(define-module (guix-cran packages p)
  #: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 python-science)
  #:use-module (gnu packages python)
  #:use-module (gnu packages gcc)
  #:use-module (gnu packages bioconductor)
  #:use-module (gnu packages algebra)
  #:use-module (gnu packages web)
  #:use-module (gnu packages curl)
  #:use-module (gnu packages pkg-config)
  #:use-module (gnu packages ssh)
  #:use-module (gnu packages tls)
  #:use-module (gnu packages geo)
  #:use-module (gnu packages compression)
  #:use-module (gnu packages graphviz)
  #:use-module (gnu packages machine-learning)
  #:use-module (gnu packages version-control)
  #:use-module (gnu packages gettext)
  #:use-module (gnu packages sqlite)
  #:use-module (gnu packages bioinformatics)
  #:use-module (gnu packages haskell-xyz)
  #:use-module (gnu packages maths)
  #:use-module (gnu packages pdf)
  #:use-module (gnu packages gtk)
  #:use-module (gnu packages java)
  #: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 s)
  #:use-module (guix-cran packages r)
  #:use-module (guix-cran packages q)
  #: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-pzfx
  (package
    (name "r-pzfx")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pzfx" version))
       (sha256
        (base32 "0f8bvwnk44wq8j58rw80b9bclknrh4a3iqdl3p4y26q100cpisgr"))))
    (properties `((upstream-name . "pzfx")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Yue-Jiang/pzfx")
    (synopsis "Read and Write 'GraphPad Prism' Files")
    (description "Read and write @code{GraphPad} Prism .pzfx files in R.")
    (license license:expat)))

(define-public r-pytrendslongitudinalr
  (package
    (name "r-pytrendslongitudinalr")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PytrendsLongitudinalR" version))
       (sha256
        (base32 "00y9v9kv8a4cyfkxzzr8cbq6l2b8cfp2svnkkmq3rbrn3wrwl6d6"))))
    (properties `((upstream-name . "PytrendsLongitudinalR")))
    (build-system r-build-system)
    (inputs (list python python-pandas))
    (propagated-inputs (list r-reticulate r-lubridate r-jsonlite))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PytrendsLongitudinalR")
    (synopsis "Create Longitudinal Google Trends Data")
    (description
     "Google Trends provides cross-sectional and time-series data on searches, but
lacks readily available longitudinal data.  Researchers, who want to create
longitudinal Google Trends on their own, face practical challenges, such as
normalized counts that make it difficult to combine cross-sectional and
time-series data and limitations in data formats and timelines that limit data
granularity over extended time periods.  This package addresses these issues and
enables researchers to generate longitudinal Google Trends data.  This package
is built on pytrends', a Python library that acts as the unofficial Google
Trends API to collect Google Trends data.  As long as the Google Trends API',
pytrends and all their dependencies are working, this package will work.  During
testing, we noticed that for the same input (keyword, topic, data_format,
timeline), the output index can vary from time to time.  Besides, if the keyword
is not very popular, then the resulting dataset will contain a lot of zeros,
which will greatly affect the final result.  While this package has no control
over the accuracy or quality of Google Trends data, once the data is created,
this package coverts it to longitudinal data.  In addition, the user may
encounter a 429 Too Many Requests error when using cross_section() and
time_series() to collect Google Trends data.  This error indicates that the user
has exceeded the rate limits set by the Google Trends API'.  For more
information about the Google Trends API - pytrends', visit
<https://pypi.org/project/pytrends/>.")
    (license license:expat)))

(define-public r-pysparklyr
  (package
    (name "r-pysparklyr")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pysparklyr" version))
       (sha256
        (base32 "1029way7vq02bi6hj78vxfqgq3ccbzyv7b758lv51rjm6fm9ckpr"))))
    (properties `((upstream-name . "pysparklyr")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidyselect
                             r-tidyr
                             r-sparklyr
                             r-rstudioapi
                             r-rsconnect
                             r-rlang
                             r-reticulate
                             r-purrr
                             r-processx
                             r-magrittr
                             r-httr2
                             r-glue
                             r-fs
                             r-dplyr
                             r-dbplyr
                             r-dbi
                             r-cli
                             r-arrow))
    (home-page "https://github.com/mlverse/pysparklyr")
    (synopsis "Provides a 'PySpark' Back-End for the 'sparklyr' Package")
    (description
     "It enables sparklyr to integrate with Spark Connect', and Databricks Connect by
providing a wrapper over the @code{PySpark} python library.")
    (license license:expat)))

