleach-bst/sim2Dpredictr")
    (synopsis "Simulate Outcomes Using Spatially Dependent Design Matrices")
    (description
     "This package provides tools for simulating spatially dependent predictors
(continuous or binary), which are used to generate scalar outcomes in a
(generalized) linear model framework.  Continuous predictors are generated using
traditional multivariate normal distributions or Gauss Markov random fields with
several correlation function approaches (e.g., see Rue (2001)
<doi:10.1111/1467-9868.00288> and Furrer and Sain (2010)
<doi:10.18637/jss.v036.i10>), while binary predictors are generated using a
Boolean model (see Cressie and Wikle (2011, ISBN: 978-0-471-69274-4)).
Parameter vectors exhibiting spatial clustering can also be easily specified by
the user.")
    (license license:gpl3)))

(define-public r-sim1000g
  (package
    (name "r-sim1000g")
    (version "1.40")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sim1000G" version))
       (sha256
        (base32 "1avq6xfc34izavvp9vb5ccc2b9nc4m7cwr06ygnsmpgwi5nswbb8"))))
    (properties `((upstream-name . "sim1000G")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-readr r-mass r-hapsim))
    (native-inputs (list r-rmarkdown r-prettydoc r-knitr))
    (home-page "https://cran.r-project.org/package=sim1000G")
    (synopsis
     "Genotype Simulations for Rare or Common Variants Using Haplotypes from 1000 Genomes")
    (description
     "Generates realistic simulated genetic data in families or unrelated individuals.")
    (license license:gpl2+)))

(define-public r-sim-plfn
  (package
    (name "r-sim-plfn")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Sim.PLFN" version))
       (sha256
        (base32 "1jmsydhpfv2z9cr99hfy8cairhkkxpwk4wnz1adixwxazkn7qfah"))))
    (properties `((upstream-name . "Sim.PLFN")))
    (build-system r-build-system)
    (propagated-inputs (list r-fuzzynumbers r-distrib))
    (home-page "https://cran.r-project.org/package=Sim.PLFN")
    (synopsis "Simulation of Piecewise Linear Fuzzy Numbers")
    (description
     "The definition of fuzzy random variable and the methods of simulation from fuzzy
random variables are two challenging statistical problems in three recent
decades.  This package is organized based on a special definition of fuzzy
random variable and simulate fuzzy random variable by Piecewise Linear Fuzzy
Numbers (PLFNs); see Coroianua et al. (2013) <doi:10.1016/j.fss.2013.02.005> for
details about PLFNs.  Some important statistical functions are considered for
obtaining the membership function of main statistics, such as mean, variance,
summation, standard deviation and coefficient of variance.  Some of applied
advantages of Sim.PLFN package are: (1) Easily generating / simulation a random
sample of PLFN, (2) drawing the membership functions of the simulated PLFNs or
the membership function of the statistical result, and (3) Considering the
simulated PLFNs for arithmetic operation or importing into some statistical
computation.  Finally, it must be mentioned that Sim.PLFN package works on the
basis of @code{FuzzyNumbers} package.")
    (license license:lgpl3+)))

(define-public r-sim-diffproc
  (package
    (name "r-sim-diffproc")
    (version "4.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Sim.DiffProc" version))
       (sha256
        (base32 "059dzkl0jw1hfswa4wj1wwal7svy4sb5x79l25dk9hwdvlpy77v4"))))
    (properties `((upstream-name . "Sim.DiffProc")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-deriv))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/acguidoum/Sim.DiffProc")
    (synopsis "Simulation of Diffusion Processes")
    (description
     "It provides users with a wide range of tools to simulate, estimate, analyze, and
visualize the dynamics of stochastic differential systems in both forms Ito and
Stratonovich.  Statistical analysis with parallel Monte Carlo and moment
equations methods of SDEs <doi:10.18637/jss.v096.i02>.  Enabled many searchers
in different domains to use these equations to modeling practical problems in
financial and actuarial modeling and other areas of application, e.g., modeling
and simulate of first passage time problem in shallow water using the attractive
center (Boukhetala K, 1996) ISBN:1-56252-342-2.")
    (license license:gpl2+)))

(define-public r-sim-ba
  (package
    (name "r-sim-ba")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sim.BA" version))
       (sha256
        (base32 "0rwr77n7bg5w1f8ikz88lzm4kq32nxs4h2zr2fqwrwwwwp8paixf"))))
    (properties `((upstream-name . "sim.BA")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-scales
                             r-rlang
                             r-pbapply
                             r-ggplot2
                             r-cobalt
                             r-chk))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=sim.BA")
    (synopsis "Simulation-Based Bias Analysis for Observational Studies")
    (description
     "Allows user to conduct a simulation based quantitative bias analysis using
covariate structures generated with individual-level data to characterize the
bias arising from unmeasured confounding.  Users can specify their desired data
generating mechanisms to simulate data and quantitatively summarize findings in
an end-to-end application using this package.")
    (license license:gpl2+)))

(define-public r-silp
  (package
    (name "r-silp")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "silp" version))
       (sha256
        (base32 "1a0ng4lmibcx1afkxxd17593h5dn6iizi6x5kgdy9660llfs1q57"))))
    (properties `((upstream-name . "silp")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-semtools
                             r-purrr
                             r-matrix
                             r-mass
                             r-lavaan))
    (home-page "https://github.com/TomBJJJ/silp")
    (synopsis
     "Conditional Process Analysis (CPA) via Structural Equation Modeling (SEM) Approach")
    (description
     "This package provides Reliability-Adjusted Product Indicator (RAPI) method to
estimate effects among latent variables, thus allowing for more precise
definition and analysis of mediation and moderation models.  Our simulation
studies reveal that while silp may exhibit instability with smaller sample sizes
and lower reliability scores (e.g., N = 100, omega = 0.7), implementing nearest
positive definite matrix correction and bootstrap confidence interval estimation
can significantly ameliorate this volatility.  When these adjustments are
applied, silp achieves estimations akin in quality to those derived from latent
moderated structural equations (LMS).  In conclusion, the silp package is a
valuable tool for researchers seeking to explore complex relational structures
between variables without resorting to commercial software.  Hsiao et
al.(2018)<doi:10.1177/0013164416679877> Kline &
Moosbrugger(2000)<doi:10.1007/BF02296338> Cheung et
al.(2021)<doi:10.1007/s10869-020-09717-0>.")
    (license license:expat)))

(define-public r-silm
  (package
    (name "r-silm")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SILM" version))
       (sha256
        (base32 "1iaivpdx18djfm5dqak0q9kfl1xfrnx3gk5x9a4y53h1d7jhl9p6"))))
    (properties `((upstream-name . "SILM")))
    (build-system r-build-system)
    (propagated-inputs (list r-sis r-scalreg r-hdi r-glmnet))
    (home-page "https://cran.r-project.org/package=SILM")
    (synopsis "Simultaneous Inference for Linear Models")
    (description
     "Simultaneous inference procedures for high-dimensional linear models as
described by Zhang, X., and Cheng, G. (2017)
<doi:10.1080/01621459.2016.1166114>.")
    (license license:gpl3)))

(define-public r-sillyputty
  (package
    (name "r-sillyputty")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SillyPutty" version))
       (sha256
        (base32 "18qc7dwd29zghx9klsslvr2pianbf3av0pvc0wc09781hiv3xksg"))))
    (properties `((upstream-name . "SillyPutty")))
    (build-system r-build-system)
    (propagated-inputs (list r-thresher r-polychrome r-oompabase r-cluster))
    (native-inputs (list r-knitr))
    (home-page "http://oompa.r-forge.r-project.org/")
    (synopsis "Silly Putty Clustering")
    (description
     "This package implements a simple, novel clustering algorithm based on optimizing
the silhouette width.  See <doi:10.1101/2023.11.07.566055> for details.")
    (license license:asl2.0)))

(define-public r-silicate
  (package
    (name "r-silicate")
    (version "0.7.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "silicate" version))
       (sha256
        (base32 "193n0gh2kg5900si1m94cliyh0fbmp9dybs17vl1xxn34pg9c9s9"))))
    (properties `((upstream-name . "silicate")))
    (build-system r-build-system)
    (propagated-inputs (list r-unjoin
                             r-tibble
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-gridbase
                             r-gibble
                             r-dplyr
                             r-decido
                             r-crsmeta))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/hypertidy/silicate")
    (synopsis
     "Common Forms for Complex Hierarchical and Relational Data Structures")
    (description
     "Generate common data forms for complex data suitable for conversions and
transmission by decomposition as paths or primitives.  Paths are
sequentially-linked records, primitives are basic atomic elements and both can
model many forms and be grouped into hierarchical structures.  The universal
models SC0 (structural) and SC (labelled, relational) are composed of edges and
can represent any hierarchical form.  Specialist models PATH', ARC and TRI
provide the most common intermediate forms used for converting from one form to
another.  The methods are inspired by the simplicial complex
<https://en.wikipedia.org/wiki/Simplicial_complex> and provide intermediate
forms that relate spatial data structures to this mathematical construct.")
    (license license:gpl3)))

(define-public r-silggm
  (package
    (name "r-silggm")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SILGGM" version))
       (sha256
        (base32 "1lhmisgg2zbfksl7czz0fqag3732gkjc44n615ipxbdi2pvnc7m0"))))
    (properties `((upstream-name . "SILGGM")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape r-rcpp r-mass r-glasso))
    (home-page "https://cran.r-project.org/package=SILGGM")
    (synopsis
     "Statistical Inference of Large-Scale Gaussian Graphical Model in Gene Networks")
    (description
     "This package provides a general framework to perform statistical inference of
each gene pair and global inference of whole-scale gene pairs in gene networks
using the well known Gaussian graphical model (GGM) in a time-efficient manner.
We focus on the high-dimensional settings where p (the number of genes) is
allowed to be far larger than n (the number of subjects).  Four main approaches
are supported in this package: (1) the bivariate nodewise scaled Lasso (Ren et
al (2015) <doi:10.1214/14-AOS1286>) (2) the de-sparsified nodewise scaled Lasso
(Jankova and van de Geer (2017) <doi:10.1007/s11749-016-0503-5>) (3) the
de-sparsified graphical Lasso (Jankova and van de Geer (2015)
<doi:10.1214/15-EJS1031>) (4) the GGM estimation with false discovery rate
control (FDR) using scaled Lasso or Lasso (Liu (2013) <doi:10.1214/13-AOS1169>).
 Windows users should install Rtools before the installation of this package.")
    (license license:gpl2+)))

(define-public r-silfs
  (package
    (name "r-silfs")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SILFS" version))
       (sha256
        (base32 "0rr27bmmjw2hb6w3d6jw1kpk3y212n652qlglnlpryhp7gcckq26"))))
    (properties `((upstream-name . "SILFS")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-glmnet r-ckmeans-1d-dp))
    (home-page "https://cran.r-project.org/package=SILFS")
    (synopsis "Subgroup Identification with Latent Factor Structure")
    (description
     "In various domains, many datasets exhibit both high variable dependency and
group structures, which necessitates their simultaneous estimation.  This
package provides functions for two subgroup identification methods based on
penalized functions, both of which utilize factor model structures to adapt to
data with cross-sectional dependency.  The first method is the Subgroup
Identification with Latent Factor Structure Method (SILFSM) we proposed.  By
employing Center-Augmented Regularization and factor structures, the SILFSM
effectively eliminates data dependencies while identifying subgroups within
datasets.  For this model, we offer optimization functions based on two
different methods: Coordinate Descent and our newly developed Difference of
Convex-Alternating Direction Method of Multipliers (DC-ADMM) algorithms; the
latter can be applied to cases where the distance function in Center-Augmented
Regularization takes L1 and L2 forms.  The other method is the Factor-Adjusted
Pairwise Fusion Penalty (FA-PFP) model, which incorporates factor augmentation
into the Pairwise Fusion Penalty (PFP) developed by Ma, S. and Huang, J. (2017)
<doi:10.1080/01621459.2016.1148039>.  Additionally, we provide a function for
the Standard CAR (S-CAR) method, which does not consider the dependency and is
for comparative analysis with other approaches.  Furthermore, functions based on
the Bayesian Information Criterion (BIC) of the SILFSM and the FA-PFP method are
also included in SILFS for selecting tuning parameters.  For more details of
Subgroup Identification with Latent Factor Structure Method, please refer to He
et al. (2024) <doi:10.48550/@code{arXiv.2407.00882>}.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-siland
  (package
    (name "r-siland")
    (version "3.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "siland" version))
       (sha256
        (base32 "1dhjwl8akkziiax7a369rbxy8bmmnggnnd44v3zj1d2fqskskvdg"))))
    (properties `((upstream-name . "siland")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-sf
                             r-reshape2
                             r-raster
                             r-lme4
                             r-ggplot2
                             r-ggforce
                             r-fields
                             r-fasterize))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=siland")
    (synopsis "Spatial Influence of Landscape")
    (description
     "Method to estimate the spatial influence scales of landscape variables on a
response variable.  The method is based on Chandler and Hepinstall-Cymerman
(2016) Estimating the spatial scales of landscape effects on abundance,
Landscape ecology, 31: 1383-1394, <doi:10.1007/s10980-016-0380-z>.")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-siie
  (package
    (name "r-siie")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "siie" version))
       (sha256
        (base32 "1i095ys39q9p9bz12ia93k45ayl8cip956vh5wylw05m82vms2f0"))))
    (properties `((upstream-name . "siie")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=siie")
    (synopsis "Superior Identification Index and Its Extensions")
    (description
     "Calculate superior identification index and its extensions.  Measure the
performance of journals based on how well they could identify the top papers by
any index (e.g. citation indices) according to Huang & Yang. (2022)
<doi:10.1007/s11192-022-04372-z>.  These methods could be extended to evaluate
other entities such as institutes, countries, etc.")
    (license license:expat)))

(define-public r-sihr
  (package
    (name "r-sihr")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SIHR" version))
       (sha256
        (base32 "03i78kpp5gnq9v4b8rq6v0ndlsjfj2rr1m9gnl10v7z8j021qw1q"))))
    (properties `((upstream-name . "SIHR")))
    (build-system r-build-system)
    (propagated-inputs (list r-glmnet r-cvxr))
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "https://zywang0701.github.io/SIHR/")
    (synopsis "Statistical Inference in High Dimensional Regression")
    (description
     "The goal of SIHR is to provide inference procedures in the high-dimensional
generalized linear regression setting for: (1) linear functionals
<doi:10.48550/@code{arXiv.1904.12891>} <doi:10.48550/@code{arXiv.2012.07133>},
(2) conditional average treatment effects, (3) quadratic functionals
<doi:10.48550/@code{arXiv.1909.01503>}, (4) inner product, (5) distance.")
    (license license:gpl3)))

(define-public r-sigtree
  (package
    (name "r-sigtree")
    (version "1.10.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SigTree" version))
       (sha256
        (base32 "18gh7azjr979ijc2y4yyskj24ay697rw3j7znc5p4a63s4vpxr9w"))))
    (properties `((upstream-name . "SigTree")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-rcolorbrewer
                             r-phyloseq
                             r-phylobase
                             r-phyext2
                             r-mass
                             r-ape))
    (home-page "https://cran.r-project.org/package=SigTree")
    (synopsis
     "Identify and Visualize Significantly Responsive Branches in a Phylogenetic Tree")
    (description
     "This package provides tools to identify and visualize branches in a phylogenetic
tree that are significantly responsive to some intervention, taking as primary
inputs a phylogenetic tree (of class phylo) and a data frame (or matrix) of
corresponding tip (OTU) labels and p-values.")
    (license license:gpl3)))

(define-public r-sigr
  (package
    (name "r-sigr")
    (version "1.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sigr" version))
       (sha256
        (base32 "16v6f25pdbfm10b3cicf11nzqz5ykq70vppll7rxqbyafkcfwcra"))))
    (properties `((upstream-name . "sigr")))
    (build-system r-build-system)
    (propagated-inputs (list r-wrapr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/WinVector/sigr/")
    (synopsis "Succinct and Correct Statistical Summaries for Reports")
    (description
     "Succinctly and correctly format statistical summaries of various models and
tests (F-test, Chi-Sq-test, Fisher-test, T-test, and rank-significance).  This
package also includes empirical tests, such as Monte Carlo and bootstrap
distribution estimates.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-sigqc
  (package
    (name "r-sigqc")
    (version "0.1.24")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sigQC" version))
       (sha256
        (base32 "0z728hsicnx6j95y9q4gs9drv568bjqxckvgp08g3ahk5iymcym2"))))
    (properties `((upstream-name . "sigQC")))
    (build-system r-build-system)
    (propagated-inputs (list r-nnet
                             r-moments
                             r-mclust
                             r-mass
                             r-lattice
                             r-kernsmooth
                             r-gsva
                             r-gridgraphics
                             r-gplots
                             r-fmsb
                             r-complexheatmap
                             r-cluster
                             r-class
                             r-circlize
                             r-biclust))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=sigQC")
    (synopsis "Quality Control Metrics for Gene Signatures")
    (description
     "This package provides gene signature quality control metrics in publication
ready plots.  Namely, enables the visualization of properties such as
expression, variability, correlation, and comparison of methods of
standardisation and scoring metrics.")
    (license license:gpl3+)))

(define-public r-sigora
  (package
    (name "r-sigora")
    (version "3.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sigora" version))
       (sha256
        (base32 "11abbvynyhbb7y7zndbkivb13yxj8l86nrn361mfv495g1d3ba8s"))))
    (properties `((upstream-name . "sigora")))
    (build-system r-build-system)
    (propagated-inputs (list r-slam))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/wolski/sigora")
    (synopsis "Signature Overrepresentation Analysis")
    (description
     "Pathway Analysis is statistically linking observations on the molecular level to
biological processes or pathways on the systems(i.e., organism, organ, tissue,
cell) level.  Traditionally, pathway analysis methods regard pathways as
collections of single genes and treat all genes in a pathway as equally
informative.  However, this can lead to identifying spurious pathways as
statistically significant since components are often shared amongst pathways.
SIGORA seeks to avoid this pitfall by focusing on genes or gene pairs that are
(as a combination) specific to a single pathway.  In relying on such pathway
gene-pair signatures (Pathway-GPS), SIGORA inherently uses the status of other
genes in the experimental context to identify the most relevant pathways.  The
current version allows for pathway analysis of human and mouse datasets.  In
addition, it contains pre-computed Pathway-GPS data for pathways in the KEGG and
Reactome pathway repositories and mechanisms for extracting GPS for
user-supplied repositories.")
    (license license:gpl3)))

(define-public r-sigoptr
  (package
    (name "r-sigoptr")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SigOptR" version))
       (sha256
        (base32 "1sylchhhz6kx4r8jx95cvsmjsacjh8pin7acf2fyw2a4nlx9r1a6"))))
    (properties `((upstream-name . "SigOptR")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-httr))
    (home-page "https://cran.r-project.org/package=SigOptR")
    (synopsis "R API Wrapper for SigOpt")
    (description
     "Interfaces with the @code{SigOpt} API. More info at <https://sigopt.com>.")
    (license license:expat)))

(define-public r-signs
  (package
    (name "r-signs")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "signs" version))
       (sha256
        (base32 "0dzf5pydvs93rvj6idw9l2asv1bxwz20df7qis2db84hfww2kc6m"))))
    (properties `((upstream-name . "signs")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-rlang))
    (native-inputs (list r-knitr))
    (home-page "https://benjaminwolfe.github.io/signs")
    (synopsis "Insert Proper Minus Signs")
    (description
     "This package provides convenience functions to replace hyphen-minuses (ASCII 45)
with proper minus signs (Unicode character 2212).  The true minus matches the
plus symbol in width, line thickness, and height above the baseline.  It was
designed for mathematics, looks better in presentation, and is understood
properly by screen readers.")
    (license license:expat)))

(define-public r-signnet
  (package
    (name "r-signnet")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "signnet" version))
       (sha256
        (base32 "04w8wik8q15hi7yc9rp30bc31pvw15bkrvr3lnif46zrrhsq6cv8"))))
    (properties `((upstream-name . "signnet")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-matrix r-igraph))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/schochastics/signnet")
    (synopsis "Methods to Analyse Signed Networks")
    (description
     "This package provides methods for the analysis of signed networks.  This
includes several measures for structural balance as introduced by Cartwright and
Harary (1956) <doi:10.1037/h0046049>, blockmodeling algorithms from Doreian
(2008) <doi:10.1016/j.socnet.2008.03.005>, various centrality indices, and
projections of signed two-mode networks introduced by Schoch (2020)
<doi:10.1080/0022250X.2019.1711376>.")
    (license license:expat)))

(define-public r-signifreg
  (package
    (name "r-signifreg")
    (version "4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SignifReg" version))
       (sha256
        (base32 "1f57zbc7746jr2hfgri29whw5nb7w8h9nwk2p2n851b1rf7q0mgp"))))
    (properties `((upstream-name . "SignifReg")))
    (build-system r-build-system)
    (propagated-inputs (list r-car))
    (home-page "https://cran.r-project.org/package=SignifReg")
    (synopsis
     "Consistent Significance Controlled Variable Selection in Generalized Linear Regression")
    (description
     "This package provides significance controlled variable selection algorithms with
different directions (forward, backward, stepwise) based on diverse criteria
(AIC, BIC, adjusted r-square, PRESS, or p-value).  The algorithm selects a final
model with only significant variables defined as those with significant p-values
after multiple testing correction such as Bonferroni, False Discovery Rate, etc.
 See Zambom and Kim (2018) <doi:10.1002/sta4.210>.")
    (license license:gpl2+)))

(define-public r-signibox
  (package
    (name "r-signibox")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "signibox" version))
       (sha256
        (base32 "0yjdpcyjdlmr6brb3a21wcs5rxryvjsnkhl8v73g2cygbhqba8rr"))))
    (properties `((upstream-name . "signibox")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=signibox")
    (synopsis "Statistical Significance Marks on Boxplots")
    (description
     "Add significance marks to any R Boxplot, including a given significance niveau.")
    (license license:gpl2+)))

(define-public r-signed-backbones
  (package
    (name "r-signed-backbones")
    (version "0.91.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "signed.backbones" version))
       (sha256
        (base32 "0xk8sncblzihqzqfpaq7158slvrb6ww3dc4v5di1m2bqwhykncwi"))))
    (properties `((upstream-name . "signed.backbones")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2))
    (home-page "https://cran.r-project.org/package=signed.backbones")
    (synopsis "Extract the Signed Backbones of Weighted Networks")
    (description
     "Extract the signed backbones of intrinsically dense weighted networks based on
the significance filter and vigor filter as described in the following paper.
Please cite it if you find this software useful in your work.  Furkan Gursoy and
Bertan Badur. \"Extracting the signed backbone of intrinsically dense weighted
networks.\" Journal of Complex Networks. <@code{arXiv:2012.05216>}.")
    (license license:gpl3)))

(define-public r-signaturesurvival
  (package
    (name "r-signaturesurvival")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "signatureSurvival" version))
       (sha256
        (base32 "0c3wjhpsswi1l76m4nlh9xfi27g1d18c960q31lwd7b659w0x1fp"))))
    (properties `((upstream-name . "signatureSurvival")))
    (build-system r-build-system)
    (propagated-inputs (list r-survminer
                             r-survival
                             r-gtools
                             r-gplots
                             r-ggplot2
                             r-forestplot
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=signatureSurvival")
    (synopsis "Signature Survival Analysis")
    (description
     "When multiple Cox proportional hazard models are performed on clinical data
(month or year and status) and a set of differential expressions of genes, the
results (Hazard risks, z-scores and p-values) can be used to create
gene-expression signatures.  Weights are calculated using the survival p-values
of genes and are utilized to calculate expression values of the signature across
the selected genes in all patients in a cohort.  A Single or multiple univariate
or multivariate Cox proportional hazard survival analyses of the patients in one
cohort can be performed by using the gene-expression signature and visualized
using our survival plots.")
    (license license:gpl3+)))

(define-public r-signalhsmm
  (package
    (name "r-signalhsmm")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "signalHsmm" version))
       (sha256
        (base32 "1d4v6cbvcbi8pb5zmn58s01h3jb0kv7yr7qvcnx4p2v82knvhik7"))))
    (properties `((upstream-name . "signalHsmm")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-seqinr r-rcpp))
    (home-page "https://github.com/michbur/signalhsmm")
    (synopsis "Predict Presence of Signal Peptides")
    (description
     "Predicts the presence of signal peptides in eukaryotic protein using hidden
semi-Markov models.  The implemented algorithm can be accessed from both the
command line and GUI.")
    (license license:gpl3)))

(define-public r-signacx
  (package
    (name "r-signacx")
    (version "2.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SignacX" version))
       (sha256
        (base32 "0b38k3lxca985mqwvbbdczkjnn2x79kga05yw995gb6d20cjh96v"))))
    (properties `((upstream-name . "SignacX")))
    (build-system r-build-system)
    (propagated-inputs (list r-seurat
                             r-rjsonio
                             r-rcolorbrewer
                             r-pbmcapply
                             r-neuralnet
                             r-matrix
                             r-lme4
                             r-jsonlite
                             r-igraph))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mathewchamberlain/SignacX")
    (synopsis
     "Cell Type Identification and Discovery from Single Cell Gene Expression Data")
    (description
     "An implementation of neural networks trained with flow-sorted gene expression
data to classify cellular phenotypes in single cell RNA-sequencing data.  See
Chamberlain M et al. (2021) <doi:10.1101/2021.02.01.429207> for more details.")
    (license license:gpl3)))

(define-public r-sign
  (package
    (name "r-sign")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SIGN" version))
       (sha256
        (base32 "1ak4zv5a50iknrxpfw1iffn8gv8mc0rz2gk96nzky9zx6li5drhk"))))
    (properties `((upstream-name . "SIGN")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-survcomp r-gsva))
    (home-page "https://cran.r-project.org/package=SIGN")
    (synopsis "Similarity Identification in Gene Expression")
    (description
     "This package provides a classification framework to use expression patterns of
pathways as features to identify similarity between biological samples.  It
provides a new measure for quantifying similarity between expression patterns of
pathways.")
    (license license:gpl3+)))

(define-public r-sigmoid
  (package
    (name "r-sigmoid")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sigmoid" version))
       (sha256
        (base32 "067ygikaaniikig1ypd6ry187981xar7i539917zf0avszshisxq"))))
    (properties `((upstream-name . "sigmoid")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=sigmoid")
    (synopsis "Sigmoid Functions for Machine Learning")
    (description
     "Several different sigmoid functions are implemented, including a wrapper
function, @code{SoftMax} preprocessing and inverse functions.")
    (license license:gpl3)))

(define-public r-sigminer
  (package
    (name "r-sigminer")
    (version "2.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sigminer" version))
       (sha256
        (base32 "0fwmq6p6qyz5pq1vhh26ddrivcqaqnfv1bsnhzfplz1glmv6r0xj"))))
    (properties `((upstream-name . "sigminer")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rlang
                             r-rcpp
                             r-purrr
                             r-nmf
                             r-magrittr
                             r-maftools
                             r-ggpubr
                             r-ggplot2
                             r-future
                             r-furrr
                             r-dplyr
                             r-data-table
                             r-cowplot
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ShixiangWang/sigminer")
    (synopsis
     "Extract, Analyze and Visualize Mutational Signatures for Genomic Variations")
    (description
     "Genomic alterations including single nucleotide substitution, copy number
alteration, etc.  are the major force for cancer initialization and development.
 Due to the specificity of molecular lesions caused by genomic alterations, we
can generate characteristic alteration spectra, called signature (Wang,
Shixiang, et al. (2021) <DOI:10.1371/journal.pgen.1009557> & Alexandrov, Ludmil
B., et al. (2020) <DOI:10.1038/s41586-020-1943-3> & Steele Christopher D., et
al. (2022) <DOI:10.1038/s41586-022-04738-6>).  This package helps users to
extract, analyze and visualize signatures from genomic alteration records, thus
providing new insight into cancer study.")
    (license license:expat)))

(define-public r-sigmajs
  (package
    (name "r-sigmajs")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sigmajs" version))
       (sha256
        (base32 "1d26llpgnj687b8rwgbyhw88zakp334n4a9gabf72qwrg4awm1v5"))))
    (properties `((upstream-name . "sigmajs")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-shiny
                             r-scales
                             r-purrr
                             r-magrittr
                             r-jsonlite
                             r-igraph
                             r-htmlwidgets
                             r-htmltools
                             r-dplyr
                             r-crosstalk))
    (native-inputs (list esbuild))
    (home-page "http://sigmajs.john-coene.com/")
    (synopsis "Interface to 'Sigma.js' Graph Visualization Library")
    (description
     "Interface to sigma.js graph visualization library including animations, plugins
and shiny proxies.")
    (license license:expat)))

(define-public r-sigint
  (package
    (name "r-sigint")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sigInt" version))
       (sha256
        (base32 "1y3qbfsynssam05q4947zqd0dkrpcnq5pla409i3xy511wv9702d"))))
    (properties `((upstream-name . "sigInt")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-stringr
                             r-randomforest
                             r-pbivnorm
                             r-maxlik
                             r-mass
                             r-formula))
    (home-page "https://github.com/ccrismancox/sigint")
    (synopsis "Estimate the Parameters of a Discrete Crisis-Bargaining Game")
    (description
     "This package provides pseudo-likelihood methods for empirically analyzing common
signaling games in international relations as described in Crisman-Cox and
Gibilisco (2019) <doi:10.1017/psrm.2019.58>.")
    (license license:gpl3+)))

(define-public r-sightabilitymodel
  (package
    (name "r-sightabilitymodel")
    (version "1.5.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SightabilityModel" version))
       (sha256
        (base32 "1fj871q0mav3dffm3sjg4zsci0lm8z74nfgbbrv6jzab155vfrmc"))))
    (properties `((upstream-name . "SightabilityModel")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey
                             r-plyr
                             r-mvtnorm
                             r-msm
                             r-matrix
                             r-formula-tools))
    (native-inputs (list r-rmarkdown r-r-rsp r-knitr))
    (home-page "https://github.com/jfieberg/SightabilityModel")
    (synopsis "Wildlife Sightability Modeling")
    (description
     "Uses logistic regression to model the probability of detection as a function of
covariates.  This model is then used with observational survey data to estimate
population size, while accounting for uncertain detection.  See Steinhorst and
Samuel (1989).")
    (license license:gpl2)))

(define-public r-sigclust
  (package
    (name "r-sigclust")
    (version "1.1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sigclust" version))
       (sha256
        (base32 "1q3jdalm7cbsl78gssl3jsifh56wyczrlx2ya4p74r4fb6sv9kga"))))
    (properties `((upstream-name . "sigclust")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sigclust")
    (synopsis "Statistical Significance of Clustering")
    (description
     "@code{SigClust} is a statistical method for testing the significance of
clustering results. @code{SigClust} can be applied to assess the statistical
significance of splitting a data set into two clusters.  For more than two
clusters, @code{SigClust} can be used iteratively.")
    (license license:gpl2+)))

(define-public r-sig
  (package
    (name "r-sig")
    (version "0.0-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sig" version))
       (sha256
        (base32 "02narc84by24rldna6qi8vyygxfarhbaddv1kdmb1ywhbsq86597"))))
    (properties `((upstream-name . "sig")))
    (build-system r-build-system)
    (home-page "https://bitbucket.org/richierocks/sig")
    (synopsis "Print Function Signatures")
    (description "Print function signatures and find overly complicated code.")
    (license (license:fsdg-compatible "Unlimited"))))

(define-public r-sift
  (package
    (name "r-sift")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sift" version))
       (sha256
        (base32 "030vv2rha5d20045bwsp6ihdcgiw9glnchyahpzwym3m6fnkg0y5"))))
    (properties `((upstream-name . "sift")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-pastecs
                             r-glue
                             r-dplyr
                             r-cpp11))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=sift")
    (synopsis "Intelligently Peruse Data")
    (description
     "Facilitate extraction of key information from common datasets.")
    (license license:expat)))

(define-public r-sievetest
  (package
    (name "r-sievetest")
    (version "1.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sievetest" version))
       (sha256
        (base32 "17qrm7axvlr2yzp8brbibp26f8qdpqm85hc2kjzxm5zqwfvd6dfs"))))
    (properties `((upstream-name . "sievetest")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sievetest")
    (synopsis "Laboratory Sieve Test Reporting Functions")
    (description
     "This package provides functions for making particle-size analysis.  Sieve tests
are widely used to obtain particle-size distribution of powders or granular
materials.")
    (license license:gpl2+)))

(define-public r-sieveph
  (package
    (name "r-sieveph")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sievePH" version))
       (sha256
        (base32 "0bzf7k3qk8ih9qvkr196wcy8b6wq5q6x6cn311z312713n9jly10"))))
    (properties `((upstream-name . "sievePH")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-scales
                             r-rcpparmadillo
                             r-rcpp
                             r-plyr
                             r-np
                             r-ggpubr
                             r-ggplot2))
    (home-page "https://github.com/mjuraska/sievePH")
    (synopsis "Sieve Analysis Methods for Proportional Hazards Models")
    (description
     "This package implements a suite of semiparametric and nonparametric
kernel-smoothed estimation and testing procedures for continuous mark-specific
stratified hazard ratio (treatment/placebo) models in a randomized treatment
efficacy trial with a time-to-event endpoint.  Semiparametric methods, allowing
multivariate marks, are described in Juraska M and Gilbert PB (2013),
Mark-specific hazard ratio model with multivariate continuous marks: an
application to vaccine efficacy.  Biometrics 69(2):328-337
<doi:10.1111/biom.12016>, and in Juraska M and Gilbert PB (2016), Mark-specific
hazard ratio model with missing multivariate marks.  Lifetime Data Analysis
22(4):606-25 <doi:10.1007/s10985-015-9353-9>.  Nonparametric kernel-smoothed
methods, allowing univariate marks only, are described in Sun Y and Gilbert PB
(2012), Estimation of stratified markâspecific proportional hazards models
with missing marks.  Scandinavian Journal of Statistics}, 39(1):34-52
<doi:10.1111/j.1467-9469.2011.00746.x>, and in Gilbert PB and Sun Y (2015),
Inferences on relative failure rates in stratified mark-specific proportional
hazards models with missing marks, with application to human immunodeficiency
virus vaccine efficacy trials.  Journal of the Royal Statistical Society Series
C: Applied Statistics, 64(1):49-73 <doi:10.1111/rssc.12067>.  Both
semiparametric and nonparametric approaches consider two scenarios: (1) the mark
is fully observed in all subjects who experience the event of interest, and (2)
the mark is subject to missingness-at-random in subjects who experience the
event of interest.  For models with missing marks, estimators are implemented
based on (i) inverse probability weighting (IPW) of complete cases (for the
semiparametric framework), and (ii) augmentation of the IPW estimating functions
by leveraging correlations between the mark and auxiliary data to impute the
augmentation term for subjects with missing marks (for both the semiparametric
and nonparametric framework).  The augmented IPW estimators are doubly robust
and recommended for use with incomplete mark data.  The semiparametric methods
make two key assumptions: (i) the time-to-event is assumed to be conditionally
independent of the mark given treatment, and (ii) the weight function in the
semiparametric density ratio/biased sampling model is assumed to be exponential.
 Diagnostic testing procedures for evaluating validity of both assumptions are
implemented.  Summary and plotting functions are provided for estimation and
inferential results.")
    (license license:gpl2)))

(define-public r-sieve
  (package
    (name "r-sieve")
    (version "2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Sieve" version))
       (sha256
        (base32 "1smsr073zl91jxdrawy12rn2ygs2zhj79npb20c98zs7npp5yrj6"))))
    (properties `((upstream-name . "Sieve")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mass r-glmnet r-combinat))
    (home-page "https://cran.r-project.org/package=Sieve")
    (synopsis "Nonparametric Estimation by the Method of Sieves")
    (description
     "This package performs multivariate nonparametric regression/classification by
the method of sieves (using orthogonal basis).  The method is suitable for
moderate high-dimensional features (dimension < 100).  The l1-penalized sieve
estimator, a nonparametric generalization of Lasso, is adaptive to the feature
dimension with provable theoretical guarantees.  We also include a nonparametric
stochastic gradient descent estimator, Sieve-SGD, for online or large scale
batch problems.  Details of the methods can be found in:
<@code{arXiv:2206.02994>} <@code{arXiv:2104.00846><arXiv:2310.12140>}.")
    (license license:gpl2)))

(define-public r-sier
  (package
    (name "r-sier")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SiER" version))
       (sha256
        (base32 "1ng6vnh30z6z1nvclsdzc9gdbrsrb2kqdqvs6fwm0j1zqlk12x0y"))))
    (properties `((upstream-name . "SiER")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SiER")
    (synopsis
     "Signal Extraction Approach for Sparse Multivariate Response Regression")
    (description
     "This package provides methods for regression with high-dimensional predictors
and univariate or maltivariate response variables.  It considers the
decomposition of the coefficient matrix that leads to the best approximation to
the signal part in the response given any rank, and estimates the decomposition
by solving a penalized generalized eigenvalue problem followed by a least
squares procedure.  Ruiyan Luo and Xin Qi (2017)
<doi:10.1016/j.jmva.2016.09.005>.")
    (license license:gpl2)))

(define-public r-siebanxicor
  (package
    (name "r-siebanxicor")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "siebanxicor" version))
       (sha256
        (base32 "0bnpqqhqvqq9kxvzljzp4d5aw9drh78cwyxyqzdvshijl7mmqjwc"))))
    (properties `((upstream-name . "siebanxicor")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-httr))
    (home-page "https://cran.r-project.org/package=siebanxicor")
    (synopsis "Query Data Series from Bank of Mexico")
    (description
     "Allows to retrieve time series of all indicators available in the Bank of
Mexico's Economic Information System
(<http://www.banxico.org.mx/@code{SieInternet/>}).")
    (license license:expat)))

(define-public r-sie2nts
  (package
    (name "r-sie2nts")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Sie2nts" version))
       (sha256
        (base32 "14s13v5y33kjgd779wrdzy985sp9qg0a0rh5qx66qs9gmff6r663"))))
    (properties `((upstream-name . "Sie2nts")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rcurl r-plotly r-matrix r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=Sie2nts")
    (synopsis "Sieve Methods for Non-Stationary Time Series")
    (description
     "We provide functions for estimation and inference of locally-stationary time
series using the sieve methods and bootstrapping procedure.  In addition, it
also contains functions to generate Daubechies and Coiflet wavelet by Cascade
algorithm and to process data visualization.")
    (license license:expat)))

(define-public r-sidrar
  (package
    (name "r-sidrar")
    (version "0.2.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sidrar" version))
       (sha256
        (base32 "1q3imj1nfjrgs9hxwwzf9bq7pfcyphjj8967mmkwgdyakkpxrzz3"))))
    (properties `((upstream-name . "sidrar")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-stringr
                             r-rvest
                             r-rjson
                             r-magrittr
                             r-httr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rpradosiqueira/sidrar/")
    (synopsis "An Interface to IBGE's SIDRA API")
    (description
     "Allows the user to connect with IBGE's (Instituto Brasileiro de Geografia e
Estatistica, see <https://www.ibge.gov.br/> for more information) SIDRA API in a
flexible way.  SIDRA is the acronym to \"Sistema IBGE de Recuperacao Automatica\"
and is the system where IBGE turns available aggregate data from their
researches.")
    (license license:gpl3)))

(define-public r-sidier
  (package
    (name "r-sidier")
    (version "4.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sidier" version))
       (sha256
        (base32 "0zayhvhaq0q00rpgy0z01rhf3lcc3791klc8b30pdj627cl9xj01"))))
    (properties `((upstream-name . "sidier")))
    (build-system r-build-system)
    (propagated-inputs (list r-network
                             r-igraph
                             r-gridbase
                             r-ggplot2
                             r-ggmap
                             r-ape))
    (home-page "https://cran.r-project.org/package=sidier")
    (synopsis
     "Substitution and Indel Distances to Infer Evolutionary Relationships")
    (description
     "Evolutionary reconstruction based on substitutions and insertion-deletion
(indels) analyses in a distance-based framework.")
    (license license:gpl2)))

(define-public r-sides
  (package
    (name "r-sides")
    (version "1.18")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SIDES" version))
       (sha256
        (base32 "12a87kdbdhqqjjs0k99c1fv0mxi0gjhvq71rm9wvx3wkf0s5p5sv"))))
    (properties `((upstream-name . "SIDES")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-nnet
                             r-multicool
                             r-memoise
                             r-mass
                             r-foreach
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=SIDES")
    (synopsis "Subgroup Identification Based on Differential Effect Search")
    (description
     "This package provides function to apply \"Subgroup Identification based on
Differential Effect Search\" (SIDES) method proposed by Lipkovich et al. (2011)
<doi:10.1002/sim.4289>.")
    (license license:gpl3)))

(define-public r-sid
  (package
    (name "r-sid")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SID" version))
       (sha256
        (base32 "0wqbv9mlrcf0kjdd8n43vf7fjlhp2nialaaid5dfjsxnlzbf1sl6"))))
    (properties `((upstream-name . "SID")))
    (build-system r-build-system)
    (propagated-inputs (list r-rbgl r-pcalg r-matrix r-igraph))
    (home-page "https://github.com/fkgruber/SID_cran")
    (synopsis "Structural Intervention Distance")
    (description
     "The code computes the structural intervention distance (SID) between a true
directed acyclic graph (DAG) and an estimated DAG. Definition and details about
the implementation can be found in J. Peters and P. BÃ¼hlmann: \"Structural
intervention distance (SID) for evaluating causal graphs\", Neural Computation
27, pages 771-799, 2015 <doi:10.1162/NECO_a_00708>.")
    (license (license:fsdg-compatible "FreeBSD"))))

(define-public r-siconvr
  (package
    (name "r-siconvr")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "siconvr" version))
       (sha256
        (base32 "178bqjdx96lk8npa26w29aanal7g6x22b6p1p8gsbyi6h8acgkh0"))))
    (properties `((upstream-name . "siconvr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-magrittr
                             r-httr
                             r-dplyr
                             r-data-table
                             r-cli))
    (home-page "https://github.com/meirelesff/siconvr")
    (synopsis "Fetch Data from Plataforma +Brasil (SICONV)")
    (description
     "Fetch data on targeted public investments from Plataforma +Brasil (SICONV)
<http://plataformamaisbrasil.gov.br/>, the responsible system for requests,
execution, and monitoring of federal discretionary transfers in Brazil.")
    (license license:gpl3+)))

(define-public r-sicegar
  (package
    (name "r-sicegar")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sicegar" version))
       (sha256
        (base32 "14xv3rir65fmrx89gjxskk32ldp5zz8m8jv5ybgh5jrwm3x1k0fb"))))
    (properties `((upstream-name . "sicegar")))
    (build-system r-build-system)
    (propagated-inputs (list r-minpack-lm r-ggplot2 r-fbasics r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/wilkelab/sicegar")
    (synopsis "Analysis of Single-Cell Viral Growth Curves")
    (description
     "Aims to quantify time intensity data by using sigmoidal and double sigmoidal
curves.  It fits straight lines, sigmoidal, and double sigmoidal curves on to
time vs intensity data.  Then all the fits are used to make decision on which
model best describes the data.  This method was first developed in the context
of single-cell viral growth analysis (for details, see Caglar et al. (2018)
<doi:10.7717/peerj.4251>), and the package name stands for \"SIngle CEll Growth
Analysis in R\".")
    (license (list license:gpl2 license:gpl3))))

(define-public r-siberg
  (package
    (name "r-siberg")
    (version "2.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SIBERG" version))
       (sha256
        (base32 "1d8kx5m86qil50d8fhwn7lwaigdnppzk0cwyh4hv54lwffpclnki"))))
    (properties `((upstream-name . "SIBERG")))
    (build-system r-build-system)
    (propagated-inputs (list r-mclust))
    (home-page "http://oompa.r-forge.r-project.org/")
    (synopsis
     "Systematic Identification of Bimodally Expressed Genes Using RNAseq Data")
    (description
     "This package provides models to identify bimodally expressed genes from RNAseq
data based on the Bimodality Index.  SIBERG models the RNAseq data in the finite
mixture modeling framework and incorporates mechanisms for dealing with RNAseq
normalization.  Three types of mixture models are implemented, namely, the
mixture of log normal, negative binomial, or generalized Poisson distribution.
See Tong et al. (2013) <doi:10.1093/bioinformatics/bts713>.")
    (license license:asl2.0)))

(define-public r-siber
  (package
    (name "r-siber")
    (version "2.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SIBER" version))
       (sha256
        (base32 "00sr2h0fry102z2jdzsf20jav04gfskdcznzfpkjr4y5h4vv62cb"))))
    (properties `((upstream-name . "SIBER")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-tidyr
                             r-spatstat-utils
                             r-spatstat-geom
                             r-rjags
                             r-purrr
                             r-mnormt
                             r-magrittr
                             r-hdrcde
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SIBER")
    (synopsis "Stable Isotope Bayesian Ellipses in R")
    (description
     "Fits bi-variate ellipses to stable isotope data using Bayesian inference with
the aim being to describe and compare their isotopic niche.")
    (license license:gpl2+)))

(define-public r-siatools
  (package
    (name "r-siatools")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SIAtools" version))
       (sha256
        (base32 "17gw4qz9k6m7wys9ik37b72x554ig240cb3ppbbihwryy3wzygw3"))))
    (properties `((upstream-name . "SIAtools")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-usethis
                             r-shiny
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-fs
                             r-desc
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://applstat.github.io/SIAtools/")
    (synopsis "'ShinyItemAnalysis' Modules Development Toolkit")
    (description
     "This package provides a comprehensive suite of functions designed for
constructing and managing @code{ShinyItemAnalysis} modules, supplemented with
detailed guides, ready-to-use templates, linters, and tests.  This package
allows developers to seamlessly create and integrate one or more modules into
their existing packages or to start a new module project from scratch.")
    (license license:gpl3+)))

(define-public r-siamodules
  (package
    (name "r-siamodules")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SIAmodules" version))
       (sha256
        (base32 "1x6a1apscxdrqlh0dnz9slrfiz4vhxx32rp32lfg27sc1awlx6xy"))))
    (properties `((upstream-name . "SIAmodules")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-shinyjs
                             r-shinyitemanalysis
                             r-shiny
                             r-scales
                             r-rlang
                             r-purrr
                             r-plotly
                             r-mirtcat
                             r-mirt
                             r-magrittr
                             r-lme4
                             r-glue
                             r-ggplot2
                             r-forcats
                             r-dt
                             r-dplyr
                             r-difnlr
                             r-cli))
    (home-page "https://www.ShinyItemAnalysis.org")
    (synopsis "Modules for 'ShinyItemAnalysis'")
    (description
     "Package including additional modules for interactive @code{ShinyItemAnalysis}
application for the psychometric analysis of educational tests, psychological
assessments, health-related and other types of multi-item measurements, or
ratings from multiple raters.")
    (license license:gpl3)))

(define-public r-si
  (package
    (name "r-si")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SI" version))
       (sha256
        (base32 "0i6kpaw5yk39skm77nf56ai25clkparz3l8qx0223jrmdqbf97b7"))))
    (properties `((upstream-name . "SI")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SI")
    (synopsis "Stochastic Integrating")
    (description
     "An implementation of four stochastic methods of integrating in R, including: 1.
Stochastic Point Method (or Monte Carlo Method); 2.  Mean Value Method; 3.
Important Sampling Method; 4.  Stratified Sampling Method.  It can be used to
estimate one-dimension or multi-dimension integration by Monte Carlo methods.
And the estimated variance (precision) is given.  Reference: Caflisch, R. E.
(1998) <doi:10.1017/S0962492900002804>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-shutterplot
  (package
    (name "r-shutterplot")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shutterplot" version))
       (sha256
        (base32 "129jv20m0dinb07lwiz9fxv4z7sxshcmjab2g0p5vrv8jlp9a0yv"))))
    (properties `((upstream-name . "shutterplot")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=shutterplot")
    (synopsis "The R Shutter Plot Package")
    (description
     "Shows the scatter plot along with the fitted regression lines.  It depicts min,
max, the three quartiles, mean, and sd for each variable.  It also depicts
sd-line, sd-box, r, r-square, prediction boundaries, and regression outliers.")
    (license license:gpl3)))

(define-public r-shuffleci
  (package
    (name "r-shuffleci")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shuffleCI" version))
       (sha256
        (base32 "1n9k28m1fcnw1my9a7v0gy9waygd48lab45vj9k2wwijbc325zai"))))
    (properties `((upstream-name . "shuffleCI")))
    (build-system r-build-system)
    (propagated-inputs (list r-plotrix))
    (home-page "https://cran.r-project.org/package=shuffleCI")
    (synopsis "Confidence Intervals Compared via Shuffling")
    (description
     "Scripts and exercises that use card shuffling to teach confidence interval
comparisons for different estimators.")
    (license license:gpl3)))

(define-public r-shuffle
  (package
    (name "r-shuffle")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shuffle" version))
       (sha256
        (base32 "0aqb11h340picx7z2jg3k46l3pr6fkknlpyzc0z89hhrc90pss48"))))
    (properties `((upstream-name . "shuffle")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=shuffle")
    (synopsis "The Shuffle Estimator for Explainable Variance")
    (description
     "Implementation of the shuffle estimator, a non-parametric estimator for signal
and noise variance under mild noise correlations.")
    (license license:gpl2+)))

(define-public r-sht
  (package
    (name "r-sht")
    (version "0.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SHT" version))
       (sha256
        (base32 "03mzjm0hx1i8jz7gam19zyr5w6whwk7a3gfid68h5hj8x60p04wx"))))
    (properties `((upstream-name . "SHT")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-rcpparmadillo r-rcpp r-pracma r-flare))
    (home-page "https://www.kisungyou.com/SHT/")
    (synopsis "Statistical Hypothesis Testing Toolbox")
    (description
     "We provide a collection of statistical hypothesis testing procedures ranging
from classical to modern methods for non-trivial settings such as
high-dimensional scenario.  For the general treatment of statistical hypothesis
testing, see the book by Lehmann and Romano (2005) <doi:10.1007/0-387-27605-X>.")
    (license license:expat)))

(define-public r-shroomdk
  (package
    (name "r-shroomdk")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shroomDK" version))
       (sha256
        (base32 "19i06kwk1sj5x73w4ajxkn3wpiq6qxjvg3jgv7v048m0fp70i4dn"))))
    (properties `((upstream-name . "shroomDK")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-httr))
    (home-page "https://cran.r-project.org/package=shroomDK")
    (synopsis "Accessing the Flipside Crypto ShroomDK API")
    (description
     "Programmatic access to Flipside Crypto data via the Compass RPC API:
<https://api-docs.flipsidecrypto.xyz/>.  As simple as auto_paginate_query() but
with core functions as needed for troubleshooting.  Note, 0.1.1 support
deprecated 2023-05-31.")
    (license license:expat)))

(define-public r-shrinktvpvar
  (package
    (name "r-shrinktvpvar")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shrinkTVPVAR" version))
       (sha256
        (base32 "0938fmh45nnvvkpxskqrzgkv25yghzy8pq8313l8q50xxpskp1rm"))))
    (properties `((upstream-name . "shrinkTVPVAR")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-stochvol
                             r-shrinktvp
                             r-rcppprogress
                             r-rcpparmadillo
                             r-rcpp
                             r-rcolorbrewer
                             r-lattice
                             r-coda))
    (home-page "https://cran.r-project.org/package=shrinkTVPVAR")
    (synopsis
     "Efficient Bayesian Inference for TVP-VAR-SV Models with Shrinkage")
    (description
     "Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian
estimation of time-varying parameter vector autoregressive models with shrinkage
priors.  Details on the algorithms used are provided in Cadonna et al. (2020)
<doi:10.3390/econometrics8020020> and Knaus et al. (2021)
<doi:10.18637/jss.v100.i13>.")
    (license license:gpl2+)))

(define-public r-shrinktvp
  (package
    (name "r-shrinktvp")
    (version "3.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shrinkTVP" version))
       (sha256
        (base32 "0fcvx7pz4mn89g9g8x32wgqil9xyh29vjxx8hlvv5385ig2wd4lw"))))
    (properties `((upstream-name . "shrinkTVP")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-stochvol
                             r-rcppprogress
                             r-rcppgsl
                             r-rcpparmadillo
                             r-rcpp
                             r-gigrvg
                             r-coda))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=shrinkTVP")
    (synopsis
     "Efficient Bayesian Inference for Time-Varying Parameter Models with Shrinkage")
    (description
     "Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian
estimation of time-varying parameter models with shrinkage priors, both dynamic
and static.  Details on the algorithms used are provided in Bitto and
FrÃ¼hwirth-Schnatter (2019) <doi:10.1016/j.jeconom.2018.11.006> and Cadonna et
al. (2020) <doi:10.3390/econometrics8020020> and Knaus and FrÃ¼hwirth-Schnatter
(2023) <doi:10.48550/@code{arXiv.2312.10487>}.  For details on the package,
please see Knaus et al. (2021) <doi:10.18637/jss.v100.i13>.")
    (license license:gpl2+)))

(define-public r-shrinkem
  (package
    (name "r-shrinkem")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shrinkem" version))
       (sha256
        (base32 "0frnmri4ycmfjj6375iwjxldfcspfyhfvr1wyaxpqcxqa0c7gdxh"))))
    (properties `((upstream-name . "shrinkem")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-matrixcalc r-extradistr r-cholwishart
                             r-brms))
    (home-page "https://cran.r-project.org/package=shrinkem")
    (synopsis "Approximate Bayesian Regularization for Parsimonious Estimates")
    (description
     "Approximate Bayesian regularization using Gaussian approximations.  The input is
a vector of estimates and a Gaussian error covariance matrix of the key
parameters.  Bayesian shrinkage is then applied to obtain parsimonious
solutions.  The method is described on Karimova, van Erp, Leenders, and Mulder
(2024) <DOI:10.31234/osf.io/2g8qm>.  Gibbs samplers are used for model fitting.
The shrinkage priors that are supported are Gaussian (ridge) priors, Laplace
(lasso) priors (Park and Casella, 2008 <DOI:10.1198/016214508000000337>), and
horseshoe priors (Carvalho, et al., 2010; <DOI:10.1093/biomet/asq017>).  These
priors include an option for grouped regularization of different subsets of
parameters (Meier et al., 2008; <DOI:10.1111/j.1467-9868.2007.00627.x>).  F
priors are used for the penalty parameters lambda^2 (Mulder and Pericchi, 2018
<DOI:10.1214/17-BA1092>).  This correspond to half-Cauchy priors on lambda
(Carvalho, Polson, Scott, 2010 <DOI:10.1093/biomet/asq017>).")
    (license license:gpl3+)))

(define-public r-shrinkdsm
  (package
    (name "r-shrinkdsm")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shrinkDSM" version))
       (sha256
        (base32 "0vl3cl9njl0irz9hsli7xkf49jawkywww91j5gxlmz5vdrxnwz9c"))))
    (properties `((upstream-name . "shrinkDSM")))
    (build-system r-build-system)
    (propagated-inputs (list r-stochvol
                             r-shrinktvp
                             r-rcppprogress
                             r-rcpparmadillo
                             r-rcpp
                             r-coda))
    (home-page "https://cran.r-project.org/package=shrinkDSM")
    (synopsis
     "Efficient Bayesian Inference for Dynamic Survival Models with Shrinkage")
    (description
     "Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian
estimation of dynamic survival models with shrinkage priors.  Details on the
algorithms used are provided in Wagner (2011) <doi:10.1007/s11222-009-9164-5>,
Bitto and FrÃ¼hwirth-Schnatter (2019) <doi:10.1016/j.jeconom.2018.11.006> and
Cadonna et al. (2020) <doi:10.3390/econometrics8020020>.")
    (license license:gpl2+)))

(define-public r-shrinkcovmat
  (package
    (name "r-shrinkcovmat")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ShrinkCovMat" version))
       (sha256
        (base32 "0iyri3syjk9xv49d87fdyhnxg5c5x827vnak8vgckkkp62sdln2q"))))
    (properties `((upstream-name . "ShrinkCovMat")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "http://github.com/AnestisTouloumis/ShrinkCovMat")
    (synopsis "Shrinkage Covariance Matrix Estimators")
    (description
     "This package provides nonparametric Steinian shrinkage estimators of the
covariance matrix that are suitable in high dimensional settings, that is when
the number of variables is larger than the sample size.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-shrink
  (package
    (name "r-shrink")
    (version "1.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shrink" version))
       (sha256
        (base32 "0f66m6w2wh0g38g3s2fj3mj90gpdffjffj74cbg4nbl6mdb0lqaf"))))
    (properties `((upstream-name . "shrink")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rms r-mfp r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/biometrician/shrink")
    (synopsis "Global, Parameterwise and Joint Shrinkage Factor Estimation")
    (description
     "The predictive value of a statistical model can often be improved by applying
shrinkage methods.  This can be achieved, e.g., by regularized regression or
empirical Bayes approaches.  Various types of shrinkage factors can also be
estimated after a maximum likelihood.  While global shrinkage modifies all
regression coefficients by the same factor, parameterwise shrinkage factors
differ between regression coefficients.  With variables which are either highly
correlated or associated with regard to contents, such as several columns of a
design matrix describing a nonlinear effect, parameterwise shrinkage factors are
not interpretable and a compromise between global and parameterwise shrinkage,
termed joint shrinkage', is a useful extension.  A computational shortcut to
resampling-based shrinkage factor estimation based on DFBETA residuals can be
applied.  Global, parameterwise and joint shrinkage for models fitted by lm(),
glm(), coxph(), or mfp() is available.")
    (license license:gpl3)))

(define-public r-shp2graph
  (package
    (name "r-shp2graph")
    (version "1-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shp2graph" version))
       (sha256
        (base32 "140rzcy2swfh8k0b94azs04vfhpzv3cxyv0vfb85d2f674dwq3ns"))))
    (properties `((upstream-name . "shp2graph")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp r-igraph))
    (home-page "https://cran.r-project.org/package=shp2graph")
    (synopsis
     "Convert a 'SpatialLinesDataFrame' -Class Object to an 'igraph'-Class Object")
    (description
     "This package provides functions for converting and processing network data from
a @code{SpatialLinesDataFrame} -Class object to an igraph'-Class object.")
    (license license:gpl2+)))

(define-public r-showimage
  (package
    (name "r-showimage")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "showimage" version))
       (sha256
        (base32 "1c0x3iqjdjsz3cdhc02b3qm2pwxjr1q2k87jwvxj9lnzzw81f1pl"))))
    (properties `((upstream-name . "showimage")))
    (build-system r-build-system)
    (propagated-inputs (list r-png))
    (home-page "https://github.com/r-lib/showimage#readme")
    (synopsis "Show an Image on an 'R' Graphics Device")
    (description
     "Sometimes it is handy to be able to view an image file on an R graphics device.
This package just does that.  Currently it supports PNG files.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-shotgroups
  (package
    (name "r-shotgroups")
    (version "0.8.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shotGroups" version))
       (sha256
        (base32 "0m3n3ja6ny45rdd13s9f7nm1ml3p3y7nisbhfg0fj369wym95r1p"))))
    (properties `((upstream-name . "shotGroups")))
    (build-system r-build-system)
    (propagated-inputs (list r-robustbase r-kernsmooth r-compquadform r-coin
                             r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=shotGroups")
    (synopsis "Analyze Shot Group Data")
    (description
     "Analyzes shooting data with respect to group shape, precision, and accuracy.
This includes graphical methods, descriptive statistics, and inference tests
using standard, but also non-parametric and robust statistical methods.
Implements distributions for radial error in bivariate normal variables.  Works
with files exported by @code{OnTarget} PC/TDS', Silver Mountain e-target,
@code{ShotMarker} e-target, or Taran', as well as with custom data files in text
format.  Supports inference from range statistics such as extreme spread.
Includes a set of web-based graphical user interfaces.")
    (license license:gpl2+)))

(define-public r-shorts
  (package
    (name "r-shorts")
    (version "3.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shorts" version))
       (sha256
        (base32 "1ajqvm6y5djdjh5prci31hyv1ilycn1vf5gniq92gxqhzab7bnxc"))))
    (properties `((upstream-name . "shorts")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-purrr r-minpack-lm r-lambertw r-ggplot2))
    (home-page "https://mladenjovanovic.github.io/shorts/")
    (synopsis "Short Sprints")
    (description
     "Create short sprint acceleration-velocity (AVP) and force-velocity (FVP)
profiles and predict kinematic and kinetic variables using the timing-gate split
times, laser or radar gun data, tether devices data, as well as the data
provided by the GPS and LPS monitoring systems.  The modeling method utilized in
this package is based on the works of Furusawa K, Hill AV, Parkinson JL (1927)
<doi: 10.1098/rspb.1927.0035>, Greene PR. (1986) <doi:
10.1016/0025-5564(86)90063-5>, Chelly SM, Denis C. (2001) <doi:
10.1097/00005768-200102000-00024>, Clark KP, Rieger RH, Bruno RF, Stearne DJ.
(2017) <doi: 10.1519/JSC.0000000000002081>, Samozino P. (2018) <doi:
10.1007/978-3-319-05633-3_11>, Samozino P. and Peyrot N., et al (2022) <doi:
10.1111/sms.14097>, Clavel, P., et al (2023) <doi:
10.1016/j.jbiomech.2023.111602>, Jovanovic M. (2023) <doi:
10.1080/10255842.2023.2170713>, and Jovanovic M., et al (2024) <doi:
10.3390/s24092894>.")
    (license license:expat)))

(define-public r-shortirt
  (package
    (name "r-shortirt")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shortIRT" version))
       (sha256
        (base32 "1z2db31r332dayaprdjrm9r3yihj7jnpk7j106m74q5ckwsaw859"))))
    (properties `((upstream-name . "shortIRT")))
    (build-system r-build-system)
    (propagated-inputs (list r-tam r-ggplot2 r-dplyr))
    (home-page "https://cran.r-project.org/package=shortIRT")
    (synopsis
     "Procedures Based on Item Response Theory Models for the Development of Short Test Forms")
    (description
     "Implement different Item Response Theory (IRT) based procedures for the
development of static short test forms (STFs) from a test.  Two main procedures
are considered, specifically the typical IRT-based procedure for the development
of STF, and a recently introduced procedure (Epifania, Anselmi & Robusto, 2022
<doi:10.1007/978-3-031-27781-8_7>).  The procedures differ in how the most
informative items are selected for the inclusion in the STF, either by
considering their item information functions without considering any specific
level of the latent trait (typical procedure) or by considering their
informativeness with respect to specific levels of the latent trait, denoted as
theta targets (the newly introduced procedure).  Regarding the latter procedure,
three methods are implemented for the definition of the theta targets: (i) theta
targets are defined by segmenting the latent trait in equal intervals and
considering the midpoint of each interval (equal interval procedure, eip), (ii)
by clustering the latent trait to obtain unequal intervals and considering the
centroids of the clusters as the theta targets (unequal intervals procedure,
uip), and (iii) by letting the user set the specific theta targets of interest
(user-defined procedure, udp).  For further details on the procedure, please
refer to Epifania, Anselmi & Robusto (2022) <doi:10.1007/978-3-031-27781-8_7>.")
    (license license:expat)))

(define-public r-shortform
  (package
    (name "r-shortform")
    (version "0.5.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ShortForm" version))
       (sha256
        (base32 "14qypmb51w5k6xndjdgrwawyr3ccnp9llsh93sah7xkwl1hkkbdl"))))
    (properties `((upstream-name . "ShortForm")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-lavaan
                             r-ggrepel
                             r-ggplot2
                             r-foreach
                             r-dosnow))
    (home-page "https://github.com/AnthonyRaborn/ShortForm")
    (synopsis "Automatic Short Form Creation")
    (description
     "This package performs automatic creation of short forms of scales with an ant
colony optimization algorithm and a Tabu search.  As implemented in the package,
the ant colony algorithm randomly selects items to build a model of a specified
length, then updates the probability of item selection according to the fit of
the best model within each set of searches.  The algorithm continues until the
same items are selected by multiple ants a given number of times in a row.  On
the other hand, the Tabu search changes one parameter at a time to be either
free, constrained, or fixed while keeping track of the changes made and putting
changes that result in worse fit in a \"tabu\" list so that the algorithm does not
revisit them for some number of searches.  See Leite, Huang, & Marcoulides
(2008) <doi:10.1080/00273170802285743> for an applied example of the ant colony
algorithm, and Marcoulides & Falk (2018) <doi:10.1080/10705511.2017.1409074> for
an applied example of the Tabu search.")
    (license (list (license:fsdg-compatible "LGPL (>= 2.0, < 3)")
                   (license:fsdg-compatible "Mozilla Public License")))))

(define-public r-shortcuts
  (package
    (name "r-shortcuts")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shortcuts" version))
       (sha256
        (base32 "0y77napfz1v3s667cmyn8357xg5lncbw1hc3hm5kra6nalhq1jps"))))
    (properties `((upstream-name . "shortcuts")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-rstudioapi))
    (home-page "https://github.com/jcval94/shortcuts")
    (synopsis "Useful Shortcuts to Interact with 'RStudio' Scripts")
    (description
     "Integrates clipboard copied data in R Studio, loads and installs libraries
within a R script and returns all valid arguments of a selected function.")
    (license license:gpl3)))

(define-public r-shoredate
  (package
    (name "r-shoredate")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shoredate" version))
       (sha256
        (base32 "1zhfaj2wqpmi3ar71czbss32k00sqv1inj6rvj9kqhzshsi0w3zv"))))
    (properties `((upstream-name . "shoredate")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-sf
                             r-ggspatial
                             r-ggridges
                             r-ggrepel
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/isakro/shoredate")
    (synopsis "Shoreline Dating Coastal Stone Age Sites")
    (description
     "This package provides tools for shoreline dating coastal Stone Age sites.  The
implemented method was developed in Roalkvam (2023)
<doi:10.1016/j.quascirev.2022.107880> for the Norwegian Skagerrak coast.
Although it can be extended to other areas, this also forms the core area for
application of the package.  Shoreline dating is based on the present-day
elevation of a site, a reconstruction of past relative sea-level change, and
empirically derived estimates of the likely elevation of the sites above the
contemporaneous sea-level when they were in use.  The geographical and temporal
coverage of the method thus follows from the availability of local geological
reconstructions of shoreline displacement and the degree to which the
settlements to be dated have been located on or close to the shoreline when they
were in use.  Methods for numerical treatment and visualisation of the dates are
provided, along with basic tools for visualising and evaluating the location of
sites.")
    (license license:gpl3+)))

(define-public r-shopifyr
  (package
    (name "r-shopifyr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shopifyr" version))
       (sha256
        (base32 "05hih1v5vvr7vmwarizsdx8517g74s96fdsz3klcz3g12idx1nqn"))))
    (properties `((upstream-name . "shopifyr")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6 r-jsonlite r-curl))
    (home-page "https://github.com/charliebone/shopifyr/")
    (synopsis "An R Interface to the Shopify API")
    (description
     "An interface to the Admin API of the E-commerce service Shopify,
(<https://help.shopify.com/en/api/reference>).")
    (license license:gpl3)))

(define-public r-shopifyadsr
  (package
    (name "r-shopifyadsr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shopifyadsR" version))
       (sha256
        (base32 "0vd8md4w4v05l9d5v7kx21kw3rkbkx8iqf4da44x95kfnnxiqjcx"))))
    (properties `((upstream-name . "shopifyadsR")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite))
    (native-inputs (list r-knitr))
    (home-page "https://windsor.ai/")
    (synopsis "Get 'Shopify' Ads Data via the 'Windsor.ai' API")
    (description
     "Collect your data on digital marketing campaigns from Shopify Ads using the
Windsor.ai API <https://windsor.ai/api-fields/>.")
    (license license:gpl3)))

(define-public r-shock
  (package
    (name "r-shock")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shock" version))
       (sha256
        (base32 "11m52al591xjznl62q1waxsg5m1a1afmd0yqcc5zsjlrplykg4lp"))))
    (properties `((upstream-name . "shock")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-igraph r-glasso r-ggmselect r-capushe))
    (home-page "https://cran.r-project.org/package=shock")
    (synopsis
     "Slope Heuristic for Block-Diagonal Covariance Selection in High Dimensional Gaussian Graphical Models")
    (description
     "Block-diagonal covariance selection for high dimensional Gaussian graphical
models.  The selection procedure is based on the slope heuristics.")
    (license license:gpl3+)))

(define-public r-shipunov
  (package
    (name "r-shipunov")
    (version "1.17.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shipunov" version))
       (sha256
        (base32 "1kwywckhml47qi0i1xiqwkg3lv24xhk3d0brz2jpljv6v1namgk3"))))
    (properties `((upstream-name . "shipunov")))
    (build-system r-build-system)
    (propagated-inputs (list r-pbsmapping))
    (home-page "https://cran.r-project.org/package=shipunov")
    (synopsis "Miscellaneous Functions from Alexey Shipunov")
    (description
     "This package provides a collection of functions for data manipulation, plotting
and statistical computing, to use separately or with the book \"Visual
Statistics.  Use R!\": Shipunov (2020)
<http://ashipunov.info/shipunov/software/r/r-en.htm>.  Dr Alexey Shipunov died
in December 2022.  Most useful functions: Bclust(), Jclust() and @code{BootA}()
which bootstrap hierarchical clustering; Recode() which does multiple recoding
in a fast, simple and flexible way; Misclass() which outputs confusion matrix
even if classes are not concerted; Overlap() which measures group separation on
any projection; Biarrows() which converts any scatterplot into biplot; and
Pleiad() which is fast and flexible correlogram.")
    (license license:gpl2+)))

(define-public r-shinyxypad
  (package
    (name "r-shinyxypad")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyXYpad" version))
       (sha256
        (base32 "08acqbh41379p2jh0wc30m4h7rlx81cf578qc604jxnsvilvjr9n"))))
    (properties `((upstream-name . "shinyXYpad")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny))
    (home-page "https://github.com/stla/shinyXYpad")
    (synopsis "XY Controller for 'Shiny'")
    (description
     "This package provides an XY pad input for the Shiny framework.  An XY pad is
like a bivariate slider.  It allows to pick up a pair of numbers.")
    (license license:gpl3)))

(define-public r-shinywidgets
  (package
    (name "r-shinywidgets")
    (version "0.8.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyWidgets" version))
       (sha256
        (base32 "08kpf98spkyx7vq9jk5g3wnhdhzfmkkzq04p8gv44hjw2b2v9m5d"))))
    (properties `((upstream-name . "shinyWidgets")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-shiny
                             r-sass
                             r-rlang
                             r-jsonlite
                             r-htmltools
                             r-bslib))
    (native-inputs (list esbuild))
    (home-page "https://github.com/dreamRs/shinyWidgets")
    (synopsis "Custom Inputs Widgets for Shiny")
    (description
     "Collection of custom input controls and user interface components for Shiny
applications.  Give your applications a unique and colorful style !")
    (license license:gpl3)))

(define-public r-shinyvalidate
  (package
    (name "r-shinyvalidate")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyvalidate" version))
       (sha256
        (base32 "01qlqzp0g6782maj73yygmwlchhv4xcnlc7j0w3lp6dcj7qkzz68"))))
    (properties `((upstream-name . "shinyvalidate")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-rlang r-htmltools r-glue))
    (home-page "https://rstudio.github.io/shinyvalidate/")
    (synopsis "Input Validation for Shiny Apps")
    (description
     "Improves the user experience of Shiny apps by helping to provide feedback when
required inputs are missing, or input values are not valid.")
    (license license:expat)))

(define-public r-shinytoastr
  (package
    (name "r-shinytoastr")
    (version "2.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinytoastr" version))
       (sha256
        (base32 "1zcf4ya9anlq6n97iq0b86ybcqbbd2h66vrr41m80zvlkav3z46z"))))
    (properties `((upstream-name . "shinytoastr")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-shiny))
    (native-inputs (list esbuild))
    (home-page "https://github.com/gaborcsardi/shinytoastr")
    (synopsis "Notifications from 'Shiny'")
    (description "Browser notifications in Shiny apps, using toastr':
<https://github.com/@code{CodeSeven/toastr#readme>}.")
    (license license:expat)))

(define-public r-shinytoastify
  (package
    (name "r-shinytoastify")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyToastify" version))
       (sha256
        (base32 "0ipqwgl89pjiycwg78p0db45lbzry989kn6aqjsf79lpvghnk5qs"))))
    (properties `((upstream-name . "shinyToastify")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-reactr r-htmltools r-fontawesome))
    (home-page "https://github.com/stla/shinyToastify")
    (synopsis "Pretty Notifications for 'Shiny'")
    (description
     "This is a wrapper of the React library React-Toastify'.  It allows to show some
notifications (toasts) in Shiny applications.  There are options for the style,
the position, the transition effect, and more.")
    (license license:gpl3)))

(define-public r-shinytitle
  (package
    (name "r-shinytitle")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinytitle" version))
       (sha256
        (base32 "1k05s2fmqvz3drnqkxcpxvvmn0sisdqivg7i4x5c6649b97rsbsx"))))
    (properties `((upstream-name . "shinytitle")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny))
    (home-page "https://github.com/ashbaldry/shinytitle")
    (synopsis "Update Browser Window Title in 'shiny' Session")
    (description
     "Enables the ability to change or flash the title of the browser window during a
shiny session.")
    (license license:gpl2)))

(define-public r-shinytime
  (package
    (name "r-shinytime")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyTime" version))
       (sha256
        (base32 "04vpq9fpp1iwila9l33cljig3hfyw7lsg03fqny0q260ka8chhgz"))))
    (properties `((upstream-name . "shinyTime")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-htmltools))
    (home-page "https://burgerga.github.io/shinyTime/")
    (synopsis "Time Input Widget for Shiny")
    (description
     "This package provides a time input widget for Shiny.  This widget allows
intuitive time input in the [hh]:[mm]:[ss] or [hh]:[mm] (24H) format by using a
separate numeric input for each time component.  The interface with R uses
date-time objects.  See the project page for more information and examples.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-shinytester
  (package
    (name "r-shinytester")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ShinyTester" version))
       (sha256
        (base32 "0wm4rl2p8ggw6v2chk9b6ygh5y8p8cwzbyra0nr0qr2ka97didp8"))))
    (properties `((upstream-name . "ShinyTester")))
    (build-system r-build-system)
    (propagated-inputs (list r-visnetwork
                             r-tidyr
                             r-stringr
                             r-readr
                             r-purrr
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=ShinyTester")
    (synopsis
     "Functions to Minimize Bonehead Moves While Working with 'shiny'")
    (description
     "It's my experience that working with shiny is intuitive once you're into it, but
can be quite daunting at first.  Several common mistakes are fairly predictable,
and therefore we can control for these.  The functions in this package help
match up the assets listed in the UI and the SERVER files, and Visualize the ad
hoc structure of the shiny App.")
    (license license:gpl2)))

(define-public r-shinytest2
  (package
    (name "r-shinytest2")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinytest2" version))
       (sha256
        (base32 "1qrnh9cfgi67szv1yiaqkv10vf1r70i9l88h6dssgiv1q2fw8yx1"))))
    (properties `((upstream-name . "shinytest2")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-testthat
                             r-shiny
                             r-rmarkdown
                             r-rlang
                             r-r6
                             r-pingr
                             r-jsonlite
                             r-httr
                             r-globals
                             r-fs
                             r-crayon
                             r-cpp11
                             r-chromote
                             r-checkmate
                             r-callr))
    (native-inputs (list r-knitr))
    (home-page "https://rstudio.github.io/shinytest2/")
    (synopsis "Testing for Shiny Applications")
    (description
     "Automated unit testing of Shiny applications through a headless Chromium
browser.")
    (license license:expat)))

(define-public r-shinytest
  (package
    (name "r-shinytest")
    (version "1.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinytest" version))
       (sha256
        (base32 "0h9ciivic0fg7w5yq9cycdj1fh4zhdg4fz1gkn2fdx87c0d5j7a4"))))
    (properties `((upstream-name . "shinytest")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (inputs (list))
    (propagated-inputs (list r-withr
                             r-webdriver
                             r-testthat
                             r-shiny
                             r-rstudioapi
                             r-rlang
                             r-rematch
                             r-r6
                             r-pingr
                             r-parsedate
                             r-jsonlite
                             r-httr
                             r-httpuv
                             r-htmlwidgets
                             r-digest
                             r-debugme
                             r-crayon
                             r-callr
                             r-assertthat))
    (native-inputs (list esbuild))
    (home-page "https://github.com/rstudio/shinytest")
    (synopsis "Test Shiny Apps")
    (description
     "Please see the shinytest to shinytest2 migration guide at
<https://rstudio.github.io/shinytest2/articles/z-migration.html>.")
    (license license:expat)))

(define-public r-shinytempsignal
  (package
    (name "r-shinytempsignal")
    (version "0.0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyTempSignal" version))
       (sha256
        (base32 "1ww164zqqc9rh158wfi6c4jcp4pn61mz56zbhh2fkmyc2mcv6xbb"))))
    (properties `((upstream-name . "shinyTempSignal")))
    (build-system r-build-system)
    (propagated-inputs (list r-yulab-utils
                             r-treeio
                             r-shinywidgets
                             r-shinyjs
                             r-shinydashboard
                             r-shiny
                             r-nlme
                             r-golem
                             r-ggtree
                             r-ggprism
                             r-ggpmisc
                             r-ggplot2
                             r-forecast
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/YuLab-SMU/shinyTempSignal")
    (synopsis "Explore Temporal and Other Phylogenetic Signals")
    (description
     "Sequences sampled at different time points can be used to infer molecular
phylogenies on natural time scales, but if the sequences records inaccurate
sampling times, that are not the actual sampling times, then it will affect the
molecular phylogenetic analysis.  This shiny application helps exploring
temporal characteristics of the evolutionary trees through linear regression
analysis and with the ability to identify and remove incorrect labels.  The
method was extended to support exploring other phylogenetic signals under strict
and relaxed models.")
    (license license:gpl3)))

(define-public r-shinysurveys
  (package
    (name "r-shinysurveys")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinysurveys" version))
       (sha256
        (base32 "01bj0g8r71jng03psw9dcjann78divy2xr5yn6y1pa82d96s46b5"))))
    (properties `((upstream-name . "shinysurveys")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-sass r-jsonlite r-htmltools))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=shinysurveys")
    (synopsis "Create and Deploy Surveys in 'Shiny'")
    (description
     "Easily create and deploy surveys in Shiny'.  This package includes a
minimalistic framework similar to Google Forms that allows for url-based user
tracking, customizable submit actions, easy survey-theming, and more.")
    (license license:expat)))

(define-public r-shinystoreplus
  (package
    (name "r-shinystoreplus")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyStorePlus" version))
       (sha256
        (base32 "08xyd26wcfv629a4791s20f690faf472rn33qhnlsjbddnmla62z"))))
    (properties `((upstream-name . "shinyStorePlus")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinywidgets r-shiny r-jsonlite r-htmltools))
    (native-inputs (list r-knitr))
    (home-page "https://shinystoreplus.obi.obianom.com")
    (synopsis
     "Secure in-Browser Storage for 'Shiny' Inputs, Outputs and Variables")
    (description
     "Store persistent and synchronized data from Shiny inputs within the browser in a
secure format.  Refresh Shiny applications and preserve user-inputs over
multiple sessions.  A database-like storage format is implemented using Dexie.js
<https://dexie.org>, a minimal wrapper for @code{IndexedDB}'.  Transfer browser
link parameters to Shiny input or output values.")
    (license license:expat)))

(define-public r-shinysir
  (package
    (name "r-shinysir")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinySIR" version))
       (sha256
        (base32 "1g2xcm0n7chswrnw9zq3wz6mimzzf6avhkp1rcd24ga809346jcd"))))
    (properties `((upstream-name . "shinySIR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-shiny r-ggplot2 r-dplyr r-desolve))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=shinySIR")
    (synopsis
     "Interactive Plotting for Mathematical Models of Infectious Disease Spread")
    (description
     "This package provides interactive plotting for mathematical models of infectious
disease spread.  Users can choose from a variety of common built-in ordinary
differential equation (ODE) models (such as the SIR, SIRS, and SIS models), or
create their own.  This latter flexibility allows @code{shinySIR} to be applied
to simple ODEs from any discipline.  The package is a useful teaching tool as
students can visualize how changing different parameters can impact model
dynamics, with minimal knowledge of coding in R. The built-in models are
inspired by those featured in Keeling and Rohani (2008)
<doi:10.2307/j.ctvcm4gk0> and Bjornstad (2018) <doi:10.1007/978-3-319-97487-3>.")
    (license license:expat)))

(define-public r-shinyshortcut
  (package
    (name "r-shinyshortcut")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyShortcut" version))
       (sha256
        (base32 "101wz9s4rk7fbaqgm0r2v3v8za20vdxwq4vhkz8xr7y5pr65sxiq"))))
    (properties `((upstream-name . "shinyShortcut")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=shinyShortcut")
    (synopsis "Creates an Executable Shortcut for Shiny Applications")
    (description
     "This package provides function @code{shinyShortcut}() that, when given the base
directory of a shiny application, will produce an executable file that runs the
shiny app directly in the user's default browser.  Tested on both windows and
unix machines.  Inspired by and borrowing from
<http://www.mango-solutions.com/wp/2017/03/shiny-based-tablet-or-desktop-app/>.")
    (license (list license:gpl3+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-shinyservicebot
  (package
    (name "r-shinyservicebot")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyservicebot" version))
       (sha256
        (base32 "18xbggiai6ssyl3q0nqhnszxb7zlbffaw0iy3r7vbzswzqx522la"))))
    (properties `((upstream-name . "shinyservicebot")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-htmlwidgets r-htmltools r-digest))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/capiaas/shinyservicebot")
    (synopsis "Servicebot 'Shiny' Integration")
    (description
     "Create in-app purchasing and subscriptions through Servicebot payment using the
Stripe framework.")
    (license license:expat)))

(define-public r-shinyselect
  (package
    (name "r-shinyselect")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinySelect" version))
       (sha256
        (base32 "16fnqhw2kd7mamhc4qnravrybbi2fbxkdmdpk07ycnasn00fprp0"))))
    (properties `((upstream-name . "shinySelect")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-reactr r-htmltools r-fontawesome))
    (native-inputs (list esbuild))
    (home-page "https://github.com/stla/shinySelect")
    (synopsis "Wrapper of the 'react-select' Library")
    (description
     "This package provides a select control widget for Shiny'.  It is easily
customizable, and one can easily use HTML in the items and @code{KaTeX} to type
mathematics.")
    (license license:gpl3)))

(define-public r-shinysearchbar
  (package
    (name "r-shinysearchbar")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinySearchbar" version))
       (sha256
        (base32 "1v80380vym31aw0i1ghwlbdi2xkf3n59w2as62ywqi79025x3w6y"))))
    (properties `((upstream-name . "shinySearchbar")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-shiny r-jsonlite))
    (native-inputs (list esbuild))
    (home-page "https://github.com/jes-n/shiny-searchbar")
    (synopsis
     "Shiny Searchbar - An Input Widget for Highlighting Text and More")
    (description
     "Add a searchbar widget to your Shiny application.  The widget quickly integrates
with any existing element containing text to highlight matches.  Highlighting is
done with the @code{JavaScript} library mark.js'.  The widget includes buttons
to cycle through multiple instances of the match and automatically scroll to the
matches in an overflow element (or window).  The widget also displays the total
number of matches and which match is currently being cycled through.  The widget
is structured as a Bootstrap 3 input group.")
    (license license:gpl3)))

(define-public r-shinyscreenshot
  (package
    (name "r-shinyscreenshot")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyscreenshot" version))
       (sha256
        (base32 "1pa1fc0yhsdsc2glyj77zsbpl8qv9x2i7qfha7s77k1zif74px88"))))
    (properties `((upstream-name . "shinyscreenshot")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-uuid r-shiny r-jsonlite r-htmltools r-base64enc))
    (native-inputs (list esbuild))
    (home-page "https://github.com/daattali/shinyscreenshot")
    (synopsis
     "Capture Screenshots of Entire Pages or Parts of Pages in 'Shiny'")
    (description
     "Capture screenshots in Shiny applications.  Screenshots can either be of the
entire viewable page, or a specific section of the page.  The captured image is
automatically downloaded as a PNG image, or it can also be saved on the server.
Powered by the html2canvas @code{JavaScript} library.")
    (license license:expat)))

(define-public r-shinysbm
  (package
    (name "r-shinysbm")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinySbm" version))
       (sha256
        (base32 "0r6zq235krg4rikpypwp9a2hwblgygl7mmc1phxrpxr8xppgq2da"))))
    (properties `((upstream-name . "shinySbm")))
    (build-system r-build-system)
    (propagated-inputs (list r-visnetwork
                             r-stringr
                             r-shinydashboard
                             r-shinyalert
                             r-shiny
                             r-sbm
                             r-rmarkdown
                             r-purrr
                             r-patchwork
                             r-magrittr
                             r-golem
                             r-ggplot2
                             r-fresh
                             r-flextable
                             r-dt
                             r-dplyr
                             r-data-table
                             r-config
                             r-colourpicker))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=shinySbm")
    (synopsis "'shiny' Application to Use the Stochastic Block Model")
    (description
     "This package provides a shiny interface for a simpler use of the sbm R package.
It also contains useful functions to easily explore the sbm package results.
With this package you should be able to use the stochastic block model without
any knowledge in R, get automatic reports and nice visuals, as well as learning
the basic functions of sbm'.")
    (license license:expat)))

(define-public r-shinyrgl
  (package
    (name "r-shinyrgl")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyRGL" version))
       (sha256
        (base32 "07llg1yg5vmsp89jk60ly695zvxky6n06ar77mjxzlyc294akwmy"))))
    (properties `((upstream-name . "shinyRGL")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-rgl))
    (home-page "https://cran.r-project.org/package=shinyRGL")
    (synopsis "Shiny Wrappers for RGL")
    (description
     "Shiny wrappers for the RGL package.  This package exposes RGL's ability to
export @code{WebGL} visualization in a shiny-friendly format.")
    (license license:expat)))

(define-public r-shinyreforms
  (package
    (name "r-shinyreforms")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyreforms" version))
       (sha256
        (base32 "1g1blc57c6zkca0m52bqkw8jwgpplabns07x1cwkk46dmv6sgqci"))))
    (properties `((upstream-name . "shinyreforms")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-r6 r-htmltools))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/piotrbajger/shinyreforms")
    (synopsis "Add Forms to your 'Shiny' App")
    (description
     "Allows to create modular, reusable HTML forms which can be embedded in your
shiny app with minimal effort.  Features include conditional code execution on
form submission, automatic input validation and input tooltips.")
    (license license:gpl3)))

(define-public r-shinyrecipes
  (package
    (name "r-shinyrecipes")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyrecipes" version))
       (sha256
        (base32 "1b4l27qsl1lpssh3jv1pqmw2mq8hanfj0m269yp5618nd5nxd2ck"))))
    (properties `((upstream-name . "shinyrecipes")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-sortable
                             r-shinywidgets
                             r-shinyglide
                             r-shiny
                             r-rstudioapi
                             r-recipes
                             r-purrr
                             r-miniui
                             r-esquisse
                             r-dt
                             r-dplyr))
    (home-page "https://github.com/AlbertoAlmuinha/shinyrecipes")
    (synopsis
     "Gadget to Use the Data Preprocessing 'recipes' Package Interactively")
    (description
     "This gadget allows you to use the recipes package belonging to tidymodels to
carry out the data preprocessing tasks in an interactive way.  Build your recipe
by dragging the variables, visually analyze your data to decide which steps to
use, add those steps and preprocess your data.")
    (license license:gpl3)))

(define-public r-shinyratings
  (package
    (name "r-shinyratings")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyRatings" version))
       (sha256
        (base32 "1hqgp4md0q0mqijp3fxj1z49m6bmjb1r2c2l51796a5arhljxx1y"))))
    (properties `((upstream-name . "shinyRatings")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-jsonlite r-htmltools))
    (home-page "https://cran.r-project.org/package=shinyRatings")
    (synopsis "An Intuitive Way of Providing Star Rating in a 'shiny' App")
    (description
     "This package provides a simple interface to integrate star ratings into your
shiny apps.  It can be used for customer feedback systems, user reviews, or any
application that requires user ratings. @code{shinyRatings} offers a
straightforward and customisable solution that enhances user engagement and
facilitates valuable feedback collection.")
    (license license:expat)))

(define-public r-shinyradiomatrix
  (package
    (name "r-shinyradiomatrix")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyRadioMatrix" version))
       (sha256
        (base32 "195b1990r3b91f4i4azai0i4lxjgw2ngbzdywiy35c1fnmiyy882"))))
    (properties `((upstream-name . "shinyRadioMatrix")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny))
    (home-page "https://cran.r-project.org/package=shinyRadioMatrix")
    (synopsis "Create a Matrix with Radio Buttons")
    (description
     "An input controller for R Shiny: a matrix with radio buttons, where only one
option per row can be selected.")
    (license license:gpl3)))

(define-public r-shinyquiz
  (package
    (name "r-shinyquiz")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyquiz" version))
       (sha256
        (base32 "0d4n15dx2kizl37gvmgwc49b0n99x59xp168an8n3k0h8qzdcyd4"))))
    (properties `((upstream-name . "shinyquiz")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-stringi
                             r-shinyjs
                             r-shiny
                             r-scales
                             r-reactable
                             r-purrr
                             r-htmltools
                             r-glue
                             r-fontawesome
                             r-cli))
    (home-page "https://priism-center.github.io/shinyquiz/")
    (synopsis "Create Interactive Quizzes in 'shiny'")
    (description
     "Simple and flexible quizzes in shiny'.  Easily create quizzes from various
pre-built question and choice types or create your own using htmltools and shiny
packages as building blocks.  Integrates with larger shiny applications.  Ideal
for non-web-developers such as educators, data scientists, and anyone who wants
to assess responses interactively in a small form factor.")
    (license license:expat)))

(define-public r-shinyquickstarter
  (package
    (name "r-shinyquickstarter")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ShinyQuickStarter" version))
       (sha256
        (base32 "19k46hwcab33l6dc43dp4hvvr366qh3z2jjw3b79x7f824wampnn"))))
    (properties `((upstream-name . "ShinyQuickStarter")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '()))))
                  (add-after 'unpack 'set-HOME
                    (lambda _
                      (setenv "HOME" "/tmp"))))))
    (propagated-inputs (list r-styler
                             r-stringr
                             r-stringi
                             r-shinywidgets
                             r-shinyjs
                             r-shinyfiles
                             r-shinydashboard
                             r-shinyalert
                             r-shiny
                             r-reshape2
                             r-plotly
                             r-miniui
                             r-ggplot2
                             r-fs
                             r-dplyr
                             r-data-table
                             r-colourpicker))
    (native-inputs (list esbuild))
    (home-page "https://cran.r-project.org/package=ShinyQuickStarter")
    (synopsis "'RStudio' Addin for Building Shiny Apps per Drag & Drop")
    (description
     "This RStudio addin makes the creation of Shiny and @code{ShinyDashboard} apps
more efficient.  Besides the necessary folder structure, entire apps can be
created using a drag and drop interface and customized with respect to a
specific use case.  The addin allows the export of the required user interface
and server code at any time.  By allowing the creation of modules, the addin can
be used throughout the entire app development process.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-shinyquerybuilder
  (package
    (name "r-shinyquerybuilder")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyQueryBuilder" version))
       (sha256
        (base32 "1v6za6xpl91dfbqp5pzdgxrwcs26y0qlwj19av00ggmjgzdca29c"))))
    (properties `((upstream-name . "shinyQueryBuilder")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-shiny
                             r-rlang
                             r-r6
                             r-querybuilder
                             r-purrr
                             r-magrittr
                             r-jsonlite
                             r-htmltools
                             r-glue))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://cran.r-project.org/package=shinyQueryBuilder")
    (synopsis "Construct Complex Filtering Queries in 'Shiny'")
    (description
     "Input widget that allows to construct complex filtering queries in Shiny'.  It's
a wrapper for @code{JavaScript} library @code{jQuery-QueryBuilder}', check
<https://querybuilder.js.org/>.")
    (license license:expat)))

(define-public r-shinypredict
  (package
    (name "r-shinypredict")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyPredict" version))
       (sha256
        (base32 "086dawnwzhcfz4l555j8wvy46f8h939y17c0glm86i339jszzra3"))))
    (properties `((upstream-name . "shinyPredict")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=shinyPredict")
    (synopsis "Predictions using Shiny")
    (description
     "This package creates shiny application ('app.R') for making predictions based on
lm(), glm(), or coxph() models.")
    (license license:gpl2)))

(define-public r-shinypivottabler
  (package
    (name "r-shinypivottabler")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinypivottabler" version))
       (sha256
        (base32 "0qjk2xgbcl60aqva5jn0bbrzmcql8kaqs1air2xyjbfj73axsrig"))))
    (properties `((upstream-name . "shinypivottabler")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-pivottabler r-openxlsx r-htmltools
                             r-colourpicker))
    (home-page "https://cran.r-project.org/package=shinypivottabler")
    (synopsis "Shiny Module to Create Pivot Tables")
    (description
     "Shiny Module to create, visualize, customize and export Excel-like pivot table.")
    (license license:gpl3)))

(define-public r-shinypanels
  (package
    (name "r-shinypanels")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinypanels" version))
       (sha256
        (base32 "1rslz9wl3gx11dm6il77j4k5swxvcfy5mf0zffbs4zk8xjgpzzj6"))))
    (properties `((upstream-name . "shinypanels")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-shinyjs r-shiny r-htmltools r-glue))
    (native-inputs (list esbuild))
    (home-page "http://github.com/datasketch/shinypanels")
    (synopsis "Shiny Layout with Collapsible Panels")
    (description
     "Create Shiny Apps with collapsible vertical panels.  This package provides a new
visual arrangement for elements on top of Shiny'.  Use the expand and collapse
capabilities to leverage web applications with many elements to focus the user
attention on the panel of interest.")
    (license license:expat)))

(define-public r-shinypanel
  (package
    (name "r-shinypanel")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinypanel" version))
       (sha256
        (base32 "003sp6x78mwjf5hl8m4gqwyxlg3zilli6932l7k82s1hbaksar1j"))))
    (properties `((upstream-name . "shinypanel")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinyjs r-shinybs r-shiny r-jsonlite
                             r-htmltools))
    (home-page "https://cran.r-project.org/package=shinypanel")
    (synopsis "Shiny Control Panel")
    (description
     "Add shiny inputs with one or more inline buttons that grow and shrink with
inputs.  Also add tool tips to input buttons and styling and messages for input
validation.")
    (license license:expat)))

(define-public r-shinyobjects
  (package
    (name "r-shinyobjects")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyobjects" version))
       (sha256
        (base32 "0fg5gyssia2fqar635vrqgfwpfrk4pqachsgg10y13ljp09lcdm3"))))
    (properties `((upstream-name . "shinyobjects")))
    (build-system r-build-system)
    (arguments
     (list
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'set-HOME
                    (lambda _
                      (setenv "HOME" "/tmp"))))))
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-styler
                             r-stringr
                             r-shiny
                             r-rstudioapi
                             r-rlang
                             r-readr
                             r-purrr
                             r-pander
                             r-magrittr
                             r-knitr
                             r-glue
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=shinyobjects")
    (synopsis "Access Reactive Data Interactively")
    (description
     "Troubleshooting reactive data in shiny can be difficult.  These functions will
convert reactive data frames into functions and load all assigned objects into
your local environment.  If you create a dummy input object, as the function
will suggest, you will be able to test your server and ui functions
interactively.")
    (license license:gpl3)))

(define-public r-shinynotes
  (package
    (name "r-shinynotes")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyNotes" version))
       (sha256
        (base32 "19k4n3ikffxs24c66i9wi7ldw50wjs0gym73n9clnq3hh6l2fzz8"))))
    (properties `((upstream-name . "shinyNotes")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-shinywidgets
                             r-shinyjs
                             r-shiny
                             r-rsqlite
                             r-rlang
                             r-markdown
                             r-magrittr
                             r-dplyr
                             r-dbplyr
                             r-dbi))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/danielkovtun/shinyNotes")
    (synopsis "Shiny Module for Taking Free-Form Notes")
    (description
     "An enterprise-targeted scalable and customizable shiny module providing an easy
way to incorporate free-form note taking or discussion boards into applications.
 The package includes a shiny module that can be included in any shiny
application to create a panel containing searchable, editable text broken down
by section headers.  Can be used with a local SQLite database, or a compatible
remote database of choice.")
    (license license:expat)))

(define-public r-shinynorrrm
  (package
    (name "r-shinynorrrm")
    (version "0.8.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyNORRRM" version))
       (sha256
        (base32 "1cb6n3bwqfygqc0p25z62nmrrns6hm2by4chi45sl898is5hkhdn"))))
    (properties `((upstream-name . "shinyNORRRM")))
    (build-system r-build-system)
    (propagated-inputs (list r-ternary r-shinywidgets r-shinythemes r-shiny
                             r-pracma))
    (home-page "https://github.com/TheRFrog/shinyNORRRM")
    (synopsis "The Ultimate Igneous Norm")
    (description
     "The computer program is an efficient igneous norm algorithm and rock
classification system written in R but run as shiny app.")
    (license license:gpl3)))