(define-public r-pysd2r
  (package
    (name "r-pysd2r")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pysd2r" version))
       (sha256
        (base32 "1dqvgdxj6m683wkjyjhv685xnhl9328bm921zphm1i9p70fahs0n"))))
    (properties `((upstream-name . "pysd2r")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-reticulate r-knitr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pysd2r")
    (synopsis "API to 'Python' Library 'pysd'")
    (description
     "Using the R package reticulate', this package creates an interface to the pysd
toolset.  The package provides an R interface to a number of pysd functions, and
can read files in Vensim mdl format, and xmile format.  The resulting
simulations are returned as a tibble', and from that the results can be
processed using dplyr and ggplot2'.  The package has been tested using python3'.")
    (license license:expat)))

(define-public r-pyramid
  (package
    (name "r-pyramid")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pyramid" version))
       (sha256
        (base32 "0lakn9knmhqvhrp98sc3hhwd4bilpplxbr5j9bv8w6li0qd3d04a"))))
    (properties `((upstream-name . "pyramid")))
    (build-system r-build-system)
    (home-page "http://minato.sip21c.org/swtips/Rgraphics.html#PYRAMID")
    (synopsis "Draw Population Pyramid")
    (description
     "Drawing population pyramid using (1) data.frame or (2) vectors.  The former is
named as pyramid() and the latter pyramids(), as wrapper function of pyramid().
pyramidf() is the function to draw population pyramid within the specified
frame.")
    (license license:gpl2+)))

(define-public r-pymturkr
  (package
    (name "r-pymturkr")
    (version "1.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pyMTurkR" version))
       (sha256
        (base32 "16a16rjm71xlv57lkw0w49cv563pg1b8ihligmacrnv9vmzv0843"))))
    (properties `((upstream-name . "pyMTurkR")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml r-reticulate r-curl))
    (home-page "https://cran.r-project.org/package=pyMTurkR")
    (synopsis "Client for the 'MTurk' Requester API")
    (description
     "This package provides access to the latest Amazon Mechanical Turk ('MTurk')
<https://www.mturk.com> Requester API (version 2017â01â17'), replacing the
now deprecated M@code{TurkR} package.")
    (license license:gpl2)))

(define-public r-pylintr
  (package
    (name "r-pylintr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pylintR" version))
       (sha256
        (base32 "00k9p3igdxhyx353gq8j9mnhrs9fh0j4q2iqa2ljfjdsdw1r2asr"))))
    (properties `((upstream-name . "pylintR")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-rstudioapi r-htmlwidgets r-fansi))
    (home-page "https://github.com/stla/pylintR")
    (synopsis "Lint 'Python' Files with a R Command or a 'RStudio' Addin")
    (description
     "Allow to run pylint on Python files with a R command or a RStudio addin.  The
report appears in the RStudio viewer pane as a formatted HTML file.")
    (license license:gpl3)))

(define-public r-pyinit
  (package
    (name "r-pyinit")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pyinit" version))
       (sha256
        (base32 "0cbwip549d17ysbk4lrvn6a25vfjjnimjsd5myvnbvg8c9nq5ryn"))))
    (properties `((upstream-name . "pyinit")))
    (build-system r-build-system)
    (propagated-inputs (list r-robustbase))
    (home-page "https://github.com/dakep/pyinit")
    (synopsis "Pena-Yohai Initial Estimator for Robust S-Regression")
    (description
     "Deterministic Pena-Yohai initial estimator for robust S estimators of
regression.  The procedure is described in detail in Pena, D., & Yohai, V.
(1999) <doi:10.2307/2670164>.")
    (license license:gpl2+)))

(define-public r-pycno
  (package
    (name "r-pycno")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pycno" version))
       (sha256
        (base32 "1df7fbizm8r705dddb9xhypf39i6avsn5gpq0r7jx07nv1gmbmbw"))))
    (properties `((upstream-name . "pycno")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp r-sf))
    (home-page "https://cran.r-project.org/package=pycno")
    (synopsis "Pycnophylactic Interpolation")
    (description
     "Given a @code{SpatialPolygonsDataFrame} and a set of populations for each
polygon, compute a population density estimate based on Tobler's pycnophylactic
interpolation algorithm.  The result is a @code{SpatialGridDataFrame}.  Methods
are described in Tobler Waldo R. (1979) <doi:10.1080/01621459.1979.10481647>.")
    (license license:gpl2+)))