(define-public r-shinymonacoeditor
  (package
    (name "r-shinymonacoeditor")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyMonacoEditor" version))
       (sha256
        (base32 "12inzz9vrxi1465l82fafjva198qxyzwailx1s0fm58cqzhnqxr6"))))
    (properties `((upstream-name . "shinyMonacoEditor")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-shiny))
    (native-inputs (list esbuild))
    (home-page "https://github.com/stla/shinyMonacoEditor")
    (synopsis "The 'Monaco' Editor in 'Shiny'")
    (description
     "This package provides a Shiny app including the Monaco editor.  The Monaco
editor is the code editor which powers VS Code'.  It is particularly well
developed for @code{JavaScript}'.  In addition to the Monaco editor features,
the app provides prettifiers and minifiers for multiple languages, SCSS and
@code{TypeScript} compilers, code checking for C and C++ (requires cppcheck').")
    (license license:gpl3)))

(define-public r-shinymolbio
  (package
    (name "r-shinymolbio")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyMolBio" version))
       (sha256
        (base32 "09lq5pvis450il1agcq5n9cilnq84rfv08w2dz1d0bbq1qgrgnvg"))))
    (properties `((upstream-name . "shinyMolBio")))
    (build-system r-build-system)
    (propagated-inputs (list r-whisker
                             r-stringr
                             r-shiny
                             r-rdml
                             r-rcolorbrewer
                             r-purrr
                             r-plotly
                             r-dplyr
                             r-checkmate))
    (home-page "https://github.com/kablag/shinyMolBio")
    (synopsis "Molecular Biology Visualization Tools for 'Shiny' Apps")
    (description
     "Interactive visualization of RDML files via shiny apps.  Package provides (1)
PCR plate interface with ability to select individual tubes and (2)
amplification/melting plots with fast hiding and highlighting individual curves.")
    (license license:expat)))

(define-public r-shinymodels
  (package
    (name "r-shinymodels")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinymodels" version))
       (sha256
        (base32 "1ydj4ny94y77akbsq0asaz29s3hnkb237icbxsnrh18z2wnyhhll"))))
    (properties `((upstream-name . "shinymodels")))
    (build-system r-build-system)
    (propagated-inputs (list r-yardstick
                             r-tune
                             r-tidyselect
                             r-tidyr
                             r-shinydashboard
                             r-shiny
                             r-scales
                             r-rlang
                             r-purrr
                             r-plotly
                             r-parsnip
                             r-magrittr
                             r-htmltools
                             r-glue
                             r-ggplot2
                             r-generics
                             r-dt
                             r-dplyr))
    (home-page "https://shinymodels.tidymodels.org")
    (synopsis "Interactive Assessments of Models")
    (description
     "Launch a shiny application for tidymodels results.  For classification or
regression models, the app can be used to determine if there is lack of fit or
poorly predicted points.")
    (license license:expat)))

(define-public r-shinymobile
  (package
    (name "r-shinymobile")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyMobile" version))
       (sha256
        (base32 "0vsmvd30n4i2gqzywl0h11v5zrrcf40gmr12fvngxm3baxa5v3xc"))))
    (properties `((upstream-name . "shinyMobile")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-shiny
                             r-magrittr
                             r-lifecycle
                             r-jsonlite
                             r-htmltools
                             r-gplots))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://github.com/RinteRface/shinyMobile")
    (synopsis "Mobile Ready 'shiny' Apps with Standalone Capabilities")
    (description
     "Develop outstanding shiny apps for @code{iOS} and Android as well as beautiful
shiny gadgets. @code{shinyMobile} is built on top of the latest Framework7
template <https://framework7.io>.  Discover 14 new input widgets (sliders,
vertical sliders, stepper, grouped action buttons, toggles, picker, smart
select, ...), 2 themes (light and dark), 12 new widgets (expandable cards,
badges, chips, timelines, gauges, progress bars, ...) combined with the power of
server-side notifications such as alerts, modals, toasts, action sheets, sheets
(and more) as well as 3 layouts (single, tabs and split).")
    (license license:gpl2)))

(define-public r-shinyml
  (package
    (name "r-shinyml")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyML" version))
       (sha256
        (base32 "086iavycv1gd6scfsixf7cmw0s5a4dc6x5cylfasxrzgiixd76c9"))))
    (properties `((upstream-name . "shinyML")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-sparklyr
                             r-shinywidgets
                             r-shinyjs
                             r-shinycssloaders
                             r-shiny
                             r-plotly
                             r-lubridate
                             r-h2o
                             r-ggplot2
                             r-dygraphs
                             r-dt
                             r-dplyr
                             r-data-table
                             r-argonr
                             r-argondash))
    (home-page "https://jeanbertinr.github.io/shinyMLpackage/")
    (synopsis "Compare Supervised Machine Learning Models Using Shiny App")
    (description
     "Implementation of a shiny app to easily compare supervised machine learning
model performances.  You provide the data and configure each model parameter
directly on the shiny app.  Different supervised learning algorithms can be
tested either on Spark or H2O frameworks to suit your regression and
classification tasks.  Implementation of available machine learning models on R
has been done by Lantz (2013, ISBN:9781782162148).")
    (license license:gpl3)))

(define-public r-shinymgr
  (package
    (name "r-shinymgr")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinymgr" version))
       (sha256
        (base32 "000r7vh8myc10jxna5h65kjx1sqw45aqjj24brls5vnvz5rg4i3w"))))
    (properties `((upstream-name . "shinymgr")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinyjs
                             r-shinydashboard
                             r-shiny
                             r-rsqlite
                             r-renv
                             r-reactable
                             r-dbi))
    (home-page "https://code.usgs.gov/vtcfwru/shinymgr")
    (synopsis
     "Framework for Building, Managing, and Stitching 'shiny' Modules into Reproducible Workflows")
    (description
     "This package provides a unifying framework for managing and deploying shiny
applications that consist of modules, where an \"app\" is a tab-based workflow
that guides a user step-by-step through an analysis.  The shinymgr app builder
\"stitches\" shiny modules together so that outputs from one module serve as
inputs to the next, creating an analysis pipeline that is easy to implement and
maintain.  Users of shinymgr apps can save analyses as an RDS file that fully
reproduces the analytic steps and can be ingested into an R Markdown report for
rapid reporting.  In short, developers use the shinymgr framework to write
modules and seamlessly combine them into shiny apps, and users of these apps can
execute reproducible analyses that can be incorporated into reports for rapid
dissemination.")
    (license license:gpl3)))

(define-public r-shinymergely
  (package
    (name "r-shinymergely")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyMergely" version))
       (sha256
        (base32 "1rvv5lcbb17i263xx1zn9524iwpmw56xwh0b2qii8096l20w6cnd"))))
    (properties `((upstream-name . "shinyMergely")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-shiny))
    (native-inputs (list esbuild))
    (home-page "https://github.com/stla/shinyMergely")
    (synopsis "Compare and Merge Two Files with a 'Shiny' App")
    (description
     "This package provides a Shiny app allowing to compare and merge two files, with
syntax highlighting for several coding languages.")
    (license license:gpl3)))

(define-public r-shinymatrix
  (package
    (name "r-shinymatrix")
    (version "0.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyMatrix" version))
       (sha256
        (base32 "0dlmih1xf49vaw27r83dyzbz7w6wfvz06iz214pcp0xp51hx37gw"))))
    (properties `((upstream-name . "shinyMatrix")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-shiny r-jsonlite))
    (native-inputs (list esbuild))
    (home-page "https://inwtlab.github.io/shiny-matrix/")
    (synopsis "Shiny Matrix Input Field")
    (description "This package implements a custom matrix input field.")
    (license license:expat)))

(define-public r-shinymaterial
  (package
    (name "r-shinymaterial")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinymaterial" version))
       (sha256
        (base32 "0nqakim0cx722sydbv12kgva1nis7h2i0lrvi9c0h54733x7lbza"))))
    (properties `((upstream-name . "shinymaterial")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-shiny r-sass r-jsonlite))
    (native-inputs (list esbuild))
    (home-page "https://ericrayanderson.github.io/shinymaterial/")
    (synopsis "Implement Material Design in Shiny Applications")
    (description
     "Allows shiny developers to incorporate UI elements based on Google's Material
design.  See <https://material.io/guidelines/> for more information.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-shinylp
  (package
    (name "r-shinylp")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyLP" version))
       (sha256
        (base32 "1cdi9dbaz1q71c0wb5a910xz3rspqgqxdk14ibhh06lb0hs9n4nc"))))
    (properties `((upstream-name . "shinyLP")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny))
    (home-page "https://github.com/jasdumas/shinyLP")
    (synopsis "Bootstrap Landing Home Pages for Shiny Applications")
    (description
     "This package provides functions that wrap HTML Bootstrap components code to
enable the design and layout of informative landing home pages for Shiny
applications.  This can lead to a better user experience for the users and
writing less HTML for the developer.")
    (license license:expat)))

(define-public r-shinylottie
  (package
    (name "r-shinylottie")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyLottie" version))
       (sha256
        (base32 "1fs1cpi7qab8zqb4jn3lw4bpan3434m6jdy6zj7vbzfibdj0y2wf"))))
    (properties `((upstream-name . "shinyLottie")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-jsonlite r-htmltools r-glue))
    (native-inputs (list r-knitr))
    (home-page "https://camhowitt.github.io/shinyLottie/")
    (synopsis
     "Seamlessly Integrate 'Lottie' Animations into 'shiny' Applications")
    (description
     "Easily integrate and control Lottie animations within shiny applications',
without the need for idiosyncratic expression or use of @code{JavaScript}'.
This includes utilities for generating animation instances, controlling
playback, manipulating animation properties, and more.  For more information on
Lottie', see: <https://airbnb.io/lottie/#/>.  Additionally, see the official
Lottie @code{GitHub} repository at <https://github.com/airbnb/lottie>.")
    (license license:expat)))

(define-public r-shinylogs
  (package
    (name "r-shinylogs")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinylogs" version))
       (sha256
        (base32 "1w19z0zw2k3ni7ckg04q7j0jx4k3cm490z0qccj1smq01wn3czjj"))))
    (properties `((upstream-name . "shinylogs")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny
                             r-nanotime
                             r-jsonlite
                             r-htmltools
                             r-digest
                             r-data-table
                             r-bit64
                             r-anytime))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/dreamRs/shinylogs")
    (synopsis "Record Everything that Happens in a 'Shiny' Application")
    (description
     "Track and record the use of applications and the user's interactions with Shiny
inputs.  Allows to trace the inputs with which the user interacts, the outputs
generated, as well as the errors displayed in the interface.")
    (license license:gpl3)))

(define-public r-shinyloadtest
  (package
    (name "r-shinyloadtest")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyloadtest" version))
       (sha256
        (base32 "01y75imwjbg1wdw7xx50a48py23ya0hgl4jm24lgfl60llm2lfrz"))))
    (properties `((upstream-name . "shinyloadtest")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-xml2
                             r-websocket
                             r-vroom
                             r-svglite
                             r-stringr
                             r-scales
                             r-rlang
                             r-r6
                             r-magrittr
                             r-jsonlite
                             r-httpuv
                             r-ggplot2
                             r-dplyr
                             r-curl
                             r-cli))
    (native-inputs (list esbuild))
    (home-page "https://rstudio.github.io/shinyloadtest/")
    (synopsis "Load Test Shiny Applications")
    (description
     "Assesses the number of concurrent users shiny applications are capable of
supporting, and for directing application changes in order to support a higher
number of users.  Provides facilities for recording shiny application sessions,
playing recorded sessions against a target server at load, and analyzing the
resulting metrics.")
    (license license:gpl3)))

(define-public r-shinylive
  (package
    (name "r-shinylive")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinylive" version))
       (sha256
        (base32 "16r3fbzgv9qjdr08cdazddjxanliymi2mhlx56p0bdrn10miiyp9"))))
    (properties `((upstream-name . "shinylive")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-whisker
                             r-rlang
                             r-renv
                             r-rappdirs
                             r-progress
                             r-pkgdepends
                             r-jsonlite
                             r-httr2
                             r-glue
                             r-gh
                             r-fs
                             r-cli
                             r-brio
                             r-archive))
    (home-page "https://posit-dev.github.io/r-shinylive/")
    (synopsis "Run 'shiny' Applications in the Browser")
    (description
     "Exporting shiny applications with shinylive allows you to run them entirely in a
web browser, without the need for a separate R server.  The traditional way of
deploying shiny applications involves in a separate server and client: the
server runs R and shiny', and clients connect via the web browser.  When an
application is deployed with shinylive', R and shiny run in the web browser (via
@code{webR}'): the browser is effectively both the client and server for the
application.  This allows for your shiny application exported by shinylive to be
hosted by a static web server.")
    (license license:expat)))

(define-public r-shinylink
  (package
    (name "r-shinylink")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ShinyLink" version))
       (sha256
        (base32 "1idb95hdjllrhzm4fx1lrx3d2palbwjshm6ppjw916p7wg7y7wqn"))))
    (properties `((upstream-name . "ShinyLink")))
    (build-system r-build-system)
    (propagated-inputs (list r-vroom
                             r-tidyselect
                             r-tidyr
                             r-shinywidgets
                             r-shinydashboardplus
                             r-shinydashboard
                             r-shiny
                             r-readxl
                             r-magrittr
                             r-lubridate
                             r-jsonlite
                             r-htmlwidgets
                             r-haven
                             r-golem
                             r-ggvenn
                             r-ggplot2
                             r-gender
                             r-fastlink
                             r-dt
                             r-config))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ShinyLink")
    (synopsis "'Shiny' Based Record Linkage Tool")
    (description
     "This package provides a bridge is created between existing robust open-source
record linkage algorithms and an urgently needed user-friendly platform that
removes financial and technical barriers, setting a new standard for data
interoperability in public health and bioinformatics.  The @code{fastLink}
algorithms are used for matching.  Ted Enamorado et al. (2019)
<doi:10.1017/S0003055418000783>.")
    (license license:gpl3)))

(define-public r-shinylight
  (package
    (name "r-shinylight")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinylight" version))
       (sha256
        (base32 "0kqblyxzdknnnhi8ma4aviqrsysnfarz5pfqjzs86wsrp1ghplzj"))))
    (properties `((upstream-name . "shinylight")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-later r-jsonlite r-httpuv))
    (native-inputs (list esbuild))
    (home-page "https://cran.r-project.org/package=shinylight")
    (synopsis "Web Interface to 'R' Functions")
    (description
     "Web front end for your R functions producing plots or tables.  If you have a
function or set of related functions, you can make them available over the
internet through a web browser.  This is the same motivation as the shiny
package, but note that the development of shinylight is not in any way linked to
that of shiny (beyond the use of the httpuv package).  You might prefer
shinylight to shiny if you want a lighter weight deployment with easier
horizontal scaling, or if you want to develop your front end yourself in
@code{JavaScript} and HTML just using a lightweight remote procedure call
interface to your R code on the server.")
    (license license:gpl3)))

(define-public r-shinyknobs
  (package
    (name "r-shinyknobs")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyKnobs" version))
       (sha256
        (base32 "1rxlvq4vr87krf7d6mpx0y53hyz8q1jnx6nv7mlhc1ck13q6fgac"))))
    (properties `((upstream-name . "shinyKnobs")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-htmltools))
    (home-page "https://github.com/cotepat/shinyKnobs")
    (synopsis "Collection of Knob Inputs for 'shiny'")
    (description
     "This package provides a collection of highly configurable, touch-enabled knob
input controls for shiny'.  These components can be styled to fit in perfectly
in any app, and allow users to set precise values through many input modalities.
 Users can touch-and-drag, click-and-drag, scroll their mouse wheel, double
click, or use keyboard input.")
    (license license:expat)))

(define-public r-shinykgode
  (package
    (name "r-shinykgode")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyKGode" version))
       (sha256
        (base32 "004zwz4kqfijzvvwkq8v1gi25w1wrdlg8230g93w36gbi6cl2vk1"))))
    (properties `((upstream-name . "shinyKGode")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml
                             r-shinyjs
                             r-shiny
                             r-reshape2
                             r-pspline
                             r-pracma
                             r-mvtnorm
                             r-kgode
                             r-gridextra
                             r-ggplot2))
    (home-page "https://github.com/joewandy/shinyKGode")
    (synopsis
     "An Interactive Application for ODE Parameter Inference Using Gradient Matching")
    (description
     "An interactive Shiny application to perform fast parameter inference on
dynamical systems (described by ordinary differential equations) using gradient
matching.  Please see the project page for more details.")
    (license license:gpl2)))

(define-public r-shinyitemanalysis
  (package
    (name "r-shinyitemanalysis")
    (version "1.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ShinyItemAnalysis" version))
       (sha256
        (base32 "12knai3ar8c091pwba34dz74yhnx7kd0f6si0hcwamdcnvb6i324"))))
    (properties `((upstream-name . "ShinyItemAnalysis")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rstudioapi
                             r-rlang
                             r-purrr
                             r-psych
                             r-nnet
                             r-mirt
                             r-magrittr
                             r-lme4
                             r-ggplot2
                             r-dplyr
                             r-difr))
    (home-page "https://www.ShinyItemAnalysis.org")
    (synopsis "Test and Item Analysis via Shiny")
    (description
     "Package including functions and interactive shiny application for the
psychometric analysis of educational tests, psychological assessments,
health-related and other types of multi-item measurements, or ratings from
multiple raters.")
    (license license:gpl3)))

(define-public r-shinyirt
  (package
    (name "r-shinyirt")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyIRT" version))
       (sha256
        (base32 "01x7nhjs1dx2ldwznhbavc5vzqhgkn73xg395vlf2hgzbxa2ydvj"))))
    (properties `((upstream-name . "shinyIRT")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinydashboard
                             r-shinycssloaders
                             r-shiny
                             r-readxl
                             r-psych
                             r-mirt
                             r-magrittr
                             r-irtoys))
    (home-page "https://cran.r-project.org/package=shinyIRT")
    (synopsis "Item Response Theory Analysis with a 'shiny' Application")
    (description
     "Performing Item Response Theory analysis such as parameter estimation, ability
estimation, item and model fit analyse, local independence assumption,
dimensionality assumption, characteristic and information curves under various
models with a user friendly shiny interface.")
    (license license:gpl3+)))

(define-public r-shinyinvoice
  (package
    (name "r-shinyinvoice")
    (version "0.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyInvoice" version))
       (sha256
        (base32 "024dnyrdkb4ws0glj6yib0fxz22biysvndsppffdhxri3afvs2by"))))
    (properties `((upstream-name . "shinyInvoice")))
    (build-system r-build-system)
    (inputs (list pandoc))
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-shiny
                             r-rlang
                             r-quantmod
                             r-lubridate
                             r-dplyr))
    (home-page "https://github.com/fernandoroa/invoice-public")
    (synopsis "Shiny App - Generate a Pdf Invoice with 'Rmarkdown'")
    (description
     "Generate an invoice containing a header with invoice number and businesses
details.  The invoice table contains any of: salary, one-liner costs, grouped
costs.  Under the table signature and bank account details appear.  Pages are
numbered when more than one.  Source .json and .Rmd files are editable in the
app.  A .csv file with raw data can be downloaded.  This package includes
functions for getting exchange rates between currencies based on quantmod (Ryan
and Ulrich, 2023 <https://CRAN.R-project.org/package=quantmod>).")
    (license license:gpl2+)))

(define-public r-shinyhugeplot
  (package
    (name "r-shinyhugeplot")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyHugePlot" version))
       (sha256
        (base32 "0i5qgj24ar1ig5p7wmd5acf4k3yb879k4zsm7575jnz8cs3z13vl"))))
    (properties `((upstream-name . "shinyHugePlot")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-shinyjs
                             r-shiny
                             r-rlang
                             r-r6
                             r-purrr
                             r-plotly
                             r-nanotime
                             r-lazyeval
                             r-jsonlite
                             r-htmltools
                             r-duckdb
                             r-dplyr
                             r-dbi
                             r-data-table
                             r-bit64
                             r-assertthat))
    (home-page "https://cran.r-project.org/package=shinyHugePlot")
    (synopsis "Efficient Plotting of Large-Sized Data")
    (description
     "This package provides a tool to plot data with a large sample size using shiny
and plotly'.  Relatively small samples are obtained from the original data using
a specific algorithm.  The samples are updated according to a user-defined x
range.  Jonas Van Der Donckt, Jeroen Van Der Donckt, Emiel Deprost (2022)
<https://github.com/predict-idlab/plotly-resampler>.")
    (license license:expat)))

(define-public r-shinyhttr
  (package
    (name "r-shinyhttr")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyhttr" version))
       (sha256
        (base32 "1snrl7bg1bc5jxqs1c5a2ir6qnf1nq576aigk19k0ms4gb9hh66m"))))
    (properties `((upstream-name . "shinyhttr")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinywidgets r-httr))
    (home-page "https://github.com/curso-r/shinyhttr")
    (synopsis "Progress Bars for Downloads in 'shiny' Apps")
    (description
     "Modifies the progress() function from httr package to let it send output to
@code{progressBar}() function from @code{shinyWidgets} package.  It is just a
tweak at the original functions from httr package to make it smooth for shiny
developers.")
    (license license:expat)))

(define-public r-shinyheatmaply
  (package
    (name "r-shinyheatmaply")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyHeatmaply" version))
       (sha256
        (base32 "1n7c7dvvb9kqay2phcf0bkwi990x1cjl7js6pq05xx7rli5bc5h4"))))
    (properties `((upstream-name . "shinyHeatmaply")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-shiny
                             r-rmarkdown
                             r-readxl
                             r-plotly
                             r-htmltools
                             r-heatmaply
                             r-dplyr))
    (home-page "https://github.com/yonicd/shinyHeatmaply")
    (synopsis "Deploy 'heatmaply' using 'shiny'")
    (description
     "Access functionality of the heatmaply package through Shiny UI'.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-shinygovstyle
  (package
    (name "r-shinygovstyle")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyGovstyle" version))
       (sha256
        (base32 "1irhcyj4ixlfz4qilz2vwls6a6sc0zzzixgvizh7cwnkjf962cg9"))))
    (properties `((upstream-name . "shinyGovstyle")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-stringr
                             r-shinyjs
                             r-shiny
                             r-purrr
                             r-magrittr
                             r-jsonlite
                             r-htmltools))
    (native-inputs (list esbuild))
    (home-page "https://github.com/moj-analytical-services/shinyGovstyle")
    (synopsis "Custom Gov Style Inputs for Shiny")
    (description
     "Collection of shiny application styling that are the based on the GOV.UK Design
System.  See <https://design-system.service.gov.uk/components/> for details.")
    (license license:gpl3)))

(define-public r-shinyglide
  (package
    (name "r-shinyglide")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyglide" version))
       (sha256
        (base32 "1w4bxpnpnxk1v2904i94hkrk6hv5i7kp2paajm5xnfps7gqc0f1r"))))
    (properties `((upstream-name . "shinyglide")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-htmltools))
    (native-inputs (list r-knitr))
    (home-page "https://juba.github.io/shinyglide/")
    (synopsis "Glide Component for Shiny Applications")
    (description
     "Insert Glide @code{JavaScript} component into Shiny applications for carousel or
assistant-like user interfaces.")
    (license license:gpl3+)))

(define-public r-shinygizmo
  (package
    (name "r-shinygizmo")
    (version "0.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyGizmo" version))
       (sha256
        (base32 "0pjmin2qmfqg5ppqx8ld6pv8y91ci6bagmp4bi476i9bk9q50hz8"))))
    (properties `((upstream-name . "shinyGizmo")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-shinywidgets
                             r-shiny
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-htmlwidgets
                             r-htmltools
                             r-glue))
    (native-inputs (list esbuild))
    (home-page "https://cran.r-project.org/package=shinyGizmo")
    (synopsis "Custom Components for Shiny Applications")
    (description
     "This package provides useful UI components and input widgets for Shiny
applications.  The offered components allow to apply non-standard operations and
view to your Shiny application, but also help to overcome common performance
issues.")
    (license license:expat)))

(define-public r-shinyfullscreen
  (package
    (name "r-shinyfullscreen")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyfullscreen" version))
       (sha256
        (base32 "0wx3jhma27701k4cmmls46rfvnix5zylxf09wdn7h6v3zdwvicq8"))))
    (properties `((upstream-name . "shinyfullscreen")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-shiny r-jsonlite))
    (native-inputs (list esbuild))
    (home-page "https://github.com/etiennebacher/shinyfullscreen")
    (synopsis "Display 'HTML' Elements on Full Screen in 'Shiny' Apps")
    (description
     "In Shiny apps, it is sometimes useful to see a plot or a table in full screen.
Using Shinyfullscreen', you can easily designate the HTML elements that can be
displayed on fullscreen and use buttons to trigger the fullscreen view.")
    (license license:expat)))

(define-public r-shinyfilter
  (package
    (name "r-shinyfilter")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyfilter" version))
       (sha256
        (base32 "00n1l2jzzviz0f2gialj4zc6xfymzqi99raqhmlm0x9fbj1syfga"))))
    (properties `((upstream-name . "shinyfilter")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-shinyjs r-shinybs r-shiny r-reactable))
    (home-page "https://github.com/jsugarelli/shinyfilter/")
    (synopsis "Use Interdependent Filters on Table Columns in Shiny Apps")
    (description
     "Allows to connect @code{selectizeInputs} widgets as filters to a reactable
table.  As known from spreadsheet applications, column filters are
interdependent, so each filter only shows the values that are really available
at the moment based on the current selection in other filters.  Filter values
currently not available (and also those being available) can be shown via
popovers or tooltips.")
    (license license:gpl3)))

(define-public r-shinyfeedback
  (package
    (name "r-shinyfeedback")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyFeedback" version))
       (sha256
        (base32 "1xfznq2d4wkk0b0y4k25rwg4d3bhn3gxlkf3chfqk44f4rgril11"))))
    (properties `((upstream-name . "shinyFeedback")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-shiny r-jsonlite r-htmltools r-fontawesome))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://github.com/merlinoa/shinyFeedback")
    (synopsis "Display User Feedback in Shiny Apps")
    (description "Easily display user feedback in Shiny apps.")
    (license license:expat)))

(define-public r-shinyexprportal
  (package
    (name "r-shinyexprportal")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyExprPortal" version))
       (sha256
        (base32 "1cldz5m7w3db3izgqcpichpixfixhdy7rz4iksnsdywih0kdhqwc"))))
    (properties `((upstream-name . "shinyExprPortal")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-yaml
                             r-vegawidget
                             r-tidyr
                             r-shinyhelper
                             r-shiny
                             r-rlang
                             r-rfast
                             r-qvalue
                             r-markdown
                             r-iheatmapr
                             r-htmltools
                             r-dt
                             r-dplyr
                             r-data-table
                             r-config
                             r-cli
                             r-bslib))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://c4tb.github.io/shinyExprPortal/")
    (synopsis
     "Configurable 'shiny' Portal for Sharing Analysis of Molecular Expression Data")
    (description
     "Enables deploying configuration file-based shiny apps with minimal programming
for interactive exploration and analysis showcase of molecular expression data.
For exploration, supports visualization of correlations between rows of an
expression matrix and a table of observations, such as clinical measures, and
comparison of changes in expression over time.  For showcase, enables
visualizing the results of differential expression from package such as limma',
co-expression modules from WGCNA and lower dimensional projections.")
    (license license:expat)))

(define-public r-shinyeffects
  (package
    (name "r-shinyeffects")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyEffects" version))
       (sha256
        (base32 "1ajar09hmrk7vcw89wkgvkz2yl0cfnkz915b57w1lmbvpxzgsq3i"))))
    (properties `((upstream-name . "shinyEffects")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-htmltools))
    (home-page "https://github.com/RinteRface/shinyEffects")
    (synopsis "Customize Your Web Apps with Fancy Effects")
    (description
     "Add fancy CSS effects to your shinydashboards or shiny apps.  100% compatible
with @code{shinydashboardPlus} and bs4Dash'.")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-shinydrive
  (package
    (name "r-shinydrive")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinydrive" version))
       (sha256
        (base32 "16yfc9grmjzkpl6xzqn5rpkqd2qnlj4qdgia8img794l6j0y8dgp"))))
    (properties `((upstream-name . "shinydrive")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-shiny
                             r-r-utils
                             r-knitr
                             r-htmltools
                             r-dt))
    (home-page "https://cran.r-project.org/package=shinydrive")
    (synopsis "File Sharing Shiny Module")
    (description
     "Shiny module for easily sharing files between users.  Admin can add, remove,
edit and download file.  User can only download file.  It's also possible to
manage files using R functions directly.")
    (license license:gpl3)))

(define-public r-shinydnd
  (package
    (name "r-shinydnd")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyDND" version))
       (sha256
        (base32 "0nkvz4hmjzmxlzj4vkjrdy8xlbxapg1amk180irgvwicqldi4jpm"))))
    (properties `((upstream-name . "shinyDND")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-htmltools))
    (home-page "https://github.com/ayayron/shinydnd")
    (synopsis "Shiny Drag-n-Drop")
    (description
     "Add functionality to create drag and drop div elements in shiny.")
    (license license:gpl3)))

(define-public r-shinydlplot
  (package
    (name "r-shinydlplot")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinydlplot" version))
       (sha256
        (base32 "1gxr98p2zyvwa6q8a80bz4h1pkgbdn58cwqbd19dbn4liszfxsy8"))))
    (properties `((upstream-name . "shinydlplot")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinyjs
                             r-shinybs
                             r-shiny
                             r-plotly
                             r-htmlwidgets
                             r-htmltools))
    (home-page "https://cran.r-project.org/package=shinydlplot")
    (synopsis "Add a Download Button to a 'shiny' Plot or 'plotly'")
    (description
     "Add a download button to a shiny plot or plotly that appears when the plot is
hovered.  A tooltip, styled to resemble plotly buttons, is displayed on hover of
the download button.  The download button can be used to allow users to download
the dataset used for a plot.")
    (license license:expat)))

(define-public r-shinydbauth
  (package
    (name "r-shinydbauth")
    (version "1.0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinydbauth" version))
       (sha256
        (base32 "04f8cq5vismbl05cvkd4kh5h1bzank5fhsgfmxdqwpy3jp68a1kf"))))
    (properties `((upstream-name . "shinydbauth")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-shiny
                             r-scrypt
                             r-r6
                             r-r-utils
                             r-openssl
                             r-htmltools
                             r-glue
                             r-dt
                             r-billboarder))
    (home-page "https://github.com/diegoefe/shinydbauth")
    (synopsis "Simple Authentification for 'shiny' Applications")
    (description
     "This package provides a simple authentification mechanism for single shiny
applications.  Authentification and password change functionality are performed
calling user provided functions that typically access some database backend.
Source code of main applications is protected until authentication is
successful.")
    (license license:gpl3)))

(define-public r-shinydatetimepickers
  (package
    (name "r-shinydatetimepickers")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyDatetimePickers" version))
       (sha256
        (base32 "1vl61mp3skpzq7cbcxz6ylkiajj7krk00ci9kzqb8d0hynrgx1ii"))))
    (properties `((upstream-name . "shinyDatetimePickers")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-reactr r-lubridate r-htmltools))
    (home-page "https://github.com/stla/shinyDatetimePickers")
    (synopsis "Some Datetime Pickers for 'Shiny'")
    (description
     "This package provides three types of datetime pickers for usage in a Shiny UI. A
datetime picker is an input field for selecting both a date and a time.")
    (license license:gpl3)))

(define-public r-shinycyjs
  (package
    (name "r-shinycyjs")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyCyJS" version))
       (sha256
        (base32 "1xgrjk7iyg313x0wkg9q3mp63aidbbhxmjp3jv8cgr4w6qcv0ny6"))))
    (properties `((upstream-name . "shinyCyJS")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-htmlwidgets))
    (native-inputs (list esbuild))
    (home-page "https://github.com/jhk0530/shinyCyJS")
    (synopsis "Create Interactive Network Visualizations in R and 'shiny'")
    (description
     "Create Interactive Graph (Network) Visualizations. @code{shinyCyJS} can be used
in Shiny apps or viewed from Rstudio Viewer. @code{shinyCyJS} includes API to
build Graph model like node or edge with customized attributes for R.
@code{shinyCyJS} is built with cytoscape.js and htmlwidgets R package.")
    (license license:expat)))

(define-public r-shinycroneditor
  (package
    (name "r-shinycroneditor")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinycroneditor" version))
       (sha256
        (base32 "14z2yzzsycy5vplrim40j295mcw74rk3bxwpncavk3widjbmvjhx"))))
    (properties `((upstream-name . "shinycroneditor")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-htmlwidgets r-htmltools))
    (native-inputs (list esbuild))
    (home-page "https://cran.r-project.org/package=shinycroneditor")
    (synopsis "'shiny' Cron Expression Input Widget")
    (description
     "This package provides a widget for shiny apps to handle schedule expression
input, using the cron-expression-input @code{JavaScript} component.  Note that
this does not edit the crontab file, it is just an input element for the
schedules.  See
<https://github.com/@code{DatalabFabriek/shinycroneditor/blob/main/inst/examples/shiny-app.R>}
for an example implementation.")
    (license license:gpl3+)))

(define-public r-shinycox
  (package
    (name "r-shinycox")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyCox" version))
       (sha256
        (base32 "16hba9y3629x0vvl6gal70nffssmy04p7xsrnq2j0gk7gk1p6gbg"))))
    (properties `((upstream-name . "shinyCox")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-shiny))
    (home-page "https://github.com/harryc598/shinyCox")
    (synopsis
     "Create 'shiny' Applications for Cox Proportional Hazards Models")
    (description
     "Takes one or more fitted Cox proportional hazards models and writes a shiny
application to a directory specified by the user.  The shiny application
displays predicted survival curves based on user input, and contains none of the
original data used to create the Cox model or models.  The goal is towards
visualization and presentation of predicted survival curves.")
    (license license:lgpl3+)))

(define-public r-shinycohortbuilder
  (package
    (name "r-shinycohortbuilder")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyCohortBuilder" version))
       (sha256
        (base32 "0l22fwq6mk9z8bv3vpxbf6gfimknwidbx55gjxjsww85v7qmh6r5"))))
    (properties `((upstream-name . "shinyCohortBuilder")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-trycatchlog
                             r-tibble
                             r-shinywidgets
                             r-shinygizmo
                             r-shiny
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-lifecycle
                             r-jsonlite
                             r-htmlwidgets
                             r-htmltools
                             r-highr
                             r-glue
                             r-ggplot2
                             r-ggiraph
                             r-dplyr
                             r-cohortbuilder
                             r-bslib))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://r-world-devs.github.io/shinyCohortBuilder/")
    (synopsis "Modular Cohort-Building Framework for Analytical Dashboards")
    (description
     "You can easily add advanced cohort-building component to your analytical
dashboard or simple Shiny app.  Then you can instantly start building cohorts
using multiple filters of different types, filtering datasets, and filtering
steps.  Filters can be complex and data-specific, and together with multiple
filtering steps you can use complex filtering rules.  The cohort-building
sidebar panel allows you to easily work with filters, add and remove filtering
steps.  It helps you with handling missing values during filtering, and provides
instant filtering feedback with filter feedback plots.  The GUI panel is not
only compatible with native shiny bookmarking, but also provides reproducible R
code.")
    (license license:expat)))

(define-public r-shinychatr
  (package
    (name "r-shinychatr")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyChatR" version))
       (sha256
        (base32 "174jm8a52h7lr4bvl88ajllhknzczq8lwvbrldh07hiphbid071d"))))
    (properties `((upstream-name . "shinyChatR")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-r6 r-purrr r-dbi r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/julianschmocker/shinyChatR")
    (synopsis "R Shiny Chat Module")
    (description
     "This package provides an easy-to-use module for adding a chat to a Shiny app.
Allows users to send messages and view messages from other users.  Messages can
be stored in a database or a .rds file.")
    (license license:gpl3+)))

(define-public r-shinychakraui
  (package
    (name "r-shinychakraui")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyChakraUI" version))
       (sha256
        (base32 "08qy6qrrprqlarwd3kab9s92dlx51jkhk7mikkf0mdhl6n2q26py"))))
    (properties `((upstream-name . "shinyChakraUI")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-shiny
                             r-rlang
                             r-reactr
                             r-jsonlite
                             r-htmltools
                             r-formatr
                             r-fontawesome))
    (home-page "https://github.com/stla/shinyChakraUI")
    (synopsis "Wrapper of the 'React' Library 'Chakra UI' for 'Shiny'")
    (description
     "Makes the React library Chakra UI usable in Shiny apps.  Chakra UI components
include alert dialogs, drawers (sliding panels), menus, modals, popovers,
sliders, and more.")
    (license license:gpl3+)))

(define-public r-shinychakraslider
  (package
    (name "r-shinychakraslider")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyChakraSlider" version))
       (sha256
        (base32 "1vsbw9dm8ai2w6mfdaksxh13ryh2awajqgkcwcxb99l47h17rvpy"))))
    (properties `((upstream-name . "shinyChakraSlider")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-reactr r-htmltools))
    (home-page "https://github.com/stla/shinyChakraSlider")
    (synopsis "Combined Slider and Numeric Input for 'Shiny'")
    (description
     "This package provides a combined slider and numeric input for usage in a Shiny
app.  The slider and the numeric input are linked together: each one is updated
when the other one changes.  Many styling properties are customizable (e.g.
colors and size).")
    (license license:gpl3)))

(define-public r-shinybusy
  (package
    (name "r-shinybusy")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinybusy" version))
       (sha256
        (base32 "1bjfi5xdidj8n84mylghvrncvcyg625kj8pnizl59m649k3n10v2"))))
    (properties `((upstream-name . "shinybusy")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-shiny r-jsonlite r-htmlwidgets r-htmltools))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://github.com/dreamRs/shinybusy")
    (synopsis "Busy Indicators and Notifications for 'Shiny' Applications")
    (description
     "Add indicators (spinner, progress bar, gif) in your shiny applications to show
the user that the server is busy.  And other tools to let your users know
something is happening (send notifications, reports, ...).")
    (license license:gpl3)))

(define-public r-shinybrowser
  (package
    (name "r-shinybrowser")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinybrowser" version))
       (sha256
        (base32 "0ah25x2r92crc761ixmzs9mnzdvwrnmdqfjv410138x8kb8f9pm8"))))
    (properties `((upstream-name . "shinybrowser")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny))
    (home-page "https://cran.r-project.org/package=shinybrowser")
    (synopsis "Find Out Information About a User's Web Browser in 'Shiny'")
    (description
     "Sometimes it's useful to know some information about your user in a Shiny app.
The available information is: browser name (such as Chrome or Safari') and
version, device type (mobile or desktop), operating system (such as Windows or
Mac or Android') and version, and browser dimensions.")
    (license license:expat)))

(define-public r-shinybrms
  (package
    (name "r-shinybrms")
    (version "1.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinybrms" version))
       (sha256
        (base32 "0qsmgswkk7l7nn4sabisg4gwiqiicjdxi0g96778ad4d4prnx107"))))
    (properties `((upstream-name . "shinybrms")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-rstan r-rlang r-brms))
    (home-page "https://fweber144.github.io/shinybrms/")
    (synopsis "Graphical User Interface ('shiny' App) for 'brms'")
    (description
     "This package provides a graphical user interface (GUI) for fitting Bayesian
regression models using the package brms which in turn relies on Stan
(<https://mc-stan.org/>).  The shinybrms GUI is a shiny app.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-shinyauthr
  (package
    (name "r-shinyauthr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyauthr" version))
       (sha256
        (base32 "0apaqjkxpr96kx9indl0pifk5y75gdx5npfcvxfg5k2crpx9vmxk"))))
    (properties `((upstream-name . "shinyauthr")))
    (build-system r-build-system)
    (propagated-inputs (list r-sodium
                             r-shinyjs
                             r-shiny
                             r-rlang
                             r-glue
                             r-dplyr))
    (home-page "https://github.com/paulc91/shinyauthr")
    (synopsis "'Shiny' Authentication Modules")
    (description
     "Add in-app user authentication to shiny', allowing you to secure publicly hosted
apps and build dynamic user interfaces from user information.")
    (license license:expat)))

(define-public r-shinyanimate
  (package
    (name "r-shinyanimate")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyanimate" version))
       (sha256
        (base32 "1n8nf42imw0qcgdh1h0yhqid2g688cxrp22npj3z1mnynmjvsxp9"))))
    (properties `((upstream-name . "shinyanimate")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny))
    (home-page "https://github.com/Swechhya/shinyanimate")
    (synopsis "Animation for 'shiny' Elements")
    (description
     "An extension of animate.css that allows user to easily add animations to any UI
element in shiny app using the elements id.")
    (license license:expat)))

(define-public r-shinyalert
  (package
    (name "r-shinyalert")
    (version "3.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyalert" version))
       (sha256
        (base32 "1gips6hr8h8njv2hx1dv0nqcmihjknqkkyydhff9cgfgs242g27f"))))
    (properties `((upstream-name . "shinyalert")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-uuid r-shiny r-htmltools))
    (native-inputs (list esbuild))
    (home-page "https://github.com/daattali/shinyalert")
    (synopsis "Easily Create Pretty Popup Messages (Modals) in 'Shiny'")
    (description
     "Easily create pretty popup messages (modals) in Shiny'.  A modal can contain
text, images, OK/Cancel buttons, an input to get a response from the user, and
many more customizable options.")
    (license license:expat)))

(define-public r-shinyaframe
  (package
    (name "r-shinyaframe")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinyaframe" version))
       (sha256
        (base32 "1flhgsm4q7p5acb41v73pf8ni92y9dwdppxjb9czlz1sxwf03p1s"))))
    (properties `((upstream-name . "shinyaframe")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-shiny r-htmlwidgets r-htmltools))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://cran.r-project.org/package=shinyaframe")
    (synopsis
     "'WebVR' Data Visualizations with 'RStudio Shiny' and 'Mozilla A-Frame'")
    (description
     "Make R data available in Web-based virtual reality experiences for immersive,
cross-platform data visualizations.  Includes the gg-aframe @code{JavaScript}
package for a Grammar of Graphics declarative HTML syntax to create
3-dimensional data visualizations with Mozilla A-Frame <https://aframe.io>.")
    (license license:agpl3)))

(define-public r-shiny-telemetry
  (package
    (name "r-shiny-telemetry")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shiny.telemetry" version))
       (sha256
        (base32 "1kn89gfz9a8j3ymmv2mpgazixyig6g1vxjd1a5incnj5qd58inv5"))))
    (properties `((upstream-name . "shiny.telemetry")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-shiny
                             r-rsqlite
                             r-rlang
                             r-r6
                             r-purrr
                             r-odbc
                             r-lubridate
                             r-logger
                             r-lifecycle
                             r-jsonlite
                             r-httr2
                             r-htmltools
                             r-glue
                             r-dplyr
                             r-digest
                             r-checkmate))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://appsilon.github.io/shiny.telemetry/")
    (synopsis "'Shiny' App Usage Telemetry")
    (description
     "Enables instrumentation of Shiny apps for tracking user session events such as
input changes, browser type, and session duration.  These events can be sent to
any of the available storage backends and analyzed using the included Shiny app
to gain insights about app usage and adoption.")
    (license license:lgpl3)))

(define-public r-shiny-tailwind
  (package
    (name "r-shiny-tailwind")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shiny.tailwind" version))
       (sha256
        (base32 "12z6cnir7gyh01pw2l2wmsx1kw4yjh7i4p6afr05mqik9kfiljw2"))))
    (properties `((upstream-name . "shiny.tailwind")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-htmltools))
    (home-page "https://github.com/kylebutts/shiny.tailwind")
    (synopsis "'TailwindCSS' for Shiny Apps")
    (description
     "Allows @code{TailwindCSS} to be used in Shiny apps with just-in-time compiling,
custom css with @@apply directive, and custom tailwind configurations.")
    (license license:expat)))

(define-public r-shiny-semantic
  (package
    (name "r-shiny-semantic")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shiny.semantic" version))
       (sha256
        (base32 "1gfkwgij365j0i9wz7dqdadpa9mdarn8n70cygm46fcak7lp08h8"))))
    (properties `((upstream-name . "shiny.semantic")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny
                             r-semantic-assets
                             r-r6
                             r-purrr
                             r-magrittr
                             r-jsonlite
                             r-htmlwidgets
                             r-htmltools
                             r-glue))
    (native-inputs (list r-knitr))
    (home-page "https://appsilon.github.io/shiny.semantic/")
    (synopsis "Semantic UI Support for Shiny")
    (description
     "Creating a great user interface for your Shiny apps can be a hassle, especially
if you want to work purely in R and don't want to use, for instance HTML
templates.  This package adds support for a powerful UI library Fomantic UI -
<https://fomantic-ui.com/> (before Semantic).  It also supports universal UI
input binding that works with various DOM elements.")
    (license license:expat)))

(define-public r-shiny-router
  (package
    (name "r-shiny-router")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shiny.router" version))
       (sha256
        (base32 "1zg8cdxmw620i1iv7jrghd768gw7iv52hi6lx79xvnfjz8w4si3x"))))
    (properties `((upstream-name . "shiny.router")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-rlang r-htmltools r-glue))
    (home-page "https://appsilon.github.io/shiny.router/")
    (synopsis "Basic Routing for Shiny Web Applications")
    (description
     "It is a simple router for your Shiny apps.  The router allows you to create
dynamic web applications with real-time User Interface and easily share url to
pages within your Shiny apps.")
    (license license:expat)))

(define-public r-shiny-reglog
  (package
    (name "r-shiny-reglog")
    (version "0.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shiny.reglog" version))
       (sha256
        (base32 "018dnnisidmn799api7kgwknvm3ka2sk1vvq2wcx65acqc450i18"))))
    (properties `((upstream-name . "shiny.reglog")))
    (build-system r-build-system)
    (propagated-inputs (list r-uuid
                             r-stringi
                             r-shinyjs
                             r-shiny
                             r-scrypt
                             r-r6
                             r-lubridate
                             r-lifecycle
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://statismike.github.io/shiny.reglog/")
    (synopsis "Optional Login and Registration Module System for ShinyApps")
    (description
     "@code{RegLog} system provides a set of shiny modules to handle register
procedure for your users, alongside with login, edit credentials and password
reset functionality.  It provides support for popular SQL databases and
optionally googlesheet-based database for easy setup.  For email sending it
provides support for emayili and gmailr backends.  Architecture makes
customizing usability pretty straightforward.  The authentication system created
with shiny.reglog is designed to be optional: user don't need to be logged-in to
access your application, but when logged-in the user data can be used to read
from and write to relational databases.")
    (license license:expat)))

(define-public r-shiny-react
  (package
    (name "r-shiny-react")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shiny.react" version))
       (sha256
        (base32 "115kxwfpwirj34djzk7r4akdq1a6ril3lg9xzii60wiqc1panbdd"))))
    (properties `((upstream-name . "shiny.react")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-stringi
                             r-shiny
                             r-rlang
                             r-purrr
                             r-logger
                             r-jsonlite
                             r-htmltools
                             r-glue))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://appsilon.github.io/shiny.react/")
    (synopsis "Tools for Using React in Shiny")
    (description
     "This package provides a toolbox for defining React component wrappers which can
be used seamlessly in Shiny apps.")
    (license license:lgpl3+)))

(define-public r-shiny-pwa
  (package
    (name "r-shiny-pwa")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shiny.pwa" version))
       (sha256
        (base32 "120wqqy7fh7nm2bib3dq02a92jb3lykkl7qayiw6bnax8sdr18yw"))))
    (properties `((upstream-name . "shiny.pwa")))
    (build-system r-build-system)
    (propagated-inputs (list r-urltools r-shiny r-readr r-htmltools))
    (home-page "https://github.com/pedrocoutinhosilva/shiny.pwa")
    (synopsis "Progressive Web App Support for Shiny")
    (description
     "Adds Progressive Web App support for Shiny apps, including desktop and mobile
installations.")
    (license license:expat)))

(define-public r-shiny-info
  (package
    (name "r-shiny-info")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shiny.info" version))
       (sha256
        (base32 "162wvb02z5h1y7xgsyl6wv88zlfmbm1jlnz48f56fyybs54baw41"))))
    (properties `((upstream-name . "shiny.info")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-glue r-git2r))
    (home-page "https://cran.r-project.org/package=shiny.info")
    (synopsis "'shiny' Info")
    (description
     "Displays simple diagnostic information of the shiny project in the user
interface of the app.")
    (license license:expat)))

(define-public r-shiny-i18n
  (package
    (name "r-shiny-i18n")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shiny.i18n" version))
       (sha256
        (base32 "0kcdvan8ds1kdqdxk6yvqpxlxv6xj4nxr8mp1qns3fzklyf4n4gy"))))
    (properties `((upstream-name . "shiny.i18n")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-stringr
                             r-shiny
                             r-rstudioapi
                             r-r6
                             r-jsonlite
                             r-glue))
    (home-page "https://appsilon.github.io/shiny.i18n/")
    (synopsis "Shiny Applications Internationalization")
    (description
     "It provides easy internationalization of Shiny applications.  It can be used as
standalone translation package to translate reports, interactive visualizations
or graphical elements as well.")
    (license license:expat)))

(define-public r-shiny-fluent
  (package
    (name "r-shiny-fluent")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shiny.fluent" version))
       (sha256
        (base32 "0d6925apv6vwbhyzxlzli7j18yzz4raxf32p7mgwsrwirjkhj21s"))))
    (properties `((upstream-name . "shiny.fluent")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny-react r-shiny r-purrr r-jsonlite
                             r-htmltools))
    (home-page "https://appsilon.github.io/shiny.fluent/")
    (synopsis "Microsoft Fluent UI for Shiny Apps")
    (description
     "This package provides a rich set of UI components for building Shiny
applications, including inputs, containers, overlays, menus, and various
utilities.  All components from Fluent UI (the underlying @code{JavaScript}
library) are available and have usage examples in R.")
    (license license:lgpl3)))

(define-public r-shiny-exe
  (package
    (name "r-shiny-exe")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shiny.exe" version))
       (sha256
        (base32 "0657ywbnkg3k67321v9rkmfqqrkn89qzyidri0gfhs53c96yrijm"))))
    (properties `((upstream-name . "shiny.exe")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/AODiakite")
    (synopsis "Launch a Shiny Application without Opening R or RStudio")
    (description
     "Launch an application by a simple click without opening R or RStudio.  The
package has 3 functions of which only one is essential in its use,
`shiny.exe()`.  It generates a script in the open shiny project then create a
shortcut in the same folder that allows you to launch the app by clicking.If you
set `host = public'`, the application will be launched on the public server to
which you are connected.  Thus, all other devices connected to the same server
will be able to access the application through the link of your `IPv4` extended
by the port.  You can stop the application by leaving the terminal opened by the
shortcut.")
    (license license:gpl2)))

(define-public r-shiny-emptystate
  (package
    (name "r-shiny-emptystate")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shiny.emptystate" version))
       (sha256
        (base32 "07vw8dw6yv3gqp400h2b1x2nv2v3hzgfgmhb8fxxdicbx08vxv47"))))
    (properties `((upstream-name . "shiny.emptystate")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-r6 r-htmltools r-fontawesome))
    (home-page "https://appsilon.github.io/shiny.emptystate/")
    (synopsis "Empty State Components for 'Shiny'")
    (description
     "Offers a comprehensive solution for managing empty states in Shiny applications.
 It provides tools to create both default and customizable components for
scenarios where data is absent or doesn't match user-defined filters.  The
package prioritizes user experience, ensuring clarity and consistency even when
data is not available to display.")
    (license license:lgpl3)))

(define-public r-shiny-destroy
  (package
    (name "r-shiny-destroy")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shiny.destroy" version))
       (sha256
        (base32 "07hn079pmyilmjh7gjlz608987j4z42l7s3fskwpc5asnjnx2brq"))))
    (properties `((upstream-name . "shiny.destroy")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-rlang r-purrr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=shiny.destroy")
    (synopsis "Create Destroyable Modules in 'Shiny'")
    (description
     "Enables the complete removal of various Shiny components, such as inputs,
outputs and modules.  It also aids in the removal of observers that have been
created in dynamically created modules.")
    (license license:expat)))

(define-public r-shiny-blueprint
  (package
    (name "r-shiny-blueprint")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shiny.blueprint" version))
       (sha256
        (base32 "1hxn9z565g34fx7wbx5648sx2q4q1w2nggbfcjplrjwg709swbdr"))))
    (properties `((upstream-name . "shiny.blueprint")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-shiny-react r-shiny r-htmltools r-checkmate))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://cran.r-project.org/package=shiny.blueprint")
    (synopsis "Palantir's 'Blueprint' for 'Shiny' Apps")
    (description
     "Easily use Blueprint', the popular React library from Palantir, in your Shiny
app.  Blueprint provides a rich set of UI components for creating visually
appealing applications and is optimized for building complex, data-dense web
interfaces.  This package provides most components from the underlying library,
as well as special wrappers for some components to make it easy to use them in R
without writing @code{JavaScript} code.")
    (license license:lgpl3)))

(define-public r-shiny-benchmark
  (package
    (name "r-shiny-benchmark")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shiny.benchmark" version))
       (sha256
        (base32 "14k51vhd3gngdpg67kn9gsd6i47sn9vkxx1qni139jlza7g9n0jx"))))
    (properties `((upstream-name . "shiny.benchmark")))
    (build-system r-build-system)
    (propagated-inputs (list r-testthat
                             r-stringr
                             r-shinytest2
                             r-renv
                             r-progress
                             r-jsonlite
                             r-glue
                             r-ggplot2
                             r-fs
                             r-dplyr))
    (home-page "https://github.com/Appsilon/shiny.benchmark")
    (synopsis "Benchmark the Performance of 'shiny' Applications")
    (description
     "Compare performance between different versions of a shiny application based on
git references.")
    (license license:lgpl3)))

(define-public r-shinipsum
  (package
    (name "r-shinipsum")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shinipsum" version))
       (sha256
        (base32 "1l6skq9y5417x174l7svbvvlh10vv5kdnp2s4q7g76l62y710ks4"))))
    (properties `((upstream-name . "shinipsum")))
    (build-system r-build-system)
    (propagated-inputs (list r-plotly
                             r-magrittr
                             r-ggplot2
                             r-dygraphs
                             r-dt
                             r-attempt))
    (home-page "https://github.com/Thinkr-open/shinipsum")
    (synopsis "Lorem-Ipsum-Like Helpers for Fast Shiny Prototyping")
    (description
     "Prototype your shiny apps quickly with these Lorem-Ipsum-like Helpers.")
    (license license:expat)))

(define-public r-shiftsharese
  (package
    (name "r-shiftsharese")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ShiftShareSE" version))
       (sha256
        (base32 "1zjqr0a6gs6k6mh8a5bzq48fz143g79p2k1ngcbrd9py8pdj0ss1"))))
    (properties `((upstream-name . "ShiftShareSE")))
    (build-system r-build-system)
    (propagated-inputs (list r-formula))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/kolesarm/ShiftShareSE")
    (synopsis "Inference in Regressions with Shift-Share Structure")
    (description
     "This package provides confidence intervals in least-squares regressions when the
variable of interest has a shift-share structure, and in instrumental variables
regressions when the instrument has a shift-share structure.  The confidence
intervals implement the AKM and AKM0 methods developed in AdÃ£o, KolesÃ¡r, and
Morales (2019) <doi:10.1093/qje/qjz025>.")
    (license license:gpl3)))

(define-public r-shiftr
  (package
    (name "r-shiftr")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shiftR" version))
       (sha256
        (base32 "18y2qzqx8hd6yph1wnsq4ygm16pcdszynh174xsrxrhvx2h9mi4x"))))
    (properties `((upstream-name . "shiftR")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/andreyshabalin/shiftR")
    (synopsis "Fast Enrichment Analysis via Circular Permutations")
    (description
     "Fast enrichment analysis for locally correlated statistics via circular
permutations.  The analysis can be performed at multiple significance thresholds
for both primary and auxiliary data sets with efficient correction for multiple
testing.")
    (license license:lgpl3)))

(define-public r-shiftconvolvepoibin
  (package
    (name "r-shiftconvolvepoibin")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ShiftConvolvePoibin" version))
       (sha256
        (base32 "0pj3vynm8afns2kcwvmramxgbd0s2ksyyc5iha6p4dw2nz4hahc7"))))
    (properties `((upstream-name . "ShiftConvolvePoibin")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=ShiftConvolvePoibin")
    (synopsis
     "Exactly Computing the Tail of the Poisson-Binomial Distribution")
    (description
     "An exact method for computing the Poisson-Binomial Distribution (PBD).  The
package provides a function for generating a random sample from the PBD, as well
as two distinct approaches for computing the density, distribution, and quantile
functions of the PBD. The first method uses direct-convolution, or a
dynamic-programming approach which is numerically stable but can be slow for a
large input due to its quadratic complexity.  The second method is much faster
on large inputs thanks to its use of Fast Fourier Transform (FFT) based
convolutions.  Notably in this case the package uses an exponential shift to
practically guarantee the relative accuracy of the computation of an arbitrarily
small tail of the PBD -- something that FFT-based methods often struggle with.
This @code{ShiftConvolvePoiBin} method is described in Peres, Lee and Keich
(2020) <@code{arXiv:2004.07429>} where it is also shown to be competitive with
the fastest implementations for exactly computing the entire Poisson-Binomial
distribution.")
    (license license:gpl2+)))

(define-public r-shide
  (package
    (name "r-shide")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shide" version))
       (sha256
        (base32 "0wf84npx431c9hb09yxgbi0w1apzrfh7qisamlybvq3mhs8aaqdr"))))
    (properties `((upstream-name . "shide")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs r-tzdb r-rlang r-cpp11 r-cli))
    (home-page "https://github.com/mmollayi/shide")
    (synopsis "Date/Time Classes Based on Jalali Calendar")
    (description
     "This package implements S3 classes for storing dates and date-times based on the
Jalali calendar.  The main design goal of shide is consistency with base R's
Date and POSIXct'.  It provide features such as: date-time parsing, formatting
and arithmetic.")
    (license license:expat)))

(define-public r-shidashi
  (package
    (name "r-shidashi")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shidashi" version))
       (sha256
        (base32 "1zdyn0vsz5mnpbv3cj66q6y38h9gh5s02vnidhanwzz9c79q4ylj"))))
    (properties `((upstream-name . "shidashi")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-yaml
                             r-shiny
                             r-jsonlite
                             r-httr
                             r-formatr
                             r-fastmap
                             r-digest))
    (native-inputs (list esbuild))
    (home-page "https://dipterix.org/shidashi/")
    (synopsis "Shiny Dashboard Template System")
    (description
     "This package provides a template system based on @code{AdminLTE3}
(<https://adminlte.io/themes/v3/>) theme.  Comes with default theme that can be
easily customized.  Developers can upload modified templates on Github', and
users can easily download templates with RStudio project wizard.  The key
features of the default template include light and dark theme switcher, resizing
graphs, synchronizing inputs across sessions, new notification system, fancy
progress bars, and card-like flip panels with back sides, as well as various of
HTML tool widgets.")
    (license license:expat)))

(define-public r-sherlockholmes
  (package
    (name "r-sherlockholmes")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SherlockHolmes" version))
       (sha256
        (base32 "162iw7qzi5cb73lawfvsnbq3cxgx0si9bz0avv60rrkzdb0xlr05"))))
    (properties `((upstream-name . "SherlockHolmes")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-textboxplacement
                             r-tablehtml
                             r-stringr
                             r-stargazer
                             r-qpdf
                             r-plotrix
                             r-plot-matrix
                             r-dpseg
                             r-devtools))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SherlockHolmes")
    (synopsis "Building a Concordance of Terms in a Series of Texts")
    (description
     "Compute the frequency distribution of a search term in a series of texts.  For
example, Arthur Conan Doyle wrote a total of 60 Sherlock Holmes stories,
comprised of 54 short stories and 4 longer novels.  I wanted to test my own
subjective impression that, in many of the stories, Sherlock Holmes popularity
was used as bait to induce the reader to read a story that is essentially not
primarily a Sherlock Holmes story.  I used the term \"Holmes\" as a search
pattern, since Watson would frequently address him by name, or use his name to
describe something that he was doing.  My hypothesis is that the frequency
distribution of the search pattern \"Holmes\" is a good proxy for the degree to
which a story is or is not truly a Sherlock Holmes story.  The results are
presented in a manuscript that is available as a vignette and online at
<https://barryzee.github.io/Concordance/index.html>.")
    (license license:gpl2+)))

(define-public r-sherlock
  (package
    (name "r-sherlock")
    (version "0.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sherlock" version))
       (sha256
        (base32 "1hqi2gd5xjczkic34gq35467k3ccswpvy3qlch66blqpppadmzgk"))))
    (properties `((upstream-name . "sherlock")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidytext
                             r-tidyr
                             r-stringr
                             r-scales
                             r-rstudioapi
                             r-rlang
                             r-readr
                             r-purrr
                             r-plotly
                             r-openxlsx
                             r-magrittr
                             r-ggplot2
                             r-ggh4x
                             r-fs
                             r-forcats
                             r-dplyr
                             r-cowplot))
    (home-page "https://github.com/gaboraszabo/sherlock")
    (synopsis
     "Graphical Displays for Structured Problem Solving and Diagnosis")
    (description
     "Powerful graphical displays and statistical tools for structured problem solving
and diagnosis.  The functions of the sherlock package are especially useful for
applying the process of elimination as a problem diagnosis technique.  The
sherlock package was designed to seamlessly work with the tidyverse set of
packages and provides a collection of graphical displays built on top of the
ggplot and plotly packages, such as different kinds of small multiple plots as
well as helper functions such as adding reference lines, normalizing
observations, reading in data or saving analysis results in an Excel file.
References: David Hartshorne (2019, ISBN: 978-1-5272-5139-7).  Stefan H.
Steiner, R. Jock @code{MacKay} (2005, ISBN: 0873896467).")
    (license license:expat)))

(define-public r-shelltrace
  (package
    (name "r-shelltrace")
    (version "3.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shelltrace" version))
       (sha256
        (base32 "1xgbavaa26185i6q3907ds3bzq4xrw027x1sw5vsybqrxdz04jiz"))))
    (properties `((upstream-name . "shelltrace")))
    (build-system r-build-system)
    (propagated-inputs (list r-xlsx r-tiff r-bmp))
    (home-page "https://github.com/nielsjdewinter/ShellTrace")
    (synopsis "Bivalve Growth and Trace Element Accumulation Model")
    (description
     "This package contains all the formulae of the growth and trace element uptake
model described in the equally-named Geoscientific Model Development paper (de
Winter, 2017, <doi:10.5194/gmd-2017-137>).  The model takes as input a file with
X- and Y-coordinates of digitized growth increments recognized on a longitudinal
cross section through the bivalve shell, as well as a BMP file of an elemental
map of the cross section surface with chemically distinct phases separated by
phase analysis.  It proceeds by a step-by-step process described in the paper,
by which digitized growth increments are used to calculate changes in shell
height, shell thickness, shell volume, shell mass and shell growth rate through
the bivalve's life time.  Then, results of this growth modelling are combined
with the trace element mapping results to trace the incorporation of trace
elements into the bivalve shell.  Results of various modelling parameters can be
exported in the form of XLSX files.")
    (license license:gpl3)))

(define-public r-shellchron
  (package
    (name "r-shellchron")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ShellChron" version))
       (sha256
        (base32 "1xb06zszwirn5l63qa9g99q9by58sdqpg8s6kiq70z5qqnlgiq5c"))))
    (properties `((upstream-name . "ShellChron")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-tidyr
                             r-scales
                             r-rtop
                             r-magrittr
                             r-ggpubr
                             r-ggplot2
                             r-dplyr))
    (home-page "https://github.com/nielsjdewinter/ShellChron")
    (synopsis "Builds Chronologies from Oxygen Isotope Profiles in Shells")
    (description
     "Takes as input a stable oxygen isotope (d18O) profile measured in growth
direction (D) through a shell + uncertainties in both variables (d18O_err &
D_err).  It then models the seasonality in the d18O record by fitting a
combination of a growth and temperature sine wave to year-length chunks of the
data (see Judd et al., (2018) <doi:10.1016/j.palaeo.2017.09.034>).  This
modeling is carried out along a sliding window through the data and yields
estimates of the day of the year (Julian Day) and local growth rate for each
data point.  Uncertainties in both modeling routine and the data itself are
propagated and pooled to obtain a confidence envelope around the age of each
data point in the shell.  The end result is a shell chronology consisting of
estimated ages of shell formation relative to the annual cycle with their
uncertainties.  All formulae in the package serve this purpose, but the user can
customize the model (e.g. number of days in a year and the mineralogy of the
shell carbonate) through input parameters.")
    (license license:gpl3)))

(define-public r-shelf
  (package
    (name "r-shelf")
    (version "1.11.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SHELF" version))
       (sha256
        (base32 "1pmbn3bqb6d5mdvyadxxf8dpdvkl8kc7g0cc1b0qvd3531b7zzaw"))))
    (properties `((upstream-name . "SHELF")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-survminer
                             r-survival
                             r-sn
                             r-shinymatrix
                             r-shiny
                             r-scales
                             r-rmarkdown
                             r-hmisc
                             r-ggridges
                             r-ggplot2
                             r-ggextra
                             r-flexsurv))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/OakleyJ/SHELF")
    (synopsis "Tools to Support the Sheffield Elicitation Framework")
    (description
     "This package implements various methods for eliciting a probability distribution
for a single parameter from an expert or a group of experts.  The expert
provides a small number of probability judgements, corresponding to points on
his or her cumulative distribution function.  A range of parametric
distributions can then be fitted and displayed, with feedback provided in the
form of fitted probabilities and percentiles.  For multiple experts, a weighted
linear pool can be calculated.  Also includes functions for eliciting beliefs
about population distributions; eliciting multivariate distributions using a
Gaussian copula; eliciting a Dirichlet distribution; eliciting distributions for
variance parameters in a random effects meta-analysis model; survival
extrapolation.  R Shiny apps for most of the methods are included.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-sheetreader
  (package
    (name "r-sheetreader")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SheetReader" version))
       (sha256
        (base32 "0b9cgyh4lls8m6xq5hl2pmhlk72yl7bhbc3ssm81d84rsshhyxvl"))))
    (properties `((upstream-name . "SheetReader")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://github.com/fhenz/SheetReader-r")
    (synopsis "Parse xlsx Files")
    (description
     "Uses C++ via the Rcpp package to parse modern Excel files ('.xlsx').  Memory
usage is kept minimal by decompressing only parts of the file at a time, while
employing multiple threads to achieve significant runtime reduction.  Uses
<https://github.com/richgel999/miniz> and
<https://github.com/lemire/fast_double_parser>.")
    (license license:expat)))

(define-public r-shazam
  (package
    (name "r-shazam")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shazam" version))
       (sha256
        (base32 "16bkwm903v4hv6ng6ncfzax2s5x8qfa4ldlwzclry7iggniq7dzh"))))
    (properties `((upstream-name . "shazam")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-stringi
                             r-seqinr
                             r-scales
                             r-rlang
                             r-progress
                             r-mass
                             r-lazyeval
                             r-kernsmooth
                             r-iterators
                             r-igraph
                             r-ggplot2
                             r-foreach
                             r-dplyr
                             r-doparallel
                             r-diptest
                             r-ape
                             r-alakazam))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "http://shazam.readthedocs.io")
    (synopsis "Immunoglobulin Somatic Hypermutation Analysis")
    (description
     "This package provides a computational framework for analyzing mutations in
immunoglobulin (Ig) sequences.  Includes methods for Bayesian estimation of
antigen-driven selection pressure, mutational load quantification, building of
somatic hypermutation (SHM) models, and model-dependent distance calculations.
Also includes empirically derived models of SHM for both mice and humans.
Citations: Gupta and Vander Heiden, et al (2015)
<doi:10.1093/bioinformatics/btv359>, Yaari, et al (2012)
<doi:10.1093/nar/gks457>, Yaari, et al (2013) <doi:10.3389/fimmu.2013.00358>,
Cui, et al (2016) <doi:10.4049/jimmunol.1502263>.")
    (license license:agpl3)))

(define-public r-shattering
  (package
    (name "r-shattering")
    (version "1.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shattering" version))
       (sha256
        (base32 "0dmb57pdgzwm001gfm1zm8ba7x71bp2vjk4ml0cy4r05hpdswfh3"))))
    (properties `((upstream-name . "shattering")))
    (build-system r-build-system)
    (propagated-inputs (list r-slam
                             r-ryacas
                             r-rmarkdown
                             r-pracma
                             r-pdist
                             r-nmf
                             r-fnn
                             r-e1071))
    (home-page "https://cran.r-project.org/package=shattering")
    (synopsis "Estimate the Shattering Coefficient for a Particular Dataset")
    (description
     "The Statistical Learning Theory (SLT) provides the theoretical background to
ensure that a supervised algorithm generalizes the mapping f:X -> Y given f is
selected from its search space bias F. This formal result depends on the
Shattering coefficient function N(F,2n) to upper bound the empirical risk
minimization principle, from which one can estimate the necessary training
sample size to ensure the probabilistic learning convergence and, most
importantly, the characterization of the capacity of F, including its under and
overfitting abilities while addressing specific target problems.  In this
context, we propose a new approach to estimate the maximal number of hyperplanes
required to shatter a given sample, i.e., to separate every pair of points from
one another, based on the recent contributions by Har-Peled and Jones in the
dataset partitioning scenario, and use such foundation to analytically compute
the Shattering coefficient function for both binary and multi-class problems.
As main contributions, one can use our approach to study the complexity of the
search space bias F, estimate training sample sizes, and parametrize the number
of hyperplanes a learning algorithm needs to address some supervised task, what
is specially appealing to deep neural networks.  Reference: de Mello, R.F.
(2019) \"On the Shattering Coefficient of Supervised Learning Algorithms\"
<@code{arXiv:1911.05461>}; de Mello, R.F., Ponti, M.A. (2018, ISBN:
978-3319949888) \"Machine Learning: A Practical Approach on the Statistical
Learning Theory\".")
    (license license:gpl3)))

(define-public r-sharx
  (package
    (name "r-sharx")
    (version "1.0-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sharx" version))
       (sha256
        (base32 "0fyz7m8zx2i4r1kj5svzmn6l43f7zg2sj4c3ynyjwa1lzi5wf75v"))))
    (properties `((upstream-name . "sharx")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-formula r-dcmle r-dclone))
    (home-page "https://github.com/psolymos/sharx")
    (synopsis
     "Models and Data Sets for the Study of Species-Area Relationships")
    (description
     "Hierarchical models for the analysis of species-area relationships (SARs) by
combining several data sets and covariates; with a global data set combining
individual SAR studies; as described in Solymos and Lele (2012)
<doi:10.1111/j.1466-8238.2011.00655.x>.")
    (license license:gpl2)))

(define-public r-sharpshootr
  (package
    (name "r-sharpshootr")
    (version "2.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sharpshootR" version))
       (sha256
        (base32 "1cvq2wh05dkaijkmwm20bs7xcmcv0xwv1gcf535xnd9gkzpy8xdr"))))
    (properties `((upstream-name . "sharpshootR")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringi
                             r-soildb
                             r-scales
                             r-reshape2
                             r-rcolorbrewer
                             r-plyr
                             r-lattice
                             r-e1071
                             r-digest
                             r-curl
                             r-cluster
                             r-circular
                             r-aqp
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ncss-tech/sharpshootR")
    (synopsis "Soil Survey Toolkit")
    (description
     "Miscellaneous soil data management, summary, visualization, and conversion
utilities to support soil survey.")
    (license license:gpl3+)))

(define-public r-sharpr2
  (package
    (name "r-sharpr2")
    (version "1.1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sharpr2" version))
       (sha256
        (base32 "1r7anfr296l5sbhgry6rb15hlca6mpcm24qssdzz9bckavyfvqgi"))))
    (properties `((upstream-name . "sharpr2")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-matrix))
    (home-page "https://cran.r-project.org/package=sharpr2")
    (synopsis "Estimating Regulatory Scores and Identifying ATAC-STARR Data")
    (description
     "An algorithm for identifying high-resolution driver elements for datasets from a
high-definition reporter assay library.  Xinchen Wang, Liang He, Sarah Goggin,
Alham Saadat, Li Wang, Melina Claussnitzer, Manolis Kellis (2017)
<doi:10.1101/193136>.")
    (license license:gpl2+)))

(define-public r-sharppen
  (package
    (name "r-sharppen")
    (version "1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sharpPen" version))
       (sha256
        (base32 "0ra1iq6bfiy87arj7nb4s4adiji3pcp7vrjhsfbm2agfw05k4zj4"))))
    (properties `((upstream-name . "sharpPen")))
    (build-system r-build-system)
    (propagated-inputs (list r-np r-matrix r-locpol r-kernsmooth r-glmnet))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=sharpPen")
    (synopsis "Penalized Data Sharpening for Local Polynomial Regression")
    (description
     "This package provides functions and data sets for data sharpening.
Nonparametric regressions are computed subject to smoothness and other kinds of
penalties.")
    (license (license:fsdg-compatible "Unlimited"))))

(define-public r-sharperratio
  (package
    (name "r-sharperratio")
    (version "1.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sharpeRratio" version))
       (sha256
        (base32 "0ndqckz7pi099yynzfwzcbz9jcz93bb9jcd2a4px2sjbagfmx7ag"))))
    (properties `((upstream-name . "sharpeRratio")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-ghyp))
    (home-page "https://cran.r-project.org/package=sharpeRratio")
    (synopsis "Moment-Free Estimation of Sharpe Ratios")
    (description
     "An efficient moment-free estimator of the Sharpe ratio, or signal-to-noise
ratio, for heavy-tailed data (see <@code{arXiv:1505.01333>}).")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-sharper
  (package
    (name "r-sharper")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SharpeR" version))
       (sha256
        (base32 "1xq2m53n2gc706lyd16gccllx2wgwdrqkfpf79jwwdmipqfqnxjx"))))
    (properties `((upstream-name . "SharpeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrixcalc))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/shabbychef/SharpeR")
    (synopsis "Statistical Significance of the Sharpe Ratio")
    (description
     "This package provides a collection of tools for analyzing significance of
assets, funds, and trading strategies, based on the Sharpe ratio and overfit of
the same.  Provides density, distribution, quantile and random generation of the
Sharpe ratio distribution based on normal returns, as well as the optimal Sharpe
ratio over multiple assets.  Computes confidence intervals on the Sharpe and
provides a test of equality of Sharpe ratios based on the Delta method.  The
statistical foundations of the Sharpe can be found in the author's Short Sharpe
Course <doi:10.2139/ssrn.3036276>.")
    (license license:lgpl3)))

(define-public r-sharpdata
  (package
    (name "r-sharpdata")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sharpData" version))
       (sha256
        (base32 "1pnza52wqczjds4vp2fy9ky41y7lyj9ywam11nsr0mpv63jglnc5"))))
    (properties `((upstream-name . "sharpData")))
    (build-system r-build-system)
    (propagated-inputs (list r-quadprog r-kernsmooth))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=sharpData")
    (synopsis "Data Sharpening")
    (description
     "This package provides functions and data sets inspired by data sharpening - data
perturbation to achieve improved performance in nonparametric estimation, as
described in Choi, E., Hall, P. and Rousson, V. (2000).  Capabilities for
enhanced local linear regression function and derivative estimation are
included, as well as an asymptotically correct iterated data sharpening
estimator for any degree of local polynomial regression estimation.  A
cross-validation-based bandwidth selector is included which, in concert with the
iterated sharpener, will often provide superior performance, according to a
median integrated squared error criterion.  Sample data sets are provided to
illustrate function usage.")
    (license (license:fsdg-compatible "Unlimited"))))

(define-public r-sharp
  (package
    (name "r-sharp")
    (version "1.4.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sharp" version))
       (sha256
        (base32 "0sa0l5bnglmbm4ykmna1z1b1janggpah8g29505n4pfabw0r4lpl"))))
    (properties `((upstream-name . "sharp")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-rdpack
                             r-plotrix
                             r-nloptr
                             r-mclust
                             r-igraph
                             r-glmnet
                             r-glassofast
                             r-future-apply
                             r-future
                             r-fake
                             r-beepr
                             r-abind))
    (home-page "https://github.com/barbarabodinier/sharp")
    (synopsis "Stability-enHanced Approaches using Resampling Procedures")
    (description
     "In stability selection (N Meinshausen, P BÃ¼hlmann (2010)
<doi:10.1111/j.1467-9868.2010.00740.x>) and consensus clustering (S Monti et al
(2003) <doi:10.1023/A:1023949509487>), resampling techniques are used to enhance
the reliability of the results.  In this package, hyper-parameters are
calibrated by maximising model stability, which is measured under the null
hypothesis that all selection (or co-membership) probabilities are identical (B
Bodinier et al (2023a) <doi:10.1093/jrsssc/qlad058> and B Bodinier et al (2023b)
<doi:10.1093/bioinformatics/btad635>).  Functions are readily implemented for
the use of LASSO regression, sparse PCA, sparse (group) PLS or graphical LASSO
in stability selection, and hierarchical clustering, partitioning around
medoids, K means or Gaussian mixture models in consensus clustering.")
    (license license:gpl3+)))

(define-public r-sharkdemography
  (package
    (name "r-sharkdemography")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SharkDemography" version))
       (sha256
        (base32 "188qzbixz44qvi1jqr76hmmamkxn43lc9sayrpsvwi3bhkj9ablw"))))
    (properties `((upstream-name . "SharkDemography")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-readr
                             r-popbio
                             r-mass
                             r-magrittr
                             r-iterators
                             r-interp
                             r-foreach
                             r-dplyr
                             r-doparallel
                             r-dofuture))
    (home-page "https://github.com/jonathansmart/SharkDemography")
    (synopsis "Shark Demographic Analyses Using Leslie Matrix Models")
    (description
     "Run Leslie Matrix models using Monte Carlo simulations for any specified shark
species.  This package was developed during the publication of Smart, JJ, White,
WT, Baje, L, et al. (2020) \"Can multi-species shark longline fisheries be
managed sustainably using size limits? Theoretically, yes.  Realistically, no\".J
Appl Ecol.  2020; 57; 1847â1860. <doi:10.1111/1365-2664.13659>.")
    (license license:gpl3+)))

(define-public r-shar
  (package
    (name "r-shar")
    (version "2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shar" version))
       (sha256
        (base32 "1j2sxlym7n6l1phgwww1fapgp6ndll18i2385z1fx2nz4l22kd4h"))))
    (properties `((upstream-name . "shar")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-spatstat-random
                             r-spatstat-model
                             r-spatstat-geom
                             r-spatstat-explore
                             r-classint))
    (native-inputs (list r-knitr))
    (home-page "https://r-spatialecology.github.io/shar/")
    (synopsis "Species-Habitat Associations")
    (description
     "Analyse species-habitat associations in R. Therefore, information about the
location of the species (as a point pattern) is needed together with
environmental conditions (as a categorical raster).  To test for significance
habitat associations, one of the two components is randomized.  Methods are
mainly based on Plotkin et al. (2000) <doi:10.1006/jtbi.2000.2158> and Harms et
al. (2001) <doi:10.1111/j.1365-2745.2001.00615.x>.")
    (license license:gpl3+)))

(define-public r-shapviz
  (package
    (name "r-shapviz")
    (version "0.9.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shapviz" version))
       (sha256
        (base32 "14p24c9pxbqiagy27gd6iam4dgh7rbgnzp04w49y337pd99m4qj7"))))
    (properties `((upstream-name . "shapviz")))
    (build-system r-build-system)
    (propagated-inputs (list r-xgboost
                             r-rlang
                             r-patchwork
                             r-ggrepel
                             r-ggplot2
                             r-gggenes
                             r-ggfittext))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ModelOriented/shapviz")
    (synopsis "SHAP Visualizations")
    (description
     "Visualizations for SHAP (SHapley Additive @code{exPlanations}), such as
waterfall plots, force plots, various types of importance plots, dependence
plots, and interaction plots.  These plots act on a shapviz object created from
a matrix of SHAP values and a corresponding feature dataset.  Wrappers for the R
packages xgboost', lightgbm', fastshap', shapr', h2o', treeshap', DALEX', and
kernelshap are added for convenience.  By separating visualization and
computation, it is possible to display factor variables in graphs, even if the
SHAP values are calculated by a model that requires numerical features.  The
plots are inspired by those provided by the shap package in Python, but there is
no dependency on it.")
    (license license:gpl2+)))

(define-public r-shapr
  (package
    (name "r-shapr")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shapr" version))
       (sha256
        (base32 "0c8vivr1ia7x4jvdsffv06nl2yahxx38r5xbws5cdkalhbhlwv4h"))))
    (properties `((upstream-name . "shapr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-mvnfast
                             r-matrix
                             r-data-table
                             r-condmvnorm))
    (native-inputs (list r-knitr))
    (home-page "https://norskregnesentral.github.io/shapr/")
    (synopsis "Prediction Explanation with Dependence-Aware Shapley Values")
    (description
     "Complex machine learning models are often hard to interpret.  However, in many
situations it is crucial to understand and explain why a model made a specific
prediction.  Shapley values is the only method for such prediction explanation
framework with a solid theoretical foundation.  Previously known methods for
estimating the Shapley values do, however, assume feature independence.  This
package implements the method described in Aas, Jullum and LÃ¸land (2019)
<@code{arXiv:1903.10464>}, which accounts for any feature dependence, and
thereby produces more accurate estimates of the true Shapley values.")
    (license license:expat)))

(define-public r-shapper
  (package
    (name "r-shapper")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shapper" version))
       (sha256
        (base32 "1jaj9j894md45611gdrz05n0kfakawf7vlcc3zxhwdh3f7kwz4lp"))))
    (properties `((upstream-name . "shapper")))
    (build-system r-build-system)
    (propagated-inputs (list r-reticulate r-ggplot2 r-dalex))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ModelOriented/shapper")
    (synopsis "Wrapper of Python Library 'shap'")
    (description
     "This package provides SHAP explanations of machine learning models.  In applied
machine learning, there is a strong belief that we need to strike a balance
between interpretability and accuracy.  However, in field of the Interpretable
Machine Learning, there are more and more new ideas for explaining black-box
models.  One of the best known method for local explanations is SHapley Additive
@code{exPlanations} (SHAP) introduced by Lundberg, S., et al., (2016)
<@code{arXiv:1705.07874>} The SHAP method is used to calculate influences of
variables on the particular observation.  This method is based on Shapley
values, a technique used in game theory.  The R package shapper is a port of the
Python library shap'.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-shapleyvalue
  (package
    (name "r-shapleyvalue")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ShapleyValue" version))
       (sha256
        (base32 "05ziyvvd81l0lx572in9snndnjdq8y4wkd8bxqs0y979l49yr6fd"))))
    (properties `((upstream-name . "ShapleyValue")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyverse r-mass r-kableextra))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ShapleyValue")
    (synopsis "Shapley Value Regression for Relative Importance of Attributes")
    (description
     "Shapley Value Regression for calculating the relative importance of independent
variables in linear regression with avoiding the collinearity.")
    (license license:expat)))

(define-public r-shapleyoutlier
  (package
    (name "r-shapleyoutlier")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ShapleyOutlier" version))
       (sha256
        (base32 "0k96jf8p5h6m81v900rj6110gq7jg3imxvlqsp4a93y67rgx58vn"))))
    (properties `((upstream-name . "ShapleyOutlier")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-robustbase
                             r-rdpack
                             r-rcolorbrewer
                             r-magrittr
                             r-gridextra
                             r-ggplot2
                             r-forcats
                             r-egg
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ShapleyOutlier")
    (synopsis
     "Multivariate Outlier Explanations using Shapley Values and Mahalanobis Distances")
    (description
     "Based on Shapley values to explain multivariate outlyingness and to detect and
impute cellwise outliers.  Includes implementations of methods described in
Mayrhofer and Filzmoser (2023) <doi:10.1016/j.ecosta.2023.04.003>.")
    (license license:gpl3)))

(define-public r-shapley
  (package
    (name "r-shapley")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shapley" version))
       (sha256
        (base32 "0zr1mjpsn7s1wb708vza5vlv5biqcx3h7ng36rfq7v08lzrakwqk"))))
    (properties `((upstream-name . "shapley")))
    (build-system r-build-system)
    (propagated-inputs (list r-waffle r-h2o r-ggplot2 r-curl))
    (home-page "https://github.com/haghish/shapley")
    (synopsis
     "Weighted Mean SHAP for Feature Selection in ML Grid and Ensemble")
    (description
     "This R package introduces Weighted Mean SHapley Additive @code{exPlanations}
(WMSHAP), an innovative method for calculating SHAP values for a grid of
fine-tuned base-learner machine learning models as well as stacked ensembles, a
method not previously available due to the common reliance on single
best-performing models.  By integrating the weighted mean SHAP values from
individual base-learners comprising the ensemble or individual base-learners in
a tuning grid search, the package weights SHAP contributions according to each
model's performance, assessed by multiple either R squared (for both regression
and classification models).  alternatively, this software also offers weighting
SHAP values based on the area under the precision-recall curve (AUCPR), the area
under the curve (AUC), and F2 measures for binary classifiers.  It further
extends this framework to implement weighted confidence intervals for weighted
mean SHAP values, offering a more comprehensive and robust feature importance
evaluation over a grid of machine learning models, instead of solely computing
SHAP values for the best model.  This methodology is particularly beneficial for
addressing the severe class imbalance (class rarity) problem by providing a
transparent, generalized measure of feature importance that mitigates the risk
of reporting SHAP values for an overfitted or biased model and maintains
robustness under severe class imbalance, where there is no universal criteria of
identifying the absolute best model.  Furthermore, the package implements
hypothesis testing to ascertain the statistical significance of SHAP values for
individual features, as well as comparative significance testing of SHAP
contributions between features.  Additionally, it tackles a critical gap in
feature selection literature by presenting criteria for the automatic feature
selection of the most important features across a grid of models or stacked
ensembles, eliminating the need for arbitrary determination of the number of top
features to be extracted.  This utility is invaluable for researchers analyzing
feature significance, particularly within severely imbalanced outcomes where
conventional methods fall short.  Moreover, it is also expected to report
democratic feature importance across a grid of models, resulting in a more
comprehensive and generalizable feature selection.  The package further
implements a novel method for visualizing SHAP values both at subject level and
feature level as well as a plot for feature selection based on the weighted mean
SHAP ratios.")
    (license license:expat)))

(define-public r-shapeselectforest
  (package
    (name "r-shapeselectforest")
    (version "1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ShapeSelectForest" version))
       (sha256
        (base32 "096v3i0g4877zp883vi5n9cijbjdqga3dbinjlm5ws4j2zlghvb1"))))
    (properties `((upstream-name . "ShapeSelectForest")))
    (build-system r-build-system)
    (propagated-inputs (list r-raster r-coneproj))
    (home-page "https://cran.r-project.org/package=ShapeSelectForest")
    (synopsis "Shape Selection for Landsat Time Series of Forest Dynamics")
    (description
     "Landsat satellites collect important data about global forest conditions.
Documentation about Landsat's role in forest disturbance estimation is available
at the site <https://landsat.gsfc.nasa.gov/>.  By constrained quadratic
B-splines, this package delivers an optimal shape-restricted trajectory to a
time series of Landsat imagery for the purpose of modeling annual forest
disturbance dynamics to behave in an ecologically sensible manner assuming one
of seven possible \"shapes\", namely, flat, decreasing, one-jump (decreasing, jump
up, decreasing), inverted vee (increasing then decreasing), vee (decreasing then
increasing), linear increasing, and double-jump (decreasing, jump up,
decreasing, jump up, decreasing).  The main routine selects the best shape
according to the minimum Bayes information criterion (BIC) or the cone
information criterion (CIC), which is defined as the log of the estimated
predictive squared error.  The package also provides parameters summarizing the
temporal pattern including year(s) of inflection, magnitude of change, pre- and
post-inflection rates of growth or recovery.  In addition, it contains routines
for converting a flat map of disturbance agents to time-series disturbance maps
and a graphical routine displaying the fitted trajectory of Landsat imagery.")
    (license license:gpl2+)))

(define-public r-shaper
  (package
    (name "r-shaper")
    (version "1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shapeR" version))
       (sha256
        (base32 "02al1g9ycfb63mcx4s16abniggl825ihjanx6silbpv6dn6g1ms8"))))
    (properties `((upstream-name . "shapeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-wavethresh
                             r-vegan
                             r-plotrix
                             r-pixmap
                             r-mass
                             r-jpeg))
    (home-page "https://github.com/lisalibungan/shapeR")
    (synopsis "Collection and Analysis of Otolith Shape Data")
    (description
     "Studies otolith shape variation among fish populations.  Otoliths are calcified
structures found in the inner ear of teleost fish and their shape has been known
to vary among several fish populations and stocks, making them very useful in
taxonomy, species identification and to study geographic variations.  The
package extends previously described software used for otolith shape analysis by
allowing the user to automatically extract closed contour outlines from a large
number of images, perform smoothing to eliminate pixel noise described in Haines
and Crampton (2000) <doi:10.1111/1475-4983.00148>, choose from conducting either
a Fourier or wavelet see GenÃ§ay et al (2001)
<doi:10.1016/S0378-4371(00)00463-5> transform to the outlines and visualize the
mean shape.  The output of the package are independent Fourier or wavelet
coefficients which can be directly imported into a wide range of statistical
packages in R. The package might prove useful in studies of any two dimensional
objects.")
    (license license:gpl2+)))

(define-public r-shapepattern
  (package
    (name "r-shapepattern")
    (version "3.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ShapePattern" version))
       (sha256
        (base32 "1vlzvfns6grxc2g0d4slsckg6lzzr0k38brncf2akb41jkczbyak"))))
    (properties `((upstream-name . "ShapePattern")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-sp r-raster r-landscapemetrics r-igraph))
    (home-page "https://cran.r-project.org/package=ShapePattern")
    (synopsis "Tools for Analyzing Shapes and Patterns")
    (description
     "This is an evolving and growing collection of tools for the quantification,
assessment, and comparison of shape and pattern.  This collection provides tools
for: (1) the spatial decomposition of planar shapes using @code{ShrinkShape} to
incrementally shrink shapes to extinction while computing area, perimeter, and
number of parts at each iteration of shrinking; the spectra of results are
returned in graphic and tabular formats (Remmel 2015) <doi:10.1111/cag.12222>,
(2) simulating landscape patterns, (3) provision of tools for estimating
composition and configuration parameters from a categorical (binary) landscape
map (grid) and then simulates a selected number of statistically similar
landscapes.  Class-focused pattern metrics are computed for each simulated map
to produce empirical distributions against which statistical comparisons can be
made.  The code permits the analysis of single maps or pairs of maps (Remmel and
Fortin 2013) <doi:10.1007/s10980-013-9905-x>, (4) counting the number of each
first-order pattern element and converting that information into both frequency
and empirical probability vectors (Remmel 2020) <doi:10.3390/e22040420>, and (5)
computing the porosity of raster patches <doi:10.3390/su10103413>.  NOTE: This
is a consolidation of existing packages ('@code{PatternClass}',
@code{ShapePattern}') to begin warehousing all shape and pattern code in a
common package.  Additional utility tools for handling data are provided and
this package will be added to as more tools are created, cleaned-up, and
documented.  Note that all future developments will appear in this package and
that @code{PatternClass} will eventually be archived.")
    (license license:gpl3)))

(define-public r-shapena
  (package
    (name "r-shapena")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shapeNA" version))
       (sha256
        (base32 "1ivp8kvnacr2fx3rs7ckzdpinb6bifzzw7riskzj95lygjlkvabw"))))
    (properties `((upstream-name . "shapeNA")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=shapeNA")
    (synopsis "M-Estimation of Shape for Data with Missing Values")
    (description
     "M-estimators of location and shape following the power family (Frahm,
Nordhausen, Oja (2020) <doi:10.1016/j.jmva.2019.104569>) are provided in the
case of complete data and also when observations have missing values together
with functions aiding their visualization.")
    (license license:gpl3)))

(define-public r-shapefiles
  (package
    (name "r-shapefiles")
    (version "0.7.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shapefiles" version))
       (sha256
        (base32 "03sdcxbah05x0j6cpygx3ivkzrdlz2c0frxi30cinb05q6a41yjb"))))
    (properties `((upstream-name . "shapefiles")))
    (build-system r-build-system)
    (propagated-inputs (list r-foreign))
    (home-page "https://cran.r-project.org/package=shapefiles")
    (synopsis "Read and Write ESRI Shapefiles")
    (description
     "This package provides functions to read and write ESRI shapefiles.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-shapechange
  (package
    (name "r-shapechange")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ShapeChange" version))
       (sha256
        (base32 "0m9v5ykw8gfvyr9v149bs8sqjf9712yhvlg2rin6b5f1ww3mzl37"))))
    (properties `((upstream-name . "ShapeChange")))
    (build-system r-build-system)
    (propagated-inputs (list r-quadprog r-coneproj))
    (home-page "https://cran.r-project.org/package=ShapeChange")
    (synopsis "Change-Point Estimation using Shape-Restricted Splines")
    (description
     "In a scatterplot where the response variable is Gaussian, Poisson or binomial,
we consider the case in which the mean function is smooth with a change-point,
which is a mode, an inflection point or a jump point.  The main routine
estimates the mean curve and the change-point as well using shape-restricted
B-splines.  An optional subroutine delivering a bootstrap confidence interval
for the change-point is incorporated in the main routine.")
    (license license:gpl2+)))

(define-public r-shapdoe
  (package
    (name "r-shapdoe")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ShapDoE" version))
       (sha256
        (base32 "1lv0kci2yxlyswfmc0wm3iqzw1mgsjhmwilm5mbf7d2l643rhwf1"))))
    (properties `((upstream-name . "ShapDoE")))
    (build-system r-build-system)
    (propagated-inputs (list r-gtools))
    (home-page "https://cran.r-project.org/package=ShapDoE")
    (synopsis
     "Approximation of the Shapley Values Based on Experimental Designs")
    (description
     "Estimating the Shapley values using the algorithm in the paper Liuqing Yang,
Yongdao Zhou, Haoda Fu, Min-Qian Liu and Wei Zheng (2024)
<doi:10.1080/01621459.2023.2257364> \"Fast Approximation of the Shapley Values
Based on Order-of-Addition Experimental Designs\".  You provide the data and
define the value function, it retures the estimated Shapley values based on
sampling methods or experimental designs.")
    (license license:expat)))

(define-public r-shannon
  (package
    (name "r-shannon")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shannon" version))
       (sha256
        (base32 "010w30pj9ci1qgsi0zv2nixvqcwbhgpd998fvqn248458lrf0jdi"))))
    (properties `((upstream-name . "shannon")))
    (build-system r-build-system)
    (propagated-inputs (list r-vares r-extradistr))
    (home-page "https://cran.r-project.org/package=shannon")
    (synopsis "Computation of Entropy Measures and Relative Loss")
    (description
     "The functions allow for the numerical evaluation of some commonly used entropy
measures, such as Shannon entropy, RÃ©nyi entropy, Havrda and Charvat entropy,
and Arimoto entropy, at selected parametric values from several well-known and
widely used probability distributions.  Moreover, the functions also compute the
relative loss of these entropies using the truncated distributions.  Related
works include: Awad, A. M., & Alawneh, A. J. (1987).  Application of entropy to
a life-time model.  IMA Journal of Mathematical Control and Information, 4(2),
143-148. <doi:10.1093/imamci/4.2.143>.")
    (license license:gpl2)))

(define-public r-shadowr
  (package
    (name "r-shadowr")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "shadowr" version))
       (sha256
        (base32 "0b5x7pwz6zf7466yxrrl4yzq57ifhiwb5cmly7vq36f98gnsmzvl"))))
    (properties `((upstream-name . "shadowr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rselenium))
    (home-page "https://github.com/ricilandolt/shadowr")
    (synopsis
     "Selenium Plugin to Manage Multi Level Shadow Elements on Web Page")
    (description
     "Shadow Document Object Model is a web standard that offers component style and
markup encapsulation.  It is a critically important piece of the Web Components
story as it ensures that a component will work in any environment even if other
CSS or @code{JavaScript} is at play on the page.  Custom HTML tags can't be
directly identified with selenium tools, because Selenium doesn't provide any
way to deal with shadow elements.  Using this plugin you can handle any custom
HTML tags.")
    (license license:asl2.0)))

(define-public r-sgt
  (package
    (name "r-sgt")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sgt" version))
       (sha256
        (base32 "0qb3maj5idwafs40fpdfrwzkadnh5yg8fvfzfs51p9yy69kbmlkx"))))
    (properties `((upstream-name . "sgt")))
    (build-system r-build-system)
    (propagated-inputs (list r-optimx r-numderiv))
    (home-page "https://cran.r-project.org/package=sgt")
    (synopsis "Skewed Generalized T Distribution Tree")
    (description
     "Density, distribution function, quantile function and random generation for the
skewed generalized t distribution.  This package also provides a function that
can fit data to the skewed generalized t distribution using maximum likelihood
estimation.")
    (license license:gpl3+)))