(define-public r-pxwebapidata
  (package
    (name "r-pxwebapidata")
    (version "0.9.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PxWebApiData" version))
       (sha256
        (base32 "0xch2l9c3dana419p9zfs6vwcqgacxamzjx1c6v8bpnb49gc3dp8"))))
    (properties `((upstream-name . "PxWebApiData")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjstat r-pxweb r-jsonlite r-httr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/statisticsnorway/PxWebApiData")
    (synopsis "PX-Web Data by API")
    (description
     "Function to read PX-Web data into R via API. The example code reads data from
the three national statistical institutes, Statistics Norway, Statistics Sweden
and Statistics Finland.")
    (license (list license:asl2.0
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-pxweb
  (package
    (name "r-pxweb")
    (version "0.17.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pxweb" version))
       (sha256
        (base32 "10c3cdlgjkp0bfr6xdn5h1221krv4c4i1j4mgbh38ykrzrm4d6am"))))
    (properties `((upstream-name . "pxweb")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-httr r-curl r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rOpenGov/pxweb/")
    (synopsis "R Interface to PXWEB APIs")
    (description
     "Generic interface for the PX-Web/PC-Axis API. The PX-Web/PC-Axis API is used by
organizations such as Statistics Sweden and Statistics Finland to disseminate
data.  The R package can interact with all PX-Web/PC-Axis APIs to fetch
information about the data hierarchy, extract metadata and extract and parse
statistics to R data.frame format.  PX-Web is a solution to disseminate PC-Axis
data files in dynamic tables on the web.  Since 2013 PX-Web contains an API to
disseminate PC-Axis files.")
    (license license:bsd-2)))

(define-public r-pxr
  (package
    (name "r-pxr")
    (version "0.42.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pxR" version))
       (sha256
        (base32 "05i1cg35hq0z37xnh2qfcp8mnrphadrg7av5d0mcgi9nx6c1w80j"))))
    (properties `((upstream-name . "pxR")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rjsonio r-reshape2 r-plyr))
    (home-page "https://github.com/cjgb/pxR")
    (synopsis "PC-Axis with R")
    (description
     "This package provides a set of functions for reading and writing PC-Axis files,
used by different statistical organizations around the globe for data
dissemination.")
    (license license:gpl3)))

(define-public r-pwt9
  (package
    (name "r-pwt9")
    (version "9.1-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pwt9" version))
       (sha256
        (base32 "0m2df89lali9945avcv4gdjpxch9in7vdd042nx0q8nv0alb4ijd"))))
    (properties `((upstream-name . "pwt9")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pwt9")
    (synopsis "Penn World Table (Version 9.x)")
    (description
     "The Penn World Table 9.x (<http://www.ggdc.net/pwt/>) provides information on
relative levels of income, output, inputs, and productivity for 182 countries
between 1950 and 2017.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-pwt8
  (package
    (name "r-pwt8")
    (version "8.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pwt8" version))
       (sha256
        (base32 "1iig0x90ilzh3hdki0h33qgrra8r94rw4bk1x8y7i6c1may8y0v6"))))
    (properties `((upstream-name . "pwt8")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pwt8")
    (synopsis "Penn World Table (Version 8.x)")
    (description
     "The Penn World Table 8.x provides information on relative levels of income,
output, inputs, and productivity for 167 countries between 1950 and 2011.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-pwt10
  (package
    (name "r-pwt10")
    (version "10.01-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pwt10" version))
       (sha256
        (base32 "156w50zhkbbdlxmwsdkiw89240xbhq6wmagashz94z798s5ssisv"))))
    (properties `((upstream-name . "pwt10")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pwt10")
    (synopsis "Penn World Table (Version 10.x)")
    (description
     "The Penn World Table 10.x (<https://www.rug.nl/ggdc/productivity/pwt/>) provides
information on relative levels of income, output, input, and productivity for
183 countries between 1950 and 2019.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-pwt
  (package
    (name "r-pwt")
    (version "7.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pwt" version))
       (sha256
        (base32 "0926viwmwldmzlzbnjfijh00wrhgb0h4h0mlrls71pi5pjfldifa"))))
    (properties `((upstream-name . "pwt")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pwt")
    (synopsis "Penn World Table (Versions 5.6, 6.x, 7.x)")
    (description
     "The Penn World Table provides purchasing power parity and national income
accounts converted to international prices for 189 countries for some or all of
the years 1950-2010.")
    (license license:gpl2)))