(define-public r-sgstar
  (package
    (name "r-sgstar")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sgstar" version))
       (sha256
        (base32 "0b3bw10lf5h4kgg3c0s3lxsafaffhn8gc9kxmg7bg35lbxv0r1vn"))))
    (properties `((upstream-name . "sgstar")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-ggplot2 r-dplyr))
    (home-page "https://github.com/yogasatria30/sgstar")
    (synopsis "Seasonal Generalized Space Time Autoregressive (S-GSTAR) Model")
    (description
     "This package provides a set of function that implements for seasonal
multivariate time series analysis based on Seasonal Generalized Space Time
Autoregressive with Seemingly Unrelated Regression (S-GSTAR-SUR) Model by
Setiawan(2016)<https://www.researchgate.net/publication/316517889_S-GSTAR-SUR_model_for_seasonal_spatio_temporal_data_forecasting>.")
    (license license:gpl3)))

(define-public r-sgsr
  (package
    (name "r-sgsr")
    (version "1.4.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sgsR" version))
       (sha256
        (base32 "1ircbwlc6ysv8v8vxn5snjqx47qhi5rdqyd3vvf3cx54jndsnvah"))))
    (properties `((upstream-name . "sgsR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-terra
                             r-spatstat-geom
                             r-sf
                             r-samplingbigdata
                             r-ggplot2
                             r-dplyr
                             r-clhs
                             r-balancedsampling))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/tgoodbody/sgsR")
    (synopsis "Structurally Guided Sampling")
    (description
     "Structurally guided sampling (SGS) approaches for airborne laser scanning (ALS;
LIDAR).  Primary functions provide means to generate data-driven stratifications
& methods for allocating samples.  Intermediate functions for calculating and
extracting important information about input covariates and samples are also
included.  Processing outcomes are intended to help forest and environmental
management practitioners better optimize field sample placement as well as
assess and augment existing sample networks in the context of data distributions
and conditions.  ALS data is the primary intended use case, however any
rasterized remote sensing data can be used, enabling data-driven stratifications
and sampling approaches.")
    (license license:gpl3+)))

(define-public r-sgs
  (package
    (name "r-sgs")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sgs" version))
       (sha256
        (base32 "0n8vyia3k3lafl7kd6dqrrgp6vjr954jxc74kchncaqx8p7ays3d"))))
    (properties `((upstream-name . "sgs")))
    (build-system r-build-system)
    (propagated-inputs (list r-slope
                             r-rlab
                             r-rcpparmadillo
                             r-rcpp
                             r-matrix
                             r-mass
                             r-faux
                             r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ff1201/sgs")
    (synopsis
     "Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control")
    (description
     "Implementation of Sparse-group SLOPE (SGS) (Feser and Evangelou (2023)
<doi:10.48550/@code{arXiv.2305.09467>}) models.  Linear and logistic regression
models are supported, both of which can be fit using k-fold cross-validation.
Dense and sparse input matrices are supported.  In addition, a general adaptive
three operator splitting (ATOS) implementation is provided.  Group SLOPE
(@code{gSLOPE}) (Brzyski et al. (2019) <doi:10.1080/01621459.2017.1411269>) and
group-based OSCAR models (Feser and Evangelou (2024)
<doi:10.48550/@code{arXiv.2405.15357>}) are also implemented.  All models are
available with strong screening rules (Feser and Evangelou (2024)
<doi:10.48550/@code{arXiv.2405.15357>}) for computational speed-up.")
    (license license:gpl3+)))

(define-public r-sgraph
  (package
    (name "r-sgraph")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sgraph" version))
       (sha256
        (base32 "00vwzjn09cdpng7ss852ha98v3shaznvspbm8687w0gzgd86974d"))))
    (properties `((upstream-name . "sgraph")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-stringi
                             r-rcolorbrewer
                             r-magrittr
                             r-jsonlite
                             r-igraph
                             r-htmlwidgets
                             r-ggplot2
                             r-cowplot))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://gitlab.com/thomaschln/sgraph")
    (synopsis "Network Visualization Using 'sigma.js'")
    (description
     "Interactive visualizations of graphs created with the igraph package using a
htmlwidgets wrapper for the sigma.js network visualization v2.4.0
<https://www.sigmajs.org/>, enabling to display several thousands of nodes.
While several R packages have been developed to interface sigma.js', all were
developed for v1.x.x and none have migrated to v2.4.0 nor are they planning to.
This package builds upon the @code{sigmaNet} package, and users familiar with it
will recognize the similar design approach.  Two extensions have been added to
the classic sigma.js visualizations by overriding the underlying
@code{JavaScript} code, enabling to draw a frame around node labels, and to
display labels on multiple lines by parsing line breaks.  Other additional
functionalities that did not require overriding sigma.js code include toggling
node visibility when clicked using a node attribute and highlighting specific
edges.  sigma.js is currently preparing a stable release v3.0.0, and this
package plans to update to it when it is available.")
    (license license:gpl3)))

(define-public r-sgr
  (package
    (name "r-sgr")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sgr" version))
       (sha256
        (base32 "0m7rl3yii6zz4p21aiaifj1ram6gi6x5kywpyx9yjan79180q8h7"))))
    (properties `((upstream-name . "sgr")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=sgr")
    (synopsis "Sample Generation by Replacement")
    (description
     "Sample Generation by Replacement simulations (SGR; Lombardi & Pastore, 2014;
Pastore & Lombardi, 2014).  The package can be used to perform fake data
analysis according to the sample generation by replacement approach.  It
includes functions for making simple inferences about discrete/ordinal fake
data.  The package allows to study the implications of fake data for empirical
results.")
    (license license:gpl2+)))

(define-public r-sgpv
  (package
    (name "r-sgpv")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sgpv" version))
       (sha256
        (base32 "0w0slnnw239a34c0h7xr76c4m0rfwl0hq61g9n12vzxd6lfh3gqz"))))
    (properties `((upstream-name . "sgpv")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sgpv")
    (synopsis "Calculate Second-Generation p-Values and Associated Measures")
    (description
     "Computation of second-generation p-values as described in Blume et al. (2018)
<doi:10.1371/journal.pone.0188299> and Blume et al. (2019)
<doi:10.1080/00031305.2018.1537893>.  There are additional functions which
provide power and type I error calculations, create graphs (particularly suited
for large-scale inference usage), and a function to estimate false discovery
rates based on second-generation p-value inference.")
    (license license:expat)))

(define-public r-sgpr
  (package
    (name "r-sgpr")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SGPR" version))
       (sha256
        (base32 "1bywllnf7gsk3gi3y77qshjd20735vf54g164qsxm3x1n7zjr0p3"))))
    (properties `((upstream-name . "SGPR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=SGPR")
    (synopsis
     "Sparse Group Penalized Regression for Bi-Level Variable Selection")
    (description
     "Fits the regularization path of regression models (linear and logistic) with
additively combined penalty terms.  All possible combinations with Least
Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute
Deviation (SCAD), Minimax Concave Penalty (MCP) and Exponential Penalty (EP) are
supported.  This includes Sparse Group LASSO (SGL), Sparse Group SCAD (SGS),
Sparse Group MCP (SGM) and Sparse Group EP (SGE).  For more information, see
Buch, G., Schulz, A., Schmidtmann, I., Strauch, K., & Wild, P. S. (2024)
<doi:10.1002/bimj.202200334>.")
    (license license:gpl3+)))

(define-public r-sgpls
  (package
    (name "r-sgpls")
    (version "1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sgPLS" version))
       (sha256
        (base32 "16cjhrla1f4pf2gqfnbi5n1ai69nbr80crwn1f29ldk483d6hjc5"))))
    (properties `((upstream-name . "sgPLS")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-mixomics))
    (home-page "https://cran.r-project.org/package=sgPLS")
    (synopsis "Sparse Group Partial Least Square Methods")
    (description
     "Regularized version of partial least square approaches providing sparse, group,
and sparse group versions of partial least square regression models (Liquet, B.,
Lafaye de Micheaux, P., Hejblum B., Thiebaut, R. (2016)
<doi:10.1093/bioinformatics/btv535>).  Version of PLS Discriminant analysis is
also provided.")
    (license license:gpl2+)))

(define-public r-sgpdata
  (package
    (name "r-sgpdata")
    (version "28.0-0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SGPdata" version))
       (sha256
        (base32 "03x4ihxhwisrq6bwqclxh56n88b7z0b4yfcrfxxfrgc7zqs9adv2"))))
    (properties `((upstream-name . "SGPdata")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table r-crayon))
    (native-inputs (list r-knitr))
    (home-page "https://CenterForAssessment.github.io/SGPdata/")
    (synopsis
     "Exemplar Data Sets for Student Growth Percentiles (SGP) Analyses")
    (description
     "Data sets utilized by the SGP package as exemplars for users to conduct their
own student growth percentiles (SGP) analyses.")
    (license license:gpl3)))

(define-public r-sgp
  (package
    (name "r-sgp")
    (version "2.2-0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SGP" version))
       (sha256
        (base32 "08gxwixrhnhsxf3xhx612d05jpiz7d5wl9csi612afhy3psblysq"))))
    (properties `((upstream-name . "SGP")))
    (build-system r-build-system)
    (propagated-inputs (list r-toordinal
                             r-svglite
                             r-sn
                             r-rsqlite
                             r-rngtools
                             r-randomnames
                             r-quantreg
                             r-matrixstats
                             r-jsonlite
                             r-iterators
                             r-gtools
                             r-gridbase
                             r-foreach
                             r-equate
                             r-doparallel
                             r-digest
                             r-data-table
                             r-crayon
                             r-colorspace
                             r-collapse
                             r-callr
                             r-cairo))
    (native-inputs (list r-knitr))
    (home-page "https://sgp.io")
    (synopsis "Student Growth Percentiles & Percentile Growth Trajectories")
    (description
     "An analytic framework for the calculation of norm- and criterion-referenced
academic growth estimates using large scale, longitudinal education assessment
data as developed in Betebenner (2009) <doi:10.1111/j.1745-3992.2009.00161.x>.")
    (license license:gpl3)))

(define-public r-sgolay
  (package
    (name "r-sgolay")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sgolay" version))
       (sha256
        (base32 "0qry7286kw6ks5kc3wp89ax43smmb6b77gral72izpbw5wkblphm"))))
    (properties `((upstream-name . "sgolay")))
    (build-system r-build-system)
    (propagated-inputs (list r-signal))
    (home-page "https://github.com/zeehio/sgolay")
    (synopsis "Efficient Savitzky-Golay Filtering")
    (description
     "Smoothing signals and computing their derivatives is a common requirement in
signal processing workflows.  Savitzky-Golay filters are a established method
able to do both (Savitzky and Golay, 1964 <doi:10.1021/ac60214a047>).  This
package implements one dimensional Savitzky-Golay filters that can be applied to
vectors and matrices (either row-wise or column-wise).  Vectorization and memory
allocations have been profiled to reduce computational fingerprint.  Short
filter lengths are implemented in the direct space, while longer filters are
implemented in frequency space, using a Fast Fourier Transform (FFT).")
    (license license:gpl2+)))

(define-public r-sgof
  (package
    (name "r-sgof")
    (version "2.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sgof" version))
       (sha256
        (base32 "028gas18jrfg6ww5r0p32am7x0rjk0a9bcb84pdg9k091dz4b120"))))
    (properties `((upstream-name . "sgof")))
    (build-system r-build-system)
    (propagated-inputs (list r-poibin))
    (home-page "https://cran.r-project.org/package=sgof")
    (synopsis "Multiple Hypothesis Testing")
    (description
     "Seven different methods for multiple testing problems.  The S@code{GoF-type}
methods (see for example, Carvajal RodrÃ­guez et al., 2009
<doi:10.1186/1471-2105-10-209>; de UÃ±a Ãlvarez, 2012
<doi:10.1515/1544-6115.1812>; Castro Conde et al., 2015
<doi:10.1177/0962280215597580>) and the BH and BY false discovery rate
controlling procedures.")
    (license license:gpl2)))

(define-public r-sgo
  (package
    (name "r-sgo")
    (version "0.9.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sgo" version))
       (sha256
        (base32 "1vd1ak6j3c7kg3chqjz3v0aijicba2fcq1h8zy4ah1kjf8cfxklw"))))
    (properties `((upstream-name . "sgo")))
    (build-system r-build-system)
    (home-page "https://github.com/clozanoruiz/sgo")
    (synopsis "Simple Geographical Operations (with OSGB36)")
    (description
     "This package provides methods focused in performing the OSGB36/ETRS89
transformation (Great Britain and the Isle of Man only) by using the Ordnance
Survey's OSTN15/OSGM15 transformation model.  Calculation of distances and areas
from sets of points defined in any of the supported Coordinated Systems is also
available.")
    (license license:bsd-2)))

(define-public r-sgmrfmix
  (package
    (name "r-sgmrfmix")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sGMRFmix" version))
       (sha256
        (base32 "183i3159k2cxpanm09zcy2y1vvjd6185gjjf81c3284prw3aky2c"))))
    (properties `((upstream-name . "sGMRFmix")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-tidyr r-mvtnorm r-glasso r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=sGMRFmix")
    (synopsis
     "Sparse Gaussian Markov Random Field Mixtures for Anomaly Detection")
    (description
     "An implementation of sparse Gaussian Markov random field mixtures presented by
Ide et al. (2016) <doi:10.1109/ICDM.2016.0119>.  It provides a novel anomaly
detection method for multivariate noisy sensor data.  It can automatically
handle multiple operational modes.  And it can also compute variable-wise
anomaly scores.")
    (license license:expat)))

(define-public r-sgmodel
  (package
    (name "r-sgmodel")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sgmodel" version))
       (sha256
        (base32 "1aq2klppwv1hn1nmicfxmb3vnkfw5jnmaa8gxxz4gnkk59k5rxpr"))))
    (properties `((upstream-name . "sgmodel")))
    (build-system r-build-system)
    (propagated-inputs (list r-rtauchen r-ramify r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=sgmodel")
    (synopsis
     "Solves a Generic Stochastic Growth Model with a Representative Agent")
    (description
     "It computes the solutions to a generic stochastic growth model for a given set
of user supplied parameters.  It includes the solutions to the model, plots of
the solution, a summary of the features of the model, a function that covers
different types of consumption preferences, and a function that computes the
moments of a Markov process.  Merton, Robert C (1971)
<doi:10.1016/0022-0531(71)90038-X>, Tauchen, George (1986)
<doi:10.1016/0165-1765(86)90168-0>, Wickham, Hadley (2009,
ISBN:978-0-387-98140-6 ).")
    (license license:gpl3)))

(define-public r-sglr
  (package
    (name "r-sglr")
    (version "0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sglr" version))
       (sha256
        (base32 "0f4ndx0a50km7iwwqxbxv10rb5c96210vgimaskjlmsbpaqna601"))))
    (properties `((upstream-name . "sglr")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-rlang r-ggplot2))
    (home-page "https://cran.r-project.org/package=sglr")
    (synopsis
     "Sequential Generalized Likelihood Ratio Decision Boundaries Proposed by Shih, Lai, Heyse and Chen (2010, <doi:10.1002/Sim.4036>)")
    (description
     "We provide functions for computing the decision boundaries for pre-licensure
vaccine trials using the Generalized Likelihood Ratio tests proposed by Shih,
Lai, Heyse and Chen (2010, <doi:10.1002/sim.4036>).")
    (license license:gpl2+)))

(define-public r-sglg
  (package
    (name "r-sglg")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sglg" version))
       (sha256
        (base32 "0h1f31ksygg4cpsnqi87lknxyv39mygjj7sirwgxdha6wmzhl1dc"))))
    (properties `((upstream-name . "sglg")))
    (build-system r-build-system)
    (propagated-inputs (list r-teachingsampling
                             r-survival
                             r-rcpp
                             r-progress
                             r-pracma
                             r-plotly
                             r-plot3d
                             r-moments
                             r-magrittr
                             r-gridextra
                             r-ggplot2
                             r-formula
                             r-adequacymodel))
    (home-page "https://cran.r-project.org/package=sglg")
    (synopsis
     "Fitting Semi-Parametric Generalized log-Gamma Regression Models")
    (description
     "Set of tools to fit a linear multiple or semi-parametric regression models with
the possibility of non-informative random right-censoring.  Under this setup,
the localization parameter of the response variable distribution is modeled by
using linear multiple regression or semi-parametric functions, whose
non-parametric components may be approximated by natural cubic spline or
P-splines.  The supported distribution for the model error is a generalized
log-gamma distribution which includes the generalized extreme value and standard
normal distributions as important special cases.  Inference is based on
penalized likelihood and bootstrap methods.  Also, some numerical and graphical
devices for diagnostic of the fitted models are offered.")
    (license license:gpl3)))

(define-public r-sglasso
  (package
    (name "r-sglasso")
    (version "1.2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sglasso" version))
       (sha256
        (base32 "01162sh2cndqhcyswk7qprsychxik1yvkk7lr7d5vf25sq85h1k1"))))
    (properties `((upstream-name . "sglasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-igraph))
    (native-inputs (list gfortran))
    (home-page "http://www.unipa.it/persone/docenti/a/luigi.augugliaro")
    (synopsis "Lasso Method for RCON(V,E) Models")
    (description
     "RCON(V, E) models are a kind of restriction of the Gaussian Graphical Models
defined by a set of equality constraints on the entries of the concentration
matrix.  sglasso package implements the structured graphical lasso (sglasso)
estimator proposed in Abbruzzo et al. (2014) for the weighted l1-penalized
RCON(V, E) model.  Two cyclic coordinate algorithms are implemented to compute
the sglasso estimator, i.e.  a cyclic coordinate minimization (CCM) and a cyclic
coordinate descent (CCD) algorithm.")
    (license license:gpl2+)))

(define-public r-sgl
  (package
    (name "r-sgl")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SGL" version))
       (sha256
        (base32 "055mx876ydg4kzvcm6rxfkxqz849zgzkzsskvrfgwj9fs1k6ja2v"))))
    (properties `((upstream-name . "SGL")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SGL")
    (synopsis
     "Fit a GLM (or Cox Model) with a Combination of Lasso and Group Lasso Regularization")
    (description
     "Fit a regularized generalized linear model via penalized maximum likelihood.
The model is fit for a path of values of the penalty parameter.  Fits linear,
logistic and Cox models.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-sgeostat
  (package
    (name "r-sgeostat")
    (version "1.0-27")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sgeostat" version))
       (sha256
        (base32 "1iq9p2jk8bpv1h853a1l91d5c5dxnhkk3cmkd01siqqvj04hv4vb"))))
    (properties `((upstream-name . "sgeostat")))
    (build-system r-build-system)
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=sgeostat")
    (synopsis "An Object-Oriented Framework for Geostatistical Modeling in S+")
    (description
     "An Object-oriented Framework for Geostatistical Modeling in S+ containing
functions for variogram estimation, variogram fitting and kriging as well as
some plot functions.  Written entirely in S, therefore works only for small data
sets in acceptable computing time.")
    (license license:gpl2+)))

(define-public r-sgee
  (package
    (name "r-sgee")
    (version "0.6-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sgee" version))
       (sha256
        (base32 "055iydainzywb1s5id5rl9i94dj5arr9b6qz15p8wps49vm2mlp3"))))
    (properties `((upstream-name . "sgee")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-copula))
    (home-page "https://cran.r-project.org/package=sgee")
    (synopsis "Stagewise Generalized Estimating Equations")
    (description
     "Stagewise techniques implemented with Generalized Estimating Equations to handle
individual, group, bi-level, and interaction selection.  Stagewise approaches
start with an empty model and slowly build the model over several iterations,
which yields a path of candidate models from which model selection can be
performed.  This slow brewing approach gives stagewise techniques a unique
flexibility that allows simple incorporation of Generalized Estimating
Equations; see Vaughan, G., Aseltine, R., Chen, K., Yan, J., (2017)
<doi:10.1111/biom.12669> for details.")
    (license license:gpl3+)))

(define-public r-sgdinference
  (package
    (name "r-sgdinference")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SGDinference" version))
       (sha256
        (base32 "13sqchniiq5d8k0rkilmn33lhl5lrqkswwny3zzk6d6alh6x7jh8"))))
    (properties `((upstream-name . "SGDinference")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/SGDinference-Lab/SGDinference/")
    (synopsis "Inference with Stochastic Gradient Descent")
    (description
     "Estimation and inference methods for large-scale mean and quantile regression
models via stochastic (sub-)gradient descent (S-@code{subGD}) algorithms.  The
inference procedure handles cross-sectional data sequentially: (i) updating the
parameter estimate with each incoming \"new observation\", (ii) aggregating it as
a Polyak-Ruppert average, and (iii) computing an asymptotically pivotal
statistic for inference through random scaling.  The methodology used in the
SGDinference package is described in detail in the following papers: (i) Lee,
S., Liao, Y., Seo, M.H. and Shin, Y. (2022) <doi:10.1609/aaai.v36i7.20701> \"Fast
and robust online inference with stochastic gradient descent via random
scaling\". (ii) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2023)
<@code{arXiv:2209.14502>} \"Fast Inference for Quantile Regression with Tens of
Millions of Observations\".")
    (license license:gpl3)))

(define-public r-sgd
  (package
    (name "r-sgd")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sgd" version))
       (sha256
        (base32 "0spcv9dysl2vlgrpjxicai9nkg7hylf2wci45pjpl8jph6yj8c70"))))
    (properties `((upstream-name . "sgd")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-mass
                             r-ggplot2
                             r-bigmemory
                             r-bh))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/airoldilab/sgd")
    (synopsis "Stochastic Gradient Descent for Scalable Estimation")
    (description
     "This package provides a fast and flexible set of tools for large scale
estimation.  It features many stochastic gradient methods, built-in models,
visualization tools, automated hyperparameter tuning, model checking, interval
estimation, and convergence diagnostics.")
    (license license:gpl2)))

(define-public r-sgboost
  (package
    (name "r-sgboost")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sgboost" version))
       (sha256
        (base32 "1x3ppg2sivl2r3w1pbqvam7cvrd86agxbqdmkgf2qkvy3hf5gdga"))))
    (properties `((upstream-name . "sgboost")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-rlang
                             r-mboost
                             r-ggplot2
                             r-ggforce
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/FabianObster/sgboost")
    (synopsis "Sparse-Group Boosting")
    (description
     "Sparse-group boosting to be used in conjunction with the mboost for modeling
grouped data.  Applicable to all sparse-group lasso type problems where
within-group and between-group sparsity is desired.  Interprets and visualizes
individual variables and groups.")
    (license license:expat)))

(define-public r-sgbj
  (package
    (name "r-sgbj")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sGBJ" version))
       (sha256
        (base32 "145mxfpl8gpycz8fl1ddlxy61rwpcqlfdrva6yhwzv4awwsv1fvn"))))
    (properties `((upstream-name . "sGBJ")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-gbj))
    (home-page "https://github.com/lauravillain/sGBJ")
    (synopsis "Survival Extension of the Generalized Berk-Jones Test")
    (description
     "This package implements an extension of the Generalized Berk-Jones (GBJ)
statistic for survival data, @code{sGBJ}.  It computes the @code{sGBJ} statistic
and its p-value for testing the association between a gene set and a
time-to-event outcome with possible adjustment on additional covariates.
Detailed method is available at Villain L, Ferte T, Thiebaut R and Hejblum BP
(2021) <doi:10.1101/2021.09.07.459329>.")
    (license license:gpl3+)))

(define-public r-sgb
  (package
    (name "r-sgb")
    (version "1.0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SGB" version))
       (sha256
        (base32 "0lhb0rqih154lxzczdk8i613my3zwym9479n3r4h9xym9bjq80lr"))))
    (properties `((upstream-name . "SGB")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv r-mass r-formula r-alabama))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SGB")
    (synopsis "Simplicial Generalized Beta Regression")
    (description
     "Main properties and regression procedures using a generalization of the
Dirichlet distribution called Simplicial Generalized Beta distribution.  It is a
new distribution on the simplex (i.e.  on the space of compositions or positive
vectors with sum of components equal to 1).  The Dirichlet distribution can be
constructed from a random vector of independent Gamma variables divided by their
sum.  The SGB follows the same construction with generalized Gamma instead of
Gamma variables.  The Dirichlet exponents are supplemented by an overall shape
parameter and a vector of scales.  The scale vector is itself a composition and
can be modeled with auxiliary variables through a log-ratio transformation.
Graf, M. (2017, ISBN: 978-84-947240-0-8).  See also the vignette enclosed in the
package.")
    (license license:gpl2+)))

(define-public r-sgat
  (package
    (name "r-sgat")
    (version "0.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sgat" version))
       (sha256
        (base32 "14sniq5yfh7xmbnd8d0ighw97352qjgfdg587a6hxafisx9is61p"))))
    (properties `((upstream-name . "sgat")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rselenium
                             r-rcurl
                             r-qdapregex
                             r-plyr
                             r-dplyr
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/matiaspoullain/sgat")
    (synopsis "Extract Information from Google's \"Popular Times\"")
    (description
     "Once you've identified a real life place, such as a shop, a restaurant, a bar,
etc.  use this package to simulate a Google search and retrieve its \"Popular
Times\" and geographic location information and save them in Comma-Separated
Values files.  This package also downloads a list of restaurants and bars of
Ushuaia city, Argentina.")
    (license license:expat)))

(define-public r-sgapi
  (package
    (name "r-sgapi")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sgapi" version))
       (sha256
        (base32 "0q0qamhzsn4421wzbjfqlwrs2w4qj61ynvjh9pjv7fkk89szh7xx"))))
    (properties `((upstream-name . "sgapi")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-sf
                             r-readr
                             r-magrittr
                             r-httr
                             r-dplyr))
    (home-page
     "https://defra-data-science-centre-of-excellence.github.io/sgapi/")
    (synopsis
     "Aid Querying 'nomis' and 'Office for National Statistics Open Geography' APIs")
    (description
     "Facilitates extraction of geospatial data from the Office for National
Statistics Open Geography and nomis Application Programming Interfaces (APIs).
Simplifies process of querying nomis datasets <https://www.nomisweb.co.uk/> and
extracting desired datasets in dataframe format.  Extracts area shapefiles at
chosen resolution from Office for National Statistics Open Geography
<https://geoportal.statistics.gov.uk/>.")
    (license license:expat)))

(define-public r-sg
  (package
    (name "r-sg")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sg" version))
       (sha256
        (base32 "1nbj4qrhm8kqcd5620hjdz57yfc3dkchvrxrp9qdrqaf6k1kc5vq"))))
    (properties `((upstream-name . "sg")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-mime r-httr2 r-base64enc))
    (home-page "https://github.com/botan/sg")
    (synopsis "'SendGrid' Email API Client")
    (description
     "Simple @code{SendGrid} Email API client for creating and sending emails.  For
more information, visit the official @code{SendGrid} Email API documentation:
<https://sendgrid.com/en-us/solutions/email-api>.")
    (license license:expat)))

(define-public r-sftrack
  (package
    (name "r-sftrack")
    (version "0.5.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sftrack" version))
       (sha256
        (base32 "0cm87fla8bnhp1j35wq2llw1jziqhgfcj1yn503z4ds40gh4wnqa"))))
    (properties `((upstream-name . "sftrack")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf))
    (native-inputs (list r-knitr))
    (home-page "https://mablab.org/sftrack/")
    (synopsis "Modern Classes for Tracking and Movement Data")
    (description
     "Modern classes for tracking and movement data, building on sf spatial
infrastructure, and early theoretical work from Turchin (1998, ISBN:
9780878938476), and Calenge et al. (2009) <doi:10.1016/j.ecoinf.2008.10.002>.
Tracking data are series of locations with at least 2-dimensional spatial
coordinates (x,y), a time index (t), and individual identification (id) of the
object being monitored; movement data are made of trajectories, i.e.  the line
representation of the path, composed by steps (the straight-line segments
connecting successive locations).  sftrack is designed to handle movement of
both living organisms and inanimate objects.")
    (license license:expat)))

(define-public r-sftools
  (package
    (name "r-sftools")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SFtools" version))
       (sha256
        (base32 "08k2ywkpk1pzp0qcwm7qx9gnv9hglspl9hrsrrfblagdpkw6rm2x"))))
    (properties `((upstream-name . "SFtools")))
    (build-system r-build-system)
    (propagated-inputs (list r-wordspace r-ff r-doparallel))
    (home-page "https://sites.google.com/site/mohamedlaibwebpage/")
    (synopsis "Space Filling Based Tools for Data Mining")
    (description
     "This package contains space filling based tools for machine learning and data
mining.  Some functions offer several computational techniques and deal with the
out of memory for large big data by using the ff package.")
    (license license:gpl3)))

(define-public r-sft
  (package
    (name "r-sft")
    (version "2.2-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sft" version))
       (sha256
        (base32 "0fxz64ba58n6ghfqsgq64n1c7qkvi8c3lsa86732mil9w71yxw33"))))
    (properties `((upstream-name . "sft")))
    (build-system r-build-system)
    (propagated-inputs (list r-suppdists r-fda))
    (home-page "https://cran.r-project.org/package=sft")
    (synopsis "Functions for Systems Factorial Technology Analysis of Data")
    (description
     "This package provides a series of tools for analyzing Systems Factorial
Technology data.  This includes functions for plotting and statistically testing
capacity coefficient functions and survivor interaction contrast functions.
Houpt, Blaha, @code{McIntire}, Havig, and Townsend (2013)
<doi:10.3758/s13428-013-0377-3> provide a basic introduction to Systems
Factorial Technology along with examples using the sft R package.")
    (license license:gpl2+)))

(define-public r-sfsi
  (package
    (name "r-sfsi")
    (version "1.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SFSI" version))
       (sha256
        (base32 "1l4fqg55jjlyidbdfksvc80zkvjbg4vz89ibqxpm38j2c0vi4h9a"))))
    (properties `((upstream-name . "SFSI")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-tensorevd
                             r-stringr
                             r-scales
                             r-reshape2
                             r-igraph
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MarcooLopez/SFSI")
    (synopsis "Sparse Family and Selection Index")
    (description
     "Here we provide tools for the estimation of coefficients in penalized
regressions when the (co)variance matrix of predictors and the covariance vector
between predictors and response, are provided.  These methods are extended to
the context of a Selection Index (commonly used for breeding value prediction).
The approaches offer opportunities such as the integration of high-throughput
traits in genetic evaluations ('Lopez-Cruz et al., 2020')
<doi:10.1038/s41598-020-65011-2> and solutions for training set optimization in
Genomic Prediction ('Lopez-Cruz & de los Campos, 2021')
<doi:10.1093/genetics/iyab030>.")
    (license license:gpl3)))

(define-public r-sfs
  (package
    (name "r-sfs")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SFS" version))
       (sha256
        (base32 "1pig64pj30yyfzyv97f74gshw4lnklrx0npp4da3absk6anb3iv7"))))
    (properties `((upstream-name . "SFS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=SFS")
    (synopsis "Similarity-First Search Seriation Algorithm")
    (description
     "An implementation of the Similarity-First Search algorithm (SFS), a
combinatorial algorithm which can be used to solve the seriation problem and to
recognize some structured weighted graphs.  The SFS algorithm represents a
generalization to weighted graphs of the graph search algorithm Lexicographic
Breadth-First Search (Lex-BFS), a variant of Breadth-First Search.  The SFS
algorithm reduces to Lex-BFS when applied to binary matrices (or, equivalently,
unweighted graphs).  Hence this library can be also considered for Lex-BFS
applications such as recognition of graph classes like chordal or unit interval
graphs.  In fact, the SFS seriation algorithm implemented in this package is a
multisweep algorithm, which consists in repeating a finite number of SFS
iterations (at most n sweeps for a matrix of size n).  If the data matrix has a
Robinsonian structure, then the ranking returned by the multistep SFS algorithm
is a Robinson ordering of the input matrix.  Otherwise the algorithm can be used
as a heuristic to return a ranking partially satisfying the Robinson property.")
    (license license:gpl3)))

(define-public r-sfpl
  (package
    (name "r-sfpl")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SFPL" version))
       (sha256
        (base32 "03axwipil1k1dcjc2zsc62a464qqrncyl2z828w7dqkhp2mfyz2s"))))
    (properties `((upstream-name . "SFPL")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma r-gtools))
    (home-page "https://cran.r-project.org/package=SFPL")
    (synopsis "Sparse Fused Plackett-Luce")
    (description
     "This package implements the methodological developments found in Hermes, van
Heerwaarden, and Behrouzi (2024) <doi:10.48550/@code{arXiv.2308.04325>}, and
allows for the statistical modeling of multi-group rank data in combination with
object variables.  The package also allows for the simulation of synthetic
multi-group rank data.")
    (license license:gpl3)))

(define-public r-sfo
  (package
    (name "r-sfo")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sfo" version))
       (sha256
        (base32 "0q821i9w4kbzpmiym1sha086nr59yydbyig84jy7hws186lkvi7m"))))
    (properties `((upstream-name . "sfo")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=sfo")
    (synopsis "San Francisco International Airport Monthly Air Passengers")
    (description
     "This package provides monthly statistics on the number of monthly air passengers
at SFO airport such as operating airline, terminal, geo, etc.  Data source: San
Francisco data portal (@code{DataSF})
<https://data.sfgov.org/Transportation/Air-Traffic-Passenger-Statistics/rkru-6vcg>.")
    (license license:expat)))

(define-public r-sfnetworks
  (package
    (name "r-sfnetworks")
    (version "0.6.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sfnetworks" version))
       (sha256
        (base32 "0q64ki4ylgkwnz4g43pq43hv9lspi1akyssjpbd4947x69hlyhiz"))))
    (properties `((upstream-name . "sfnetworks")))
    (build-system r-build-system)
    (propagated-inputs (list r-units
                             r-tidygraph
                             r-tibble
                             r-sfheaders
                             r-sf
                             r-rlang
                             r-lwgeom
                             r-igraph
                             r-dplyr
                             r-crayon))
    (native-inputs (list r-knitr))
    (home-page "https://luukvdmeer.github.io/sfnetworks/")
    (synopsis "Tidy Geospatial Networks")
    (description
     "This package provides a tidy approach to spatial network analysis, in the form
of classes and functions that enable a seamless interaction between the network
analysis package tidygraph and the spatial analysis package sf'.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-sfislands
  (package
    (name "r-sfislands")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sfislands" version))
       (sha256
        (base32 "1x3jybr0nhms375ynik8v8nx9n2r9gada1fpnx9vhbykdiz5w8jm"))))
    (properties `((upstream-name . "sfislands")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-spdep
                             r-sf
                             r-purrr
                             r-ggplot2
                             r-dplyr
                             r-broom-mixed))
    (home-page "https://github.com/horankev/sfislands")
    (synopsis "Streamlines the Process of Fitting Areal Spatial Models")
    (description
     "Helpers for addressing the issue of disconnected spatial units.  It allows for
convenient adding and removal of neighbourhood connectivity between areal units
prior to modelling, with the visual aid of maps.  Post-modelling, it reduces the
human workload for extracting, tidying and mapping predictions from areal
models.")
    (license license:expat)))

(define-public r-sfinx
  (package
    (name "r-sfinx")
    (version "1.7.99")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sfinx" version))
       (sha256
        (base32 "14v47y00qwvc2s0vzjw19hwn85nzj8lna539c75qq6zkn6kir6va"))))
    (properties `((upstream-name . "sfinx")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "http://sfinx.ugent.be")
    (synopsis
     "Straightforward Filtering Index for AP-MS Data Analysis (SFINX)")
    (description
     "The straightforward filtering index (SFINX) identifies true positive protein
interactions in a fast, user-friendly, and highly accurate way.  It is not only
useful for the filtering of affinity purification - mass spectrometry (AP-MS)
data, but also for similar types of data resulting from other co-complex
interactomics technologies, such as TAP-MS, Virotrap and @code{BioID}.  SFINX
can also be used via the website interface at <http://sfinx.ugent.be>.")
    (license license:asl2.0)))

(define-public r-sfhotspot
  (package
    (name "r-sfhotspot")
    (version "0.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sfhotspot" version))
       (sha256
        (base32 "17vlayqqhv59p9mhrxzxs5r6gfqd6wyyi1k6p05n2sd9c06jzadb"))))
    (properties `((upstream-name . "sfhotspot")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-spdep
                             r-spatialkde
                             r-sf
                             r-rlang
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "http://pkgs.lesscrime.info/sfhotspot/")
    (synopsis "Hot-Spot Analysis with Simple Features")
    (description
     "Identify and understand clusters of points (typically representing the locations
of places or events) stored in simple-features (SF) objects.  This is useful for
analysing, for example, hot-spots of crime events.  The package emphasises
producing results from point SF data in a single step using reasonable default
values for all other arguments, to aid rapid data analysis by users who are
starting out.  Functions available include kernel density estimation (for
details, see Yip (2020) <doi:10.22224/gistbok/2020.1.12>), analysis of spatial
association (Getis and Ord (1992) <doi:10.1111/j.1538-4632.1992.tb00261.x>) and
hot-spot classification (Chainey (2020) ISBN:158948584X).")
    (license license:expat)))

(define-public r-sfflhd
  (package
    (name "r-sfflhd")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sFFLHD" version))
       (sha256
        (base32 "014gy3nj6gr740z4apw82jh4n7s57zmnr95bm3g9xxnwnb224npq"))))
    (properties `((upstream-name . "sFFLHD")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6 r-doe-base r-conf-design))
    (home-page "https://github.com/CollinErickson/sFFLHD")
    (synopsis "Sequential Full Factorial-Based Latin Hypercube Design")
    (description
     "Gives design points from a sequential full factorial-based Latin hypercube
design, as described in Duan, Ankenman, Sanchez, and Sanchez (2015,
Technometrics, <doi:10.1080/00401706.2015.1108233>).")
    (license license:gpl3)))

(define-public r-sfdep
  (package
    (name "r-sfdep")
    (version "0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sfdep" version))
       (sha256
        (base32 "0x3wwa3s5aa402ab0p07lx0cs3vg5qhhrhz4p3sdjw4lxwfw1hks"))))
    (properties `((upstream-name . "sfdep")))
    (build-system r-build-system)
    (propagated-inputs (list r-spdep r-sf r-rlang r-cli))
    (home-page "https://sfdep.josiahparry.com")
    (synopsis "Spatial Dependence for Simple Features")
    (description
     "An interface to spdep to integrate with sf objects and the tidyverse'.")
    (license license:gpl3)))

(define-public r-sfdct
  (package
    (name "r-sfdct")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sfdct" version))
       (sha256
        (base32 "1v86s8nb021swfq53jmj3vjcyjkzm8aqjlw55xjjxsk29rmq4694"))))
    (properties `((upstream-name . "sfdct")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-sp r-sf r-rtriangle r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/hypertidy/sfdct")
    (synopsis "Constrained Triangulation for Simple Features")
    (description
     "Build a constrained high quality Delaunay triangulation from simple features
objects, applying constraints based on input line segments, and triangle
properties including maximum area, minimum internal angle.  The triangulation
code in RTriangle uses the method of Cheng, Dey and Shewchuk (2012,
ISBN:9781584887300).  For a low-dependency alternative with low-quality
path-based constrained triangulation see
<https://CRAN.R-project.org/package=decido> and for high-quality configurable
triangulation see <https://github.com/hypertidy/anglr>.  Also consider
comparison with the GEOS lib which since version 3.10.0 includes a low quality
polygon triangulation method that starts with ear clipping and refines to
Delaunay.")
    (license (license:fsdg-compatible "CC BY-NC-SA 4.0"))))

(define-public r-sfcurve
  (package
    (name "r-sfcurve")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sfcurve" version))
       (sha256
        (base32 "02r87znczsvx8k4yrwjyayg9hynk97dgy5i3xbhal0cc6rlf1h5m"))))
    (properties `((upstream-name . "sfcurve")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-colorramp2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jokergoo/sfcurve")
    (synopsis "2x2, 3x3 and Nxn Space-Filling Curves")
    (description
     "Implementation of all possible forms of 2x2 and 3x3 space-filling curves, i.e.,
the generalized forms of the Hilbert curve
<https://en.wikipedia.org/wiki/Hilbert_curve>, the Peano curve
<https://en.wikipedia.org/wiki/Peano_curve> and the Peano curve in the meander
type (Figure 5 in <https://eudml.org/doc/141086>).  It can generates nxn curves
expanded from any specific level-1 units.  It also implements the H-curve and
the three-dimensional Hilbert curve.")
    (license license:expat)))

(define-public r-sfcr
  (package
    (name "r-sfcr")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sfcr" version))
       (sha256
        (base32 "05ip1z6rh0k7az0y7nwp9c6rv1s53i0i698yfhx46lfnhzz87anf"))))
    (properties `((upstream-name . "sfcr")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rootsolve
                             r-rlang
                             r-rdpack
                             r-rcpparmadillo
                             r-rcpp
                             r-purrr
                             r-magrittr
                             r-kableextra
                             r-igraph
                             r-forcats
                             r-expm
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/joaomacalos/sfcr")
    (synopsis "Simulate Stock-Flow Consistent Models")
    (description
     "Routines to write, simulate, and validate stock-flow consistent (SFC) models.
The accounting structure of SFC models are described in Godley and Lavoie (2007,
ISBN:978-1-137-08599-3).  The algorithms implemented to solve the models
(Gauss-Seidel and Broyden) are described in Kinsella and O'Shea (2010)
<doi:10.2139/ssrn.1729205> and Peressini and Sullivan (1988,
ISBN:0-387-96614-5).")
    (license license:expat)))

(define-public r-sfcentral
  (package
    (name "r-sfcentral")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sfcentral" version))
       (sha256
        (base32 "1jvdyalrvk7kzrkwm19fhqrqy1ji6jmz1jp5xdq36myamkj790lg"))))
    (properties `((upstream-name . "sfcentral")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-scales r-lwgeom r-hmisc r-geodist))
    (home-page "https://gavg712.gitlab.io/sfcentral/")
    (synopsis "Spatial Centrality and Dispersion Statistics")
    (description
     "Computing centrographic statistics (central points, standard distance, standard
deviation ellipse, standard deviation box) for observations taken at point
locations in 2D or 3D. The sfcentral library was inspired in aspace package but
conceived to be used in a spatial tidyverse context.")
    (license license:gpl3+)))

(define-public r-sfc
  (package
    (name "r-sfc")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sfc" version))
       (sha256
        (base32 "0cm4mfcfd9bhf2j5fppsihzrfipnldb6q3xradd88z9pwgrkfx2a"))))
    (properties `((upstream-name . "sfc")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-triangle r-tidyr r-sna r-dplyr))
    (home-page "https://github.com/ctfysh/sfc")
    (synopsis "Substance Flow Computation")
    (description
     "This package provides a function sfc() to compute the substance flow with the
input files --- \"data\" and \"model\".  If sample.size is set more than 1,
uncertainty analysis will be executed while the distributions and parameters are
supplied in the file \"data\".")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-sfarrow
  (package
    (name "r-sfarrow")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sfarrow" version))
       (sha256
        (base32 "1hslyha1w5x1ncawbwbx792cqa3031v26g9lssmb0n1hihzvfp8g"))))
    (properties `((upstream-name . "sfarrow")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-jsonlite r-dplyr r-arrow))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/wcjochem/sfarrow")
    (synopsis "Read/Write Simple Feature Objects ('sf') with 'Apache' 'Arrow'")
    (description
     "Support for reading/writing simple feature ('sf') spatial objects from/to
Parquet files.  Parquet files are an open-source, column-oriented data storage
format from Apache (<https://parquet.apache.org/>), now popular across
programming languages.  This implementation converts simple feature list
geometries into well-known binary format for use by arrow', and coordinate
reference system information is maintained in a standard metadata format.")
    (license license:expat)))

(define-public r-sfar
  (package
    (name "r-sfar")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sfaR" version))
       (sha256
        (base32 "1c7rnnsmdlhby765avb8134f7424qf0b8gdl5q6vz3ialalfg49d"))))
    (properties `((upstream-name . "sfaR")))
    (build-system r-build-system)
    (propagated-inputs (list r-ucminf
                             r-trustoptim
                             r-texreg
                             r-sandwich
                             r-randtoolbox
                             r-qrng
                             r-plm
                             r-nleqslv
                             r-mnorm
                             r-maxlik
                             r-marqlevalg
                             r-formula
                             r-fastghquad
                             r-cubature))
    (home-page "https://github.com/hdakpo/sfaR")
    (synopsis "Stochastic Frontier Analysis Routines")
    (description
     "Maximum likelihood estimation for stochastic frontier analysis (SFA) of
production (profit) and cost functions.  The package includes the basic
stochastic frontier for cross-sectional or pooled data with several
distributions for the one-sided error term (i.e., Rayleigh, gamma, Weibull,
lognormal, uniform, generalized exponential and truncated skewed Laplace), the
latent class stochastic frontier model (LCM) as described in Dakpo et al. (2021)
<doi:10.1111/1477-9552.12422>, for cross-sectional and pooled data, and the
sample selection model as described in Greene (2010)
<doi:10.1007/s11123-009-0159-1>, and applied in Dakpo et al. (2021)
<doi:10.1111/agec.12683>.  Several possibilities in terms of optimization
algorithms are proposed.")
    (license license:gpl3+)))

(define-public r-sfadv
  (package
    (name "r-sfadv")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sfadv" version))
       (sha256
        (base32 "1rfpm6km5pckwhqgpvidm6qsj5sjdaqqj5b0sgvpslivfa4c80cr"))))
    (properties `((upstream-name . "sfadv")))
    (build-system r-build-system)
    (propagated-inputs (list r-minpack-lm r-gmm))
    (home-page "https://cran.r-project.org/package=sfadv")
    (synopsis "Advanced Methods for Stochastic Frontier Analysis")
    (description
     "Stochastic frontier analysis with advanced methods.  In particular, it applies
the approach proposed by Latruffe et al. (2017) <DOI:10.1093/ajae/aaw077> to
estimate a stochastic frontier with technical inefficiency effects when one
input is endogenous.")
    (license license:gpl3)))

(define-public r-sewage
  (package
    (name "r-sewage")
    (version "0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sewage" version))
       (sha256
        (base32 "171rd59w90q29a9ywdx5225gw6m57bz9rcj25ql38jk62xdmrx69"))))
    (properties `((upstream-name . "sewage")))
    (build-system r-build-system)
    (propagated-inputs (list r-glue r-diagrammer r-cli))
    (home-page "https://github.com/mwhalen18/sewage")
    (synopsis "Light-Weight Data Pipelining Tool")
    (description
     "This package provides a simple interface to developing complex data pipelines
which can be executed in a single call.  sewage makes it easy to test, debug,
and share data pipelines through it's interface and visualizations.")
    (license license:expat)))

(define-public r-sever
  (package
    (name "r-sever")
    (version "0.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sever" version))
       (sha256
        (base32 "10jckf6q31blsl1yhs5f1mx7b8sm8sqb86iakcvaslf651ijx81m"))))
    (properties `((upstream-name . "sever")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-htmltools r-cli))
    (home-page "https://sever.john-coene.com/")
    (synopsis "Customise 'Shiny' Disconnected Screens and Error Messages")
    (description
     "Customise Shiny disconnected screens as well as sanitize error messages to make
them clearer and friendlier to the user.")
    (license license:expat)))

(define-public r-sevenbridges2
  (package
    (name "r-sevenbridges2")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sevenbridges2" version))
       (sha256
        (base32 "065r7qggr3hb4jzwbw5sfkimyvaqr9pgyqa5k7aagmy5xdsgzczp"))))
    (properties `((upstream-name . "sevenbridges2")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-stringr
                             r-rlang
                             r-readr
                             r-r6
                             r-purrr
                             r-jsonlite
                             r-httr
                             r-glue
                             r-desctools
                             r-data-table
                             r-curl
                             r-cli
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://www.sevenbridges.com")
    (synopsis "The 'Seven Bridges Platform' API Client")
    (description
     "R client and utilities for Seven Bridges Platform API, from Cancer Genomics
Cloud to other Seven Bridges supported platforms.  API documentation is hosted
publicly at <https://docs.sevenbridges.com/docs/the-api>.")
    (license license:asl2.0)))

(define-public r-settingssync
  (package
    (name "r-settingssync")
    (version "3.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "settingsSync" version))
       (sha256
        (base32 "0bb8cdj68473hi4fvwhcqyhpxclx4x42dpc9ghvhp42lhw1lgbi4"))))
    (properties `((upstream-name . "settingsSync")))
    (build-system r-build-system)
    (propagated-inputs (list r-yesno
                             r-tibble
                             r-rappdirs
                             r-jsonlite
                             r-googledrive
                             r-glue
                             r-dplyr
                             r-cli))
    (home-page "https://github.com/notPlancha/settingsSync")
    (synopsis "'Rstudio' Addin to Sync Settings and Keymaps")
    (description
     "This package provides a Rstudio addin to download, merge and upload Rstudio
settings and keymaps, essentially syncing them at will.  It uses Google Drive as
a cloud storage to keep the settings and keymaps files.")
    (license (license:fsdg-compatible "Apache License (== 2)"))))

(define-public r-settings
  (package
    (name "r-settings")
    (version "0.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "settings" version))
       (sha256
        (base32 "1i9ij5k8p2v9gxr48vas8hj337mbyx9c4ywv9bbdwkz6kzah46k5"))))
    (properties `((upstream-name . "settings")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/markvanderloo/settings")
    (synopsis "Software Option Settings Manager for R")
    (description
     "This package provides option settings management that goes beyond R's default
options function.  With this package, users can define their own option settings
manager holding option names, default values and (if so desired) ranges or sets
of allowed option values that will be automatically checked.  Settings can then
be retrieved, altered and reset to defaults with ease.  For R programmers and
package developers it offers cloning and merging functionality which allows for
conveniently defining global and local options, possibly in a multilevel options
hierarchy.  See the package vignette for some examples concerning functions, S4
classes, and reference classes.  There are convenience functions to reset par()
and options() to their factory defaults'.")
    (license license:gpl3)))

(define-public r-settest
  (package
    (name "r-settest")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SetTest" version))
       (sha256
        (base32 "12w297xmq76afiwj1h8ygk24rjhhd7a77fj12dsksw20y1g0f30d"))))
    (properties `((upstream-name . "SetTest")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SetTest")
    (synopsis
     "Group Testing Procedures for Signal Detection and Goodness-of-Fit")
    (description
     "It provides cumulative distribution function (CDF), quantile, p-value,
statistical power calculator and random number generator for a collection of
group-testing procedures, including the Higher Criticism tests, the one-sided
Kolmogorov-Smirnov tests, the one-sided Berk-Jones tests, the one-sided
phi-divergence tests, etc.  The input are a group of p-values.  The null
hypothesis is that they are i.i.d.  Uniform(0,1).  In the context of signal
detection, the null hypothesis means no signals.  In the context of the
goodness-of-fit testing, which contrasts a group of i.i.d.  random variables to
a given continuous distribution, the input p-values can be obtained by the CDF
transformation.  The null hypothesis means that these random variables follow
the given distribution.  For reference, see [1]Hong Zhang, Jiashun Jin and
Zheyang Wu. \"Distributions and power of optimal signal-detection statistics in
finite case\", IEEE Transactions on Signal Processing (2020) 68, 1021-1033; [2]
Hong Zhang and Zheyang Wu. \"The general goodness-of-fit tests for correlated
data\", Computational Statistics & Data Analysis (2022) 167, 107379.")
    (license license:gpl2)))

(define-public r-setmethods
  (package
    (name "r-setmethods")
    (version "4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SetMethods" version))
       (sha256
        (base32 "0pzxcqbc7jnsxhv902qw2q9rcqskib11z36d5nav9wny6bj2z658"))))
    (properties `((upstream-name . "SetMethods")))
    (build-system r-build-system)
    (propagated-inputs (list r-stargazer
                             r-scatterplot3d
                             r-qca
                             r-ggrepel
                             r-ggplot2
                             r-fmsb
                             r-betareg
                             r-admisc))
    (home-page "https://cran.r-project.org/package=SetMethods")
    (synopsis
     "Functions for Set-Theoretic Multi-Method Research and Advanced QCA")
    (description
     "This package provides functions for performing set-theoretic multi-method
research, QCA for clustered data, theory evaluation, Enhanced Standard Analysis,
indirect calibration, radar visualisations.  Additionally it includes data to
replicate the 	examples in the books by Oana, I.E, C. Q. Schneider, and E.
Thomann.  Qualitative Comparative Analysis (QCA) using R: A Beginner's Guide.
Cambridge University Press and C. Q. Schneider and C. Wagemann \"Set Theoretic
Methods for the Social Sciences\", Cambridge University Press.")
    (license license:gpl2)))

(define-public r-setartree
  (package
    (name "r-setartree")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "setartree" version))
       (sha256
        (base32 "13j0shmnnwnbkzgl1wnirvfd9yi29smn3a5cjxg5xg89bjh77wc6"))))
    (properties `((upstream-name . "setartree")))
    (build-system r-build-system)
    (propagated-inputs (list r-generics))
    (home-page "https://github.com/rakshitha123/setartree")
    (synopsis
     "SETAR-Tree - A Novel and Accurate Tree Algorithm for Global Time Series Forecasting")
    (description
     "The implementation of a forecasting-specific tree-based model that is in
particular suitable for global time series forecasting, as proposed in Godahewa
et al. (2022) <@code{arXiv:2211.08661v1>}.  The model uses the concept of Self
Exciting Threshold Autoregressive (SETAR) models to define the node splits and
thus, the model is named SETAR-Tree.  The SETAR-Tree uses some
time-series-specific splitting and stopping procedures.  It trains global pooled
regression models in the leaves allowing the models to learn cross-series
information.  The depth of the tree is controlled by conducting a statistical
linearity test as well as measuring the error reduction percentage at each node
split.  Thus, the SETAR-Tree requires minimal external hyperparameter tuning and
provides competitive results under its default configuration.  A forest is
developed by extending the SETAR-Tree.  The SETAR-Forest combines the forecasts
provided by a collection of diverse SETAR-Trees during the forecasting process.")
    (license license:expat)))

(define-public r-set
  (package
    (name "r-set")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "set" version))
       (sha256
        (base32 "0f36dp9k0nxp1amsd7ybwgbv0vxvgd1kns7cbfsbsh0kljxyplzp"))))
    (properties `((upstream-name . "set")))
    (build-system r-build-system)
    (propagated-inputs (list r-do))
    (home-page "https://github.com/yikeshu0611/set")
    (synopsis "Set Operation")
    (description
     "More easy to get intersection, union or complementary set and combinations.")
    (license license:gpl3)))

(define-public r-sesraster
  (package
    (name "r-sesraster")
    (version "0.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SESraster" version))
       (sha256
        (base32 "08lj7prqfb7ngjh6km9q2zcmmjvivnpw3f2pvakjarl312f53kla"))))
    (properties `((upstream-name . "SESraster")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-rlang))
    (native-inputs (list r-knitr))
    (home-page "https://CRAN.R-project.org/package=SESraster")
    (synopsis "Raster Randomization for Null Hypothesis Testing")
    (description
     "Randomization of presence/absence species distribution raster data with or
without including spatial structure for calculating standardized effect sizes
and testing null hypothesis.  The randomization algorithms are based on
classical algorithms for matrices (Gotelli 2000, <doi:10.2307/177478>)
implemented for raster data.")
    (license license:gpl3+)))

(define-public r-seset
  (package
    (name "r-seset")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SEset" version))
       (sha256
        (base32 "127z85m95xn46s7lk3sdzrs73mi7pa2smx9r80fnl678f2kyz6lm"))))
    (properties `((upstream-name . "SEset")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-matrix r-combinat))
    (home-page "https://cran.r-project.org/package=SEset")
    (synopsis "Computing Statistically-Equivalent Path Models")
    (description
     "This package provides tools to compute and analyze the set of
statistically-equivalent (Gaussian, linear) path models which generate the input
precision or (partial) correlation matrix.  This procedure is useful for
understanding how statistical network models such as the Gaussian Graphical
Model (GGM) perform as causal discovery tools.  The statistical-equivalence set
of a given GGM expresses the uncertainty we have about the sign, size and
direction of directed relationships based on the weights matrix of the GGM
alone.  The derivation of the equivalence set and its use for understanding GGMs
as causal discovery tools is described by Ryan, O., Bringmann, L.F., &
Schuurman, N.K. (2022) <doi: 10.31234/osf.io/ryg69>.")
    (license license:gpl3)))

(define-public r-sesem
  (package
    (name "r-sesem")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sesem" version))
       (sha256
        (base32 "0b1xzv38wpkhhlq87xrvlh45b8ksbi8bg4ar5hrf8gxa19r9rmi5"))))
    (properties `((upstream-name . "sesem")))
    (build-system r-build-system)
    (propagated-inputs (list r-mgcv r-lavaan r-gplots))
    (home-page "http://www.r-project.org")
    (synopsis "Spatially Explicit Structural Equation Modeling")
    (description
     "Structural equation modeling is a powerful statistical approach for the testing
of networks of direct and indirect theoretical causal relationships in complex
data sets with inter-correlated dependent and independent variables.  Here we
implement a simple method for spatially explicit structural equation modeling
based on the analysis of variance co-variance matrices calculated across a range
of lag distances.  This method provides readily interpreted plots of the change
in path coefficients across scale.")
    (license license:gpl2+)))

(define-public r-servospherer
  (package
    (name "r-servospherer")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "servosphereR" version))
       (sha256
        (base32 "0m1fjd3f1s2ss54xmcrm3p0c33hhi2dj0vim790g4y0mw2aicc8l"))))
    (properties `((upstream-name . "servosphereR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-purrr r-magrittr r-dplyr r-data-table))
    (native-inputs (list r-knitr))
    (home-page "http://github.com/wittja01/servosphereR")
    (synopsis "Analyze Data Generated from Syntech Servosphere Trials")
    (description
     "This package provides functions that facilitate and speed up the analysis of
data produced by a Syntech servosphere
<http://www.ockenfels-syntech.com/products/locomotion-compensation/>, which is
equipment for studying the movement behavior of arthropods.  This package is
designed to make working with data produced from a servosphere easy for someone
new to or unfamiliar with R. The functions provided in this package fall into
three broad-use categories: functions for cleaning raw data produced by the
servosphere software, functions for deriving movement variables based on
position data, and functions for summarizing movement variables for easier
analysis.  These functions are built with functions from the tidyverse package
to work efficiently, as a single servosphere file may consist of hundreds of
thousands of rows of data and a user may wish to analyze hundreds of files at a
time.  Many of the movement variables derivable through this package are
described in the following papers: OtÃ¡lora-Luna, Fernando; Dickens, Joseph C.
(2011) <doi:10.1371/journal.pone.0020990> Party, Virginie; Hanot, Christophe;
Busser, Daniela Schmidt; Rochat, Didier; Renou, Michel (2013)
<doi:10.1371/journal.pone.0052897> Bell, William J.; Kramer, Ernest (1980)
<doi:10.1007/BF01402908> Becher, Paul G; Guerin, Patrick M. (2009)
<doi:10.1016/j.jinsphys.2009.01.006>.")
    (license license:gpl3)))

(define-public r-serrsbayes
  (package
    (name "r-serrsbayes")
    (version "0.5-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "serrsBayes" version))
       (sha256
        (base32 "1w8fs9k4zhy0hwkm30dvi360n28cl2gn9vdqaak64g0nlvb9jy1w"))))
    (properties `((upstream-name . "serrsBayes")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm r-rcppeigen r-rcpp r-matrix))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mooresm/serrsBayes")
    (synopsis "Bayesian Modelling of Raman Spectroscopy")
    (description
     "Sequential Monte Carlo (SMC) algorithms for fitting a generalised additive mixed
model (GAMM) to surface-enhanced resonance Raman spectroscopy (SERRS), using the
method of Moores et al. (2016) <@code{arXiv:1604.07299>}.  Multivariate
observations of SERRS are highly collinear and lend themselves to a reduced-rank
representation.  The GAMM separates the SERRS signal into three components: a
sequence of Lorentzian, Gaussian, or pseudo-Voigt peaks; a smoothly-varying
baseline; and additive white noise.  The parameters of each component of the
model are estimated iteratively using SMC. The posterior distributions of the
parameters given the observed spectra are represented as a population of
weighted particles.")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-serpstatr
  (package
    (name "r-serpstatr")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "serpstatr" version))
       (sha256
        (base32 "087rlfqdx74rx9fil4lgchl0v8mzs3ld46ji89nx885913wfhjf2"))))
    (properties `((upstream-name . "serpstatr")))
    (build-system r-build-system)
    (propagated-inputs (list r-httr))
    (home-page "https://serpstat.com/api/")
    (synopsis "'Serpstat' API Wrapper")
    (description
     "The primary goal of Serpstat API <https://serpstat.com/api/> is to reduce manual
SEO (search engine optimization) and PPC (pay-per-click) tasks.  You can
automate your keywords research or competitors analysis with this API wrapper.")
    (license license:expat)))

(define-public r-serp
  (package
    (name "r-serp")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "serp" version))
       (sha256
        (base32 "0hd25rxknj636ssy2289xjvq5fflp2p561yb9w0siqihfc7cfjpc"))))
    (properties `((upstream-name . "serp")))
    (build-system r-build-system)
    (propagated-inputs (list r-ordinal r-crayon))
    (home-page "https://github.com/ejikeugba/serp")
    (synopsis "Smooth Effects on Response Penalty for CLM")
    (description
     "This package provides a regularization method for the cumulative link models.
The smooth-effect-on-response penalty (SERP) provides flexible modelling of the
ordinal model by enabling the smooth transition from the general cumulative link
model to a coarser form of the same model.  In other words, as the tuning
parameter goes from zero to infinity, the subject-specific effects associated
with each variable in the model tend to a unique global effect.  The parameter
estimates of the general cumulative model are mostly unidentifiable or at least
only identifiable within a range of the entire parameter space.  Thus, by
maximizing a penalized rather than the usual non-penalized log-likelihood, this
and other numerical problems common with the general model are to a large extent
eliminated.  Fitting is via a modified Newton's method.  Several standard model
performance and descriptive methods are also available.  For more details on the
penalty implemented here, see, Ugba (2021) <doi:10.21105/joss.03705> and Ugba et
al. (2021) <doi:10.3390/stats4030037>.")
    (license license:gpl2)))

(define-public r-serosv
  (package
    (name "r-serosv")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "serosv" version))
       (sha256
        (base32 "11nwpbbzcp6lch79kfz2v63l899dvjdg58qdlc4z2r1c68yxk4vp"))))
    (properties `((upstream-name . "serosv")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-patchwork
                             r-mixdist
                             r-mgcv
                             r-magrittr
                             r-locfit
                             r-ggplot2
                             r-dplyr
                             r-desolve
                             r-boot
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://oucru-modelling.github.io/serosv/")
    (synopsis "Model Infectious Disease Parameters from Serosurveys")
    (description
     "An easy-to-use and efficient tool to estimate infectious diseases parameters
using serological data.  Implemented models include SIR models
(basic_sir_model(), static_sir_model(), mseir_model(), sir_subpops_model()),
parametric models (polynomial_model(), fp_model()), nonparametric models
(lp_model()), semiparametric models (penalized_splines_model()), hierarchical
models (hierarchical_bayesian_model()).  The package is based on the book
\"Modeling Infectious Disease Parameters Based on Serological and Social Contact
Data: A Modern Statistical Perspective\" (Hens, Niel & Shkedy, Ziv & Aerts, Marc
& Faes, Christel & Damme, Pierre & Beutels, Philippe., 2013)
<doi:10.1007/978-1-4614-4072-7>.")
    (license license:expat)))

(define-public r-serocalculator
  (package
    (name "r-serocalculator")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "serocalculator" version))
       (sha256
        (base32 "1cxwii35gp9dybjp2989wbmsrv9f3s1qxrrsl4s57lj3q1by2kpk"))))
    (properties `((upstream-name . "serocalculator")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-scales
                             r-rngtools
                             r-rlang
                             r-rcpp
                             r-mixtools
                             r-magrittr
                             r-lifecycle
                             r-ggpubr
                             r-ggplot2
                             r-foreach
                             r-dplyr
                             r-doparallel
                             r-cli))
    (home-page "https://github.com/UCD-SERG/serocalculator")
    (synopsis "Estimating Infection Rates from Serological Data")
    (description
     "Translates antibody levels measured in cross-sectional population samples into
estimates of the frequency with which seroconversions (infections) occur in the
sampled populations.  Replaces the previous seroincidence package.  Methods
originally published in Simonsen et al. (2009) <doi:10.1002/sim.3592> and Teunis
et al. (2012) <doi:10.1002/sim.5322>, and further developed in subsequent
publications by de Graaf et al. (2014) <doi:10.1016/j.epidem.2014.08.002>,
Teunis et al. (2016) <doi:10.1016/j.epidem.2016.04.001>, and Teunis et al.
(2020) <doi:10.1002/sim.8578>.")
    (license license:gpl3)))

(define-public r-serieslcb
  (package
    (name "r-serieslcb")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "serieslcb" version))
       (sha256
        (base32 "1ip4k2xzg06hwsni62ai1whs1zzhwlf94507inpfpqz3jmmafrhq"))))
    (properties `((upstream-name . "serieslcb")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-gplots))
    (home-page "https://cran.r-project.org/package=serieslcb")
    (synopsis "Lower Confidence Bounds for Binomial Series System")
    (description
     "Calculate and compare lower confidence bounds for binomial series system
reliability.  The R shiny application, launched by the function launch_app(),
weaves together a workflow of customized simulations and delta coverage
calculations to output recommended lower confidence bound methods.")
    (license license:gpl3)))

(define-public r-serial
  (package
    (name "r-serial")
    (version "3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "serial" version))
       (sha256
        (base32 "1y1gj0lcdn5apiv6c6arks6pqhsbvnrfym6n2izjarx2c27ijrjj"))))
    (properties `((upstream-name . "serial")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=serial")
    (synopsis "The Serial Interface Package")
    (description
     "Enables reading and writing binary and ASCII data to RS232/RS422/RS485 or any
other virtual serial interface of the computer.")
    (license license:gpl2)))

(define-public r-sergeant
  (package
    (name "r-sergeant")
    (version "0.9.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sergeant" version))
       (sha256
        (base32 "0mgrrlny1qfddznyc34awh8vkbjnyskbyqrxszdhsm2c5wgp2nwh"))))
    (properties `((upstream-name . "sergeant")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringi
                             r-scales
                             r-readr
                             r-purrr
                             r-magrittr
                             r-jsonlite
                             r-httr
                             r-htmltools
                             r-dplyr
                             r-dbplyr
                             r-dbi
                             r-bit64))
    (home-page "https://gitlab.com/hrbrmstr/sergeant")
    (synopsis "Tools to Transform and Query Data with Apache Drill")
    (description
     "Apache Drill is a low-latency distributed query engine designed to enable data
exploration and analysis on both relational and non-relational data stores,
scaling to petabytes of data.  Methods are provided that enable working with
Apache Drill instances via the REST API, DBI methods and using dplyr'/'dbplyr
idioms.  Helper functions are included to facilitate using official Drill Docker
images/containers.")
    (license license:expat)))

(define-public r-sequoia
  (package
    (name "r-sequoia")
    (version "2.11.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sequoia" version))
       (sha256
        (base32 "09ji88hylic73m23gq24m9fp2zqlfz06xypd8sajj2k6q8105nbn"))))
    (properties `((upstream-name . "sequoia")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (inputs (list))
    (propagated-inputs (list r-plyr r-cli))
    (native-inputs (list r-r-rsp r-knitr gfortran esbuild))
    (home-page "https://jiscah.github.io/")
    (synopsis "Pedigree Inference from SNPs")
    (description
     "Multi-generational pedigree inference from incomplete data on hundreds of SNPs,
including parentage assignment and sibship clustering.  See Huisman (2017)
(<DOI:10.1111/1755-0998.12665>) for more information.")
    (license license:gpl2)))

(define-public r-sequentialdesign
  (package
    (name "r-sequentialdesign")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SequentialDesign" version))
       (sha256
        (base32 "1gi37pixwbpy7358id1c75rckr352hs8vjs8sk8qgsr97pkm5xdq"))))
    (properties `((upstream-name . "SequentialDesign")))
    (build-system r-build-system)
    (propagated-inputs (list r-sequential))
    (home-page "https://cran.r-project.org/package=SequentialDesign")
    (synopsis
     "Observational Database Study Planning using Exact Sequential Analysis for Poisson and Binomial Data")
    (description
     "This package provides functions to be used in conjunction with the Sequential
package that allows for planning of observational database studies that will be
analyzed with exact sequential analysis.  This package supports Poisson- and
binomial-based data.  The primary function, seq_wrapper(...), accepts parameters
for simulation of a simple exposure pattern and for the Sequential package setup
and analysis functions.  The exposure matrix is used to simulate the true and
false positive and negative populations (Green (1983)
<doi:10.1093/oxfordjournals.aje.a113521>, Brenner (1993)
<doi:10.1093/oxfordjournals.aje.a116805>).  Functions are then run from the
Sequential package on these populations, which allows for the exploration of
outcome misclassification in data.")
    (license license:gpl2)))

(define-public r-sequential
  (package
    (name "r-sequential")
    (version "4.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Sequential" version))
       (sha256
        (base32 "0k5iikj16iqlahr01y6nikd04vp0v5jk292kwdw9isnvx735fvkr"))))
    (properties `((upstream-name . "Sequential")))
    (build-system r-build-system)
    (propagated-inputs (list r-boot))
    (home-page "https://cran.r-project.org/package=Sequential")
    (synopsis "Exact Sequential Analysis for Poisson and Binomial Data")
    (description
     "This package provides functions to calculate exact critical values, statistical
power, expected time to signal, and required sample sizes for performing exact
sequential analysis.  All these	calculations can be done for either Poisson or
binomial data, for continuous or group sequential analyses, and for different
types of rejection boundaries.  In case of group sequential analyses, the group
sizes do not have to be specified in advance and the alpha spending can be
arbitrarily settled.")
    (license license:gpl2)))

(define-public r-sequencespikeslab
  (package
    (name "r-sequencespikeslab")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SequenceSpikeSlab" version))
       (sha256
        (base32 "0kxcz2xqf8hiz7j0hb20ikl2mwjpplbmsis5906f7lqvzg85v97x"))))
    (properties `((upstream-name . "SequenceSpikeSlab")))
    (build-system r-build-system)
    (propagated-inputs (list r-selectiveinference r-rcppprogress r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SequenceSpikeSlab")
    (synopsis
     "Exact Bayesian Model Selection Methods for the Sparse Normal Sequence Model")
    (description
     "This package contains fast functions to calculate the exact Bayes posterior for
the Sparse Normal Sequence Model, implementing the algorithms described in Van
Erven and Szabo (2021, <doi:10.1214/20-BA1227>).  For general hierarchical
priors, sample sizes up to 10,000 are feasible within half an hour on a standard
laptop.  For beta-binomial spike-and-slab priors, a faster algorithm is
provided, which can handle sample sizes of 100,000 in half an hour.  In the
implementation, special care has been taken to assure numerical stability of the
methods even for such large sample sizes.")
    (license license:gpl2+)))

(define-public r-seqtrie
  (package
    (name "r-seqtrie")
    (version "0.2.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seqtrie" version))
       (sha256
        (base32 "12pkcb5ylqlr7ljzrwna6fjqvnikgvsr7k664bw6xkbx1g370s2a"))))
    (properties `((upstream-name . "seqtrie")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringi
                             r-rlang
                             r-rcppparallel
                             r-rcpp
                             r-r6
                             r-dplyr
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/traversc/seqtrie")
    (synopsis "Radix Tree and Trie-Based String Distances")
    (description
     "This package provides a collection of Radix Tree and Trie algorithms for finding
similar sequences and calculating sequence distances (Levenshtein and other
distance metrics).  This work was inspired by a trie implementation in Python:
\"Fast and Easy Levenshtein distance using a Trie.\" Hanov (2011)
<http://stevehanov.ca/blog/index.php?id=114>.")
    (license license:gpl3)))

(define-public r-seqtest
  (package
    (name "r-seqtest")
    (version "0.1-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seqtest" version))
       (sha256
        (base32 "1bdfww9szh7h278id5xpfc39mxkcix3yvsdwc68wx3q3x0wxhqhq"))))
    (properties `((upstream-name . "seqtest")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=seqtest")
    (synopsis "Sequential Triangular Test")
    (description
     "Sequential triangular test for the arithmetic mean in one- and two- samples,
proportions in one- and two-samples, and the Pearson's correlation coefficient.")
    (license license:gpl3)))

(define-public r-seqshp
  (package
    (name "r-seqshp")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seqSHP" version))
       (sha256
        (base32 "1p1flpw055jb135jpqlgdyyn19p6hwiqj9kx085irlyczi5kiimi"))))
    (properties `((upstream-name . "seqSHP")))
    (build-system r-build-system)
    (propagated-inputs (list r-haven r-dplyr))
    (home-page "https://cran.r-project.org/package=seqSHP")
    (synopsis "Building Sequences from SHP Waves")
    (description
     "Based on the structure of the SPSS version of the Swiss Household Panel (SHP)
data, provides a function @code{seqFromWaves}() that seeks the data of variables
specified by the user in each of the wave files and collects them as sequences.
The function also matches the sequences with variables from other files such as
the master files of persons (MP) and households (MH), and social origins (SO).
It can also match with activity calendar data (CA).")
    (license license:gpl3+)))

(define-public r-seqnet
  (package
    (name "r-seqnet")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SeqNet" version))
       (sha256
        (base32 "1ix3c249q758si6670hqa56g1snqqwqdrwshfwajvynl7k2zqzmv"))))
    (properties `((upstream-name . "SeqNet")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-rlang
                             r-rdpack
                             r-rcpp
                             r-purrr
                             r-mvtnorm
                             r-igraph
                             r-ggplot2
                             r-fitdistrplus))
    (home-page "https://cran.r-project.org/package=SeqNet")
    (synopsis "Generate RNA-Seq Data from Gene-Gene Association Networks")
    (description
     "This package provides methods to generate random gene-gene association networks
and simulate RNA-seq data from them, as described in Grimes and Datta (2021)
<doi:10.18637/jss.v098.i12>.  Includes functions to generate random networks of
any size and perturb them to obtain differential networks.  Network objects are
built from individual, overlapping modules that represent pathways.  The
resulting network has various topological properties that are characteristic of
gene regulatory networks.  RNA-seq data can be generated such that the
association among gene expression profiles reflect the underlying network.  A
reference RNA-seq dataset can be provided to model realistic marginal
distributions.  Plotting functions are available to visualize a network, compare
two networks, and compare the expression of two genes across multiple networks.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-seqmon
  (package
    (name "r-seqmon")
    (version "2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seqmon" version))
       (sha256
        (base32 "0y08xn0r6sj6priksz72mgfdasm167smvqi4rjiwrcv5qqv0blbl"))))
    (properties `((upstream-name . "seqmon")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=seqmon")
    (synopsis "Group Sequential Design Class for Clinical Trials")
    (description
     "S4 class object for creating and managing group sequential designs.  It
calculates the efficacy and futility boundaries at each look.  It allows
modifying the design and tracking the design update history.")
    (license license:expat)))

(define-public r-seqmagick
  (package
    (name "r-seqmagick")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seqmagick" version))
       (sha256
        (base32 "1rdq24192yl7w0jsnw1zhj7qzi8qb08dw95iyxyf0rm2q34q1kwm"))))
    (properties `((upstream-name . "seqmagick")))
    (build-system r-build-system)
    (propagated-inputs (list r-yulab-utils r-magrittr r-biostrings))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/YuLab-SMU/seqmagick")
    (synopsis "Sequence Manipulation Utilities")
    (description
     "Supports reading and writing sequences for different formats (currently
interleaved and sequential formats for FASTA and PHYLIP'), file conversion, and
manipulation (e.g. filter sequences that contain specify pattern, export
consensus sequence from an alignment).")
    (license license:artistic2.0)))

(define-public r-seqmade
  (package
    (name "r-seqmade")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SeqMADE" version))
       (sha256
        (base32 "0nf1xjhk0kpmmzgcxycg3ccxvwq6gydjq7xq6n9m7k7v35v9v3qf"))))
    (properties `((upstream-name . "SeqMADE")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=SeqMADE")
    (synopsis
     "Network Module-Based Model in the Differential Expression Analysis for RNA-Seq")
    (description
     "This package provides a network module-based generalized linear model for
differential expression analysis with the count-based sequence data from
RNA-Seq.")
    (license license:gpl2+)))

(define-public r-seqkat
  (package
    (name "r-seqkat")
    (version "0.0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SeqKat" version))
       (sha256
        (base32 "09b5zrp41241ha5q8bxll9n12sn11hsbv46g2ld1lqsy2razivr4"))))
    (properties `((upstream-name . "SeqKat")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-foreach r-doparallel))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://cran.r-project.org/package=SeqKat")
    (synopsis "Detection of Kataegis")
    (description
     "Kataegis is a localized hypermutation occurring when a region is enriched in
somatic SNVs.  Kataegis can result from multiple cytosine deaminations catalyzed
by the AID/APOBEC family of proteins.  This package contains functions to detect
kataegis from SNVs in BED format.  This package reports two scores per kataegic
event, a hypermutation score and an APOBEC mediated kataegic score.  Yousif, F.
et al.; The Origins and Consequences of Localized and Global Somatic
Hypermutation; Biorxiv 2018 <doi:10.1101/287839>.")
    (license license:gpl2)))

(define-public r-seqimpute
  (package
    (name "r-seqimpute")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seqimpute" version))
       (sha256
        (base32 "1g60drnx12j6wzkisxlmhidvqzzgsrmmmmp8dljq94bb5iv8xjmz"))))
    (properties `((upstream-name . "seqimpute")))
    (build-system r-build-system)
    (propagated-inputs (list r-traminerextras
                             r-traminer
                             r-stringr
                             r-rms
                             r-ranger
                             r-plyr
                             r-nnet
                             r-mlr
                             r-mice
                             r-foreach
                             r-dplyr
                             r-dosnow
                             r-dorng
                             r-dfidx
                             r-cluster
                             r-amelia))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=seqimpute")
    (synopsis "Imputation of Missing Data in Sequence Analysis")
    (description
     "Multiple imputation of missing data present in a dataset through the prediction
based on either a random forest or a multinomial regression model.  Covariates
and time-dependent covariates can be included in the model.  The prediction of
the missing values is based on the method of Halpin (2012)
<https://researchrepository.ul.ie/articles/report/Multiple_imputation_for_life-course_sequence_data/19839736>.")
    (license license:gpl2)))

(define-public r-seqicp
  (package
    (name "r-seqicp")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seqICP" version))
       (sha256
        (base32 "1dfhl7g47icjw38p9c0hyvnn3pwzw7pndywk22vgcyfhiz23xjcp"))))
    (properties `((upstream-name . "seqICP")))
    (build-system r-build-system)
    (propagated-inputs (list r-mgcv r-dhsic))
    (home-page "https://cran.r-project.org/package=seqICP")
    (synopsis "Sequential Invariant Causal Prediction")
    (description
     "This package contains an implementation of invariant causal prediction for
sequential data.  The main function in the package is @code{seqICP}', which
performs linear sequential invariant causal prediction and has guaranteed type I
error control.  For non-linear dependencies the package also contains a
non-linear method @code{seqICPnl}', which allows to input any regression
procedure and performs tests based on a permutation approach that is only
approximately correct.  In order to test whether an individual set S is
invariant the package contains the subroutines @code{seqICP.s} and
@code{seqICPnl.s} corresponding to the respective main methods.")
    (license license:gpl3)))

(define-public r-seqhmm
  (package
    (name "r-seqhmm")
    (version "1.2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seqHMM" version))
       (sha256
        (base32 "17rphq6n2pzgxma0v27vaixi4a343gzc6sk15czfd96kfz7m0gad"))))
    (properties `((upstream-name . "seqHMM")))
    (build-system r-build-system)
    (propagated-inputs (list r-traminer
                             r-rcpparmadillo
                             r-rcpp
                             r-numderiv
                             r-nloptr
                             r-matrix
                             r-igraph
                             r-gridbase))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=seqHMM")
    (synopsis
     "Mixture Hidden Markov Models for Social Sequence Data and Other Multivariate, Multichannel Categorical Time Series")
    (description
     "Designed for fitting hidden (latent) Markov models and mixture hidden Markov
models for social sequence data and other categorical time series.  Also some
more restricted versions of these type of models are available: Markov models,
mixture Markov models, and latent class models.  The package supports models for
one or multiple subjects with one or multiple parallel sequences (channels).
External covariates can be added to explain cluster membership in mixture
models.  The package provides functions for evaluating and comparing models, as
well as functions for visualizing of multichannel sequence data and hidden
Markov models.  Models are estimated using maximum likelihood via the EM
algorithm and/or direct numerical maximization with analytical gradients.  All
main algorithms are written in C++ with support for parallel computation.
Documentation is available via several vignettes in this page, and the paper by
Helske and Helske (2019, <doi:10.18637/jss.v088.i03>).")
    (license license:gpl2+)))

(define-public r-seqhandbook
  (package
    (name "r-seqhandbook")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seqhandbook" version))
       (sha256
        (base32 "0pr4h19g4sa9g7x3di3k18abmabn6ywigdhx2h4b5y88zcnizfam"))))
    (properties `((upstream-name . "seqhandbook")))
    (build-system r-build-system)
    (propagated-inputs (list r-traminer))
    (native-inputs (list r-r-rsp))
    (home-page "https://nicolas-robette.github.io/seqhandbook/")
    (synopsis "Miscellaneous Tools for Sequence Analysis")
    (description
     "It provides miscellaneous sequence analysis functions for describing episodes in
individual sequences, measuring association between domains in multidimensional
sequence analysis (see Piccarreta (2017) <doi:10.1177/0049124115591013>), heat
maps of sequence data, Globally Interdependent Multidimensional Sequence
Analysis (see Robette et al (2015) <doi:10.1177/0081175015570976>), smoothing
sequences for index plots (see Piccarreta (2012)
<doi:10.1177/0049124112452394>), coding sequences for Qualitative Harmonic
Analysis (see Deville (1982)), measuring stress from multidimensional scaling
factors (see Piccarreta and Lior (2010) <doi:10.1111/j.1467-985X.2009.00606.x>),
symmetrical (or canonical) Partial Least Squares (see Bry (1996)).")
    (license license:gpl2+)))

(define-public r-seqgendiff
  (package
    (name "r-seqgendiff")
    (version "1.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seqgendiff" version))
       (sha256
        (base32 "0gc07dpmixf5cw8i2i42jyfjayzqy4c0w2ggwiyc7vrpxsrkxc02"))))
    (properties `((upstream-name . "seqgendiff")))
    (build-system r-build-system)
    (propagated-inputs (list r-sva
                             r-pdist
                             r-matchingr
                             r-irlba
                             r-clue
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/dcgerard/seqgendiff")
    (synopsis "RNA-Seq Generation/Modification for Simulation")
    (description
     "Generates/modifies RNA-seq data for use in simulations.  We provide a suite of
functions that will add a known amount of signal to a real RNA-seq dataset.  The
advantage of using this approach over simulating under a theoretical
distribution is that common/annoying aspects of the data are more preserved,
giving a more realistic evaluation of your method.  The main functions are
select_counts(), thin_diff(), thin_lib(), thin_gene(), thin_2group(),
thin_all(), and effective_cor().  See Gerard (2020)
<doi:10.1186/s12859-020-3450-9> for details on the implemented methods.")
    (license license:gpl3)))

(define-public r-seqfeatr
  (package
    (name "r-seqfeatr")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SeqFeatR" version))
       (sha256
        (base32 "1dvl65n60j7wl0jdb0myi9hprlr9grq6nf9m68gzxm9sz3h8lpvh"))))
    (properties `((upstream-name . "SeqFeatR")))
    (build-system r-build-system)
    (propagated-inputs (list r-widgettools
                             r-tcltk2
                             r-scales
                             r-r2jags
                             r-qvalue
                             r-plyr
                             r-plotrix
                             r-phangorn
                             r-ggplot2
                             r-coda
                             r-calibrate
                             r-biostrings
                             r-ape))
    (home-page "https://cran.r-project.org/package=SeqFeatR")
    (synopsis "Tool to Associate FASTA Sequences and Features")
    (description
     "This package provides user friendly methods for the identification of sequence
patterns that are statistically significantly associated with a property of the
sequence.  For instance, @code{SeqFeatR} allows to identify viral immune escape
mutations for hosts of given HLA types.  The underlying statistical method is
Fisher's exact test, with appropriate corrections for multiple testing, or
Bayes.  Patterns may be point mutations or n-tuple of mutations. @code{SeqFeatR}
offers several ways to visualize the results of the statistical analyses, see
Budeus (2016) <doi:10.1371/journal.pone.0146409>.")
    (license license:gpl3+)))

(define-public r-seqexpmatch
  (package
    (name "r-seqexpmatch")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SeqExpMatch" version))
       (sha256
        (base32 "0i4hsmkapsavhqx99zk9d2qajaqh8kchdf8qy3rbdwgchiiwzybv"))))
    (properties `((upstream-name . "SeqExpMatch")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6 r-doparallel r-checkmate))
    (home-page
     "https://github.com/kapelner/matching_on_the_fly_designs_R_package_and_paper_repr")
    (synopsis "Sequential Experimental Design via Matching on-the-Fly")
    (description
     "Generates the following sequential two-arm experimental designs: (1) completely
randomized (Bernoulli) (2) balanced completely randomized (3) Efron's (1971)
Biased Coin (4) Atkinson's (1982) Covariate-Adjusted Biased Coin (5) Kapelner
and Krieger's (2014) Covariate-Adjusted Matching on the Fly (6) Kapelner and
Krieger's (2021) CARA Matching on the Fly with Differential Covariate Weights
(Naive) (7) Kapelner and Krieger's (2021) CARA Matching on the Fly with
Differential Covariate Weights (Stepwise) and also provides the following types
of inference: (1) estimation (with both Z-style estimators and OLS estimators),
(2) frequentist testing (via asymptotic distribution results and via employing
the nonparametric randomization test) and (3) frequentist confidence intervals
(only under the superpopulation sampling assumption currently).  Details can be
found in our publication: Kapelner and Krieger \"A Matching Procedure for
Sequential Experiments that Iteratively Learns which Covariates Improve Power\"
(2020) <@code{arXiv:2010.05980>}.")
    (license license:gpl3)))

(define-public r-seqdetect
  (package
    (name "r-seqdetect")
    (version "1.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SeqDetect" version))
       (sha256
        (base32 "054b5v0jzk9pj9hrxk31kvcdm789lx8gzh70ivskixz57kajmiss"))))
    (properties `((upstream-name . "SeqDetect")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-igraph r-eventdatar r-dplyr))
    (native-inputs (list r-xtable r-dplyr))
    (home-page "https://cran.r-project.org/package=SeqDetect")
    (synopsis "Sequence and Latent Process Detector")
    (description
     "Sequence detector in this package contains a specific automaton model that can
be used to learn and detect data and process sequences.  Automaton model in this
package is capable of learning and tracing sequences.  Automaton model can be
found in KrleÅ¾a, Vrdoljak, BrÄiÄ (2019) <doi:10.1109/ACCESS.2019.2955245>.
This research has been partly supported under Competitiveness and Cohesion
Operational Programme from the European Regional and Development Fund, as part
of the Integrated Anti-Fraud System project no.  KK.01.2.1.01.0041.  This
research has also been partly supported by the European Regional Development
Fund under the grant KK.01.1.1.01.0009.")
    (license license:lgpl3)))

(define-public r-seqdesign
  (package
    (name "r-seqdesign")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seqDesign" version))
       (sha256
        (base32 "10p8ldj83mvhpcz1yas4xns5qh8zbs533na8nv87phngi8dsb76m"))))
    (properties `((upstream-name . "seqDesign")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "https://github.com/mjuraska/seqDesign")
    (synopsis
     "Simulation and Group Sequential Monitoring of Randomized Two-Stage Treatment Efficacy Trials with Time-to-Event Endpoints")
    (description
     "This package provides a modification of the preventive vaccine efficacy trial
design of Gilbert, Grove et al. (2011, Statistical Communications in Infectious
Diseases) is implemented, with application generally to individual-randomized
clinical trials with multiple active treatment groups and a shared control
group, and a study endpoint that is a time-to-event endpoint subject to
right-censoring.  The design accounts for the issues that the efficacy of the
treatment/vaccine groups may take time to accrue while the multiple treatment
administrations/vaccinations are given; there is interest in assessing the
durability of treatment efficacy over time; and group sequential monitoring of
each treatment group for potential harm, non-efficacy/efficacy futility, and
high efficacy is warranted.  The design divides the trial into two stages of
time periods, where each treatment is first evaluated for efficacy in the first
stage of follow-up, and, if and only if it shows significant treatment efficacy
in stage one, it is evaluated for longer-term durability of efficacy in stage
two.  The package produces plots and tables describing operating characteristics
of a specified design including an unconditional power for intention-to-treat
and per-protocol/as-treated analyses; trial duration; probabilities of the
different possible trial monitoring outcomes (e.g., stopping early for
non-efficacy); unconditional power for comparing treatment efficacies; and
distributions of numbers of endpoint events occurring after the
treatments/vaccinations are given, useful as input parameters for the design of
studies of the association of biomarkers with a clinical outcome (surrogate
endpoint problem).  The code can be used for a single active treatment versus
control design and for a single-stage design.")
    (license license:gpl2)))

(define-public r-seqalloc
  (package
    (name "r-seqalloc")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SeqAlloc" version))
       (sha256
        (base32 "04rhr3gb2p9i35a3x4k8m0lv42ncfqlhx6sf3bq8yihppwrag8x3"))))
    (properties `((upstream-name . "SeqAlloc")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SeqAlloc")
    (synopsis "Sequential Allocation for Prospective Experiments")
    (description
     "Potential randomization schemes are prospectively evaluated when units are
assigned to treatment arms upon entry into the experiment.  The schemes are
evaluated for balance on covariates and on predictability (i.e., how well could
a site worker guess the treatment of the next unit enrolled).")
    (license license:gpl2)))

(define-public r-seqalignr
  (package
    (name "r-seqalignr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SeqAlignR" version))
       (sha256
        (base32 "1p5gbvndl73kj4bdxqga2brw88zlk4bh5c86wy0jqz2y01rc1611"))))
    (properties `((upstream-name . "SeqAlignR")))
    (build-system r-build-system)
    (propagated-inputs (list r-plot-matrix))
    (home-page "https://cran.r-project.org/package=SeqAlignR")
    (synopsis "Sequence Alignment and Visualization Tool")
    (description
     "Computes the optimal alignment of two character sequences.  Visualizes the
result of the alignment in a matrix plot.  Needleman, Saul B.; Wunsch, Christian
D. (1970) \"A general method applicable to the search for similarities in the
amino acid sequence of two proteins\" <doi:10.1016/0022-2836(70)90057-4>.")
    (license license:gpl3)))

(define-public r-seq2r
  (package
    (name "r-seq2r")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seq2R" version))
       (sha256
        (base32 "1g5zg3v5fjv2vnnskv4az8kk3cm0spnnz6q8b5wa4ic5izlg4fcf"))))
    (properties `((upstream-name . "seq2R")))
    (build-system r-build-system)
    (propagated-inputs (list r-seqinr))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=seq2R")
    (synopsis
     "Simple Method to Detect Compositional Changes in Genomic Sequences")
    (description
     "This software is useful for loading .fasta or .gbk files, and for retrieving
sequences from @code{GenBank} dataset <https://www.ncbi.nlm.nih.gov/genbank/>.
This package allows to detect differences or asymmetries based on nucleotide
composition by using local linear kernel smoothers.  Also, it is possible to
draw inference about critical points (i.  e.  maximum or minimum points) related
with the derivative curves.  Additionally, bootstrap methods have been used for
estimating confidence intervals and speed computational techniques (binning
techniques) have been implemented in seq2R'.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-sepkoski
  (package
    (name "r-sepkoski")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sepkoski" version))
       (sha256
        (base32 "1jx1ilbz7md501035vklk5bsw8krg8dn18pwkqynsacqy8wjwl2s"))))
    (properties `((upstream-name . "sepkoski")))
    (build-system r-build-system)
    (home-page "https://github.com/LewisAJones/sepkoski")
    (synopsis "Sepkoski's Fossil Marine Animal Genera Compendium")
    (description
     "Stratigraphic ranges of fossil marine animal genera from Sepkoski's (2002)
published compendium.  No changes have been made to any taxonomic names.
However, first and last appearance intervals have been updated to be consistent
with stages of the International Geological Timescale.  Functionality for
generating a plot of Sepkoski's evolutionary fauna is also included.  For
specific details on the compendium see: Sepkoski, J. J. (2002).  A compendium of
fossil marine animal genera.  Bulletins of American Paleontology, 363, pp.
1â560 (ISBN 0-87710-450-6).  Access:
<https://www.biodiversitylibrary.org/item/40634#page/5/mode/1up>.")
    (license license:gpl3+)))

(define-public r-sephora
  (package
    (name "r-sephora")
    (version "0.1.31")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sephora" version))
       (sha256
        (base32 "1axh5qd8mp55d45k34935sm0wc6ib4b309a92d1vaxz3hzgvbygi"))))
    (properties `((upstream-name . "sephora")))
    (build-system r-build-system)
    (propagated-inputs (list r-spiralize
                             r-rootsolve
                             r-nlme
                             r-mass
                             r-ggplot2
                             r-ggnewscale
                             r-geots
                             r-foreach
                             r-ebsc
                             r-dtwclust
                             r-dplyr
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=sephora")
    (synopsis "Statistical Estimation of Phenological Parameters")
    (description
     "This package provides functions and methods for estimating phenological dates
(green up, start of a season, maturity, senescence, end of a season and
dormancy) from (nearly) periodic Earth Observation time series.  These dates are
critical points of some derivatives of an idealized curve which, in turn, is
obtained through a functional principal component analysis-based regression
model.  Some of the methods implemented here are based on T. Krivobokova, P.
Serra and F. Rosales (2022)
<https://www.sciencedirect.com/science/article/pii/S0167947322000998>.  Methods
for handling and plotting Earth observation time series are also provided.")
    (license license:gpl2+)))

(define-public r-separationplot
  (package
    (name "r-separationplot")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "separationplot" version))
       (sha256
        (base32 "1dvm073mckn6mkyf6fvclc8glsr8d3p2413x5i91w00hphm2lg8z"))))
    (properties `((upstream-name . "separationplot")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer r-mass r-hmisc r-foreign))
    (home-page "https://cran.r-project.org/package=separationplot")
    (synopsis "Separation Plots")
    (description
     "Visual representations of model fit or predictive success in the form of
\"separation plots.\" See Greenhill, Brian, Michael D. Ward, and Audrey Sacks.
\"The separation plot: A new visual method for evaluating the fit of binary
models.\" American Journal of Political Science 55.4 (2011): 991-1002.")
    (license license:artistic2.0)))

(define-public r-separate
  (package
    (name "r-separate")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sEparaTe" version))
       (sha256
        (base32 "1nxiqsqz0sva9mxk1jscdq6ragc073gmqlrpa68zkfnfdzfrkb32"))))
    (properties `((upstream-name . "sEparaTe")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sEparaTe")
    (synopsis
     "Maximum Likelihood Estimation and Likelihood Ratio Test Functions for Separable Variance-Covariance Structures")
    (description
     "Maximum likelihood estimation of the parameters of matrix and 3rd-order tensor
normal distributions with unstructured factor variance covariance matrices, two
procedures, and for unbiased modified likelihood ratio testing of simple and
double separability for variance-covariance structures, two procedures.
References: Dutilleul P. (1999) <doi:10.1080/00949659908811970>, Manceur AM,
Dutilleul P. (2013) <doi:10.1016/j.cam.2012.09.017>, and Manceur AM, Dutilleul
P. (2013) <doi:10.1016/j.spl.2012.10.020>.")
    (license license:expat)))

(define-public r-sepals
  (package
    (name "r-sepals")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SEPaLS" version))
       (sha256
        (base32 "13bdrbx08whjw0i0dir6nvb1brfa6rrdhpr0b9qlr3bixafhak03"))))
    (properties `((upstream-name . "SEPaLS")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SEPaLS")
    (synopsis "Shrinkage for Extreme Partial Least-Squares (SEPaLS)")
    (description
     "Regression context for the Partial Least Squares framework for Extreme values.
Estimations of the Shrinkage for Extreme Partial Least-Squares (SE@code{PaLS})
estimators, an adaptation of the original Partial Least Squares (PLS) method
tailored to the extreme-value framework.  The SE@code{PaLS} project is a joint
work by Stephane Girard, Hadrien Lorenzo and Julyan Arbel.  R code to replicate
the results of the paper is available at
<https://github.com/hlorenzo/SE@code{PaLS_simus>}.  Extremes within PLS was
already studied by one of the authors, see M Bousebeta, G Enjolras, S Girard
(2023) <doi:10.1016/j.jmva.2022.105101>.")
    (license license:expat)))

(define-public r-seofm
  (package
    (name "r-seofm")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SEofM" version))
       (sha256
        (base32 "0662f4ayr05h33myxbfal682fxfwpl3a94kv4bzc8hil9bcqcznf"))))
    (properties `((upstream-name . "SEofM")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SEofM")
    (synopsis "Standard Error of Measurement")
    (description
     "To calculate the standard error of measurement (SEM) to assess the observer
variability (inter- and intra-observer variation).  The methods used in this
package are referenced from Zoran B. PopoviÄ (2017)
<doi:10.21037/cdt.2017.03.12>.")
    (license license:gpl3)))

(define-public r-sentryr
  (package
    (name "r-sentryr")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sentryR" version))
       (sha256
        (base32 "08k4ws4jrd50ymkp7v0i5jjss5brqxyxr36x1jnyqw1nzmchas5x"))))
    (properties `((upstream-name . "sentryR")))
    (build-system r-build-system)
    (propagated-inputs (list r-uuid r-tibble r-stringr r-jsonlite r-httr))
    (home-page "https://github.com/jcpsantiago/sentryR")
    (synopsis "Send Errors and Messages to Sentry")
    (description
     "Unofficial client for Sentry <https://sentry.io>, a self-hosted or cloud-based
error-monitoring service.  It will inform about errors in real-time, and
includes integration with the Plumber package.")
    (license license:expat)))

(define-public r-sentopics
  (package
    (name "r-sentopics")
    (version "0.7.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sentopics" version))
       (sha256
        (base32 "1sqlblsy75i0bqn5jplmc6ghrak9d6whn0xzx5mbr6vc3802r28d"))))
    (properties `((upstream-name . "sentopics")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppprogress
                             r-rcpphungarian
                             r-rcpparmadillo
                             r-rcpp
                             r-quanteda
                             r-generics
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/odelmarcelle/sentopics")
    (synopsis "Tools for Joint Sentiment and Topic Analysis of Textual Data")
    (description
     "This package provides a framework that joins topic modeling and sentiment
analysis of textual data.  The package implements a fast Gibbs sampling
estimation of Latent Dirichlet Allocation (Griffiths and Steyvers (2004)
<doi:10.1073/pnas.0307752101>) and Joint Sentiment/Topic Model (Lin, He, Everson
and Ruger (2012) <doi:10.1109/TKDE.2011.48>).  It offers a variety of helpers
and visualizations to analyze the result of topic modeling.  The framework also
allows enriching topic models with dates and externally computed sentiment
measures.  A flexible aggregation scheme enables the creation of time series of
sentiment or topical proportions from the enriched topic models.  Moreover, a
novel method jointly aggregates topic proportions and sentiment measures to
derive time series of topical sentiment.")
    (license license:gpl3+)))

(define-public r-sentometrics
  (package
    (name "r-sentometrics")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sentometrics" version))
       (sha256
        (base32 "06qvwfgshr70n1qgf2zqjvycwwrak8d5hfyn3v5k2qzkfaxkihmm"))))
    (properties `((upstream-name . "sentometrics")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringi
                             r-rcpproll
                             r-rcppparallel
                             r-rcpparmadillo
                             r-rcpp
                             r-quanteda
                             r-isoweek
                             r-glmnet
                             r-ggplot2
                             r-foreach
                             r-data-table
                             r-caret))
    (home-page "https://sentometrics-research.com/sentometrics/")
    (synopsis
     "An Integrated Framework for Textual Sentiment Time Series Aggregation and Prediction")
    (description
     "Optimized prediction based on textual sentiment, accounting for the intrinsic
challenge that sentiment can be computed and pooled across texts and time in
various ways.  See Ardia et al. (2021) <doi:10.18637/jss.v099.i02>.")
    (license license:gpl2+)))

(define-public r-sentinmixt
  (package
    (name "r-sentinmixt")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SenTinMixt" version))
       (sha256
        (base32 "1yja63087nhb5c0wlql7yynjkhkrjw69amvz4w55xqvp605rh46f"))))
    (properties `((upstream-name . "SenTinMixt")))
    (build-system r-build-system)
    (propagated-inputs (list r-zipfr
                             r-withr
                             r-tsdist
                             r-tidyr
                             r-snow
                             r-rlist
                             r-mclust
                             r-foreach
                             r-expint
                             r-dosnow
                             r-data-table))
    (home-page "https://cran.r-project.org/package=SenTinMixt")
    (synopsis "Parsimonious Mixtures of MSEN and MTIN Distributions")
    (description
     "This package implements parsimonious mixtures of MSEN and MTIN distributions via
expectation- maximization based algorithms for model-based clustering.  For each
mixture component, parsimony is reached via the eigen-decomposition of the scale
matrices and by imposing a constraint on the tailedness parameter.  This
produces a family of 28 parsimonious mixture models for each distribution.")
    (license license:gpl3+)))

(define-public r-sentimentr
  (package
    (name "r-sentimentr")
    (version "2.9.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sentimentr" version))
       (sha256
        (base32 "0i0lg2fl079wjv30lpqcxhx62miqakb9kzwwhrr4c18kl0bxbaav"))))
    (properties `((upstream-name . "sentimentr")))
    (build-system r-build-system)
    (propagated-inputs (list r-textshape
                             r-textclean
                             r-syuzhet
                             r-stringi
                             r-lexicon
                             r-ggplot2
                             r-data-table))
    (home-page "https://github.com/trinker/sentimentr")
    (synopsis "Calculate Text Polarity Sentiment")
    (description
     "Calculate text polarity sentiment at the sentence level and optionally aggregate
by rows or grouping variable(s).")
    (license license:expat)))

(define-public r-sentimentanalysis
  (package
    (name "r-sentimentanalysis")
    (version "1.3-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SentimentAnalysis" version))
       (sha256
        (base32 "184nzmi17s86lvxa87ddq7vkmq6cmr7kss8bzz1ncp1nfcm2cczw"))))
    (properties `((upstream-name . "SentimentAnalysis")))
    (build-system r-build-system)
    (propagated-inputs (list r-tm
                             r-stringdist
                             r-spikeslab
                             r-qdapdictionaries
                             r-ngramrr
                             r-moments
                             r-glmnet
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sfeuerriegel/SentimentAnalysis")
    (synopsis "Dictionary-Based Sentiment Analysis")
    (description
     "This package performs a sentiment analysis of textual contents in R. This
implementation utilizes various existing dictionaries, such as Harvard IV, or
finance-specific dictionaries.  Furthermore, it can also create customized
dictionaries.  The latter uses LASSO regularization as a statistical approach to
select relevant terms based on an exogenous response variable.")
    (license license:expat)))

(define-public r-sentiment-ai
  (package
    (name "r-sentiment-ai")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sentiment.ai" version))
       (sha256
        (base32 "1wz34m5imzfwzf1ywidgb1xscsvf9nhri3w3wa2d8r7bk0nvlfjs"))))
    (properties `((upstream-name . "sentiment.ai")))
    (build-system r-build-system)
    (propagated-inputs (list r-xgboost
                             r-tfhub
                             r-tensorflow
                             r-roperators
                             r-reticulate
                             r-jsonlite
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://benwiseman.github.io/sentiment.ai/")
    (synopsis "Simple Sentiment Analysis Using Deep Learning")
    (description
     "Sentiment Analysis via deep learning and gradient boosting models with a lot of
the underlying hassle taken care of to make the process as simple as possible.
In addition to out-performing traditional, lexicon-based sentiment analysis (see
<https://benwiseman.github.io/sentiment.ai/#Benchmarks>), it also allows the
user to create embedding vectors for text which can be used in other analyses.
GPU acceleration is supported on Windows and Linux.")
    (license license:expat)))

(define-public r-sentencepiece
  (package
    (name "r-sentencepiece")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sentencepiece" version))
       (sha256
        (base32 "1cfk450asadp3bbv1px64kf62rdhfak2drnq4y913jjjq4radxwh"))))
    (properties `((upstream-name . "sentencepiece")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://github.com/bnosac/sentencepiece")
    (synopsis
     "Text Tokenization using Byte Pair Encoding and Unigram Modelling")
    (description
     "Unsupervised text tokenizer allowing to perform byte pair encoding and unigram
modelling.  Wraps the sentencepiece library
<https://github.com/google/sentencepiece> which provides a language independent
tokenizer to split text in words and smaller subword units.  The techniques are
explained in the paper \"@code{SentencePiece}: A simple and language independent
subword tokenizer and detokenizer for Neural Text Processing\" by Taku Kudo and
John Richardson (2018) <doi:10.18653/v1/D18-2012>.  Provides as well
straightforward access to pretrained byte pair encoding models and subword
embeddings trained on Wikipedia using word2vec', as described in \"BPEmb:
Tokenization-free Pre-trained Subword Embeddings in 275 Languages\" by Benjamin
Heinzerling and Michael Strube (2018)
<http://www.lrec-conf.org/proceedings/lrec2018/pdf/1049.pdf>.")
    (license (license:fsdg-compatible "MPL-2.0"))))

(define-public r-sensusr
  (package
    (name "r-sensusr")
    (version "2.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SensusR" version))
       (sha256
        (base32 "1x1a8vnpn6h7905wa4lvf7vibkl2dsa4yyg2sg4bbi719lcki2q0"))))
    (properties `((upstream-name . "SensusR")))
    (build-system r-build-system)
    (propagated-inputs (list r-r-utils
                             r-plyr
                             r-openssl
                             r-lubridate
                             r-jsonlite
                             r-ggplot2
                             r-ggmap))
    (home-page "https://predictive-technology-laboratory.github.io/sensus/")
    (synopsis "Sensus Analytics")
    (description
     "This package provides access and analytic functions for Sensus data.")
    (license license:gpl3)))

(define-public r-senstrat
  (package
    (name "r-senstrat")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "senstrat" version))
       (sha256
        (base32 "0j6mb55v5ivqvk8nn8fjlzrbdgj6csa58yc6gy1g07m7gk2qz2np"))))
    (properties `((upstream-name . "senstrat")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-biasedurn))
    (home-page "https://cran.r-project.org/package=senstrat")
    (synopsis "Sensitivity Analysis for Stratified Observational Studies")
    (description
     "Sensitivity analysis in unmatched observational studies, with or without strata.
 The main functions are sen2sample() and senstrat().  See Rosenbaum, P. R. and
Krieger, A. M. (1990), JASA, 85, 493-498, <doi:10.1080/01621459.1990.10476226>
and Gastwirth, Krieger and Rosenbaum (2000), JRSS-B, 62, 545â555
<doi:10.1111/1467-9868.00249> .")
    (license license:gpl2)))

(define-public r-sensrivastava
  (package
    (name "r-sensrivastava")
    (version "2015.6.25.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SenSrivastava" version))
       (sha256
        (base32 "101qq84pii8m9naw0nnaqnbqbzjpfvvc2dql4a8hbihjhvyxbjxa"))))
    (properties `((upstream-name . "SenSrivastava")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SenSrivastava")
    (synopsis "Datasets from Sen & Srivastava")
    (description
     "Collection of datasets from Sen & Srivastava: \"Regression Analysis, Theory,
Methods and Applications\", Springer.  Sources for individual data files are more
fully documented in the book.")
    (license license:gpl2+)))

(define-public r-sensr
  (package
    (name "r-sensr")
    (version "1.5-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sensR" version))
       (sha256
        (base32 "0lmy7ivjm4plb7n6mf8ycv44ir34f82s3a7wamxj3w6v1n653ji6"))))
    (properties `((upstream-name . "sensR")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv r-multcomp r-mass))
    (home-page "https://github.com/aigorahub/sensR")
    (synopsis "Thurstonian Models for Sensory Discrimination")
    (description
     "This package provides methods for sensory discrimination methods; duotrio,
tetrad, triangle, 2-AFC, 3-AFC, A-not A, same-different, 2-AC and
degree-of-difference.  This enables the calculation of d-primes, standard errors
of d-primes, sample size and power computations, and comparisons of different
d-primes.  Methods for profile likelihood confidence intervals and plotting are
included.  Most methods are described in Brockhoff, P.B. and Christensen, R.H.B.
(2010) <doi:10.1016/j.foodqual.2009.04.003>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-senspe
  (package
    (name "r-senspe")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SenSpe" version))
       (sha256
        (base32 "0mmf2pyh0nbf7jpb49bplh9gh3ijx0dybzsjdrprqz3gicm6qlly"))))
    (properties `((upstream-name . "SenSpe")))
    (build-system r-build-system)
    (native-inputs (list r-knitr gfortran))
    (home-page "https://cran.r-project.org/package=SenSpe")
    (synopsis
     "Estimating Specificity at Controlled Sensitivity, or Vice Versa")
    (description
     "Perform biomarker evaluation and comparison in terms of specificity at a
controlled sensitivity level, or sensitivity at a controlled specificity level.
Point estimation and exact bootstrap of Huang, Parakati, Patil, and Sanda (2023)
<doi:10.5705/ss.202021.0020> for the one- and two-biomarker problems are
implemented.")
    (license license:gpl2+)))

(define-public r-sensory
  (package
    (name "r-sensory")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sensory" version))
       (sha256
        (base32 "1zd0ajrymxi6gygcq9fqgwgy0g6c3cqz53x0k5m0ihbmh11rc7s7"))))
    (properties `((upstream-name . "sensory")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-mass r-gtools))
    (home-page "https://cran.r-project.org/package=sensory")
    (synopsis
     "Simultaneous Model-Based Clustering and Imputation via a Progressive Expectation-Maximization Algorithm")
    (description
     "This package contains the function CUUimpute() which performs model-based
clustering and imputation simultaneously.")
    (license license:gpl2+)))

(define-public r-sensominer
  (package
    (name "r-sensominer")
    (version "1.27")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SensoMineR" version))
       (sha256
        (base32 "0si7zklv94li8payg4zxcac1a7ivk90q4ckcyak536sqfinihpb3"))))
    (properties `((upstream-name . "SensoMineR")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2
                             r-kernsmooth
                             r-gtools
                             r-ggrepel
                             r-ggplot2
                             r-factominer
                             r-cluster
                             r-algdesign))
    (home-page "http://sensominer.free.fr")
    (synopsis "Sensory Data Analysis")
    (description
     "Statistical Methods to Analyse Sensory Data. @code{SensoMineR}: A package for
sensory data analysis.  S. Le and F. Husson (2008).")
    (license license:gpl2+)))

(define-public r-sensobol
  (package
    (name "r-sensobol")
    (version "1.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sensobol" version))
       (sha256
        (base32 "0vw53aqlgdqncy8cpwlc959bavrsfqam7yq99ic6ij8kxqf4h9xl"))))
    (properties `((upstream-name . "sensobol")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-scales
                             r-rlang
                             r-rfast
                             r-rdpack
                             r-rcpparmadillo
                             r-rcpp
                             r-randtoolbox
                             r-matrixstats
                             r-magrittr
                             r-lhs
                             r-ggplot2
                             r-desolve
                             r-data-table
                             r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/arnaldpuy/sensobol")
    (synopsis "Computation of Variance-Based Sensitivity Indices")
    (description
     "It allows to rapidly compute, bootstrap and plot up to fourth-order Sobol'-based
sensitivity indices using several state-of-the-art first and total-order
estimators.  Sobol indices can be computed either for models that yield a scalar
as a model output or for systems of differential equations.  The package also
provides a suit of benchmark tests functions and several options to obtain
publication-ready figures of the model output uncertainty and
sensitivity-related analysis.  An overview of the package can be found in Puy et
al. (2022) <doi:10.18637/jss.v102.i05>.")
    (license license:gpl3)))

(define-public r-sensmediation
  (package
    (name "r-sensmediation")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sensmediation" version))
       (sha256
        (base32 "10m5z651nij61rh9pq7yh03q06crdhl9p9msiangd8r3lkm77sbi"))))
    (properties `((upstream-name . "sensmediation")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-maxlik))
    (home-page "https://cran.r-project.org/package=sensmediation")
    (synopsis
     "Parametric Estimation and Sensitivity Analysis of Direct and Indirect Effects")
    (description
     "We implement functions to estimate and perform sensitivity analysis to
unobserved confounding of direct and indirect effects introduced in Lindmark, de
Luna and Eriksson (2018) <doi:10.1002/sim.7620> and Lindmark (2022)
<doi:10.1007/s10260-021-00611-4>.  The estimation and sensitivity analysis are
parametric, based on probit and/or linear regression models.  Sensitivity
analysis is implemented for unobserved confounding of the exposure-mediator,
mediator-outcome and exposure-outcome relationships.")
    (license license:gpl2)))

(define-public r-sensmap
  (package
    (name "r-sensmap")
    (version "0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SensMap" version))
       (sha256
        (base32 "1aac3mr7jq6fl0r230igdwcmpivpd9by5sx1vbrz3rpircgy28nz"))))
    (properties `((upstream-name . "SensMap")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny
                             r-reshape2
                             r-plotly
                             r-mgcv
                             r-mcmcpack
                             r-lattice
                             r-glmulti
                             r-ggplot2
                             r-ggdendro
                             r-fields
                             r-factominer
                             r-factoextra
                             r-doby))
    (home-page "https://github.com/IbtihelRebhi/SensMap")
    (synopsis "Sensory and Consumer Data Mapping")
    (description
     "This package provides Sensory and Consumer Data mapping and analysis
<doi:10.14569/IJACSA.2017.081266>.  The mapping visualization is made available
from several features : options in dimension reduction methods and prediction
models ranging from linear to non linear regressions.  A smoothed version of the
map performed using locally weighted regression algorithm is available.  A
selection process of map stability is provided.  A shiny application is
included.  It presents an easy GUI for the implemented functions as well as a
comparative tool of fit models using several criteria.  Basic analysis such as
characterization of products, panelists and sessions likewise consumer
segmentation are also made available.")
    (license license:gpl2+)))

(define-public r-sensitivitymw
  (package
    (name "r-sensitivitymw")
    (version "2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sensitivitymw" version))
       (sha256
        (base32 "1sjq8lxw9iy5s5a19xaxm05wnz9yg4llpbnljydini511pyshzzx"))))
    (properties `((upstream-name . "sensitivitymw")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sensitivitymw")
    (synopsis
     "Sensitivity Analysis for Observational Studies Using Weighted M-Statistics")
    (description
     "Sensitivity analysis for tests, confidence intervals and estimates in matched
observational studies with one or more controls using weighted or unweighted
Huber-Maritz M-tests (including the permutational t-test).  The method is from
Rosenbaum (2014) Weighted M-statistics with superior design sensitivity in
matched observational studies with multiple controls JASA, 109(507), 1145-1158
<doi:10.1080/01621459.2013.879261>.")
    (license license:gpl2)))

(define-public r-sensitivitymv
  (package
    (name "r-sensitivitymv")
    (version "1.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sensitivitymv" version))
       (sha256
        (base32 "1ga8mf92niiv1z3g3kv5ha5xdd1089aaqp19dz68kv8a396vhy1j"))))
    (properties `((upstream-name . "sensitivitymv")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sensitivitymv")
    (synopsis "Sensitivity Analysis in Observational Studies")
    (description
     "The package performs a sensitivity analysis in an observational study using an
M-statistic, for instance, the mean.  The main function in the package is
senmv(), but amplify() and @code{truncatedP}() are also useful.  The method is
developed in Rosenbaum Biometrics, 2007, 63, 456-464,
<doi:10.1111/j.1541-0420.2006.00717.x>.")
    (license license:gpl2)))

(define-public r-sensitivitymult
  (package
    (name "r-sensitivitymult")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sensitivitymult" version))
       (sha256
        (base32 "19pyq6c7pvr3zkz3dz5p2hhfcvwm0im0bwaw4yhhgin4kqfnfjd5"))))
    (properties `((upstream-name . "sensitivitymult")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sensitivitymult")
    (synopsis
     "Sensitivity Analysis for Observational Studies with Multiple Outcomes")
    (description
     "Sensitivity analysis for multiple outcomes in observational studies.  For
instance, all linear combinations of several outcomes may be explored using
Scheffe projections in the comparison() function; see Rosenbaum (2016, Annals of
Applied Statistics) <doi:10.1214/16-AOAS942>.  Alternatively, attention may
focus on a few principal components in the principal() function.  The package
includes parallel methods for individual outcomes, including tests in the senm()
function and confidence intervals in the @code{senmCI}() function.")
    (license license:gpl2)))

(define-public r-sensitivityfull
  (package
    (name "r-sensitivityfull")
    (version "1.5.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sensitivityfull" version))
       (sha256
        (base32 "0g2gc4rms6gd34d23kxj50pjh0yq1h6yiybj4dxwyf6i2652irhq"))))
    (properties `((upstream-name . "sensitivityfull")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sensitivityfull")
    (synopsis
     "Sensitivity Analysis for Full Matching in Observational Studies")
    (description
     "Sensitivity to unmeasured biases in an observational study that is a full match.
 Function senfm() performs tests and function @code{senfmCI}() creates
confidence intervals.  The method uses Huber's M-statistics, including least
squares, and is described in Rosenbaum (2007, Biometrics)
<DOI:10.1111/j.1541-0420.2006.00717.x>.")
    (license license:gpl2)))

(define-public r-sensitivitycasecontrol
  (package
    (name "r-sensitivitycasecontrol")
    (version "2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SensitivityCaseControl" version))
       (sha256
        (base32 "04gakb7pc88lygdixxxjwj0p6ylwq5jsy6zidyh5w37ch83m3hj6"))))
    (properties `((upstream-name . "SensitivityCaseControl")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SensitivityCaseControl")
    (synopsis "Sensitivity Analysis for Case-Control Studies")
    (description
     "Sensitivity analysis for case-control studies in which some cases may meet a
more narrow definition of being a case compared to other cases which only meet a
broad definition.  The sensitivity analyses are described in Small, Cheng,
Halloran and Rosenbaum (2013, \"Case Definition and Sensitivity Analysis\",
Journal of the American Statistical Association, 1457-1468).  The functions
sens.analysis.mh and sens.analysis.aberrant.rank provide sensitivity analyses
based on the Mantel-Haenszel test statistic and aberrant rank test statistic as
described in Rosenbaum (1991, \"Sensitivity Analysis for Matched Case Control
Studies\", Biometrics); see also Section 1 of Small et al.  The function
adaptive.case.test provides adaptive inferences as described in Section 5 of
Small et al.  The function adaptive.noether.brown provides a sensitivity
analysis for a matched cohort study based on an adaptive test.  The other
functions in the package are internal functions.")
    (license license:gpl2+)))

(define-public r-sensitivitycalibration
  (package
    (name "r-sensitivitycalibration")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sensitivityCalibration" version))
       (sha256
        (base32 "1fn07dra7dhpsg6f1yn1ayfpmvwaxma8cps94070nrpfwmdvi04g"))))
    (properties `((upstream-name . "sensitivityCalibration")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringi
                             r-splitstackshape
                             r-relaimpo
                             r-plotly
                             r-ggrepel
                             r-ggplot2))
    (home-page "https://cran.r-project.org/package=sensitivityCalibration")
    (synopsis
     "Calibrated Sensitivity Analysis for Matched Observational Studies")
    (description
     "This package implements the calibrated sensitivity analysis approach for matched
observational studies.  Our sensitivity analysis framework views matched sets as
drawn from a super-population.  The unmeasured confounder is modeled as a random
variable.  We combine matching and model-based covariate-adjustment methods to
estimate the treatment effect.  The hypothesized unmeasured confounder enters
the picture as a missing covariate.  We adopt a state-of-art Expectation
Maximization (EM) algorithm to handle this missing covariate problem in
generalized linear models (GLMs).  As our method also estimates the effect of
each observed covariate on the outcome and treatment assignment, we are able to
calibrate the unmeasured confounder to observed covariates.  Zhang, B., Small,
D. S. (2018). <@code{arXiv:1812.00215>}.")
    (license license:expat)))

(define-public r-sensitivity2x2xk
  (package
    (name "r-sensitivity2x2xk")
    (version "1.01")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sensitivity2x2xk" version))
       (sha256
        (base32 "1r829k939zzmi0j4chdaniajchcflmmjrl3a9hwnkg0wkfnjbvdl"))))
    (properties `((upstream-name . "sensitivity2x2xk")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-biasedurn))
    (home-page "https://cran.r-project.org/package=sensitivity2x2xk")
    (synopsis "Sensitivity Analysis for 2x2xk Tables in Observational Studies")
    (description
     "This package performs exact or approximate adaptive or nonadaptive
Cochran-Mantel-Haenszel-Birch tests and sensitivity analyses for one or two
2x2xk tables in observational studies.")
    (license license:gpl2)))

(define-public r-sensitivity
  (package
    (name "r-sensitivity")
    (version "1.30.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sensitivity" version))
       (sha256
        (base32 "1k9ka5jszlrbgz57383632znv7n2vdnbf81scmqh87jyay7bp55m"))))
    (properties `((upstream-name . "sensitivity")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-numbers
                             r-ggplot2
                             r-foreach
                             r-dtwclust
                             r-boot))
    (home-page "https://cran.r-project.org/package=sensitivity")
    (synopsis
     "Global Sensitivity Analysis of Model Outputs and Importance Measures")
    (description
     "This package provides a collection of functions for sensitivity analysis of
model outputs (factor screening, global sensitivity analysis and robustness
analysis), for variable importance measures of data, as well as for
interpretability of machine learning models.  Most of the functions have to be
applied on scalar output, but several functions support multi-dimensional
outputs.")
    (license license:gpl2)))

(define-public r-sensiphy
  (package
    (name "r-sensiphy")
    (version "0.8.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sensiPhy" version))
       (sha256
        (base32 "1kjkdklva5wzmw071gbn1v9g5cf0wzmvwgy5y0dgi530v6byaw8j"))))
    (properties `((upstream-name . "sensiPhy")))
    (build-system r-build-system)
    (propagated-inputs (list r-phytools
                             r-phylolm
                             r-ggplot2
                             r-geiger
                             r-caper
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/paternogbc/sensiPhy")
    (synopsis "Sensitivity Analysis for Comparative Methods")
    (description
     "An implementation of sensitivity analysis for phylogenetic comparative methods.
The package is an umbrella of statistical and graphical methods that estimate
and report different types of uncertainty in PCM: (i) Species Sampling
uncertainty (sample size; influential species and clades). (ii) Phylogenetic
uncertainty (different topologies and/or branch lengths). (iii) Data uncertainty
(intraspecific variation and measurement error).")
    (license license:gpl2)))

(define-public r-sensibo-sky
  (package
    (name "r-sensibo-sky")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sensibo.sky" version))
       (sha256
        (base32 "0s8gbj7qrcbcnflp7v0s93ykh2xgk7jwpyvzndj1f90hjy20x3bc"))))
    (properties `((upstream-name . "sensibo.sky")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-httr r-glue))
    (home-page "https://github.com/theclue/sensibo.sky")
    (synopsis
     "Access to 'Sensibo Sky' API V2 for Air Conditioners Remote Control")
    (description
     "This package provides an interface to the Sensibo Sky API which allows to
remotely control non-smart air conditioning units.  See <https://sensibo.com>
for more informations.")
    (license license:expat)))

(define-public r-sensemakr
  (package
    (name "r-sensemakr")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sensemakr" version))
       (sha256
        (base32 "1r4vmprwq91bda9klmw6aaqg1fklqs9da094ggf6qx7lh4kvj9xz"))))
    (properties `((upstream-name . "sensemakr")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/carloscinelli/sensemakr")
    (synopsis "Sensitivity Analysis Tools for Regression Models")
    (description
     "This package implements a suite of sensitivity analysis tools that extends the
traditional omitted variable bias framework and makes it easier to understand
the impact of omitted variables in regression models, as discussed in Cinelli,
C. and Hazlett, C. (2020), \"Making Sense of Sensitivity: Extending Omitted
Variable Bias.\" Journal of the Royal Statistical Society, Series B (Statistical
Methodology) <doi:10.1111/rssb.12348>.")
    (license license:gpl3)))

(define-public r-sense
  (package
    (name "r-sense")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sense" version))
       (sha256
        (base32 "10cvjhvzfz1y8372wryxxzi0p6xlcchgdhs8idjgk3isvjbps953"))))
    (properties `((upstream-name . "sense")))
    (build-system r-build-system)
    (propagated-inputs (list r-visnetwork
                             r-tictoc
                             r-readr
                             r-purrr
                             r-paradox
                             r-mlr3viz
                             r-mlr3tuning
                             r-mlr3pipelines
                             r-mlr3learners
                             r-mlr3filters
                             r-mlr3
                             r-metrics
                             r-lubridate
                             r-forcats
                             r-data-table
                             r-bbotk))
    (home-page "https://mlr3.mlr-org.com/")
    (synopsis "Automatic Stacked Ensemble for Regression Tasks")
    (description
     "Stacked ensemble for regression tasks based on mlr3 framework with a pipeline
for preprocessing numeric and factor features and hyper-parameter tuning using
grid or random search.")
    (license license:gpl3)))

(define-public r-sendigr
  (package
    (name "r-sendigr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sendigR" version))
       (sha256
        (base32 "0zc9hbxfvgb9dgmyqj5dsm2pcfykx6sk3wjcbxmpk7shyb34b6yl"))))
    (properties `((upstream-name . "sendigR")))
    (build-system r-build-system)
    (inputs (list python))
    (propagated-inputs (list r-xfun
                             r-stringr
                             r-sjlabelled
                             r-shinydashboard
                             r-shiny
                             r-rsqlite
                             r-reticulate
                             r-readxl
                             r-plotly
                             r-parsedate
                             r-magrittr
                             r-htmltools
                             r-hmisc
                             r-haven
                             r-ggplot2
                             r-dt
                             r-dplyr
                             r-desctools
                             r-data-table
                             r-cicerone))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/phuse-org/sendigR")
    (synopsis "Enable Cross-Study Analysis of 'CDISC' 'SEND' Datasets")
    (description
     "This package provides a system enables cross study Analysis by extracting and
filtering study data for control animals from CDISC SEND Study Repository.
These data types are supported: Body Weights, Laboratory test results and
Microscopic findings.  These database types are supported: SQLite and Oracle'.")
    (license license:expat)))

(define-public r-sendgridr
  (package
    (name "r-sendgridr")
    (version "0.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sendgridr" version))
       (sha256
        (base32 "1lw17lhd3iwkp5mcbpwvwhlzgwk1nbz7xxic8fid8i22z1js22gc"))))
    (properties `((upstream-name . "sendgridr")))
    (build-system r-build-system)
    (propagated-inputs (list r-usethis
                             r-magrittr
                             r-keyring
                             r-jsonlite
                             r-httr
                             r-emayili
                             r-cli
                             r-base64enc))
    (home-page "https://github.com/mrchypark/sendgridr")
    (synopsis "Mail Sender Using 'Sendgrid' Service")
    (description
     "Send email using Sendgrid <https://sendgrid.com/> mail API(v3)
<https://docs.sendgrid.com/api-reference/how-to-use-the-sendgrid-v3-api/authentication>.")
    (license license:expat)))

(define-public r-semverutils
  (package
    (name "r-semverutils")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "semverutils" version))
       (sha256
        (base32 "0y3zh8n8bgs3ag8yy9cnp0i0xrn0iqflcbrya70glyb40148w9sw"))))
    (properties `((upstream-name . "semverutils")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6 r-foreach))
    (home-page "https://github.com/ajwtech/semverutils")
    (synopsis "Semantic Version Utilities")
    (description
     "Semantic Versions allow for standardized management versions.  This package
implements semantic versioning handling in R. using R6 to create a mutable
object that can handle deciphering and checking versions.")
    (license license:expat)))

(define-public r-semtree
  (package
    (name "r-semtree")
    (version "0.9.20")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "semtree" version))
       (sha256
        (base32 "1dcxnlnl5j9dha5bdmj4rn117cbssdzp1gwdnch2pc0234ziq4ys"))))
    (properties `((upstream-name . "semtree")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-tidyr
                             r-strucchange
                             r-sandwich
                             r-rpart-plot
                             r-rpart
                             r-openmx
                             r-lavaan
                             r-gridbase
                             r-ggplot2
                             r-future-apply
                             r-expm
                             r-data-table
                             r-crayon
                             r-cluster
                             r-clisymbols))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/brandmaier/semtree")
    (synopsis "Recursive Partitioning for Structural Equation Models")
    (description
     "SEM Trees and SEM Forests -- an extension of model-based decision trees and
forests to Structural Equation Models (SEM).  SEM trees hierarchically split
empirical data into homogeneous groups each sharing similar data patterns with
respect to a SEM by recursively selecting optimal predictors of these
differences.  SEM forests are an extension of SEM trees.  They are ensembles of
SEM trees each built on a random sample of the original data.  By aggregating
over a forest, we obtain measures of variable importance that are more robust
than measures from single trees.  A description of the method was published by
Brandmaier, von Oertzen, @code{McArdle}, & Lindenberger (2013)
<doi:10.1037/a0030001> and Arnold, Voelkle, & Brandmaier (2020)
<doi:10.3389/fpsyg.2020.564403>.")
    (license license:gpl3)))

(define-public r-semsfa
  (package
    (name "r-semsfa")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "semsfa" version))
       (sha256
        (base32 "1k6i1m4r9y7j85rlrsgfjm8w15ax4xgf70xyvqwsszmf9950ldxx"))))
    (properties `((upstream-name . "semsfa")))
    (build-system r-build-system)
    (propagated-inputs (list r-np
                             r-moments
                             r-mgcv
                             r-iterators
                             r-gamlss
                             r-foreach
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=semsfa")
    (synopsis "Semiparametric Estimation of Stochastic Frontier Models")
    (description
     "Semiparametric Estimation of Stochastic Frontier Models following a two step
procedure: in the first step semiparametric or nonparametric regression
techniques are used to relax parametric restrictions of the functional form
representing technology and in the second step variance parameters are obtained
by pseudolikelihood estimators or by method of moments.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-semsens
  (package
    (name "r-semsens")
    (version "1.5.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SEMsens" version))
       (sha256
        (base32 "0jgc78xxjbm2b1l0b72s84y1dmg9vvw23y07p4gh66jzl07dh5gf"))))
    (properties `((upstream-name . "SEMsens")))
    (build-system r-build-system)
    (propagated-inputs (list r-lavaan))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://cran.r-project.org/package=SEMsens")
    (synopsis "Tool for Sensitivity Analysis in Structural Equation Modeling")
    (description
     "Perform sensitivity analysis in structural equation modeling using
meta-heuristic optimization methods (e.g., ant colony optimization and others).
The references for the proposed methods are: (1) Leite, W., & Shen, Z.,
Marcoulides, K., Fish, C., & Harring, J. (2022).
<doi:10.1080/10705511.2021.1881786> (2) Harring, J. R., @code{McNeish}, D. M., &
Hancock, G. R. (2017) <doi:10.1080/10705511.2018.1506925>; (3) Fisk, C.,
Harring, J., Shen, Z., Leite, W., Suen, K., & Marcoulides, K. (2022).
<doi:10.1177/00131644211073121>; (4) Socha, K., & Dorigo, M. (2008)
<doi:10.1016/j.ejor.2006.06.046>.  We also thank Dr. Krzysztof Socha for sharing
his research on ant colony optimization algorithm with continuous domains and
associated R code, which provided the base for the development of this package.")
    (license license:gpl3)))

(define-public r-semptools
  (package
    (name "r-semptools")
    (version "0.2.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "semptools" version))
       (sha256
        (base32 "15rzkavr3njaln9mgn93ypbxw93prskvxw1s0865xqp58mh14k8y"))))
    (properties `((upstream-name . "semptools")))
    (build-system r-build-system)
    (propagated-inputs (list r-semplot r-rlang r-lavaan))
    (native-inputs (list r-knitr))
    (home-page "https://sfcheung.github.io/semptools/")
    (synopsis "Customizing Structural Equation Modelling Plots")
    (description
     "Most function focus on specific ways to customize a graph.  They use a qgraph
output as the first argument, and return a modified qgraph object.  This allows
the functions to be chained by a pipe operator.")
    (license license:gpl3)))

(define-public r-sempower
  (package
    (name "r-sempower")
    (version "2.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "semPower" version))
       (sha256
        (base32 "000daccgvw0ld4iihj6zih85wirsfmj5wk7r8zhbzkmjyiw3xf3s"))))
    (properties `((upstream-name . "semPower")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/moshagen/semPower")
    (synopsis "Power Analyses for SEM")
    (description
     "This package provides a-priori, post-hoc, and compromise power-analyses for
structural equation models (SEM).")
    (license license:lgpl2.0+)))

(define-public r-semnova
  (package
    (name "r-semnova")
    (version "0.1-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "semnova" version))
       (sha256
        (base32 "1vghf8gy0vahwn3nq752wm0rbj33jdy69yqjzin94sk8xa42d0wi"))))
    (properties `((upstream-name . "semnova")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-mass r-lavaan))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=semnova")
    (synopsis "Latent Repeated Measures ANOVA")
    (description
     "Latent repeated measures ANOVA (L-RM-ANOVA) is a structural equation modeling
based alternative to traditional repeated measures ANOVA. L-RM-ANOVA extends the
latent growth components approach by Mayer et al. (2012)
<doi:10.1080/10705511.2012.713242> and introduces latent variables to repeated
measures analysis.")
    (license license:gpl2+)))

(define-public r-semnetdictionaries
  (package
    (name "r-semnetdictionaries")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SemNetDictionaries" version))
       (sha256
        (base32 "1yhhva79hhj1imzw0n8wkyrb6pmxcqs7kq9czs392xzc6lqk9xr2"))))
    (properties `((upstream-name . "SemNetDictionaries")))
    (build-system r-build-system)
    (propagated-inputs (list r-knitr r-easycsv))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/AlexChristensen/SemNetDictionaries")
    (synopsis "Dictionaries for the 'SemNetCleaner' Package")
    (description
     "This package implements dictionaries that can be used in the
@code{SemNetCleaner} package.  Also includes several functions aimed at
facilitating the text cleaning analysis in the @code{SemNetCleaner} package.
This package is designed to integrate and update word lists and dictionaries
based on each user's individual needs by allowing users to store and save their
own dictionaries.  Dictionaries can be added to the @code{SemNetDictionaries}
package by submitting user-defined dictionaries to
<https://github.com/@code{AlexChristensen/SemNetDictionaries>}.")
    (license license:gpl3+)))

(define-public r-semnetcleaner
  (package
    (name "r-semnetcleaner")
    (version "1.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SemNetCleaner" version))
       (sha256
        (base32 "1fll6i4bz2z07h23z5m3x8hqw5rkianh0w6v8gnnkjqlrnacpfp9"))))
    (properties `((upstream-name . "SemNetCleaner")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringi
                             r-stringdist
                             r-shiny
                             r-semnetdictionaries
                             r-searcher
                             r-rstudioapi
                             r-readxl
                             r-r-matlab
                             r-miniui
                             r-foreign
                             r-editdata
                             r-easycsv))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/AlexChristensen/SemNetCleaner")
    (synopsis "An Automated Cleaning Tool for Semantic and Linguistic Data")
    (description
     "This package implements several functions that automates the cleaning and
spell-checking of text data.  Also converges, finalizes, removes plurals and
continuous strings, and puts text data in binary format for semantic network
analysis.  Uses the @code{SemNetDictionaries} package to make the cleaning
process more accurate, efficient, and reproducible.")
    (license license:gpl3+)))

(define-public r-semnet
  (package
    (name "r-semnet")
    (version "1.4.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SemNeT" version))
       (sha256
        (base32 "0240r7vsr39s53756gmg3s70cv0qaipxaw6b2bgr8fipf11dw15m"))))
    (properties `((upstream-name . "SemNeT")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-qgraph
                             r-plyr
                             r-philentropy
                             r-pbapply
                             r-magrittr
                             r-igraph
                             r-ggplot2
                             r-effects
                             r-dplyr
                             r-car
                             r-broom))
    (home-page "https://github.com/AlexChristensen/SemNeT")
    (synopsis "Methods and Measures for Semantic Network Analysis")
    (description
     "This package implements several functions for the analysis of semantic networks
including different network estimation algorithms, partial node bootstrapping
(Kenett, Anaki, & Faust, 2014 <doi:10.3389/fnhum.2014.00407>), random walk
simulation (Kenett & Austerweil, 2016
<http://alab.psych.wisc.edu/papers/files/Kenett16@code{CreativityRW.pdf>}), and
a function to compute global network measures.  Significance tests and plotting
features are also implemented.")
    (license license:gpl3+)))

(define-public r-semnar
  (package
    (name "r-semnar")
    (version "0.8.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "semnar" version))
       (sha256
        (base32 "0f63k60z41sdh70acx1x1k1abgf19l8l0nawxjvnmj5ywal1cv5a"))))
    (properties `((upstream-name . "semnar")))
    (build-system r-build-system)
    (propagated-inputs (list r-urlshortener r-parsedate r-lubridate r-leaflet
                             r-jsonlite))
    (home-page "https://cran.r-project.org/package=semnar")
    (synopsis "Constructing and Interacting with Databases of Presentations")
    (description
     "This package provides methods for constructing and maintaining a database of
presentations in R. The presentations are either ones that the user gives or
gave or presentations at a particular event or event series.  The package also
provides a plot method for the interactive mapping of the presentations using
leaflet by grouping them according to country, city, year and other presentation
attributes.  The markers on the map come with popups providing presentation
details (title, institution, event, links to materials and events, and so on).")
    (license license:gpl3)))

(define-public r-semmcmc
  (package
    (name "r-semmcmc")
    (version "0.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "semmcmc" version))
       (sha256
        (base32 "13asqiw3zazh9k3p39582a0bzm00gmagls2w76kvpygilj4r5391"))))
    (properties `((upstream-name . "semmcmc")))
    (build-system r-build-system)
    (propagated-inputs (list r-msm r-mass))
    (home-page "https://cran.r-project.org/package=semmcmc")
    (synopsis
     "Bayesian Structural Equation Modeling in Multiple Omics Data Integration")
    (description
     "This package provides Markov Chain Monte Carlo (MCMC) routine for the structural
equation modelling described in Maity et.  al. (2020)
<doi:10.1093/bioinformatics/btaa286>.  This MCMC sampler is useful when one
attempts to perform an integrative survival analysis for multiple platforms of
the Omics data where the response is time to event and the predictors are
different omics expressions for different platforms.")
    (license license:gpl3)))

(define-public r-semmcci
  (package
    (name "r-semmcci")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "semmcci" version))
       (sha256
        (base32 "1j65z1h267pn941gcm5qbxgr6h7wf75iy6wz1jlzg76yfzsa47va"))))
    (properties `((upstream-name . "semmcci")))
    (build-system r-build-system)
    (propagated-inputs (list r-mice r-lavaan))
    (home-page "https://github.com/jeksterslab/semmcci")
    (synopsis
     "Monte Carlo Confidence Intervals in Structural Equation Modeling")
    (description
     "Monte Carlo confidence intervals for free and defined parameters in models
fitted in the structural equation modeling package lavaan can be generated using
the semmcci package.  semmcci has three main functions, namely, MC(), MCMI(),
and MCStd().  The output of lavaan is passed as the first argument to the MC()
function or the MCMI() function to generate Monte Carlo confidence intervals.
Monte Carlo confidence intervals for the standardized estimates can also be
generated by passing the output of the MC() function or the MCMI() function to
the MCStd() function.  A description of the package and code examples are
presented in Pesigan and Cheung (2023) <doi:10.3758/s13428-023-02114-4>.")
    (license license:expat)))

(define-public r-semlrtp
  (package
    (name "r-semlrtp")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "semlrtp" version))
       (sha256
        (base32 "089hw80rwi4rxlz1b04jyylc7rnqpv6b1nl89w6pzdkk0dv98h4g"))))
    (properties `((upstream-name . "semlrtp")))
    (build-system r-build-system)
    (propagated-inputs (list r-pbapply r-lavaan))
    (native-inputs (list r-knitr))
    (home-page "https://sfcheung.github.io/semlrtp/")
    (synopsis "Likelihood Ratio Test P-Values for Structural Equation Models")
    (description
     "Computes likelihood ratio test (LRT) p-values for free parameters in a
structural equation model.  Currently supports models fitted by the lavaan
package by Rosseel (2012) <doi:10.18637/jss.v048.i02>.")
    (license license:gpl3+)))

(define-public r-semlbci
  (package
    (name "r-semlbci")
    (version "0.11.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "semlbci" version))
       (sha256
        (base32 "131nbwq8g0l2imbjv61l83c5avhwqndhdgwkaf0hj0h6a0qwvhqa"))))
    (properties `((upstream-name . "semlbci")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-pbapply
                             r-nloptr
                             r-mass
                             r-lavaan
                             r-ggrepel
                             r-ggplot2
                             r-callr))
    (native-inputs (list r-knitr))
    (home-page "https://sfcheung.github.io/semlbci/")
    (synopsis
     "Likelihood-Based Confidence Interval in Structural Equation Models")
    (description
     "Forms likelihood-based confidence intervals (LBCIs) for parameters in structural
equation modeling, introduced in Cheung and Pesigan (2023)
<doi:10.1080/10705511.2023.2183860>.  Currently implements the algorithm
illustrated by Pek and Wu (2018) <doi:10.1037/met0000163>, and supports the
robust LBCI proposed by Falk (2018) <doi:10.1080/10705511.2017.1367254>.")
    (license license:gpl3)))

(define-public r-semipar-depcens
  (package
    (name "r-semipar-depcens")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SemiPar.depCens" version))
       (sha256
        (base32 "0ig7w36by5970p02859p8z9licmw1myy5r84hfd31ndkymv3hca7"))))
    (properties `((upstream-name . "SemiPar.depCens")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-pbivnorm r-foreach r-doparallel
                             r-copula))
    (home-page "https://github.com/Nago2020/SemiPar.depCens")
    (synopsis
     "Copula Based Cox Proportional Hazards Models for Dependent Censoring")
    (description
     "Copula based Cox proportional hazards models for survival data subject to
dependent censoring.  This approach does not assume that the parameter defining
the copula is known.  The dependency parameter is estimated with other finite
model parameters by maximizing a Pseudo likelihood function.  The cumulative
hazard function is estimated via estimating equations derived based on
martingale ideas.  Available copula functions include Frank, Gumbel and Normal
copulas.  Only Weibull and lognormal models are allowed for the censoring model,
even though any parametric model that satisfies certain identifiability
conditions could be used.  Implemented methods are described in the article
\"Copula based Cox proportional hazards models for dependent censoring\" by Deresa
and Van Keilegom (2024) <doi:10.1080/01621459.2022.2161387>.")
    (license license:gpl3)))

(define-public r-semipar
  (package
    (name "r-semipar")
    (version "1.0-4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SemiPar" version))
       (sha256
        (base32 "0pa3drpvclkw81ji5m1h5arj5c2rh03dnlff97cnnr1v5kvg1i4w"))))
    (properties `((upstream-name . "SemiPar")))
    (build-system r-build-system)
    (propagated-inputs (list r-nlme r-mass r-cluster))
    (home-page "http://matt-wand.utsacademics.info/SPmanu.pdf")
    (synopsis "Semiparametic Regression")
    (description
     "This package provides functions for semiparametric regression analysis, to
complement the book: Ruppert, D., Wand, M.P. and Carroll, R.J. (2003).
Semiparametric Regression.  Cambridge University Press.")
    (license license:gpl2+)))

(define-public r-seminr
  (package
    (name "r-seminr")
    (version "2.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seminr" version))
       (sha256
        (base32 "0zh5slwrsv20z1irfy944cd0x7940yv84p5mjj7jj9isgr9a8wbz"))))
    (properties `((upstream-name . "seminr")))
    (build-system r-build-system)
    (propagated-inputs (list r-webp
                             r-testthat
                             r-rmarkdown
                             r-lavaan
                             r-knitr
                             r-glue
                             r-diagrammersvg
                             r-diagrammer))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sem-in-r/seminr")
    (synopsis "Building and Estimating Structural Equation Models")
    (description
     "This package provides a powerful, easy to syntax for specifying and estimating
complex Structural Equation Models.  Models can be estimated using Partial Least
Squares Path Modeling or Covariance-Based Structural Equation Modeling or
covariance based Confirmatory Factor Analysis.  Methods described in Ray, Danks,
and Valdez (2021).")
    (license license:gpl3)))

(define-public r-semimarkov
  (package
    (name "r-semimarkov")
    (version "1.4.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SemiMarkov" version))
       (sha256
        (base32 "0nga790kcrvmbrx5asp4062711x0kjsccc246l4syiiw30b769ig"))))
    (properties `((upstream-name . "SemiMarkov")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsolnp r-numderiv r-mass))
    (home-page "https://cran.r-project.org/package=SemiMarkov")
    (synopsis "Multi-States Semi-Markov Models")
    (description
     "This package provides functions for fitting multi-state semi-Markov models to
longitudinal data.  A parametric maximum likelihood estimation method adapted to
deal with Exponential, Weibull and Exponentiated Weibull distributions is
considered.  Right-censoring can be taken into account and both constant and
time-varying covariates can be included using a Cox proportional model.
Reference: A. Krol and P. Saint-Pierre (2015) 	<doi:10.18637/jss.v066.i06>.")
    (license license:gpl2+)))

(define-public r-semiestimate
  (package
    (name "r-semiestimate")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SemiEstimate" version))
       (sha256
        (base32 "0zqxy50glx3xkzz6z41nifqxjgva5fk2x6fbbhdm39fham2yb34f"))))
    (properties `((upstream-name . "SemiEstimate")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SemiEstimate")
    (synopsis "Solve Semi-Parametric Estimation by Implicit Profiling")
    (description
     "Semi-parametric estimation problem can be solved by two-step Newton-Raphson
iteration.  The implicit profiling method<@code{arXiv:2108.07928>} is an
improved method of two-step NR iteration especially for the implicit-bundled
type of the parametric part and non-parametric part.  This package provides a
function semislv() supporting the above two methods and numeric derivative
approximation for unprovided Jacobian matrix.")
    (license license:expat)))

(define-public r-semidist
  (package
    (name "r-semidist")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "semidist" version))
       (sha256
        (base32 "0bgl7z78psd2b3v4c4fzhq0r8w0zfn408ji21cw1x7h87dg70qr2"))))
    (properties `((upstream-name . "semidist")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-purrr
                             r-furrr
                             r-fnn
                             r-energy))
    (home-page "https://github.com/wzhong41/semidist")
    (synopsis
     "Measure Dependence Between Categorical and Continuous Variables")
    (description
     "Semi-distance and mean-variance (MV) index are proposed to measure the
dependence between a categorical random variable and a continuous variable.
Test of independence and feature screening for classification problems can be
implemented via the two dependence measures.  For the details of the methods,
see Zhong et al. (2023) <doi:10.1080/01621459.2023.2284988>; Cui and Zhong
(2019) <doi:10.1016/j.csda.2019.05.004>; Cui, Li and Zhong (2015)
<doi:10.1080/01621459.2014.920256>.")
    (license license:expat)))

(define-public r-semid
  (package
    (name "r-semid")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SEMID" version))
       (sha256
        (base32 "14x2vir17fp9wm9hzsrc1sw0qgavkpfdvcsfnnklhznl0h526xrr"))))
    (properties `((upstream-name . "SEMID")))
    (build-system r-build-system)
    (propagated-inputs (list r-r-utils r-r-oo r-r-methodss3 r-igraph))
    (home-page "https://github.com/Lucaweihs/SEMID")
    (synopsis "Identifiability of Linear Structural Equation Models")
    (description
     "This package provides routines to check identifiability or non-identifiability
of linear structural equation models as described in Drton, Foygel, and
Sullivant (2011) <doi:10.1214/10-AOS859>, Foygel, Draisma, and Drton (2012)
<doi:10.1214/12-AOS1012>, and other works.  The routines are based on the
graphical representation of structural equation models.")
    (license license:gpl2+)))

(define-public r-semicontmanova
  (package
    (name "r-semicontmanova")
    (version "0.1-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "semicontMANOVA" version))
       (sha256
        (base32 "08z4r8klp2drch89jd4j3ji0439p6dr93db51vnm3bzic0dbrm01"))))
    (properties `((upstream-name . "semicontMANOVA")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-matrixcalc))
    (home-page "https://cran.r-project.org/package=semicontMANOVA")
    (synopsis
     "Multivariate ANalysis of VAriance with Ridge Regularization for Semicontinuous High-Dimensional Data")
    (description
     "This package implements Multivariate ANalysis Of VAriance (MANOVA) parameters
inference and test with regularization for semicontinuous high-dimensional data.
 The method can be applied also in presence of low-dimensional data.  The
p-value can be obtained through asymptotic distribution or using a permutation
procedure.  The package gives also the possibility to simulate this type of
data.  Method is described in Elena Sabbioni, Claudio Agostinelli and Alessio
Farcomeni (2024) <@code{arXiv:2401.04036>}.")
    (license license:gpl2)))

(define-public r-semicomprisks
  (package
    (name "r-semicomprisks")
    (version "3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SemiCompRisks" version))
       (sha256
        (base32 "0xxdb3k7d9rqq94d2x862jl67s3pkzwigwlcvrybclm6b5gb86x4"))))
    (properties `((upstream-name . "SemiCompRisks")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mass r-formula))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=SemiCompRisks")
    (synopsis
     "Hierarchical Models for Parametric and Semi-Parametric Analyses of Semi-Competing Risks Data")
    (description
     "Hierarchical multistate models are considered to perform the analysis of
independent/clustered semi-competing risks data.  The package allows to choose
the specification for model components from a range of options giving users
substantial flexibility, including: accelerated failure time or proportional
hazards regression models; parametric or non-parametric specifications for
baseline survival functions and cluster-specific random effects distribution; a
Markov or semi-Markov specification for terminal event following non-terminal
event.  While estimation is mainly performed within the Bayesian paradigm, the
package also provides the maximum likelihood estimation approach for several
parametric models.  The package also includes functions for univariate survival
analysis as complementary analysis tools.")
    (license license:gpl2+)))

(define-public r-semicmprskcoxmsm
  (package
    (name "r-semicmprskcoxmsm")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "semicmprskcoxmsm" version))
       (sha256
        (base32 "00nxmyp4g1pxzqrw3zd5il92vinzpy12wnjnpm33nbh4wx2i5356"))))
    (properties `((upstream-name . "semicmprskcoxmsm")))
    (build-system r-build-system)
    (propagated-inputs (list r-twang r-survival r-rcpp r-ggplot2 r-fastghquad))
    (home-page "https://cran.r-project.org/package=semicmprskcoxmsm")
    (synopsis
     "Use Inverse Probability Weighting to Estimate Treatment Effect for Semi Competing Risks Data")
    (description
     "Use inverse probability weighting methods to estimate treatment effect under
marginal structure model (MSM) for the transition hazard of semi competing risk
data, i.e.  illness death model.  We implement two specific such models, the
usual Markov illness death structural model and the general Markov illness death
structural model.  We also provide the predicted three risks functions from the
marginal structure models.  Zhang, Y. and Xu, R. (2022)
<@code{arXiv:2204.10426>}.")
    (license license:gpl2+)))

(define-public r-semiartificial
  (package
    (name "r-semiartificial")
    (version "2.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "semiArtificial" version))
       (sha256
        (base32 "0zsn3r5n7n3dsa02i194lr6ail7mizq9xp8x3kc5k17z4ckcfysd"))))
    (properties `((upstream-name . "semiArtificial")))
    (build-system r-build-system)
    (propagated-inputs (list r-timedate
                             r-statmatch
                             r-rsnns
                             r-robustbase
                             r-nnet
                             r-mcclust
                             r-mass
                             r-logspline
                             r-ks
                             r-fpc
                             r-flexclust
                             r-corelearn
                             r-cluster))
    (home-page "http://lkm.fri.uni-lj.si/rmarko/software/")
    (synopsis "Generator of Semi-Artificial Data")
    (description
     "This package contains methods to generate and evaluate semi-artificial data
sets.  Based on a given data set different methods learn data properties using
machine learning algorithms and generate new data with the same properties.  The
package currently includes the following data generators: i) a RBF network based
generator using @code{rbfDDA}() from package RSNNS', ii) a Random Forest based
generator for both classification and regression problems iii) a density forest
based generator for unsupervised data Data evaluation support tools include: a)
single attribute based statistical evaluation: mean, median, standard deviation,
skewness, kurtosis, medcouple, L/RMC, KS test, Hellinger distance b) evaluation
based on clustering using Adjusted Rand Index (ARI) and FM c) evaluation based
on classification performance with various learning models, e.g., random
forests.")
    (license license:gpl3)))

(define-public r-semhelpinghands
  (package
    (name "r-semhelpinghands")
    (version "0.1.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "semhelpinghands" version))
       (sha256
        (base32 "1v6gyjk2awlpz8zpczkxifb2pdnd7js6l75kmrh9lbyy5s7q9xxi"))))
    (properties `((upstream-name . "semhelpinghands")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-lavaan r-ggrepel r-ggplot2 r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://sfcheung.github.io/semhelpinghands/")
    (synopsis "Helper Functions for Structural Equation Modeling")
    (description
     "An assortment of helper functions for doing structural equation modeling, mainly
by lavaan for now.  Most of them are time-saving functions for common tasks in
doing structural equation modeling and reading the output.  This package is not
for functions that implement advanced statistical procedures.  It is a
light-weight package for simple functions that do simple tasks conveniently,
with as few dependencies as possible.")
    (license license:gpl3+)))

(define-public r-semgraph
  (package
    (name "r-semgraph")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SEMgraph" version))
       (sha256
        (base32 "08lka7zv3w8m4l855whcjwijqyhv7x45m6bcqaivbw5clxhidmgw"))))
    (properties `((upstream-name . "SEMgraph")))
    (build-system r-build-system)
    (propagated-inputs (list r-rgraphviz
                             r-rbgl
                             r-protoclust
                             r-pbapply
                             r-mvtnorm
                             r-mgcv
                             r-lavaan
                             r-igraph
                             r-graph
                             r-glmnet
                             r-glasso
                             r-ggm
                             r-gdata
                             r-flip
                             r-dagitty
                             r-corpcor
                             r-boot
                             r-aspect))
    (home-page "https://github.com/fernandoPalluzzi/SEMgraph")
    (synopsis
     "Network Analysis and Causal Inference Through Structural Equation Modeling")
    (description
     "Estimate networks and causal relationships in complex systems through Structural
Equation Modeling.  This package also includes functions to import, weight,
manipulate, and fit biological network models within the Structural Equation
Modeling framework proposed in Grassi M, Palluzzi F, Tarantino B (2022)
<doi:10.1093/bioinformatics/btac567>.")
    (license license:gpl3)))

(define-public r-semgram
  (package
    (name "r-semgram")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "semgram" version))
       (sha256
        (base32 "0l5n176mhjkn5kcjd1kfq9s897rg510m1acnmjxcaw0kmmhi4ggs"))))
    (properties `((upstream-name . "semgram")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rsyntax r-data-table))
    (home-page "https://github.com/omstuhler/semgram")
    (synopsis "Extracting Semantic Motifs from Textual Data")
    (description
     "This package provides a framework for extracting semantic motifs around entities
in textual data.  It implements an entity-centered semantic grammar that
distinguishes six classes of motifs: actions of an entity, treatments of an
entity, agents acting upon an entity, patients acted upon by an entity,
characterizations of an entity, and possessions of an entity.  Motifs are
identified by applying a set of extraction rules to a parsed text object that
includes part-of-speech tags and dependency annotations - such as those
generated by spacyr'.  For further reference, see: Stuhler (2022) <doi:
10.1177/00491241221099551>.")
    (license license:gpl3)))

(define-public r-semfindr
  (package
    (name "r-semfindr")
    (version "0.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "semfindr" version))
       (sha256
        (base32 "02p2l5xsy53v6485as8azqamqy6cnjfmxb9zav1wnhm9p7d1f9ss"))))
    (properties `((upstream-name . "semfindr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-matrix r-lavaan r-ggrepel r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://sfcheung.github.io/semfindr/")
    (synopsis "Influential Cases in Structural Equation Modeling")
    (description
     "Sensitivity analysis in structural equation modeling using influence measures
and diagnostic plots.  Support leave-one-out casewise sensitivity analysis
presented by Pek and @code{MacCallum} (2011) <doi:10.1080/00273171.2011.561068>
and approximate casewise influence using scores and casewise likelihood.")
    (license license:gpl3)))

(define-public r-semeff
  (package
    (name "r-semeff")
    (version "0.7.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "semEff" version))
       (sha256
        (base32 "1a71yaw20l5wsmgchwf4p3yhwbb2g65aw5i8cmajyhwp0hhvgyls"))))
    (properties `((upstream-name . "semEff")))
    (build-system r-build-system)
    (propagated-inputs (list r-lme4 r-gsl r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://murphymv.github.io/semEff/")
    (synopsis
     "Automatic Calculation of Effects for Piecewise Structural Equation Models")
    (description
     "Automatically calculate direct, indirect, and total effects for piecewise
structural equation models, comprising lists of fitted models representing
structured equations (Lefcheck, 2016 <doi:10/f8s8rb>).  Confidence intervals are
provided via bootstrapping.")
    (license license:gpl3+)))

(define-public r-semds
  (package
    (name "r-semds")
    (version "0.9-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "semds" version))
       (sha256
        (base32 "10kvcmcy6qa2r5sram6azqgzl528rghfxgw2f2bq2fvhhq127dzq"))))
    (properties `((upstream-name . "semds")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma r-minpack-lm))
    (home-page "https://cran.r-project.org/package=semds")
    (synopsis "Structural Equation Multidimensional Scaling")
    (description
     "Fits a structural equation multidimensional scaling (SEMDS) model for asymmetric
and three-way input dissimilarities.  It assumes that the dissimilarities are
measured with errors.  The latent dissimilarities are estimated as factor scores
within an SEM framework while the objects are represented in a low-dimensional
space as in MDS.")
    (license license:gpl2+)))

(define-public r-semdrw
  (package
    (name "r-semdrw")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "semdrw" version))
       (sha256
        (base32 "16hm1ifgspnxnlf3rfg5ylmql2fq91ig8nc5953ggyhfrjwbqx15"))))
    (properties `((upstream-name . "semdrw")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinyace
                             r-shiny
                             r-semtools
                             r-semplot
                             r-psych
                             r-lavaan
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=semdrw")
    (synopsis "'SEM Shiny'")
    (description
     "Interactive shiny application for working with Structural Equation Modelling
technique.  Runtime examples are provided in the package function as well as at
<https://kartikeyab.shinyapps.io/semwebappk/> .")
    (license license:gpl2)))

(define-public r-semdeep
  (package
    (name "r-semdeep")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SEMdeep" version))
       (sha256
        (base32 "0jwaliqxcw2rja7h87j096z02fkvrmfv8ssn10m16lcng9xy5gmm"))))
    (properties `((upstream-name . "SEMdeep")))
    (build-system r-build-system)
    (propagated-inputs (list r-xgboost
                             r-torch
                             r-shapr
                             r-semgraph
                             r-ranger
                             r-nnet
                             r-neuralnettools
                             r-mgcv
                             r-lavaan
                             r-igraph
                             r-corpcor
                             r-cito))
    (home-page "https://github.com/BarbaraTarantino/SEMdeep")
    (synopsis
     "Structural Equation Modeling with Deep Neural Network and Machine Learning")
    (description
     "Training and validation of a custom (or data-driven) Structural Equation Models
using layer-wise Deep Neural Networks or node-wise Machine Learning algorithms,
which extend the fitting procedures of the	'SEMgraph R package
<doi:10.32614/CRAN.package.SEMgraph>.")
    (license license:gpl3+)))

(define-public r-semblance
  (package
    (name "r-semblance")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Semblance" version))
       (sha256
        (base32 "1kzrg5z3244nx9y37p092wpangni3fxpx04i5fb4dhrmav4rvgab"))))
    (properties `((upstream-name . "Semblance")))
    (build-system r-build-system)
    (propagated-inputs (list r-performanceanalytics r-msos r-fields
                             r-desctools))
    (home-page "https://cran.r-project.org/package=Semblance")
    (synopsis "Data-Driven Similarity Kernel on Probability Spaces")
    (description
     "We present a rank-based Mercer kernel to compute a pair-wise similarity metric
corresponding to informative representation of data.  We tailor the development
of a kernel to encode our prior knowledge about the data distribution over a
probability space.  The philosophical concept behind our construction is that
objects whose feature values fall on the extreme of that featureâs probability
mass distribution are more similar to each other, than objects whose feature
values lie closer to the mean.  Semblance emphasizes features whose values lie
far away from the mean of their probability distribution.  The kernel relies on
properties empirically determined from the data and does not assume an
underlying distribution.  The use of feature ranks on a probability space
ensures that Semblance is computational efficacious, robust to outliers, and
statistically stable, thus making it widely applicable algorithm for pattern
analysis.  The output from the kernel is a square, symmetric matrix that gives
proximity values between pairs of observations.")
    (license license:gpl2)))

(define-public r-semantic-dashboard
  (package
    (name "r-semantic-dashboard")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "semantic.dashboard" version))
       (sha256
        (base32 "0518pdxgjjv8ql1aaysk64pv1pa01bbg3s7qdibw1v0c6pcf3adb"))))
    (properties `((upstream-name . "semantic.dashboard")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-shiny-semantic r-shiny r-htmltools r-glue
                             r-checkmate))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://cran.r-project.org/package=semantic.dashboard")
    (synopsis "Dashboard with Fomantic UI Support for Shiny")
    (description
     "It offers functions for creating dashboard with Fomantic UI.")
    (license license:expat)))

(define-public r-semantic-assets
  (package
    (name "r-semantic-assets")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "semantic.assets" version))
       (sha256
        (base32 "000kgsbrgw8dlc0zi3f85g87xhpkzi8qbkrj717a076hr3h1bms7"))))
    (properties `((upstream-name . "semantic.assets")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-htmlwidgets))
    (native-inputs (list esbuild))
    (home-page "https://github.com/Appsilon/semantic.assets")
    (synopsis "Assets for 'shiny.semantic'")
    (description
     "Style sheets and @code{JavaScript} assets for shiny.semantic package.")
    (license license:lgpl3)))

(define-public r-selfingtree
  (package
    (name "r-selfingtree")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "selfingTree" version))
       (sha256
        (base32 "18ylxmg2ms4ccgm4ahzfl65x614wiq5id7zazjjz5y75h8gs7gzj"))))
    (properties `((upstream-name . "selfingTree")))
    (build-system r-build-system)
    (propagated-inputs (list r-foreach))
    (home-page "https://cran.r-project.org/package=selfingTree")
    (synopsis
     "Genotype Probabilities in Intermediate Generations of Inbreeding Through Selfing")
    (description
     "This package provides a probability tree allows to compute probabilities of
complex events, such as genotype probabilities in intermediate generations of
inbreeding through recurrent self-fertilization (selfing).  This package
implements functionality to compute probability trees for two- and three-marker
genotypes in the F2 to F7 selfing generations.  The conditional probabilities
are derived automatically and in symbolic form.  The package also provides
functionality to extract and evaluate the relevant probabilities.")
    (license license:bsd-3)))

(define-public r-self
  (package
    (name "r-self")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SELF" version))
       (sha256
        (base32 "1yafjhxwsanr33dqy7w9x61ghldr29s8k6h68idq6nnfb77ghjyx"))))
    (properties `((upstream-name . "SELF")))
    (build-system r-build-system)
    (propagated-inputs (list r-xgboost r-rcpp r-data-table
                             r-comparecausalnetworks r-bnlearn))
    (home-page "https://cran.r-project.org/package=SELF")
    (synopsis
     "Structural Equation Embedded Likelihood Framework for Causal Discovery")
    (description
     "This package provides the SELF criteria to learn causal structure.  Please cite
\"Ruichu Cai, Jie Qiao, Zhenjie Zhang, Zhifeng Hao.  SELF: Structural Equational
Embedded Likelihood Framework for Causal Discovery.  AAAI. 2018.\".")
    (license license:gpl2)))

(define-public r-seleniumpipes
  (package
    (name "r-seleniumpipes")
    (version "0.3.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seleniumPipes" version))
       (sha256
        (base32 "1krpzh2gy3yl07c2n1d8q21xjc2qcv5ac1kv7212710bxkdyfad6"))))
    (properties `((upstream-name . "seleniumPipes")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-whisker r-magrittr r-jsonlite r-httr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/johndharrison/seleniumPipes")
    (synopsis "R Client Implementing the W3C WebDriver Specification")
    (description
     "The W3C @code{WebDriver} specification defines a way for out-of-process programs
to remotely instruct the behaviour of web browsers.  It is detailed at
<https://w3c.github.io/webdriver/webdriver-spec.html>.  This package provides an
R client implementing the W3C specification.")
    (license license:gpl3)))

(define-public r-selenium
  (package
    (name "r-selenium")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "selenium" version))
       (sha256
        (base32 "0dnz7zz1rxkf80ayxmp56kcx3blbl5hxwcy66rhrs1s090mbhyxr"))))
    (properties `((upstream-name . "selenium")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-rappdirs
                             r-r6
                             r-processx
                             r-lifecycle
                             r-jsonlite
                             r-httr2
                             r-base64enc))
    (home-page "https://ashbythorpe.github.io/selenium-r/")
    (synopsis "Low-Level Browser Automation Interface")
    (description
     "An implementation of W3C @code{WebDriver} 2.0
(<https://w3c.github.io/webdriver/>), allowing interaction with a Selenium
Server (<https://www.selenium.dev/documentation/grid/>) instance from R'.
Allows a web browser to be automated from R'.")
    (license license:expat)))

(define-public r-selenider
  (package
    (name "r-selenider")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "selenider" version))
       (sha256
        (base32 "1pxc8vxrvx5dfvx4gqj025wddsmpys8grs33nmz7hlnj1vichlp3"))))
    (properties `((upstream-name . "selenider")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-vctrs
                             r-rlang
                             r-processx
                             r-prettyunits
                             r-lifecycle
                             r-curl
                             r-coro
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ashbythorpe/selenider")
    (synopsis
     "Concise, Lazy and Reliable Wrapper for 'chromote' and 'selenium'")
    (description
     "This package provides a user-friendly wrapper for web automation, using either
chromote or selenium'.  Provides a simple and consistent API to make web
scraping and testing scripts easy to write and understand.  Elements are lazy,
and automatically wait for the website to be valid, resulting in reliable and
reproducible code, with no visible impact on the experience of the programmer.")
    (license license:expat)))

(define-public r-selemix
  (package
    (name "r-selemix")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SeleMix" version))
       (sha256
        (base32 "03q5pdgxdfx944zwpl2w7gzf23va4flbd8in3g03980smq0ai2c1"))))
    (properties `((upstream-name . "SeleMix")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://cran.r-project.org/package=SeleMix")
    (synopsis "Selective Editing via Mixture Models")
    (description
     "Detection of outliers and influential errors using a latent variable model.")
    (license (license:fsdg-compatible "EUPL"))))

(define-public r-selectspm
  (package
    (name "r-selectspm")
    (version "0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "selectspm" version))
       (sha256
        (base32 "02gzbzjdr8ij9gsrvx8wmz9q8r35zjniwcri2hmiz57h0svybvll"))))
    (properties `((upstream-name . "selectspm")))
    (build-system r-build-system)
    (propagated-inputs (list r-spatstat-random
                             r-spatstat-model
                             r-spatstat-geom
                             r-spatstat-explore
                             r-spatstat
                             r-ecespa))
    (home-page "https://cran.r-project.org/package=selectspm")
    (synopsis
     "Select Point Pattern Models Based on Minimum Contrast, AIC and Goodness of Fit")
    (description
     "Fit and selects point pattern models based on minimum contrast, AIC and and
goodness of fit.")
    (license license:gpl2+)))

(define-public r-selectmeta
  (package
    (name "r-selectmeta")
    (version "1.0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "selectMeta" version))
       (sha256
        (base32 "0i0wzx5ggd60y26lnn4qk4n8h27ahll9732026ppks1djx14cdy0"))))
    (properties `((upstream-name . "selectMeta")))
    (build-system r-build-system)
    (propagated-inputs (list r-deoptim))
    (home-page "http://www.kasparrufibach.ch")
    (synopsis "Estimation of Weight Functions in Meta Analysis")
    (description
     "Publication bias, the fact that studies identified for inclusion in a meta
analysis do not represent all studies on the topic of interest, is commonly
recognized as a threat to the validity of the results of a meta analysis.  One
way to explicitly model publication bias is via selection models or weighted
probability distributions.  In this package we provide implementations of
several parametric and nonparametric weight functions.  The novelty in Rufibach
(2011) is the proposal of a non-increasing variant of the nonparametric weight
function of Dear & Begg (1992).  The new approach potentially offers more
insight in the selection process than other methods, but is more flexible than
parametric approaches.  To maximize the log-likelihood function proposed by Dear
& Begg (1992) under a monotonicity constraint we use a differential evolution
algorithm proposed by Ardia et al (2010a, b) and implemented in Mullen et al
(2009).  In addition, we offer a method to compute a confidence interval for the
overall effect size theta, adjusted for selection bias as well as a function
that computes the simulation-based p-value to assess the null hypothesis of no
selection as described in Rufibach (2011, Section 6).")
    (license license:gpl2+)))

(define-public r-selectiveinference
  (package
    (name "r-selectiveinference")
    (version "1.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "selectiveInference" version))
       (sha256
        (base32 "07kzpvdmflca1sriqfzn20f84fx2b9kwwiqm91lqjpx854bsx5kh"))))
    (properties `((upstream-name . "selectiveInference")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rcpp
                             r-mass
                             r-intervals
                             r-glmnet
                             r-adaptmcmc))
    (home-page "https://cran.r-project.org/package=selectiveInference")
    (synopsis "Tools for Post-Selection Inference")
    (description
     "New tools for post-selection inference, for use with forward stepwise
regression, least angle regression, the lasso, and the many means problem.  The
lasso function implements Gaussian, logistic and Cox survival models.")
    (license license:gpl2)))

(define-public r-selectiongain
  (package
    (name "r-selectiongain")
    (version "2.0.710")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "selectiongain" version))
       (sha256
        (base32 "0gbfzm1kzlc2lf1jfd0gxv8nlnm5bw6w7b55m48338lxb4zyhjcc"))))
    (properties `((upstream-name . "selectiongain")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://cran.r-project.org/package=selectiongain")
    (synopsis
     "Tool for Calculation and Optimization of the Expected Gain from Multi-Stage Selection")
    (description
     "Multi-stage selection is practiced in numerous fields of life and social
sciences and particularly in breeding.  A special characteristic of multi-stage
selection is that candidates are evaluated in successive stages with increasing
intensity and effort, and only a fraction of the superior candidates is selected
and promoted to the next stage.  For the optimum design of such selection
programs, the selection gain plays a crucial role.  It can be calculated by
integration of a truncated multivariate normal (MVN) distribution.  While
mathematical formulas for calculating the selection gain and the variance among
selected candidates were developed long time ago, solutions for numerical
calculation were not available.  This package can also be used for optimizing
multi-stage selection programs for a given total budget and different costs of
evaluating the candidates in each stage.")
    (license license:gpl2)))

(define-public r-selectionbias
  (package
    (name "r-selectionbias")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SelectionBias" version))
       (sha256
        (base32 "00fk3ljm3x1slffni6g4j61s2766znlmamg16vmbkfjmrbb80nfy"))))
    (properties `((upstream-name . "SelectionBias")))
    (build-system r-build-system)
    (propagated-inputs (list r-lifecycle r-arm))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/stizet/SelectionBias")
    (synopsis
     "Calculates Bounds for the Selection Bias for Binary Treatment and Outcome Variables")
    (description
     "Computes bounds and sensitivity parameters as part of sensitivity analysis for
selection bias.  Different bounds are provided: the SV (Smith and
@code{VanderWeele}), AF (assumption-free) bound, GAF (generalized AF), and CAF
(counterfactual AF) bounds.  The calculation of the sensitivity parameters for
the SV and GAF bounds assume an additional dependence structure in form of a
generalized M-structure.  The bounds can be calculated for any structure as long
as the necessary assumptions hold.  See Smith and @code{VanderWeele} (2019)
<doi:10.1097/EDE.0000000000001032>, Zetterstrom and Waernbaum (2022)
<doi:10.1515/em-2022-0108> and Zetterstrom (2024) <doi:10.1515/em-2023-0033>.")
    (license license:expat)))

(define-public r-selection-index
  (package
    (name "r-selection-index")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "selection.index" version))
       (sha256
        (base32 "0yqg0j0sifpc25qpndrmnam64qy1qbzazqqs8rz99lynkwn3z1fh"))))
    (properties `((upstream-name . "selection.index")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/zankrut20/selection.index")
    (synopsis "Analysis of Selection Index in Plant Breeding")
    (description
     "The aim of most plant breeding programmes is simultaneous improvement of several
characters.  An objective method involving simultaneous selection for several
attributes then becomes necessary.  It has been recognised that most rapid
improvements in the economic value is expected from selection applied
simultaneously to all the characters which determine the economic value of a
plant, and appropriate assigned weights to each character according to their
economic importance, heritability and correlations between characters.  So the
selection for economic value is a complex matter.  If the component characters
are combined together into an index in such a way that when selection is applied
to the index, as if index is the character to be improved, most rapid
improvement of economic value is expected.  Such an index was first proposed by
Smith (1937 <doi:10.1111/j.1469-1809.1936.tb02143.x>) based on the Fisher's
(1936 <doi:10.1111/j.1469-1809.1936.tb02137.x>) \"discriminant function\"
Dabholkar (1999
<https://books.google.co.in/books?id=@code{mlFtumAXQ0oC&lpg=PA4&ots=Xgxp1qLuxS&dq=elements%20of%20biometrical%20genetics&lr&pg=PP1#v=onepage&q&f=false>}).
 In this package selection index is calculated based on the Smith (1937)
selection index method.")
    (license license:gpl3+)))

(define-public r-selectboost
  (package
    (name "r-selectboost")
    (version "2.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SelectBoost" version))
       (sha256
        (base32 "189hdr50sikg6pr9x9amjmgg9z0c95j65bddmifsy7zvjhllzjiy"))))
    (properties `((upstream-name . "SelectBoost")))
    (build-system r-build-system)
    (propagated-inputs (list r-varbvs
                             r-spls
                             r-rfast
                             r-msgps
                             r-lars
                             r-igraph
                             r-glmnet
                             r-cascade
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://fbertran.github.io/SelectBoost/")
    (synopsis
     "General Algorithm to Enhance the Performance of Variable Selection Methods in Correlated Datasets")
    (description
     "An implementation of the selectboost algorithm (Bertrand et al.  2020,
Bioinformatics', <doi:10.1093/bioinformatics/btaa855>), which is a general
algorithm that improves the precision of any existing variable selection method.
 This algorithm is based on highly intensive simulations and takes into account
the correlation structure of the data.  It can either produce a confidence index
for variable selection or it can be used in an experimental design planning
perspective.")
    (license license:gpl3)))

(define-public r-selectapref
  (package
    (name "r-selectapref")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "selectapref" version))
       (sha256
        (base32 "0njibljh3hiqdn4wdb031bdg1bh3xpixgczbi5i01gaavybsq4cc"))))
    (properties `((upstream-name . "selectapref")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=selectapref")
    (synopsis "Analysis of Field and Laboratory Foraging")
    (description
     "This package provides indices such as Manly's alpha, foraging ratio, and Ivlev's
selectivity to allow for analysis of dietary selectivity and preference.  Can
accommodate multiple experimental designs such as constant prey number of prey
depletion.  Please contact the package maintainer with any publications making
use of this package in an effort to maintain a repository of dietary selections
studies.")
    (license license:gpl2+)))

(define-public r-select
  (package
    (name "r-select")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Select" version))
       (sha256
        (base32 "1qx4wwxxwjq31vf645xvwb0y2z5h4v6ca8fcrfpaj5kc33f333v2"))))
    (properties `((upstream-name . "Select")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsolnp r-latticeextra r-lattice r-fd r-ade4))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=Select")
    (synopsis "Determines Species Probabilities Based on Functional Traits")
    (description
     "The objective of these functions is to derive a species assemblage that
satisfies a functional trait profile.  Restoring resilient ecosystems requires a
flexible framework for selecting assemblages that are based on the functional
traits of species.  However, current trait-based models have been limited to
algorithms that can only select species by optimising specific trait values, and
could not elegantly accommodate the common desire among restoration ecologists
to produce functionally diverse assemblages.  We have solved this problem by
applying a non-linear optimisation algorithm that optimises Rao Q, a closed-form
functional trait diversity index that incorporates species abundances, subject
to other linear constraints.  This framework generalises previous models that
only optimised the entropy of the community, and can optimise both functional
diversity and entropy simultaneously.  This package can also be used to generate
experimental assemblages to test the effects of community-level traits on
community dynamics and ecosystem function.  The method is based on theory
discussed in Laughlin (2014, Ecology Letters) <doi.org/10.1111/ele.12288>.")
    (license license:gpl2+)))

(define-public r-selcorr
  (package
    (name "r-selcorr")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "selcorr" version))
       (sha256
        (base32 "0xasxjb0hk84ai4338pkddy7w02ih2q8v4vgn2zdc744jb9f0gqb"))))
    (properties `((upstream-name . "selcorr")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=selcorr")
    (synopsis "Post-Selection Inference for Generalized Linear Models")
    (description
     "Calculates (unconditional) post-selection confidence intervals and p-values for
the coefficients of (generalized) linear models.")
    (license license:gpl3)))

(define-public r-sel
  (package
    (name "r-sel")
    (version "1.0-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SEL" version))
       (sha256
        (base32 "1inahd5hajy3cmwas5zz1nlnzzrv7rraf3xb6n0vrk1fxm7s42wj"))))
    (properties `((upstream-name . "SEL")))
    (build-system r-build-system)
    (propagated-inputs (list r-quadprog r-lattice))
    (home-page "https://cran.r-project.org/package=SEL")
    (synopsis "Semiparametric Elicitation")
    (description
     "This package implements a method for fitting a bounded probability distribution
to quantiles (for example stated by an expert), see Bornkamp and Ickstadt (2009)
for details.  For this purpose B-splines are used, and the density is obtained
by penalized least squares based on a Brier entropy penalty.  The package
provides methods for fitting the distribution as well as methods for evaluating
the underlying density and cdf.  In addition methods for plotting the
distribution, drawing random numbers and calculating quantiles of the obtained
distribution are provided.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-sejong
  (package
    (name "r-sejong")
    (version "0.01")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Sejong" version))
       (sha256
        (base32 "0j10szvdn32b7xnvhcbj89xma64bq62936b3r18n1limjpismfws"))))
    (properties `((upstream-name . "Sejong")))
    (build-system r-build-system)
    (home-page "https://github.com/haven-jeon/Sejong")
    (synopsis "KoNLP static dictionaries and Sejong project resources")
    (description
     "Sejong(http://www.sejong.or.kr/) corpus and
Hannanum(http://semanticweb.kaist.ac.kr/home/index.php/@code{HanNanum})
dictionaries for @code{KoNLP}.")
    (license license:gpl3)))

(define-public r-sejmrp
  (package
    (name "r-sejmrp")
    (version "1.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sejmRP" version))
       (sha256
        (base32 "1j3sadbp12fip3n96s0hx1sg4kzwx8z01p1c905kv8gffhkz9bh0"))))
    (properties `((upstream-name . "sejmRP")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-xml
                             r-tidyr
                             r-stringi
                             r-rvest
                             r-rpostgresql
                             r-factoextra
                             r-dplyr
                             r-dbi
                             r-cluster))
    (home-page "https://cran.r-project.org/package=sejmRP")
    (synopsis
     "An Information About Deputies and Votings in Polish Diet from Seventh to Eighth Term of Office")
    (description
     "Set of functions that access information about deputies and votings in Polish
diet from webpage <http://www.sejm.gov.pl>.  The package was developed as a
result of an internship in MI2 Group - <http://mi2.mini.pw.edu.pl>, Faculty of
Mathematics and Information Science, Warsaw University of Technology.")
    (license license:gpl2)))

(define-public r-seismicroll
  (package
    (name "r-seismicroll")
    (version "1.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seismicRoll" version))
       (sha256
        (base32 "1c0aiqm83w25p5x3rk7niibraxx6m470y0d0d17r61b746sp85ms"))))
    (properties `((upstream-name . "seismicRoll")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=seismicRoll")
    (synopsis "Fast Rolling Functions for Seismology using 'Rcpp'")
    (description
     "Fast versions of seismic analysis functions that roll over a vector of values.
See the @code{RcppRoll} package for alternative versions of basic statistical
functions such as rolling mean, median, etc.")
    (license license:gpl2+)))

(define-public r-seismic
  (package
    (name "r-seismic")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seismic" version))
       (sha256
        (base32 "0sxvbknx5ws04nzvj1p9wimrs5swzzds613sflf13v9757vm7pxf"))))
    (properties `((upstream-name . "seismic")))
    (build-system r-build-system)
    (home-page "http://snap.stanford.edu/seismic/")
    (synopsis "Predict Information Cascade by Self-Exciting Point Process")
    (description
     "An implementation of self-exciting point process model for information cascades,
which occurs when many people engage in the same acts after observing the
actions of others (e.g. post resharings on Facebook or Twitter).  It provides
functions to estimate the infectiousness of an information cascade and predict
its popularity given the observed history.  See
<http://snap.stanford.edu/seismic/> for more information and datasets.")
    (license license:gpl3)))

(define-public r-seirfansy
  (package
    (name "r-seirfansy")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SEIRfansy" version))
       (sha256
        (base32 "0xnyrsv04n22s71v1wajwpccwk78q27fgry9dhxxzw8a6yd082x8"))))
    (properties `((upstream-name . "SEIRfansy")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-rlang
                             r-pbapply
                             r-patchwork
                             r-magrittr
                             r-knitr
                             r-ggpubr
                             r-ggplot2
                             r-dplyr
                             r-desctools
                             r-arm))
    (home-page "https://github.com/umich-biostatistics/SEIRfansy")
    (synopsis "Extended Susceptible-Exposed-Infected-Recovery Model")
    (description
     "Extended Susceptible-Exposed-Infected-Recovery Model for handling high false
negative rate and symptom based administration of diagnostic tests.
<doi:10.1101/2020.09.24.20200238>.")
    (license license:gpl2)))

(define-public r-sei
  (package
    (name "r-sei")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SEI" version))
       (sha256
        (base32 "14qcj2bw11xqh9ff3mpxfmcd0mg61lsdqc1cg4ffd507knf0kb2m"))))
    (properties `((upstream-name . "SEI")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xts
                             r-lmom
                             r-ggplot2
                             r-gamlss-dist
                             r-gamlss
                             r-flexsurv
                             r-fitdistrplus))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/noeliaof/SEI")
    (synopsis "Calculating Standardised Indices")
    (description
     "Convert a time series of observations to a time series of standardised indices
that can be used to monitor variables on a common and probabilistically
interpretable scale.  The indices can be aggregated and rescaled to different
time scales, visualised using plot capabilities, and calculated using a range of
distributions.  This includes flexible non-parametric and non-stationary
methods.")
    (license license:gpl2+)))

(define-public r-sehrnett
  (package
    (name "r-sehrnett")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sehrnett" version))
       (sha256
        (base32 "0nsgaf79cs2fnjb42vzlmbi92290vrlp5bpgg6mnik3yh5fq2ndr"))))
    (properties `((upstream-name . "sehrnett")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-rsqlite
                             r-purrr
                             r-magrittr
                             r-dplyr
                             r-dbi))
    (home-page "https://github.com/chainsawriot/sehrnett")
    (synopsis "Very Nice Interface to 'WordNet'")
    (description
     "This package provides a very nice interface to Princeton's @code{WordNet}
without @code{rJava} dependency. @code{WordNet} data is not included.  Princeton
University makes @code{WordNet} available to research and commercial users free
of charge provided the terms of their license
(<https://wordnet.princeton.edu/license-and-commercial-use>) are followed, and
proper reference is made to the project using an appropriate citation
(<https://wordnet.princeton.edu/citing-wordnet>).")
    (license license:gpl3+)))

(define-public r-seguid
  (package
    (name "r-seguid")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seguid" version))
       (sha256
        (base32 "0r623548w736jf4pannc4mdm9bkpd999wr43xkhqxz00z32w80rb"))))
    (properties `((upstream-name . "seguid")))
    (build-system r-build-system)
    (propagated-inputs (list r-digest r-base64enc))
    (home-page "https://www.seguid.org/")
    (synopsis "Sequence Globally Unique Identifier (SEGUID) Checksums")
    (description
     "Implementation of the original Sequence Globally Unique Identifier (SEGUID)
algorithm [Babnigg and Giometti (2006) <doi:10.1002/pmic.200600032>] and SEGUID
v2 (<https://www.seguid.org>), which extends SEGUID v1 with support for linear,
circular, single- and double-stranded biological sequences, e.g. DNA, RNA, and
proteins.")
    (license license:expat)))

(define-public r-segregatr
  (package
    (name "r-segregatr")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "segregatr" version))
       (sha256
        (base32 "12par5appi94jliz8lghrhj3q6ws99hw49r2mcgs8g5c0mzgmp4x"))))
    (properties `((upstream-name . "segregatr")))
    (build-system r-build-system)
    (propagated-inputs (list r-pedtools r-pedprobr))
    (home-page "https://github.com/magnusdv/segregatr")
    (synopsis "Segregation Analysis for Variant Interpretation")
    (description
     "An implementation of the full-likelihood Bayes factor (FLB) for evaluating
segregation evidence in clinical medical genetics.  The method was introduced by
Thompson et al. (2003) <doi:10.1086/378100>.  This implementation supports
custom penetrance values and liability classes, and allows visualisations and
robustness analysis as presented in Ratajska et al. (2023)
<doi:10.1002/mgg3.2107>.  See also the online app shinyseg',
<https://chrcarrizosa.shinyapps.io/shinyseg>, which offers interactive
segregation analysis with many additional features (Carrizosa et al. (2024)
<doi:10.1093/bioinformatics/btae201>).")
    (license license:gpl3)))

(define-public r-segregation
  (package
    (name "r-segregation")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "segregation" version))
       (sha256
        (base32 "1l2dlqva58l9glpsn1bflbggk0645zhsr3a1n1ydikp7q54wf72m"))))
    (properties `((upstream-name . "segregation")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppprogress r-rcpp r-data-table r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://elbersb.github.io/segregation/")
    (synopsis "Entropy-Based Segregation Indices")
    (description
     "Computes segregation indices, including the Index of Dissimilarity, as well as
the information-theoretic indices developed by Theil (1971)
<isbn:978-0471858454>, namely the Mutual Information Index (M) and Theil's
Information Index (H).  The M, further described by Mora and Ruiz-Castillo
(2011) <doi:10.1111/j.1467-9531.2011.01237.x> and Frankel and Volij (2011)
<doi:10.1016/j.jet.2010.10.008>, is a measure of segregation that is highly
decomposable.  The package provides tools to decompose the index by units and
groups (local segregation), and by within and between terms.  The package also
provides a method to decompose differences in segregation as described by Elbers
(2021) <doi:10.1177/0049124121986204>.  The package includes standard error
estimation by bootstrapping, which also corrects for small sample bias.  The
package also contains functions for visualizing segregation patterns.")
    (license license:expat)))

(define-public r-segrda
  (package
    (name "r-segrda")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "segRDA" version))
       (sha256
        (base32 "0az27z4pppyz5slckwmgk6qqix710m1s96drlq4fh8bqn2qmz812"))))
    (properties `((upstream-name . "segRDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/DaniloCVieira/segRDA")
    (synopsis "Modeling Non-Continuous Linear Responses of Ecological Data")
    (description
     "This package provides tools for modeling non-continuous linear responses of
ecological communities to environmental data.  The package is straightforward
through three steps: (1) data ordering (function @code{OrdData}()), (2)
split-moving-window analysis (function SMW()) and (3) piecewise redundancy
analysis (function @code{pwRDA}()).  Relevant references include Cornelius and
Reynolds (1991) <doi:10.2307/1941559> and Legendre and Legendre (2012, ISBN:
9780444538697).")
    (license license:expat)))

(define-public r-segmgarch
  (package
    (name "r-segmgarch")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "segMGarch" version))
       (sha256
        (base32 "0chw41h25jka9wa3rf3d8dq2ym47379jflv33q6qxaak8xy1kmd9"))))
    (properties `((upstream-name . "segMGarch")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-mvtnorm
                             r-iterators
                             r-foreach
                             r-fgarch
                             r-doparallel
                             r-corpcor))
    (home-page "https://cran.r-project.org/package=segMGarch")
    (synopsis
     "Multiple Change-Point Detection for High-Dimensional GARCH Processes")
    (description
     "This package implements a segmentation algorithm for multiple change-point
detection in high-dimensional GARCH processes.  It simultaneously segments GARCH
processes by identifying common change-points, each of which can be shared by a
subset or all of the component time series as a change-point in their
within-series and/or cross-sectional correlation structure.")
    (license license:gpl2+)))

(define-public r-segmetric
  (package
    (name "r-segmetric")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "segmetric" version))
       (sha256
        (base32 "1cpkak3d91xjz6z4ls29ys1m5gmkqm7pq86vs88xy51z0av4kji7"))))
    (properties `((upstream-name . "segmetric")))
    (build-system r-build-system)
    (propagated-inputs (list r-units r-sf r-magrittr))
    (home-page "https://michellepicoli.github.io/segmetric/")
    (synopsis
     "Metrics for Assessing Segmentation Accuracy for Geospatial Data")
    (description
     "This package provides a system that computes metrics to assess the segmentation
accuracy of geospatial data.  These metrics calculate the discrepancy between
segmented and reference objects, and indicate the segmentation accuracy.  For
more details on choosing evaluation metrics, we suggest seeing Costa et al.
(2018) <doi:10.1016/j.rse.2017.11.024> and Jozdani et al. (2020)
<doi:10.1016/j.isprsjprs.2020.01.002>.")
    (license license:expat)))

(define-public r-segmentr
  (package
    (name "r-segmentr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "segmentr" version))
       (sha256
        (base32 "0q5kxxiw0akpk7w2a2l9zp7v4pvqblzdvrld5fjyl5f6h8hsb40z"))))
    (properties `((upstream-name . "segmentr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-glue r-foreach))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/thalesmello/segmentr")
    (synopsis "Segment Data With Maximum Likelihood")
    (description
     "Given a likelihood provided by the user, this package applies it to a given
matrix dataset in order to find change points in the data that maximize the sum
of the likelihoods of all the segments.  This package provides a handful of
algorithms with different time complexities and assumption compromises so the
user is able to choose the best one for the problem at hand.  The implementation
of the segmentation algorithms in this package are based on the paper by Bruno
M. de Castro, Florencia Leonardi (2018) <@code{arXiv:1501.01756>}.  The Berlin
weather sample dataset was provided by Deutscher Wetterdienst <https://dwd.de/>.
 You can find all the references in the Acknowledgments section of this
package's repository via the URL below.")
    (license license:expat)))

(define-public r-segmentier
  (package
    (name "r-segmentier")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "segmenTier" version))
       (sha256
        (base32 "01xbcddinjbkbqkwq2lnsxd3gw8fxys58p498dc2lw8psz3g2wgc"))))
    (properties `((upstream-name . "segmenTier")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/raim/segmenTier")
    (synopsis "Similarity-Based Segmentation of Multidimensional Signals")
    (description
     "This package provides a dynamic programming solution to segmentation based on
maximization of arbitrary similarity measures within segments.  The general
idea, theory and this implementation are described in Machne, Murray & Stadler
(2017) <doi:10.1038/s41598-017-12401-8>.  In addition to the core algorithm, the
package provides time-series processing and clustering functions as described in
the publication.  These are generally applicable where a `k-means` clustering
yields meaningful results, and have been specifically developed for clustering
of the Discrete Fourier Transform of periodic gene expression data (`circadian
or `yeast metabolic oscillations').  This clustering approach is outlined in the
supplemental material of Machne & Murray (2012)
<doi:10.1371/journal.pone.0037906>), and here is used as a basis of segment
similarity measures.  Notably, the time-series processing and clustering
functions can also be used as stand-alone tools, independent of segmentation,
e.g., for transcriptome data already mapped to genes.")
    (license license:gpl2+)))

(define-public r-segmag
  (package
    (name "r-segmag")
    (version "1.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "segmag" version))
       (sha256
        (base32 "1i717xg1z7s35pkwzywgjf9wx7zj9xksv0k87h7p1q62y073qbqm"))))
    (properties `((upstream-name . "segmag")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-plyr))
    (home-page "https://cran.r-project.org/package=segmag")
    (synopsis "Determine Event Boundaries in Event Segmentation Experiments")
    (description
     "This package contains functions that help to determine event boundaries in event
segmentation experiments by bootstrapping a critical segmentation magnitude
under the null hypothesis that all key presses were randomly distributed across
the experiment.  Segmentation magnitude is defined as the sum of Gaussians
centered at the times of the segmentation key presses performed by the
participants.  Within a participant, the maximum of the overlaid Gaussians is
used to prevent an excessive influence of a single participant on the overall
outcome (e.g. if a participant is pressing the key multiple times in
succession).  Further functions are included, such as plotting the results.")
    (license license:gpl3+)))

(define-public r-segen
  (package
    (name "r-segen")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "segen" version))
       (sha256
        (base32 "1l60rb1w0rfp1rv5favi4yxzyqs1nb1881mcz66xv789r3awhp11"))))
    (properties `((upstream-name . "segen")))
    (build-system r-build-system)
    (propagated-inputs (list r-tictoc
                             r-scales
                             r-rfast
                             r-readr
                             r-purrr
                             r-philentropy
                             r-narray
                             r-moments
                             r-modeest
                             r-lubridate
                             r-imputets
                             r-greybox
                             r-ggplot2
                             r-fastdummies
                             r-fancova
                             r-entropy))
    (home-page "https://rpubs.com/giancarlo_vercellino/segen")
    (synopsis "Sequence Generalization Through Similarity Network")
    (description
     "Proposes an application for sequence prediction generalizing the similarity
within the network of previous sequences.")
    (license license:gpl3)))