(define-public r-pwrss
  (package
    (name "r-pwrss")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pwrss" version))
       (sha256
        (base32 "1qi2k0bk47yaqyzc6zv714ggs7s17zifazpsidfbfhikb5cs4gba"))))
    (properties `((upstream-name . "pwrss")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pwrss")
    (synopsis "Statistical Power and Sample Size Calculation Tools")
    (description
     "Statistical power and minimum required sample size calculations for (1) testing
a proportion (one-sample) against a constant, (2) testing a mean (one-sample)
against a constant, (3) testing difference between two proportions (independent
samples), (4) testing difference between two means or groups (parametric and
non-parametric tests for independent and paired samples), (5) testing a
correlation (one-sample) against a constant, (6) testing difference between two
correlations (independent samples), (7) testing a single coefficient in multiple
linear regression, logistic regression, and Poisson regression (with
standardized or unstandardized coefficients, with no covariates or covariate
adjusted), (8) testing an indirect effect (with standardized or unstandardized
coefficients, with no covariates or covariate adjusted) in the mediation
analysis (Sobel, Joint, and Monte Carlo tests), (9) testing an R-squared against
zero in linear regression, (10) testing an R-squared difference against zero in
hierarchical regression, (11) testing an eta-squared or f-squared (for main and
interaction effects) against zero in analysis of variance (could be one-way,
two-way, and three-way), (12) testing an eta-squared or f-squared (for main and
interaction effects) against zero in analysis of covariance (could be one-way,
two-way, and three-way), (13) testing an eta-squared or f-squared (for between,
within, and interaction effects) against zero in one-way repeated measures
analysis of variance (with non-sphericity correction and repeated measures
correlation), and (14) testing goodness-of-fit or independence for contingency
tables.  Alternative hypothesis can be formulated as \"not equal\", \"less\",
\"greater\", \"non-inferior\", \"superior\", or \"equivalent\" in (1), (2), (3), and
(4); as \"not equal\", \"less\", or \"greater\" in (5), (6), (7) and (8); but always
as \"greater\" in (9), (10), (11), (12), (13), and (14).  Reference: Bulus and
Polat (2023) <https://osf.io/ua5fc>.")
    (license license:gpl3+)))

(define-public r-pwrrasch
  (package
    (name "r-pwrrasch")
    (version "0.1-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pwrRasch" version))
       (sha256
        (base32 "13fr4yfk8aky1vv36pllx673l4lg9q7i661vbyn2zabyizd2rw3b"))))
    (properties `((upstream-name . "pwrRasch")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pwrRasch")
    (synopsis "Statistical Power Simulation for Testing the Rasch Model")
    (description
     "Statistical power simulation for testing the Rasch Model based on a three-way
analysis of variance design with mixed classification.")
    (license license:gpl3)))

(define-public r-pwrgsd
  (package
    (name "r-pwrgsd")
    (version "2.3.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PwrGSD" version))
       (sha256
        (base32 "0rz0an16g3dwzbjcr0iln05cdpzx0rl0fiaj30y79n5f38di5ac2"))))
    (properties `((upstream-name . "PwrGSD")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "https://cran.r-project.org/package=PwrGSD")
    (synopsis "Power in a Group Sequential Design")
    (description
     "This package provides tools for the evaluation of interim analysis plans for
sequentially monitored trials on a survival endpoint; tools to construct
efficacy and futility boundaries, for deriving power of a sequential design at a
specified alternative, template for evaluating the performance of candidate
plans at a set of time varying alternatives.  See Izmirlian, G. (2014)
<doi:10.4310/SII.2014.v7.n1.a4>.")
    (license license:gpl2+)))

(define-public r-pwrfdr
  (package
    (name "r-pwrfdr")
    (version "2.8.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pwrFDR" version))
       (sha256
        (base32 "16qlxwhljhf878a7jif2kjj7hvga01bgr8zb6bh555yacxxh4yiz"))))
    (properties `((upstream-name . "pwrFDR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pwrFDR")
    (synopsis "FDR Power")
    (description
     "This is a package for calculating two differing notions of power, or deriving
sample sizes for specified requisite power in multiple testing experiments under
a variety of methods for control of the distribution of the False Discovery
Proportion (FDP).  More specifically, one can choose to control the FDP
distribution according to control of its (i) mean, e.g. the usual BH-FDR
procedure, or via the probability that it exceeds a given value, delta, via (ii)
the Romano procedure, or via (iii) my procedure based upon asymptotic
approximation.  Likewise, we can think of the power in multiple testing
experiments in terms of a summary of the distribution of the True Positive
Proportion (TPP).  The package will compute power, sample size or any other
missing parameter required for power based upon (i) the mean of the TPP which is
the average power (ii) the probability that the TPP exceeds a given value,
lambda, via my asymptotic approximation procedure.  The theoretical results are
described in Izmirlian, G. (2020) Stat.  and Prob.  letters,
<doi:10.1016/j.spl.2020.108713>, and an applied paper describing the methodology
with a simulation study is in preparation.")
    (license license:gpl2+)))

(define-public r-pwrab
  (package
    (name "r-pwrab")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pwrAB" version))
       (sha256
        (base32 "0ifyw40m9gsijw3vm9qb8qk4yydmdixfdpdp45x2hk93j8v61jm1"))))
    (properties `((upstream-name . "pwrAB")))
    (build-system r-build-system)
    (home-page "http://github.com/williamcha/pwrAB")
    (synopsis "Power Analysis for AB Testing")
    (description
     "Power analysis for AB testing.  The calculations are based on the Welch's
unequal variances t-test, which is generally preferred over the Student's t-test
when sample sizes and variances of the two groups are unequal, which is
frequently the case in AB testing.  In such situations, the Student's t-test
will give biased results due to using the pooled standard deviation, unlike the
Welch's t-test.")
    (license license:gpl3+)))

(define-public r-pwr4exp
  (package
    (name "r-pwr4exp")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pwr4exp" version))
       (sha256
        (base32 "081ci87idiqxfr0xa0mws0lzqg2rd4hq0yq6y7izr1al6nfiwffj"))))
    (properties `((upstream-name . "pwr4exp")))
    (build-system r-build-system)
    (propagated-inputs (list r-lmertest r-lme4 r-emmeans r-car))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/an-ethz/pwr4exp")
    (synopsis "Power Analysis for Research Experiments")
    (description
     "This package provides tools for calculating statistical power and determining
sample size for a variety of experimental designs used in agricultural and
biological research, including completely randomized, block, and split-plot
designs.  Supports customized designs and allows specification of main effects,
interactions, and contrasts for accurate power analysis.")
    (license license:expat)))

(define-public r-pwr2ppl
  (package
    (name "r-pwr2ppl")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pwr2ppl" version))
       (sha256
        (base32 "0vq7xw6nlaf3vi3415640752988vjj0cdrpjmpl7fjh0z2m5w758"))))
    (properties `((upstream-name . "pwr2ppl")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-semtools
                             r-quantreg
                             r-pls
                             r-phia
                             r-nlme
                             r-mbess
                             r-mass
                             r-lmtest
                             r-lmperm
                             r-lavaan
                             r-ez
                             r-dplyr
                             r-car
                             r-broom
                             r-afex))
    (home-page "https://cran.r-project.org/package=pwr2ppl")
    (synopsis "Power Analyses for Common Designs (Power to the People)")
    (description
     "Statistical power analysis for designs including t-tests, correlations, multiple
regression, ANOVA, mediation, and logistic regression.  Functions accompany
Aberson (2019) <doi:10.4324/9781315171500>.")
    (license license:gpl2+)))

(define-public r-pwr2
  (package
    (name "r-pwr2")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pwr2" version))
       (sha256
        (base32 "0zfv7z5yymw310r1sqm8ivsc2pv6dgk2k4b1axmm92gsaj53cn7p"))))
    (properties `((upstream-name . "pwr2")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pwr2")
    (synopsis
     "Power and Sample Size Analysis for One-way and Two-way ANOVA Models")
    (description
     "User friendly functions for power and sample size analysis at one-way and
two-way ANOVA settings take either effect size or delta and sigma as arguments.
They are designed for both one-way and two-way ANOVA settings.  In addition, a
function for plotting power curves is available for power comparison, which can
be easily visualized by statisticians and clinical researchers.")
    (license license:gpl2)))

(define-public r-pwlmm
  (package
    (name "r-pwlmm")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pwlmm" version))
       (sha256
        (base32 "0mqf26mx6bamnasknxj3fwsn60nkwh4h74wbr2950qwz1s320hmz"))))
    (properties `((upstream-name . "pwlmm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp r-matrix r-lme4))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pwlmm")
    (synopsis "PWIGLS for Two-Level Multivariate and Multilevel Linear Models")
    (description
     "Estimates two-level multilevel linear model and two-level multivariate linear
multilevel model with weights following Probability Weighted Iterative
Generalised Least Squares approach.  For details see Veiga et al.(2014)
<doi:10.1111/rssc.12020>.")
    (license license:gpl2+)))

(define-public r-pwir
  (package
    (name "r-pwir")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PWIR" version))
       (sha256
        (base32 "05f4mj8id8ikfz63c8c1lkbxl7s1b2q5xri8iicn1xv2m3w89p2v"))))
    (properties `((upstream-name . "PWIR")))
    (build-system r-build-system)
    (propagated-inputs (list r-progressr r-igraph r-bibliometrix))
    (home-page "https://cran.r-project.org/package=PWIR")
    (synopsis
     "Provides a Function to Calculate Prize Winner Indices Based on Bibliometric Data")
    (description
     "This package provides a function PWI() that calculates prize winner indices
based on bibliometric data is provided.  The default is the Derek de Solla Price
Memorial Medal'.  Users can provide recipients of other prizes.")
    (license (license:fsdg-compatible "EUPL"))))