(define-public r-segcorr
  (package
    (name "r-segcorr")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SegCorr" version))
       (sha256
        (base32 "1g4bwfs539jf7ckzjyindi3v1av7vd38yz16m7cb20qqa0a33kbj"))))
    (properties `((upstream-name . "SegCorr")))
    (build-system r-build-system)
    (propagated-inputs (list r-jointseg))
    (home-page "https://cran.r-project.org/package=SegCorr")
    (synopsis "Detecting Correlated Genomic Regions")
    (description
     "This package performs correlation matrix segmentation and applies a test
procedure to detect highly correlated regions in gene expression.")
    (license license:gpl2)))

(define-public r-segclust2d
  (package
    (name "r-segclust2d")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "segclust2d" version))
       (sha256
        (base32 "0vsdmlpf9nd8lbv8iwvywrzajbpc76ifvx2fx7mlai1pskgyqfwm"))))
    (properties `((upstream-name . "segclust2d")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-scales
                             r-rlang
                             r-reshape2
                             r-rcpparmadillo
                             r-rcpp
                             r-rcolorbrewer
                             r-plyr
                             r-magrittr
                             r-ggplot2
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rpatin/segclust2d")
    (synopsis "Bivariate Segmentation/Clustering Methods and Tools")
    (description
     "This package provides two methods for segmentation and joint
segmentation/clustering of bivariate time-series.  Originally intended for
ecological segmentation (home-range and behavioural modes) but easily applied on
other series, the package also provides tools for analysing outputs from R
packages @code{moveHMM} and marcher'.  The segmentation method is a bivariate
extension of Lavielle's method available in @code{adehabitatLT} (Lavielle, 1999
<doi:10.1016/S0304-4149(99)00023-X> and 2005
<doi:10.1016/j.sigpro.2005.01.012>).  This method rely on dynamic programming
for efficient segmentation.  The segmentation/clustering method alternates steps
of dynamic programming with an Expectation-Maximization algorithm.  This is an
extension of Picard et al (2007) <doi:10.1111/j.1541-0420.2006.00729.x> method
(formerly available in cghseg package) to the bivariate case.  The method is
fully described in Patin et al (2018) <doi:10.1101/444794>.")
    (license license:gpl3)))

(define-public r-seerabomb
  (package
    (name "r-seerabomb")
    (version "2019.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SEERaBomb" version))
       (sha256
        (base32 "0rlah68jbaw4qbqq5rnlrm6m9ckz7igbsay6iq1yvk4gy24p9bfh"))))
    (properties `((upstream-name . "SEERaBomb")))
    (build-system r-build-system)
    (propagated-inputs (list r-writexls
                             r-tidyr
                             r-tibble
                             r-survival
                             r-stringr
                             r-scales
                             r-rsqlite
                             r-rgl
                             r-reshape2
                             r-readr
                             r-rcpp
                             r-purrr
                             r-plyr
                             r-openxlsx
                             r-mgcv
                             r-laf
                             r-labelled
                             r-ggplot2
                             r-forcats
                             r-dplyr
                             r-demography
                             r-dbi))
    (home-page "http://epbi-radivot.cwru.edu/SEERaBomb/SEERaBomb.html")
    (synopsis "SEER and Atomic Bomb Survivor Data Analysis Tools")
    (description
     "This package creates SEER (Surveillance, Epidemiology and End Results) and
A-bomb data binaries from ASCII sources and provides tools for estimating SEER
second cancer risks.  Methods are described in <doi:10.1038/leu.2015.258>.")
    (license license:gpl2+)))

(define-public r-seer
  (package
    (name "r-seer")
    (version "1.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seer" version))
       (sha256
        (base32 "13r26iyciq9wbzn4r3447s4j4m6k5sdymp704i6hkf0fphv4340c"))))
    (properties `((upstream-name . "seer")))
    (build-system r-build-system)
    (propagated-inputs (list r-urca
                             r-tsfeatures
                             r-tibble
                             r-stringr
                             r-randomforest
                             r-purrr
                             r-magrittr
                             r-future
                             r-furrr
                             r-forectheta
                             r-forecast
                             r-dplyr))
    (home-page "https://thiyangt.github.io/seer/")
    (synopsis "Feature-Based Forecast Model Selection")
    (description
     "This package provides a novel meta-learning framework for forecast model
selection using time series features.  Many applications require a large number
of time series to be forecast.  Providing better forecasts for these time series
is important in decision and policy making.  We propose a classification
framework which selects forecast models based on features calculated from the
time series.  We call this framework FFORMS (Feature-based FORecast Model
Selection).  FFORMS builds a mapping that relates the features of time series to
the best forecast model using a random forest.  seer package is the
implementation of the FFORMS algorithm.  For more details see our paper at
<https://www.monash.edu/business/econometrics-and-business-statistics/research/publications/ebs/wp06-2018.pdf>.")
    (license license:gpl3)))

(define-public r-seeker
  (package
    (name "r-seeker")
    (version "1.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seeker" version))
       (sha256
        (base32 "0xzlm0ph1g88ccv1f4k7nicsmmzcsdxmwbzpdx8vvpfihjy808qa"))))
    (properties `((upstream-name . "seeker")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-withr
                             r-tximport
                             r-sessioninfo
                             r-readr
                             r-rcurl
                             r-r-utils
                             r-qs
                             r-jsonlite
                             r-glue
                             r-geoquery
                             r-foreach
                             r-data-table
                             r-curl
                             r-checkmate
                             r-biomart
                             r-biocmanager
                             r-annotationdbi
                             r-affy))
    (native-inputs (list r-knitr))
    (home-page "https://seeker.hugheylab.org")
    (synopsis
     "Simplified Fetching and Processing of Microarray and RNA-Seq Data")
    (description
     "Wrapper around various existing tools and command-line interfaces, providing a
standard interface, simple parallelization, and detailed logging.  For
microarray data, maps probe sets to standard gene IDs, building on GEOquery
Davis and Meltzer (2007) <doi:10.1093/bioinformatics/btm254>,
@code{ArrayExpress} Kauffmann et al. (2009) <doi:10.1093/bioinformatics/btp354>,
Robust multi-array average RMA Irizarry et al. (2003)
<doi:10.1093/biostatistics/4.2.249>, and @code{BrainArray} Dai et al. (2005)
<doi:10.1093/nar/gni179>.  For RNA-seq data, fetches metadata and raw reads from
National Center for Biotechnology Information (NCBI) Sequence Read Archive
(SRA), performs standard adapter and quality trimming using @code{TrimGalore}
Krueger <https://github.com/@code{FelixKrueger/TrimGalore>}, performs quality
control checks using @code{FastQC} Andrews
<https://github.com/s-andrews/@code{FastQC>}, quantifies transcript abundances
using salmon Patro et al. (2017) <doi:10.1038/nmeth.4197> and potentially
refgenie Stolarczyk et al. (2020) <doi:10.1093/gigascience/giz149>, aggregates
the results using @code{MultiQC} Ewels et al. (2016)
<doi:10.1093/bioinformatics/btw354>, maps transcripts to genes using
@code{biomaRt} Durinkck et al. (2009) <doi:10.1038/nprot.2009.97>, and
summarizes transcript-level quantifications for gene-level analyses using
tximport Soneson et al. (2015) <doi:10.12688/f1000research.7563.2>.")
    (license license:expat)))

(define-public r-seedvigorindex
  (package
    (name "r-seedvigorindex")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SeedVigorIndex" version))
       (sha256
        (base32 "00r7n7h3b81pnq0bbgm6pgbz7w3algk1apawmc15apg9zi85x8dp"))))
    (properties `((upstream-name . "SeedVigorIndex")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SeedVigorIndex")
    (synopsis "Seed Vigor Index")
    (description
     "Seed vigor is defined as the sum total of those properties of the seed which
determine the level of activity and performance of the seed or seed lot during
germination and seedling emergence.  Testing for vigor becomes more important
for carryover seeds, especially if seeds were stored under unknown conditions or
under unfavorable storage conditions.  Seed vigor testing is also used as
indicator of the storage potential of a seed lot and in ranking various seed
lots with different qualities.  The vigour index is calculated using the
equation given by (Ling et al.  2014) <doi:10.1038/srep05859>.")
    (license license:gpl3)))

(define-public r-seeds
  (package
    (name "r-seeds")
    (version "0.9.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seeds" version))
       (sha256
        (base32 "0adk7y5ripg78bwkdgnlfwq9w9h6mlpdrikgbnn5z8az2qahc0in"))))
    (properties `((upstream-name . "seeds")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-statmod
                             r-ryacas
                             r-r-utils
                             r-pracma
                             r-mvtnorm
                             r-matrixstats
                             r-mass
                             r-hmisc
                             r-ggplot2
                             r-dplyr
                             r-desolve
                             r-deriv
                             r-coda
                             r-callr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Newmi1988/seeds")
    (synopsis "Estimate Hidden Inputs using the Dynamic Elastic Net")
    (description
     "Algorithms to calculate the hidden inputs of systems of differential equations.
These hidden inputs can be interpreted as a control that tries to minimize the
discrepancies between a given model and taken measurements.  The idea is also
called the Dynamic Elastic Net, as proposed in the paper \"Learning (from) the
errors of a systems biology model\" (Engelhardt, Froelich, Kschischo 2016)
<doi:10.1038/srep20772>.  To use the experimental SBML import function, the
rsbml package is required.  For installation I refer to the official rsbml page:
<https://bioconductor.org/packages/release/bioc/html/rsbml.html>.")
    (license license:expat)))

(define-public r-seedreg
  (package
    (name "r-seedreg")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seedreg" version))
       (sha256
        (base32 "07wg48pn2v3wcwf5a34b4hc0s1flfpphrylr4d9bayaxsvi3k0xk"))))
    (properties `((upstream-name . "seedreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-sf
                             r-multcompview
                             r-multcomp
                             r-hnp
                             r-gridextra
                             r-ggplot2
                             r-emmeans
                             r-drc
                             r-dplyr
                             r-crayon
                             r-car
                             r-boot))
    (home-page "https://cran.r-project.org/package=seedreg")
    (synopsis
     "Regression Analysis for Seed Germination as a Function of Temperature")
    (description
     "Regression analysis using common models in seed temperature studies, such as the
Gaussian model (Martins, JF, Barroso, AAM, & Alves, PLCA (2017)
<doi:10.1590/s0100-83582017350100039>), quadratic (Nunes, AL, Sossmeier, S,
Gotz, AP, & Bispo, NB (2018) <doi: 10.17265/2161-6264/2018.06.002>) and others
with potential for use, such as those implemented in the drc package (Ritz, C,
Baty, F, Streibig, JC, & Gerhard, D (2015). <doi:10.1371/journal.pone.0146021>),
in the estimation of the ideal and cardinal temperature for the occurrence of
plant seed germination.  The functions return graphs with the equations
automatically.")
    (license license:gpl2+)))

(define-public r-seedr
  (package
    (name "r-seedr")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seedr" version))
       (sha256
        (base32 "15drbc0iplb97a2sym6jnrwij7ak5qckcw98n3m65x45apjwdmhq"))))
    (properties `((upstream-name . "seedr")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table r-binom))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/efernandezpascual/seedr")
    (synopsis "Hydro and Thermal Time Seed Germination Models in R")
    (description
     "Analysis of seed germination data using the physiological time modelling
approach.  Includes functions to fit hydrotime and thermal-time models with the
traditional approaches of Bradford (1990) <doi:10.1104/pp.94.2.840> and
Garcia-Huidobro (1982) <doi:10.1093/jxb/33.2.288>.  Allows to fit models to
grouped datasets, i.e.  datasets containing multiple species, seedlots or
experiments.")
    (license license:gpl3)))

(define-public r-seedmatchr
  (package
    (name "r-seedmatchr")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SeedMatchR" version))
       (sha256
        (base32 "138chb0z1gyhvl56g3a7zf4nash1v3v5l3qfxiqai2kxvladmz9y"))))
    (properties `((upstream-name . "SeedMatchR")))
    (build-system r-build-system)
    (propagated-inputs (list r-testit
                             r-stringr
                             r-msa
                             r-lifecycle
                             r-ggplot2
                             r-genomicfeatures
                             r-genomeinfodb
                             r-dplyr
                             r-cowplot
                             r-biostrings
                             r-annotationhub))
    (native-inputs (list r-knitr))
    (home-page "https://tacazares.github.io/SeedMatchR/")
    (synopsis "Find Matches to Canonical SiRNA Seeds in Genomic Features")
    (description
     "On-target gene knockdown using @code{siRNA} ideally results from binding fully
complementary regions in @code{mRNA} transcripts to induce cleavage.  Off-target
@code{siRNA} gene knockdown can occur through several modes, one being a
seed-mediated mechanism mimicking @code{miRNA} gene regulation.  Seed-mediated
off-target effects occur when the ~8 nucleotides at the 5â end of the guide
strand, called a seed region, bind the 3â untranslated regions of @code{mRNA},
causing reduced translation.  Experiments using @code{siRNA} knockdown paired
with RNA-seq can be used to detect @code{siRNA} sequences with potential
off-target effects driven by the seed region. @code{SeedMatchR} provides tools
for exploring and detecting potential seed-mediated off-target effects of
@code{siRNA} in RNA-seq experiments. @code{SeedMatchR} is designed to extend
current differential expression analysis tools, such as DESeq2', by annotating
results with predicted seed matches.  Using publicly available data, we
demonstrate the ability of @code{SeedMatchR} to detect cumulative changes in
differential gene expression attributed to @code{siRNA} seed regions.")
    (license license:expat)))

(define-public r-seedimbibition
  (package
    (name "r-seedimbibition")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SeedImbibition" version))
       (sha256
        (base32 "05gxixj69nqvb9d4v81d7359nl3dihglxqysr1ys6qz3vzjr8zq4"))))
    (properties `((upstream-name . "SeedImbibition")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SeedImbibition")
    (synopsis "Seed Imbibition Percentage")
    (description
     "Imbibition causes seeds to expand, which results in the seed coat or testa being
broken.  Seed germination begins with imbibition.  Imbibition aids in the
transport of water into the developing ovules.  Imbibition is required during
the first stages of root water absorption.")
    (license license:gpl3)))

(define-public r-seededlda
  (package
    (name "r-seededlda")
    (version "1.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seededlda" version))
       (sha256
        (base32 "1v5cy47asihjsxkywfa3rpc93a7s6qkmfmk2sbqygx9gr08r8ac0"))))
    (properties `((upstream-name . "seededlda")))
    (build-system r-build-system)
    (inputs (list tbb))
    (propagated-inputs (list r-testthat
                             r-rcpparmadillo
                             r-rcpp
                             r-quanteda
                             r-proxyc
                             r-matrix))
    (native-inputs (list pkg-config))
    (home-page "https://github.com/koheiw/seededlda")
    (synopsis "Seeded Sequential LDA for Topic Modeling")
    (description
     "Seeded Sequential LDA can classify sentences of texts into pre-define topics
with a small number of seed words (Watanabe & Baturo, 2023)
<doi:10.1177/08944393231178605>.  Implements Seeded LDA (Lu et al., 2010)
<doi:10.1109/ICDMW.2011.125> and Sequential LDA (Du et al., 2012)
<doi:10.1007/s10115-011-0425-1> with the distributed LDA algorithm (Newman, et
al., 2009) for parallel computing.")
    (license license:gpl3)))

(define-public r-seedcca
  (package
    (name "r-seedcca")
    (version "3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seedCCA" version))
       (sha256
        (base32 "01v8hwhw5pap69n11czn0ma22vakhi3kfbly0rzasw9fsvf66xhg"))))
    (properties `((upstream-name . "seedCCA")))
    (build-system r-build-system)
    (propagated-inputs (list r-corpcor r-cca))
    (home-page "https://cran.r-project.org/package=seedCCA")
    (synopsis "Seeded Canonical Correlation Analysis")
    (description
     "This package provides functions for dimension reduction through the seeded
canonical correlation analysis are provided.  A classical canonical correlation
analysis (CCA) is one of useful statistical methods in multivariate data
analysis, but it is limited in use due to the matrix inversion for large p small
n data.  To overcome this, a seeded CCA has been proposed in Im, Gang and Yoo
(2015) \\doi{10.1002/cem.2691}.  The seeded CCA is a two-step procedure.  The
sets of variables are initially reduced by successively projecting cov(X,Y) or
cov(Y,X) onto cov(X) and cov(Y), respectively, without loss of information on
canonical correlation analysis, following Cook, Li and Chiaromonte (2007)
\\doi{10.1093/biomet/asm038} and Lee and Yoo (2014) \\doi{10.1111/anzs.12057}.
Then, the canonical correlation is finalized with the initially-reduced two sets
of variables.")
    (license license:gpl2+)))

(define-public r-seedcalc
  (package
    (name "r-seedcalc")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SeedCalc" version))
       (sha256
        (base32 "1p8ncf3l2zhpbbblpjagg8cg9gf7f2izdcgc48n1aq4f7bmjbqgk"))))
    (properties `((upstream-name . "SeedCalc")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SeedCalc")
    (synopsis "Seed Germination and Seedling Growth Indexes")
    (description
     "This package provides functions to calculate seed germination and seedling
emergence and growth indexes.  The main indexes for germination and seedling
emergence, considering the time for seed germinate are: T10, T50 and T90, in
Farooq et al. (2005) <10.1111/j.1744-7909.2005.00031.x>; and MGT, in Labouriau
(1983).  Considering the germination speed are: Germination Speed Index, in
Maguire (1962), Mean Germination Rate, in Labouriau (1983); considering the
homogeneity of germination are: Coefficient of Variation of the Germination
Time, in Carvalho et al. (2005) <10.1590/S0100-84042005000300018>, and Variance
of Germination, in Labouriau (1983); Uncertainty, in Labouriau and Valadares
(1976) <ISSN:0001-3765>; and Synchrony, in Primack (1980).  The main seedling
indexes are Growth, in Sako (2001), Uniformity, in Sako (2001) and Castan et al.
(2018) <doi:10.1590/1678-992x-2016-0401>; and Vigour, in Medeiros and Pereira
(2018) <doi:10.1590/1983-40632018v4852340>.")
    (license license:gpl2+)))

(define-public r-seecolor
  (package
    (name "r-seecolor")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seecolor" version))
       (sha256
        (base32 "1qfiq8dmakban9mh7rizm54risq48vw1ck7i5yl48ggqmr47mvzf"))))
    (properties `((upstream-name . "seecolor")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rstudioapi
                             r-purrr
                             r-magrittr
                             r-ggplot2
                             r-fansi
                             r-dplyr
                             r-crayon))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/lovestat/seecolor")
    (synopsis "View Colors Used in R Objects in the Console")
    (description
     "Output colors used in literal vectors, palettes and plot objects (ggplot).")
    (license license:expat)))

(define-public r-seeclickfixr
  (package
    (name "r-seeclickfixr")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seeclickfixr" version))
       (sha256
        (base32 "1agsqq2msrqrssffc6liyjjs6nqm90xy2inlcjbkdac5dhinjc4n"))))
    (properties `((upstream-name . "seeclickfixr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcurl r-jsonlite))
    (home-page "https://cran.r-project.org/package=seeclickfixr")
    (synopsis "Access Data from the SeeClickFix Web API")
    (description
     "This package provides a wrapper to access data from the @code{SeeClickFix} web
API for R. @code{SeeClickFix} is a central platform employed by many cities that
allows citizens to request their city's services.  This package creates several
functions to work with all the built-in calls to the @code{SeeClickFix} API.
Allows users to download service request data from numerous locations in
easy-to-use dataframe format manipulable in standard R functions.")
    (license license:gpl3)))

(define-public r-see
  (package
    (name "r-see")
    (version "0.9.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "see" version))
       (sha256
        (base32 "002cszzsykwgvxwbqhpfxbf6vsbwq6rqkb1qhbdvfmfvgfplr8wi"))))
    (properties `((upstream-name . "see")))
    (build-system r-build-system)
    (propagated-inputs (list r-performance
                             r-parameters
                             r-modelbased
                             r-insight
                             r-ggplot2
                             r-effectsize
                             r-datawizard
                             r-correlation
                             r-bayestestr))
    (home-page "https://easystats.github.io/see/")
    (synopsis "Model Visualisation Toolbox for 'easystats' and 'ggplot2'")
    (description
     "This package provides plotting utilities supporting packages in the easystats
ecosystem (<https://github.com/easystats/easystats>) and some extra themes,
geoms, and scales for ggplot2'.  Color scales are based on
<https://materialui.co/>.  References: LÃ¼decke et al. (2021)
<doi:10.21105/joss.03393>.")
    (license license:expat)))

(define-public r-sedproxy
  (package
    (name "r-sedproxy")
    (version "0.7.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sedproxy" version))
       (sha256
        (base32 "1n5970pbdc0zl9vh0fslirlww56as5c090mnhzbic1fdzkhli245"))))
    (properties `((upstream-name . "sedproxy")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-rlang r-mvtnorm r-ggplot2 r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://earthsystemdiagnostics.github.io/sedproxy/")
    (synopsis "Simulation of Sediment Archived Climate Proxy Records")
    (description
     "Proxy forward modelling for sediment archived climate proxies such as Mg/Ca,
d18O or Alkenones.  The user provides a hypothesised \"true\" past climate, such
as output from a climate model, and details of the sedimentation rate and
sampling scheme of a sediment core.  Sedproxy returns simulated proxy records.
Implements the methods described in Dolman and Laepple (2018)
<doi:10.5194/cp-14-1851-2018>.")
    (license license:expat)))

(define-public r-secutrialr
  (package
    (name "r-secutrialr")
    (version "1.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "secuTrialR" version))
       (sha256
        (base32 "1g6xfvgq4qgpgbhihpjxgxk87dwa9k2lhwrka4w1icyabchk6yg2"))))
    (properties `((upstream-name . "secuTrialR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-readxl
                             r-readr
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-haven
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/SwissClinicalTrialOrganisation/secuTrialR")
    (synopsis
     "Handling of Data from the Clinical Data Management System 'secuTrial'")
    (description
     "Seamless and standardized interaction with data exported from the clinical data
management system (CDMS) @code{secuTrial'<https://www.secutrial.com>}.  The
primary data export the package works with is a standard non-rectangular export.")
    (license license:expat)))

(define-public r-secure
  (package
    (name "r-secure")
    (version "0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "secure" version))
       (sha256
        (base32 "1i0csl90w4qvhpnrxwrdhxzlmjw7hn30py36r41cd28igycz2bw3"))))
    (properties `((upstream-name . "secure")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mass))
    (home-page "https://cran.r-project.org/package=secure")
    (synopsis "Sequential Co-Sparse Factor Regression")
    (description
     "Sequential factor extraction via co-sparse unit-rank estimation (@code{SeCURE}).")
    (license license:gpl3+)))

(define-public r-sectorgap
  (package
    (name "r-sectorgap")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sectorgap" version))
       (sha256
        (base32 "0nn9cpy7svil2y9snvk5zznnlqd825gb4kxp31kzwcvwf5frx8q2"))))
    (properties `((upstream-name . "sectorgap")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-tidyr
                             r-tempdisagg
                             r-mcmcpack
                             r-kfas
                             r-ggplot2
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=sectorgap")
    (synopsis "Consistent Economic Trend Cycle Decomposition")
    (description
     "Determining potential output and the output gap - two inherently unobservable
variables - is a major challenge for macroeconomists.  sectorgap features a
flexible modeling and estimation framework for a multivariate Bayesian state
space model identifying economic output fluctuations consistent with subsectors
of the economy.  The proposed model is able to capture various correlations
between output and a set of aggregate as well as subsector indicators.
Estimation of the latent states and parameters is achieved using a simple Gibbs
sampling procedure and various plotting options facilitate the assessment of the
results.  For details on the methodology and an illustrative example, see
Streicher (2024)
<https://www.research-collection.ethz.ch/handle/20.500.11850/653682>.")
    (license license:gpl3)))

(define-public r-secsse
  (package
    (name "r-secsse")
    (version "3.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "secsse" version))
       (sha256
        (base32 "0v4qjsjmc7shqx8cy92c99fh6bfy2zdkwi2as05r2h4c2vk3p73z"))))
    (properties `((upstream-name . "secsse")))
    (build-system r-build-system)
    (propagated-inputs (list r-treestats
                             r-tibble
                             r-rlang
                             r-rcppparallel
                             r-rcpp
                             r-ggplot2
                             r-geiger
                             r-ddd
                             r-bh
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://rsetienne.github.io/secsse/")
    (synopsis
     "Several Examined and Concealed States-Dependent Speciation and Extinction")
    (description
     "Simultaneously infers state-dependent diversification across two or more states
of a single or multiple traits while accounting for the role of a possible
concealed trait.  See Herrera-Alsina et al. (2019) <doi:10.1093/sysbio/syy057>.")
    (license (list license:gpl3+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-secrlinear
  (package
    (name "r-secrlinear")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "secrlinear" version))
       (sha256
        (base32 "1ivhl5imc7ic72h4v5276d2n02s39j78lcdr0rcx3dqycf3yyqag"))))
    (properties `((upstream-name . "secrlinear")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp r-sf r-secr r-mass r-igraph))
    (native-inputs (list r-knitr))
    (home-page "https://www.otago.ac.nz/density/")
    (synopsis "Spatially Explicit Capture-Recapture for Linear Habitats")
    (description
     "This package provides tools for spatially explicit capture-recapture analysis of
animal populations in linear habitats, extending package secr'.")
    (license license:gpl2+)))

(define-public r-secrettext
  (package
    (name "r-secrettext")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "secrettext" version))
       (sha256
        (base32 "129v524yyb464mcij7hvzv05cxliaz5nm7v30g9g9ppskcdqhsx2"))))
    (properties `((upstream-name . "secrettext")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-testthat
                             r-stringr
                             r-rlang
                             r-magrittr
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=secrettext")
    (synopsis "Encrypt Text Using a Shifting Substitution Cipher")
    (description
     "Encrypt text using a simple shifting substitution cipher with setcode(),
providing two numeric keys used to define the encryption algorithm.  The
resulting text can be decoded using decode() function and the two numeric keys
specified during encryption.")
    (license license:expat)))

(define-public r-secret
  (package
    (name "r-secret")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "secret" version))
       (sha256
        (base32 "063gpyc2s2l6zw1j8b8ppzsv9kjsf0s77s5wcn7ipmwkj8qr2g4b"))))
    (properties `((upstream-name . "secret")))
    (build-system r-build-system)
    (propagated-inputs (list r-rprojroot r-openssl r-jsonlite r-curl
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/gaborcsardi/secret#readme")
    (synopsis "Share Sensitive Information in R Packages")
    (description
     "Allow sharing sensitive information, for example passwords, API keys, etc., in R
packages, using public key cryptography.")
    (license license:expat)))

(define-public r-secrdesign
  (package
    (name "r-secrdesign")
    (version "2.9.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "secrdesign" version))
       (sha256
        (base32 "12n0xy838r2a1ilgf4d8kxmpbhrzwnx64r14194ma8v6sdj44xqi"))))
    (properties `((upstream-name . "secrdesign")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf
                             r-secr
                             r-rcpparmadillo
                             r-rcpp
                             r-kofnga
                             r-bh
                             r-abind))
    (home-page "https://www.otago.ac.nz/density/")
    (synopsis "Sampling Design for Spatially Explicit Capture-Recapture")
    (description
     "This package provides tools for designing spatially explicit capture-recapture
studies of animal populations.  This is primarily a simulation manager for
package secr'.  Extensions in version 2.5.0 include costing and evaluation of
detector spacing.")
    (license license:gpl2+)))

(define-public r-secr
  (package
    (name "r-secr")
    (version "5.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "secr" version))
       (sha256
        (base32 "1s34k8z91dq3iijgwnm2w3mcapf4mmcfyc17w518wsynpir7k576"))))
    (properties `((upstream-name . "secr")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-stringr
                             r-sf
                             r-rcppparallel
                             r-rcppnumerical
                             r-rcppeigen
                             r-rcpp
                             r-raster
                             r-nlme
                             r-mvtnorm
                             r-mgcv
                             r-mass
                             r-bh
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://www.otago.ac.nz/density/")
    (synopsis "Spatially Explicit Capture-Recapture")
    (description
     "This package provides functions to estimate the density and size of a spatially
distributed animal population sampled with an array of passive detectors, such
as traps, or by searching polygons or transects.  Models incorporating
distance-dependent detection are fitted by maximizing the likelihood.  Tools are
included for data manipulation and model selection.")
    (license license:gpl2+)))

(define-public r-secp
  (package
    (name "r-secp")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SECP" version))
       (sha256
        (base32 "01nq30ap06n2inqb59qmv4wkc9wh1hdkyfblqs0inn55842d009z"))))
    (properties `((upstream-name . "SECP")))
    (build-system r-build-system)
    (propagated-inputs (list r-spsl))
    (home-page "https://cran.r-project.org/package=SECP")
    (synopsis "Statistical Estimation of Cluster Parameters")
    (description
     "Estimating parameters of site clusters on 2D & 3D square lattice with various
lattice sizes, relative fractions of open sites (occupation probability), iso- &
anisotropy, von Neumann & Moore (1,d)-neighborhoods, described by Moskalev P.V.
et al. (2011) <@code{arXiv:1105.2334v1>}.")
    (license license:gpl3)))

(define-public r-secfish
  (package
    (name "r-secfish")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SECFISH" version))
       (sha256
        (base32 "0yd9k6anz9g4psg5kh1bhp0lr1i4y7in1m0mxk4qp14nl1zqy4fy"))))
    (properties `((upstream-name . "SECFISH")))
    (build-system r-build-system)
    (propagated-inputs (list r-optimization r-hmisc r-ggplot2))
    (home-page "https://cran.r-project.org/package=SECFISH")
    (synopsis "Disaggregate Variable Costs")
    (description
     "These functions were developed within SECFISH project (Strengthening regional
cooperation in the area of fisheries data collection-Socio-economic data
collection for fisheries, aquaculture and the processing industry at EU level).
They are aimed at identifying correlations between costs and transversal
variables by metier using individual vessel data and for disaggregating variable
costs from fleet segment to metier level.")
    (license license:gpl2)))

(define-public r-secdim
  (package
    (name "r-secdim")
    (version "3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SecDim" version))
       (sha256
        (base32 "04l4qpw409dj5i494vmf7vmksis6910599gh259nbyv0pdfw5svr"))))
    (properties `((upstream-name . "SecDim")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-geosphere))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SecDim")
    (synopsis "The Second Dimension of Spatial Association")
    (description
     "Most of the current methods explore spatial association using observations at
sample locations, which are defined as the first dimension of spatial
association (FDA).  The proposed concept of the second dimension of spatial
association (SDA), as described in Yongze Song (2022)
<doi:10.1016/j.jag.2022.102834>, aims to extract in-depth information about the
geographical environment from locations outside sample locations for exploring
spatial association.")
    (license license:gpl2)))

(define-public r-sebr
  (package
    (name "r-sebr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SeBR" version))
       (sha256
        (base32 "0xk002y4nim5f3mcfbl3zk484cf2s5mvg1paj2jg4f5v60vr4pwp"))))
    (properties `((upstream-name . "SeBR")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod
                             r-spikeslabgam
                             r-quantreg
                             r-mass
                             r-gpgp
                             r-fields))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/drkowal/SeBR")
    (synopsis "Semiparametric Bayesian Regression Analysis")
    (description
     "Monte Carlo and MCMC sampling algorithms for semiparametric Bayesian regression
analysis.  These models feature a nonparametric (unknown) transformation of the
data paired with widely-used regression models including linear regression,
spline regression, quantile regression, and Gaussian processes.  The
transformation enables broader applicability of these key models, including for
real-valued, positive, and compactly-supported data with challenging
distributional features.  The samplers prioritize computational scalability and,
for most cases, Monte Carlo (not MCMC) sampling for greater efficiency.  Details
of the methods and algorithms are provided in Kowal and Wu (2023)
<@code{arXiv:2306.05498>}.")
    (license license:expat)))

(define-public r-seawaveq
  (package
    (name "r-seawaveq")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seawaveQ" version))
       (sha256
        (base32 "1x4vvassal1lwb9xnwisrhlx2maaqxl84h7klfy8yg9x80fdrhsw"))))
    (properties `((upstream-name . "seawaveQ")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rms r-reshape2 r-plyr r-lubridate))
    (home-page "https://doi.org/10.3133/ofr20201082")
    (synopsis "SEAWAVE-Q Model")
    (description
     "This package provides a model and utilities for analyzing trends in chemical
concentrations in streams with a seasonal wave (seawave) and adjustment for
streamflow (Q) and other ancillary variables.  See Ryberg and York, 2020,
<doi:10.3133/ofr20201082>.")
    (license (list (license:fsdg-compatible "Unlimited")
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-seaval
  (package
    (name "r-seaval")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SeaVal" version))
       (sha256
        (base32 "1j4y96pqh6h791hiz07f6c4bhacm8n5v720bkwpkmbkky39yi2bf"))))
    (properties `((upstream-name . "SeaVal")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-scales
                             r-rcolorbrewer
                             r-patchwork
                             r-ncdf4
                             r-maps
                             r-lifecycle
                             r-ggplotify
                             r-ggplot2
                             r-ggnewscale
                             r-data-table))
    (home-page "https://seasonalforecastingengine.github.io/SeaValDoc/")
    (synopsis "Validation of Seasonal Weather Forecasts")
    (description
     "This package provides tools for processing and evaluating seasonal weather
forecasts, with an emphasis on tercile forecasts.  We follow the World
Meteorological Organization's \"Guidance on Verification of Operational Seasonal
Climate Forecasts\", S.J.Mason (2018, ISBN: 978-92-63-11220-0, URL:
<https://library.wmo.int/idurl/4/56227>).  The development was supported by the
European Unionâs Horizon 2020 research and innovation programme under grant
agreement no.  869730 (CONFER).  A comprehensive online tutorial is available at
<https://seasonalforecastingengine.github.io/@code{SeaValDoc/>}.")
    (license license:gpl3+)))

(define-public r-seastests
  (package
    (name "r-seastests")
    (version "0.15.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seastests" version))
       (sha256
        (base32 "1xy2hacd57v75y5snhn4al7bi71wr994jy6m2sdr6qzzsd5pbgpc"))))
    (properties `((upstream-name . "seastests")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-xts r-forecast))
    (home-page "https://cran.r-project.org/package=seastests")
    (synopsis "Seasonality Tests")
    (description
     "An overall test for seasonality of a given time series in addition to a set of
individual seasonality tests as described by Ollech and Webel (forthcoming): An
overall seasonality test.  Bundesbank Discussion Paper.")
    (license license:gpl3)))

(define-public r-seasonalview
  (package
    (name "r-seasonalview")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seasonalview" version))
       (sha256
        (base32 "1l705yc7ssldsfckbgnvd95sh3zzhpkmf1rr6ar2s60s3wsyly4n"))))
    (properties `((upstream-name . "seasonalview")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xts
                             r-xtable
                             r-shinydashboard
                             r-shiny
                             r-seasonal
                             r-openxlsx
                             r-htmlwidgets
                             r-dygraphs))
    (home-page "http://www.seasonal.website")
    (synopsis "Graphical User Interface for Seasonal Adjustment")
    (description
     "This package provides a graphical user interface to the seasonal package and
X-13ARIMA-SEATS', the U.S. Census Bureau's seasonal adjustment software.
Unifies the code base of <http://www.seasonal.website> and the GUI in the
seasonal package.")
    (license license:gpl3)))

(define-public r-seasonalityplot
  (package
    (name "r-seasonalityplot")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seasonalityPlot" version))
       (sha256
        (base32 "17a5i6gs1bb10fklmb8rkhpss56bp82nz96z5xz5a8qx81k08iww"))))
    (properties `((upstream-name . "seasonalityPlot")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-ttr
                             r-quantmod
                             r-plotrix
                             r-magrittr
                             r-lubridate
                             r-htmltools
                             r-dygraphs
                             r-crypto2
                             r-assertthat))
    (home-page "https://github.com/kumeS/seasonalityPlot")
    (synopsis
     "Seasonality Variation Plots of Stock Prices and Cryptocurrencies")
    (description
     "The price action at any given time is determined by investor sentiment and
market conditions.  Although there is no established principle, over a long
period of time, things often move with a certain periodicity.  This is sometimes
referred to as anomaly.  The @code{seasonPlot}() function in this package
calculates and visualizes the average value of price movements over a year for
any given period.  In addition, the monthly increase or decrease in price
movement is represented with a colored background.  This @code{seasonPlot}()
function can use the same symbols as the quantmod package (e.g. ^IXIC, ^DJI,
SPY, BTC-USD, and ETH-USD etc).")
    (license license:artistic2.0)))

(define-public r-seasonal
  (package
    (name "r-seasonal")
    (version "1.10.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seasonal" version))
       (sha256
        (base32 "11d67bwkhrsphv7nb1ilimpcfv6n89ykd5sbx8p123dr3h02sgss"))))
    (properties `((upstream-name . "seasonal")))
    (build-system r-build-system)
    (propagated-inputs (list r-x13binary))
    (native-inputs (list r-knitr))
    (home-page "http://www.seasonal.website")
    (synopsis "R Interface to X-13-ARIMA-SEATS")
    (description
     "Easy-to-use interface to X-13-ARIMA-SEATS, the seasonal adjustment software by
the US Census Bureau.  It offers full access to almost all options and outputs
of X-13, including X-11 and SEATS, automatic ARIMA model search, outlier
detection and support for user defined holiday variables, such as Chinese New
Year or Indian Diwali.  A graphical user interface can be used through the
seasonalview package.  Uses the X-13-binaries from the x13binary package.")
    (license license:gpl3)))

(define-public r-season
  (package
    (name "r-season")
    (version "0.3.15")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "season" version))
       (sha256
        (base32 "0f63ichlnj49g0ni9560avr4415iyj5061pixkjmqj83y7bbsz22"))))
    (properties `((upstream-name . "season")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mass r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=season")
    (synopsis "Seasonal Analysis of Health Data")
    (description
     "Routines for the seasonal analysis of health data, including regression models,
time-stratified case-crossover, plotting functions and residual checks, see
Barnett and Dobson (2010) ISBN 978-3-642-10748-1.  Thanks to Yuming Guo for
checking the case-crossover code.")
    (license license:gpl3+)))

(define-public r-seas
  (package
    (name "r-seas")
    (version "0.6-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seas" version))
       (sha256
        (base32 "0cr4lsbd599gzjzc6px7hkm3np34zh6sjzgzamlp4mdrphpvlvn7"))))
    (properties `((upstream-name . "seas")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://github.com/mwtoews/seas")
    (synopsis "Seasonal Analysis and Graphics, Especially for Climatology")
    (description
     "Capable of deriving seasonal statistics, such as \"normals\", and analysis of
seasonal data, such as departures.  This package also has graphics capabilities
for representing seasonal data, including boxplots for seasonal parameters, and
bars for summed normals.  There are many specific functions related to
climatology, including precipitation normals, temperature normals, cumulative
precipitation departures and precipitation interarrivals.  However, this package
is designed to represent any time-varying parameter with a discernible seasonal
signal, such as found in hydrology and ecology.")
    (license license:gpl2+)))

(define-public r-sears
  (package
    (name "r-sears")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SEARS" version))
       (sha256
        (base32 "14zc6k8haj6d3hs7hrsz2yp71v0zqlzvsigx9wzl101cjslx841d"))))
    (properties `((upstream-name . "SEARS")))
    (build-system r-build-system)
    (propagated-inputs (list r-boin))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SEARS")
    (synopsis
     "Seamless Dose Escalation/Expansion with Adaptive Randomization Scheme")
    (description
     "This package provides a seamless design that combines phase I dose escalation
based on toxicity with phase II dose expansion and dose comparison based on
efficacy.")
    (license license:gpl2)))

(define-public r-searchtrees
  (package
    (name "r-searchtrees")
    (version "0.5.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SearchTrees" version))
       (sha256
        (base32 "1n0y91y1l657g61klbcjz82ddj2csdpnralc3rbk1hbwr4nxk069"))))
    (properties `((upstream-name . "SearchTrees")))
    (build-system r-build-system)
    (home-page "https://github.com/gmbecker/SearchTrees")
    (synopsis "Spatial Search Trees")
    (description
     "The @code{QuadTree} data structure is useful for fast, neighborhood-restricted
lookups.  We use it to implement fast k-Nearest Neighbor and Rectangular range
lookups in 2 dimenions.  The primary target is high performance interactive
graphics.")
    (license license:lgpl2.0+)))

(define-public r-searcher
  (package
    (name "r-searcher")
    (version "0.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "searcher" version))
       (sha256
        (base32 "09h2jzm7y5c79z8an2nm392xmcn2ay7brcch7h1a7h9dbaakf1fb"))))
    (properties `((upstream-name . "searcher")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/coatless-rpkg/searcher")
    (synopsis "Query Search Interfaces")
    (description
     "This package provides a search interface to look up terms on Google', Bing',
@code{DuckDuckGo}', Startpage', Ecosia', rseek', Twitter',
@code{StackOverflow}', RStudio Community', @code{GitHub}', and
@code{BitBucket}'.  Upon searching, a browser window will open with the
aforementioned search results.")
    (license license:gpl2+)))

(define-public r-sealasso
  (package
    (name "r-sealasso")
    (version "0.1-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sealasso" version))
       (sha256
        (base32 "0j43my9mj73ada3bzzxazgwcahm2bhzbmqc128xara46gxvwrbzz"))))
    (properties `((upstream-name . "sealasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-lars))
    (home-page "https://cran.r-project.org/package=sealasso")
    (synopsis "Standard Error Adjusted Adaptive Lasso")
    (description
     "Standard error adjusted adaptive lasso (SEA-lasso) is a version of the adaptive
lasso, which incorporates OLS standard error to the L1 penalty weight.  This
method is intended for variable selection under linear regression settings (n >
p).  This new weight assignment strategy is especially useful when the
collinearity of the design matrix is a concern.")
    (license license:gpl2+)))

(define-public r-seahors
  (package
    (name "r-seahors")
    (version "1.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SEAHORS" version))
       (sha256
        (base32 "172wqna58sfmdaparvy14nva8lsflvsz55ndkd2ficfmp28qqp6n"))))
    (properties `((upstream-name . "SEAHORS")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-stringr
                             r-shinywidgets
                             r-shinythemes
                             r-shinyjs
                             r-shiny
                             r-rmarkdown
                             r-readxl
                             r-raster
                             r-plotly
                             r-mass
                             r-htmlwidgets
                             r-gridextra
                             r-ggplot2
                             r-dt
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/AurelienRoyer/SEAHORS")
    (synopsis "Spatial Exploration of ArcHaeological Objects in R Shiny")
    (description
     "An R Shiny application dedicated to the intra-site spatial analysis of
piece-plotted archaeological remains, making the two and three-dimensional
spatial exploration of archaeological data as user-friendly as possible.
Documentation about SEAHORS is provided by the vignette included in this package
and by the companion scientific paper: Royer, Discamps, Plutniak, Thomas (2023,
PCI Archaeology, <doi:10.5281/zenodo.7674698>).")
    (license license:gpl3)))

(define-public r-seagle
  (package
    (name "r-seagle")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SEAGLE" version))
       (sha256
        (base32 "1j5wjjw7fl2m4h9brg8dyahf1vmn9wgc7jaajaz6z1xqm6fc0d9k"))))
    (properties `((upstream-name . "SEAGLE")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-compquadform))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jocelynchi/SEAGLE")
    (synopsis
     "Scalable Exact Algorithm for Large-Scale Set-Based Gene-Environment Interaction Tests")
    (description
     "The explosion of biobank data offers immediate opportunities for
gene-environment (@code{GxE}) interaction studies of complex diseases because of
the large sample sizes and rich collection in genetic and non-genetic
information.  However, the extremely large sample size also introduces new
computational challenges in @code{GxE} assessment, especially for set-based
@code{GxE} variance component (VC) tests, a widely used strategy to boost
overall @code{GxE} signals and to evaluate the joint @code{GxE} effect of
multiple variants from a biologically meaningful unit (e.g., gene).  We present
SEAGLE', a Scalable Exact @code{AlGorithm} for Large-scale Set-based @code{GxE}
tests, to permit @code{GxE} VC test scalable to biobank data.  SEAGLE employs
modern matrix computations to achieve the same âexactâ results as the
original @code{GxE} VC tests, and does not impose additional assumptions nor
relies on approximations.  SEAGLE can easily accommodate sample sizes in the
order of 10^5, is implementable on standard laptops, and does not require
specialized equipment.  The accompanying manuscript for this package can be
found at Chi, Ipsen, Hsiao, Lin, Wang, Lee, Lu, and Tzeng. (2021+)
<@code{arXiv:2105.03228>}.")
    (license license:gpl3)))

(define-public r-seacarb
  (package
    (name "r-seacarb")
    (version "3.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "seacarb" version))
       (sha256
        (base32 "1xrwvkal60dkj4i6h27dskbliizkz6q476s06627asjacy8vdls3"))))
    (properties `((upstream-name . "seacarb")))
    (build-system r-build-system)
    (propagated-inputs (list r-solvesaphe r-oce r-gsw))
    (home-page "https://CRAN.R-project.org/package=seacarb")
    (synopsis "Seawater Carbonate Chemistry")
    (description
     "Calculates parameters of the seawater carbonate system and assists the design of
ocean acidification perturbation experiments.")
    (license license:gpl2+)))

(define-public r-sea
  (package
    (name "r-sea")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SEA" version))
       (sha256
        (base32 "03iqvs95v2q252dl62z39i0nwg8n44g1p9ljbmb1nx6zq98fd7bw"))))
    (properties `((upstream-name . "SEA")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny
                             r-mass
                             r-kscorrect
                             r-foreach
                             r-doparallel
                             r-data-table))
    (home-page "https://cran.r-project.org/package=SEA")
    (synopsis "Segregation Analysis")
    (description
     "This package provides a few major genes and a series of polygene are responsive
for each quantitative trait.  Major genes are individually identified while
polygene is collectively detected.  This is mixed major genes plus polygene
inheritance analysis or segregation analysis (SEA).  In the SEA, phenotypes from
a single or multiple bi-parental segregation populations along with their
parents are used to fit all the possible models and the best model of the trait
for population phenotypic distributions is viewed as the model of the trait.
There are fourteen types of population combinations available.  Zhang Yuan-Ming,
Gai Jun-Yi, Yang Yong-Hua (2003, <doi:10.1017/S0016672303006141>).")
    (license license:gpl2+)))

(define-public r-se-eq
  (package
    (name "r-se-eq")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SE.EQ" version))
       (sha256
        (base32 "09fqx2rcqpiv072l6gqgfz9pdhas98q4kkkm7m4y28k13q2gicgl"))))
    (properties `((upstream-name . "SE.EQ")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=SE.EQ")
    (synopsis "SE-Test for Equivalence")
    (description
     "This package implements the SE-test for equivalence according to Hoffelder et
al. (2015) <DOI:10.1080/10543406.2014.920344>.  The SE-test for equivalence is a
multivariate two-sample equivalence test.  Distance measure of the test is the
sum of standardized differences between the expected values or in other words:
the sum of effect sizes (SE) of all components of the two multivariate samples.
The test is an asymptotically valid test for normally distributed data (see
Hoffelder et al.,2015).  The function SE.EQ() implements the SE-test for
equivalence according to Hoffelder et al. (2015).  The function
SE.EQ.dissolution.profiles() implements a variant of the SE-test for equivalence
for similarity analyses of dissolution profiles as mentioned in Suarez-Sharp et
al.(2020) <DOI:10.1208/s12248-020-00458-9>).  The equivalence margin used in
SE.EQ.dissolution.profiles() is analogically defined as for the T2EQ approach
according to Hoffelder (2019) <DOI:10.1002/bimj.201700257>) by means of a
systematic shift in location of 10 [\\% of label claim] of both dissolution
profile populations.  SE.EQ.dissolution.profiles() checks whether the weighted
mean of the differences of the expected values of both dissolution profile
populations is statistically significantly smaller than 10 [\\% of label claim].
The weights are built up by the inverse variances.")
    (license license:gpl3)))

(define-public r-sdwd
  (package
    (name "r-sdwd")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sdwd" version))
       (sha256
        (base32 "14x5hjdbkr5lsh8x3wninxg88acqh6q50famf6qjlfm7727rdysc"))))
    (properties `((upstream-name . "sdwd")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=sdwd")
    (synopsis "Sparse Distance Weighted Discrimination")
    (description
     "Formulates a sparse distance weighted discrimination (SDWD) for high-dimensional
classification and implements a very fast algorithm for computing its solution
path with the L1, the elastic-net, and the adaptive elastic-net penalties.  More
details about the methodology SDWD is seen on Wang and Zou (2016)
(<doi:10.1080/10618600.2015.1049700>).")
    (license license:gpl2)))

(define-public r-sdtmval
  (package
    (name "r-sdtmval")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sdtmval" version))
       (sha256
        (base32 "1alwjknkgbabmivn1wrrbd0wyrs0hxpvx47ibdn7zl7ksfbhlsld"))))
    (properties `((upstream-name . "sdtmval")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-stringr
                             r-rlang
                             r-readxl
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-knitr
                             r-haven
                             r-glue
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/skgithub14/sdtmval")
    (synopsis "Validate SDTM Domains")
    (description
     "This package provides a set of tools to assist statistical programmers in
validating Study Data Tabulation Model (SDTM) domain data sets.  Statistical
programmers are required to validate that a SDTM data set domain has been
programmed correctly, per the SDTM Implementation Guide (SDTMIG) by CDISC
(<https://www.cdisc.org/standards/foundational/sdtmig>), study specification,
and study protocol using a process called double programming.  Double
programming involves two different programmers independently converting the raw
electronic data cut (EDC) data into a SDTM domain data table and comparing their
results to ensure accurate standardization of the data.  One of these attempts
is termed production and the other validation'.  Generally, production runs are
the official programs for submittals and these are written in SAS'.  Validation
runs can be programmed in another language, in this case R'.")
    (license license:expat)))

(define-public r-sdtmchecks
  (package
    (name "r-sdtmchecks")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sdtmchecks" version))
       (sha256
        (base32 "0dpnvyfgvaqj3n4ci7hjqb1mvj3f5hsab4h02vz5q2brx7fq4ll0"))))
    (properties `((upstream-name . "sdtmchecks")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect r-openxlsx r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://pharmaverse.github.io/sdtmchecks/")
    (synopsis
     "Data Quality Checks for Study Data Tabulation Model (SDTM) Datasets")
    (description
     "This package provides a series of checks to identify common issues in Study Data
Tabulation Model (SDTM) datasets.  These checks are intended to be
generalizable, actionable, and meaningful for analysis.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-sdtm-oak
  (package
    (name "r-sdtm-oak")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sdtm.oak" version))
       (sha256
        (base32 "0sldjys6aam6kg3jq2sz6m3i71k5hf1ydhxmfmg9kdk5hmnf7f1j"))))
    (properties `((upstream-name . "sdtm.oak")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-purrr
                             r-pillar
                             r-dplyr
                             r-cli
                             r-assertthat
                             r-admiraldev))
    (native-inputs (list r-knitr))
    (home-page "https://pharmaverse.github.io/sdtm.oak/")
    (synopsis "SDTM Data Transformation Engine")
    (description
     "An Electronic Data Capture system (EDC) and Data Standard agnostic solution that
enables the pharmaceutical programming community to develop Clinical Data
Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM)
datasets in R. The reusable algorithms concept in sdtm.oak provides a framework
for modular programming and can potentially automate the conversion of raw
clinical data to SDTM through standardized SDTM specifications.  SDTM is one of
the required standards for data submission to the Food and Drug Administration
(FDA) in the United States and Pharmaceuticals and Medical Devices Agency (PMDA)
in Japan.  SDTM standards are implemented following the SDTM Implementation
Guide as defined by CDISC <https://www.cdisc.org/standards/foundational/sdtmig>.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-sdt
  (package
    (name "r-sdt")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SDT" version))
       (sha256
        (base32 "1jwpfd1pnzy9wcl90qv5bgwi19shsw9064dvml9zwbif8yw8dzjj"))))
    (properties `((upstream-name . "SDT")))
    (build-system r-build-system)
    (propagated-inputs (list r-quadprog))
    (home-page "http://www.meb.edu.tum.de")
    (synopsis "Self-Determination Theory Measures")
    (description
     "This package provides functions for self-determination motivation theory (SDT)
to compute measures of motivation internalization, motivation simplex structure,
and of the original and adjusted self-determination or relative autonomy index.
SDT was introduced by Deci and Ryan (1985) <doi:10.1007/978-1-4899-2271-7>.  See
package?SDT for an overview.")
    (license license:gpl2+)))

(define-public r-sdsfun
  (package
    (name "r-sdsfun")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sdsfun" version))
       (sha256
        (base32 "00armg1ly0xd0p4n2b63sb2rj0rwinqx7c6cilh1j5z9s1ib1z9k"))))
    (properties `((upstream-name . "sdsfun")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-spdep
                             r-sf
                             r-rcpparmadillo
                             r-rcpp
                             r-purrr
                             r-pander
                             r-magrittr
                             r-geosphere
                             r-dplyr))
    (home-page "https://stscl.github.io/sdsfun/")
    (synopsis "Spatial Data Science Complementary Features")
    (description
     "Wrapping and supplementing commonly used functions in the R ecosystem related to
spatial data science, while serving as a basis for other packages maintained by
Wenbo Lv.")
    (license license:gpl3)))

(define-public r-sdrt
  (package
    (name "r-sdrt")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sdrt" version))
       (sha256
        (base32 "1kiq73m1wzqa8d0542nhm2bh7rc0bib0jnrwccawjyxmhm4jix73"))))
    (properties `((upstream-name . "sdrt")))
    (build-system r-build-system)
    (propagated-inputs (list r-tseries r-psych r-pracma))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=sdrt")
    (synopsis
     "Estimating the Sufficient Dimension Reduction Subspaces in Time Series")
    (description
     "The sdrt() function is designed for estimating subspaces for Sufficient
Dimension Reduction (SDR) in time series, with a specific focus on the Time
Series Central Mean subspace (TS-CMS).  The package employs the Fourier
transformation method proposed by Samadi and De Alwis (2023)
<doi:10.48550/@code{arXiv.2312.02110>} and the Nadaraya-Watson kernel smoother
method proposed by Park et al. (2009) <doi:10.1198/jcgs.2009.08076> for
estimating the TS-CMS. The package provides tools for estimating distances
between subspaces and includes functions for selecting model parameters using
the Fourier transformation method.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-sdpt3r
  (package
    (name "r-sdpt3r")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sdpt3r" version))
       (sha256
        (base32 "02qv889mkzcilxzszzqcqgn9z5zmki0093mdriradbl1y8a27bp5"))))
    (properties `((upstream-name . "sdpt3r")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (home-page "https://cran.r-project.org/package=sdpt3r")
    (synopsis "Semi-Definite Quadratic Linear Programming Solver")
    (description
     "Solves the general Semi-Definite Linear Programming formulation using an R
implementation of SDPT3 (K.C. Toh, M.J. Todd, and R.H. Tutuncu (1999)
<doi:10.1080/10556789908805762>).  This includes problems such as the nearest
correlation matrix problem (Higham (2002) <doi:10.1093/imanum/22.3.329>),
D-optimal experimental design (Smith (1918) <doi:10.2307/2331929>), Distance
Weighted Discrimination (Marron and Todd (2012)
<doi:10.1198/016214507000001120>), as well as graph theory problems including
the maximum cut problem.  Technical details surrounding SDPT3 can be found in
R.H Tutuncu, K.C. Toh, and M.J. Todd (2003) <doi:10.1007/s10107-002-0347-5>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-sdprism2d
  (package
    (name "r-sdprism2d")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SDPrism2D" version))
       (sha256
        (base32 "1f1k6lk0fh9dz11703fpbmvjl2gyq4y7l4khigl1h69j9xv97yf3"))))
    (properties `((upstream-name . "SDPrism2D")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SDPrism2D")
    (synopsis "Visualizing the Standard Deviation as the Size of a Prism")
    (description
     "We visualize the standard deviation of a data set as the size of a prism whose
volume equals the total volume of several prisms made from the Empirical
Cumulative Distribution Function.")
    (license license:gpl3)))

(define-public r-sdprisk
  (package
    (name "r-sdprisk")
    (version "1.1-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sdprisk" version))
       (sha256
        (base32 "061x78irryc5phd8bv9cs2mfgiajgwmr85ryp1r47anadydb2haa"))))
    (properties `((upstream-name . "sdprisk")))
    (build-system r-build-system)
    (propagated-inputs (list r-rootsolve r-polynomf r-numderiv))
    (home-page "https://cran.r-project.org/package=sdprisk")
    (synopsis
     "Measures of Risk for the Compound Poisson Risk Process with Diffusion")
    (description
     "Based on the compound Poisson risk process that is perturbed by a Brownian
motion, saddlepoint approximations to some measures of risk are provided.
Various approximation methods for the probability of ruin are also included.
Furthermore, exact values of both the risk measures as well as the probability
of ruin are available if the individual claims follow a hypo-exponential
distribution (i.  e., if it can be represented as a sum of independent
exponentially distributed random variables with different rate parameters).  For
more details see Gatto and Baumgartner (2014) <doi:10.1007/s11009-012-9316-5>.")
    (license license:agpl3)))

(define-public r-sdprior
  (package
    (name "r-sdprior")
    (version "1.0-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sdPrior" version))
       (sha256
        (base32 "0g4crlasnvxz752n3az5jc681jvvj2g87c3a8s2h1i2vh64p5h37"))))
    (properties `((upstream-name . "sdPrior")))
    (build-system r-build-system)
    (propagated-inputs (list r-pscl
                             r-mvtnorm
                             r-mgcv
                             r-mass
                             r-gb2
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=sdPrior")
    (synopsis
     "Scale-Dependent Hyperpriors in Structured Additive Distributional Regression")
    (description
     "Utility functions for scale-dependent and alternative hyperpriors.  The
distribution parameters may capture location, scale, shape, etc.  and every
parameter may depend on complex additive terms (fixed, random, smooth, spatial,
etc.) similar to a generalized additive model.  Hyperpriors for all effects can
be elicitated within the package.  Including complex tensor product interaction
terms and variable selection priors.  The basic model is explained in in Klein
and Kneib (2016) <doi:10.1214/15-BA983>.")
    (license license:gpl2)))

(define-public r-sdpdth
  (package
    (name "r-sdpdth")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sdpdth" version))
       (sha256
        (base32 "1yn8fsjxpbagargf3mv0p23vsgnl64dxfkggrzvfp0ylxsx8nnvc"))))
    (properties `((upstream-name . "sdpdth")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjava
                             r-rcpparmadillo
                             r-rcpp
                             r-rcma
                             r-matrixcalc
                             r-matrix))
    (home-page "https://cran.r-project.org/package=sdpdth")
    (synopsis "M-Estimator for Threshold Spatial Dynamic Panel Data Model")
    (description
     "M-estimator for threshold and non-threshold spatial dynamic panel data model.
Yang, Z (2018) <doi:10.1016/j.jeconom.2017.08.019>.  Wu, J., Matsuda, Y (2021)
<doi:10.1007/s43071-021-00008-1>.")
    (license license:expat)))

(define-public r-sdpdmod
  (package
    (name "r-sdpdmod")
    (version "0.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SDPDmod" version))
       (sha256
        (base32 "1f136nb32y3lnnhyri03y8p5akc2c06g9rynx53w702fyi72bgy6"))))
    (properties `((upstream-name . "SDPDmod")))
    (build-system r-build-system)
    (propagated-inputs (list r-spdep
                             r-sp
                             r-sf
                             r-rspectra
                             r-plm
                             r-matrix))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SDPDmod")
    (synopsis "Spatial Dynamic Panel Data Modeling")
    (description
     "Spatial model calculation for static and dynamic panel data models, weights
matrix creation and Bayesian model comparison.  Bayesian model comparison
methods were described by @code{LeSage} (2014)
<doi:10.1016/j.spasta.2014.02.002>.  The Lee'-'Yu transformation approach is
described in Yu', De Jong and Lee (2008) <doi:10.1016/j.jeconom.2008.08.002>,
Lee and Yu (2010) <doi:10.1016/j.jeconom.2009.08.001> and Lee and Yu (2010)
<doi:10.1017/S0266466609100099>.")
    (license license:gpl3+)))

(define-public r-sdmvspecies
  (package
    (name "r-sdmvspecies")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sdmvspecies" version))
       (sha256
        (base32 "19avkag13ij1k65vqhmvcy8j50j8vrgw4mjc49x8i63w3d4z1wxh"))))
    (properties `((upstream-name . "sdmvspecies")))
    (build-system r-build-system)
    (propagated-inputs (list r-raster r-psych))
    (home-page "http://www.sdmserialsoftware.org/sdmvspecies/")
    (synopsis "Create Virtual Species for Species Distribution Modelling")
    (description
     "This package provides a software package help user to create virtual species for
species distribution modelling.  It includes several methods to help user to
create virtual species distribution map.  Those maps can be used for Species
Distribution Modelling (SDM) study.  SDM use environmental data for sites of
occurrence of a species to predict all the sites where the environmental
conditions are suitable for the species to persist, and may be expected to
occur.")
    (license license:agpl3)))

(define-public r-sdmtune
  (package
    (name "r-sdmtune")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SDMtune" version))
       (sha256
        (base32 "0zzra0vig3gyz3b0h66fdlkpl2ii939d7k6p7ym5mzhz1vqdaljz"))))
    (properties `((upstream-name . "SDMtune")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (inputs (list openjdk))
    (propagated-inputs (list r-whisker
                             r-terra
                             r-stringr
                             r-rstudioapi
                             r-rlang
                             r-rcpp
                             r-randomforest
                             r-nnet
                             r-maxnet
                             r-jsonlite
                             r-ggplot2
                             r-gbm
                             r-dismo
                             r-cli))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://consbiol-unibern.github.io/SDMtune/")
    (synopsis "Species Distribution Model Selection")
    (description
     "User-friendly framework that enables the training and the evaluation of species
distribution models (SDMs).  The package implements functions for data driven
variable selection and model tuning and includes numerous utilities to display
the results.  All the functions used to select variables or to tune model
hyperparameters have an interactive real-time chart displayed in the RStudio
viewer pane during their execution.")
    (license license:gpl3)))

(define-public r-sdmtmb
  (package
    (name "r-sdmtmb")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sdmTMB" version))
       (sha256
        (base32 "0v2kmyppax5f1g1jji7l2m6v49kxj65r81giv84hnkwmcqdzsic8"))))
    (properties `((upstream-name . "sdmTMB")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmb
                             r-rlang
                             r-rcppeigen
                             r-nlme
                             r-mvtnorm
                             r-mgcv
                             r-matrix
                             r-lme4
                             r-lifecycle
                             r-generics
                             r-fmesher
                             r-fishmod
                             r-clisymbols
                             r-cli
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://pbs-assess.github.io/sdmTMB/")
    (synopsis "Spatial and Spatiotemporal SPDE-Based GLMMs with 'TMB'")
    (description
     "This package implements spatial and spatiotemporal GLMMs (Generalized Linear
Mixed Effect Models) using TMB', fmesher', and the SPDE (Stochastic Partial
Differential Equation) Gaussian Markov random field approximation to Gaussian
random fields.  One common application is for spatially explicit species
distribution models (SDMs).  See Anderson et al. (2024)
<doi:10.1101/2022.03.24.485545>.")
    (license license:gpl3)))

(define-public r-sdmpredictors
  (package
    (name "r-sdmpredictors")
    (version "0.2.15")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sdmpredictors" version))
       (sha256
        (base32 "19znyvz98b3ykwqllzs2sjrw4sd6pd90130ws5d739xgpgsj1zwv"))))
    (properties `((upstream-name . "sdmpredictors")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-raster r-r-utils))
    (native-inputs (list r-knitr))
    (home-page "http://lifewatch.github.io/sdmpredictors/")
    (synopsis "Species Distribution Modelling Predictor Datasets")
    (description
     "Terrestrial and marine predictors for species distribution modelling from
multiple sources, including @code{WorldClim} <https://www.worldclim.org/>,,
ENVIREM <https://envirem.github.io/>, Bio-ORACLE <https://bio-oracle.org/> and
MARSPEC <http://www.marspec.org/>.")
    (license license:expat)))

(define-public r-sdm
  (package
    (name "r-sdm")
    (version "1.2-46")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sdm" version))
       (sha256
        (base32 "035x08bml5d3yw5ikh8kicvlzd2hnv679cpfab3ssib96scd6662"))))
    (properties `((upstream-name . "sdm")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-sp r-raster))
    (native-inputs (list r-r-rsp))
    (home-page "https://www.biogeoinformatics.org")
    (synopsis "Species Distribution Modelling")
    (description
     "An extensible framework for developing species distribution models using
individual and community-based approaches, generate ensembles of models,
evaluate the models, and predict species potential distributions in space and
time.  For more information, please check the following paper: Naimi, B.,
Araujo, M.B. (2016) <doi:10.1111/ecog.01881>.")
    (license license:gpl3+)))

(define-public r-sdlfilter
  (package
    (name "r-sdlfilter")
    (version "2.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SDLfilter" version))
       (sha256
        (base32 "0f565626kbi5rigsla5843rczmfi0hwmrwn2410smfhjvhywmlqy"))))
    (properties `((upstream-name . "SDLfilter")))
    (build-system r-build-system)
    (propagated-inputs (list r-stars
                             r-sf
                             r-pracma
                             r-maps
                             r-lubridate
                             r-gridextra
                             r-ggspatial
                             r-ggplot2
                             r-ggmap
                             r-geosphere
                             r-emmeans
                             r-dplyr
                             r-data-table))
    (home-page "https://github.com/TakahiroShimada/SDLfilter")
    (synopsis "Filtering and Assessing the Sample Size of Tracking Data")
    (description
     "This package provides functions to filter GPS/Argos locations, as well as
assessing the sample size for the analysis of animal distributions.  The filters
remove temporal and spatial duplicates, fixes located at a given height from
estimated high tide line, and locations with high error as described in Shimada
et al. (2012) <doi:10.3354/meps09747> and Shimada et al. (2016)
<doi:10.1007/s00227-015-2771-0>.  Sample size for the analysis of animal
distributions can be assessed by the conventional area-based approach or the
alternative probability-based approach as described in Shimada et al. (2021)
<doi:10.1111/2041-210X.13506>.")
    (license (list license:gpl2
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-sdgdetector
  (package
    (name "r-sdgdetector")
    (version "2.7.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SDGdetector" version))
       (sha256
        (base32 "0gm23f0xqw2cgig44d17qw2mfp685bz0dr8wrly4r90w44l121z9"))))
    (properties `((upstream-name . "SDGdetector")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-scales
                             r-rnaturalearth
                             r-magrittr
                             r-magick
                             r-ggplot2
                             r-dplyr))
    (home-page "https://github.com/Yingjie4Science/SDGdetector")
    (synopsis "Detect SDGs and Targets in Text")
    (description
     "Identify 17 Sustainable Development Goals and associated 169 targets in text.")
    (license license:gpl3+)))

(define-public r-sdetorus
  (package
    (name "r-sdetorus")
    (version "0.1.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sdetorus" version))
       (sha256
        (base32 "09y23s5pxrhfv2l4q08vi8rf1nwainyy7dm0aij10sfwr86k0brn"))))
    (properties `((upstream-name . "sdetorus")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mvtnorm))
    (home-page "https://github.com/egarpor/sdetorus")
    (synopsis "Statistical Tools for Toroidal Diffusions")
    (description
     "Implementation of statistical methods for the estimation of toroidal diffusions.
 Several diffusive models are provided, most of them belonging to the Langevin
family of diffusions on the torus.  Specifically, the wrapped normal and von
Mises processes are included, which can be seen as toroidal analogues of the
Ornstein-Uhlenbeck diffusion.  A collection of methods for approximate maximum
likelihood estimation, organized in four blocks, is given: (i) based on the
exact transition probability density, obtained as the numerical solution to the
Fokker-Plank equation; (ii) based on wrapped pseudo-likelihoods; (iii) based on
specific analytic approximations by wrapped processes; (iv) based on maximum
likelihood of the stationary densities.  The package allows the replicability of
the results in GarcÃ­a-PortuguÃ©s et al. (2019) <doi:10.1007/s11222-017-9790-2>.")
    (license license:gpl3)))

(define-public r-sdefsr
  (package
    (name "r-sdefsr")
    (version "0.7.22")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SDEFSR" version))
       (sha256
        (base32 "10rxq2y7a9rbzgl5s5fdh93jm4w5kqbyyflw5xfsdwymjj9p4b7m"))))
    (properties `((upstream-name . "SDEFSR")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-ggplot2 r-foreign))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/SIMIDAT/SDEFSR")
    (synopsis "Subgroup Discovery with Evolutionary Fuzzy Systems")
    (description
     "Implementation of evolutionary fuzzy systems for the data mining task called
\"subgroup discovery\".  In particular, the algorithms presented in this package
are: M. J. del Jesus, P. Gonzalez, F. Herrera, M. Mesonero (2007)
<doi:10.1109/TFUZZ.2006.890662> M. J. del Jesus, P. Gonzalez, F. Herrera (2007)
<doi:10.1109/MCDM.2007.369416> C. J. Carmona, P. Gonzalez, M. J. del Jesus, F.
Herrera (2010) <doi:10.1109/TFUZZ.2010.2060200> C. J. Carmona, V. Ruiz-Rodado,
M. J. del Jesus, A. Weber, M. Grootveld, P. GonzÃ¡lez, D. Elizondo (2015)
<doi:10.1016/j.ins.2014.11.030> It also provide a Shiny App to ease the
analysis.  The algorithms work with data sets provided in KEEL, ARFF and CSV
format and also with data.frame objects.")
    (license license:lgpl3+)))

(define-public r-sde
  (package
    (name "r-sde")
    (version "2.0.18")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sde" version))
       (sha256
        (base32 "1rk9wqaam21fipsinag3ychfkl6p7fpgbamr0119v8gjgskajxl8"))))
    (properties `((upstream-name . "sde")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-mass r-fda))
    (home-page "https://cran.r-project.org/package=sde")
    (synopsis "Simulation and Inference for Stochastic Differential Equations")
    (description
     "Companion package to the book Simulation and Inference for Stochastic
Differential Equations With R Examples, ISBN 978-0-387-75838-1, Springer, NY.")
    (license license:gpl2+)))

(define-public r-sdctable
  (package
    (name "r-sdctable")
    (version "0.32.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sdcTable" version))
       (sha256
        (base32 "15jjc25zmxlhh6jvf1ml9q2ngf3blr6c1rarfsx993i4lbb6lg12"))))
    (properties `((upstream-name . "sdcTable")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-ssbtools
                             r-slam
                             r-sdchierarchies
                             r-rlang
                             r-rglpk
                             r-rcpp
                             r-progress
                             r-matrix
                             r-knitr
                             r-glpkapi
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sdcTools/sdcTable")
    (synopsis "Methods for Statistical Disclosure Control in Tabular Data")
    (description
     "This package provides methods for statistical disclosure control in tabular data
such as primary and secondary cell suppression as described for example in
Hundepol et al. (2012) <doi:10.1002/9781118348239> are covered in this package.")
    (license license:gpl2+)))

(define-public r-sdcspatial
  (package
    (name "r-sdcspatial")
    (version "0.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sdcSpatial" version))
       (sha256
        (base32 "0bnq63akzngvf6r9csg11cb98mhwd8ybrqrk4v2i0iiln70sf7b6"))))
    (properties `((upstream-name . "sdcSpatial")))
    (build-system r-build-system)
    (propagated-inputs (list r-raster))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/edwindj/sdcSpatial")
    (synopsis "Statistical Disclosure Control for Spatial Data")
    (description
     "Privacy protected raster maps can be created from spatial point data.
Protection methods include smoothing of dichotomous variables by de Jonge and de
Wolf (2016) <doi:10.1007/978-3-319-45381-1_9>, continuous variables by de Wolf
and de Jonge (2018) <doi:10.1007/978-3-319-99771-1_23>, suppressing revealing
values and a generalization of the quad tree method by SuÃ±Ã©, Rovira, IbÃ¡Ã±ez
and FarrÃ© (2017) <doi:10.2901/EUROSTAT.C2017.001>.")
    (license license:gpl2)))

(define-public r-sdcnway
  (package
    (name "r-sdcnway")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SDCNway" version))
       (sha256
        (base32 "0zmjc3xh4qvwyrq4ma2k6q08f4ny4iqnl5a640pj17jk8awzwbg0"))))
    (properties `((upstream-name . "SDCNway")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-plyr r-mass r-ggplot2 r-dplyr))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=SDCNway")
    (synopsis "Tools to Evaluate Disclosure Risk")
    (description
     "This package provides tools for calculating disclosure risk measures for
microdata, including record-level and file-level measures.  The record-level
disclosure risk is estimated primarily using exhaustive tabulation.  The
file-level disclosure risk is estimated by fitting loglinear models on the
observed sample counts in cells formed by key variables and their interactions.
Funded by the National Center for Education Statistics.  See Skinner and Shlomo
(2008) <doi:10.1198/016214507000001328> for a description of the file-level risk
measures and the loglinear model approach.")
    (license license:gpl2)))

(define-public r-sdcmicro
  (package
    (name "r-sdcmicro")
    (version "5.7.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sdcMicro" version))
       (sha256
        (base32 "18qd30rvhqxyh8sxmajc0cs4wys0wvpl7nqw0dhapjkgygwk5nrq"))))
    (properties `((upstream-name . "sdcMicro")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-vim
                             r-shinybs
                             r-shiny
                             r-robustbase
                             r-rmarkdown
                             r-rhandsontable
                             r-rcpp
                             r-prettydoc
                             r-mass
                             r-knitr
                             r-haven
                             r-ggplot2
                             r-e1071
                             r-dt
                             r-data-table
                             r-cluster
                             r-cardata
                             r-car))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sdcTools/sdcMicro")
    (synopsis
     "Statistical Disclosure Control Methods for Anonymization of Data and Risk Estimation")
    (description
     "Data from statistical agencies and other institutions are mostly confidential.
This package, introduced in Templ, Kowarik and Meindl (2017)
<doi:10.18637/jss.v067.i04>, can be used for the generation of anonymized
(micro)data, i.e.  for the creation of public- and scientific-use files.  The
theoretical basis for the methods implemented can be found in Templ (2017)
<doi:10.1007/978-3-319-50272-4>.  Various risk estimation and anonymization
methods are included.  Note that the package includes a graphical user interface
published in Meindl and Templ (2019) <doi:10.3390/a12090191> that allows to use
various methods of this package.")
    (license license:gpl2)))

(define-public r-sdclog
  (package
    (name "r-sdclog")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sdcLog" version))
       (sha256
        (base32 "0ssjgpfg3z8j9s47nmiw6a5jcsi4pppdxidvzm3sryjz4klallwg"))))
    (properties `((upstream-name . "sdcLog")))
    (build-system r-build-system)
    (propagated-inputs (list r-mathjaxr r-data-table r-cli r-checkmate r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/matthiasgomolka/sdcLog")
    (synopsis
     "Tools for Statistical Disclosure Control in Research Data Centers")
    (description
     "This package provides tools for researchers to explicitly show that their
results comply to rules for statistical disclosure control imposed by research
data centers.  These tools help in checking descriptive statistics and models
and in calculating extreme values that are not individual data.  Also included
is a simple function to create log files.  The methods used here are described
in the \"Guidelines for the checking of output based on microdata research\" by
Bond, Brandt, and de Wolf (2015)
<https://ec.europa.eu/eurostat/cros/system/files/dwb_standalone-document_output-checking-guidelines.pdf>.")
    (license license:gpl3)))

(define-public r-sdchierarchies
  (package
    (name "r-sdchierarchies")
    (version "0.21.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sdcHierarchies" version))
       (sha256
        (base32 "0ihfddjkqdmzpr62dackblwbjcvbkhzm4ap1xzbn4m8i3z1nlbi0"))))
    (properties `((upstream-name . "sdcHierarchies")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinytree
                             r-shinythemes
                             r-shinyjs
                             r-shiny
                             r-rlang
                             r-rcpp
                             r-jsonlite
                             r-data-table
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bernhard-da/sdcHierarchies")
    (synopsis "Create and (Interactively) Modify Nested Hierarchies")
    (description
     "This package provides functionality to generate, (interactively) modify (by
adding, removing and renaming nodes) and convert nested hierarchies between
different formats.  These tree like structures can be used to define for example
complex hierarchical tables used for statistical disclosure control.")
    (license license:gpl3)))

(define-public r-sdaresources
  (package
    (name "r-sdaresources")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SDAResources" version))
       (sha256
        (base32 "13zb8sx7913wpj1jhi5sb0ps8fgzxfvjx75p218n166w54rxjqjs"))))
    (properties `((upstream-name . "SDAResources")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SDAResources")
    (synopsis
     "Datasets and Functions for 'Sampling: Design and Analysis, 3rd Edition'")
    (description
     "Includes all the datasets of Sampling: Design and Analysis (3rd edition by
Sharon Lohr) in R format and additional functions for analyzing and graphing
probability samples.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-sdar
  (package
    (name "r-sdar")
    (version "0.9-55")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SDAR" version))
       (sha256
        (base32 "1a1dy4wpzhfaqxv4x8knazh4kjx042rnkmvxf62r6wkgbfvq4r6b"))))
    (properties `((upstream-name . "SDAR")))
    (build-system r-build-system)
    (propagated-inputs (list r-readxl r-linbin r-grimport2))
    (native-inputs (list r-knitr))
    (home-page "https://doi.org/10.25573/data.13118426.v2")
    (synopsis "Stratigraphic Data Analysis")
    (description
     "This package provides a fast, consistent tool for plotting and facilitating the
analysis of stratigraphic and sedimentological data.  Taking advantage of the
flexible plotting tools available in R, SDAR uses stratigraphic and
sedimentological data to produce detailed graphic logs for outcrop sections and
borehole logs.  These logs can include multiple features (e.g., bed thickness,
lithology, samples, sedimentary structures, colors, fossil content, bioturbation
index, gamma ray logs) (Johnson, 1992, <ISSN 0037-0738>).")
    (license license:gpl2+)))

(define-public r-sdamr
  (package
    (name "r-sdamr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sdamr" version))
       (sha256
        (base32 "1hcgdysa62mrzk2wmy366jpwab65m3qyi8bh7kzgifpwwxh5pv9k"))))
    (properties `((upstream-name . "sdamr")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-dplyr r-car))
    (home-page "https://mspeekenbrink.github.io/sdam-r/")
    (synopsis "Statistics: Data Analysis and Modelling")
    (description
     "Data sets and functions to support the books \"Statistics: Data analysis and
modelling\" by Speekenbrink, M. (2021)
<https://mspeekenbrink.github.io/sdam-book/> and \"An R companion to Statistics:
data analysis and modelling\" by Speekenbrink, M. (2021)
<https://mspeekenbrink.github.io/sdam-r-companion/>.  All datasets analysed in
these books are provided in this package.  In addition, the package provides
functions to compute sample statistics (variance, standard deviation, mode),
create raincloud and enhanced Q-Q plots, and expand Anova results into omnibus
tests and tests of individual contrasts.")
    (license license:gpl3)))

(define-public r-sdam
  (package
    (name "r-sdam")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sdam" version))
       (sha256
        (base32 "0mqhg47z4knzcy1lxcd9fv8qk8ayszbl6sbxnmq5z0hx6wabkdf8"))))
    (properties `((upstream-name . "sdam")))
    (build-system r-build-system)
    (propagated-inputs (list r-multiplex r-grimport2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sdam-au/sdam/")
    (synopsis "Social Dynamics and Complexity in the Ancient Mediterranean")
    (description
     "This package provides digital tools for performing analyses within Social
Dynamics and complexity in the Ancient Mediterranean (SDAM), which is a research
group based at the Department of History and Classical Studies at Aarhus
University.")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-sdafilter
  (package
    (name "r-sdafilter")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sdafilter" version))
       (sha256
        (base32 "0y692j25pln46dp6xvaq17fkym0461yqfa6rv5avfw2b240k8jiq"))))
    (properties `((upstream-name . "sdafilter")))
    (build-system r-build-system)
    (propagated-inputs (list r-poet r-huge r-glmnet r-glasso))
    (home-page "https://cran.r-project.org/package=sdafilter")
    (synopsis "Symmetrized Data Aggregation")
    (description
     "We develop a new class of distribution free multiple testing rules for false
discovery rate (FDR) control under general dependence.  A key element in our
proposal is a symmetrized data aggregation (SDA) approach to incorporating the
dependence structure via sample splitting, data screening and information
pooling.  The proposed SDA filter first constructs a sequence of ranking
statistics that fulfill global symmetry properties, and then chooses a data
driven threshold along the ranking to control the FDR. For more information, see
the website below and the accompanying paper: Du et al. (2020), \"False Discovery
Rate Control Under General Dependence By Symmetrized Data Aggregation\",
<@code{arXiv:2002.11992>}.")
    (license license:gpl2+)))

(define-public r-sdaa
  (package
    (name "r-sdaa")
    (version "0.1-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SDaA" version))
       (sha256
        (base32 "1gs29jaj46i2bhbnpa0s6bhdrylz1s6wnif5cyc5m7xl19s413gb"))))
    (properties `((upstream-name . "SDaA")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SDaA")
    (synopsis "Sampling: Design and Analysis")
    (description
     "This package provides functions and Datasets from Lohr, S. (1999), Sampling:
Design and Analysis, Duxbury.")
    (license license:gpl3)))

(define-public r-sda
  (package
    (name "r-sda")
    (version "1.3.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sda" version))
       (sha256
        (base32 "14cpl7zj9aamdwyc8c9s7hnf660srvjv66lpnsm837d4r7k19icv"))))
    (properties `((upstream-name . "sda")))
    (build-system r-build-system)
    (propagated-inputs (list r-fdrtool r-entropy r-corpcor))
    (home-page "https://strimmerlab.github.io/software/sda/")
    (synopsis
     "Shrinkage Discriminant Analysis and CAT Score Variable Selection")
    (description
     "This package provides an efficient framework for high-dimensional linear and
diagonal discriminant analysis with variable selection.  The classifier is
trained using James-Stein-type shrinkage estimators and predictor variables are
ranked using correlation-adjusted t-scores (CAT scores).  Variable selection
error is controlled using false non-discovery rates or higher criticism.")
    (license license:gpl3+)))

(define-public r-scva
  (package
    (name "r-scva")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCVA" version))
       (sha256
        (base32 "1gdicpnbr8bdgrbwwslpwpzw2adpp94p8nhsrkdx0bjlpfhd4g9h"))))
    (properties `((upstream-name . "SCVA")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-plotly r-ggplot2 r-ggextra))
    (home-page "https://cran.r-project.org/package=SCVA")
    (synopsis "Single-Case Visual Analysis")
    (description
     "Make graphical representations of single case data and transform graphical
displays back to raw data, as discussed in Bulte and Onghena (2013)
<doi:10.22237/jmasm/1383280020>.  The package also includes tools for visually
analyzing single-case data, by displaying central location, variability and
trend.")
    (license license:gpl2+)))

(define-public r-scutr
  (package
    (name "r-scutr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scutr" version))
       (sha256
        (base32 "0n3lsj6cmkzwg6rc7s75vh54xaddxdin7p3v6269kb0v1brmkbi3"))))
    (properties `((upstream-name . "scutr")))
    (build-system r-build-system)
    (propagated-inputs (list r-smotefamily r-mclust))
    (home-page "https://github.com/s-kganz/scutr")
    (synopsis "Balancing Multiclass Datasets for Classification Tasks")
    (description
     "Imbalanced training datasets impede many popular classifiers.  To balance
training data, a combination of oversampling minority classes and undersampling
majority classes is useful.  This package implements the SCUT (SMOTE and
Cluster-based Undersampling Technique) algorithm as described in Agrawal et.
al. (2015) <doi:10.5220/0005595502260234>.  Their paper uses model-based
clustering and synthetic oversampling to balance multiclass training datasets,
although other resampling methods are provided in this package.")
    (license license:expat)))

(define-public r-scutils
  (package
    (name "r-scutils")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scUtils" version))
       (sha256
        (base32 "09c4vwrjj4r0nfcwb8q0a243m0v5wa3qisdlcy7ik58gyxp02zv8"))))
    (properties `((upstream-name . "scUtils")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridislite
                             r-viridis
                             r-scales
                             r-matrix
                             r-ggplot2
                             r-dplyr
                             r-assertthat))
    (home-page "https://cran.r-project.org/package=scUtils")
    (synopsis "Utility Functions for Single-Cell RNA Sequencing Data")
    (description
     "Analysis of single-cell RNA sequencing data can be simple and clear with the
right utility functions.  This package collects such functions, aiming to
fulfill the following criteria: code clarity over performance (i.e.  plain R
code instead of C code), most important analysis steps over completeness
(analysis by hand', not automated integration etc.), emphasis on quantitative
visualization (intensity-coded color scale, etc.).")
    (license license:gpl3)))

(define-public r-scuba
  (package
    (name "r-scuba")
    (version "1.11-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scuba" version))
       (sha256
        (base32 "1gmwzwkj0ylaqwz33djrv3hrqmwhr23q53yp94hv04mhijdh9myl"))))
    (properties `((upstream-name . "scuba")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=scuba")
    (synopsis "Diving Calculations and Decompression Models")
    (description
     "Code for describing and manipulating scuba diving profiles (depth-time curves)
and decompression models, for calculating the predictions of decompression
models, for calculating maximum no-decompression time and decompression tables,
and for performing mixed gas calculations.")
    (license license:gpl2+)))

(define-public r-sctools
  (package
    (name "r-sctools")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCtools" version))
       (sha256
        (base32 "0mwpia145swzqgxfh0h6fzmd1v5jxwigbw2rqm4rmla60yapn5hz"))))
    (properties `((upstream-name . "SCtools")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-synth
                             r-stringr
                             r-purrr
                             r-magrittr
                             r-ggplot2
                             r-future
                             r-furrr
                             r-dplyr
                             r-cvtools))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SCtools")
    (synopsis "Extensions for Synthetic Controls Analysis")
    (description
     "Extends the functionality of the package Synth as detailed in Abadie, Diamond,
and Hainmueller (2011) <doi:10.18637/jss.v042.i13>.  Includes generating and
plotting placebos, post/pre-MSPE (Mean Squared Prediction Error) significance
tests and plots, and calculating average treatment effects for multiple treated
units.")
    (license license:gpl3)))

(define-public r-sctenifoldnet
  (package
    (name "r-sctenifoldnet")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scTenifoldNet" version))
       (sha256
        (base32 "02ijwi487pfwhgdmpals2snx5dvdnkbkyp9g1yldw12a9v8rz8q7"))))
    (properties `((upstream-name . "scTenifoldNet")))
    (build-system r-build-system)
    (propagated-inputs (list r-rspectra r-rhpcblasctl r-pbapply r-matrix
                             r-mass))
    (home-page "https://github.com/cailab-tamu/scTenifoldNet")
    (synopsis
     "Construct and Compare scGRN from Single-Cell Transcriptomic Data")
    (description
     "This package provides a workflow based on machine learning methods to construct
and compare single-cell gene regulatory networks (@code{scGRN}) using
single-cell RNA-seq (@code{scRNA-seq}) data collected from different conditions.
 Uses principal component regression, tensor decomposition, and manifold
alignment, to accurately identify even subtly shifted gene expression programs.
See <doi:10.1016/j.patter.2020.100139> for more details.")
    (license license:gpl2+)))

(define-public r-sctenifoldknk
  (package
    (name "r-sctenifoldknk")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scTenifoldKnk" version))
       (sha256
        (base32 "0d6gcr0qrm7mc36n2lihl7bq9vj40dblqswznbwvxichv8kc5svz"))))
    (properties `((upstream-name . "scTenifoldKnk")))
    (build-system r-build-system)
    (propagated-inputs (list r-sctenifoldnet r-rspectra r-pbapply r-matrix
                             r-mass))
    (home-page "https://github.com/cailab-tamu/scTenifoldKnk")
    (synopsis
     "In-Silico Knockout Experiments from Single-Cell Gene Regulatory Networks")
    (description
     "This package provides a workflow based on @code{scTenifoldNet} to perform
in-silico knockout experiments using single-cell RNA sequencing
(@code{scRNA-seq}) data from wild-type (WT) control samples as input.  First,
the package constructs a single-cell gene regulatory network (@code{scGRN}) and
knocks out a target gene from the adjacency matrix of the WT @code{scGRN} by
setting the geneâs outdegree edges to zero.  Then, it compares the knocked out
@code{scGRN} with the WT @code{scGRN} to identify differentially regulated
genes, called virtual-knockout perturbed genes, which are used to assess the
impact of the gene knockout and reveal the geneâs function in the analyzed
cells.")
    (license license:gpl2+)))

(define-public r-scspatialsim
  (package
    (name "r-scspatialsim")
    (version "0.1.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scSpatialSIM" version))
       (sha256
        (base32 "04r3fqsv0ycjycy11r03ga05s4wzjlmhxifw8cm92qkcl8h4av40"))))
    (properties `((upstream-name . "scSpatialSIM")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-spatstat-random
                             r-spatstat-geom
                             r-proxy
                             r-pbmcapply
                             r-magrittr
                             r-ggpubr
                             r-ggplot2
                             r-dplyr
                             r-crayon))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/FridleyLab/scSpatialSIM")
    (synopsis "Point Pattern Simulator for Spatial Cellular Data")
    (description
     "Single cell resolution data has been valuable in learning about tissue
microenvironments and interactions between cells or spots.  This package allows
for the simulation of this level of data, be it single cell or âspotsâ, in
both a univariate (single metric or cell type) and bivariate (2 or more metrics
or cell types) ways.  As more technologies come to marker, more methods will be
developed to derive spatial metrics from the data which will require a way to
benchmark methods against each other.  Additionally, as the field currently
stands, there is not a gold standard method to be compared against.  We set out
to develop an R package that will allow users to simulate point patterns that
can be biologically informed from different tissue domains, holes, and varying
degrees of clustering/colocalization.  The data can be exported as spatial files
and a summary file (like HALO').
<https://github.com/@code{FridleyLab/scSpatialSIM/>}.")
    (license license:expat)))

(define-public r-scsorter
  (package
    (name "r-scsorter")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scSorter" version))
       (sha256
        (base32 "04gnw9i4j8sk3fjh74idk0d6wflw17z1c3afz6wir5pc4y3lzn2j"))))
    (properties `((upstream-name . "scSorter")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=scSorter")
    (synopsis "Implementation of 'scSorter' Algorithm")
    (description
     "This package implements the algorithm described in Guo, H., and Li, J.,
\"@code{scSorter}: assigning cells to known cell types according to known marker
genes\".  Cluster cells to known cell types based on marker genes specified for
each cell type.")
    (license license:gpl3)))

(define-public r-scryr
  (package
    (name "r-scryr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scryr" version))
       (sha256
        (base32 "040x2qvgrr8j5k3y2g24kngjys271radqhh3jsqi07dhg9bybr5q"))))
    (properties `((upstream-name . "scryr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-purrr r-httr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://curso-r.github.io/scryr/")
    (synopsis "An Interface to the 'Scryfall' API")
    (description
     "This package provides a simple, light, and robust interface between R and the
Scryfall card data API <https://scryfall.com/docs/api>.")
    (license license:expat)))

(define-public r-scrutiny
  (package
    (name "r-scrutiny")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scrutiny" version))
       (sha256
        (base32 "0f03nd378ks6cr15gkjmq0756cbzvrz5gp912nvdgc5r59q7j665"))))
    (properties `((upstream-name . "scrutiny")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-lifecycle
                             r-glue
                             r-ggrepel
                             r-ggplot2
                             r-dplyr
                             r-corrr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://lhdjung.github.io/scrutiny/")
    (synopsis "Error Detection in Science")
    (description
     "Test published summary statistics for consistency (Brown and Heathers, 2017,
<doi:10.1177/1948550616673876>; Allard, 2018,
<https://aurelienallard.netlify.app/post/anaytic-grimmer-possibility-standard-deviations/>;
Heathers and Brown, 2019, <https://osf.io/5vb3u/>).  The package also provides
infrastructure for implementing new error detection techniques.")
    (license license:expat)))

(define-public r-scrt
  (package
    (name "r-scrt")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCRT" version))
       (sha256
        (base32 "0wkxc22hv08riivjl09lhxvffqxxdy4xnvdmc2y69gssyinkbln1"))))
    (properties `((upstream-name . "SCRT")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SCRT")
    (synopsis "Single-Case Randomization Tests")
    (description
     "Design single-case phase, alternation and multiple-baseline experiments, and
conduct randomization tests on data gathered by means of such designs, as
discussed in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>.")
    (license license:gpl2+)))

(define-public r-scroshi
  (package
    (name "r-scroshi")
    (version "1.0.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scROSHI" version))
       (sha256
        (base32 "15mgbgb21wz1gnkw6mxkvckxj345cxi4w5hzwgxfqjvxq9wx6bik"))))
    (properties `((upstream-name . "scROSHI")))
    (build-system r-build-system)
    (propagated-inputs (list r-uwot r-summarizedexperiment
                             r-singlecellexperiment r-s4vectors r-limma))
    (home-page "https://cran.r-project.org/package=scROSHI")
    (synopsis "Robust Supervised Hierarchical Identification of Single Cells")
    (description
     "Identifying cell types based on expression profiles is a pillar of single cell
analysis. @code{scROSHI} identifies cell types based on expression profiles of
single cell analysis by utilizing previously obtained cell type specific gene
sets.  It takes into account the hierarchical nature of cell type relationship
and does not require training or annotated data.  A detailed description of the
method can be found at: Prummer, Bertolini, Bosshard, Barkmann, Yates, Boeva,
The Tumor Profiler Consortium, Stekhoven, and Singer (2022)
<doi:10.1101/2022.04.05.487176>.")
    (license license:expat)))

(define-public r-scrollrevealr
  (package
    (name "r-scrollrevealr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scrollrevealR" version))
       (sha256
        (base32 "0k8ffapwxpb25wphm76arma095hrh3wf0gpkx1z6g4rplahf4jxa"))))
    (properties `((upstream-name . "scrollrevealR")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-htmltools r-glue))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://github.com/feddelegrand7/scrollrevealR")
    (synopsis
     "Animate 'shiny' Elements when They Scroll into View using the 'scrollrevealjs' Library")
    (description
     "Allows the user to animate shiny elements when scrolling to view them.  The
animations are activated using the scrollrevealjs library.  See
<https://scrollrevealjs.org/> for more information.")
    (license license:expat)))

(define-public r-scrobbler
  (package
    (name "r-scrobbler")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scrobbler" version))
       (sha256
        (base32 "1f8vcx79yzhy2698xgr7a863hqhqad1kx1zc9xy83bigghsjq1py"))))
    (properties `((upstream-name . "scrobbler")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-httr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/condwanaland/scrobbler")
    (synopsis "Download 'Scrobbles' from 'Last.fm'")
    (description
     "Last.fm'<https://www.last.fm> is a music platform focussed on building a
detailed profile of a users listening habits.  It does this by scrobbling
(recording) every track you listen to on other platforms ('spotify', youtube',
soundcloud etc) and transferring them to your Last.fm database.  This allows
Last.fm to act as a complete record of your entire listening history.  scrobbler
provides helper functions to download and analyse your listening history in R.")
    (license license:gpl3)))

(define-public r-scrnatools
  (package
    (name "r-scrnatools")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scRNAtools" version))
       (sha256
        (base32 "0x0lniqhq6q87y08kkkyvmk1g5b8i73hm3h86ba8rcv0v0n7ap9a"))))
    (properties `((upstream-name . "scRNAtools")))
    (build-system r-build-system)
    (propagated-inputs (list r-tpea
                             r-survival
                             r-scatterplot3d
                             r-rtsne
                             r-rmisc
                             r-reshape2
                             r-plyr
                             r-performanceanalytics
                             r-limma
                             r-lattice
                             r-igraph
                             r-hmisc
                             r-ggthemes
                             r-ggplot2
                             r-foreach
                             r-edger
                             r-corrplot
                             r-consensusclusterplus
                             r-all))
    (home-page "https://cran.r-project.org/package=scRNAtools")
    (synopsis "Single Cell RNA Sequencing Data Analysis Tools")
    (description
     "We integrated the common analysis methods utilized in single cell RNA sequencing
data, which included cluster method, principal components analysis (PCA), the
filter of differentially expressed genes, pathway enrichment analysis and
correlated analysis methods.")
    (license license:gpl2)))