(define-public r-pwexp
  (package
    (name "r-pwexp")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PWEXP" version))
       (sha256
        (base32 "1xvpabfi04alhmm640bdlgx1al19nrs43qmichpcv3sllw9bc74x"))))
    (properties `((upstream-name . "PWEXP")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-segmented r-foreach r-fastmatch
                             r-dosnow))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/zjph602xtc/PWEXP")
    (synopsis "Piecewise Exponential Distribution Prediction Model")
    (description
     "Build piecewise exponential survival model for study design (planning) and
event/timeline prediction.")
    (license license:expat)))

(define-public r-pwev
  (package
    (name "r-pwev")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PWEV" version))
       (sha256
        (base32 "1jl63wp6nl2zyznc63zcb97yi1fvzj4mic4ri0ni515s0nb5jd5b"))))
    (properties `((upstream-name . "PWEV")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xts
                             r-weightedensemble
                             r-rumidas
                             r-rugarch
                             r-metrics))
    (home-page "https://cran.r-project.org/package=PWEV")
    (synopsis "PSO Based Weighted Ensemble Algorithm for Volatility Modelling")
    (description
     "Price volatility refers to the degree of variation in series over a certain
period of time.  This volatility is especially noticeable in agricultural
commodities, adding uncertainty for farmers, traders, and others in the
agricultural supply chain.  Commonly and popularly used four volatility models
viz, GARCH, Glosten Jagannatan Runkle-GARCH (GJR-GARCH) model, exponentially
weighted moving average (EWMA) model and Multiplicative Error Model (MEM) are
selected and implemented.  PWAVE, weighted ensemble model based on particle
swarm optimization (PSO) is proposed to combine the forecast obtained from all
the candidate models.  This package has been developed using algorithm of Paul
et al. <doi:10.1007/s40009-023-01218-x> and Yeasin and Paul (2024)
<doi:10.1007/s11227-023-05542-3>.")
    (license license:gpl3)))

(define-public r-pweall
  (package
    (name "r-pweall")
    (version "1.3.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PWEALL" version))
       (sha256
        (base32 "1p6wq7khpzs6cw2chswp17bq383ppm6q2ia9p6y1gggrazsymdvg"))))
    (properties `((upstream-name . "PWEALL")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=PWEALL")
    (synopsis
     "Design and Monitoring of Survival Trials Accounting for Complex Situations")
    (description
     "Calculates various functions needed for design and monitoring survival trials
accounting for complex situations such as delayed treatment effect, treatment
crossover, non-uniform accrual, and different censoring distributions between
groups.  The event time distribution is assumed to be piecewise exponential
(PWE) distribution and the entry time is assumed to be piecewise uniform
distribution.  As compared with Version 1.2.1, two more types of hybrid
crossover are added.  A bug is corrected in the function \"pwecx\" that calculates
the crossover-adjusted survival, distribution, density, hazard and cumulative
hazard functions.  Also, to generate the crossover-adjusted event time random
variable, a more efficient algorithm is used and the output includes crossover
indicators.")
    (license license:gpl2+)))

(define-public r-pvstatem
  (package
    (name "r-pvstatem")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PvSTATEM" version))
       (sha256
        (base32 "18jk2kwzd2avym5mslagvxvs31m0awqnafl21ad8mva0gr5c8shc"))))
    (properties `((upstream-name . "PvSTATEM")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-stringi
                             r-readxl
                             r-readr
                             r-r6
                             r-png
                             r-nplr
                             r-ggrepel
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "<https://github.com/mini-pw/PvSTATEM>")
    (synopsis
     "Reading, Quality Control and Preprocessing of MBA (Multiplex Bead Assay) Data")
    (description
     "Speeds up the process of loading raw data from MBA (Multiplex Bead Assay)
examinations, performs quality control checks, and automatically normalizes the
data, preparing it for more advanced, downstream tasks.  The main objective of
the package is to create a simple environment for a user, who does not
necessarily have experience with R language.  The package is developed within
the project of the same name - @code{PvSTATEM}', which is an international
project aiming for malaria elimination.")
    (license license:bsd-3)))

(define-public r-pvr
  (package
    (name "r-pvr")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PVR" version))
       (sha256
        (base32 "0pcnazkiwmicr3k6k70r8skqk0pd46fcqhhxghawxjs4slgnygkj"))))
    (properties `((upstream-name . "PVR")))
    (build-system r-build-system)
    (propagated-inputs (list r-splancs r-mass r-ape))
    (home-page "https://cran.r-project.org/package=PVR")
    (synopsis
     "Phylogenetic Eigenvectors Regression and Phylogentic Signal-Representation Curve")
    (description
     "Estimates (and controls for) phylogenetic signal through phylogenetic
eigenvectors regression (PVR) and phylogenetic signal-representation (PSR)
curve, along with some plot utilities.")
    (license license:gpl2+)))