(define-public r-scrnastat
  (package
    (name "r-scrnastat")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scRNAstat" version))
       (sha256
        (base32 "0yv5np7jafmygg77bwisrha5y2rdb1va906ngzs4zc8mvxr0dvlm"))))
    (properties `((upstream-name . "scRNAstat")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-seurat
                             r-patchwork
                             r-matrix
                             r-magrittr
                             r-ggplot2
                             r-dplyr
                             r-clustree))
    (home-page "https://cran.r-project.org/package=scRNAstat")
    (synopsis "Pipeline to Process Single Cell RNAseq Data")
    (description
     "This package provides a pipeline that can process single or multiple Single Cell
RNAseq samples primarily specializes in Clustering and Dimensionality Reduction.
 Meanwhile we use common cell type marker genes for T cells, B cells, Myeloid
cells, Epithelial cells, and stromal cells (Fiboblast, Endothelial cells,
Pericyte, Smooth muscle cells) to visualize the Seurat clusters, to facilitate
labeling them by biological names.  Once users named each cluster, they can
evaluate the quality of them again and find the de novo marker genes also.")
    (license license:agpl3+)))

(define-public r-scrnaimm
  (package
    (name "r-scrnaimm")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ScRNAIMM" version))
       (sha256
        (base32 "0r69nbmpd42j547li34psj9kbkf5wckayzs9mz0ybjpzi92wx99y"))))
    (properties `((upstream-name . "ScRNAIMM")))
    (build-system r-build-system)
    (propagated-inputs (list r-scdha r-nortest r-mclust r-magrittr r-dplyr))
    (home-page "https://cran.r-project.org/package=ScRNAIMM")
    (synopsis
     "Performing Single-Cell RNA-Seq Imputation by Using Mean/Median Imputation")
    (description
     "Performing single-cell imputation in a way that preserves the biological
variations in the data.  The package clusters the input data to do imputation
for each cluster, and do a distribution check using the Anderson-Darling
normality test to impute dropouts using mean or median (Yazici, B., & Yolacan,
S. (2007) <DOI:10.1080/10629360600678310>).")
    (license license:gpl3)))

(define-public r-scrm
  (package
    (name "r-scrm")
    (version "1.7.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scrm" version))
       (sha256
        (base32 "195ilhqfcag5zljw5h031vxisy69mn0jjl2n4r3bxirywim1d5vg"))))
    (properties `((upstream-name . "scrm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/scrm/scrm-r")
    (synopsis "Simulating the Evolution of Biological Sequences")
    (description
     "This package provides a coalescent simulator that allows the rapid simulation of
biological sequences under neutral models of evolution, see Staab et al. (2015)
<doi:10.1093/bioinformatics/btu861>.  Different to other coalescent based
simulations, it has an optional approximation parameter that allows for high
accuracy while maintaining a linear run time cost for long sequences.  It is
optimized for simulating massive data sets as produced by Next- Generation
Sequencing technologies for up to several thousand sequences.")
    (license license:gpl3+)))

(define-public r-scripturs
  (package
    (name "r-scripturs")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scriptuRs" version))
       (sha256
        (base32 "1wbvfqcc0raazhdc6pyp93q859ilxfz9rz338p3sb7fjyxvparbl"))))
    (properties `((upstream-name . "scriptuRs")))
    (build-system r-build-system)
    (home-page "https://github.com/andrewheiss/scriptuRs")
    (synopsis "Complete Text of the LDS Scriptures")
    (description
     "Full text, in data frames containing one row per verse, of the Standard Works of
The Church of Jesus Christ of Latter-day Saints (LDS).  These are the Old
Testament, (KJV), the New Testament (KJV), the Book of Mormon, the Doctrine and
Covenants, and the Pearl of Great Price.")
    (license license:expat)))

(define-public r-scriptname
  (package
    (name "r-scriptname")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scriptName" version))
       (sha256
        (base32 "1rd3qj7c7pg454a5mxhr64g4kp15cqf5lf8lbsh9x88llvkjci5v"))))
    (properties `((upstream-name . "scriptName")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-purrr))
    (home-page "https://github.com/MullinsLab/scriptName")
    (synopsis "Determine a Script's Filename from Within the Script Itself")
    (description
     "This package provides a small set of functions wrapping up the call stack and
command line inspection needed to determine a running script's filename from
within the script itself.")
    (license license:expat)))

(define-public r-scriptmapr
  (package
    (name "r-scriptmapr")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ScriptMapR" version))
       (sha256
        (base32 "18pd23yqn3ai1yfl149081n42hbh0d57hm71s2lhm1z7a5ccjkmf"))))
    (properties `((upstream-name . "ScriptMapR")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rcy3 r-formatr r-dplyr))
    (home-page "https://cran.r-project.org/package=ScriptMapR")
    (synopsis "R Script Visualization in Cytoscape")
    (description
     "Displays the content of a R script into the Cytoscape network-visualization app
<https://cytoscape.org/>.")
    (license license:expat)))

(define-public r-scriptloc
  (package
    (name "r-scriptloc")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scriptloc" version))
       (sha256
        (base32 "0k0dmh833chyvcbw5b7i85m0xgj5kd635h70x5y891jcwdrbccvh"))))
    (properties `((upstream-name . "scriptloc")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=scriptloc")
    (synopsis
     "Get the Location of the R Script that is Being Sourced/Executed")
    (description
     "This package provides functions to retrieve the location of R scripts loaded
through the source() function or run from the command line using the Rscript
command.  This functionality is analogous to the Bash shell's ${BASH_SOURCE[0]}.
 Users can first set the project root's path relative to the script path and
then all subsequent paths relative to the root.  This system ensures that all
paths lead to the same location regardless of where any script is
executed/loaded from without resorting to the use of setwd() at the top of the
scripts.")
    (license license:expat)))

(define-public r-scriptexec
  (package
    (name "r-scriptexec")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scriptexec" version))
       (sha256
        (base32 "0k10x0npdga1nmchzz9xxv0qijnq2n9pb49z1g06bmva1z8wb4wg"))))
    (properties `((upstream-name . "scriptexec")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sagiegurari/scriptexec")
    (synopsis "Execute Native Scripts")
    (description
     "Run complex native scripts with a single command, similar to system commands.")
    (license license:asl2.0)))

(define-public r-script
  (package
    (name "r-script")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "script" version))
       (sha256
        (base32 "1ba0mr77g4v4nv7yfs5yaj7x8yqrl3q8a1yia105pclgwq9ybs8i"))))
    (properties `((upstream-name . "script")))
    (build-system r-build-system)
    (propagated-inputs (list r-rstudioapi))
    (home-page "https://cran.r-project.org/package=script")
    (synopsis "Identify Script Name")
    (description
     "Identifies the name of the current script in a variety of contexts, e.g.
interactively or when sourced.  Attempts to support RStudio environment.  Based
on <https://stackoverflow.com/a/32016824/2292993> and
<https://stackoverflow.com/a/35842176/2292993>.")
    (license license:gpl3)))

(define-public r-scrip
  (package
    (name "r-scrip")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCRIP" version))
       (sha256
        (base32 "1cv8443y2s67q3krsyj7r2d1vqv01w8xr0iz8dz4kijmhksyg7ng"))))
    (properties `((upstream-name . "SCRIP")))
    (build-system r-build-system)
    (propagated-inputs (list r-summarizedexperiment
                             r-singlecellexperiment
                             r-seurat
                             r-s4vectors
                             r-mgcv
                             r-knitr
                             r-fitdistrplus
                             r-edger
                             r-crayon
                             r-checkmate
                             r-biocmanager
                             r-biocgenerics))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/thecailab/SCRIP")
    (synopsis "An Accurate Simulator for Single-Cell RNA Sequencing Data")
    (description
     "We provide a comprehensive scheme that is capable of simulating Single Cell RNA
Sequencing data for various parameters of Biological Coefficient of Variation,
busting kinetics, differential expression (DE), cell or sample groups, cell
trajectory, batch effect and other experimental designs.  SCRIP proposed and
compared two frameworks with Gamma-Poisson and Beta-Gamma-Poisson models for
simulating Single Cell RNA Sequencing data.  Other reference is available in
Zappia et al. (2017)
<https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1305-0>.")
    (license license:gpl3)))

(define-public r-scribe
  (package
    (name "r-scribe")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scribe" version))
       (sha256
        (base32 "02gwv7c107vaphkpr3giz2lqbaabk32fg6yf6zi0sirkp8dph93d"))))
    (properties `((upstream-name . "scribe")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://jmbarbone.github.io/scribe/")
    (synopsis "Command Argument Parsing")
    (description
     "This package provides a base dependency solution with basic argument parsing for
use with Rscript'.")
    (license license:expat)))

(define-public r-screenshot
  (package
    (name "r-screenshot")
    (version "0.9.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "screenshot" version))
       (sha256
        (base32 "16b1dzagy92jk1y1dymq6r2yzri3lynaj9a2bjqqjn0bh21aqsxr"))))
    (properties `((upstream-name . "screenshot")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-rlang
                             r-purrr
                             r-imager
                             r-fs
                             r-dplyr))
    (home-page "https://github.com/matutosi/screenshot")
    (synopsis "Take Screenshots (Screen Capture) from R Command")
    (description
     "Take screenshots from R command and locate an image position.")
    (license license:expat)))

(define-public r-screenot
  (package
    (name "r-screenot")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ScreeNOT" version))
       (sha256
        (base32 "1vgrfwsl096w2az3dwq7pvfsjdvsmzrrkkkzsnfxld018raf4fvg"))))
    (properties `((upstream-name . "ScreeNOT")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=ScreeNOT")
    (synopsis
     "'ScreeNOT': MSE-Optimal Singular Value Thresholding in Correlated Noise")
    (description
     "Optimal hard thresholding of singular values.  The procedure adaptively
estimates the best singular value threshold under unknown noise characteristics.
 The threshold chosen by @code{ScreeNOT} is optimal (asymptotically, in the
sense of minimum Frobenius error) under the the so-called \"Spiked model\" of a
low-rank matrix observed in additive noise.  In contrast to previous works, the
noise is not assumed to be i.i.d.  or white; it can have an essentially
arbitrary and unknown correlation structure, across either rows, columns or
both. @code{ScreeNOT} is proposed to practitioners as a mathematically solid
alternative to Cattell's ever-popular but vague Scree Plot heuristic from 1966.
If you use this package, please cite our paper: David L. Donoho, Matan Gavish
and Elad Romanov (2023). \"@code{ScreeNOT}: Exact MSE-optimal singular value
thresholding in correlated noise.\" Annals of Statistics, 2023 (To appear).
<@code{arXiv:2009.12297>}.")
    (license license:expat)))

(define-public r-screenclean
  (package
    (name "r-screenclean")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ScreenClean" version))
       (sha256
        (base32 "0haanr05g4vwp5apncyzv8i3r61g4xf9ihm8ilcabcgpri56gpjk"))))
    (properties `((upstream-name . "ScreenClean")))
    (build-system r-build-system)
    (propagated-inputs (list r-quadprog r-matrix r-mass))
    (home-page "https://cran.r-project.org/package=ScreenClean")
    (synopsis "Screen and clean variable selection procedures")
    (description
     "Routines for a collection of screen-and-clean type variable selection
procedures, including UPS and GS.")
    (license license:gpl2+)))

(define-public r-scrappy
  (package
    (name "r-scrappy")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scrappy" version))
       (sha256
        (base32 "0dckq2pdqabnv5cinfqgbrjxp55x4z81lp8xh4yhyb2cnz9gdsqy"))))
    (properties `((upstream-name . "scrappy")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-rvest r-magrittr))
    (home-page "https://github.com/villegar/scrappy/")
    (synopsis "Simple Web Scraper")
    (description
     "This package provides a group of functions to scrape data from different
websites, for academic purposes.")
    (license license:expat)))

(define-public r-scraper
  (package
    (name "r-scraper")
    (version "0.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scrapeR" version))
       (sha256
        (base32 "1hrxlj3x05vksa7pj8qkgyrc50l1b3d006nlzn174nn9ywhw6ypg"))))
    (properties `((upstream-name . "scrapeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rvest r-magrittr r-httr))
    (home-page "https://cran.r-project.org/package=scrapeR")
    (synopsis
     "These Functions Fetch and Extract Text Content from Specified Web Pages")
    (description
     "The @code{scrapeR} package utilizes functions that fetch and extract text
content from specified web pages.  It handles HTTP errors and parses HTML
efficiently.  The package can handle hundreds of websites at a time using the
@code{scrapeR_in_batches}() command.")
    (license license:expat)))

(define-public r-scraep
  (package
    (name "r-scraep")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scraEP" version))
       (sha256
        (base32 "1qkfd4bp3sd3xrh9bdaqwmmvx7f9rvjcch6hi2p387ky04ywwqn4"))))
    (properties `((upstream-name . "scraEP")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-xml r-rvest r-data-table))
    (home-page "https://cran.r-project.org/package=scraEP")
    (synopsis "Scrape the Web with Extra Power")
    (description
     "This package provides tools for scraping information from webpages and other XML
contents, using XPath or CSS selectors.")
    (license license:gpl3+)))

(define-public r-scquantum
  (package
    (name "r-scquantum")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scquantum" version))
       (sha256
        (base32 "0v263qxf3qp7hra3l8fyw8a625z9ym2h2zg5b9lsysdiznbi9bww"))))
    (properties `((upstream-name . "scquantum")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=scquantum")
    (synopsis
     "Estimate Ploidy and Absolute Copy Number from Single Cell Sequencing")
    (description
     "Given bincount data from single-cell copy number profiling (segmented or
unsegmented), estimates ploidy, and uses the ploidy estimate to scale the data
to absolute copy numbers.  Uses the modular quantogram proposed by Kendall
(1986) <doi:10.1002/0471667196.ess2129.pub2>, modified by weighting segments
according to confidence, and quantifying confidence in the estimate using a
theoretical quantogram.  Includes optional fused-lasso segmentation with the
algorithm in Johnson (2013) <doi:10.1080/10618600.2012.681238>, using the
implementation from glmgen by Arnold, Sadhanala, and Tibshirani.")
    (license license:gpl2+)))

(define-public r-scqe
  (package
    (name "r-scqe")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scqe" version))
       (sha256
        (base32 "1113zbc2jyq03fh8l1h6yidf25hh5dcqrk6wkk9flqwnbrvfkwrr"))))
    (properties `((upstream-name . "scqe")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-aer))
    (home-page "https://cran.r-project.org/package=scqe")
    (synopsis "Stability Controlled Quasi-Experimentation")
    (description
     "This package provides functions to implement the stability controlled
quasi-experiment (SCQE) approach to study the effects of newly adopted
treatments that were not assigned at random.  This package contains tools to
help users avoid making statistical assumptions that rely on infeasible
assumptions.  Methods developed in Hazlett (2019) <doi:10.1002/sim.8717>.")
    (license license:expat)))

(define-public r-scpubr
  (package
    (name "r-scpubr")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCpubr" version))
       (sha256
        (base32 "121q8f823rcmn56sz17d2khismvf9hnr3d7dwmjavxwy25bhb76b"))))
    (properties `((upstream-name . "SCpubr")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/enblacar/SCpubr/")
    (synopsis
     "Generate Publication Ready Visualizations of Single Cell Transcriptomics Data")
    (description
     "This package provides a system that provides a streamlined way of generating
publication ready plots for known Single-Cell transcriptomics data in a
âpublication readyâ format.  This is, the goal is to automatically generate
plots with the highest quality possible, that can be used right away or with
minimal modifications for a research article.")
    (license license:gpl3)))

(define-public r-scpoisson
  (package
    (name "r-scpoisson")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scpoisson" version))
       (sha256
        (base32 "0v8is1y9mqbvcvhqay1a0ycbhfxwxqkxifmbxb0sjzxg02mla65x"))))
    (properties `((upstream-name . "scpoisson")))
    (build-system r-build-system)
    (propagated-inputs (list r-wgcna
                             r-tidyr
                             r-seuratobject
                             r-seurat
                             r-rdpack
                             r-purrr
                             r-matrixstats
                             r-matrix
                             r-magrittr
                             r-glmpca
                             r-ggplot2
                             r-dplyr
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=scpoisson")
    (synopsis "Single Cell Poisson Probability Paradigm")
    (description
     "Useful to visualize the Poissoneity (an independent Poisson statistical
framework, where each RNA measurement for each cell comes from its own
independent Poisson distribution) of Unique Molecular Identifier (UMI) based
single cell RNA sequencing (@code{scRNA-seq}) data, and explore cell clustering
based on model departure as a novel data representation.")
    (license license:expat)))

(define-public r-scplot
  (package
    (name "r-scplot")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scplot" version))
       (sha256
        (base32 "0ksk93i7vj616kbzs1i6wrngrzwqjb70imb1vmbk5r5lf9sningn"))))
    (properties `((upstream-name . "scplot")))
    (build-system r-build-system)
    (propagated-inputs (list r-scan r-mblm r-ggplot2))
    (home-page "https://cran.r-project.org/package=scplot")
    (synopsis "Plot Function for Single-Case Data Frames")
    (description
     "Add-on for the scan package that creates plots from single-case data frames
('scdf').  It includes functions for styling single-case plots, adding
phase-based lines to indicate various statistical parameters, and predefined
themes for presentations and publications.  More information and in depth
examples can be found in the online book \"Analyzing Single-Case Data with R and
scan\" JÃ¼rgen Wilbert (2023) <https://jazznbass.github.io/scan-Book/>.")
    (license license:gpl3+)))

(define-public r-scploidy
  (package
    (name "r-scploidy")
    (version "0.6.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scPloidy" version))
       (sha256
        (base32 "0p8rn2c58601yjkampgw360w6r8sbwg0rk007jnvkayfad5xhdvd"))))
    (properties `((upstream-name . "scPloidy")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rsamtools
                             r-rlang
                             r-nimble
                             r-mixtools
                             r-matrixstats
                             r-mass
                             r-magrittr
                             r-genomicranges
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=scPloidy")
    (synopsis "Infer Ploidy of Single Cells")
    (description
     "Compute ploidy of single cells (or nuclei) based on single-cell (or
single-nucleus) ATAC-seq (Assay for Transposase-Accessible Chromatin using
sequencing) data <https://github.com/fumi-github/@code{scPloidy>}.")
    (license license:expat)))

(define-public r-scpi
  (package
    (name "r-scpi")
    (version "2.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scpi" version))
       (sha256
        (base32 "12wr7kk57qv28kypk5czr0gv2xfib040m8k5047kdgd4fj3i6fhr"))))
    (properties `((upstream-name . "scpi")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-reshape2
                             r-qtools
                             r-purrr
                             r-matrix
                             r-mass
                             r-magrittr
                             r-ggplot2
                             r-foreach
                             r-fastdummies
                             r-ecosolver
                             r-dplyr
                             r-dosnow
                             r-cvxr
                             r-abind))
    (home-page "https://nppackages.github.io/scpi/")
    (synopsis
     "Prediction Intervals for Synthetic Control Methods with Multiple Treated Units and Staggered Adoption")
    (description
     "Implementation of prediction and inference procedures for Synthetic Control
methods using least square, lasso, ridge, or simplex-type constraints.
Uncertainty is quantified with prediction intervals as developed in Cattaneo,
Feng, and Titiunik (2021)
<https://nppackages.github.io/references/Cattaneo-Feng-Titiunik_2021_JASA.pdf>
for a single treated unit and in Cattaneo, Feng, Palomba, and Titiunik (2023)
<@code{arXiv:2210.05026>} for multiple treated units and staggered adoption.
More details about the software implementation can be found in Cattaneo, Feng,
Palomba, and Titiunik (2022) <@code{arXiv:2202.05984>}.")
    (license license:gpl2)))

(define-public r-scperturbr
  (package
    (name "r-scperturbr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scperturbR" version))
       (sha256
        (base32 "0bddsfms1zaq8hfviw68n5n5mjiwg47hiyzbnnwbimxaamz4rkkb"))))
    (properties `((upstream-name . "scperturbR")))
    (build-system r-build-system)
    (propagated-inputs (list r-seurat r-rdist r-energy r-dplyr))
    (home-page "https://github.com/sanderlab/scPerturb/tree/master/package_r")
    (synopsis "E-Statistics for Seurat Objects")
    (description
     "R version of scperturb tool for single-cell perturbation analysis.  Contains
wrappers for performing E-statistics for Seurat objects.  More details on the
method can be found in Peidli et al. (2023) <doi:10.1101/2022.08.20.504663> and
in SzÃ©kely and Rizzo (2004).")
    (license license:expat)))

(define-public r-scperf
  (package
    (name "r-scperf")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCperf" version))
       (sha256
        (base32 "1kqi3sv9ds58l20pdcnjrrbf7fin82j73yqj5rbx4kjdw560ylb2"))))
    (properties `((upstream-name . "SCperf")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SCperf")
    (synopsis
     "Functions for Planning and Managing Inventories in a Supply Chain")
    (description
     "This package implements different inventory models, the bullwhip effect and
other supply chain performance variables.  Marchena Marlene (2010)
<@code{arXiv:1009.3977>}.")
    (license license:gpl3)))

(define-public r-scouter
  (package
    (name "r-scouter")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCOUTer" version))
       (sha256
        (base32 "1s0mmzfz0zhr5v4wagcjwdba8a5f57xr26vwml92dnw6lai37pj4"))))
    (properties `((upstream-name . "SCOUTer")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggpubr r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SCOUTer")
    (synopsis "Simulate Controlled Outliers")
    (description
     "Using principal component analysis as a base model, SCOUTer offers a new
approach to simulate outliers in a simple and precise way.  The user can
generate new observations defining them by a pair of well-known statistics: the
Squared Prediction Error (SPE) and the Hotelling's T^2 (T^2) statistics.  Just
by introducing the target values of the SPE and T^2, SCOUTer returns a new set
of observations with the desired target properties.  Authors: Alba GonzÃ¡lez,
Abel Folch-Fortuny, Francisco Arteaga and Alberto Ferrer (2020).")
    (license license:gpl3)))

(define-public r-scout
  (package
    (name "r-scout")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scout" version))
       (sha256
        (base32 "0vr497g7g1xhf75cwjbjsns2fvdzy86iibbf5w0g2xylw82s4lh2"))))
    (properties `((upstream-name . "scout")))
    (build-system r-build-system)
    (propagated-inputs (list r-glasso))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=scout")
    (synopsis
     "Implements the Scout Method for Covariance-Regularized Regression")
    (description
     "This package implements the Scout method for regression, described in
\"Covariance-regularized regression and classification for high-dimensional
problems\", by Witten and Tibshirani (2008), Journal of the Royal Statistical
Society, Series B 71(3): 615-636.")
    (license license:gpl2+)))

(define-public r-scottknottesd
  (package
    (name "r-scottknottesd")
    (version "2.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ScottKnottESD" version))
       (sha256
        (base32 "0gjpjd8ljnp3v4fc72vhwjgscfw9vfivx710yg683pp30z0sv9nf"))))
    (properties `((upstream-name . "ScottKnottESD")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-forecast r-effsize r-car))
    (home-page "https://github.com/klainfo/ScottKnottESD")
    (synopsis "The Scott-Knott Effect Size Difference (ESD) Test")
    (description
     "The Scott-Knott Effect Size Difference (ESD) test is a mean comparison approach
that leverages a hierarchical clustering to partition the set of treatment means
(e.g., means of variable importance scores, means of model performance) into
statistically distinct groups with non-negligible difference [Tantithamthavorn
et al., (2018) <doi:10.1109/TSE.2018.2794977>].")
    (license license:gpl2+)))

(define-public r-scottknott
  (package
    (name "r-scottknott")
    (version "1.3-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ScottKnott" version))
       (sha256
        (base32 "01cgzqwfabn31s3y1dk6d8z1s5qbsjim191d2yv3zq3vdc4drpma"))))
    (properties `((upstream-name . "ScottKnott")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable r-doby))
    (home-page "https://github.com/ivanalaman/ScottKnott")
    (synopsis "The ScottKnott Clustering Algorithm")
    (description "Perform the balanced (Scott and Knott, 1974) and unbalanced
<doi:10.1590/1984-70332017v17n1a1> Scott & Knott algorithm.")
    (license license:gpl2+)))

(define-public r-scorpius
  (package
    (name "r-scorpius")
    (version "1.0.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCORPIUS" version))
       (sha256
        (base32 "0i464i6fbxxcrvc48ajmz7i75sqfwizjypbbmc0mmj0dgmnxgby9"))))
    (properties `((upstream-name . "SCORPIUS")))
    (build-system r-build-system)
    (propagated-inputs (list r-tsp
                             r-tidyr
                             r-reshape2
                             r-rcolorbrewer
                             r-rann
                             r-ranger
                             r-purrr
                             r-princurve
                             r-pheatmap
                             r-pbapply
                             r-mclust
                             r-matrix
                             r-mass
                             r-lmds
                             r-ggplot2
                             r-dynwrap
                             r-dynutils
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rcannood/SCORPIUS")
    (synopsis
     "Inferring Developmental Chronologies from Single-Cell RNA Sequencing Data")
    (description
     "An accurate and easy tool for performing linear trajectory inference on single
cells using single-cell RNA sequencing data.  In addition, SCORPIUS provides
functions for discovering the most important genes with respect to the
reconstructed trajectory, as well as nice visualisation tools.  Cannoodt et al.
(2016) <doi:10.1101/079509>.")
    (license license:gpl3)))

(define-public r-scorpion
  (package
    (name "r-scorpion")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCORPION" version))
       (sha256
        (base32 "0bkhg3y7irbigy14l9a747032ljhssvgcj1gdqs07p76k0x6fv5y"))))
    (properties `((upstream-name . "SCORPION")))
    (build-system r-build-system)
    (propagated-inputs (list r-rhpcblasctl
                             r-rann
                             r-pbapply
                             r-matrix
                             r-irlba
                             r-igraph
                             r-cli))
    (home-page "https://cran.r-project.org/package=SCORPION")
    (synopsis
     "Single Cell Oriented Reconstruction of PANDA Individual Optimized Networks")
    (description
     "Constructs gene regulatory networks from single-cell gene expression data using
the PANDA (Passing Attributes between Networks for Data Assimilation) algorithm.")
    (license license:gpl3)))

(define-public r-scornet
  (package
    (name "r-scornet")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCORNET" version))
       (sha256
        (base32 "13aibikvrc3ril4gqki4fk4y7ha85bniqabmaifzbr1dw5xy2wxm"))))
    (properties `((upstream-name . "SCORNET")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rcpparmadillo
                             r-rcpp
                             r-pracma
                             r-matrix
                             r-foreach
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/celehs/SCORNET")
    (synopsis "Semi-Supervised Calibration of Risk with Noisy Event Times")
    (description
     "This package provides a consistent, semi-supervised, non-parametric survival
curve estimator optimized for efficient use of Electronic Health Record (EHR)
data with a limited number of current status labels.  See van der Laan and
Robins (1997) <doi:10.2307/2670119>.")
    (license license:gpl3)))

(define-public r-scoringutils
  (package
    (name "r-scoringutils")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scoringutils" version))
       (sha256
        (base32 "1ylw7ql5ayfn6fcxnmmmr3zpvczax6mhmis28r03kwm8val5q233"))))
    (properties `((upstream-name . "scoringutils")))
    (build-system r-build-system)
    (propagated-inputs (list r-scoringrules
                             r-rlang
                             r-lifecycle
                             r-ggplot2
                             r-ggdist
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://doi.org/10.48550/arXiv.2205.07090")
    (synopsis "Utilities for Scoring and Assessing Predictions")
    (description
     "This package provides a collection of metrics and proper scoring rules (Tilmann
Gneiting & Adrian E Raftery (2007) <doi:10.1198/016214506000001437>, Jordan, A.,
KrÃ¼ger, F., & Lerch, S. (2019) <doi:10.18637/jss.v090.i12>) within a consistent
framework for evaluation, comparison and visualisation of forecasts.  In
addition to proper scoring rules, functions are provided to assess bias,
sharpness and calibration (Sebastian Funk, Anton Camacho, Adam J. Kucharski,
Rachel Lowe, Rosalind M. Eggo, W. John Edmunds (2019)
<doi:10.1371/journal.pcbi.1006785>) of forecasts.  Several types of predictions
(e.g. binary, discrete, continuous) which may come in different formats (e.g.
forecasts represented by predictive samples or by quantiles of the predictive
distribution) can be evaluated.  Scoring metrics can be used either through a
convenient data.frame format, or can be applied as individual functions in a
vector / matrix format.  All functionality has been implemented with a focus on
performance and is robustly tested.  Find more information about the package in
the accompanying paper (<doi:10.48550/@code{arXiv.2205.07090>}).")
    (license license:expat)))

(define-public r-scoringrules
  (package
    (name "r-scoringrules")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scoringRules" version))
       (sha256
        (base32 "01p918qj6gmnkyh79b2hhd1h6a5v3b7ssnnx5a1ppf1a5xmx2ps6"))))
    (properties `((upstream-name . "scoringRules")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mass r-knitr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/FK83/scoringRules")
    (synopsis
     "Scoring Rules for Parametric and Simulated Distribution Forecasts")
    (description
     "Dictionary-like reference for computing scoring rules in a wide range of
situations.  Covers both parametric forecast distributions (such as mixtures of
Gaussians) and distributions generated via simulation.  Further details can be
found in the package vignettes <doi:10.18637/jss.v090.i12>,
<doi:10.18637/jss.v110.i08>.")
    (license license:gpl2+)))

(define-public r-scoringfunctions
  (package
    (name "r-scoringfunctions")
    (version "0.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scoringfunctions" version))
       (sha256
        (base32 "05bxqj8pq8halwljsy89l2p8hq8gghvivyhk1078w6yrrn8w18lq"))))
    (properties `((upstream-name . "scoringfunctions")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=scoringfunctions")
    (synopsis "Collection of Scoring Functions for Assessing Point Forecasts")
    (description
     "This package implements multiple consistent scoring functions (Gneiting T (2011)
<doi:10.1198/jasa.2011.r10138>) for assessing point forecasts and point
predictions.  Detailed documentation of scoring functions properties is included
for facilitating interpretation of results.")
    (license license:gpl3)))

(define-public r-scoring
  (package
    (name "r-scoring")
    (version "0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scoring" version))
       (sha256
        (base32 "114jlpbnm15fdan5lr40adxdzwajy3yhdw6dm4kvkvky6qhcx7q5"))))
    (properties `((upstream-name . "scoring")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=scoring")
    (synopsis "Proper Scoring Rules")
    (description
     "Evaluating probabilistic forecasts via proper scoring rules.  scoring implements
the beta, power, and pseudospherical families of proper scoring rules, along
with ordered versions of the latter two families.  Included among these families
are popular rules like the Brier (quadratic) score, logarithmic score, and
spherical score.  For two-alternative forecasts, also includes functionality for
plotting scores that one would obtain under specific scoring rules.")
    (license license:gpl2)))

(define-public r-scoreplus
  (package
    (name "r-scoreplus")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ScorePlus" version))
       (sha256
        (base32 "07hmrx1k256i4cs605j5bpg6brzypryxlcnjkzqak8cw4f26k24q"))))
    (properties `((upstream-name . "ScorePlus")))
    (build-system r-build-system)
    (propagated-inputs (list r-rspectra r-limsolve r-igraphdata r-igraph
                             r-combinat))
    (home-page "https://cran.r-project.org/package=ScorePlus")
    (synopsis "Implementation of SCORE, SCORE+ and Mixed-SCORE")
    (description
     "Implementation of community detection algorithm SCORE in the paper J. Jin (2015)
<@code{arXiv:1211.5803>}, and SCORE+ in J. Jin, Z. Ke and S. Luo (2018)
<@code{arXiv:1811.05927>}.  Membership estimation algorithm called Mixed-SCORE
in J. Jin, Z. Ke and S. Luo (2017) <@code{arXiv:1708.07852>}.")
    (license license:gpl2)))

(define-public r-scorepeak
  (package
    (name "r-scorepeak")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scorepeak" version))
       (sha256
        (base32 "1g9n7kjqb9qk5b7hw8ysrc069w9dzc4z29ay6z1kbm0v4kkzp7bi"))))
    (properties `((upstream-name . "scorepeak")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ShotaOchi/scorepeak")
    (synopsis "Peak Functions for Peak Detection in Univariate Time Series")
    (description
     "This package provides peak functions, which enable us to detect peaks in time
series.  The methods implemented in this package are based on Girish Keshav
Palshikar (2009)
<https://www.researchgate.net/publication/228853276_Simple_Algorithms_for_Peak_Detection_in_Time-Series>.")
    (license license:gpl3)))

(define-public r-scorematchingad
  (package
    (name "r-scorematchingad")
    (version "0.0.67")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scorematchingad" version))
       (sha256
        (base32 "1dfnnb5fd16i5m6mwxw60jq42cn8migpqmy0s2rbpd9jdg96lwpj"))))
    (properties `((upstream-name . "scorematchingad")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-rdpack
                             r-rcppeigen
                             r-rcpp
                             r-r6
                             r-optimx
                             r-mcmcpack
                             r-fixedpoint))
    (home-page "https://github.com/kasselhingee/scorematchingad")
    (synopsis "Score Matching Estimation by Automatic Differentiation")
    (description
     "HyvÃ¤rinen's score matching (HyvÃ¤rinen, 2005)
<https://jmlr.org/papers/v6/hyvarinen05a.html> is a useful estimation technique
when the normalising constant for a probability distribution is difficult to
compute.  This package implements score matching estimators using automatic
differentiation in the @code{CppAD} library
<https://github.com/coin-or/@code{CppAD>} and is designed for quickly
implementing score matching estimators for new models.  Also available is
general robustification (Windham, 1995) <https://www.jstor.org/stable/2346159>.
Already in the package are estimators for directional distributions (Mardia,
Kent and Laha, 2016) <doi:10.48550/@code{arXiv.1604.08470>} and the flexible
Polynomially-Tilted Pairwise Interaction model for compositional data.  The
latter estimators perform well when there are zeros in the compositions (Scealy
and Wood, 2023) <doi:10.1080/01621459.2021.2016422>, even many zeros (Scealy,
Hingee, Kent, and Wood, 2024) <doi:10.1007/s11222-024-10412-w>.")
    (license license:gpl3+)))

(define-public r-scoreeb
  (package
    (name "r-scoreeb")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ScoreEB" version))
       (sha256
        (base32 "0lvdywkw2g0xdgf7zj39h1903rd0jg2lzgqq7cfni11pniv6265n"))))
    (properties `((upstream-name . "ScoreEB")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table))
    (home-page "https://cran.r-project.org/package=ScoreEB")
    (synopsis
     "Score Test Integrated with Empirical Bayes for Association Study")
    (description
     "Perform association test within linear mixed model framework using score test
integrated with Empirical Bayes for genome-wide association study.  Firstly,
score test was conducted for each marker under linear mixed model framework,
taking into account the genetic relatedness and population structure.  And then
all the potentially associated markers were selected with a less stringent
criterion.  Finally, all the selected markers were placed into a multi-locus
model to identify the true quantitative trait nucleotide.")
    (license license:gpl3)))

(define-public r-scoredec
  (package
    (name "r-scoredec")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scoredec" version))
       (sha256
        (base32 "02dypr3kakha8rfhjmp5pzqlxd9mz0p1x5gk4hfc0ps8vn226xwh"))))
    (properties `((upstream-name . "scoredec")))
    (build-system r-build-system)
    (propagated-inputs (list r-rfast r-rcpp r-igraph))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/cadam00/scoredec")
    (synopsis "S-Core Graph Decomposition")
    (description
     "S-Core Graph Decomposition algorithm for graphs.  This is a method for
decomposition of a weighted graph, as proposed by Eidsaa and Almaas (2013)
<doi:10.1103/@code{PhysRevE.88.062819>}.  The high speed and the low memory
usage make it suitable for large graphs.")
    (license license:gpl3)))

(define-public r-scorecardmodelutils
  (package
    (name "r-scorecardmodelutils")
    (version "0.0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scorecardModelUtils" version))
       (sha256
        (base32 "09znaxrv14m58hsk5pac458xbpcsidc6ygcsw4q9j0p4yd80qbvl"))))
    (properties `((upstream-name . "scorecardModelUtils")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-sqldf
                             r-reshape2
                             r-randomforest
                             r-partykit
                             r-ggplot2
                             r-gbm
                             r-e1071
                             r-car))
    (home-page "https://cran.r-project.org/package=scorecardModelUtils")
    (synopsis "Credit Scorecard Modelling Utils")
    (description
     "This package provides infrastructure functionalities such as missing value
treatment, information value calculation, GINI calculation etc.  which are used
for developing a traditional credit scorecard as well as a machine learning
based model.  The functionalities defined are standard steps for any credit
underwriting scorecard development, extensively used in financial domain.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-scorecard
  (package
    (name "r-scorecard")
    (version "0.4.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scorecard" version))
       (sha256
        (base32 "0aql9rfvq1fhlq13vj5dmz37nd31c1rrm808zh8mzx6425vx72d1"))))
    (properties `((upstream-name . "scorecard")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-xefun
                             r-stringi
                             r-openxlsx
                             r-gridextra
                             r-ggplot2
                             r-foreach
                             r-doparallel
                             r-data-table
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ShichenXie/scorecard")
    (synopsis "Credit Risk Scorecard")
    (description
     "The `scorecard` package makes the development of credit risk scorecard easier
and efficient by providing functions for some common tasks, such as data
partition, variable selection, woe binning, scorecard scaling, performance
evaluation and report generation.  These functions can also used in the
development of machine learning models.  The references including: 1.  Refaat,
M. (2011, ISBN: 9781447511199).  Credit Risk Scorecard: Development and
Implementation Using SAS. 2.  Siddiqi, N. (2006, ISBN: 9780471754510).  Credit
risk scorecards.  Developing and Implementing Intelligent Credit Scoring.")
    (license license:expat)))

(define-public r-score
  (package
    (name "r-score")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "score" version))
       (sha256
        (base32 "1p289k1vmc7qg70rv15x05dyb92r7s6315whr1ibi40sqln62a5s"))))
    (properties `((upstream-name . "score")))
    (build-system r-build-system)
    (propagated-inputs (list r-msm))
    (home-page "https://cran.r-project.org/package=score")
    (synopsis "Package to Score Behavioral Questionnaires")
    (description
     "This package provides routines for scoring behavioral questionnaires.  Includes
scoring procedures for the International Physical Activity Questionnaire (IPAQ)
<http://www.ipaq.ki.se>.  Compares physical functional performance to the age-
and gender-specific normal ranges.")
    (license license:gpl3+)))

(define-public r-scor
  (package
    (name "r-scor")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCOR" version))
       (sha256
        (base32 "0isws2ajla2nhdg5hhfab0s5ihihm4w8h6zyvncapchfaj2c8vmf"))))
    (properties `((upstream-name . "SCOR")))
    (build-system r-build-system)
    (propagated-inputs (list r-iterators r-foreach r-doparallel))
    (home-page "https://github.com/synx21/SCOR")
    (synopsis "Spherically Constrained Optimization Routine")
    (description
     "This package provides a non convex optimization package that optimizes any
function under the criterion, combination of variables are on the surface of a
unit sphere, as described in the paper : Das et al. (2019)
<@code{arXiv:1909.04024>} .")
    (license license:gpl3)))

(define-public r-scopro
  (package
    (name "r-scopro")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCOPRO" version))
       (sha256
        (base32 "08pxr7kn2d77g614s6rb2qbd8g5vf6b2w1pwcp9i191b524n844p"))))
    (properties `((upstream-name . "SCOPRO")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SCOPRO")
    (synopsis "Score Projection Between in 'Vivo' and in 'Vitro' Datasets")
    (description
     "Assigns a score projection from 0 to 1 between a given in vivo stage and each
single cluster from an in vitro dataset.  The score is assigned based on the the
fraction of specific markers of the in vivo stage that are conserved in the in
vitro clusters <https://github.com/@code{ScialdoneLab>}.")
    (license license:gpl3)))

(define-public r-scoper
  (package
    (name "r-scoper")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scoper" version))
       (sha256
        (base32 "0y0bqdxr4g929paddrgs8qqxzp0pwsc62kxl6lbzp4l2xmg3j2j2"))))
    (properties `((upstream-name . "scoper")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringi
                             r-shazam
                             r-scales
                             r-rlang
                             r-rcpp
                             r-ggplot2
                             r-foreach
                             r-dplyr
                             r-doparallel
                             r-data-table
                             r-alakazam))
    (native-inputs (list r-knitr))
    (home-page "https://scoper.readthedocs.io")
    (synopsis "Spectral Clustering-Based Method for Identifying B Cell Clones")
    (description
     "This package provides a computational framework for identification of B cell
clones from Adaptive Immune Receptor Repertoire sequencing (AIRR-Seq) data.
Three main functions are included (@code{identicalClones},
@code{hierarchicalClones}, and @code{spectralClones}) that perform clustering
among sequences of BCRs/IGs (B cell receptors/immunoglobulins) which share the
same V gene, J gene and junction length.  Nouri N and Kleinstein SH (2018) <doi:
10.1093/bioinformatics/bty235>.  Nouri N and Kleinstein SH (2019) <doi:
10.1101/788620>.  Gupta NT, et al. (2017) <doi: 10.4049/jimmunol.1601850>.")
    (license license:agpl3)))

(define-public r-scontomatch
  (package
    (name "r-scontomatch")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scOntoMatch" version))
       (sha256
        (base32 "1x08qm492yjkbi0srbx6l2z92cd7740iajrvv34hzfdbgjvx289s"))))
    (properties `((upstream-name . "scOntoMatch")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr r-ontologyplot r-ontologyindex))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Papatheodorou-Group/scOntoMatch")
    (synopsis
     "Aligning Ontology Annotation Across Single Cell Datasets with 'scOntoMatch'")
    (description
     "Unequal granularity of cell type annotation makes it difficult to compare
@code{scRNA-seq} datasets at scale.  Leveraging the ontology system for defining
cell type hierarchy, @code{scOntoMatch} aims to align cell type annotations to
make them comparable across studies.  The alignment involves two core steps:
first is to trim the cell type tree within each dataset so each cell type does
not have descendants, and then map cell type labels cross-studies by direct
matching and mapping descendants to ancestors.  Various functions for plotting
cell type trees and manipulating ontology terms are also provided.  In the
Single Cell Expression Atlas hosted at EBI, a compendium of datasets with
curated ontology labels are great inputs to this package.")
    (license license:expat)))

(define-public r-scmodels
  (package
    (name "r-scmodels")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scModels" version))
       (sha256
        (base32 "01minq4b0pdjlh2r56lvr2ab6r7sp7l9sjz8a9hmx5xmizmygmvr"))))
    (properties `((upstream-name . "scModels")))
    (build-system r-build-system)
    (inputs (list mpfr gmp))
    (propagated-inputs (list r-rcpp r-gamlss-dist))
    (home-page "https://cran.r-project.org/package=scModels")
    (synopsis "Fitting Discrete Distribution Models to Count Data")
    (description
     "This package provides functions for fitting discrete distribution models to
count data.  Included are the Poisson, the negative binomial, the
Poisson-inverse gaussian and, most importantly, a new implementation of the
Poisson-beta distribution (density, distribution and quantile functions, and
random number generator) together with a needed new implementation of Kummer's
function (also: confluent hypergeometric function of the first kind).  Three
different implementations of the Gillespie algorithm allow data simulation based
on the basic, switching or bursting @code{mRNA} generating processes.  Moreover,
likelihood functions for four variants of each of the three aforementioned
distributions are also available.  The variants include one population and two
population mixtures, both with and without zero-inflation.  The package depends
on the MPFR libraries (<https://www.mpfr.org/>) which need to be installed
separately (see description at <https://github.com/fuchslab/@code{scModels>}).
This package is supplement to the paper \"A mechanistic model for the negative
binomial distribution of single-cell @code{mRNA} counts\" by Lisa Amrhein, Kumar
Harsha and Christiane Fuchs (2019) <doi:10.1101/657619> available on
@code{bioRxiv}.")
    (license license:gpl3)))

(define-public r-scmappr
  (package
    (name "r-scmappr")
    (version "1.0.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scMappR" version))
       (sha256
        (base32 "0a2jm2a10lawqrlcglaz31gx3kbvjz19f4ynhllkj0px61awxjah"))))
    (properties `((upstream-name . "scMappR")))
    (build-system r-build-system)
    (propagated-inputs (list r-seurat
                             r-reshape
                             r-pheatmap
                             r-pcamethods
                             r-pbapply
                             r-limsolve
                             r-gsva
                             r-gprofiler2
                             r-gprofiler
                             r-ggplot2
                             r-downloader
                             r-adapts))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=scMappR")
    (synopsis "Single Cell Mapper")
    (description
     "The single cell mapper (@code{scMappR}) R package contains a suite of
bioinformatic tools that provide experimentally relevant cell-type specific
information to a list of differentially expressed genes (DEG).  The function
\"@code{scMappR_and_pathway_analysis}\" reranks DEGs to generate cell-type
specificity scores called cell-weighted fold-changes.  Users input a list of
DEGs, normalized counts, and a signature matrix into this function.
@code{scMappR} then re-weights bulk DEGs by cell-type specific expression from
the signature matrix, cell-type proportions from RNA-seq deconvolution and the
ratio of cell-type proportions between the two conditions to account for changes
in cell-type proportion.  With @code{cwFold-changes} calculated, @code{scMappR}
uses two approaches to utilize @code{cwFold-changes} to complete cell-type
specific pathway analysis.  The \"process_@code{dgTMatrix_lists}\" function in the
@code{scMappR} package contains an automated @code{scRNA-seq} processing
pipeline where users input @code{scRNA-seq} count data, which is made compatible
for @code{scMappR} and other R packages that analyze @code{scRNA-seq} data.  We
further used this to store hundreds up regularly updating signature matrices.
The functions \"tissue_by_celltype_enrichment\", \"tissue_@code{scMappR_internal}\",
and \"tissue_@code{scMappR_custom}\" combine these consistently processed
@code{scRNAseq} count data with gene-set enrichment tools to allow for cell-type
marker enrichment of a generic gene list (e.g. GWAS hits).  Reference:
Sokolowski,D.J., Faykoo-Martinez,M., Erdman,L., Hou,H., Chan,C., Zhu,H.,
Holmes,M.M., Goldenberg,A. and Wilson,M.D. (2021) Single-cell mapper
(@code{scMappR}): using @code{scRNA-seq} to infer cell-type specificities of
differentially expressed genes.  NAR Genomics and Bioinformatics.  3(1).
Iqab011. <doi:10.1093/nargab/lqab011>.")
    (license license:gpl3)))

(define-public r-scma
  (package
    (name "r-scma")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCMA" version))
       (sha256
        (base32 "0j0np99mcr8vgpcg664769gdb3rxm665h4jgj3zplihbh5ihxfj1"))))
    (properties `((upstream-name . "SCMA")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SCMA")
    (synopsis "Single-Case Meta-Analysis")
    (description
     "Perform meta-analysis of single-case experiments, including calculating various
effect size measures (SMD, PND, PEM and NAP) and probability combining (additive
and multiplicative method), as discussed in Bulte and Onghena (2013)
<doi:10.22237/jmasm/1383280020>.")
    (license license:gpl2+)))

(define-public r-sclust
  (package
    (name "r-sclust")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sClust" version))
       (sha256
        (base32 "0bvvbb360454d39bkynpbaf6ll9xsc3gxgxivy0igbr2pipfwbap"))))
    (properties `((upstream-name . "sClust")))
    (build-system r-build-system)
    (propagated-inputs (list r-cluster r-class))
    (home-page "https://cran.r-project.org/package=sClust")
    (synopsis "R Toolbox for Unsupervised Spectral Clustering")
    (description
     "Toolbox containing a variety of spectral clustering tools functions.  Among the
tools available are the hierarchical spectral clustering algorithm, the Shi and
Malik clustering algorithm, the Perona and Freeman algorithm, the non-normalized
clustering, the Von Luxburg algorithm, the Partition Around Medoids clustering
algorithm, a multi-level clustering algorithm, recursive clustering and the fast
method for all clustering algorithm.  As well as other tools needed to run these
algorithms or useful for unsupervised spectral clustering.  This toolbox aims to
gather the main tools for unsupervised spectral classification.  See
<http://mawenzi.univ-littoral.fr/> for more information and documentation.")
    (license license:gpl2+)))

(define-public r-sclr
  (package
    (name "r-sclr")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sclr" version))
       (sha256
        (base32 "10pihx35889w509ijbyq5hzslk9n7kakjnfscg7p0dq198nbpyca"))))
    (properties `((upstream-name . "sclr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-rlang r-purrr r-dplyr r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://khvorov45.github.io/sclr/")
    (synopsis "Scaled Logistic Regression")
    (description
     "Maximum likelihood estimation of the scaled logit model parameters proposed in
Dunning (2006) <doi:10.1002/sim.2282>.")
    (license license:expat)))

(define-public r-sclink
  (package
    (name "r-sclink")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scLink" version))
       (sha256
        (base32 "0gcwx8kfik1lv949r9d9rqqnvrfkr4bj5cj2i9h1r7l1b76kna74"))))
    (properties `((upstream-name . "scLink")))
    (build-system r-build-system)
    (propagated-inputs (list r-glasso))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=scLink")
    (synopsis
     "Inferring Functional Gene Co-Expression Networks from Single Cell Data")
    (description
     "Uses statistical network modeling to understand the co-expression relationships
among genes and to construct sparse gene co-expression networks from single-cell
gene expression data.")
    (license license:gpl3)))

(define-public r-sciviews
  (package
    (name "r-sciviews")
    (version "0.9-13.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SciViews" version))
       (sha256
        (base32 "0wm4vnjh65jxyg82m8l08kanl414qiaz0cc6srshb8sdx2sixkfi"))))
    (properties `((upstream-name . "SciViews")))
    (build-system r-build-system)
    (propagated-inputs (list r-ellipse))
    (home-page "https://github.com/SciViews/SciViews")
    (synopsis "SciViews - Main package")
    (description
     "This package provides functions to install @code{SciViews} additions to R, and
more tools.")
    (license license:gpl2)))

(define-public r-scitd
  (package
    (name "r-scitd")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scITD" version))
       (sha256
        (base32 "0xcx8bvl2w2qa4c68w5hvfx9412qvscjvj8d6hp4q62z58xqy1f8"))))
    (properties `((upstream-name . "scITD")))
    (build-system r-build-system)
    (propagated-inputs (list r-sva
                             r-sccore
                             r-rtensor
                             r-rmisc
                             r-reshape2
                             r-rcppprogress
                             r-rcpparmadillo
                             r-rcpp
                             r-rcolorbrewer
                             r-nmf
                             r-msigdbr
                             r-mgcv
                             r-matrix
                             r-ica
                             r-ggpubr
                             r-ggplot2
                             r-fgsea
                             r-edger
                             r-dplyr
                             r-complexheatmap
                             r-circlize))
    (home-page "https://cran.r-project.org/package=scITD")
    (synopsis "Single-Cell Interpretable Tensor Decomposition")
    (description
     "Single-cell Interpretable Tensor Decomposition (@code{scITD}) employs the Tucker
tensor decomposition to extract multicell-type gene expression patterns that
vary across donors/individuals.  This tool is geared for use with single-cell
RNA-sequencing datasets consisting of many source donors.  The method has a wide
range of potential applications, including the study of inter-individual
variation at the population-level, patient sub-grouping/stratification, and the
analysis of sample-level batch effects.  Each \"multicellular process\" that is
extracted consists of (A) a multi cell type gene loadings matrix and (B) a
corresponding donor scores vector indicating the level at which the
corresponding loadings matrix is expressed in each donor.  Additional methods
are implemented to aid in selecting an appropriate number of factors and to
evaluate stability of the decomposition.  Additional tools are provided for
downstream analysis, including integration of gene set enrichment analysis and
ligand-receptor analysis.  Tucker, L.R. (1966) <doi:10.1007/BF02289464>.  Unkel,
S., Hannachi, A., Trendafilov, N. T., & Jolliffe, I. T. (2011)
<doi:10.1007/s13253-011-0055-9>.  Zhou, G., & Cichocki, A. (2012)
<doi:10.2478/v10175-012-0051-4>.")
    (license license:gpl3)))

(define-public r-scitb
  (package
    (name "r-scitb")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scitb" version))
       (sha256
        (base32 "11gv2whjc62vxrgk3hc175pz3f56zfpnyk62hxg4hvqlypl6g75r"))))
    (properties `((upstream-name . "scitb")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringi r-reshape2 r-nortest r-mass r-ggplot2))
    (home-page "https://cran.r-project.org/package=scitb")
    (synopsis "Provides Some Useful Functions for Making Statistical Tables")
    (description
     "You can use the functions provided by the package to make various statistical
tables, such as baseline data tables.  Creates Table 1', i.e., a description of
the baseline patient characteristics, which is essential in every medical
research.  Supports both continuous and categorical variables, as well as
p-values and standardized mean differences.  This method was described by Mary L
@code{McHugh} (2013) <doi:10.11613/bm.2013.018>.")
    (license license:gpl3)))

(define-public r-scisr
  (package
    (name "r-scisr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scISR" version))
       (sha256
        (base32 "0m4sgbyc05qy1zklw45zj4w4wx0fhiwqr90fc3gpkqq7xkd7ihbw"))))
    (properties `((upstream-name . "scISR")))
    (build-system r-build-system)
    (propagated-inputs (list r-pinsplus
                             r-matrixstats
                             r-markdown
                             r-irlba
                             r-entropy
                             r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/duct317/scISR")
    (synopsis "Single-Cell Imputation using Subspace Regression")
    (description
     "This package provides an imputation pipeline for single-cell RNA sequencing
data.  The @code{scISR} method uses a hypothesis-testing technique to identify
zero-valued entries that are most likely affected by dropout events and
estimates the dropout values using a subspace regression model (Tran et.al.
(2022) <DOI:10.1038/s41598-022-06500-4>).")
    (license license:lgpl2.0+)))

(define-public r-scirmdtheme
  (package
    (name "r-scirmdtheme")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sciRmdTheme" version))
       (sha256
        (base32 "17pyvi38qg9r2yshw02car7d08qda6mscxkhh5jdm4ncc91y9i17"))))
    (properties `((upstream-name . "sciRmdTheme")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/oobianom/sciRmdTheme")
    (synopsis "Upgraded 'Rmarkdown' Themes for Scientific Writing")
    (description
     "This package provides a set of Rmarkdown themes for creating scientific and
professional documents.  Simple interface with features to ease navigation
across the page and sub-pages.")
    (license license:expat)))

(define-public r-scipub
  (package
    (name "r-scipub")
    (version "1.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scipub" version))
       (sha256
        (base32 "1491xdlxfqh1h178lrl01b9f4wwdgxaid3dxippyf3b1wxb1gqcf"))))
    (properties `((upstream-name . "scipub")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-purrr
                             r-ggplot2
                             r-gghalves
                             r-forcats
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/dpagliaccio/scipub")
    (synopsis "Summarize Data for Scientific Publication")
    (description
     "Create and format tables and APA statistics for scientific publication.  This
includes making a Table 1 to summarize demographics across groups, correlation
tables with significance indicated by stars, and extracting formatted
statistical summarizes from simple tests for in-text notation.  The package also
includes functions for Winsorizing data based on a Z-statistic cutoff.")
    (license license:gpl3)))

(define-public r-sciplot
  (package
    (name "r-sciplot")
    (version "1.2-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sciplot" version))
       (sha256
        (base32 "097zcgjsv54f6wd8fjwh2xb406j6yrw94v9pcxa0zln1q7fs93vk"))))
    (properties `((upstream-name . "sciplot")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sciplot")
    (synopsis "Scientific Graphing Functions for Factorial Designs")
    (description
     "This package provides a collection of functions that creates graphs with error
bars for data collected from one-way or higher factorial designs.")
    (license license:gpl2+)))

(define-public r-scintruler
  (package
    (name "r-scintruler")
    (version "0.99.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCIntRuler" version))
       (sha256
        (base32 "1ap23iighbx1x3blfh0b9srgk5mzinl3gy0addfwd6i40a160brp"))))
    (properties `((upstream-name . "SCIntRuler")))
    (build-system r-build-system)
    (propagated-inputs (list r-summarizedexperiment
                             r-singlecellexperiment
                             r-seuratobject
                             r-seurat
                             r-rcpp
                             r-matrixgenerics
                             r-matrix
                             r-magrittr
                             r-harmony
                             r-gridextra
                             r-ggplot2
                             r-dplyr
                             r-cowplot
                             r-coin
                             r-batchelor))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/yuelyu21/SCIntRuler")
    (synopsis
     "Guiding the Integration of Multiple Single-Cell RNA-Seq Datasets")
    (description
     "The accumulation of single-cell RNA-seq ('@code{scRNA-seq}') studies highlights
the potential benefits of integrating multiple datasets.  By augmenting sample
sizes and enhancing analytical robustness, integration can lead to more
insightful biological conclusions.  However, challenges arise due to the
inherent diversity and batch discrepancies within and across studies.
SC@code{IntRuler}', a novel R package, addresses these challenges by guiding the
integration of multiple @code{scRNA-seq} datasets.")
    (license license:expat)))

(define-public r-scinsight
  (package
    (name "r-scinsight")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scINSIGHT" version))
       (sha256
        (base32 "1zkm2n19px3yh3y2z1g5l84ym36ar31i96i54mm7wa0ki179swap"))))
    (properties `((upstream-name . "scINSIGHT")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rcpparmadillo r-rcpp r-rann r-igraph))
    (home-page "https://github.com/Vivianstats/scINSIGHT")
    (synopsis
     "Interpretation of Heterogeneous Single-Cell Gene Expression Data")
    (description
     "We develop a novel matrix factorization tool named @code{scINSIGHT} to jointly
analyze multiple single-cell gene expression samples from biologically
heterogeneous sources, such as different disease phases, treatment groups, or
developmental stages.  Given multiple gene expression samples from different
biological conditions, @code{scINSIGHT} simultaneously identifies common and
condition-specific gene modules and quantify their expression levels in each
sample in a lower-dimensional space.  With the factorized results, the inferred
expression levels and memberships of common gene modules can be used to cluster
cells and detect cell identities, and the condition-specific gene modules can
help compare functional differences in transcriptomes from distinct conditions.
Please also see Qian K, Fu SW, Li HW, Li WV (2022)
<doi:10.1186/s13059-022-02649-3>.")
    (license license:gpl3)))

(define-public r-scina
  (package
    (name "r-scina")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCINA" version))
       (sha256
        (base32 "1gv9widjwvk5j535r1zx6f41ylpa2r4168ya580llgblx85z402d"))))
    (properties `((upstream-name . "SCINA")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-gplots))
    (home-page "https://cran.r-project.org/package=SCINA")
    (synopsis "Semi-Supervised Category Identification and Assignment Tool")
    (description
     "An automatic cell type detection and assignment algorithm for single cell
RNA-Seq and Cytof/FACS data.  SCINA is capable of assigning cell type identities
to a pool of cells profiled by @code{scRNA-Seq} or Cytof/FACS data with prior
knowledge of markers, such as genes and protein symbols that are highly or lowly
expressed in each category.  See Zhang Z, et al (2019)
<doi:10.3390/genes10070531> for more details.")
    (license license:gpl2)))

(define-public r-scimo
  (package
    (name "r-scimo")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scimo" version))
       (sha256
        (base32 "1fvljxn6m01wv4hmljcrki2wzbc0nlr3zrbyw80jbzw3p55vqgls"))))
    (properties `((upstream-name . "scimo")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rlang
                             r-recipes
                             r-magrittr
                             r-generics
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/abichat/scimo")
    (synopsis "Extra Recipes Steps for Dealing with Omics Data")
    (description
     "Omics data (e.g. transcriptomics, proteomics, metagenomics...) offer a detailed
and multi-dimensional perspective on the molecular components and interactions
within complex biological (eco)systems.  Analyzing these data requires adapted
procedures, which are implemented as steps according to the recipes package.")
    (license license:gpl3+)))

(define-public r-scifigure
  (package
    (name "r-scifigure")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scifigure" version))
       (sha256
        (base32 "1zc88r1lnb0cws130hq4rjc34rrp6zkmi8yhkwsm3vkznbhi4iba"))))
    (properties `((upstream-name . "scifigure")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=scifigure")
    (synopsis
     "Visualize 'Reproducibility' and 'Replicability' in a Comparison of Scientific Studies")
    (description
     "Users may specify what fundamental qualities of a new study have or have not
changed in an attempt to reproduce or replicate an original study.  A comparison
of the differences is visualized.  Visualization approach follows Patil', Peng',
and Leek (2016) <doi:10.1101/066803>.")
    (license license:expat)))

(define-public r-scientotext
  (package
    (name "r-scientotext")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scientoText" version))
       (sha256
        (base32 "0f18hg9s50lbbgl0fqjv45yhynpnsxj8wwm07g126snzr68gbd21"))))
    (properties `((upstream-name . "scientoText")))
    (build-system r-build-system)
    (propagated-inputs (list r-tm r-stringr))
    (home-page "https://cran.r-project.org/package=scientoText")
    (synopsis "Text & Scientometric Analytics")
    (description
     "It involves bibliometric indicators calculation from bibliometric data.It also
deals pattern analysis using the text part of bibliometric data.The bibliometric
data are obtained from mainly Web of Science and Scopus.")
    (license license:gpl3)))

(define-public r-scientific
  (package
    (name "r-scientific")
    (version "2024.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scientific" version))
       (sha256
        (base32 "04pnc8fnc7ijvagp4mxfhg6kkrsczcc1a9j6m72sbyavv0k4dcin"))))
    (properties `((upstream-name . "scientific")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-xfun r-rmarkdown r-knitr r-htmltools))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://scientific.obi.obianom.com")
    (synopsis "Highly Customizable 'rmarkdown' Theme for Scientific Reporting")
    (description
     "Offers markdown output formats designed with various scientific styles, allowing
users to generate PDF and HTML outputs.  The output has a contemporary
appearance with vibrant visuals, providing numerous styles for effective
highlighting.  The package also includes additional features specifically
tailored for front-page slides, enhancing the overall presentation and
customization options.  The package was created using the tufte
<https://rstudio.github.io/tufte/> package code as a starting point.")
    (license license:expat)))

(define-public r-scidesignr
  (package
    (name "r-scidesignr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scidesignR" version))
       (sha256
        (base32 "0krr7kn3l4c0dyn9ln3dz0h9ivr21dnrlmncpxiv560wqxnwi7z0"))))
    (properties `((upstream-name . "scidesignR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=scidesignR")
    (synopsis
     "Data Sets from Design and Analysis of Experiments and Observational Studies using R")
    (description
     "Data used in Taback, N. (2022).  Design and Analysis of Experiments and
Observational Studies using R. Chapman & Hall/CRC.")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-scicomptools
  (package
    (name "r-scicomptools")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scicomptools" version))
       (sha256
        (base32 "193byqr8sjb0hk1k4qj26pnqr5dgqfsxn47ckddgcvzk6ybd6cpl"))))
    (properties `((upstream-name . "scicomptools")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyxl
                             r-tidytext
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-semnetcleaner
                             r-readxl
                             r-purrr
                             r-magrittr
                             r-googledrive
                             r-gitcreds
                             r-ggwordcloud
                             r-ggplot2
                             r-dplyr
                             r-data-tree))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/NCEAS/scicomptools")
    (synopsis "Tools Developed by the NCEAS Scientific Computing Support Team")
    (description
     "Set of tools to import, summarize, wrangle, and visualize data.  These functions
were originally written based on the needs of the various synthesis working
groups that were supported by the National Center for Ecological Analysis and
Synthesis (NCEAS).  These tools are meant to be useful inside and outside of the
context for which they were designed.")
    (license license:bsd-3)))

(define-public r-sciber
  (package
    (name "r-sciber")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCIBER" version))
       (sha256
        (base32 "0mnpqwimvwln29lha7ylrrhd9j7nhlgkrg78vwkgqjmni8p6b351"))))
    (properties `((upstream-name . "SCIBER")))
    (build-system r-build-system)
    (propagated-inputs (list r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/RavenGan/SCIBER")
    (synopsis "Single-Cell Integrator and Batch Effect Remover")
    (description
     "Remove batch effects by projecting query batches into the reference batch space.")
    (license license:expat)))

(define-public r-sci
  (package
    (name "r-sci")
    (version "1.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCI" version))
       (sha256
        (base32 "1jvzkdv15ifgf6a3zjfzzcgw2y2vg0wp7yhiamiaqp8xkm142w49"))))
    (properties `((upstream-name . "SCI")))
    (build-system r-build-system)
    (propagated-inputs (list r-lmomco r-fitdistrplus))
    (home-page "https://cran.r-project.org/package=SCI")
    (synopsis "Standardized Climate Indices Such as SPI, SRI or SPEI")
    (description
     "This package provides functions for generating Standardized Climate Indices
(SCI).  SCI is a transformation of (smoothed) climate (or environmental) time
series that removes seasonality and forces the data to take values of the
standard normal distribution.  SCI was originally developed for precipitation.
In this case it is known as the Standardized Precipitation Index (SPI).")
    (license license:gpl2+)))

(define-public r-schumaker
  (package
    (name "r-schumaker")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "schumaker" version))
       (sha256
        (base32 "15i39lzkd1qggd35sp39vy922y3y1n9drjkidz7asniv1miali6j"))))
    (properties `((upstream-name . "schumaker")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=schumaker")
    (synopsis "Schumaker Shape-Preserving Spline")
    (description
     "This is a shape preserving spline <doi:10.1137/0720057> which is guaranteed to
be monotonic and concave or convex if the data is monotonic and concave or
convex.  It does not use any optimisation and is therefore quick and smoothly
converges to a fixed point in economic dynamics problems including value
function iteration.  It also automatically gives the first two derivatives of
the spline and options for determining behaviour when evaluated outside the
interpolation domain.")
    (license license:expat)))

(define-public r-schtools
  (package
    (name "r-schtools")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "schtools" version))
       (sha256
        (base32 "1y3qm08m3sflb5p57z6glxbrc37limm9nyrab09mvqifz5xh4z3h"))))
    (properties `((upstream-name . "schtools")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-rlang
                             r-glue
                             r-formatr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "http://www.schlosslab.org/schtools/")
    (synopsis "Schloss Lab Tools for Reproducible Microbiome Research")
    (description
     "This package provides a collection of useful functions and example code created
and used by the Schloss Lab for reproducible microbiome research.  Perform
common tasks like read files created by mothur <https://mothur.org/>, tidy up
your microbiome data, and format R Markdown documents for publication.  See the
website <http://www.schlosslab.org/schtools/> for more information,
documentation, and examples.")
    (license license:expat)))

(define-public r-schrute
  (package
    (name "r-schrute")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "schrute" version))
       (sha256
        (base32 "14mjz63g2z4m4rgh7yf05gz5cjcriqvwgrd0an9xzj9pk36arisb"))))
    (properties `((upstream-name . "schrute")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bradlindblad/schrute")
    (synopsis "The Entire Transcript from the Office in Tidy Format")
    (description
     "The complete scripts from the American version of the Office television show in
tibble format.  Use this package to analyze and have fun with text from the best
series of all time.")
    (license license:expat)))

(define-public r-schorsch
  (package
    (name "r-schorsch")
    (version "1.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "schoRsch" version))
       (sha256
        (base32 "0bf4lahfzi6dchqfnykh4saiagcka12h82d9mvd1ddv5phmbyi10"))))
    (properties `((upstream-name . "schoRsch")))
    (build-system r-build-system)
    (home-page "https://www.tqmp.org/RegularArticles/vol12-2/p147/index.html")
    (synopsis "Tools for Analyzing Factorial Experiments")
    (description
     "Offers a helping hand to psychologists and other behavioral scientists who
routinely deal with experimental data from factorial experiments.  It includes
several functions to format output from other R functions according to the style
guidelines of the APA (American Psychological Association).  This formatted
output can be copied directly into manuscripts to facilitate data reporting.
These features are backed up by a toolkit of several small helper functions,
e.g., offering out-of-the-box outlier removal.  The package lends its name to
Georg \"Schorsch\" Schuessler, ingenious technician at the Department of
Psychology III, University of Wuerzburg.  For details on the implemented
methods, see Roland Pfister and Markus Janczyk (2016) <doi:
10.20982/tqmp.12.2.p147>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-schoolmath
  (package
    (name "r-schoolmath")
    (version "0.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "schoolmath" version))
       (sha256
        (base32 "0r0hipficyjkvqhy9jv2p7s44ww4zwxs8x52sd5hss2wz4vvkkv7"))))
    (properties `((upstream-name . "schoolmath")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=schoolmath")
    (synopsis "Functions and Datasets for Math Used in School")
    (description
     "This package contains functions and datasets for math taught in school.  A main
focus is set to prime-calculation.")
    (license license:gpl2+)))

(define-public r-schooldatait
  (package
    (name "r-schooldatait")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SchoolDataIT" version))
       (sha256
        (base32 "0mgxhj5xz9nf7if7qiif3x4dbjjblvb96riyn327s920pzwy9f2l"))))
    (properties `((upstream-name . "SchoolDataIT")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tidyr
                             r-stringr
                             r-sf
                             r-rvest
                             r-rlang
                             r-readr
                             r-mapview
                             r-magrittr
                             r-leafpop
                             r-httr
                             r-ggplot2
                             r-dplyr
                             r-curl))
    (home-page "https://github.com/lcef97/SchoolDataIT")
    (synopsis
     "Retrieve, Harmonise and Map Open Data Regarding the Italian School System")
    (description
     "Compiles and displays the available data sets regarding the Italian school
system, with a focus on the infrastructural aspects.  Input datasets are
downloaded from the web, with the aim of updating everything to real time.  The
functions are divided in four main modules, namely Get', to scrape raw data from
the web Util', various utilities needed to process raw data Group', to aggregate
data at the municipality or province level Map', to visualize the output
datasets.")
    (license license:gpl3+)))

(define-public r-scholar
  (package
    (name "r-scholar")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scholar" version))
       (sha256
        (base32 "12r4j1s71szh77nsnqzsi0q5cvkp0cyr2fxzcagk02f42bnp5aww"))))
    (properties `((upstream-name . "scholar")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tidygraph
                             r-stringr
                             r-rvest
                             r-rlang
                             r-r-cache
                             r-httr
                             r-ggraph
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/YuLab-SMU/scholar")
    (synopsis "Analyse Citation Data from Google Scholar")
    (description
     "This package provides functions to extract citation data from Google Scholar.
Convenience functions are also provided for comparing multiple scholars and
predicting future h-index values.")
    (license license:expat)))

(define-public r-schoenberg
  (package
    (name "r-schoenberg")
    (version "2.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "schoenberg" version))
       (sha256
        (base32 "1d6pflj7kafcx4xwcqyxgvxgygnf51zk9b0a28fiz05gxnb4bhp6"))))
    (properties `((upstream-name . "schoenberg")))
    (build-system r-build-system)
    (propagated-inputs (list r-crayon))
    (home-page "https://cran.r-project.org/package=schoenberg")
    (synopsis "Tools for 12-Tone Musical Composition")
    (description
     "This package provides functions for creating and manipulating 12-tone (i.e.,
dodecaphonic) musical matrices using Arnold Schoenberg's (1923) serialism
technique.  This package can generate random 12-tone matrices and can generate
matrices using a pre-determined sequence of notes.")
    (license license:gpl3+)))

(define-public r-schemr
  (package
    (name "r-schemr")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "schemr" version))
       (sha256
        (base32 "0g27w49mg3sd2mc82cj18dafhg757nig1gax8aiqyraga55a4hb1"))))
    (properties `((upstream-name . "schemr")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-purrr
                             r-openimager
                             r-magrittr
                             r-dplyr
                             r-apcluster))
    (home-page "https://cran.r-project.org/package=schemr")
    (synopsis "Convert Images to Usable Color Schemes")
    (description
     "This package provides a fast and adaptable tool to convert photos and images
into usable colour schemes for data visualisation.  Contains functionality to
extract colour palettes from images, as well for the conversion of images
between colour spaces.")
    (license license:gpl3)))

(define-public r-sched
  (package
    (name "r-sched")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sched" version))
       (sha256
        (base32 "1n6r8g5cd3y6yiplhf0jlb8zm8z37cz1ds66qqpipiva4vx26xr3"))))
    (properties `((upstream-name . "sched")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcurl
                             r-r6
                             r-openssl
                             r-lgr
                             r-fscache
                             r-chk))
    (native-inputs (list r-knitr))
    (home-page "https://gitlab.com/cnrgh/databases/r-sched")
    (synopsis "Request Scheduler")
    (description
     "Offers classes and functions to contact web servers while enforcing scheduling
rules required by the sites.  The URL class makes it easy to construct a URL by
providing parameters as a vector.  The Request class allows to describes SOAP
(Simple Object Access Protocol) or standard requests: URL, method (POST or GET),
header, body.  The Scheduler class controls the request frequency for each
server address by mean of rules (Rule class).  The @code{RequestResult} class
permits to get the request status to handle error cases and the content.")
    (license license:agpl3)))

(define-public r-scgwr
  (package
    (name "r-scgwr")
    (version "0.1.2-21")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scgwr" version))
       (sha256
        (base32 "15pwasxkwirr2wwf8w02cy8zx0bqavzyfixjz3m37c1llrhddpka"))))
    (properties `((upstream-name . "scgwr")))
    (build-system r-build-system)
    (propagated-inputs (list r-spdata r-sp r-optimparallel r-fnn r-dplyr))
    (home-page "https://cran.r-project.org/package=scgwr")
    (synopsis "Scalable Geographically Weighted Regression")
    (description
     "Fast and regularized version of GWR for large dataset, detailed in Murakami,
Tsutsumida, Yoshida, Nakaya, and Lu (2019) <@code{arXiv:1905.00266>}.")
    (license license:gpl2+)))

(define-public r-scgoclust
  (package
    (name "r-scgoclust")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scGOclust" version))
       (sha256
        (base32 "0jbmjrc5xmxvkc6q93blbryqvyxcy6ia6bmca03vq05l6isybal6"))))
    (properties `((upstream-name . "scGOclust")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-slanter
                             r-seurat
                             r-networkd3
                             r-matrix
                             r-magrittr
                             r-limma
                             r-dplyr
                             r-biomart))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Papatheodorou-Group/scGOclust")
    (synopsis
     "Measuring Cell Type Similarity with Gene Ontology in Single-Cell RNA-Seq")
    (description
     "Traditional methods for analyzing single cell RNA-seq datasets focus solely on
gene expression, but this package introduces a novel approach that goes beyond
this limitation.  Using Gene Ontology terms as features, the package allows for
the functional profile of cell populations, and comparison within and between
datasets from the same or different species.  Our approach enables the discovery
of previously unrecognized functional similarities and differences between cell
types and has demonstrated success in identifying cell types functional
correspondence even between evolutionarily distant species.")
    (license license:gpl3+)))

(define-public r-scglr
  (package
    (name "r-scglr")
    (version "3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCGLR" version))
       (sha256
        (base32 "10hsvcjgsycap6wkp06snp2zab1ppsp78kyncpbjnm3vp84qm0nz"))))
    (properties `((upstream-name . "SCGLR")))
    (build-system r-build-system)
    (propagated-inputs (list r-proc
                             r-matrix
                             r-ggplot2
                             r-formula
                             r-expm
                             r-ade4))
    (home-page "https://scnext.github.io/SCGLR")
    (synopsis "Supervised Component Generalized Linear Regression")
    (description
     "An extension of the Fisher Scoring Algorithm to combine PLS regression with GLM
estimation in the multivariate context.  Covariates can also be grouped in
themes.")
    (license (list (license:fsdg-compatible "CeCILL-2") license:gpl2))))

(define-public r-scfmonitor
  (package
    (name "r-scfmonitor")
    (version "0.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCFMonitor" version))
       (sha256
        (base32 "0gr84vj7n4vz61a5pqq3iqx0ng4j7dn11smhb93rdglj0v76kdf9"))))
    (properties `((upstream-name . "SCFMonitor")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-readr
                             r-magrittr
                             r-ggplot2
                             r-dplyr))
    (home-page "https://github.com/AzuleneG/SCFMonitor")
    (synopsis
     "Clear Monitor and Graphing Software Processing Gaussian .log File")
    (description
     "Self-Consistent Field(SCF) calculation method is one of the most important steps
in the calculation methods of quantum chemistry.  Ehrenreich, H., & Cohen, M. H.
(1959). <doi:10.1103/@code{PhysRev.115.786>} However, the most prevailing
software in this area, Gaussian''s SCF convergence process is hard to monitor,
especially while the job is still running, causing researchers difficulty in
knowing whether the oscillation has started or not, wasting time and energy on
useless configurations or abandoning the jobs that can actually work.  M.J.
Frisch, G.W. Trucks, H.B. Schlegel et al. (2016). <https://gaussian.com>
SCFMonitor enables Gaussian quantum chemistry calculation software users to
easily read the Gaussian .log files and monitor the SCF convergence and geometry
optimization process with little effort and clear, beautiful, and clean outputs.
 It can generate graphs using tidyverse to let users check SCF convergence and
geometry optimization processes in real-time.  The software supports processing
.log files remotely using with rbase::url().  This software is a suitcase for
saving time and energy for the researchers, supporting multiple versions of
Gaussian'.")
    (license license:expat)))

(define-public r-scepterbinary
  (package
    (name "r-scepterbinary")
    (version "0.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCEPtERbinary" version))
       (sha256
        (base32 "0rab0widfndx94dn1nchhs06q0d57vq2n3xy79p130l9rgp9v489"))))
    (properties `((upstream-name . "SCEPtERbinary")))
    (build-system r-build-system)
    (propagated-inputs (list r-scepter r-mass))
    (home-page "https://cran.r-project.org/package=SCEPtERbinary")
    (synopsis
     "Stellar CharactEristics Pisa Estimation gRid for Binary Systems")
    (description
     "SCE@code{PtER} pipeline for estimating the stellar age for double-lined detached
binary systems.  The observational constraints adopted in the recovery are the
effective temperature, the metallicity [Fe/H], the mass, and the radius of the
two stars.  The results are obtained adopting a maximum likelihood technique
over a grid of pre-computed stellar models.")
    (license license:gpl2+)))

(define-public r-scepter
  (package
    (name "r-scepter")
    (version "0.2-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCEPtER" version))
       (sha256
        (base32 "1098pvq3a7k9czxapszkakc8lr9sfvj7a643iivil5fcqfk0j50r"))))
    (properties `((upstream-name . "SCEPtER")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=SCEPtER")
    (synopsis "Stellar CharactEristics Pisa Estimation gRid")
    (description
     "This package provides a pipeline for estimating the stellar age, mass, and
radius given observational effective temperature, [Fe/H], and astroseismic
parameters.  The results are obtained adopting a maximum likelihood technique
over a grid of pre-computed stellar models, as described in Valle et al. (2014)
<doi:10.1051/0004-6361/201322210>.")
    (license license:gpl2+)))

(define-public r-scenes
  (package
    (name "r-scenes")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scenes" version))
       (sha256
        (base32 "068x0f9j9apdrv07m4facxxs60axv43q57laypfavxsns3r4dk30"))))
    (properties `((upstream-name . "scenes")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny
                             r-rlang
                             r-purrr
                             r-glue
                             r-cookies
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/r4ds/scenes")
    (synopsis "Switch Between Alternative 'shiny' UIs")
    (description
     "Sometimes it is useful to serve up alternative shiny UIs depending on
information passed in the request object, such as the value of a cookie or a
query parameter.  This packages facilitates such switches.")
    (license license:expat)))

(define-public r-scem
  (package
    (name "r-scem")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCEM" version))
       (sha256
        (base32 "1fxxkv965gb0wq06rclv05xxlzk8p9l8hzbnqcf0nbbymyn73fqr"))))
    (properties `((upstream-name . "SCEM")))
    (build-system r-build-system)
    (propagated-inputs (list r-mathjaxr r-devtools))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/kserkcho/SCEM")
    (synopsis "Splitting-Coalescence-Estimation Method")
    (description
     "We introduce improved methods for statistically assessing birth seasonality and
intra-annual variation.  The first method we propose is a new idea that uses a
nonparametric clustering procedure to group individuals with similar time series
data and estimate birth seasonality based on the clusters.  One can use the
function SCEM() to implement this method.  The second method estimates input
parameters for use with a previously-developed parametric approach (Tornero et
al., 2013).  The relevant code for this approach is @code{makeFits_OLS}(), while
@code{makeFits_initial}() is the code to implement the same method but with
given initial conditions for two parameters.  The latter can be used to show the
disadvantage of the existing approach.  One can use the function
@code{makeFits}() to generate parametric birth seasonality estimates using
either initialization.  Detailed description can be found here: Chazin Hannah,
Soudeep Deb, Joshua Falk, and Arun Srinivasan. (2019) \"New Statistical
Approaches to Intra-Individual Isotopic Analysis and Modeling Birth Seasonality
in Studies of Herd Animals.\" <doi:10.1111/arcm.12432>.")
    (license license:gpl3)))

(define-public r-scellpam
  (package
    (name "r-scellpam")
    (version "1.4.6.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scellpam" version))
       (sha256
        (base32 "0hxbvgq6mq365q8hw91k0pm0ky9psivcd98bxafrgrncqc117cd1"))))
    (properties `((upstream-name . "scellpam")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-memuse r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=scellpam")
    (synopsis
     "Applying Partitioning Around Medoids to Single Cell Data with High Number of Cells")
    (description
     "PAM (Partitioning Around Medoids) algorithm application to samples of single
cell sequencing techniques with a high number of cells (as many as the computer
memory allows).  The package uses a binary format to store matrices (either
full, sparse or symmetric) in files written in the disk that can contain any
data type (not just double) which allows its manipulation when memory is
sufficient to load them as int or float, but not as double.  The PAM
implementation is done in parallel, using several/all the cores of the machine,
if it has them.  This package shares a great part of its code with packages
jmatrix and parallelpam but their functionality is included here so there is no
need to install them.")
    (license license:gpl2+)))

(define-public r-scdtb
  (package
    (name "r-scdtb")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scdtb" version))
       (sha256
        (base32 "1gb5asgqgymczih5x4078m3ksnvm9j06pqhyl37cxrzxx4ahw87s"))))
    (properties `((upstream-name . "scdtb")))
    (build-system r-build-system)
    (propagated-inputs (list r-sn
                             r-shinythemes
                             r-shiny
                             r-nlme
                             r-mmints
                             r-mmcards
                             r-mass
                             r-ggplot2
                             r-dt
                             r-broom-mixed))
    (home-page "https://github.com/mightymetrika/scdtb")
    (synopsis "Single Case Design Tools")
    (description
     "In some situations where researchers would like to demonstrate causal effects,
it is hard to obtain a sample size that would allow for a well-powered
randomized controlled trial.  Single case designs are experimental designs that
can be used to demonstrate causal effects with only one participant or with only
a few participants.  The scdtb package provides a suite of tools for analyzing
data from studies that use single case designs.  The nap() function can be used
to compute the nonoverlap of all pairs as outlined by the What Works
Clearinghouse (2022) <https://ies.ed.gov/ncee/wwc/Handbooks>.  The package also
offers the mixed_model_analysis() and cross_lagged() functions which implement
mixed effects models and cross lagged analyses as described in Maric & van der
Werff (2020) <doi:10.4324/9780429273872-9>.  The randomization_test() function
implements randomization tests based on methods presented in Onghena (2020)
<doi:10.4324/9780429273872-8>.  The scdtb() shiny application can be used to
upload single case design data and access various scdtb tools for plotting and
analysis.")
    (license license:expat)))

(define-public r-scdiftest
  (package
    (name "r-scdiftest")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scDIFtest" version))
       (sha256
        (base32 "1hq68g7zf7372cach2qfc7l1l8gh5aqkp66k0cxjnrhmvvb1qcax"))))
    (properties `((upstream-name . "scDIFtest")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-strucchange r-sandwich r-mirt))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=scDIFtest")
    (synopsis "Item-Wise Score-Based DIF Detection")
    (description
     "Detection of item-wise Differential Item Functioning (DIF) in fitted mirt',
@code{multipleGroup} or bfactor models using score-based structural change
tests.  Under the hood the sctest() function from the strucchange package is
used.")
    (license license:gpl3)))

(define-public r-scdiffcom
  (package
    (name "r-scdiffcom")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scDiffCom" version))
       (sha256
        (base32 "17scb8hhx2d67cbhzxd0l2hzy4sj22x5j9rvc71ba2d958wk4ysa"))))
    (properties `((upstream-name . "scDiffCom")))
    (build-system r-build-system)
    (propagated-inputs (list r-seurat
                             r-magrittr
                             r-future-apply
                             r-future
                             r-delayedarray
                             r-data-table))
    (home-page "https://cyrillagger.github.io/scDiffCom/")
    (synopsis
     "Differential Analysis of Intercellular Communication from scRNA-Seq Data")
    (description
     "Analysis tools to investigate changes in intercellular communication from
@code{scRNA-seq} data.  Using a Seurat object as input, the package infers which
cell-cell interactions are present in the dataset and how these interactions
change between two conditions of interest (e.g. young vs old).  It relies on an
internal database of ligand-receptor interactions (available for human, mouse
and rat) that have been gathered from several published studies.  Detection and
differential analyses rely on permutation tests.  The package also contains
several tools to perform over-representation analysis and visualize the results.
 See Lagger, C. et al. (2023) <doi:10.1038/s43587-023-00514-x> for a full
description of the methodology.")
    (license license:expat)))

(define-public r-scdhlm
  (package
    (name "r-scdhlm")
    (version "0.7.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scdhlm" version))
       (sha256
        (base32 "03bfn02x5gfbp7fd0fhcvpm9pf1h71kz1lcd0jkmddqmbj7v2i48"))))
    (properties `((upstream-name . "scdhlm")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-rlang
                             r-readxl
                             r-nlme
                             r-magrittr
                             r-lmeinfo
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://jepusto.github.io/scdhlm/")
    (synopsis "Estimating Hierarchical Linear Models for Single-Case Designs")
    (description
     "This package provides a set of tools for estimating hierarchical linear models
and effect sizes based on data from single-case designs.  Functions are provided
for calculating standardized mean difference effect sizes that are directly
comparable to standardized mean differences estimated from between-subjects
randomized experiments, as described in Hedges, Pustejovsky, and Shadish (2012)
<DOI:10.1002/jrsm.1052>; Hedges, Pustejovsky, and Shadish (2013)
<DOI:10.1002/jrsm.1086>; Pustejovsky, Hedges, and Shadish (2014)
<DOI:10.3102/1076998614547577>; and Chen, Pustejovsky, Klingbeil, and Van Norman
(2023) <DOI:10.1016/j.jsp.2023.02.002>.  Includes an interactive web interface.")
    (license license:gpl3)))

(define-public r-scdha
  (package
    (name "r-scdha")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scDHA" version))
       (sha256
        (base32 "1l9i5r7z4n387mrfmq43md2a0wrhvshrggb4nc3varjyq3drh3yd"))))
    (properties `((upstream-name . "scDHA")))
    (build-system r-build-system)
    (propagated-inputs (list r-uwot
                             r-torch
                             r-rhpcblasctl
                             r-rcppparallel
                             r-rcpparmadillo
                             r-rcppannoy
                             r-rcpp
                             r-matrixstats
                             r-matrix
                             r-igraph
                             r-foreach
                             r-doparallel
                             r-coro
                             r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/duct317/scDHA")
    (synopsis "Single-Cell Decomposition using Hierarchical Autoencoder")
    (description
     "This package provides a fast and accurate pipeline for single-cell analyses.
The @code{scDHA} software package can perform clustering, dimension reduction
and visualization, classification, and time-trajectory inference on single-cell
data (Tran et.al. (2021) <DOI:10.1038/s41467-021-21312-2>).")
    (license license:gpl3)))

(define-public r-scdensity
  (package
    (name "r-scdensity")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scdensity" version))
       (sha256
        (base32 "00r06syfhg26mhnpnkvswgyqyagxh35j1jxg4ay72l4d05b9pnkg"))))
    (properties `((upstream-name . "scdensity")))
    (build-system r-build-system)
    (propagated-inputs (list r-quadprog r-lpsolve))
    (home-page "https://cran.r-project.org/package=scdensity")
    (synopsis "Shape-Constrained Kernel Density Estimation")
    (description
     "This package implements methods for obtaining kernel density estimates subject
to a variety of shape constraints (unimodality, bimodality, symmetry, tail
monotonicity, bounds, and constraints on the number of inflection points).
Enforcing constraints can eliminate unwanted waves or kinks in the estimate,
which improves its subjective appearance and can also improve statistical
performance.  The main function scdensity() is very similar to the density()
function in stats', allowing shape-restricted estimates to be obtained with
little effort.  The methods implemented in this package are described in Wolters
and Braun (2017) <doi:10.1080/03610918.2017.1288247>, Wolters (2012)
<doi:10.18637/jss.v047.i06>, and Hall and Huang (2002)
<https://www3.stat.sinica.edu.tw/statistica/j12n4/j12n41/j12n41.htm>.  See the
scdensity() help for for full citations.")
    (license license:gpl2)))

(define-public r-scdeconr
  (package
    (name "r-scdeconr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCdeconR" version))
       (sha256
        (base32 "0wp7l226hw75jgi9j7n4c2f0nxlrmi3z3iv5wi5vhflvndgr95ym"))))
    (properties `((upstream-name . "SCdeconR")))
    (build-system r-build-system)
    (propagated-inputs (list r-seurat
                             r-reshape2
                             r-nmf
                             r-mgcv
                             r-matrix
                             r-mass
                             r-harmony
                             r-gtools
                             r-glmgampoi
                             r-ggplot2
                             r-foreach
                             r-edger
                             r-dplyr
                             r-data-table
                             r-biobase))
    (home-page "https://github.com/Liuy12/SCdeconR/")
    (synopsis
     "Deconvolution of Bulk RNA-Seq Data using Single-Cell RNA-Seq Data as Reference")
    (description
     "Streamlined workflow from deconvolution of bulk RNA-seq data to downstream
differential expression and gene-set enrichment analysis.  Provide various
visualization functions.")
    (license license:gpl3+)))

(define-public r-scdeco
  (package
    (name "r-scdeco")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scDECO" version))
       (sha256
        (base32 "0gm7xlkv3rr6mzwbgggyaby75ia1avrflasrsszniph13bcndb9g"))))
    (properties `((upstream-name . "scDECO")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjags r-msm r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/YenYiHo-Lab/scDECO")
    (synopsis "Estimating Dynamic Correlation")
    (description
     "Implementations for two different Bayesian models of differential co-expression.
 scdeco.cop() fits the bivariate Gaussian copula model from Zichen Ma, Shannon
W. Davis, Yen-Yi Ho (2023) <doi:10.1111/biom.13701>, while scdeco.pg() fits the
bivariate Poisson-Gamma model from Zhen Yang, Yen-Yi Ho (2022)
<doi:10.1111/biom.13457>.")
    (license license:gpl3+)))

(define-public r-scdb
  (package
    (name "r-scdb")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCDB" version))
       (sha256
        (base32 "0cvb69vh5s74827ni61yp5s6kflijf0zf1q2nl82ik3cv4gjpszx"))))
    (properties `((upstream-name . "SCDB")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-stringr
                             r-rlang
                             r-r6
                             r-purrr
                             r-parallelly
                             r-openssl
                             r-lifecycle
                             r-glue
                             r-dplyr
                             r-dbplyr
                             r-dbi
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ssi-dk/SCDB")
    (synopsis
     "Easily Access and Maintain Time-Based Versioned Data (Slowly-Changing-Dimension)")
    (description
     "This package provides a collection of functions that enable easy access and
updating of a database of data over time.  More specifically, the package
facilitates type-2 history for data-warehouses and provides a number of Quality
of life improvements for working on SQL databases with R. For reference see
Ralph Kimball and Margy Ross (2013, ISBN 9781118530801).")
    (license license:gpl3)))

(define-public r-scda
  (package
    (name "r-scda")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCDA" version))
       (sha256
        (base32 "12r8rw6fs5wgrk38z31m7s7asj4w6adlk5abrbf0vhmh5v3ahfha"))))
    (properties `((upstream-name . "SCDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-spdep
                             r-spatialreg
                             r-sp
                             r-rlang
                             r-performance
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=SCDA")
    (synopsis "Spatially-Clustered Data Analysis")
    (description
     "This package contains functions for statistical data analysis based on
spatially-clustered techniques.  The package allows estimating the
spatially-clustered spatial regression models presented in Cerqueti, Maranzano
\\& Mattera (2024), \"Spatially-clustered spatial autoregressive models with
application to agricultural market concentration in Europe\", @code{arXiv}
preprint 2407.15874 <doi:10.48550/@code{arXiv.2407.15874>}.  Specifically, the
current release allows the estimation of the spatially-clustered linear
regression model (SCLM), the spatially-clustered spatial autoregressive model
(SCSAR), the spatially-clustered spatial Durbin model (SCSEM), and the
spatially-clustered linear regression model with spatially-lagged exogenous
covariates (SCSLX).")
    (license license:gpl2+)))

(define-public r-sccs
  (package
    (name "r-sccs")
    (version "1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCCS" version))
       (sha256
        (base32 "0cx7xw15pwkk2nza8ng4ic6mq07vgf5idcclkwdyp3ry5j3rqnvl"))))
    (properties `((upstream-name . "SCCS")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-r-methodss3 r-gnm r-fda r-corpcor))
    (home-page "https://cran.r-project.org/package=SCCS")
    (synopsis "The Self-Controlled Case Series Method")
    (description
     "Various self-controlled case series models used to investigate associations
between time-varying exposures such as vaccines or other drugs or non drug
exposures and an adverse event can be fitted.  Detailed information on the
self-controlled case series method and its extensions with more examples can be
found in Farrington, P., Whitaker, H., and Ghebremichael Weldeselassie, Y.
(2018, ISBN: 978-1-4987-8159-6.  Self-controlled Case Series studies: A
modelling Guide with R. Boca Raton: Chapman & Hall/CRC Press) and
<https://sccs-studies.info/index.html>.")
    (license license:gpl2+)))

(define-public r-sccr
  (package
    (name "r-sccr")
    (version "2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sccr" version))
       (sha256
        (base32 "0cjlzv92b5qrs43d3ry33zcggiqvsqaf8wq4k19ws9lvgh59b62v"))))
    (properties `((upstream-name . "sccr")))
    (build-system r-build-system)
    (propagated-inputs (list r-dplyr))
    (home-page "https://cran.r-project.org/package=sccr")
    (synopsis "The Self-Consistent, Competing Risks (SC-CR) Algorithms")
    (description
     "The SC-SR Algorithm is used to calculate fully non-parametric and
self-consistent estimators of the cause-specific failure probabilities in the
presence of interval-censoring and possible making of the failure cause in a
competing risks environment.  In the version 2.0 the function creating the
probability matrix from double-censored data is added.")
    (license license:gpl2)))

(define-public r-scclust
  (package
    (name "r-scclust")
    (version "0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scclust" version))
       (sha256
        (base32 "028ssw7k4sj4vzyylb5njfbjisir2vas0xzh63hmwkalncr2dcpv"))))
    (properties `((upstream-name . "scclust")))
    (build-system r-build-system)
    (propagated-inputs (list r-distances))
    (home-page "https://github.com/fsavje/scclust-R")
    (synopsis "Size-Constrained Clustering")
    (description
     "This package provides wrappers for scclust', a C library for computationally
efficient size-constrained clustering with near-optimal performance.  See
<https://github.com/fsavje/scclust> for more information.")
    (license license:gpl3+)))

(define-public r-scci
  (package
    (name "r-scci")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCCI" version))
       (sha256
        (base32 "08ibazxiqmkm3n9g8bfp0cfq26zmg6x2fxb064g3z05nczsf72db"))))
    (properties `((upstream-name . "SCCI")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=SCCI")
    (synopsis
     "Stochastic Complexity-Based Conditional Independence Test for Discrete Data")
    (description
     "An efficient implementation of SCCI using Rcpp'.  SCCI is short for the
Stochastic Complexity-based Conditional Independence criterium (Marx and
Vreeken, 2019).  SCCI is an asymptotically unbiased and L2 consistent estimator
of (conditional) mutual information for discrete data.")
    (license license:gpl2+)))

(define-public r-sccca
  (package
    (name "r-sccca")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sccca" version))
       (sha256
        (base32 "193d6015zchnmky0kic0msdw040ykvw3isxpzw5ccmg9dqr7j1xp"))))
    (properties `((upstream-name . "sccca")))
    (build-system r-build-system)
    (propagated-inputs (list r-seurat
                             r-scales
                             r-plyr
                             r-openxlsx
                             r-hgnchelper
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=sccca")
    (synopsis "Single-Cell Correlation Based Cell Type Annotation")
    (description
     "Performing cell type annotation based on cell markers from a unified database.
The approach utilizes correlation-based approach combined with association
analysis using Fisher-exact and phyper statistical tests (Upton, Graham JG.
(1992) <DOI:10.2307/2982890>).")
    (license license:gpl3+)))

(define-public r-sccatch
  (package
    (name "r-sccatch")
    (version "3.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scCATCH" version))
       (sha256
        (base32 "0skd6gsm30bsxr7qi63y890qrx7zq8fvp0ik8jrpwfnsvfgyzx8v"))))
    (properties `((upstream-name . "scCATCH")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-progress r-matrix))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ZJUFanLab/scCATCH")
    (synopsis
     "Single Cell Cluster-Based Annotation Toolkit for Cellular Heterogeneity")
    (description
     "An automatic cluster-based annotation pipeline based on evidence-based score by
matching the marker genes with known cell markers in tissue-specific cell
taxonomy reference database for single-cell RNA-seq data.  See Shao X, et al
(2020) <doi:10.1016/j.isci.2020.100882> for more details.")
    (license license:gpl3+)))