(define-public r-pvplr
  (package
    (name "r-pvplr")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PVplr" version))
       (sha256
        (base32 "1hrd2r3m2qy8r4wjacbhpqsmlchdjqk6iyydiy5k1ccrmv8da74a"))))
    (properties `((upstream-name . "PVplr")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-tidyr
                             r-stlplus
                             r-segmented
                             r-scales
                             r-rlang
                             r-purrr
                             r-minpack-lm
                             r-magrittr
                             r-ggplot2
                             r-forecast
                             r-dplyr
                             r-cluster
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PVplr")
    (synopsis "Performance Loss Rate Analysis Pipeline")
    (description
     "The pipeline contained in this package provides tools used in the Solar
Durability and Lifetime Extension Center (SDLE) for the analysis of Performance
Loss Rates (PLR) in real world photovoltaic systems.  Functions included allow
for data cleaning, feature correction, power predictive modeling, PLR
determination, and uncertainty bootstrapping through various methods
<doi:10.1109/PVSC40753.2019.8980928>.  The vignette \"Pipeline Walkthrough\" gives
an explicit run through of typical package usage.  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-EE-0008172.  This work made use of the High Performance Computing
Resource in the Core Facility for Advanced Research Computing at Case Western
Reserve University.")
    (license license:expat)))

(define-public r-pvlrt
  (package
    (name "r-pvlrt")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pvLRT" version))
       (sha256
        (base32 "02w88z752svyk63n288dwnbwlw5v5cla294pzrms361ggs1bdkph"))))
    (properties `((upstream-name . "pvLRT")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer
                             r-progress
                             r-magrittr
                             r-glue
                             r-ggplot2
                             r-ggfittext
                             r-data-table
                             r-bit64))
    (home-page "https://cran.r-project.org/package=pvLRT")
    (synopsis "Likelihood Ratio Test-Based Approaches to Pharmacovigilance")
    (description
     "This package provides a suite of likelihood ratio test based methods to use in
pharmacovigilance.  Contains various testing and post-processing functions.")
    (license license:gpl3)))

(define-public r-pvldcurve
  (package
    (name "r-pvldcurve")
    (version "1.2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pvldcurve" version))
       (sha256
        (base32 "0vyrrgbj955jkir25svq59h8darvaf33m985mcy1dx6836zqwgap"))))
    (properties `((upstream-name . "pvldcurve")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pvldcurve")
    (synopsis
     "Simplifies the Analysis of Pressure Volume and Leaf Drying Curves")
    (description
     "Simplifies the manufacturing, analysis and display of pressure volume and leaf
drying curves.  From the progression of the curves turgor loss point, osmotic
potential, apoplastic fraction as well as minimum conductance and stomatal
closure can be derived.  Methods adapted from Bartlett, Scoffoni, Sack (2012)
<doi:10.1111/j.1461-0248.2012.01751.x> and Sack, Scoffoni,
@code{PrometheusWikiContributors} (2011)
<http://prometheuswiki.org/tiki-index.php?page=Minimum+epidermal+conductance+%28gmin%2C+a.k.a.+cuticular+conductance%29>.")
    (license license:expat)))

(define-public r-pvda
  (package
    (name "r-pvda")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pvda" version))
       (sha256
        (base32 "0m7lw5wplwv3w7bwcflk6ijw83rv85zdch3flvcnx8slqxzaidrg"))))
    (properties `((upstream-name . "pvda")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-rdpack
                             r-purrr
                             r-glue
                             r-dtplyr
                             r-dplyr
                             r-data-table
                             r-cli
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://oskargauffin.github.io/pvda/")
    (synopsis "Disproportionality Functions for Pharmacovigilance")
    (description
     "This package provides tools for performing disproportionality analysis using the
information component, proportional reporting rate and the reporting odds ratio.
 The anticipated use is passing data to the da() function, which executes the
disproportionality analysis.  See NorÃ©n et al (2011)
<doi:10.1177/0962280211403604> and Montastruc et al (2011)
<doi:10.1111/j.1365-2125.2011.04037.x> for further details.")
    (license license:gpl3+)))

(define-public r-pvcurveanalysis
  (package
    (name "r-pvcurveanalysis")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pvcurveanalysis" version))
       (sha256
        (base32 "0rj1afldz07krp8qiszbcpmf5vz5ngvxvflww9f7zyj6kj8lll8d"))))
    (properties `((upstream-name . "pvcurveanalysis")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pvcurveanalysis")
    (synopsis "Analysis of Pressure Volume Curves")
    (description
     "Enables the manufacturing, analysis and display of pressure volume curves.  From
the progression of the curves, turgor loss point, osmotic potential and
apoplastic fraction can be derived.  Methods adapted from Bartlett, Scoffoni and
Sack (2012) <doi:10.1111/j.1461-0248.2012.01751.x>.")
    (license license:expat)))

(define-public r-pvclass
  (package
    (name "r-pvclass")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pvclass" version))
       (sha256
        (base32 "1g6x75qz3xybq1xnik0hzi7mcs0gzzqd3f0iwfkzi5d1zcddnw13"))))
    (properties `((upstream-name . "pvclass")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (home-page "https://cran.r-project.org/package=pvclass")
    (synopsis "P-Values for Classification")
    (description
     "Computes nonparametric p-values for the potential class memberships of new
observations as well as cross-validated p-values for the training data.  The
p-values are based on permutation tests applied to an estimated Bayesian
likelihood ratio, using a plug-in statistic for the Gaussian model, k nearest
neighbors', weighted nearest neighbors or penalized logistic regression'.
Additionally, it provides graphical displays and quantitative analyses of the
p-values.")
    (license license:gpl2+)))

(define-public r-pvar
  (package
    (name "r-pvar")
    (version "2.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pvar" version))
       (sha256
        (base32 "1k651021ga0lwpvgr9wnm8k1zzd47r4jprfp7ki5mqys7947y6a9"))))
    (properties `((upstream-name . "pvar")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pvar")
    (synopsis "Calculation and Application of p-Variation")
    (description
     "The calculation of p-variation of the finite sample data.  This package is a
realisation of the procedure described in Butkus, V. & Norvaisa, R. Lith Math J
(2018). <doi: 10.1007/s10986-018-9414-3> The formal definitions and reference
into literature are given in vignette.")
    (license license:gpl2)))

(define-public r-pvaluefunctions
  (package
    (name "r-pvaluefunctions")
    (version "1.6.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pvaluefunctions" version))
       (sha256
        (base32 "10dxzbz6fw01qgfm6mmxb1xy14pwxgif0d603m3x146337pccc7q"))))
    (properties `((upstream-name . "pvaluefunctions")))
    (build-system r-build-system)
    (propagated-inputs (list r-zipfr r-scales r-pracma r-gsl r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/DInfanger/pvaluefunctions")
    (synopsis
     "Creates and Plots P-Value Functions, S-Value Functions, Confidence Distributions and Confidence Densities")
    (description
     "This package contains functions to compute and plot confidence distributions,
confidence densities, p-value functions and s-value (surprisal) functions for
several commonly used estimates.  Instead of just calculating one p-value and
one confidence interval, p-value functions display p-values and confidence
intervals for many levels thereby allowing to gauge the compatibility of several
parameter values with the data.  These methods are discussed by Infanger D,
Schmidt-TrucksÃ¤ss A. (2019) <doi:10.1002/sim.8293>; Poole C. (1987)
<doi:10.2105/AJPH.77.2.195>; Schweder T, Hjort NL. (2002)
<doi:10.1111/1467-9469.00285>; Bender R, Berg G, Zeeb H. (2005)
<doi:10.1002/bimj.200410104> ; Singh K, Xie M, Strawderman WE. (2007)
<doi:10.1214/074921707000000102>; Rothman KJ, Greenland S, Lash TL. (2008,
ISBN:9781451190052); Amrhein V, Trafimow D, Greenland S. (2019)
<doi:10.1080/00031305.2018.1543137>; Greenland S. (2019)
<doi:10.1080/00031305.2018.1529625> and Rafi Z, Greenland S. (2020)
<doi:10.1186/s12874-020-01105-9>.")
    (license license:gpl3)))

(define-public r-pvaclone
  (package
    (name "r-pvaclone")
    (version "0.1-7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PVAClone" version))
       (sha256
        (base32 "1fp4ivjs1980456gyzamwpwwy2rc2sv6xwb2mbk12qkpghgdgssl"))))
    (properties `((upstream-name . "PVAClone")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-dcmle r-dclone r-coda))
    (home-page "https://github.com/psolymos/PVAClone")
    (synopsis "Population Viability Analysis with Data Cloning")
    (description
     "Likelihood based population viability analysis in the presence of observation
error and missing data.  The package can be used to fit, compare, predict, and
forecast various growth model types using data cloning.")
    (license license:gpl2)))

(define-public r-puzzle
  (package
    (name "r-puzzle")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "puzzle" version))
       (sha256
        (base32 "073n074irsvn4w1jy5xmr6l24a209kn0