(define-public r-sccan
  (package
    (name "r-sccan")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scCAN" version))
       (sha256
        (base32 "0iypyh34583wmp4hkvhhkyz72aw8y58ynmx1iml82iwvivvwjk1s"))))
    (properties `((upstream-name . "scCAN")))
    (build-system r-build-system)
    (propagated-inputs (list r-scdha r-purrr r-fnn))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=scCAN")
    (synopsis "Single-Cell Clustering using Autoencoder and Network Fusion")
    (description
     "This package provides a single-cell Clustering method using Autoencoder and
Network fusion ('@code{scCAN}') Bang Tran (2022)
<doi:10.1038/s41598-022-14218-6> for segregating the cells from the
high-dimensional @code{scRNA-Seq} data.  The software automatically determines
the optimal number of clusters and then partitions the cells in a way such that
the results are robust to noise and dropouts. @code{scCAN} is fast and it
supports Windows, Linux, and Mac OS.")
    (license license:lgpl2.0+)))

(define-public r-scbursts
  (package
    (name "r-scbursts")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scbursts" version))
       (sha256
        (base32 "09yxnq6m35xmp0priavjnh0bdcwkr5hhy7j33cz13xwkrd1cgw5j"))))
    (properties `((upstream-name . "scbursts")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-readxl))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=scbursts")
    (synopsis "Single Channel Bursts Analysis")
    (description
     "This package provides tools to import and export from several existing pieces of
ion-channel analysis software such as TAC', QUB', SCAN', and Clampfit',
implements procedures such as dwell-time correction and defining bursts with a
critical time, and provides tools for analysis of bursts, such as tools for
sorting and plotting.")
    (license license:lgpl2.1)))

(define-public r-scbsp
  (package
    (name "r-scbsp")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scBSP" version))
       (sha256
        (base32 "0cabj2hizmnadq5vijaip4d1wcsycwy5bdpgjwivzgqkhy9r5ss7"))))
    (properties `((upstream-name . "scBSP")))
    (build-system r-build-system)
    (propagated-inputs (list r-sparsematrixstats r-spam r-rann r-matrix
                             r-fitdistrplus))
    (home-page "https://cran.r-project.org/package=scBSP")
    (synopsis
     "Fast Tool for Single-Cell Spatially Variable Genes Identifications on Large-Scale Data")
    (description
     "Identifying spatially variable genes is critical in linking molecular cell
functions with tissue phenotypes.  This package utilizes a granularity-based
dimension-agnostic tool, single-cell big-small patch (@code{scBSP}),
implementing sparse matrix operation and KD tree methods for distance
calculation, for the identification of spatially variable genes on large-scale
data.  The detailed description of this method is available at Wang, J. and Li,
J. et al.  2023 (Wang, J. and Li, J. (2023), <doi:10.1038/s41467-023-43256-5>).")
    (license license:gpl2+)))

(define-public r-scbmeanfd
  (package
    (name "r-scbmeanfd")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCBmeanfd" version))
       (sha256
        (base32 "045498q71zqgcg8p3665vwd99a8ybf21y0sa7y8316zw66wb1caz"))))
    (properties `((upstream-name . "SCBmeanfd")))
    (build-system r-build-system)
    (propagated-inputs (list r-kernsmooth r-boot))
    (home-page "https://CRAN.R-project.org/package=SCBmeanfd")
    (synopsis "Simultaneous Confidence Bands for the Mean of Functional Data")
    (description
     "Statistical methods for estimating and inferring the mean of functional data.
The methods include simultaneous confidence bands, local polynomial fitting,
bandwidth selection by plug-in and cross-validation, goodness-of-fit tests for
parametric models, equality tests for two-sample problems, and plotting
functions.")
    (license license:gpl3)))

(define-public r-scbio
  (package
    (name "r-scbio")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scBio" version))
       (sha256
        (base32 "1bpizxm9fbxd4sfapi1ayfg5csacb4dvqbvappd79jkq4mrsk12q"))))
    (properties `((upstream-name . "scBio")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-raster
                             r-limma
                             r-liblinear
                             r-foreach
                             r-fields
                             r-dosnow))
    (home-page "https://github.com/amitfrish/scBio")
    (synopsis
     "Single Cell Genomics for Enhancing Cell Composition Inference from Bulk Genomics Data")
    (description
     "Cellular population mapping (CPM) a deconvolution algorithm in which single-cell
genomics is required in only one or a few samples, where in other samples of the
same tissue, only bulk genomics is measured and the underlying fine resolution
cellular heterogeneity is inferred.")
    (license license:gpl2)))

(define-public r-scbiclust
  (package
    (name "r-scbiclust")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SCBiclust" version))
       (sha256
        (base32 "1vmymdrci4kynjb074c10sg27labx7axnlbb9dqhwby7s55a6c78"))))
    (properties `((upstream-name . "SCBiclust")))
    (build-system r-build-system)
    (propagated-inputs (list r-sparcl r-sigclust))
    (home-page "https://cran.r-project.org/package=SCBiclust")
    (synopsis
     "Identifies Mean, Variance, and Hierarchically Clustered Biclusters")
    (description
     "Identifies a bicluster, a submatrix of the data such that the features and
observations within the submatrix differ from those not contained in submatrix,
using a two-step method.  In the first step, observations in the bicluster are
identified to maximize the sum of weighted between cluster feature differences.
The method is described in Helgeson et al. (2020) <doi:10.1111/biom.13136>.
SCBiclust can be used to identify biclusters which differ based on feature
means, feature variances, or more general differences.")
    (license license:gpl2+)))

(define-public r-scatterplotmatrix
  (package
    (name "r-scatterplotmatrix")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scatterPlotMatrix" version))
       (sha256
        (base32 "1hg49n0qk3wlca4abfyzr5n10k2gcv4kq42fm7x9fhiw2mm1xiw9"))))
    (properties `((upstream-name . "scatterPlotMatrix")))
    (build-system r-build-system)
    (propagated-inputs (list r-htmlwidgets))
    (native-inputs (list r-knitr))
    (home-page
     "https://ifpen-gitlab.appcollaboratif.fr/detocs/scatterplotmatrix")
    (synopsis "`htmlwidget` for a Scatter Plot Matrix")
    (description
     "Create a scatter plot matrix, using `htmlwidgets` package and `d3.js`.")
    (license license:expat)))

(define-public r-scatterdensity
  (package
    (name "r-scatterdensity")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ScatterDensity" version))
       (sha256
        (base32 "126jvjgfja6vyjadl3a7ahpb8z99hazijai4ymvnr1g1wcnx1z19"))))
    (properties `((upstream-name . "ScatterDensity")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-pracma))
    (home-page "https://www.deepbionics.org/")
    (synopsis "Density Estimation and Visualization of 2D Scatter Plots")
    (description
     "The user has the option to utilize the two-dimensional density estimation
techniques called smoothed density published by Eilers and Goeman (2004)
<doi:10.1093/bioinformatics/btg454>, and pareto density which was evaluated for
univariate data by Thrun, Gehlert and Ultsch, 2020
<doi:10.1371/journal.pone.0238835>.  Moreover, it provides visualizations of the
density estimation in the form of two-dimensional scatter plots in which the
points are color-coded based on increasing density.  Colors are defined by the
one-dimensional clustering technique called 1D distribution cluster algorithm
(DDCAL) published by Lux and Rinderle-Ma (2023)
<doi:10.1007/s00357-022-09428-6>.")
    (license license:gpl3)))

(define-public r-scatterd3
  (package
    (name "r-scatterd3")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scatterD3" version))
       (sha256
        (base32 "187jq1y5va5npsmpkfyi6mhgynq956590q4z8v1ibadsrcldw34m"))))
    (properties `((upstream-name . "scatterD3")))
    (build-system r-build-system)
    (propagated-inputs (list r-htmlwidgets r-ellipse r-digest))
    (native-inputs (list r-knitr))
    (home-page "https://juba.github.io/scatterD3/")
    (synopsis "D3 JavaScript Scatterplot from R")
    (description
     "This package creates D3 @code{JavaScript} scatterplots from R with interactive
features : panning, zooming, tooltips, etc.")
    (license license:gpl3+)))

(define-public r-scatr
  (package
    (name "r-scatr")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scatr" version))
       (sha256
        (base32 "1c2z722i7d7qflh4mzb8kkix5i22wph98kwz2p98x2mhaml8802d"))))
    (properties `((upstream-name . "scatr")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6
                             r-jmvcore
                             r-ggstance
                             r-ggridges
                             r-ggplot2
                             r-cowplot))
    (home-page "https://github.com/raviselker/scatr")
    (synopsis "Create Scatter Plots with Marginal Density or Box Plots")
    (description
     "Allows you to make clean, good-looking scatter plots with the option to easily
add marginal density or box plots on the axes.  It is also available as a module
for jamovi (see <https://www.jamovi.org> for more information).  Scatr is based
on the cowplot package by Claus O. Wilke and the ggplot2 package by Hadley
Wickham.")
    (license license:gpl2+)))

(define-public r-scarabee
  (package
    (name "r-scarabee")
    (version "1.1-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scaRabee" version))
       (sha256
        (base32 "1p9828ymdm79b91z2fc2r5wy6i4hr1mx8fva6lr154c82ba3569z"))))
    (properties `((upstream-name . "scaRabee")))
    (build-system r-build-system)
    (propagated-inputs (list r-optimsimplex r-optimbase r-neldermead r-lattice
                             r-desolve))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=scaRabee")
    (synopsis
     "Optimization Toolkit for Pharmacokinetic-Pharmacodynamic Models")
    (description
     "This package provides a port of the Scarabee toolkit originally written as a
Matlab-based application. @code{scaRabee} provides a framework for simulation
and optimization of pharmacokinetic-pharmacodynamic models at the individual and
population level.  It is built on top of the neldermead package, which provides
the direct search algorithm proposed by Nelder and Mead for model optimization.")
    (license license:gpl3)))

(define-public r-scar
  (package
    (name "r-scar")
    (version "0.2-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scar" version))
       (sha256
        (base32 "0l2vm6aqi5lrc25akmn826v0zhygys9s167k9dphqn8l3yvlvqbj"))))
    (properties `((upstream-name . "scar")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=scar")
    (synopsis
     "Shape-Constrained Additive Regression: a Maximum Likelihood Approach")
    (description
     "Computes the maximum likelihood estimator of the generalised additive and index
regression with shape constraints.  Each additive component function is assumed
to obey one of the nine possible shape restrictions: linear, increasing,
decreasing, convex, convex increasing, convex decreasing, concave, concave
increasing, or concave decreasing.  For details, see Chen and Samworth (2016)
<doi:10.1111/rssb.12137>.")
    (license license:gpl2+)))

(define-public r-scapgnn
  (package
    (name "r-scapgnn")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scapGNN" version))
       (sha256
        (base32 "0cms24rf3pp87jck8yxvfm5s60ycch9b32nmrhvg5iy59j6fpdxx"))))
    (properties `((upstream-name . "scapGNN")))
    (build-system r-build-system)
    (propagated-inputs (list r-reticulate
                             r-mixtools
                             r-igraph
                             r-coop
                             r-adaptgauss
                             r-activepathways))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=scapGNN")
    (synopsis
     "Graph Neural Network-Based Framework for Single Cell Active Pathways and Gene Modules Analysis")
    (description
     "It is a single cell active pathway analysis tool based on the graph neural
network (F. Scarselli (2009) <doi:10.1109/TNN.2008.2005605>; Thomas N. Kipf
(2017) <@code{arXiv:1609.02907v4>}) to construct the gene-cell association
network, infer pathway activity scores from different single cell modalities
data, integrate multiple modality data on the same cells into one pathway
activity score matrix, identify cell phenotype activated gene modules and parse
association networks of gene modules under multiple cell phenotype.  In
addition, abundant visualization programs are provided to display the results.")
    (license license:gpl2+)))

(define-public r-scapesclassification
  (package
    (name "r-scapesclassification")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scapesClassification" version))
       (sha256
        (base32 "0kwl9mbzcmn5qzqbzabf2igbhwbf5m1b1wa8rhm8mfv2qz7psyf2"))))
    (properties `((upstream-name . "scapesClassification")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ghTaranto/scapesClassification")
    (synopsis "User-Defined Classification of Raster Surfaces")
    (description
     "Series of algorithms to translate users mental models of seascapes, landscapes
and, more generally, of geographic features into computer representations
(classifications).  Spaces and geographic objects are classified with
user-defined rules taking into account spatial data as well as spatial
relationships among different classes and objects.")
    (license license:gpl3+)))

(define-public r-scaper
  (package
    (name "r-scaper")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scaper" version))
       (sha256
        (base32 "0ygh5lhbpgxc0mbyvc2cv0m3g405aiappysb7msw7r7g2f4x0w6s"))))
    (properties `((upstream-name . "scaper")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-vam
                             r-stringr
                             r-seuratobject
                             r-seurat
                             r-magrittr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=scaper")
    (synopsis
     "Single Cell Transcriptomics-Level Cytokine Activity Prediction and Estimation")
    (description
     "Generates cell-level cytokine activity estimates using relevant information from
gene sets constructed with the @code{CytoSig} and the Reactome databases and
scored using the modified Variance-adjusted Mahalanobis (VAM) framework for
single-cell RNA-sequencing (@code{scRNA-seq}) data. @code{CytoSig} database is
described in: Jiang at al., (2021) <doi:10.1038/s41592-021-01274-5>.  Reactome
database is described in: Gillespie et al., (2021) <doi:10.1093/nar/gkab1028>.
The VAM method is outlined in: Frost (2020) <doi:10.1093/nar/gkaa582>.")
    (license license:gpl2+)))

(define-public r-scape
  (package
    (name "r-scape")
    (version "2.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scape" version))
       (sha256
        (base32 "1jb5c335qsbrpsgngs8c1qlibyb7zvvx6mwydfhm1plx4pkcq0n1"))))
    (properties `((upstream-name . "scape")))
    (build-system r-build-system)
    (propagated-inputs (list r-lattice r-hmisc r-coda))
    (home-page "https://cran.r-project.org/package=scape")
    (synopsis "Statistical Catch-at-Age Plotting Environment")
    (description
     "Import, plot, and diagnose results from statistical catch-at-age models, used in
fisheries stock assessment.")
    (license license:gpl3)))

(define-public r-scanstatistics
  (package
    (name "r-scanstatistics")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scanstatistics" version))
       (sha256
        (base32 "04pymq6bikmsjqlwszd9ihv85s5qg8ci5y4dr6jmi95dgdm988pj"))))
    (properties `((upstream-name . "scanstatistics")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-sets
                             r-rcpparmadillo
                             r-rcpp
                             r-plyr
                             r-magrittr
                             r-ismev
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/promerpr/scanstatistics")
    (synopsis "Space-Time Anomaly Detection using Scan Statistics")
    (description
     "Detection of anomalous space-time clusters using the scan statistics
methodology.  Focuses on prospective surveillance of data streams, scanning for
clusters with ongoing anomalies.  Hypothesis testing is made possible by Monte
Carlo simulation.  AllÃ©vius (2018) <doi:10.21105/joss.00515>.")
    (license license:gpl3+)))

(define-public r-scannotate
  (package
    (name "r-scannotate")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scAnnotate" version))
       (sha256
        (base32 "02hrfs5ci2ii3hr4d4ks5bj80n5ajdnx71m6swmdciiy72sdm1lc"))))
    (properties `((upstream-name . "scAnnotate")))
    (build-system r-build-system)
    (propagated-inputs (list r-seuratobject r-seurat r-harmony r-glmnet))
    (native-inputs (list r-knitr))
    (home-page "https://doi.org/10.1101/2022.02.19.481159")
    (synopsis
     "An Automated Cell Type Annotation Tool for Single-Cell RNA-Sequencing Data")
    (description
     "An entirely data-driven cell type annotation tools, which requires training data
to learn the classifier, but not biological knowledge to make subjective
decisions.  It consists of three steps: preprocessing training and test data,
model fitting on training data, and cell classification on test data.  See
Xiangling Ji,Danielle Tsao, Kailun Bai, Min Tsao, Li Xing, Xuekui
Zhang.(2022)<doi:10.1101/2022.02.19.481159> for more details.")
    (license license:gpl3)))

(define-public r-scan
  (package
    (name "r-scan")
    (version "0.61.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scan" version))
       (sha256
        (base32 "04a43vyz8brl4mzn2pzbhdap49zzb64rwb40lzc6150mqmb09bcs"))))
    (properties `((upstream-name . "scan")))
    (build-system r-build-system)
    (propagated-inputs (list r-readxl
                             r-nlme
                             r-mblm
                             r-magrittr
                             r-knitr
                             r-kableextra
                             r-gt
                             r-car))
    (home-page "https://github.com/jazznbass/scan/")
    (synopsis
     "Single-Case Data Analyses for Single and Multiple Baseline Designs")
    (description
     "This package provides a collection of procedures for analysing, visualising, and
managing single-case data.  These include piecewise linear regression models,
multilevel models, overlap indices ('PND', PEM', PAND', PET', tau-u', baseline
corrected tau', CDC'), and randomization tests.  Data preparation functions
support outlier detection, handling missing values, scaling, truncation, rank
transformation, and smoothing.  An export function helps to generate html and
latex tables in a publication friendly style.  More details can be found in the
online book Analyzing single-case data with R and scan', Juergen Wilbert (2023)
<https://jazznbass.github.io/scan-Book/>.")
    (license license:gpl3+)))

(define-public r-scam
  (package
    (name "r-scam")
    (version "1.2-17")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scam" version))
       (sha256
        (base32 "00h5wcvh8wz2xqqh7j2qpdamhzdl3yawswyvwbp0r4ys30mfcgdk"))))
    (properties `((upstream-name . "scam")))
    (build-system r-build-system)
    (propagated-inputs (list r-mgcv r-matrix))
    (home-page "https://cran.r-project.org/package=scam")
    (synopsis "Shape Constrained Additive Models")
    (description
     "Generalized additive models under shape constraints on the component functions
of the linear predictor.  Models can include multiple shape-constrained
(univariate and bivariate) and unconstrained terms.  Routines of the package
mgcv are used to set up the model matrix, print, and plot the results.  Multiple
smoothing parameter estimation by the Generalized Cross Validation or similar.
See Pya and Wood (2015) <doi:10.1007/s11222-013-9448-7> for an overview.  A
broad selection of shape-constrained smoothers, linear functionals of smooths
with shape constraints, and Gaussian models with AR1 residuals.")
    (license license:gpl2+)))

(define-public r-scalreg
  (package
    (name "r-scalreg")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scalreg" version))
       (sha256
        (base32 "0kfcgl9cpp6g1qx23s1yrjzn0hi74vmrw567vy9wpsmhk04a5bkx"))))
    (properties `((upstream-name . "scalreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-lars))
    (home-page "https://cran.r-project.org/package=scalreg")
    (synopsis "Scaled Sparse Linear Regression")
    (description
     "Algorithms for fitting scaled sparse linear regression and estimating precision
matrices.")
    (license license:gpl2)))

(define-public r-scalpel
  (package
    (name "r-scalpel")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scalpel" version))
       (sha256
        (base32 "0c8k99i5v2ki2bmcjjr6rfk7y1ds7jlykdwgn5x2zvzpdw7p4vlw"))))
    (properties `((upstream-name . "scalpel")))
    (build-system r-build-system)
    (propagated-inputs (list r-r-matlab r-protoclust r-matrix r-igraph r-gam))
    (home-page "www.ajpete.com/software")
    (synopsis "Processes Calcium Imaging Data")
    (description
     "Identifies the locations of neurons, and estimates their calcium concentrations
over time using the SCALPEL method proposed in Petersen, Ashley; Simon, Noah;
Witten, Daniela.  SCALPEL: Extracting neurons from calcium imaging data.  Ann.
Appl.  Stat.  12 (2018), no.  4, 2430--2456. <doi:10.1214/18-AOAS1159>.
<https://projecteuclid.org/euclid.aoas/1542078051>.")
    (license license:gpl2+)))

(define-public r-scalespikeslab
  (package
    (name "r-scalespikeslab")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ScaleSpikeSlab" version))
       (sha256
        (base32 "11n3pi15x77by0ikiawhnvlky6hp6qzwmsmp4l2hjng1469y7gg6"))))
    (properties `((upstream-name . "ScaleSpikeSlab")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncatednormal r-rcppeigen r-rcpp))
    (home-page "https://cran.r-project.org/package=ScaleSpikeSlab")
    (synopsis "Scalable Spike-and-Slab")
    (description
     "This package provides a scalable Gibbs sampling implementation for high
dimensional Bayesian regression with the continuous spike-and-slab prior.  Niloy
Biswas, Lester Mackey and Xiao-Li Meng, \"Scalable Spike-and-Slab\" (2022)
<@code{arXiv:2204.01668>}.")
    (license license:gpl2+)))

(define-public r-scalelink
  (package
    (name "r-scalelink")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Scalelink" version))
       (sha256
        (base32 "0likfjw5999zd0kv1ykby78bl0x3xgh0v1yjssk7fnw1ghkxgdib"))))
    (properties `((upstream-name . "Scalelink")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppparallel r-rcpp))
    (home-page "https://cran.r-project.org/package=Scalelink")
    (synopsis "Create Scale Linkage Scores")
    (description
     "Perform a probabilistic linkage of two data files using a scaling procedure
using the methods described in Goldstein, H., Harron, K. and Cortina-Borja, M.
(2017) <doi:10.1002/sim.7287>.")
    (license license:gpl2+)))

(define-public r-scaleboot
  (package
    (name "r-scaleboot")
    (version "1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scaleboot" version))
       (sha256
        (base32 "1q0bs5f1vgja5gj3id1ny6raja8ljgd8dk50fs1wn90f6080afy7"))))
    (properties `((upstream-name . "scaleboot")))
    (build-system r-build-system)
    (propagated-inputs (list r-pvclust r-mvtnorm))
    (home-page "http://stat.sys.i.kyoto-u.ac.jp/prog/scaleboot/")
    (synopsis "Approximately Unbiased P-Values via Multiscale Bootstrap")
    (description
     "Calculating approximately unbiased (AU) p-values from multiscale bootstrap
probabilities.  See Shimodaira (2004) <doi:10.1214/009053604000000823>,
Shimodaira (2008) <doi:10.1016/j.jspi.2007.04.001>, Terada ans Shimodaira (2017)
<@code{arXiv:1711.00949>}, and Shimodaira and Terada (2019)
<doi.org/10.3389/fevo.2019.00174>.")
    (license license:gpl2+)))

(define-public r-scalealign
  (package
    (name "r-scalealign")
    (version "1.0.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scaleAlign" version))
       (sha256
        (base32 "0340biwz0md6sxq5iq5wiz6q8kcrll80429f1ab9zz88k3fya2s6"))))
    (properties `((upstream-name . "scaleAlign")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=scaleAlign")
    (synopsis
     "Scale Alignment for Between-Items Multidimensional Rasch Family Models")
    (description
     "Scale alignment is a new procedure for rescaling dimensions of between-items
multidimensional Rasch family models so that dimensions scores can be compared
directly (Feuerstahler & Wilson, 2019; under review) <doi:10.1111/jedm.12209>.
This package includes functions for implementing delta-dimensional alignment
(DDA) and logistic regression alignment (LRA) for dichotomous or polytomous
data.  This function also includes a wrapper for models fit using the TAM
package.")
    (license license:gpl3)))

(define-public r-scagnostics
  (package
    (name "r-scagnostics")
    (version "0.2-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scagnostics" version))
       (sha256
        (base32 "0b5fkzhvr1js3l7s9wl12jspyklxm6ri6cn6brf949gfl4bjnnq9"))))
    (properties `((upstream-name . "scagnostics")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjava))
    (home-page "https://www.rforge.net/scagnostics/")
    (synopsis "Compute scagnostics - scatterplot diagnostics")
    (description
     "Calculates graph theoretic scagnostics.  Scagnostics describe various measures
of interest for pairs of variables, based on their appearance on a scatterplot.
They are useful tool for discovering interesting or unusual scatterplots from a
scatterplot matrix, without having to look at every individual plot.")
    (license license:bsd-2)))

(define-public r-scaffolder
  (package
    (name "r-scaffolder")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "scaffolder" version))
       (sha256
        (base32 "1va1mlhqv5qn0hm81ypdir9llr68222qxjp3jink69zpaskfbdib"))))
    (properties `((upstream-name . "scaffolder")))
    (build-system r-build-system)
    (inputs (list python))
    (propagated-inputs (list r-reticulate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/terrytangyuan/scaffolder")
    (synopsis
     "Scaffolding Interfaces to Packages in Other Programming Languages")
    (description
     "Comprehensive set of tools for scaffolding R interfaces to modules, classes,
functions, and documentations written in other programming languages, such as
Python'.")
    (license license:asl2.0)))

(define-public r-sca
  (package
    (name "r-sca")
    (version "0.9-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sca" version))
       (sha256
        (base32 "037fm92wry9njwlyn7qvd34k4k5x5k1v6isvmkzicmxfk8y94a2h"))))
    (properties `((upstream-name . "sca")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sca")
    (synopsis "Simple Component Analysis")
    (description
     "Simple Component Analysis (SCA) often provides much more interpretable
components than Principal Components (PCA) while still representing much of the
variability in the data.")
    (license license:gpl2+)))

(define-public r-sc2sc
  (package
    (name "r-sc2sc")
    (version "0.0.1-12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sc2sc" version))
       (sha256
        (base32 "0cdfx47vlcqqbl6d75zr91x8j4ayar7wdk5mkdilgw0izcxwscjf"))))
    (properties `((upstream-name . "sc2sc")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sc2sc")
    (synopsis "Spatial Transfer of Statistics among Spanish Census Sections")
    (description
     "Transfers/imputes statistics among Spanish spatial polygons (census sections or
postal code areas) from different moments in time (2001-2023) without need of
spatial files, just linking statistics to the ID codes of the spatial units.
The data available in the census sections of a partition/division (cartography)
into force in a moment of time is transferred to the census sections of another
partition/division employing the geometric approach (also known as areal
weighting or polygon overlay).  References: Goerlich (2022)
<doi:10.12842/WPIVIE_0322>.  PavÃ­a and Cantarino (2017a, b)
<doi:10.1111/gean.12112>, <doi:10.1016/j.apgeog.2017.06.021>.  Acknowledgements:
The authors wish to thank ConsellerÃ­a de EducaciÃ³n, Universidades y Empleo,
Generalitat Valenciana (grant AICO/2021/257) and Ministerio de EconomÃ­a e
InnovaciÃ³n (grant PID2021-128228NB-I00) for supporting this research.")
    (license license:gpl2+)))

(define-public r-sc2api
  (package
    (name "r-sc2api")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SC2API" version))
       (sha256
        (base32 "1z3rch5238qih5054ial8vjcpj1cdmzgc7cga9agnp58y19a1lcj"))))
    (properties `((upstream-name . "SC2API")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-httr r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SC2API")
    (synopsis "Blizzard SC2 API Wrapper")
    (description
     "This package provides a wrapper for Blizzard's Starcraft II (a 2010 real-time
strategy game) Application Programming Interface (API).  All documented API
calls are implemented in an easy-to-use and consistent manner.")
    (license license:expat)))

(define-public r-sc-meb
  (package
    (name "r-sc-meb")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SC.MEB" version))
       (sha256
        (base32 "04vv1ckirfmjvb3hfk3ldma96j4c6ykjgf77f13n550cvp2bhp3w"))))
    (properties `((upstream-name . "SC.MEB")))
    (build-system r-build-system)
    (propagated-inputs (list r-summarizedexperiment
                             r-singlecellexperiment
                             r-scran
                             r-scater
                             r-s4vectors
                             r-rcpparmadillo
                             r-rcpp
                             r-purrr
                             r-mclust
                             r-matrix
                             r-ggplot2
                             r-biocsingular))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SC.MEB")
    (synopsis
     "Spatial Clustering with Hidden Markov Random Field using Empirical Bayes")
    (description
     "Spatial clustering with hidden markov random field fitted via EM algorithm,
details of which can be found in Yi Yang (2021) <doi:10.1101/2021.06.05.447181>.
 It is not only computationally efficient and scalable to the sample size
increment, but also is capable of choosing the smoothness parameter and the
number of clusters as well.")
    (license license:gpl3)))

(define-public r-sbw
  (package
    (name "r-sbw")
    (version "1.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sbw" version))
       (sha256
        (base32 "0815xxgzz0b79bv9g4cw144dl6y6f2yi2k2n2khfhpwd8ipgmsr7"))))
    (properties `((upstream-name . "sbw")))
    (build-system r-build-system)
    (propagated-inputs (list r-spatstat-univar r-slam r-quadprog r-matrix
                             r-mass))
    (home-page "https://cran.r-project.org/package=sbw")
    (synopsis "Stable Balancing Weights for Causal Inference and Missing Data")
    (description
     "This package implements the Stable Balancing Weights by Zubizarreta (2015)
<DOI:10.1080/01621459.2015.1023805>.  These are the weights of minimum variance
that approximately balance the empirical distribution of the observed
covariates.  For an overview, see Chattopadhyay, Hase and Zubizarreta (2020)
<DOI:10.1002/sim.8659>.  To solve the optimization problem in sbw', the default
solver is quadprog', which is readily available through CRAN. The solver osqp is
also posted on CRAN. To enhance the performance of sbw', users are encouraged to
install other solvers such as gurobi and Rmosek', which require special
installation.  For the installation of gurobi and pogs, please follow the
instructions at
<https://www.gurobi.com/documentation/current/refman/r_ins_the_r_package.html>
and <http://foges.github.io/pogs/stp/r>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-sbtools
  (package
    (name "r-sbtools")
    (version "1.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sbtools" version))
       (sha256
        (base32 "14yjml5d34jkyw7rsc02zpb4wy9w30sxqzpdjk02znxpm9baxsj1"))))
    (properties `((upstream-name . "sbtools")))
    (build-system r-build-system)
    (propagated-inputs (list r-mime
                             r-keyring
                             r-jsonlite
                             r-httr
                             r-curl
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/DOI-USGS/sbtools")
    (synopsis "USGS ScienceBase Tools")
    (description
     "This package provides tools for interacting with U.S. Geological Survey
@code{ScienceBase} <https://www.sciencebase.gov> interfaces. @code{ScienceBase}
is a data cataloging and collaborative data management platform.  Functions
included for querying @code{ScienceBase}, and creating and fetching datasets.")
    (license license:cc0)))

(define-public r-sbsdiff
  (package
    (name "r-sbsdiff")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SBSDiff" version))
       (sha256
        (base32 "0mw1646dmhxw6zz4pq7j0g3mf9bjn72g43sq7m72r8ma8ciqj7r2"))))
    (properties `((upstream-name . "SBSDiff")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SBSDiff")
    (synopsis "Satorra-Bentler Scaled Chi-Squared Difference Test")
    (description
     "Calculates a Satorra-Bentler scaled chi-squared difference test between nested
models that were estimated using maximum likelihood (ML) with robust standard
errors, which cannot be calculated the traditional way.  For details see Satorra
& Bentler (2001) <doi:10.1007/bf02296192> and Satorra & Bentler (2010)
<doi:10.1007/s11336-009-9135-y>.  This package may be particularly helpful when
used in conjunction with Mplus software, specifically when implementing the
complex survey option.  In such cases, the model estimator in Mplus defaults to
ML with robust standard errors.")
    (license license:expat)))

(define-public r-sbrl
  (package
    (name "r-sbrl")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sbrl" version))
       (sha256
        (base32 "0sr57zfd75ywgv4az0y7jiacalywf36z3gmsjdgmw0adzaxxdcsg"))))
    (properties `((upstream-name . "sbrl")))
    (build-system r-build-system)
    (inputs (list gsl gsl gmp))
    (propagated-inputs (list r-rcpp r-arules))
    (home-page "https://cran.r-project.org/package=sbrl")
    (synopsis "Scalable Bayesian Rule Lists Model")
    (description
     "An efficient implementation of Scalable Bayesian Rule Lists Algorithm, a
competitor algorithm for decision tree algorithms; see Hongyu Yang, Cynthia
Rudin, Margo Seltzer (2017) <https://proceedings.mlr.press/v70/yang17h.html>.
It builds from pre-mined association rules and have a logical structure
identical to a decision list or one-sided decision tree.  Fully optimized over
rule lists, this algorithm strikes practical balance between accuracy,
interpretability, and computational speed.")
    (license license:gpl2+)))

(define-public r-sboost
  (package
    (name "r-sboost")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sboost" version))
       (sha256
        (base32 "1vh3hk15chkyrkpnj4i40542bck9ny45g424bncxdxl9fgxc4nhk"))))
    (properties `((upstream-name . "sboost")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-rcpp r-dplyr))
    (home-page "https://github.com/jadonwagstaff/sboost")
    (synopsis "Machine Learning with AdaBoost on Decision Stumps")
    (description
     "This package creates classifier for binary outcomes using Adaptive Boosting
(@code{AdaBoost}) algorithm on decision stumps with a fast C++ implementation.
For a description of @code{AdaBoost}, see Freund and Schapire (1997)
<doi:10.1006/jcss.1997.1504>.  This type of classifier is nonlinear, but easy to
interpret and visualize.  Feature vectors may be a combination of continuous
(numeric) and categorical (string, factor) elements.  Methods for classifier
assessment, predictions, and cross-validation also included.")
    (license license:expat)))

(define-public r-sbo
  (package
    (name "r-sbo")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sbo" version))
       (sha256
        (base32 "00pnvpcl6441gf8npf6b6cvzxh0kq4qbfxggjiqdny6lq2nl0d47"))))
    (properties `((upstream-name . "sbo")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-testthat r-rlang r-rcpp r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://vgherard.github.io/sbo/")
    (synopsis "Text Prediction via Stupid Back-Off N-Gram Models")
    (description
     "Utilities for training and evaluating text predictors based on Stupid Back-Off
N-gram models (Brants et al., 2007,
<https://www.aclweb.org/anthology/D07-1090/>).")
    (license license:gpl3)))

(define-public r-sbn
  (package
    (name "r-sbn")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SBN" version))
       (sha256
        (base32 "1k4x8hzjrzvaxnq9z9rykc65lfp4x75nhjrfpk6llq9hqjms2n06"))))
    (properties `((upstream-name . "SBN")))
    (build-system r-build-system)
    (propagated-inputs (list r-igraph))
    (home-page "https://flee598.github.io/SBN/")
    (synopsis "Generate Stochastic Branching Networks")
    (description
     "Generate Stochastic Branching Networks ('SBNs').  Used to model the branching
structure of rivers.")
    (license license:expat)))

(define-public r-sbmsplitmerge
  (package
    (name "r-sbmsplitmerge")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SBMSplitMerge" version))
       (sha256
        (base32 "1h44qwz9gkwwgkac25h4vn0kq3mjvjsyq99yfd8v8g6g8fkkclaq"))))
    (properties `((upstream-name . "SBMSplitMerge")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-reshape2 r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SBMSplitMerge")
    (synopsis "Inference for a Generalised SBM with a Split Merge Sampler")
    (description
     "Inference in a Bayesian framework for a generalised stochastic block model.  The
generalised stochastic block model (SBM) can capture group structure in network
data without requiring conjugate priors on the edge-states.  Two sampling
methods are provided to perform inference on edge parameters and block
structure: a split-merge Markov chain Monte Carlo algorithm and a Dirichlet
process sampler.  Green, Richardson (2001) <doi:10.1111/1467-9469.00242>; Neal
(2000) <doi:10.1080/10618600.2000.10474879>; Ludkin (2019)
<@code{arXiv:1909.09421>}.")
    (license license:expat)))

(define-public r-sbmsdp
  (package
    (name "r-sbmsdp")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sbmSDP" version))
       (sha256
        (base32 "1sl46lqi6w0s7ghv4bywhic56cm2vib3kawprga760m6igargx4y"))))
    (properties `((upstream-name . "sbmSDP")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=sbmSDP")
    (synopsis
     "Semidefinite Programming for Fitting Block Models of Equal Block Sizes")
    (description
     "An ADMM implementation of SDP-1, a semidefinite programming relaxation of the
maximum likelihood estimator for fitting a block model.  SDP-1 has a tendency to
produce equal-sized blocks and is ideal for producing a form of network
histogram approximating a nonparametric graphon model.  Alternatively, it can be
used for community detection. (This is experimental code, proceed with
caution.).")
    (license license:gpl3)))

(define-public r-sbmedian
  (package
    (name "r-sbmedian")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SBmedian" version))
       (sha256
        (base32 "1pxwsp6z1qvhmzprhr8f8i1id5zy4ppq7dxbd6ny2h1r1zvy16xm"))))
    (properties `((upstream-name . "SBmedian")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-rcpparmadillo r-rcpp r-expm))
    (home-page "https://cran.r-project.org/package=SBmedian")
    (synopsis "Scalable Bayes with Median of Subset Posteriors")
    (description
     "Median-of-means is a generic yet powerful framework for scalable and robust
estimation.  A framework for Bayesian analysis is called M-posterior, which
estimates a median of subset posterior measures.  For general exposition to the
topic, see the paper by Minsker (2015) <doi:10.3150/14-BEJ645>.")
    (license license:expat)))

(define-public r-sbm
  (package
    (name "r-sbm")
    (version "0.4.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sbm" version))
       (sha256
        (base32 "1gx8nii5r2651crc0dfns2ir3pps8lbkxymlj1ngjwl7yhh8lksw"))))
    (properties `((upstream-name . "sbm")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rlang
                             r-reshape2
                             r-rcpparmadillo
                             r-rcpp
                             r-r6
                             r-purrr
                             r-prodlim
                             r-magrittr
                             r-igraph
                             r-gremlins
                             r-ggplot2
                             r-dplyr
                             r-blockmodels
                             r-alluvial))
    (native-inputs (list r-knitr))
    (home-page "https://grosssbm.github.io/sbm/")
    (synopsis "Stochastic Blockmodels")
    (description
     "This package provides a collection of tools and functions to adjust a variety of
stochastic blockmodels (SBM).  Supports at the moment Simple, Bipartite,
Multipartite and Multiplex SBM (undirected or directed with Bernoulli, Poisson
or Gaussian emission laws on the edges, and possibly covariate for Simple and
Bipartite SBM).  See LÃ©ger (2016) <doi:10.48550/@code{arXiv.1602.07587>},
Barbillon et al. (2020) <doi:10.1111/rssa.12193> and Bar-Hen et al. (2020)
<doi:10.48550/@code{arXiv.1807.10138>}.")
    (license license:gpl3+)))

(define-public r-sbl
  (package
    (name "r-sbl")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sbl" version))
       (sha256
        (base32 "1wxzscii0kp55sngmjccrqvc8zm1zamfh406r12irrz2wbxwqnkw"))))
    (properties `((upstream-name . "sbl")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=sbl")
    (synopsis
     "Sparse Bayesian Learning for QTL Mapping and Genome-Wide Association Studies")
    (description
     "This package implements sparse Bayesian learning method for QTL mapping and
genome-wide association studies.")
    (license license:gpl3)))

(define-public r-sbicgraph
  (package
    (name "r-sbicgraph")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SBICgraph" version))
       (sha256
        (base32 "04c9xb9fdxz62qdsfkdkckvfpccrc7n5j3j5v1lgjywxq6l78wsm"))))
    (properties `((upstream-name . "SBICgraph")))
    (build-system r-build-system)
    (propagated-inputs (list r-network r-mass r-glmnet))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SBICgraph")
    (synopsis "Structural Bayesian Information Criterion for Graphical Models")
    (description
     "This is the implementation of the novel structural Bayesian information
criterion by Zhou, 2020 (under review).  In this method, the prior structure is
modeled and incorporated into the Bayesian information criterion framework.
Additionally, we also provide the implementation of a two-step algorithm to
generate the candidate model pool.")
    (license license:gpl3)))

(define-public r-sbic
  (package
    (name "r-sbic")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sBIC" version))
       (sha256
        (base32 "1fx17c9w7v6wi02i293vjdahwxx4b5a118r655cl12gsd5qwqj11"))))
    (properties `((upstream-name . "sBIC")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp
                             r-r-oo
                             r-r-methodss3
                             r-polca
                             r-mclust
                             r-igraph
                             r-hash
                             r-flexmix
                             r-combinat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Lucaweihs/sBIC")
    (synopsis "Computing the Singular BIC for Multiple Models")
    (description
     "Computes the @code{sBIC} for various singular model collections including:
binomial mixtures, factor analysis models, Gaussian mixtures, latent forests,
latent class analyses, and reduced rank regressions.")
    (license license:gpl3+)))

(define-public r-sbi
  (package
    (name "r-sbi")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SBI" version))
       (sha256
        (base32 "0kbcy95dq8kkqaknj8yi33nhvhhd3si2yj0xg1swq15v48ffnrak"))))
    (properties `((upstream-name . "SBI")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SBI")
    (synopsis "Simple Blinding Index for Randomized Controlled Trials")
    (description
     "Computes a simple blinding index for randomized controlled trials introduced in
the paper \"A simple blinding index for randomized controlled trials\" by Petroff,
Bacak, Dagres, Dilk and Wachter, which has been submitted for publication.")
    (license license:gpl3)))

(define-public r-sbgcop
  (package
    (name "r-sbgcop")
    (version "0.980")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sbgcop" version))
       (sha256
        (base32 "0vmg8b4462qghlyx6hq0raf9xqvygzgwi5y0cbqcljhmbmqjrwxs"))))
    (properties `((upstream-name . "sbgcop")))
    (build-system r-build-system)
    (home-page "http://pdhoff.github.io/")
    (synopsis
     "Semiparametric Bayesian Gaussian Copula Estimation and Imputation")
    (description
     "Estimation and inference for parameters in a Gaussian copula model, treating the
univariate marginal distributions as nuisance parameters as described in Hoff
(2007) <doi:10.1214/07-AOAS107>.  This package also provides a semiparametric
imputation procedure for missing multivariate data.")
    (license license:gpl2+)))

(define-public r-sbfc
  (package
    (name "r-sbfc")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sbfc" version))
       (sha256
        (base32 "0yfbxp3b9j2xjpd1xffkjl2bfxixc7hhzfiwicd9lyi5w2kfwavf"))))
    (properties `((upstream-name . "sbfc")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-matrix r-discretization
                             r-diagrammer))
    (home-page "https://github.com/vkrakovna/sbfc")
    (synopsis "Selective Bayesian Forest Classifier")
    (description
     "An MCMC algorithm for simultaneous feature selection and classification, and
visualization of the selected features and feature interactions.  An
implementation of SBFC by Krakovna, Du and Liu (2015),
<@code{arXiv:1506.02371>}.")
    (license license:gpl2+)))

(define-public r-sbdecomp
  (package
    (name "r-sbdecomp")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SBdecomp" version))
       (sha256
        (base32 "0p3f028c674j6bl82sgmbwlim2hljxm38qiff78pzn5yx34y3r0b"))))
    (properties `((upstream-name . "SBdecomp")))
    (build-system r-build-system)
    (propagated-inputs (list r-twang r-survey))
    (home-page "https://cran.r-project.org/package=SBdecomp")
    (synopsis "Estimation of the Proportion of SB Explained by Confounders")
    (description
     "Uses parametric and nonparametric methods to quantify the proportion of the
estimated selection bias (SB) explained by each observed confounder when
estimating propensity score weighted treatment effects.  Parast, L and Griffin,
BA (2020). \"Quantifying the Bias due to Observed Individual Confounders in
Causal Treatment Effect Estimates\".  Statistics in Medicine, 39(18): 2447- 2476
<doi: 10.1002/sim.8549>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-sbde
  (package
    (name "r-sbde")
    (version "1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sbde" version))
       (sha256
        (base32 "0npsf2s3j87j6gxjbpf46qndazjm2al43pxbhnx0jd6gd8fmr631"))))
    (properties `((upstream-name . "sbde")))
    (build-system r-build-system)
    (propagated-inputs (list r-extremefit r-coda))
    (home-page "https://cran.r-project.org/package=sbde")
    (synopsis "Semiparametric Bayesian Density Estimation")
    (description
     "Offers Bayesian semiparametric density estimation and tail-index estimation for
heavy tailed data, by using a parametric, tail-respecting transformation of the
data to the unit interval and then modeling the transformed data with a purely
nonparametric logistic Gaussian process density prior.  Based on Tokdar et al.
(2022) <doi:10.1080/01621459.2022.2104727>.")
    (license license:gpl2)))

(define-public r-sbd
  (package
    (name "r-sbd")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sbd" version))
       (sha256
        (base32 "0wf46p330l2lx6j9w4600q27n4jzhpzb2kw056654fz2ggd8x6kn"))))
    (properties `((upstream-name . "sbd")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-dplyr r-bbmle))
    (home-page "https://github.com/MarcusRowcliffe/sbd")
    (synopsis "Size Biased Distributions")
    (description
     "Fitting and plotting parametric or non-parametric size-biased non-negative
distributions, with optional covariates if parametric.  Rowcliffe, M. et al.
(2016) <doi:10.1002/rse2.17>.")
    (license license:gpl3)))

(define-public r-sbck
  (package
    (name "r-sbck")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SBCK" version))
       (sha256
        (base32 "1z5svxa639245jmv3bsikmbch8j4j51d7rjwhj62zcaxhb3ykhcs"))))
    (properties `((upstream-name . "SBCK")))
    (build-system r-build-system)
    (propagated-inputs (list r-transport r-roopsd r-rcppeigen r-rcpp r-r6))
    (home-page "https://github.com/yrobink/SBCK")
    (synopsis "Statistical Bias Correction Kit")
    (description
     "Implementation of several recent multivariate bias correction methods with a
unified interface to facilitate their use.  A description and comparison between
methods can be found in <doi:10.5194/esd-11-537-2020>.")
    (license license:gpl3)))

(define-public r-sbagm
  (package
    (name "r-sbagm")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SBAGM" version))
       (sha256
        (base32 "0vb1679016b0bhz2xlka9wgr2fxwa0447ymfncm1n5fks60rrnnf"))))
    (properties `((upstream-name . "SBAGM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rugarch r-msgarch r-forecast))
    (home-page "https://cran.r-project.org/package=SBAGM")
    (synopsis "Search Best ARIMA, GARCH, and MS-GARCH Model")
    (description
     "Get the most appropriate autoregressive integrated moving average, generalized
auto-regressive conditional heteroscedasticity and Markov switching GARCH model.
 For method details see Haas M, Mittnik S, Paolella MS (2004).
<doi:10.1093/jjfinec/nbh020>, Bollerslev T (1986).
<doi:10.1016/0304-4076(86)90063-1>.")
    (license license:gpl3)))

(define-public r-sazedr
  (package
    (name "r-sazedr")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sazedR" version))
       (sha256
        (base32 "0v8ap9ivwj5h4w2iwhww16v1jm6jpkkphvkh5vq06mv1a72p0inz"))))
    (properties `((upstream-name . "sazedR")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-pracma r-fftwtools r-dplyr r-bspec))
    (home-page "https://github.com/mtoller/autocorr_season_length_detection/")
    (synopsis
     "Parameter-Free Domain-Agnostic Season Length Detection in Time Series")
    (description
     "Spectral and Average Autocorrelation Zero Distance Density ('sazed') is a method
for estimating the season length of a seasonal time series.  sazed is aimed at
practitioners, as it employs only domain-agnostic preprocessing and does not
depend on parameter tuning or empirical constants.  The computation of sazed
relies on the efficient autocorrelation computation methods suggested by
Thibauld Nion (2012, URL:
<https://etudes.tibonihoo.net/literate_musing/autocorrelations.html>) and by Bob
Carpenter (2012, URL:
<https://lingpipe-blog.com/2012/06/08/autocorrelation-fft-kiss-eigen/>).")
    (license license:gpl2)))

(define-public r-saws
  (package
    (name "r-saws")
    (version "0.9-7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saws" version))
       (sha256
        (base32 "11mnh0m627y3w2yzsb17z5sk3v584vny36m32bxi8niscrfv9im8"))))
    (properties `((upstream-name . "saws")))
    (build-system r-build-system)
    (propagated-inputs (list r-gee))
    (home-page "https://cran.r-project.org/package=saws")
    (synopsis
     "Small-Sample Adjustments for Wald Tests Using Sandwich Estimators")
    (description
     "Tests coefficients with sandwich estimator of variance and with small samples.
Regression types supported are gee, linear regression, and conditional logistic
regression.")
    (license license:gpl2+)))

(define-public r-sawnuti
  (package
    (name "r-sawnuti")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sawnuti" version))
       (sha256
        (base32 "07abg9gqm59iz05jly47h9jnf8majnhjqss2ybjpa9mx1aclm50b"))))
    (properties `((upstream-name . "sawnuti")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sawnuti")
    (synopsis "Comparing Sequences with Non-Uniform Time Intervals")
    (description
     "The SAWNUTI algorithm performs sequence comparison for finite sequences of
discrete events with non-uniform time intervals.  Further description of the
algorithm can be found in the paper: A. Murph, A. Flynt, B. R. King (2021).
Comparing finite sequences of discrete events with non-uniform time intervals,
Sequential Analysis, 40(3), 291-313. <doi:10.1080/07474946.2021.1940491>.")
    (license license:expat)))

(define-public r-savvyr
  (package
    (name "r-savvyr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "savvyr" version))
       (sha256
        (base32 "08lrhb4nc0hcbqjdv1ar31l80gbfgxjivpc5k0farcldksybxjsg"))))
    (properties `((upstream-name . "savvyr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-etm r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://openpharma.github.io/savvyr/")
    (synopsis
     "Survival Analysis for AdVerse Events with VarYing Follow-Up Times")
    (description
     "The SAVVY (Survival Analysis for @code{AdVerse} Events with @code{VarYing}
Follow-Up Times) project is a consortium of academic and pharmaceutical industry
partners that aims to improve the analyses of adverse event (AE) data in
clinical trials through the use of survival techniques appropriately dealing
with varying follow-up times and competing events, see Stegherr, Schmoor,
Beyersmann, et al. (2021) <doi:10.1186/s13063-021-05354-x>.  Although
statistical methodologies have advanced, in AE analyses often the incidence
proportion, the incidence density or a non-parametric Kaplan-Meier estimator are
used, which either ignore censoring or competing events.  This package contains
functions to easily conduct the proposed improved AE analyses.")
    (license license:asl2.0)))

(define-public r-savonliquide
  (package
    (name "r-savonliquide")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "savonliquide" version))
       (sha256
        (base32 "1f1s2pzvpqrcb68fqk4ivpimwsphnjjx3b42jivw63y7q7ak4azh"))))
    (properties `((upstream-name . "savonliquide")))
    (build-system r-build-system)
    (propagated-inputs (list r-httr r-htmltools r-glue r-crayon))
    (home-page "https://github.com/feddelegrand7/savonliquide")
    (synopsis "Accessibility Toolbox for 'R' Users")
    (description
     "This package provides a toolbox that allows the user to implement accessibility
related concepts.")
    (license license:expat)))

(define-public r-sautomata
  (package
    (name "r-sautomata")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SAutomata" version))
       (sha256
        (base32 "0d9q7bm2f7s4aslml40n43vwcdkc8rnn9ygydccwi4b9a7y23k58"))))
    (properties `((upstream-name . "SAutomata")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SAutomata")
    (synopsis "Inference and Learning in Stochastic Automata")
    (description
     "Machine learning provides algorithms that can learn from data and make
inferences or predictions.  Stochastic automata is a class of input/output
devices which can model components.  This work provides implementation an
inference algorithm for stochastic automata which is similar to the Viterbi
algorithm.  Moreover, we specify a learning algorithm using the
expectation-maximization technique and provide a more efficient implementation
of the Baum-Welch algorithm for stochastic automata.  This work is based on
Inference and learning in stochastic automata was by Karl-Heinz Zimmermann(2017)
<doi:10.12732/ijpam.v115i3.15>.")
    (license license:gpl3+)))

(define-public r-saturnin
  (package
    (name "r-saturnin")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saturnin" version))
       (sha256
        (base32 "0cjp4h1s9ivn17v8ar48mxflaj9vgv92c8p9l2k5bc9yqx9mcs36"))))
    (properties `((upstream-name . "saturnin")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp))
    (home-page "https://cran.r-project.org/package=saturnin")
    (synopsis "Spanning Trees Used for Network Inference")
    (description
     "Bayesian inference of graphical model structures using spanning trees.")
    (license license:gpl2)))

(define-public r-satres
  (package
    (name "r-satres")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "satres" version))
       (sha256
        (base32 "0651vv381b5j8zn9gngkqz7asbz3bwrrln57448j2w0qa1lfpq87"))))
    (properties `((upstream-name . "satres")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-snakecase r-sf))
    (native-inputs (list r-knitr))
    (home-page "https://josesamos.github.io/satres/")
    (synopsis "Grouping Satellite Bands by Spectral and Spatial Resolution")
    (description
     "Given raster files directly downloaded from various websites, it generates a
raster structure where it merges them if they are tiles of the same scene and
classifies them according to their spectral and spatial resolution for easy
access by name.")
    (license license:expat)))

(define-public r-satin
  (package
    (name "r-satin")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "satin" version))
       (sha256
        (base32 "17knbgy89ljp607mkhs62fx9p6b40iqqzgpxr9karlf07l48c0ya"))))
    (properties `((upstream-name . "satin")))
    (build-system r-build-system)
    (propagated-inputs (list r-splancs
                             r-sp
                             r-pbsmapping
                             r-ncdf4
                             r-maps
                             r-geosphere))
    (home-page "https://github.com/hvillalo/satin")
    (synopsis
     "Visualisation and Analysis of Ocean Data Derived from Satellites")
    (description
     "With satin functions, visualisation, data extraction and further analysis like
producing climatologies from several images, and anomalies of satellite derived
ocean data can be easily done.  Reading functions can import a user defined
geographical extent of data stored in @code{netCDF} files.  Currently supported
ocean data sources include NASA's Oceancolor web page
<https://oceancolor.gsfc.nasa.gov/>, sensors VIIRS-SNPP; MODIS-Terra;
MODIS-Aqua; and @code{SeaWiFS}.  Available variables from this source includes
chlorophyll concentration, sea surface temperature (SST), and several others.
Data sources specific for SST that can be imported too includes Pathfinder AVHRR
<https://www.ncei.noaa.gov/products/avhrr-pathfinder-sst> and GHRSST
<https://www.ghrsst.org/>.  In addition, ocean productivity data produced by
Oregon State University
<http://sites.science.oregonstate.edu/ocean.productivity/> can also be handled
previous conversion from HDF4 to HDF5 format.  Many other ocean variables can be
processed by importing @code{netCDF} data files from two European Union's
Copernicus Marine Service databases <https://marine.copernicus.eu/>, namely
Global Ocean Physical Reanalysis and Global Ocean Biogeochemistry Hindcast.")
    (license license:gpl3)))

(define-public r-satellite
  (package
    (name "r-satellite")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "satellite" version))
       (sha256
        (base32 "1bd1f8mimx53dcy3fnlhjk7yam08jvr3lhh39zcb5c2yj0pkhk1y"))))
    (properties `((upstream-name . "satellite")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-rcpp r-raster r-plyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/environmentalinformatics-marburg/satellite")
    (synopsis "Handling and Manipulating Remote Sensing Data")
    (description
     "Herein, we provide a broad variety of functions which are useful for handling,
manipulating, and visualizing satellite-based remote sensing data.  These
operations range from mere data import and layer handling (eg subsetting), over
Raster* typical data wrangling (eg crop, extend), to more sophisticated
(pre-)processing tasks typically applied to satellite imagery (eg atmospheric
and topographic correction).  This functionality is complemented by a full
access to the satellite layers metadata at any stage and the documentation of
performed actions in a separate log file.  Currently available sensors include
Landsat 4-5 (TM), 7 (ETM+), and 8 (OLI/TIRS Combined), and additional
compatibility is ensured for the Landsat Global Land Survey data set.")
    (license license:expat)))

(define-public r-sate
  (package
    (name "r-sate")
    (version "2.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sate" version))
       (sha256
        (base32 "137q4636x0wizfiq2smf8gc8x3k31mkm5i6nmb7s3arl250qcv0n"))))
    (properties `((upstream-name . "sate")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-ellipse))
    (home-page "https://cran.r-project.org/package=sate")
    (synopsis "Scientific Analysis of Trial Errors (SATE)")
    (description
     "Bundles functions used to analyze the harmfulness of trial errors in criminal
trials.  Functions in the Scientific Analysis of Trial Errors ('SATE') package
help users estimate the probability that a jury will find a defendant guilty
given jurors preferences for a guilty verdict and the uncertainty of that
estimate.  Users can also compare actual and hypothetical trial conditions to
conduct harmful error analysis.  The relationship between individual jurors
verdict preferences and the probability that a jury returns a guilty verdict has
been studied by Davis (1973) <doi:10.1037/h0033951>; @code{MacCoun} & Kerr
(1988) <doi:10.1037/0022-3514.54.1.21>, and Devine et el. (2001)
<doi:10.1037/1076-8971.7.3.622>, among others.")
    (license license:cc0)))

(define-public r-satdad
  (package
    (name "r-satdad")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "satdad" version))
       (sha256
        (base32 "1ig7z0z4filq57sk9rc38pnx79j1lxm1sacl9hbi407y0pqx01k7"))))
    (properties `((upstream-name . "satdad")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-partitions
                             r-maps
                             r-igraph
                             r-graphicalextremes
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=satdad")
    (synopsis
     "Sensitivity Analysis Tools for Dependence and Asymptotic Dependence")
    (description
     "This package provides tools for analyzing tail dependence in any sample or in
particular theoretical models.  The package uses only theoretical and non
parametric methods, without inference.  The primary goals of the package are to
provide: (a)symmetric multivariate extreme value models in any dimension;
theoretical and empirical indices to order tail dependence; theoretical and
empirical graphical methods to visualize tail dependence.")
    (license license:gpl3+)))

(define-public r-sassy
  (package
    (name "r-sassy")
    (version "1.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sassy" version))
       (sha256
        (base32 "1w2hgkwvwzvzmd1gw9x6y5zvxvmp704zk5jny6ppq44wavm7nl52"))))
    (properties `((upstream-name . "sassy")))
    (build-system r-build-system)
    (propagated-inputs (list r-reporter
                             r-procs
                             r-logr
                             r-libr
                             r-fmtr
                             r-common))
    (native-inputs (list r-knitr))
    (home-page "https://r-sassy.org")
    (synopsis "Makes 'R' Easier for Everyone")
    (description
     "This package provides a meta-package that aims to make R easier for everyone,
especially programmers who have a background in SASÂ® software.  This set of
packages brings many useful concepts to R', including data libraries, data
dictionaries, formats and format catalogs, a data step, and a traceable log.
The flagship package is a reporting package that can output in text, rich text,
PDF', HTML', and DOCX file formats.")
    (license license:cc0)))

(define-public r-sasr
  (package
    (name "r-sasr")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sasr" version))
       (sha256
        (base32 "1cpmfclxphk5s3nr1znricm195j9xix5pw8jkyq3hhg0h7xirzi7"))))
    (properties `((upstream-name . "sasr")))
    (build-system r-build-system)
    (propagated-inputs (list r-reticulate r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/insightsengineering/sasr/")
    (synopsis "'SAS' Interface")
    (description
     "This package provides a SAS interface, through
SASPy'(<https://sassoftware.github.io/saspy/>) and
reticulate'(<https://rstudio.github.io/reticulate/>).  This package helps you
create SAS sessions, execute SAS code in remote SAS servers, retrieve execution
results and log, and exchange datasets between SAS and R'.  It also helps you to
install SASPy and create a configuration file for the connection.  Please review
the SASPy license file as instructed so that you comply with its separate and
independent license.")
    (license license:asl2.0)))

(define-public r-sasmixed
  (package
    (name "r-sasmixed")
    (version "1.0-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SASmixed" version))
       (sha256
        (base32 "0491x4a3fwiy26whclrc19alcdxccn40ghpsgwjkn9sxi8vj5wvm"))))
    (properties `((upstream-name . "SASmixed")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SASmixed")
    (synopsis "Data sets from \"SAS System for Mixed Models\"")
    (description
     "Data sets and sample lmer analyses corresponding to the examples in Littell,
Milliken, Stroup and Wolfinger (1996), \"SAS System for Mixed Models\", SAS
Institute.")
    (license license:gpl2+)))

(define-public r-sasmarkdown
  (package
    (name "r-sasmarkdown")
    (version "0.8.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SASmarkdown" version))
       (sha256
        (base32 "0xrrmb2zmm0mdg4akm5rnzbxxx690w9mv90mp830kbs42i0haqqg"))))
    (properties `((upstream-name . "SASmarkdown")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-xfun r-knitr))
    (home-page
     "https://www.ssc.wisc.edu/~hemken/SASworkshops/sas.html#writing-sas-documentation")
    (synopsis "'SAS' Markdown")
    (description "Settings and functions to extend the knitr SAS engine.")
    (license license:expat)))

(define-public r-saslm
  (package
    (name "r-saslm")
    (version "0.10.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sasLM" version))
       (sha256
        (base32 "1m44rf8mpw9pc5jlxw8ab5z62knj0fjfvbrs53yvfvlbh39xjbq4"))))
    (properties `((upstream-name . "sasLM")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://cran.r-project.org/package=sasLM")
    (synopsis "'SAS' Linear Model")
    (description
     "This is a core implementation of SAS procedures for linear models - GLM, REG,
ANOVA, TTEST, FREQ, and UNIVARIATE. Some R packages provide type II and type III
SS. However, the results of nested and complex designs are often different from
those of SAS. Different results does not necessarily mean incorrectness.
However, many wants the same results to SAS. This package aims to achieve that.
Reference: Littell RC, Stroup WW, Freund RJ (2002, ISBN:0-471-22174-0).")
    (license license:gpl3)))

(define-public r-sasfunclust
  (package
    (name "r-sasfunclust")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sasfunclust" version))
       (sha256
        (base32 "1ba7g3rxyq5h806na6bsdvh6mw6hydgcf3740qkfd4d5fx7m0mq4"))))
    (properties `((upstream-name . "sasfunclust")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-mclust
                             r-matrixcalc
                             r-matrix
                             r-mass
                             r-fda))
    (home-page "https://github.com/unina-sfere/sasfunclust")
    (synopsis "Sparse and Smooth Functional Clustering")
    (description
     "This package implements the sparse and smooth functional clustering
(@code{SaS-Funclust}) method (Centofanti et al. (2021)
<@code{arXiv:2103.15224>}) that aims to classify a sample of curves into
homogeneous groups while jointly detecting the most informative portions of
domain.")
    (license license:gpl3)))

(define-public r-sasdates
  (package
    (name "r-sasdates")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SASdates" version))
       (sha256
        (base32 "1413x0biid7972zz2qqv22h26a738mp7n97gpxxs9rykxkw3bfr5"))))
    (properties `((upstream-name . "SASdates")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SASdates")
    (synopsis "Convert the Dates to 'SAS' Formats")
    (description
     "Converts the dates to different SAS date formats.  In SAS dates are a special
case of numeric values.  Each day is assigned a specific numeric value, starting
from January 1, 1960.  This date is assigned the date value 0, and the next date
has a date value of 1 and so on.  The previous days to this date are represented
by -1 , -2 and so on.  With this approach, SAS can represent any date in the
future or any date in the past.  There are many date formats used in SAS to
represent date-time.  Here, we try to develop functions which will convert the
date to different SAS date formats.")
    (license license:expat)))

(define-public r-sascii
  (package
    (name "r-sascii")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SAScii" version))
       (sha256
        (base32 "1ib09dmvbn9d6dlw5mh4g36d503mn05y3m5453aaclqnmgarf12m"))))
    (properties `((upstream-name . "SAScii")))
    (build-system r-build-system)
    (home-page "https://github.com/ajdamico/SAScii")
    (synopsis
     "Import ASCII Files Directly into R using Only a 'SAS' Input Script")
    (description
     "Using any importation code designed for SAS users to read ASCII files into
sas7bdat files, this package parses through the INPUT block of a .sas syntax
file to design the parameters needed for a read.fwf() function call.  This
allows the user to specify the location of the ASCII (often a .dat') file and
the location of the SAS syntax file, and then load the data frame directly into
R in just one step.")
    (license license:gpl2+)))

(define-public r-sas7bdat
  (package
    (name "r-sas7bdat")
    (version "0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sas7bdat" version))
       (sha256
        (base32 "1ml9r4am13c3f0lmhzxl5m9v0lgkjb7cylfasqn7ghc27cc9yx70"))))
    (properties `((upstream-name . "sas7bdat")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sas7bdat")
    (synopsis "sas7bdat Reverse Engineering Documentation")
    (description
     "Documentation and prototypes for the earliest (circa 2010) open-source effort to
reverse engineer the sas7bdat file format.  The package includes a prototype
reader for sas7bdat files.  However, newer packages may contain more robust
readers for sas7bdat files.")
    (license license:gpl2+)))

(define-public r-sarsop
  (package
    (name "r-sarsop")
    (version "0.6.15")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sarsop" version))
       (sha256
        (base32 "0793pvfldbxnqsm0gzpwr792xvvzcr35yj75s5wc4hbv2adv9mvn"))))
    (properties `((upstream-name . "sarsop")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-xml2 r-processx r-matrix r-digest r-bh))
    (home-page "https://github.com/boettiger-lab/sarsop")
    (synopsis "Approximate POMDP Planning Software")
    (description
     "This package provides a toolkit for Partially Observed Markov Decision Processes
(POMDP).  Provides bindings to C++ libraries implementing the algorithm SARSOP
(Successive Approximations of the Reachable Space under Optimal Policies) and
described in Kurniawati et al (2008), <doi:10.15607/RSS.2008.IV.009>.  This
package also provides a high-level interface for generating, solving and
simulating POMDP problems and their solutions.")
    (license license:gpl2)))

(define-public r-sars
  (package
    (name "r-sars")
    (version "1.3.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sars" version))
       (sha256
        (base32 "1rv69i8p8bkcn2av5sa86mz4m70cn6bd1kdbvhfigg05h2kw070w"))))
    (properties `((upstream-name . "sars")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv
                             r-nortest
                             r-minpack-lm
                             r-foreach
                             r-doparallel
                             r-crayon
                             r-cli
                             r-aiccmodavg))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/txm676/sars")
    (synopsis
     "Fit and Compare Species-Area Relationship Models Using Multimodel Inference")
    (description
     "This package implements the basic elements of the multi-model inference paradigm
for up to twenty species-area relationship models (SAR), using simple R
list-objects and functions, as in Triantis et al.  2012
<DOI:10.1111/j.1365-2699.2011.02652.x>.  The package is scalable and users can
easily create their own model and data objects.  Additional SAR related
functions are provided.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-sarp-snowprofile-pyface
  (package
    (name "r-sarp-snowprofile-pyface")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sarp.snowprofile.pyface" version))
       (sha256
        (base32 "0nnl4bdvvbn6wf3zhv7n710y8fn07rnsb2f9j9lkr5g9a52356bf"))))
    (properties `((upstream-name . "sarp.snowprofile.pyface")))
    (build-system r-build-system)
    (propagated-inputs (list r-sarp-snowprofile r-reticulate r-data-table))
    (home-page "http://www.avalancheresearch.ca")
    (synopsis "'python' Modules from Snowpack and Avalanche Research")
    (description
     "The development of post-processing functionality for simulated snow profiles by
the snow and avalanche community is often done in python'.  This package aims to
make these tools accessible to R users.  Currently integrated modules contain
functions to calculate dry snow layer instabilities in support of avalache
hazard assessments following the publications of Richter, Schweizer, Rotach, and
Van Herwijnen (2019) <doi:10.5194/tc-13-3353-2019>, and Mayer, Van Herwijnen,
Techel, and Schweizer (2022) <doi:10.5194/tc-2022-34>.")
    (license license:cc-by-sa4.0)))

(define-public r-sarp-snowprofile-alignment
  (package
    (name "r-sarp-snowprofile-alignment")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sarp.snowprofile.alignment" version))
       (sha256
        (base32 "00i8j267zbv8ln52cr25drj06n3qhkgvbh184aq3rvvrpwrg846x"))))
    (properties `((upstream-name . "sarp.snowprofile.alignment")))
    (build-system r-build-system)
    (propagated-inputs (list r-sarp-snowprofile r-dtw r-data-table r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://avalancheresearch.ca/")
    (synopsis "Snow Profile Alignment, Aggregation, and Clustering")
    (description
     "Snow profiles describe the vertical (1D) stratigraphy of layered snow with
different layer characteristics, such as grain type, hardness, deposition date,
and many more.  Hence, they represent a data format similar to multivariate time
series containing categorical, ordinal, and numerical data types.  Use this
package to align snow profiles by matching their individual layers based on
Dynamic Time Warping (DTW).  The aligned profiles can then be assessed with an
independent, global similarity measure that is geared towards avalanche hazard
assessment.  Finally, through exploiting data aggregation and clustering
methods, the similarity measure provides the foundation for grouping and
summarizing snow profiles according to similar hazard conditions.  In
particular, this package allows for averaging large numbers of snow profiles
with DTW Barycenter Averaging and thereby facilitates the computation of
individual layer distributions and summary statistics that are relevant for
avalanche forecasting purposes.  For more background information refer to Herla,
Horton, Mair, and Haegeli (2021) <doi:10.5194/gmd-14-239-2021>, Herla, Mair, and
Haegeli (2022) <doi:10.5194/tc-16-3149-2022>, and Horton, Herla, and Haegeli
(2024) <doi:10.5194/egusphere-2024-1609>.")
    (license license:gpl3+)))

(define-public r-sarp-snowprofile
  (package
    (name "r-sarp-snowprofile")
    (version "1.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sarp.snowprofile" version))
       (sha256
        (base32 "1nrkifs6kq8kr3jmwi76d6jjbps1ijrnjxrslsyrpyvr2rj4p82h"))))
    (properties `((upstream-name . "sarp.snowprofile")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-data-table))
    (native-inputs (list r-knitr))
    (home-page "http://www.avalancheresearch.ca")
    (synopsis "Snow Profile Analysis for Snowpack and Avalanche Research")
    (description
     "Analysis and plotting tools for snow profile data produced from manual snowpack
observations and physical snowpack models.  The functions in this package
support snowpack and avalanche research by reading various formats of data
(including CAAML, SMET, generic csv, and outputs from the snow cover model
SNOWPACK), manipulate the data, and produce graphics such as stratigraphy and
time series profiles.  Package developed by the Simon Fraser University
Avalanche Research Program <http://www.avalancheresearch.ca>.  Graphics apply
visualization concepts from Horton, Nowak, and Haegeli (2020,
<doi:10.5194/nhess-20-1557-2020>).")
    (license license:cc-by-sa4.0)))

(define-public r-sarp-moodle
  (package
    (name "r-sarp-moodle")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SARP.moodle" version))
       (sha256
        (base32 "1zpvw5sdzrh5ci89lqra73yvvpj340wc1rl5825krj1f73lmga3s"))))
    (properties `((upstream-name . "SARP.moodle")))
    (build-system r-build-system)
    (propagated-inputs (list r-base64enc))
    (home-page "https://cran.r-project.org/package=SARP.moodle")
    (synopsis "XML Output Functions for Easy Creation of Moodle Questions")
    (description
     "This package provides a set of basic functions for creating Moodle XML output
files suited for importing questions in Moodle (a learning management system,
see <https://moodle.org/> for more information).")
    (license license:artistic2.0)))

(define-public r-sarp-compo
  (package
    (name "r-sarp-compo")
    (version "0.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SARP.compo" version))
       (sha256
        (base32 "1dgppwk7qyqdbfv1n9m7l55gqrqr4zzym7l8kpfi46f98l0knmdk"))))
    (properties `((upstream-name . "SARP.compo")))
    (build-system r-build-system)
    (propagated-inputs (list r-igraph r-car))
    (home-page "https://cran.r-project.org/package=SARP.compo")
    (synopsis "Network-Based Interpretation of Changes in Compositional Data")
    (description
     "This package provides a set of functions to interpret changes in compositional
data based on a network representation of all pairwise ratio comparisons:
computation of all pairwise ratio, construction of a p-value matrix of all
pairwise tests of these ratios between conditions, conversion of this matrix to
a network.")
    (license license:artistic2.0)))

(define-public r-saros-base
  (package
    (name "r-saros-base")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saros.base" version))
       (sha256
        (base32 "0c0h6v5r81gylz9xhd2ibf0mn78lpqslgbjb7dkwmscwc8s7ky7w"))))
    (properties `((upstream-name . "saros.base")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-zip
                             r-yaml
                             r-vctrs
                             r-tidyselect
                             r-tidyr
                             r-stringi
                             r-rstudioapi
                             r-rlang
                             r-glue
                             r-fs
                             r-forcats
                             r-dplyr
                             r-cli
                             r-bcrypt))
    (home-page "https://nifu-no.github.io/saros.base/")
    (synopsis "Base Tools for Semi-Automatic Reporting of Ordinary Surveys")
    (description
     "Scaffold an entire web-based report using template chunks, based on a small
chapter overview and a dataset.  Highly adaptable with prefixes, suffixes,
translations, etc.  Also contains tools for password-protecting, e.g. for each
organization's report on a website.  Developed for the common case of a survey
across multiple organizations/sites where each organization wants to obtain
results for their organization compared with everyone else.  See saros
(<https://CRAN.R-project.org/package=saros>) for tools used for authors in the
drafted reports.")
    (license license:expat)))

(define-public r-saros
  (package
    (name "r-saros")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saros" version))
       (sha256
        (base32 "03ignw4ah05nkfkck1dn6ghy13d892dy4sw6h8ijn2lmkxbjlgkm"))))
    (properties `((upstream-name . "saros")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidyselect
                             r-tidyr
                             r-stringi
                             r-rlang
                             r-officer
                             r-mschart
                             r-lifecycle
                             r-glue
                             r-ggplot2
                             r-ggiraph
                             r-fs
                             r-forcats
                             r-dplyr
                             r-cli))
    (home-page "https://nifu-no.github.io/saros/")
    (synopsis "Semi-Automatic Reporting of Ordinary Surveys")
    (description
     "Offers a systematic way for conditional reporting of figures and tables for many
(and bivariate combinations of) variables, typically from survey data.  Contains
interactive ggiraph'-based (<https://CRAN.R-project.org/package=ggiraph>)
plotting functions and data frame-based summary tables (bivariate significance
tests, frequencies/proportions, unique open ended responses, etc) with many
arguments for customization, and extensions possible.  Uses a global options()
system for neatly reducing redundant code.  Also contains tools for immediate
saving of objects and returning a hashed link to the object, useful for creating
download links to high resolution images upon rendering in Quarto'.  Suitable
for highly customized reports, primarily intended for survey research.")
    (license license:expat)))

(define-public r-sarima
  (package
    (name "r-sarima")
    (version "0.9.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sarima" version))
       (sha256
        (base32 "0x6wndy7mdmnliabrbs9qbpm7ibf2h7s3m4d90pasna6hw11yg3d"))))
    (properties `((upstream-name . "sarima")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack
                             r-rcpparmadillo
                             r-rcpp
                             r-polynomf
                             r-numderiv
                             r-ltsa
                             r-lagged
                             r-formula))
    (home-page
     "https://geobosh.github.io/sarima/https://github.com/GeoBosh/sarima")
    (synopsis "Simulation and Prediction with Seasonal ARIMA Models")
    (description
     "Functions, classes and methods for time series modelling with ARIMA and related
models.  The aim of the package is to provide consistent interface for the user.
 For example, a single function autocorrelations() computes various kinds of
theoretical and sample autocorrelations.  This is work in progress, see the
documentation and vignettes for the current functionality.  Function sarima()
fits extended multiplicative seasonal ARIMA models with trends, exogenous
variables and arbitrary roots on the unit circle, which can be fixed or
estimated (for the algebraic basis for this see <@code{arXiv:2208.05055>}, a
paper on the methodology is being prepared).")
    (license license:gpl2+)))

(define-public r-sara4r
  (package
    (name "r-sara4r")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sara4r" version))
       (sha256
        (base32 "1z112s7z9hcafmags76bazkyrmrrwykhf58g6z637w516nbgn314"))))
    (properties `((upstream-name . "sara4r")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-tcltk2))
    (native-inputs (list r-knitr))
    (home-page "https://hydro-geomatic-lab.com/")
    (synopsis
     "An R-GUI for Spatial Analysis of Surface Runoff using the NRCS-CN Method")
    (description
     "This package provides a Graphical user interface to calculate the
rainfall-runoff relation using the Natural Resources Conservation Service -
Curve Number method (NRCS-CN method) but include modifications by Hawkins et
al., (2002) about the Initial Abstraction.  This GUI follows the programming
logic of a previously published software (Hernandez-Guzman et al.,
2011)<doi:10.1016/j.envsoft.2011.07.006>.  It is a raster-based GIS tool that
outputs runoff estimates from Land use/land cover and hydrologic soil group
maps.  This package has already been published in Journal of Hydroinformatics
(Hernandez-Guzman et al., 2021)<doi:10.2166/hydro.2020.087> but it is under
constant development at the Institute about Natural Resources Research (INIRENA)
from the Universidad Michoacana de San Nicolas de Hidalgo and represents a
collaborative effort between the Hydro-Geomatic Lab (INIRENA) with the
Environmental Management Lab (CIAD, A.C.).")
    (license license:gpl3+)))

(define-public r-sar
  (package
    (name "r-sar")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SAR" version))
       (sha256
        (base32 "1cnf0hsq82lzssvz5xs2abvnrjxda0g6mjx70a1j223zdwjqmw64"))))
    (properties `((upstream-name . "SAR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppparallel
                             r-rcpparmadillo
                             r-rcpp
                             r-r6
                             r-matrix
                             r-jsonlite
                             r-httr
                             r-dplyr
                             r-azurestor
                             r-azurermr))
    (home-page "https://github.com/hongooi73/SAR")
    (synopsis "Smart Adaptive Recommendations")
    (description
     "Smart Adaptive Recommendations (SAR) is the name of a fast, scalable, adaptive
algorithm for personalized recommendations based on user transactions and item
descriptions.  It produces easily explainable/interpretable recommendations and
handles \"cold item\" and \"semi-cold user\" scenarios.  This package provides two
implementations of SAR': a standalone implementation, and an interface to a web
service in Microsoft's Azure cloud:
<https://github.com/Microsoft/Product-Recommendations/blob/master/doc/sar.md>.
The former allows fast and easy experimentation, and the latter provides robust
scalability and extra features for production use.")
    (license license:expat)))

(define-public r-saqgetr
  (package
    (name "r-saqgetr")
    (version "0.2.21")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saqgetr" version))
       (sha256
        (base32 "0s7mmc8gqx89697wkv369x6sp3d1wha4q2zkkwikh835i3alx4zd"))))
    (properties `((upstream-name . "saqgetr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-readr
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-httr
                             r-dplyr))
    (home-page "https://github.com/skgrange/saqgetr")
    (synopsis "Import Air Quality Monitoring Data in a Fast and Easy Way")
    (description
     "This package provides a collection of tools to access prepared air quality
monitoring data files from web servers with ease and speed.  Air quality data
are sourced from open and publicly accessible repositories and can be found in
these locations:
<https://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-8>
and <https://discomap.eea.europa.eu/map/fme/@code{AirQualityExport.htm>}.  The
web server space has been provided by Ricardo Energy & Environment.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-sapp
  (package
    (name "r-sapp")
    (version "1.0.9-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SAPP" version))
       (sha256
        (base32 "1ngyxgmb13rcgzblpa7v6v390pyymglvvb4j8x7z4038v24s7qj6"))))
    (properties `((upstream-name . "SAPP")))
    (build-system r-build-system)
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=SAPP")
    (synopsis "Statistical Analysis of Point Processes")
    (description
     "This package provides functions for statistical analysis of point processes.")
    (license license:gpl2+)))

(define-public r-sapo
  (package
    (name "r-sapo")
    (version "0.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sapo" version))
       (sha256
        (base32 "0x6hd2747v2w8hv87ymdk5xzxk57dh7zjibpgygli1y10sd2v0v7"))))
    (properties `((upstream-name . "sapo")))
    (build-system r-build-system)
    (inputs (list proj geos gdal))
    (propagated-inputs (list r-sf))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/lcgodoy/sapo/")
    (synopsis "Spatial Association of Different Types of Polygon")
    (description
     "In ecology, spatial data is often represented using polygons.  These polygons
can represent a variety of spatial entities, such as ecological patches, animal
home ranges, or gaps in the forest canopy.  Researchers often need to determine
if two spatial processes, represented by these polygons, are independent of each
other.  For instance, they might want to test if the home range of a particular
animal species is influenced by the presence of a certain type of vegetation.
To address this, Godoy et al. (2022) (<doi:10.1016/j.spasta.2022.100695>)
developed conditional Monte Carlo tests.  These tests are designed to assess
spatial independence while taking into account the shape and size of the
polygons.")
    (license license:gpl3+)))

(define-public r-sapfluxnetr
  (package
    (name "r-sapfluxnetr")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sapfluxnetr" version))
       (sha256
        (base32 "0x437nhv3g327apxnihr1wnp4mmzxn9pfk24shpj6a8amxphlzxd"))))
    (properties `((upstream-name . "sapfluxnetr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-glue
                             r-ggplot2
                             r-furrr
                             r-dplyr
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sapfluxnet/sapfluxnetr")
    (synopsis "Working with 'Sapfluxnet' Project Data")
    (description
     "Access, modify, aggregate and plot data from the Sapfluxnet project
(<http://sapfluxnet.creaf.cat>), the first global database of sap flow
measurements.")
    (license license:expat)))

(define-public r-sapevom
  (package
    (name "r-sapevom")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sapevom" version))
       (sha256
        (base32 "0vq4rx85pdjc0wjxjf7wj8j5qs3cb20hwhlc2ypbgrlw7zkcp1z4"))))
    (properties `((upstream-name . "sapevom")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=sapevom")
    (synopsis "Group Ordinal Method for Multiple Criteria Decision-Making")
    (description
     "Implementation of SAPEVO-M, a Group Ordinal Method for Multiple Criteria
Decision-Making (MCDM).  SAPEVO-M is an acronym for Simple Aggregation of
Preferences Expressed by Ordinal Vectors Group Decision Making.  This method
provides alternatives ranking given decision makers preferences: criteria
preferences and alternatives preferences for each criterion.This method is
described in Gomes et al. (2020) <doi: 10.1590/0101-7438.2020.040.00226524 >.")
    (license license:gpl3)))

(define-public r-sap
  (package
    (name "r-sap")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SAP" version))
       (sha256
        (base32 "129szqkh4snlqn5q09yaqf6sd04nvai3a0ifk0b36yrqj92v6fd1"))))
    (properties `((upstream-name . "SAP")))
    (build-system r-build-system)
    (propagated-inputs (list r-bsda))
    (home-page "https://cran.r-project.org/package=SAP")
    (synopsis "Statistical Analysis and Programming")
    (description
     "The Hypothesis tests for the means of independent or paired groups.  This
package investigates the normality assumption automatically.  Then, it tests the
hypothesis tests for two independent or paired group means by using parametric
or non-parametric tests.  It uses the Shapiro-Wilk test to test the normality
assumption.  For independent two groups, If data comes from the normal
distribution, the package uses the Z or t-test according to whether variances
are known.  For paired groups, it uses paired t-test under normal data sets.  If
data does not come from the normal distribution, the package uses the Wilcoxon
test for independent and paired cases.")
    (license license:gpl2)))

(define-public r-saotd
  (package
    (name "r-saotd")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saotd" version))
       (sha256
        (base32 "1x3zpzhs11bgxa8zhjdwr7n9fi55glhrif43zblh55ih3cqpcfc1"))))
    (properties `((upstream-name . "saotd")))
    (build-system r-build-system)
    (inputs (list mpfr gsl))
    (propagated-inputs (list r-widyr
                             r-topicmodels
                             r-tidytext
                             r-tidyr
                             r-stringr
                             r-scales
                             r-rtweet
                             r-reshape2
                             r-magrittr
                             r-lubridate
                             r-ldatuning
                             r-igraph
                             r-ggraph
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/evan-l-munson/saotd")
    (synopsis "Sentiment Analysis of Twitter Data")
    (description
     "This analytic is an in initial foray into sentiment analysis.  This analytic
will allow a user to access the Twitter API (once they create their own
developer account), ingest tweets of their interest, clean / tidy data, perform
topic modeling if interested, compute sentiment scores utilizing the Bing
Lexicon, and output visualizations.")
    (license license:gpl2+)))

(define-public r-sanzo
  (package
    (name "r-sanzo")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sanzo" version))
       (sha256
        (base32 "1fqsx2mgjsy6vjvsp2avi2i5cw7cjs2p53l1z6hb3bzid3067s87"))))
    (properties `((upstream-name . "sanzo")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jmaasch/sanzo")
    (synopsis "Color Palettes Based on the Works of Sanzo Wada")
    (description
     "Inspired by the art and color research of Sanzo Wada (1883-1967), his
\"Dictionary Of Color Combinations\" (2011, ISBN:978-4861522475), and the
interactive site by Dain M. Blodorn Kim
<https://github.com/dblodorn/sanzo-wada>, this package brings Wada's color
combinations to R for easy use in data visualizations.  This package honors 60
of Wada's color combinations: 20 duos, 20 trios, and 20 quads.")
    (license license:gpl3)))

(define-public r-sanvi
  (package
    (name "r-sanvi")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SANvi" version))
       (sha256
        (base32 "17sb8gq4nnjn0zqgdi7xs749i8f46gswkmj0skh89hlq5m3kvrv4"))))
    (properties `((upstream-name . "SANvi")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-rcpparmadillo r-rcpp r-rcolorbrewer
                             r-matrixstats))
    (home-page "https://github.com/fradenti/SANvi")
    (synopsis "Fitting Shared Atoms Nested Models via Variational Bayes")
    (description
     "An efficient tool for fitting the nested common and shared atoms models using
variational Bayes approximate inference for fast computation.  Specifically, the
package implements the common atoms model (Denti et al., 2023), its finite
version (D'Angelo et al., 2023), and a hybrid finite-infinite model.  All models
use Gaussian mixtures with a normal-inverse-gamma prior distribution on the
parameters.  Additional functions are provided to help analyze the results of
the fitting procedure.  References: Denti, Camerlenghi, Guindani, Mira (2023)
<doi:10.1080/01621459.2021.1933499>, DâAngelo, Canale, Yu, Guindani (2023)
<doi:10.1111/biom.13626>.")
    (license license:expat)))

(define-public r-santoku
  (package
    (name "r-santoku")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "santoku" version))
       (sha256
        (base32 "054bl8sgl0rcvymsqsg8br201snw6rlmac54mfb05a4hsd8y7vq4"))))
    (properties `((upstream-name . "santoku")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-rlang
                             r-rcpp
                             r-lifecycle
                             r-glue
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/hughjonesd/santoku")
    (synopsis "Versatile Cutting Tool")
    (description
     "This package provides a tool for cutting data into intervals.  Allows singleton
intervals.  Always includes the whole range of data by default.  Flexible
labelling.  Convenience functions for cutting by quantiles etc.  Handles dates,
times, units and other vectors.")
    (license license:expat)))

(define-public r-santar
  (package
    (name "r-santar")
    (version "1.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "santaR" version))
       (sha256
        (base32 "048zbx7fr71ibxzgsayzifzv8gka7dmdfsdyvmzssxvzyjcm4qwc"))))
    (properties `((upstream-name . "santaR")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny
                             r-reshape2
                             r-plyr
                             r-pcamethods
                             r-iterators
                             r-gridextra
                             r-ggplot2
                             r-foreach
                             r-dt
                             r-doparallel
                             r-bslib))
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "https://github.com/adwolfer/santaR")
    (synopsis "Short Asynchronous Time-Series Analysis")
    (description
     "This package provides a graphical and automated pipeline for the analysis of
short time-series in R ('@code{santaR}').  This approach is designed to
accommodate asynchronous time sampling (i.e.  different time points for
different individuals), inter-individual variability, noisy measurements and
large numbers of variables.  Based on a smoothing splines functional model,
@code{santaR} is able to detect variables highlighting significantly different
temporal trajectories between study groups.  Designed initially for metabolic
phenotyping, @code{santaR} is also suited for other Systems Biology disciplines.
 Command line and graphical analysis (via a shiny application) enable fast and
parallel automated analysis and reporting, intuitive visualisation and
comprehensive plotting options for non-specialist users.")
    (license license:gpl3)))

(define-public r-sansa
  (package
    (name "r-sansa")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sansa" version))
       (sha256
        (base32 "1p5qv94f62dxzkbl1jn1fx3vksyhqqa3w2ipqwiflaxlk7q9k5pq"))))
    (properties `((upstream-name . "sansa")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-fnn r-data-table))
    (home-page "https://cran.r-project.org/package=sansa")
    (synopsis "Synthetic Data Generation for Imbalanced Learning in 'R'")
    (description
     "Machine learning is widely used in information-systems design.  Yet, training
algorithms on imbalanced datasets may severely affect performance on unseen
data.  For example, in some cases in healthcare, financial, or internet-security
contexts, certain sub-classes are difficult to learn because they are
underrepresented in training data.  This R package offers a flexible and
efficient solution based on a new synthetic average neighborhood sampling
algorithm ('SANSA'), which, in contrast to other solutions, introduces a novel
âplacementâ parameter that can be tuned to adapt to each datasets unique
manifestation of the imbalance.  More information about the algorithm's
parameters can be found at Nasir et al. (2022) <https://murtaza.cc/SANSA/>.")
    (license license:gpl3+)))

(define-public r-sanple
  (package
    (name "r-sanple")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SANple" version))
       (sha256
        (base32 "1v3yhhfj93zwvngplpfxrrysrfjkyv5hfz02fgjl8fvxsif1dq0g"))))
    (properties `((upstream-name . "SANple")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-salso
                             r-rcppprogress
                             r-rcpparmadillo
                             r-rcpp
                             r-rcolorbrewer))
    (home-page "https://github.com/laura-dangelo/SANple")
    (synopsis
     "Fitting Shared Atoms Nested Models via Markov Chains Monte Carlo")
    (description
     "Estimate Bayesian nested mixture models via Markov Chain Monte Carlo methods.
Specifically, the package implements the common atoms model (Denti et al.,
2023), its finite version (D'Angelo et al., 2023), and a hybrid finite-infinite
model.  All models use Gaussian mixtures with a normal-inverse-gamma prior
distribution on the parameters.  Additional functions are provided to help
analyzing the results of the fitting procedure.  References: Denti, Camerlenghi,
Guindani, Mira (2023) <doi:10.1080/01621459.2021.1933499>, DâAngelo, Canale,
Yu, Guindani (2023) <doi:10.1111/biom.13626>.")
    (license license:expat)))

(define-public r-sanon
  (package
    (name "r-sanon")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sanon" version))
       (sha256
        (base32 "0h3l1dzgkddvb3vzg7pcgwrgidphy73jys2h49v84nbfvqr6l85z"))))
    (properties `((upstream-name . "sanon")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sanon")
    (synopsis "Stratified Analysis with Nonparametric Covariable Adjustment")
    (description
     "There are several functions to implement the method for analysis in a randomized
clinical trial with strata with following key features.  A stratified
Mann-Whitney estimator addresses the comparison between two randomized groups
for a strictly ordinal response variable.  The multivariate vector of such
stratified Mann-Whitney estimators for multivariate response variables can be
considered for one or more response variables such as in repeated measurements
and these can have missing completely at random (MCAR) data.  Non-parametric
covariance adjustment is also considered with the minimal assumption of
randomization.  The p-value for hypothesis test and confidence interval are
provided.")
    (license license:gpl2+)))

(define-public r-sankeywheel
  (package
    (name "r-sankeywheel")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sankeywheel" version))
       (sha256
        (base32 "1sigj4jz13qj4s8vxk0dl2xsddqgyfxabj8yggkasawm7pryvbyy"))))
    (properties `((upstream-name . "sankeywheel")))
    (build-system r-build-system)
    (propagated-inputs (list r-htmlwidgets))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/czxa/sankeywheel")
    (synopsis "Create Dependency Wheels and Sankey Diagrams")
    (description
     "By binding R functions and the Highcharts <http://www.highcharts.com/> charting
library, sankeywheel package provides a simple way to draw dependency wheels and
sankey diagrams.")
    (license license:expat)))

(define-public r-sankey
  (package
    (name "r-sankey")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sankey" version))
       (sha256
        (base32 "0jvcr2bn69aklfbgj4hbsrc047mymrx0jmsg26m0g6w7n1igqvcw"))))
    (properties `((upstream-name . "sankey")))
    (build-system r-build-system)
    (propagated-inputs (list r-simplegraph))
    (home-page "https://github.com/gaborcsardi/sankey#readme")
    (synopsis "Illustrate the Flow of Information or Material")
    (description "Plots that illustrate the flow of information or material.")
    (license license:gpl2+)))

(define-public r-sanketphonetictranslator
  (package
    (name "r-sanketphonetictranslator")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sanketphonetictranslator" version))
       (sha256
        (base32 "028cipwfj2v2czv04b6vn8f11ix9s0rg6fgs4561i767bmw069rb"))))
    (properties `((upstream-name . "sanketphonetictranslator")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-stringi r-readr r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=sanketphonetictranslator")
    (synopsis "Phonetic Transliteration Between Hindi and English")
    (description
     "Facilitate phonetic transliteration between different languages.  With support
for both Hindi and English, this package provides a way to convert text between
Hindi and English dataset.  Whether you're working with multilingual data or
need to convert dataset for analysis or presentation purposes, it offers a
simple and efficient solution and harness the power of phonetic transliteration
in your projects with this versatile package.")
    (license license:agpl3+)))

(define-public r-sanitytracker
  (package
    (name "r-sanitytracker")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sanityTracker" version))
       (sha256
        (base32 "0y041x840sg5c05ksljb1y4xag1cib19q5ygp443k13ryfbcgs9k"))))
    (properties `((upstream-name . "sanityTracker")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MarselScheer/sanityTracker")
    (synopsis "Keeps Track of all Performed Sanity Checks")
    (description
     "During the preparation of data set(s) one usually performs some sanity checks.
The idea is that irrespective of where the checks are performed, they are
centralized by this package in order to list all at once with examples if a
check failed.")
    (license license:gpl3)))

(define-public r-sanitizers
  (package
    (name "r-sanitizers")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sanitizers" version))
       (sha256
        (base32 "0gy4z85h639h25hxv7n7irbj7w1m2gxw9sv5480bvbfw756n2pb6"))))
    (properties `((upstream-name . "sanitizers")))
    (build-system r-build-system)
    (home-page "https://github.com/eddelbuettel/sanitizers")
    (synopsis
     "C/C++ Source Code to Trigger Address and Undefined Behaviour Sanitizers")
    (description
     "Recent gcc and clang compiler versions provide functionality to test for memory
violations and other undefined behaviour; this is often referred to as \"Address
Sanitizer\" (or ASAN') and \"Undefined Behaviour Sanitizer\" ('UBSAN').  The
Writing R Extension manual describes this in some detail in Section 4.3 title
\"Checking Memory Access\". .  This feature has to be enabled in the corresponding
binary, eg in R, which is somewhat involved as it also required a current
compiler toolchain which is not yet widely available, or in the case of Windows,
not available at all (via the common Rtools mechanism). .  As an alternative,
pre-built Docker containers such as the Rocker container r-devel-san or the
multi-purpose container r-debug can be used. .  This package then provides a
means of testing the compiler setup as the known code failures provides in the
sample code here should be detected correctly, whereas a default build of R will
let the package pass. .  The code samples are based on the examples from the
Address Sanitizer Wiki at <https://github.com/google/sanitizers/wiki>.")
    (license license:gpl2+)))

(define-public r-sanic
  (package
    (name "r-sanic")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sanic" version))
       (sha256
        (base32 "0php70zcxhx6kqcyimx4icx2460ik6frvi3h21jw6qrz631l304r"))))
    (properties `((upstream-name . "sanic")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp r-matrix))
    (home-page "https://github.com/nk027/sanic")
    (synopsis "Solving Ax = b Nimbly in C++")
    (description
     "Routines for solving large systems of linear equations and eigenproblems in R.
Direct and iterative solvers from the Eigen C++ library are made available.
Solvers include Cholesky, LU, QR, and Krylov subspace methods (Conjugate
Gradient, @code{BiCGSTAB}).  Dense and sparse problems are supported.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-sangertools
  (package
    (name "r-sangertools")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SangerTools" version))
       (sha256
        (base32 "0ijxfa1h3mx86jk1s27qp7l4sz2d7i7llkwjvgnih9zjk0ybrdzw"))))
    (properties `((upstream-name . "SangerTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-scales
                             r-rlang
                             r-readxl
                             r-readr
                             r-odbc
                             r-magrittr
                             r-janitor
                             r-ggthemes
                             r-ggtext
                             r-ggplot2
                             r-dplyr
                             r-dbi))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SangerTools")
    (synopsis "Tools for Population Health Management Analytics")
    (description
     "Created for population health analytics and monitoring.  The functions in this
package work best when working with patient level Master Patient Index-like
datasets .  Built to be used by NHS bodies and other health service providers.")
    (license license:agpl3+)))

(define-public r-sanfranbeachwater
  (package
    (name "r-sanfranbeachwater")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SanFranBeachWater" version))
       (sha256
        (base32 "18w2q3hzcrbmagnjyrn1ikwiyjh723mjb2vy0d8p1a6za0460p9w"))))
    (properties `((upstream-name . "SanFranBeachWater")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tibble
                             r-rvest
                             r-readr
                             r-magrittr
                             r-lubridate
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=SanFranBeachWater")
    (synopsis
     "Downloads and Tidies the San Francisco Public Utilities Commission Beach Water Quality Monitoring Program Data")
    (description
     "Downloads and tidies the San Francisco Public Utilities Commission Beach Water
Quality Monitoring Program data.  Data sets can be downloaded per beach, or the
raw data can be downloaded.  See
<https://sfwater.org/cfapps/lims/beachmain1.cfm>.")
    (license license:expat)))

(define-public r-sandbox
  (package
    (name "r-sandbox")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sandbox" version))
       (sha256
        (base32 "0k86dgfjg7ib79cl6nwxv1g9i15pk501kd8ynlaj3mijpnhgvbal"))))
    (properties `((upstream-name . "sandbox")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlummodel))
    (home-page "https://cran.r-project.org/package=sandbox")
    (synopsis "Probabilistic Numerical Modelling of Sediment Properties")
    (description
     "This package provides a flexible framework for definition and application of
time/depth- based rules for sets of parameters for single grains that can be
used to create artificial sediment profiles.  Such profiles can be used for
virtual sample preparation and synthetic, for instance, luminescence
measurements.")
    (license license:gpl3)))

(define-public r-sand
  (package
    (name "r-sand")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sand" version))
       (sha256
        (base32 "0a8skj1wi1n9zlm7jb5cix1i4ssdzfpj4ym2854ala9jy6qj7g7n"))))
    (properties `((upstream-name . "sand")))
    (build-system r-build-system)
    (propagated-inputs (list r-igraphdata r-igraph))
    (home-page "https://github.com/kolaczyk/sand")
    (synopsis "Statistical Analysis of Network Data with R, 2nd Edition")
    (description
     "Data sets and code blocks for the book Statistical Analysis of Network Data with
R, 2nd Edition'.")
    (license license:gpl3)))

(define-public r-samurais
  (package
    (name "r-samurais")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "samurais" version))
       (sha256
        (base32 "0s00hd3ax4r662n8iqmd9i56sgyjxbgc59la7qvh3jwcnzcvmgz9"))))
    (properties `((upstream-name . "samurais")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/fchamroukhi/SaMUraiS")
    (synopsis
     "Statistical Models for the Unsupervised Segmentation of Time-Series ('SaMUraiS')")
    (description
     "This package provides a variety of original and flexible user-friendly
statistical latent variable models and unsupervised learning algorithms to
segment and represent time-series data (univariate or multivariate), and more
generally, longitudinal data, which include regime changes.  samurais is built
upon the following packages, each of them is an autonomous time-series
segmentation approach: Regression with Hidden Logistic Process ('RHLP'), Hidden
Markov Model Regression ('HMMR'), Multivariate RHLP ('MRHLP'), Multivariate HMMR
('MHMMR'), Piece-Wise regression ('PWR').  For the advantages/differences of
each of them, the user is referred to our mentioned paper references.")
    (license license:gpl3+)))

(define-public r-samur
  (package
    (name "r-samur")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SAMUR" version))
       (sha256
        (base32 "1ihxr455hxpk3mz113j7ai36lw9i4v60yd4x50j7npzcpq5r0mwk"))))
    (properties `((upstream-name . "SAMUR")))
    (build-system r-build-system)
    (propagated-inputs (list r-matching))
    (home-page "https://cran.r-project.org/package=SAMUR")
    (synopsis
     "Stochastic Augmentation of Matched Data Using Restriction Methods")
    (description
     "Augmenting a matched data set by generating multiple stochastic, matched samples
from the data using a multi-dimensional histogram constructed from dropping the
input matched data into a multi-dimensional grid built on the full data set.
The resulting stochastic, matched sets will likely provide a collectively higher
coverage of the full data set compared to the single matched set.  Each
stochastic match is without duplication, thus allowing downstream validation
techniques such as cross-validation to be applied to each set without concern
for overfitting.")
    (license license:gpl2+)))

(define-public r-samtx
  (package
    (name "r-samtx")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SAMTx" version))
       (sha256
        (base32 "18r03w74fcra3c0xa76jnkkms9pjirdmgb2k1dnz7symqf9cvc5l"))))
    (properties `((upstream-name . "SAMTx")))
    (build-system r-build-system)
    (propagated-inputs (list r-bart))
    (home-page "https://cran.r-project.org/package=SAMTx")
    (synopsis
     "Sensitivity Assessment to Unmeasured Confounding with Multiple Treatments")
    (description
     "This package provides a sensitivity analysis approach for unmeasured confounding
in observational data with multiple treatments and a binary outcome.  This
approach derives the general bias formula and provides adjusted causal effect
estimates in response to various assumptions about the degree of unmeasured
confounding.  Nested multiple imputation is embedded within the Bayesian
framework to integrate uncertainty about the sensitivity parameters and sampling
variability.  Bayesian Additive Regression Model (BART) is used for outcome
modeling.  The causal estimands are the conditional average treatment effects
(CATE) based on the risk difference.  For more details, see paper: Hu L et al.
(2020) A flexible sensitivity analysis approach for unmeasured confounding with
multiple treatments and a binary outcome with application to SEER-Medicare lung
cancer data <@code{arXiv:2012.06093>}.")
    (license license:expat)))

(define-public r-samtool
  (package
    (name "r-samtool")
    (version "1.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SAMtool" version))
       (sha256
        (base32 "136g14lbdn07d24v46crbbmws3y2aj0qp8d0afazb0yqkb67nzxw"))))
    (properties `((upstream-name . "SAMtool")))
    (build-system r-build-system)
    (propagated-inputs (list r-vars
                             r-tmb
                             r-snowfall
                             r-rmarkdown
                             r-rcppeigen
                             r-pbapply
                             r-msetool
                             r-gplots
                             r-dplyr
                             r-abind))
    (home-page "https://openmse.com")
    (synopsis "Stock Assessment Methods Toolkit")
    (description
     "Simulation tools for closed-loop simulation are provided for the MSEtool
operating model to inform data-rich fisheries.  SAMtool provides a conditioning
model, assessment models of varying complexity with standardized reporting,
model-based management procedures, and diagnostic tools for evaluating
assessments inside closed-loop simulation.")
    (license license:gpl3)))

(define-public r-sams
  (package
    (name "r-sams")
    (version "0.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sams" version))
       (sha256
        (base32 "01rknjgxyvj3i12mqgnlr7m0wdvxp2ja0rx8hnb700vlxa1xdmhi"))))
    (properties `((upstream-name . "sams")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sams")
    (synopsis
     "Merge-Split Samplers for Conjugate Bayesian Nonparametric Models")
    (description
     "Markov chain Monte Carlo samplers for posterior simulations of conjugate
Bayesian nonparametric mixture models.  Functionality is provided for Gibbs
sampling as in Algorithm 3 of Neal (2000) <DOI:10.1080/10618600.2000.10474879>,
restricted Gibbs merge-split sampling as described in Jain & Neal (2004)
<DOI:10.1198/1061860043001>, and sequentially-allocated merge-split sampling
<DOI:10.1080/00949655.2021.1998502>, as well as summary and utility functions.")
    (license license:gpl3)))

(define-public r-sampsizeval
  (package
    (name "r-sampsizeval")
    (version "1.0.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sampsizeval" version))
       (sha256
        (base32 "06mghabwqcc92hdvirm1gcna24hwll2gvm105ljkf5hgn0fa5xii"))))
    (properties `((upstream-name . "sampsizeval")))
    (build-system r-build-system)
    (propagated-inputs (list r-sn r-pracma r-plyr r-dplyr))
    (home-page "https://github.com/mpavlou/sampsizeval")
    (synopsis "Sample Size for Validation of Risk Models with Binary Outcomes")
    (description
     "Estimation of the required sample size to validate a risk model for binary
outcomes, based on the sample size equations proposed by Pavlou et al. (2021)
<doi:10.1177/09622802211007522>.  For precision-based sample size calculations,
the user is required to enter the anticipated values of the C-statistic and
outcome prevalence, which can be obtained from a previous study.  The user also
needs to specify the required precision (standard error) for the C-statistic,
the calibration slope and the calibration in the large.  The calculations are
valid under the assumption of marginal normality for the distribution of the
linear predictor.")
    (license license:expat)))

(define-public r-samprior
  (package
    (name "r-samprior")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SAMprior" version))
       (sha256
        (base32 "1xp3wvgjp9252l8w42fsr61bh6ggs3b21df182rw3irh2ijn1mc0"))))
    (properties `((upstream-name . "SAMprior")))
    (build-system r-build-system)
    (propagated-inputs (list r-rbest r-metrics r-ggplot2 r-checkmate
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SAMprior")
    (synopsis "Self-Adapting Mixture (SAM) Priors")
    (description
     "Implementation of the SAM prior and generation of its operating characteristics
for dynamically borrowing information from historical data.  For details, please
refer to Yang et al. (2023) <doi:10.1111/biom.13927>.")
    (license license:gpl3+)))

(define-public r-samplrdata
  (package
    (name "r-samplrdata")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "samplrData" version))
       (sha256
        (base32 "09gp4hh1kbf1lmvc10i679fxa7mwmcs3j16f80zc0c3cml1shlsq"))))
    (properties `((upstream-name . "samplrData")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack))
    (home-page "https://github.com/lucas-castillo/samplrData")
    (synopsis "Datasets from the SAMPLING Project")
    (description
     "This package contains human behaviour datasets collected by the SAMPLING project
(<https://sampling.warwick.ac.uk>).")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-samplr
  (package
    (name "r-samplr")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "samplr" version))
       (sha256
        (base32 "1vjkai27hwrvz9pxis564b0iqz26r42c7fdpmfda5mrzy15my3i4"))))
    (properties `((upstream-name . "samplr")))
    (build-system r-build-system)
    (propagated-inputs (list r-testthat
                             r-rdpack
                             r-rcppdist
                             r-rcpparmadillo
                             r-rcpp
                             r-r6
                             r-pracma
                             r-lme4
                             r-latex2exp
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/lucas-castillo/samplr")
    (synopsis "Compare Human Performance to Sampling Algorithms")
    (description
     "Understand human performance from the perspective of sampling, both looking at
how people generate samples and how people use the samples they have generated.
A longer overview and other resources can be found at
<https://sampling.warwick.ac.uk>.")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-samplingvarest
  (package
    (name "r-samplingvarest")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "samplingVarEst" version))
       (sha256
        (base32 "0h78dzb18cknmg6bnjfxaqfdayyghq6skxca1dj1idqh9p4igaqq"))))
    (properties `((upstream-name . "samplingVarEst")))
    (build-system r-build-system)
    (home-page "https://www.quantos.mx/")
    (synopsis "Sampling Variance Estimation")
    (description
     "This package provides functions to calculate some point estimators and estimate
their variance under unequal probability sampling without replacement.  Single
and two-stage sampling designs are considered.  Some approximations for the
second-order inclusion probabilities (joint inclusion probabilities) are
available (sample and population based).  A variety of Jackknife variance
estimators are implemented.  Almost every function is written in C (compiled)
code for faster results.  The functions incorporate some performance
improvements for faster results with large datasets.")
    (license license:gpl2+)))

(define-public r-samplingstrata
  (package
    (name "r-samplingstrata")
    (version "1.5-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SamplingStrata" version))
       (sha256
        (base32 "093x9rfilxhgf75p0rfsvglz0sg3imhdm15azxnjiblamyxbhfjm"))))
    (properties `((upstream-name . "SamplingStrata")))
    (build-system r-build-system)
    (propagated-inputs (list r-samplingbigdata r-pbapply r-memoise r-glue
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://barcaroli.github.io/SamplingStrata/")
    (synopsis
     "Optimal Stratification of Sampling Frames for Multipurpose Sampling Surveys")
    (description
     "In the field of stratified sampling design, this package offers an approach for
the determination of the best stratification of a sampling frame, the one that
ensures the minimum sample cost under the condition to satisfy precision
constraints in a multivariate and multidomain case.  This approach is based on
the use of the genetic algorithm: each solution (i.e.  a particular partition in
strata of the sampling frame) is considered as an individual in a population;
the fitness of all individuals is evaluated applying the Bethel-Chromy algorithm
to calculate the sampling size satisfying precision constraints on the target
estimates.  Functions in the package allows to: (a) analyse the obtained results
of the optimisation step; (b) assign the new strata labels to the sampling
frame; (c) select a sample from the new frame accordingly to the best
allocation.  Functions for the execution of the genetic algorithm are a modified
version of the functions in the genalg package.  M.Ballin, G.Barcaroli (2020)
<@code{arXiv:2004.09366>} \"R package @code{SamplingStrata}: new developments and
extension to Spatial Sampling\".")
    (license license:gpl2+)))

(define-public r-samplingr
  (package
    (name "r-samplingr")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "samplingR" version))
       (sha256
        (base32 "1ikmd7dvmrzc06vgpl8f7777pmds3vcfv74r9izlqhxyz39hgc9s"))))
    (properties `((upstream-name . "samplingR")))
    (build-system r-build-system)
    (propagated-inputs (list r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=samplingR")
    (synopsis "Sampling and Estimation Methods")
    (description
     "This package provides functions to take samples of data, sample size estimation
and getting useful estimators such as total, mean, proportion about its
population using simple random, stratified, systematic and cluster sampling.")
    (license license:gpl2)))

(define-public r-samplingin
  (package
    (name "r-samplingin")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "samplingin" version))
       (sha256
        (base32 "0alcfy69wgdcp0mizw17qnd79v448if8yy98kl0frf2kmjkc7hlj"))))
    (properties `((upstream-name . "samplingin")))
    (build-system r-build-system)
    (propagated-inputs (list r-sampling r-rlang r-magrittr r-dplyr
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=samplingin")
    (synopsis "Dynamic Survey Sampling Solutions")
    (description
     "This package provides a robust solution employing the SRS (Simple Random
Sampling), systematic and PPS (Probability Proportional to Size) sampling
methods, ensuring a methodical and representative selection of data.  Seamlessly
allocate predetermined allocations to smaller levels.")
    (license license:expat)))

(define-public r-samplingdatacrt
  (package
    (name "r-samplingdatacrt")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "samplingDataCRT" version))
       (sha256
        (base32 "0ah3fi2sn642c8nicikhjhl1hdsjzshrf1j02swq0jaqggcqjdvk"))))
    (properties `((upstream-name . "samplingDataCRT")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=samplingDataCRT")
    (synopsis
     "Sampling Data Within Different Study Designs for Cluster Randomized Trials")
    (description
     "Package provides the possibility to sampling complete datasets from a normal
distribution to simulate cluster randomized trails for different study designs.")
    (license license:gpl3)))

(define-public r-samplingbook
  (package
    (name "r-samplingbook")
    (version "1.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "samplingbook" version))
       (sha256
        (base32 "08fhzanzb725hlagm0225121gdgipkbfkixizwykggaimkz5j1ad"))))
    (properties `((upstream-name . "samplingbook")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey r-sampling r-pps))
    (native-inputs (list r-knitr))
    (home-page "https://www.samplingbook.manitz.org")
    (synopsis "Survey Sampling Procedures")
    (description
     "Sampling procedures from the book Stichproben - Methoden und praktische
Umsetzung mit R by Goeran Kauermann and Helmut Kuechenhoff (2010).")
    (license license:gpl2+)))

(define-public r-samplingbigdata
  (package
    (name "r-samplingbigdata")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SamplingBigData" version))
       (sha256
        (base32 "0khrh7vfqqzpfp16x0ic5ml854wr2fj28cx95s6c0pz91hzlxkns"))))
    (properties `((upstream-name . "SamplingBigData")))
    (build-system r-build-system)
    (home-page "https://github.com/jlisic/SamplingBigData")
    (synopsis "Sampling Methods for Big Data")
    (description
     "Select sampling methods for probability samples using large data sets.  This
includes spatially balanced sampling in multi-dimensional spaces with any
prescribed inclusion probabilities.  All implementations are written in C with
efficient data structures such as k-d trees that easily scale to several million
rows on a modern desktop computer.")
    (license license:gpl2+)))

(define-public r-samplevadir
  (package
    (name "r-samplevadir")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sampleVADIR" version))
       (sha256
        (base32 "1fxqjbczw5wpdx19rmlc2jwsk8nlkbbpdagn9z1xkfba3xli9f1w"))))
    (properties `((upstream-name . "sampleVADIR")))
    (build-system r-build-system)
    (propagated-inputs (list r-splitstackshape r-lubridate))
    (home-page "https://github.com/tswanson222/sampleVADIR")
    (synopsis "Draw Stratified Samples from the VADIR Database")
    (description
     "Affords researchers the ability to draw stratified samples from the U.S.
Department of Veteran's Affairs/Department of Defense Identity Repository
(VADIR) database according to a variety of population characteristics.  The
VADIR database contains information for all veterans who were separated from the
military after 1980.  The central utility of the present package is to integrate
data cleaning and formatting for the VADIR database with the stratification
methods described by Mahto (2019)
<https://CRAN.R-project.org/package=splitstackshape>.  Data from VADIR are not
provided as part of this package.")
    (license license:gpl3+)))

(define-public r-samplesizeproportions
  (package
    (name "r-samplesizeproportions")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SampleSizeProportions" version))
       (sha256
        (base32 "1gjmzh0saba1akv2yg47y034j7cwr0g8h9w38cqcxmcclxvnvg27"))))
    (properties `((upstream-name . "SampleSizeProportions")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SampleSizeProportions")
    (synopsis
     "Calculating Sample Size Requirements when Estimating the Difference Between Two Binomial Proportions")
    (description
     "Sample size requirements calculation using three different Bayesian criteria in
the context of designing an experiment to estimate the difference between two
binomial proportions.  Functions for calculation of required sample sizes for
the Average Length Criterion, the Average Coverage Criterion and the Worst
Outcome Criterion in the context of binomial observations are provided.  In all
cases, estimation of the difference between two binomial proportions is
considered.  Functions for both the fully Bayesian and the mixed
Bayesian/likelihood approaches are provided.  For reference see Joseph L., du
Berger R. and BÃ©lisle P. (1997)
<doi:10.1002/(sici)1097-0258(19970415)16:7%3C769::aid-sim495%3E3.0.co;2-v>.")
    (license license:gpl2+)))

(define-public r-samplesizemeans
  (package
    (name "r-samplesizemeans")
    (version "1.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SampleSizeMeans" version))
       (sha256
        (base32 "1j5j5w78jp4rc7mdxr0w5qvzgcpxzj2yn4gmbnviy656sj1gj6by"))))
    (properties `((upstream-name . "SampleSizeMeans")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SampleSizeMeans")
    (synopsis "Sample Size Calculations for Normal Means")
    (description
     "Sample size requirements calculation using three different Bayesian criteria in
the context of designing an experiment to estimate a normal mean or the
difference between two normal means.  Functions for calculation of required
sample sizes for the Average Length Criterion, the Average Coverage Criterion
and the Worst Outcome Criterion in the context of normal means are provided.
Functions for both the fully Bayesian and the mixed Bayesian/likelihood
approaches are provided.  For reference see Joseph L. and BÃ©lisle P. (1997)
<https://www.jstor.org/stable/2988525>.")
    (license license:gpl2+)))

(define-public r-samplesizelogisticcasecontrol
  (package
    (name "r-samplesizelogisticcasecontrol")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "samplesizelogisticcasecontrol" version))
       (sha256
        (base32 "180c5bvrr3nb88ci1cg8kh6kj6gfg1dfmrabp8a232x2n2ykq76m"))))
    (properties `((upstream-name . "samplesizelogisticcasecontrol")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page
     "https://cran.r-project.org/package=samplesizelogisticcasecontrol")
    (synopsis "Sample Size and Power Calculations for Case-Control Studies")
    (description
     "To determine sample size or power for case-control studies to be analyzed using
logistic regression.")
    (license license:gpl2)))

(define-public r-samplesizeestimator
  (package
    (name "r-samplesizeestimator")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "samplesizeestimator" version))
       (sha256
        (base32 "0s783s2hcb9hndhsw5vdss240bkvb0a634fl6wrq42fc8fd95ixc"))))
    (properties `((upstream-name . "samplesizeestimator")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringi))
    (home-page "https://cran.r-project.org/package=samplesizeestimator")
    (synopsis "Calculate Sample Size for Various Scenarios")
    (description
     "Calculates sample size for various scenarios, such as sample size to estimate
population proportion with stated absolute or relative precision, testing a
single proportion with a reference value, to estimate the population mean with
stated absolute or relative precision, testing single mean with a reference
value and sample size for comparing two unpaired or independent means, comparing
two paired means, the sample size For case control studies, estimating the odds
ratio with stated precision, testing the odds ratio with a reference value,
estimating relative risk with stated precision, testing relative risk with a
reference value, testing a correlation coefficient with a specified value, etc.
<https://www.academia.edu/39511442/Adequacy_of_Sample_Size_in_Health_Studies#:~:text=Determining%20the%20sample%20size%20for,may%20yield%20statistically%20inconclusive%20results.>.")
    (license license:gpl2+)))

(define-public r-samplesizediagnostics
  (package
    (name "r-samplesizediagnostics")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SampleSizeDiagnostics" version))
       (sha256
        (base32 "1h4scywysya36sc6qvfn5csdghb38wicr96xgwhba289zrd9j2az"))))
    (properties `((upstream-name . "SampleSizeDiagnostics")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SampleSizeDiagnostics")
    (synopsis "Choosing Sample Size for Evaluating a Diagnostic Test")
    (description
     "Calculates the sample size needed for evaluating a diagnostic test based on
sensitivity, specificity, prevalence, and desired precision.  Based on Buderer
(1996) <doi:10.1111/j.1553-2712.1996.tb03538.x>.")
    (license license:gpl3)))

(define-public r-samplesizecmh
  (package
    (name "r-samplesizecmh")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "samplesizeCMH" version))
       (sha256
        (base32 "16vifm50yxbyd892z9vk32zpj12j2cprc856s74vn6xhvs6bp9b2"))))
    (properties `((upstream-name . "samplesizeCMH")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/pegeler/samplesizeCMH")
    (synopsis
     "Power and Sample Size Calculation for the Cochran-Mantel-Haenszel Test")
    (description
     "Calculates the power and sample size for Cochran-Mantel-Haenszel tests.  There
are also several helper functions for working with probability, odds, relative
risk, and odds ratio values.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-samplesize4surveys
  (package
    (name "r-samplesize4surveys")
    (version "4.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "samplesize4surveys" version))
       (sha256
        (base32 "0v332xb4vkkjr3nwf3231aqgzsj7mqld9ffkla1nj52lxay2ji75"))))
    (properties `((upstream-name . "samplesize4surveys")))
    (build-system r-build-system)
    (propagated-inputs (list r-timedate r-teachingsampling r-magrittr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=samplesize4surveys")
    (synopsis "Sample Size Calculations for Complex Surveys")
    (description
     "Computes the required sample size for estimation of totals, means and
proportions under complex sampling designs.")
    (license license:gpl2+)))

(define-public r-samplesize4clinicaltrials
  (package
    (name "r-samplesize4clinicaltrials")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SampleSize4ClinicalTrials" version))
       (sha256
        (base32 "0s8svsgwh79rcwnskz0my2xqasyp370hn7aixqm9m6zqlvf42zc2"))))
    (properties `((upstream-name . "SampleSize4ClinicalTrials")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SampleSize4ClinicalTrials")
    (synopsis
     "Sample Size Calculation for the Comparison of Means or Proportions in Phase III Clinical Trials")
    (description
     "There are four categories of Phase III clinical trials according to different
research goals, including (1) Testing for equality, (2) Superiority trial, (3)
Non-inferiority trial, and (4) Equivalence trial.  This package aims to help
researchers to calculate sample size when comparing means or proportions in
Phase III clinical trials with different research goals.")
    (license license:gpl3)))

(define-public r-samplesize
  (package
    (name "r-samplesize")
    (version "0.2-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "samplesize" version))
       (sha256
        (base32 "0n6z7jmf665lbj3g1mjy87a9fh53z85546bhrkmkgsysb0r9q6iq"))))
    (properties `((upstream-name . "samplesize")))
    (build-system r-build-system)
    (home-page "https://github.com/shearer/samplesize")
    (synopsis "Sample Size Calculation for Various t-Tests and Wilcoxon-Test")
    (description
     "Computes sample size for Student's t-test and for the Wilcoxon-Mann-Whitney test
for categorical data.  The t-test function allows paired and unpaired (balanced
/ unbalanced) designs as well as homogeneous and heterogeneous variances.  The
Wilcoxon function allows for ties.")
    (license license:gpl2+)))

(define-public r-sampleselection
  (package
    (name "r-sampleselection")
    (version "1.2-12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sampleSelection" version))
       (sha256
        (base32 "1am1w8c24f27gcdmrk3b4wp9j5awghnw9s9lphnkm10pl31881dy"))))
    (properties `((upstream-name . "sampleSelection")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam
                             r-systemfit
                             r-mvtnorm
                             r-misctools
                             r-maxlik
                             r-formula))
    (home-page "http://www.sampleSelection.org")
    (synopsis "Sample Selection Models")
    (description
     "Two-step and maximum likelihood estimation of Heckman-type sample selection
models: standard sample selection models (Tobit-2), endogenous switching
regression models (Tobit-5), sample selection models with binary dependent
outcome variable, interval regression with sample selection (only ML
estimation), and endogenous treatment effects models.  These methods are
described in the three vignettes that are included in this package and in
econometric textbooks such as Greene (2011, Econometric Analysis, 7th edition,
Pearson).")
    (license license:gpl2+)))

(define-public r-sampler
  (package
    (name "r-sampler")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sampler" version))
       (sha256
        (base32 "12jjx3ci1yss8dj64bjdmz73cyjfkwb7b9zk4zm48lqv78k6wlv7"))))
    (properties `((upstream-name . "sampler")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-reshape r-purrr r-dplyr))
    (home-page "https://github.com/mbaldassaro/sampler")
    (synopsis "Sample Design, Drawing & Data Analysis Using Data Frames")
    (description
     "Determine sample sizes, draw samples, and conduct data analysis using data
frames.  It specifically enables you to determine simple random sample sizes,
stratified sample sizes, and complex stratified sample sizes using a secondary
variable such as population; draw simple random samples and stratified random
samples from sampling data frames; determine which observations are missing from
a random sample, missing by strata, duplicated within a dataset; and perform
data analysis, including proportions, margins of error and upper and lower
bounds for simple, stratified and cluster sample designs.")
    (license license:expat)))

(define-public r-sampcompr
  (package
    (name "r-sampcompr")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sampcompR" version))
       (sha256
        (base32 "0jxs8kv844l5jlqzlwpa1wy5wklqqpdja1pm2fba4hw9jzq98fr9"))))
    (properties `((upstream-name . "sampcompR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-svrep
                             r-survey
                             r-sandwich
                             r-reshape2
                             r-readr
                             r-purrr
                             r-psych
                             r-magrittr
                             r-lmtest
                             r-hmisc
                             r-ggplot2
                             r-future
                             r-furrr
                             r-forcats
                             r-dplyr
                             r-data-table
                             r-boot-pval
                             r-boot))
    (home-page "https://bjoernrohr.github.io/sampcompR/")
    (synopsis "Comparing and Visualizing Differences Between Surveys")
    (description
     "Easily analyze and visualize differences between samples (e.g., benchmark
comparisons, nonresponse comparisons in surveys) on three levels.  The
comparisons can be univariate, bivariate or multivariate.  On univariate level
the variables of interest of a survey and a comparison survey (i.e.  benchmark)
are compared, by calculating one of several difference measures (e.g., relative
difference in mean), and an average difference between the surveys.  On
bivariate level a function can calculate significant differences in correlations
for the surveys.  And on multivariate levels a function can calculate
significant differences in model coefficients between the surveys of comparison.
 All of those differences can be easily plotted and outputted as a table.  For
more detailed information on the methods and example use see Rohr, B., Silber,
H., & Felderer, B. (2024).  âComparing the Accuracy of Univariate, Bivariate,
and Multivariate Estimates across Probability and Non-Probability Surveys with
Population Benchmarksâ <doi:10.31235/osf.io/n6ehf> .")
    (license license:gpl3)))

(define-public r-sampbias
  (package
    (name "r-sampbias")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sampbias" version))
       (sha256
        (base32 "09pr2cdlpxml2dxs27gy5s5i76ff3q8x1ja09f3is0mg98c872as"))))
    (properties `((upstream-name . "sampbias")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-tidyr
                             r-terra
                             r-sf
                             r-rnaturalearth
                             r-rlang
                             r-magrittr
                             r-ggplot2
                             r-forcats
                             r-dplyr
                             r-cowplot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/azizka/sampbias")
    (synopsis "Evaluating Geographic Sampling Bias in Biological Collections")
    (description
     "Evaluating the biasing impact of geographic features such as airports, cities,
roads, rivers in datasets of coordinates based biological collection datasets,
by Bayesian estimation of the parameters of a Poisson process.  Enables also
spatial visualization of sampling bias and includes a set of convenience
functions for publication level plotting.  Also available as shiny app.  The
reference for the methodology is: Zizka et al. (2020) <doi:10.1111/ecog.05102>.")
    (license license:gpl3)))

(define-public r-samon
  (package
    (name "r-samon")
    (version "4.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "samon" version))
       (sha256
        (base32 "1gv3kvl2jf6x6fcy52k61frcnsnz6xafyfhwq1n08pyzbynlsf08"))))
    (properties `((upstream-name . "samon")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=samon")
    (synopsis "Sensitivity Analysis for Missing Data")
    (description
     "In a clinical trial with repeated measures designs, outcomes are often taken
from subjects at fixed time-points.  The focus of the trial may be to compare
the mean outcome in two or more groups at some pre-specified time after
enrollment.  In the presence of missing data auxiliary assumptions are necessary
to perform such comparisons.  One commonly employed assumption is the missing at
random assumption (MAR).  The samon package allows the user to perform a
(parameterized) sensitivity analysis of this assumption.  In particular it can
be used to examine the sensitivity of tests in the difference in outcomes to
violations of the MAR assumption.  The sensitivity analysis can be performed
under two scenarios, a) where the data exhibit a monotone missing data pattern
(see the samon() function), and, b) where in addition to a monotone missing data
pattern the data exhibit intermittent missing values (see the @code{samonIM}()
function).")
    (license license:gpl2)))

(define-public r-samgep
  (package
    (name "r-samgep")
    (version "0.1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SAMGEP" version))
       (sha256
        (base32 "01f460vk60sl3f9cg2pqcpi5r2rayjhgsp73w1ik1g4rhrs4bl9s"))))
    (properties `((upstream-name . "SAMGEP")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-proc
                             r-nloptr
                             r-nlme
                             r-mvtnorm
                             r-foreach
                             r-doparallel
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/celehs/SAMGEP")
    (synopsis "Semi-Supervised Method for Prediction of Phenotype Event Times")
    (description
     "This package provides a novel semi-supervised machine learning algorithm to
predict phenotype event times using Electronic Health Record (EHR) data.")
    (license license:gpl3)))

(define-public r-same
  (package
    (name "r-same")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SAME" version))
       (sha256
        (base32 "0v7x1zl8y81affdp0cs9xxzm03xy9pg8wh9kljwmdq3anmaqhqfg"))))
    (properties `((upstream-name . "SAME")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rjags
                             r-ggplot2
                             r-extradistr
                             r-expint
                             r-coda
                             r-boot))
    (home-page "https://cran.r-project.org/package=SAME")
    (synopsis "Seamless Adaptive Multi-Arm Multi-Stage Enrichment")
    (description
     "Design a Bayesian seamless multi-arm biomarker-enriched phase II/III design with
the survival endpoint with allowing sample size re-estimation.  James M S Wason,
Jean E Abraham, Richard D Baird, Ioannis Gournaris, Anne-Laure Vallier, James D
Brenton, Helena M Earl, Adrian P Mander (2015) <doi:10.1038/bjc.2015.278>.
Guosheng Yin, Nan Chen, J. Jack Lee (2018) <doi:10.1007/s12561-017-9199-7>.
Ying Yuan, Beibei Guo, Mark Munsell, Karen Lu, Amir Jazaeri (2016)
<doi:10.1002/sim.6971>.")
    (license license:gpl2)))

(define-public r-samc
  (package
    (name "r-samc")
    (version "3.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "samc" version))
       (sha256
        (base32 "0vr6f63phzki3wsq3c4db00wzzfbfnbym33skvxn41c3xggvl324"))))
    (properties `((upstream-name . "samc")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-rcppthread
                             r-rcppeigen
                             r-rcpp
                             r-raster
                             r-matrix
                             r-circular))
    (native-inputs (list r-knitr))
    (home-page "https://andrewmarx.github.io/samc/")
    (synopsis "Spatial Absorbing Markov Chains")
    (description
     "This package implements functions for working with absorbing Markov chains.  The
implementation is based on the framework described in \"Toward a unified
framework for connectivity that disentangles movement and mortality in space and
time\" by Fletcher et al. (2019) <doi:10.1111/ele.13333>, which applies them to
spatial ecology.  This framework incorporates both resistance and absorption
with spatial absorbing Markov chains (SAMC) to provide several short-term and
long-term predictions for metrics related to connectivity in landscapes.
Despite the ecological context of the framework, this package can be used in any
application of absorbing Markov chains.")
    (license license:agpl3+)))

(define-public r-sambia
  (package
    (name "r-sambia")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sambia" version))
       (sha256
        (base32 "15yz5lrjav5bs8m52crg3l43xmi4gf7wydji76p181ssyg43z2ln"))))
    (properties `((upstream-name . "sambia")))
    (build-system r-build-system)
    (propagated-inputs (list r-smotefamily
                             r-ranger
                             r-proc
                             r-mvtnorm
                             r-fnn
                             r-e1071
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=sambia")
    (synopsis "Collection of Techniques Correcting for Sample Selection Bias")
    (description
     "This package provides a collection of various techniques correcting statistical
models for sample selection bias is provided.  In particular, the
resampling-based methods \"stochastic inverse-probability oversampling\" and
\"parametric inverse-probability bagging\" are placed at the disposal which
generate synthetic observations for correcting classifiers for biased samples
resulting from stratified random sampling.  For further information, see the
article Krautenbacher, Theis, and Fuchs (2017) <doi:10.1155/2017/7847531>.  The
methods may be used for further purposes where weighting and generation of new
observations is needed.")
    (license license:gpl3)))

(define-public r-samba
  (package
    (name "r-samba")
    (version "0.9.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SAMBA" version))
       (sha256
        (base32 "0gxc1s82bfhqj1rpmp0fm87frv77rgr8qjqai7zdgfz3jm474any"))))
    (properties `((upstream-name . "SAMBA")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey r-optimx))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SAMBA")
    (synopsis
     "Selection and Misclassification Bias Adjustment for Logistic Regression Models")
    (description
     "Health research using data from electronic health records (EHR) has gained
popularity, but misclassification of EHR-derived disease status and lack of
representativeness of the study sample can result in substantial bias in effect
estimates and can impact power and type I error for association tests.  Here,
the assumed target of inference is the relationship between binary disease
status and predictors modeled using a logistic regression model.  SAMBA
implements several methods for obtaining bias-corrected point estimates along
with valid standard errors as proposed in Beesley and Mukherjee (2020)
<doi:10.1101/2019.12.26.19015859>, currently under review.")
    (license license:gpl3)))

(define-public r-samadb
  (package
    (name "r-samadb")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "samadb" version))
       (sha256
        (base32 "1qbjjnchx9h3wrhf23cpmwfaixdkm4jcpkgniz3h5ghrhqv32grr"))))
    (properties `((upstream-name . "samadb")))
    (build-system r-build-system)
    (propagated-inputs (list r-writexl r-rmysql r-dbi r-data-table r-collapse))
    (home-page "https://cran.r-project.org/package=samadb")
    (synopsis "South Africa Macroeconomic Database API")
    (description
     "An R API providing access to a relational database with macroeconomic time
series data for South Africa, obtained from the South African Reserve Bank
(SARB) and Statistics South Africa (STATSSA), and updated on a weekly basis via
the @code{EconData} <https://www.econdata.co.za/> platform and automated
scraping of the SARB and STATSSA websites.  The database is maintained at the
Department of Economics at Stellenbosch University.")
    (license license:gpl3)))

(define-public r-sam
  (package
    (name "r-sam")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SAM" version))
       (sha256
        (base32 "0vg9hdlp0q58qwyf7iqij73kia124sn8wcplc81ydvs8z826y7yq"))))
    (properties `((upstream-name . "SAM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp))
    (home-page "https://cran.r-project.org/package=SAM")
    (synopsis "Sparse Additive Modelling")
    (description
     "Computationally efficient tools for high dimensional predictive modeling
(regression and classification).  SAM is short for sparse additive modeling, and
adopts the computationally efficient basis spline technique.  We solve the
optimization problems by various computational algorithms including the block
coordinate descent algorithm, fast iterative soft-thresholding algorithm, and
newton method.  The computation is further accelerated by warm-start and
active-set tricks.")
    (license license:gpl2)))

(define-public r-salty
  (package
    (name "r-salty")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "salty" version))
       (sha256
        (base32 "05hzyicd0qc661npkxy95547lbll693zslk48dcx6icdxvf8gbix"))))
    (properties `((upstream-name . "salty")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-purrr r-assertthat))
    (home-page "https://github.com/mdlincoln/salty")
    (synopsis "Turn Clean Data into Messy Data")
    (description
     "Take real or simulated data and salt it with errors commonly found in the wild,
such as pseudo-OCR errors, Unicode problems, numeric fields with nonsensical
punctuation, bad dates, etc.")
    (license license:expat)))

(define-public r-saltsampler
  (package
    (name "r-saltsampler")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SALTSampler" version))
       (sha256
        (base32 "1lh1w5kcx2sqv5czpfypdbq5mw7pxjbdk0vyg23zzjs26ynbdkn9"))))
    (properties `((upstream-name . "SALTSampler")))
    (build-system r-build-system)
    (propagated-inputs (list r-lattice))
    (home-page "https://cran.r-project.org/package=SALTSampler")
    (synopsis "Efficient Sampling on the Simplex")
    (description
     "The SALTSampler package facilitates Monte Carlo Markov Chain (MCMC) sampling of
random variables on a simplex.  A Self-Adjusting Logit Transform (SALT) proposal
is used so that sampling is still efficient even in difficult cases, such as
those in high dimensions or with parameters that differ by orders of magnitude.
Special care is also taken to maintain accuracy even when some coordinates
approach 0 or 1 numerically.  Diagnostic and graphic functions are included in
the package, enabling easy assessment of the convergence and mixing of the chain
within the constrained space.")
    (license license:bsd-3)))

(define-public r-salso
  (package
    (name "r-salso")
    (version "0.3.42")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "salso" version))
       (sha256
        (base32 "0j4ygcdjs241fcixzvas77rkqynrrzf84w35g791gsxgilpsl0zc"))))
    (properties `((upstream-name . "salso")))
    (build-system r-build-system)
    (inputs (list))
    (home-page "https://github.com/dbdahl/salso")
    (synopsis "Search Algorithms and Loss Functions for Bayesian Clustering")
    (description
     "The SALSO algorithm is an efficient randomized greedy search method to find a
point estimate for a random partition based on a loss function and posterior
Monte Carlo samples.  The algorithm is implemented for many loss functions,
including the Binder loss and a generalization of the variation of information
loss, both of which allow for unequal weights on the two types of clustering
mistakes.  Efficient implementations are also provided for Monte Carlo
estimation of the posterior expected loss of a given clustering estimate.  See
Dahl, Johnson, MÃ¼ller (2022) <doi:10.1080/10618600.2022.2069779>.")
    (license (list license:expat license:asl2.0))))

(define-public r-salesforcer
  (package
    (name "r-salesforcer")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "salesforcer" version))
       (sha256
        (base32 "19z4q5k0k1c1s2piqjg0z6nrwabap016nbx3jpf51lvmh5lp8ycf"))))
    (properties `((upstream-name . "salesforcer")))
    (build-system r-build-system)
    (propagated-inputs (list r-zip
                             r-xml2
                             r-xml
                             r-vctrs
                             r-tibble
                             r-rlist
                             r-rlang
                             r-readr
                             r-purrr
                             r-mime
                             r-lubridate
                             r-lifecycle
                             r-jsonlite
                             r-httr
                             r-dplyr
                             r-data-table
                             r-curl
                             r-base64enc
                             r-anytime))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/StevenMMortimer/salesforcer")
    (synopsis "An Implementation of 'Salesforce' APIs Using Tidy Principles")
    (description
     "This package provides functions connecting to the Salesforce Platform APIs
(REST, SOAP, Bulk 1.0, Bulk 2.0, Metadata, Reports and Dashboards)
<https://trailhead.salesforce.com/en/content/learn/modules/api_basics/api_basics_overview>.
\"API\" is an acronym for \"application programming interface\".  Most all calls
from these APIs are supported as they use CSV, XML or JSON data that can be
parsed into R data structures.  For more details please see the Salesforce API
documentation and this package's website
<https://stevenmmortimer.github.io/salesforcer/> for more information,
documentation, and examples.")
    (license license:expat)))

(define-public r-sales
  (package
    (name "r-sales")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SALES" version))
       (sha256
        (base32 "0a708nzj8qc2dig79a74rknj42vawm7ix40nbjq1bhakfxviam0v"))))
    (properties `((upstream-name . "SALES")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (native-inputs (list gfortran))
    (home-page "https://github.com/knightgu/SALES")
    (synopsis
     "The (Adaptive) Elastic Net and Lasso Penalized Sparse Asymmetric Least Squares (SALES) and Coupled Sparse Asymmetric Least Squares (COSALES) using Coordinate Descent and Proximal Gradient Algorithms")
    (description
     "This package provides a coordinate descent algorithm for computing the solution
paths of the sparse and coupled sparse asymmetric least squares, including the
(adaptive) elastic net and Lasso penalized SALES and COSALES regressions.")
    (license license:gpl2+)))

(define-public r-salad
  (package
    (name "r-salad")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "salad" version))
       (sha256
        (base32 "1fcs6g1ld514gkr5rxbawpxd81p9ywfxfhrh3jjwd3807gc9l2r4"))))
    (properties `((upstream-name . "salad")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=salad")
    (synopsis "Simple Automatic Differentiation")
    (description
     "Handles both vector and matrices, using a flexible S4 class for automatic
differentiation.  The method used is forward automatic differentiation.  Many
functions and methods have been defined, so that in most cases, functions
written without automatic differentiation in mind can be used without change.")
    (license license:expat)))

(define-public r-saive
  (package
    (name "r-saive")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SAiVE" version))
       (sha256
        (base32 "08f6icmr4x6kn0d7r98v9cmjqbfqgp1hzya68bl8p9fzfkjnmg4k"))))
    (properties `((upstream-name . "SAiVE")))
    (build-system r-build-system)
    (propagated-inputs (list r-vsurf
                             r-terra
                             r-rlang
                             r-proxy
                             r-doparallel
                             r-crayon
                             r-caret))
    (home-page "https://github.com/UO-SAiVE/SAiVE")
    (synopsis
     "Functions Used for SAiVE Group Research, Collaborations, and Publications")
    (description
     "Holds functions developed by the University of Ottawa's S@code{AiVE}
(Spatio-temporal Analysis of isotope Variations in the Environment) research
group with the intention of facilitating the re-use of code, foster good code
writing practices, and to allow others to benefit from the work done by the
S@code{AiVE} group.  Contributions are welcome via the @code{GitHub} repository
<https://github.com/UO-S@code{AiVE/SAiVE>} by group members as well as
non-members.")
    (license license:expat)))

(define-public r-sailor
  (package
    (name "r-sailor")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SailoR" version))
       (sha256
        (base32 "04qrnps40845ypapf37jg1c1g05nii6iwk4fv1x5xyca963hl5ii"))))
    (properties `((upstream-name . "SailoR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SailoR")
    (synopsis
     "An Extension of the Taylor Diagram to Two-Dimensional Vector Data")
    (description
     "This package provides a new diagram for the verification of vector variables
(wind, current, etc) generated by multiple models against a set of observations
is presented in this package.  It has been designed as a generalization of the
Taylor diagram to two dimensional quantities.  It is based on the analysis of
the two-dimensional structure of the mean squared error matrix between model and
observations.  The matrix is divided into the part corresponding to the relative
rotation and the bias of the empirical orthogonal functions of the data.  The
full set of diagnostics produced by the analysis of the errors between model and
observational vector datasets comprises the errors in the means, the analysis of
the total variance of both datasets, the rotation matrix corresponding to the
principal components in observation and model, the angle of rotation of
model-derived empirical orthogonal functions respect to the ones from
observations, the standard deviation of model and observations, the root mean
squared error between both datasets and the squared two-dimensional correlation
coefficient.  See the output of function UVError() in this package.")
    (license license:gpl3)))

(define-public r-saic
  (package
    (name "r-saic")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sAIC" version))
       (sha256
        (base32 "1gqcnwg7cnknfp36wqay9hibawx6n8z9z5z46vr05bl91m3xxqgs"))))
    (properties `((upstream-name . "sAIC")))
    (build-system r-build-system)
    (home-page "https://doi.org/10.1214/16-EJS1179")
    (synopsis "Akaike Information Criterion for Sparse Estimation")
    (description
     "Computes the Akaike information criterion for the generalized linear models
(logistic regression, Poisson regression, and Gaussian graphical models)
estimated by the lasso.")
    (license license:gpl2+)))

(define-public r-sahpm
  (package
    (name "r-sahpm")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sahpm" version))
       (sha256
        (base32 "1grhfxpqx9dyvqd2v91im3jgki2k3abcaal2ix9pcdzz93fc9xlw"))))
    (properties `((upstream-name . "sahpm")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://cran.r-project.org/package=sahpm")
    (synopsis "Variable Selection using Simulated Annealing")
    (description
     "Highest posterior model is widely accepted as a good model among available
models.  In terms of variable selection highest posterior model is often the
true model.  Our stochastic search process SAHPM based on simulated annealing
maximization method tries to find the highest posterior model by maximizing the
model space with respect to the posterior probabilities of the models.  This
package currently contains the SAHPM method only for linear models.  The codes
for GLM will be added in future.")
    (license license:gpl2)))

(define-public r-sagmm
  (package
    (name "r-sagmm")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SAGMM" version))
       (sha256
        (base32 "18ig7ncmrfs5cyc28xpbqk9zjhwqfp7gwix7y2v1j4j2wbdc2hzs"))))
    (properties `((upstream-name . "SAGMM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mixsim r-mclust
                             r-lowmemtkmeans))
    (home-page "https://cran.r-project.org/package=SAGMM")
    (synopsis
     "Clustering via Stochastic Approximation and Gaussian Mixture Models")
    (description
     "Computes clustering by fitting Gaussian mixture models (GMM) via stochastic
approximation following the methods of Nguyen and Jones (2018)
<doi:10.1201/9780429446177>.  It also provides some test data generation and
plotting functionality to assist with this process.")
    (license license:gpl3)))

(define-public r-sagm
  (package
    (name "r-sagm")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SAGM" version))
       (sha256
        (base32 "10hxj5ic7mq6wzpqizkxyqc165dmpc2wqc9naf6b9r560pk52hv6"))))
    (properties `((upstream-name . "SAGM")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-gigrvg r-fastmatrix))
    (home-page "https://cran.r-project.org/package=SAGM")
    (synopsis "Spatial Autoregressive Graphical Model")
    (description
     "This package implements the methodological developments found in Hermes, van
Heerwaarden, and Behrouzi (2023) <doi:10.48550/@code{arXiv.2308.04325>}, and
allows for the statistical modeling of asymmetric between-location effects, as
well as within-location effects using spatial autoregressive graphical models.
The package allows for the generation of spatial weight matrices to capture
asymmetric effects for strip-type intercropping designs, although it can handle
any type of spatial data commonly found in other sciences.")
    (license license:gpl3)))

(define-public r-sager
  (package
    (name "r-sager")
    (version "0.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sageR" version))
       (sha256
        (base32 "0j7k0sr040s6d4zmfg5hnfswcxjrv4lnq8j6j0knf44xahxm7vry"))))
    (properties `((upstream-name . "sageR")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://fbertran.github.io/homepage/")
    (synopsis "Applied Statistics for Economics and Management with R")
    (description
     "Datasets and functions for the book \"Statistiques pour lâÃ©conomie et la
gestion\", \"ThÃ©orie et applications en entreprise\", F. Bertrand, Ch.  Derquenne,
G. DufrÃ©not, F. Jawadi and M. Maumy, C. Borsenberger editor, (2021,
ISBN:9782807319448, De Boeck SupÃ©rieur, Louvain-la-Neuve).  The first chapter
of the book is dedicated to an introduction to statistics and their world.  The
second chapter deals with univariate exploratory statistics and graphics.  The
third chapter deals with bivariate and multivariate exploratory statistics and
graphics.  The fourth chapter is dedicated to data exploration with Principal
Component Analysis.  The fifth chapter is dedicated to data exploration with
Correspondance Analysis.  The sixth chapter is dedicated to data exploration
with Multiple Correspondance Analysis.  The seventh chapter is dedicated to data
exploration with automatic clustering.  The eighth chapter is dedicated to an
introduction to probability theory and classical probability distributions.  The
ninth chapter is dedicated to an estimation theory, one-sample and two-sample
tests.  The tenth chapter is dedicated to an Gaussian linear model.  The
eleventh chapter is dedicated to an introduction to time series.  The twelfth
chapter is dedicated to an introduction to probit and logit models.  Various
example datasets are shipped with the package as well as some new functions.")
    (license license:gpl3)))

(define-public r-safevote
  (package
    (name "r-safevote")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SafeVote" version))
       (sha256
        (base32 "01gkk16ynrimjyygma2dg7azgj7ag1bwd22w39rpjlfhaavkysza"))))
    (properties `((upstream-name . "SafeVote")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-knitr
                             r-ggplot2
                             r-formattable
                             r-forcats
                             r-fields
                             r-dplyr
                             r-data-table))
    (home-page "https://cran.r-project.org/package=SafeVote")
    (synopsis "Election Vote Counting with Safety Features")
    (description
     "Fork of vote_2.3-2', Raftery et al. (2021) <DOI:10.32614/RJ-2021-086>, with
additional support for stochastic experimentation.")
    (license license:gpl2+)))

(define-public r-safetygraphics
  (package
    (name "r-safetygraphics")
    (version "2.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "safetyGraphics" version))
       (sha256
        (base32 "1bvmxhy2giprf4cy1xfc42rl8hcw0pi3691kzfgqhj65sawdjwfd"))))
    (properties `((upstream-name . "safetyGraphics")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-tidyr
                             r-stringr
                             r-sortable
                             r-shinyjs
                             r-shiny
                             r-safetydata
                             r-safetycharts
                             r-rlang
                             r-rclipboard
                             r-purrr
                             r-magrittr
                             r-jsonlite
                             r-htmlwidgets
                             r-dt
                             r-dplyr
                             r-datamods))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/SafetyGraphics/safetyGraphics")
    (synopsis "Interactive Graphics for Monitoring Clinical Trial Safety")
    (description
     "This package provides a framework for evaluation of clinical trial safety.
Users can interactively explore their data using the included Shiny application.")
    (license license:expat)))

(define-public r-safetydata
  (package
    (name "r-safetydata")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "safetyData" version))
       (sha256
        (base32 "1spa1wh1z8cqsz8vrkj3abny0xs3qp8d80dla8d031d70hvfkdjc"))))
    (properties `((upstream-name . "safetyData")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=safetyData")
    (synopsis "Clinical Trial Data")
    (description
     "Example clinical trial data sets formatted for easy use in R.")
    (license license:expat)))

(define-public r-safetycharts
  (package
    (name "r-safetycharts")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "safetyCharts" version))
       (sha256
        (base32 "1riyd2rb799rr0j95s3wd45rwq8syfm0xllpihycpak0f56vg1s8"))))
    (properties `((upstream-name . "safetyCharts")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-tplyr
                             r-tendril
                             r-stringr
                             r-shiny
                             r-rlang
                             r-rcolorbrewer
                             r-purrr
                             r-plotly
                             r-pharmartf
                             r-jsonlite
                             r-huxtable
                             r-htmlwidgets
                             r-ggplot2
                             r-forcats
                             r-dt
                             r-dplyr))
    (native-inputs (list esbuild))
    (home-page "https://github.com/SafetyGraphics/safetyCharts")
    (synopsis "Charts for Monitoring Clinical Trial Safety")
    (description
     "This package contains chart code for monitoring clinical trial safety.  Charts
can be used as standalone output, but are also designed for use with the
@code{safetyGraphics} package, which makes it easy to load data and customize
the charts using an interactive web-based interface created with Shiny.")
    (license license:expat)))

(define-public r-safestats
  (package
    (name "r-safestats")
    (version "0.8.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "safestats" version))
       (sha256
        (base32 "1jxdnvmf6w8hgd5wcfjcmm796nchig1xizhngbzdgl85yi6nw1yy"))))
    (properties `((upstream-name . "safestats")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rlang
                             r-purrr
                             r-hypergeo
                             r-dplyr
                             r-boot
                             r-biasedurn))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=safestats")
    (synopsis "Safe Anytime-Valid Inference")
    (description
     "This package provides functions to design and apply tests that are anytime
valid.  The functions can be used to design hypothesis tests in the
prospective/randomised control trial setting or in the
observational/retrospective setting.  The resulting tests remain valid under
both optional stopping and optional continuation.  The current version includes
safe t-tests and safe tests of two proportions.  For details on the theory of
safe tests, see Grunwald, de Heide and Koolen (2019) \"Safe Testing\"
<@code{arXiv:1906.07801>}, for details on safe logrank tests see ter Schure,
Perez-Ortiz, Ly and Grunwald (2020) \"The Safe Logrank Test: Error Control under
Continuous Monitoring with Unlimited Horizon\" <@code{arXiv:2011.06931v3>} and
Turner, Ly and Grunwald (2021) \"Safe Tests and Always-Valid Confidence Intervals
for contingency tables and beyond\" <@code{arXiv:2106.02693>} for details on safe
contingency table tests.")
    (license license:lgpl3+)))

(define-public r-safer
  (package
    (name "r-safer")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "safer" version))
       (sha256
        (base32 "1lmhaj9y0hi4ybxfkllxl99vsagcg73sy4kkcmlvn3bs69jky1r9"))))
    (properties `((upstream-name . "safer")))
    (build-system r-build-system)
    (propagated-inputs (list r-sodium r-base64enc r-assertthat))
    (home-page "https://github.com/talegari/safer")
    (synopsis "Encrypt and Decrypt Strings, R Objects and Files")
    (description
     "This package provides a consistent interface to encrypt and decrypt strings, R
objects and files using symmetric and asymmetric key encryption.")
    (license license:gpl3)))

(define-public r-safejoin
  (package
    (name "r-safejoin")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "safejoin" version))
       (sha256
        (base32 "19ja9nxgnh7xpsdaqy866qz6zf2mkfxinh757dmha1avr7bjhmfx"))))
    (properties `((upstream-name . "safejoin")))
    (build-system r-build-system)
    (propagated-inputs (list r-lifecycle r-glue r-dplyr))
    (home-page "https://github.com/SamEdwardes/safejoin")
    (synopsis "Perform \"Safe\" Table Joins")
    (description
     "The goal of safejoin is to guarantee that when performing joins extra rows are
not added to your data.  safejoin provides a wrapper around dplyr::left_join
that will raise an error when extra rows are unexpectedly added to your data.
This can be useful when working with data where you expect there to be a many to
one relationship but you are not certain the relationship holds.")
    (license license:expat)))

(define-public r-safd
  (package
    (name "r-safd")
    (version "2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SAFD" version))
       (sha256
        (base32 "078ki9wrmcf80bwhx4d56gas79xrc17a0081i13yxvjqn6w7f7jd"))))
    (properties `((upstream-name . "SAFD")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SAFD")
    (synopsis "Statistical Analysis of Fuzzy Data")
    (description
     "The aim of the package is to provide some basic functions for doing statistics
with one dimensional Fuzzy Data (in the form of polygonal fuzzy numbers).  In
particular, the package contains functions for the basic operations on the class
of fuzzy numbers (sum, scalar product, mean, median, Hukuhara difference) as
well as for calculating (Bertoluzza) distance and sample variance.  Moreover a
function to simulate fuzzy random variables and bootstrap tests for the equality
of means is included.  Version 2.1 fixes some bugs of previous versions.")
    (license license:gpl2+)))

(define-public r-safari
  (package
    (name "r-safari")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SAFARI" version))
       (sha256
        (base32 "10fmav9dbq5kbgy8gjsp3561vi46bjvkh2wqjs39vx5z8x1ml7i3"))))
    (properties `((upstream-name . "SAFARI")))
    (build-system r-build-system)
    (propagated-inputs (list r-png r-lattice r-ebimage r-catools))
    (home-page "https://github.com/estfernandez/SAFARI")
    (synopsis "Shape Analysis for AI-Reconstructed Images")
    (description
     "This package provides functionality for image processing and shape analysis in
the context of reconstructed medical images generated by deep learning-based
methods or standard image processing algorithms and produced from different
medical imaging types, such as X-ray, Computational Tomography (CT), Magnetic
Resonance Imaging (MRI), and pathology imaging.  Specifically, offers tools to
segment regions of interest and to extract quantitative shape descriptors for
applications in signal processing, statistical analysis and modeling, and
machine learning.")
    (license license:gpl3+)))

(define-public r-saeval
  (package
    (name "r-saeval")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SAEval" version))
       (sha256
        (base32 "11427b9n3dc1jkzjqbri2h7cvfms1z130p10bbj39jjx8zjm1fyq"))))
    (properties `((upstream-name . "SAEval")))
    (build-system r-build-system)
    (propagated-inputs (list r-lmtest r-ggspatial r-ggplot2 r-car))
    (home-page "https://cran.r-project.org/package=SAEval")
    (synopsis "Small Area Estimation Evaluation")
    (description
     "Allows users to produce diagnostic procedures and graphic tools for the
evaluation of Small Area estimators.")
    (license (license:fsdg-compatible "EUPL"))))

(define-public r-saetrafo
  (package
    (name "r-saetrafo")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saeTrafo" version))
       (sha256
        (base32 "1sqmymazs3g1xlqvphbwmpvl5yhkmbmpcyc1mxqdxaj3yap44qwr"))))
    (properties `((upstream-name . "saeTrafo")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-sfsmisc
                             r-rlang
                             r-reshape2
                             r-readods
                             r-parallelmap
                             r-openxlsx
                             r-nlme
                             r-moments
                             r-hlmdiag
                             r-gridextra
                             r-ggplot2
                             r-emdi))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/NoraWuerz/saeTrafo")
    (synopsis "Transformations for Unit-Level Small Area Models")
    (description
     "The aim of this package is to offer new methodology for unit-level small area
models under transformations and limited population auxiliary information.  In
addition to this new methodology, the widely used nested error regression model
without transformations (see \"An Error-Components Model for Prediction of County
Crop Areas Using Survey and Satellite Data\" by Battese, Harter and Fuller (1988)
<doi:10.1080/01621459.1988.10478561>) and its well-known uncertainty estimate
(see \"The estimation of the mean squared error of small-area estimators\" by
Prasad and Rao (1990) <doi:10.1080/01621459.1995.10476570>) are provided.  In
this package, the log transformation and the data-driven log-shift
transformation are provided.  If a transformation is selected, an appropriate
method is chosen depending on the respective input of the population data:
Individual population data (see \"Empirical best prediction under a nested error
model with log transformation\" by Molina and MartÃ­n (2018)
<doi:10.1214/17-aos1608>) but also aggregated population data (see \"Estimating
regional income indicators under transformations and access to limited
population auxiliary information\" by WÃ¼rz, Schmid and Tzavidis <unpublished>)
can be entered.  Especially under limited data access, new methodologies are
provided in @code{saeTrafo}.  Several options are available to assess the used
model and to judge, present and export its results.  For a detailed description
of the package and the methods used see the corresponding vignette.")
    (license license:gpl2)))

(define-public r-saesim
  (package
    (name "r-saesim")
    (version "0.11.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saeSim" version))
       (sha256
        (base32 "0s0zcls4j3cx7wbvi0l3q4c3b0f9x9i2wcqnz4gzwyqik6pcfaic"))))
    (properties `((upstream-name . "saeSim")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-spdep
                             r-parallelmap
                             r-mass
                             r-ggplot2
                             r-functional
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://wahani.github.io/saeSim/")
    (synopsis "Simulation Tools for Small Area Estimation")
    (description
     "This package provides tools for the simulation of data in the context of small
area estimation.  Combine all steps of your simulation - from data generation
over drawing samples to model fitting - in one object.  This enables easy
modification and combination of different scenarios.  You can store your results
in a folder or start the simulation in parallel.")
    (license license:expat)))

(define-public r-saerobust
  (package
    (name "r-saerobust")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saeRobust" version))
       (sha256
        (base32 "0rq4az5rzjljjswkdbcz7y5lyrnhyx9b20w81s4665iwrl3fcniw"))))
    (properties `((upstream-name . "saeRobust")))
    (build-system r-build-system)
    (propagated-inputs (list r-spdep
                             r-rcpparmadillo
                             r-rcpp
                             r-pbapply
                             r-modules
                             r-memoise
                             r-matrix
                             r-mass
                             r-magrittr
                             r-ggplot2
                             r-assertthat
                             r-aoos))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=saeRobust")
    (synopsis "Robust Small Area Estimation")
    (description
     "This package provides methods to fit robust alternatives to commonly used models
used in Small Area Estimation.  The methods here used are based on best linear
unbiased predictions and linear mixed models.  At this time available models
include area level models incorporating spatial and temporal correlation in the
random effects.")
    (license license:expat)))

(define-public r-saepseudo
  (package
    (name "r-saepseudo")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saePseudo" version))
       (sha256
        (base32 "1zppb1k9rz13v0p3vgs42rhcbm93hp3xl7mavk2cxy9l36gnd40g"))))
    (properties `((upstream-name . "saePseudo")))
    (build-system r-build-system)
    (propagated-inputs (list r-sae r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/darinhuwaidaa/saePseudo")
    (synopsis "Small Area Estimation using Averaging Pseudo Area Level Model")
    (description
     "This package provides function for small area estimation at area level using
averaging pseudo area level model for variables of interest.  A dataset produced
by data generation is also provided.  This package estimates small areas at the
village level and then aggregates them to the sub-district, region, and
provincial levels.")
    (license license:gpl3)))

(define-public r-saens
  (package
    (name "r-saens")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saens" version))
       (sha256
        (base32 "1fmg7lcryc426hclrzhj26l8zaar0cc2j3vqxd752g86h4n0g6hz"))))
    (properties `((upstream-name . "saens")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-rlang r-ggplot2 r-dplyr r-cli))
    (home-page "https://github.com/Alfrzlp/sae-ns")
    (synopsis
     "Small Area Estimation with Cluster Information for Estimation of Non-Sampled Areas")
    (description
     "Implementation of small area estimation (Fay-Herriot model) with EBLUP
(Empirical Best Linear Unbiased Prediction) Approach for non-sampled area
estimation by adding cluster information and assuming that there are
similarities among particular areas.  See also Rao & Molina (2015,
ISBN:978-1-118-73578-7) and Anisa et al. (2013) <doi:10.9790/5728-10121519>.")
    (license license:expat)))

(define-public r-saemspe
  (package
    (name "r-saemspe")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saeMSPE" version))
       (sha256
        (base32 "086nmcx14sjwnydka805il11lc02jv9l39kk9j1h2j5p9gz0b3gp"))))
    (properties `((upstream-name . "saeMSPE")))
    (build-system r-build-system)
    (propagated-inputs (list r-smallarea r-rcpparmadillo r-rcpp r-matrix))
    (home-page "https://cran.r-project.org/package=saeMSPE")
    (synopsis
     "Compute MSPE Estimates for the Fay Herriot Model and Nested Error Regression Model")
    (description
     "We describe a new R package entitled @code{saeMSPE} for the well-known Fay
Herriot model and nested error regression model in small area estimation.  Based
on this package, it is possible to easily compute various common mean squared
predictive error (MSPE) estimators, as well as several existing variance
component predictors as a byproduct, for these two models.")
    (license license:gpl2+)))

(define-public r-saemix
  (package
    (name "r-saemix")
    (version "3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saemix" version))
       (sha256
        (base32 "1xx6254sj6lyarkkdkl3lajflyh8c2y8261bl9a19d1457l1h8wv"))))
    (properties `((upstream-name . "saemix")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-rlang
                             r-npde
                             r-mclust
                             r-mass
                             r-gridextra
                             r-ggplot2))
    (home-page "https://cran.r-project.org/package=saemix")
    (synopsis
     "Stochastic Approximation Expectation Maximization (SAEM) Algorithm")
    (description
     "The saemix package implements the Stochastic Approximation EM algorithm for
parameter estimation in (non)linear mixed effects models.  The SAEM algorithm
(i) computes the maximum likelihood estimator of the population parameters,
without any approximation of the model (linearisation, quadrature
approximation,...), using the Stochastic Approximation Expectation Maximization
(SAEM) algorithm, (ii) provides standard errors for the maximum likelihood
estimator (iii) estimates the conditional modes, the conditional means and the
conditional standard deviations of the individual parameters, using the
Hastings-Metropolis algorithm (see Comets et al. (2017)
<doi:10.18637/jss.v080.i03>).  Many applications of SAEM in agronomy, animal
breeding and PKPD analysis have been published by members of the Monolix group.
The full PDF documentation for the package including references about the
algorithm and examples can be downloaded on the github of the IAME research
institute for saemix':
<https://github.com/iame-@code{researchCenter/saemix/blob/7638e1b09ccb01cdff173068e01c266e906f76eb/docsaem.pdf>}.")
    (license license:gpl2+)))

(define-public r-saeme
  (package
    (name "r-saeme")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saeME" version))
       (sha256
        (base32 "071f345gpm5qwsj6k7vm8jfm80lgzk5l1gyds3zcxy955gk288cs"))))
    (properties `((upstream-name . "saeME")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=saeME")
    (synopsis "Small Area Estimation with Measurement Error")
    (description
     "This package provides a set of functions and datasets implementation of small
area estimation when auxiliary variable is measured with error.  These functions
provide a empirical best linear unbiased prediction (EBLUP) estimator and mean
squared error (MSE) estimator of the EBLUP. These models were developed by
Ybarra and Lohr (2008) <doi:10.1093/biomet/asn048>.")
    (license license:gpl2)))

(define-public r-saekernel
  (package
    (name "r-saekernel")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saekernel" version))
       (sha256
        (base32 "0zk124kkxrwr3bliy01d3ag4r0flnjg4z3lmm5wmq3w0r4gwgy4f"))))
    (properties `((upstream-name . "saekernel")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/wicaksh/saekernel")
    (synopsis
     "Small Area Estimation Non-Parametric Based Nadaraya-Watson Kernel")
    (description
     "Propose an area-level, non-parametric regression estimator based on
Nadaraya-Watson kernel on small area mean.  Adopt a two-stage estimation
approach proposed by Prasad and Rao (1990).  Mean Squared Error (MSE) estimators
are not readily available, so resampling method that called bootstrap is
applied.  This package are based on the model proposed in Two stage
non-parametric approach for small area estimation by Pushpal Mukhopadhyay and
Tapabrata Maiti(2004)
<http://www.asasrms.org/Proceedings/y2004/files/Jsm2004-000737.pdf>.")
    (license license:gpl3)))

(define-public r-saehb-zib
  (package
    (name "r-saehb-zib")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saeHB.ZIB" version))
       (sha256
        (base32 "1z7siwv96iq9cww044gy4a7hnzq02s415xav725jqqwr68chham9"))))
    (properties `((upstream-name . "saeHB.ZIB")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rjags r-coda))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=saeHB.ZIB")
    (synopsis
     "Small Area Estimation using Hierarchical Bayesian under Zero Inflated Binomial Distribution")
    (description
     "This package provides function for area level of small area estimation using
hierarchical Bayesian (HB) method with Zero-Inflated Binomial distribution for
variables of interest.  Some dataset produced by a data generation are also
provided.  The rjags package is employed to obtain parameter estimates.
Model-based estimators involves the HB estimators which include the mean and the
variation of mean.")
    (license license:gpl3)))

(define-public r-saehb-unit
  (package
    (name "r-saehb-unit")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saeHB.unit" version))
       (sha256
        (base32 "0z1p41aa398qgysm3yp7siz38any3z3rjw8hn4hrj9zxx9zn2lcy"))))
    (properties `((upstream-name . "saeHB.unit")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-rjags r-dplyr r-coda r-cli))
    (home-page "https://github.com/Alfrzlp/saeHB.unit")
    (synopsis "Basic Unit Level Model using Hierarchical Bayesian Approach")
    (description
     "Small area estimation unit level models (Battese-Harter-Fuller model) with a
Bayesian Hierarchical approach.  See also Rao & Molina (2015,
ISBN:978-1-118-73578-7) and Battese et al. (1988)
<doi:10.1080/01621459.1988.10478561>.")
    (license license:expat)))

(define-public r-saehb-panel-beta
  (package
    (name "r-saehb-panel-beta")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saeHB.panel.beta" version))
       (sha256
        (base32 "0mpq6wskfna4v7wdlq35sg2qjr6l55mq9n87pv71j41yvq3ymanm"))))
    (properties `((upstream-name . "saeHB.panel.beta")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-stringr r-rjags r-dplyr r-coda))
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "https://github.com/DianRahmawatiSalis/saeHB.panel.beta")
    (synopsis
     "Small Area Estimation using HB for Rao Yu Model under Beta Distribution")
    (description
     "Several functions are provided for small area estimation at the area level using
the hierarchical bayesian (HB) method with panel data under beta distribution
for variable interest.  This package also provides a dataset produced by data
generation.  The rjags package is employed to obtain parameter estimates.
Model-based estimators involve the HB estimators, which include the mean and the
variation of the mean.  For the reference, see Rao and Molina (2015, ISBN:
978-1-118-73578-7).")
    (license license:gpl3)))

(define-public r-saehb-panel
  (package
    (name "r-saehb-panel")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saeHB.panel" version))
       (sha256
        (base32 "0280hczz47nlrr06wihfl6j0qfnv8w8vvjjr11nzf6p61hkz6sdx"))))
    (properties `((upstream-name . "saeHB.panel")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-stringr r-rjags r-coda))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Veliatrimarliana/saeHB.panel")
    (synopsis
     "Small Area Estimation using Hierarchical Bayesian Method for Rao Yu Model")
    (description
     "We designed this package to provide several functions for area level of small
area estimation using hierarchical Bayesian (HB) method.  This package provides
model using panel data for variable interest.This package also provides a
dataset produced by a data generation.  The rjags package is employed to obtain
parameter estimates.  Model-based estimators involves the HB estimators which
include the mean and the variation of mean.  For the reference, see Rao and
Molina (2015).")
    (license license:gpl3)))

(define-public r-saehb-me-beta
  (package
    (name "r-saehb-me-beta")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saeHB.ME.beta" version))
       (sha256
        (base32 "1i1pgvh6xn26w3g4x7bn0d8qi2g9l8fd58ag1i7lrsrq3rx5m83k"))))
    (properties `((upstream-name . "saeHB.ME.beta")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rjags r-coda))
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "https://github.com/ratihrodliyah/saeHB.ME.beta")
    (synopsis "SAE with Measurement Error using HB under Beta Distribution")
    (description
     "Implementation of Small Area Estimation (SAE) using Hierarchical Bayesian (HB)
Method when auxiliary variable measured with error under Beta Distribution.  The
rjags package is employed to obtain parameter estimates.  For the references,
see J.N.K & Molina (2015) <doi:10.1002/9781118735855>, Ybarra and Sharon (2008)
<doi:10.1093/biomet/asn048>, and Ntzoufras (2009, ISBN-10: 1118210352).")
    (license license:gpl3)))

(define-public r-saehb-me
  (package
    (name "r-saehb-me")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saeHB.ME" version))
       (sha256
        (base32 "134sib6akpwns49mv3vnwflcpwalpi9jgb5qm6dw61s2b01k1m2q"))))
    (properties `((upstream-name . "saeHB.ME")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rjags r-coda))
    (home-page "https://cran.r-project.org/package=saeHB.ME")
    (synopsis
     "Small Area Estimation with Measurement Error using Hierarchical Bayesian Method")
    (description
     "Implementation of small area estimation using Hierarchical Bayesian (HB) Method
when auxiliary variable measured with error.  The rjags package is employed to
obtain parameter estimates.  For the references, see Rao and Molina (2015)
<doi:10.1002/9781118735855>, Ybarra and Lohr (2008) <doi:10.1093/biomet/asn048>,
and Ntzoufras (2009, ISBN-10: 1118210352).")
    (license license:gpl3)))

(define-public r-saehb
  (package
    (name "r-saehb")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saeHB" version))
       (sha256
        (base32 "1wv9b656s93r9vnm6j0j10gd1q92vbawhy5ijjzvzsn9drzzwzsc"))))
    (properties `((upstream-name . "saeHB")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-stringr
                             r-rjags
                             r-nimble
                             r-mass
                             r-coda
                             r-carbayesdata))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/zazaperwira/saeHB")
    (synopsis "Small Area Estimation using Hierarchical Bayesian Method")
    (description
     "This package provides several functions for area level of small area estimation
using hierarchical Bayesian (HB) methods with several univariate distributions
for variables of interest.  The dataset that is used in every function is
generated accordingly in the Example.  The rjags package is employed to obtain
parameter estimates.  Model-based estimators involve the HB estimators which
include the mean and the variation of mean.  For the reference, see Rao and
Molina (2015) <doi:10.1002/9781118735855>.")
    (license license:gpl3)))

(define-public r-saeeb
  (package
    (name "r-saeeb")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saeeb" version))
       (sha256
        (base32 "04vjxmnqcgqcamgjd9qq1dbx0msm40mnn94jjy9pzvsj2gsbws39"))))
    (properties `((upstream-name . "saeeb")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-count))
    (home-page "https://cran.r-project.org/package=saeeb")
    (synopsis "Small Area Estimation for Count Data")
    (description
     "This package provides small area estimation for count data type and gives option
whether to use covariates in the estimation or not.  By implementing Empirical
Bayes (EB) Poisson-Gamma model, each function returns EB estimators and mean
squared error (MSE) estimators for each area.  The EB estimators without
covariates are obtained using the model proposed by Clayton & Kaldor (1987)
<doi:10.2307/2532003>, the EB estimators with covariates are obtained using the
model proposed by Wakefield (2006) <doi:10.1093/biostatistics/kxl008> and the
MSE estimators are obtained using Jackknife method by Jiang et.  al. (2002)
<doi:10.1214/aos/1043351257>.")
    (license license:gpl2)))

(define-public r-saeczi
  (package
    (name "r-saeczi")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saeczi" version))
       (sha256
        (base32 "02jsmmzaig5d8lr9avra3acy93klh7f88izxsf04gn0w855y37cv"))))
    (properties `((upstream-name . "saeczi")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-rcppeigen
                             r-rcpp
                             r-purrr
                             r-progressr
                             r-lme4
                             r-future
                             r-furrr
                             r-dplyr))
    (home-page "https://harvard-ufds.github.io/saeczi/")
    (synopsis "Small Area Estimation for Continuous Zero Inflated Data")
    (description
     "This package provides functionality to fit a zero-inflated estimator for small
area estimation.  This estimator is a combines a linear mixed effects regression
model and a logistic mixed effects regression model via a two-stage modeling
approach.  The estimator's mean squared error is estimated via a parametric
bootstrap method.  Chandra and others (2012, <doi:10.1080/03610918.2011.598991>)
introduce and describe this estimator and mean squared error estimator.  White
and others (2024+, <doi:10.48550/@code{arXiv.2402.03263>}) describe the
applicability of this estimator to estimation of forest attributes and further
assess the estimator's properties.")
    (license license:expat)))

(define-public r-saebnocov
  (package
    (name "r-saebnocov")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saebnocov" version))
       (sha256
        (base32 "0dxbicvmq13wh2xj820qw2ah3j005pqjynynhgkynfvm9nmymrvr"))))
    (properties `((upstream-name . "saebnocov")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-dplyr r-descr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=saebnocov")
    (synopsis
     "Small Area Estimation using Empirical Bayes without Auxiliary Variable")
    (description
     "Estimates the parameter of small area in binary data without auxiliary variable
using Empirical Bayes technique, mainly from Rao and Molina
(2015,ISBN:9781118735787) with book entitled \"Small Area Estimation Second
Edition\".  This package provides another option of direct estimation using
weight.  This package also features alpha and beta parameter estimation on
calculating process of small area.  Those methods are Newton-Raphson and Moment
which based on Wilcox (1979) <doi:10.1177/001316447903900302> and Kleinman
(1973) <doi:10.1080/01621459.1973.10481332>.")
    (license license:gpl3+)))

(define-public r-saebest
  (package
    (name "r-saebest")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saeBest" version))
       (sha256
        (base32 "0s68w9cikhpac1kdlm1pphvnks1izacs5s87d7r380c3lkgkzd76"))))
    (properties `((upstream-name . "saeBest")))
    (build-system r-build-system)
    (propagated-inputs (list r-sae))
    (home-page "https://cran.r-project.org/package=saeBest")
    (synopsis
     "Selecting Auxiliary Variables in Small Area Estimation (SAE) Model")
    (description
     "Select best combination of auxiliary variables with certain criterion.")
    (license license:gpl3)))

(define-public r-sae2
  (package
    (name "r-sae2")
    (version "1.2-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sae2" version))
       (sha256
        (base32 "1ahq2gha8q3k6aj1a1qclibq209a3426sb77qy3fdgph9zkpij1x"))))
    (properties `((upstream-name . "sae2")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey r-mass))
    (home-page "https://cran.r-project.org/package=sae2")
    (synopsis "Small Area Estimation: Time-Series Models")
    (description
     "Time series area-level models for small area estimation.  The package
supplements the functionality of the sae package.  Specifically, it includes
EBLUP fitting of the original Rao-Yu model, which in the original form did not
have a spatial component.  The package also offers a modified ('dynamic')
version of the Rao-Yu model, replacing the assumption of stationarity.  Both
univariate and multivariate applications are supported.  Of particular note is
the allowance for covariance of the area-level sample estimates over time, as
encountered in rotating panel designs such as the U.S. National Crime
Victimization Survey or present in a time-series of 5-year estimates from the
American Community Survey.  Key references to the methods include J.N.K. Rao and
I. Molina (2015, ISBN:9781118735787), J.N.K. Rao and M. Yu (1994)
<doi:10.2307/3315407>, and R.E. Fay and R.A. Herriot (1979)
<doi:10.1080/01621459.1979.10482505>.")
    (license license:gpl2)))

(define-public r-sae-prop
  (package
    (name "r-sae-prop")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sae.prop" version))
       (sha256
        (base32 "19q86xjpvr3s3paspkzsr40zvff60mxxh57xpz0fnxldivynh6ia"))))
    (properties `((upstream-name . "sae.prop")))
    (build-system r-build-system)
    (propagated-inputs (list r-progress r-mass r-magic r-fpc r-corpcor))
    (home-page "https://github.com/mrijalussholihin/sae.prop")
    (synopsis
     "Small Area Estimation using Fay-Herriot Models with Additive Logistic Transformation")
    (description
     "This package implements Additive Logistic Transformation (alr) for Small Area
Estimation under Fay Herriot Model.  Small Area Estimation is used to borrow
strength from auxiliary variables to improve the effectiveness of a domain
sample size.  This package uses Empirical Best Linear Unbiased Prediction
(EBLUP).  The Additive Logistic Transformation (alr) are based on transformation
by Aitchison J (1986).  The covariance matrix for multivariate application is
based on covariance matrix used by Esteban M, LombardÃ­a M, LÃ³pez-VizcaÃ­no E,
Morales D, and PÃ©rez A <doi:10.1007/s11749-019-00688-w>.  The non-sampled
models are modified area-level models based on models proposed by Anisa R,
Kurnia A, and Indahwati I <doi:10.9790/5728-10121519>, with univariate model
using model-3, and multivariate model using model-1.  The MSE are estimated
using Parametric Bootstrap approach.  For non-sampled cases, MSE are estimated
using modified approach proposed by Haris F and Ubaidillah A
<doi:10.4108/eai.2-8-2019.2290339>.")
    (license license:gpl3)))

(define-public r-sae
  (package
    (name "r-sae")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sae" version))
       (sha256
        (base32 "1ncrlzgnga9pfa2j8x1y6axl2665rp08aff98zrim6gq6n6yap8z"))))
    (properties `((upstream-name . "sae")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-lme4))
    (home-page "https://cran.r-project.org/package=sae")
    (synopsis "Small Area Estimation")
    (description "This package provides functions for small area estimation.")
    (license license:gpl2)))

(define-public r-sads
  (package
    (name "r-sads")
    (version "0.6.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sads" version))
       (sha256
        (base32 "196kzsq3afzj8j69417i6xmbbjgbm5qyinrw6s6drs07yhvnzp0f"))))
    (properties `((upstream-name . "sads")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam
                             r-powerlaw
                             r-poilog
                             r-mass
                             r-guilds
                             r-bbmle))
    (home-page "https://github.com/piLaboratory/sads")
    (synopsis "Maximum Likelihood Models for Species Abundance Distributions")
    (description
     "Maximum likelihood tools to fit and compare models of species abundance
distributions and of species rank-abundance distributions.")
    (license license:gpl2)))

(define-public r-sadists
  (package
    (name "r-sadists")
    (version "0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sadists" version))
       (sha256
        (base32 "0c7bmca6yyz5hca2ghr2zc6lmwi94cywz8gkpsakrnmk0f7680xi"))))
    (properties `((upstream-name . "sadists")))
    (build-system r-build-system)
    (propagated-inputs (list r-pdqutils r-orthopolynom r-hypergeo))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/shabbychef/sadists")
    (synopsis "Some Additional Distributions")
    (description
     "This package provides the density, distribution, quantile and generation
functions of some obscure probability distributions, including the doubly
non-central t, F, Beta, and Eta distributions; the lambda-prime and K-prime; the
upsilon distribution; the (weighted) sum of non-central chi-squares to a power;
the (weighted) sum of log non-central chi-squares; the product of non-central
chi-squares to powers; the product of doubly non-central F variables; the
product of independent normals.")
    (license license:lgpl3)))

(define-public r-sadisa
  (package
    (name "r-sadisa")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SADISA" version))
       (sha256
        (base32 "0yn7f360wl5ykys5zln159xh2h8bx7zb5ddwhv1migjlqry506hp"))))
    (properties `((upstream-name . "SADISA")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma r-ddd))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=SADISA")
    (synopsis
     "Species Abundance Distributions with Independent-Species Assumption")
    (description
     "Computes the probability of a set of species abundances of a single or multiple
samples of individuals with one or more guilds under a mainland-island model.
One must specify the mainland (metacommunity) model and the island (local)
community model.  It assumes that species fluctuate independently.  The package
also contains functions to simulate under this model.  See Haegeman, B. & R.S.
Etienne (2017).  A general sampling formula for community structure data.
Methods in Ecology & Evolution 8: 1506-1519 <doi:10.1111/2041-210X.12807>.")
    (license license:gpl3)))

(define-public r-sadeg
  (package
    (name "r-sadeg")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SADEG" version))
       (sha256
        (base32 "02ilykbdanx1isbd80c43hqpzkckq6dg40y0rklcnck6v96qky3n"))))
    (properties `((upstream-name . "SADEG")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=SADEG")
    (synopsis "Stability Analysis in Differentially Expressed Genes")
    (description
     "We analyzed the nucleotide composition of genes with a special emphasis on
stability of DNA sequences.  Besides, in a variety of different organisms
unequal use of synonymous codons, or codon usage bias, occurs which also show
variation among genes in the same genome.  Seemingly, codon usage bias is
affected by both selective constraints and mutation bias which allows and
enables us to examine and detect changes in these two evolutionary forces
between genomes or along one genome.  Therefore, we determined the codon
adaptation index (CAI), effective number of codons (ENC) and codon usage
analysis with calculation of the relative synonymous codon usage (RSCU), and
subsequently predicted the translation efficiency and accuracy through GC-rich
codon usages.  Furthermore, we estimated the relative stability of the DNA
sequence following calculation of the average free energy (Delta G) and Dimer
base-stacking energy level.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-sad
  (package
    (name "r-sad")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sad" version))
       (sha256
        (base32 "0wr4xdx68hrxaw5izy36gm10smk6s1gzrzwayhfp4lhvkn75qsww"))))
    (properties `((upstream-name . "sad")))
    (build-system r-build-system)
    (propagated-inputs (list r-emdist r-dualtrees))
    (home-page "https://cran.r-project.org/package=sad")
    (synopsis
     "Verify the Scale, Anisotropy and Direction of Weather Forecasts")
    (description
     "Implementation of the wavelet-based spatial verification method of Buschow and
Friederichs \"SAD: Verifying the Scale, Anisotropy and Direction of precipitation
forecasts\" (2020, submitted to QJRMS).  Forecasts and Observations are
transformed by a decimated or redundant dual-tree complex wavelet transform to
analyze the spatial scale, degree of anisotropy and preferred direction in each
field.  These structural attributes are compared by a series of scores.  An
experimental algorithm for the correction of these errors is included as well.")
    (license license:expat)))

(define-public r-saccr
  (package
    (name "r-saccr")
    (version "3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "SACCR" version))
       (sha256
        (base32 "1sp8hjpwzyczj85saps6a6yw30yh5098ig2xmlf79jvzv86nqgn1"))))
    (properties `((upstream-name . "SACCR")))
    (build-system r-build-system)
    (propagated-inputs (list r-trading r-jsonlite r-data-tree))
    (home-page "https://openriskcalculator.com/")
    (synopsis "SA Counterparty Credit Risk under CRR2")
    (description
     "Computes the Exposure-At-Default based on the standardized approach of CRR2
(SA-CCR).  The simplified version of SA-CCR has been included, as well as the
OEM methodology.  Multiple trade types of all the five major asset classes are
being supported including the Other Exposure and, given the inheritance- based
structure of the application, the addition of further trade types is
straightforward.  The application returns a list of trees per Counterparty and
CSA after automatically separating the trades based on the Counterparty, the
CSAs, the hedging sets, the netting sets and the risk factors.  The basis and
volatility transactions are also identified and treated in specific hedging sets
whereby the corresponding penalty factors are applied.  All the examples
appearing on the regulatory papers (both for the margined and the un-margined
workflow) have been implemented including the latest CRR2 developments.")
    (license license:gpl3)))

(define-public r-saccadr
  (package
    (name "r-saccadr")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saccadr" version))
       (sha256
        (base32 "0c51zbkh8b0xi0sshzq5agbijw6nprrb3r1k77wcfqsr2mazfwi5"))))
    (properties `((upstream-name . "saccadr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-signal
                             r-rlang
                             r-rcpp
                             r-magrittr
                             r-dplyr
                             r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/alexander-pastukhov/saccadr/")
    (synopsis "Extract Saccades via an Ensemble of Methods Approach")
    (description
     "This package provides a modular and extendable approach to extract
(micro)saccades from gaze samples via an ensemble of methods.  Although there is
an agreement about a general definition of a saccade, the more specific details
are harder to agree upon.  Therefore, there are numerous algorithms that extract
saccades based on various heuristics, which differ in the assumptions about
velocity, acceleration, etc.  The package uses three methods (Engbert and Kliegl
(2003) <doi:10.1016/S0042-6989(03)00084-1>, Otero-Millan et al.
(2014)<doi:10.1167/14.2.18>, and NystrÃ¶m and Holmqvist (2010)
<doi:10.3758/BRM.42.1.188>) to label individual samples and then applies a
majority vote approach to identify saccades.  The package includes three methods
but can be extended via custom functions.  It also uses a modular approach to
compute velocity and acceleration from noisy samples.  Finally, you can obtain
methods votes per gaze sample instead of saccades.")
    (license license:gpl3+)))

(define-public r-sac
  (package
    (name "r-sac")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sac" version))
       (sha256
        (base32 "1yg133zm0db2lv0m62ij7c27bs4k3kbjic15dwrak534lk885sj2"))))
    (properties `((upstream-name . "sac")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=sac")
    (synopsis "Semiparametric Analysis of Change-Point")
    (description
     "Semiparametric empirical likelihood ratio based tests of change-point with
one-change or epidemic alternatives with data-based model diagnostic are
contained.")
    (license license:gpl2+)))

(define-public r-sabre
  (package
    (name "r-sabre")
    (version "0.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "sabre" version))
       (sha256
        (base32 "0vcfcsfc53022rglqxgf5p9b8d8lphyy161jmvlvb77qp959nbrh"))))
    (properties `((upstream-name . "sabre")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-sf
                             r-rlang
                             r-raster
                             r-entropy
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://jakubnowosad.com/sabre/")
    (synopsis "Spatial Association Between Regionalizations")
    (description
     "Calculates a degree of spatial association between regionalizations or
categorical maps using the information-theoretical V-measure (Nowosad and
Stepinski (2018) <doi:10.1080/13658816.2018.1511794>).  It also offers an R
implementation of the @code{MapCurve} method (Hargrove et al. (2006)
<doi:10.1007/s10109-006-0025-x>).")
    (license license:expat)))

(define-public r-saascnv
  (package
    (name "r-saascnv")
    (version "0.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "saasCNV" version))
       (sha256
        (base32 "12h5vrl55sb4wmx0hzib5iax8p0q2qzk19h15r0asfkh9l7qz94l"))))
    (properties `((upstream-name . "saasCNV")))
    (build-system r-build-system)
    (propagated-inputs (list r-rann r-dnacopy))
    (home-page "https://zhangz05.u.hpc.mssm.edu/saasCNV/")
    (synopsis
     "Somatic Copy Number Alteration Analysis Using Sequencing and SNP Array Data")
    (description
     "Perform joint segmentation on two signal dimensions derived from total read
depth (intensity) and allele specific read depth (intensity) for whole genome
sequencing (WGS), whole exome sequencing (WES) and SNP array data.")
    (license license:gpl2+)))

(define-public r-s7
  (package
    (name "r-s7")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "S7" version))
       (sha256
        (base32 "0r3v586xbcrww63jifdbfh2fgdkr5f2mgp72f2zljg4x70zn3sfw"))))
    (properties `((upstream-name . "S7")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rconsortium/S7/")
    (synopsis
     "An Object Oriented System Meant to Become a Successor to S3 and S4")
    (description
     "This package provides a new object oriented programming system designed to be a
successor to S3 and S4.  It includes formal class, generic, and method
specification, and a limited form of multiple dispatch.  It has been designed
and implemented collaboratively by the R Consortium Object-Oriented Programming
Working Group, which includes representatives from R-Core, Bioconductor',
Posit'/'tidyverse', and the wider R community.")
    (license license:expat)))

(define-public r-s4vd
  (package
    (name "r-s4vd")
    (version "1.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "s4vd" version))
       (sha256
        (base32 "1rp3z42nxmrvb942h3c5cl544lngzx7nrnnr4zjw7dq495bym7yp"))))
    (properties `((upstream-name . "s4vd")))
    (build-system r-build-system)
    (propagated-inputs (list r-irlba r-foreach r-biclust))
    (home-page "https://cran.r-project.org/package=s4vd")
    (synopsis
     "Biclustering via Sparse Singular Value Decomposition Incorporating Stability Selection")
    (description
     "The main function s4vd() performs a biclustering via sparse singular value
decomposition with a nested stability selection.  The results is an biclust
object and thus all methods of the biclust package can be applied.")
    (license license:gpl2)))

(define-public r-s3fs
  (package
    (name "r-s3fs")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "s3fs" version))
       (sha256
        (base32 "16692xrdgvijhdaqaysr61bl8hfjx0li7xnbk9rbnvj4g196zq2n"))))
    (properties `((upstream-name . "s3fs")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6
                             r-paws-storage
                             r-lgr
                             r-future-apply
                             r-future
                             r-fs
                             r-data-table
                             r-curl))
    (home-page "https://github.com/DyfanJones/s3fs")
    (synopsis "'Amazon Web Service S3' File System")
    (description
     "Access Amazon Web Service Simple Storage Service ('S3')
<https://aws.amazon.com/s3/> as if it were a file system.  Interface based on
the R package fs'.")
    (license license:expat)))

(define-public r-s3-resourcer
  (package
    (name "r-s3-resourcer")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "s3.resourcer" version))
       (sha256
        (base32 "0njl93c1pxsg04fr74pnrpvx3z5m8nk2xv700l0jhbygshw8k5z2"))))
    (properties `((upstream-name . "s3.resourcer")))
    (build-system r-build-system)
    (propagated-inputs (list r-sparklyr r-resourcer r-r6 r-httr r-aws-s3))
    (home-page "https://cran.r-project.org/package=s3.resourcer")
    (synopsis "S3 Resource Resolver")
    (description
     "This package provides a S3 resource is provided by Amazon Web Services S3 or a
S3-compatible object store (such as Minio).  The resource can be a tidy file to
be downloaded from the object store, or a data lake (such as Delta Lake) Parquet
file to be read by Apache Spark.")
    (license license:lgpl2.1+)))

(define-public r-s3
  (package
    (name "r-s3")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "s3" version))
       (sha256
        (base32 "10nrm45zji8k1paz7zkmq4ankwz8nisv8ngc459j1bd4578mzv9y"))))
    (properties `((upstream-name . "s3")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-purrr
                             r-prettyunits
                             r-httr
                             r-glue
                             r-fs
                             r-dplyr
                             r-cli))
    (home-page "https://github.com/brokamp-group/s3")
    (synopsis "Download Files from 'AWS S3'")
    (description
     "Download files hosted on AWS S3 (Amazon Web Services Simple Storage Service;
<https://aws.amazon.com/s3/>) to a local directory based on their URI. Avoid
downloading files that are already present locally.  Allow for customization of
where to store downloaded files.")
    (license license:expat)))

(define-public r-s2net
  (package
    (name "r-s2net")
    (version "1.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "s2net" version))
       (sha256
        (base32 "18n3zq4hd07ipba7n6qc0fhxbraam70kypzf17hiaqrsjcjpw58x"))))
    (properties `((upstream-name . "s2net")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jlaria/s2net")
    (synopsis "The Generalized Semi-Supervised Elastic-Net")
    (description
     "This package implements the generalized semi-supervised elastic-net.  This
method extends the supervised elastic-net problem, and thus it is a practical
solution to the problem of feature selection in semi-supervised contexts.  Its
mathematical formulation is presented from a general perspective, covering a
wide range of models.  We focus on linear and logistic responses, but the
implementation could be easily extended to other losses in generalized linear
models.  We develop a flexible and fast implementation, written in C++ using
@code{RcppArmadillo} and integrated into R via Rcpp modules.  See Culp, M. 2013
<doi:10.1080/10618600.2012.657139> for references on the Joint Trained
Elastic-Net.")
    (license license:gpl2+)))

(define-public r-s2dv
  (package
    (name "r-s2dv")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "s2dv" version))
       (sha256
        (base32 "0bs46hgvryaydf0hmdn02cpfc3b3schx26gpw11b78j6by95z0yw"))))
    (properties `((upstream-name . "s2dv")))
    (build-system r-build-system)
    (inputs (list cdo))
    (propagated-inputs (list r-specsverification
                             r-plyr
                             r-ncdf4
                             r-nbclust
                             r-multiapply
                             r-maps
                             r-mapproj
                             r-easyverification
                             r-easyncdf
                             r-climprojdiags
                             r-bigmemory
                             r-abind))
    (home-page "https://earth.bsc.es/gitlab/es/s2dv/")
    (synopsis "Set of Common Tools for Seasonal to Decadal Verification")
    (description
     "The advanced version of package s2dverification'.  It is intended for seasonal
to decadal (s2d) climate forecast verification, but it can also be used in other
kinds of forecasts or general climate analysis.  This package is specially
designed for the comparison between the experimental and observational datasets.
 The functionality of the included functions covers from data retrieval, data
post-processing, skill scores against observation, to visualization.  Compared
to s2dverification', s2dv is more compatible with the package @code{startR}',
able to use multiple cores for computation and handle multi-dimensional arrays
with a higher flexibility.  The CDO version used in development is 1.9.8.")
    (license license:gpl3)))

(define-public r-s20x
  (package
    (name "r-s20x")
    (version "3.1-40")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "s20x" version))
       (sha256
        (base32 "1b2bbkbkmr4d4bfihd87c3vwdfxb21hzaxhd6zqnwz1n0xg2zhb5"))))
    (properties `((upstream-name . "s20x")))
    (build-system r-build-system)
    (home-page "https://github.com/STATS-UOA/s20x")
    (synopsis
     "Functions for University of Auckland Course STATS 201/208 Data Analysis")
    (description
     "This package provides a set of functions used in teaching STATS 201/208 Data
Analysis at the University of Auckland.  The functions are designed to make
parts of R more accessible to a large undergraduate population who are mostly
not statistics majors.")
    (license (list license:gpl2
                   (license:fsdg-compatible "file://LICENSE")))))

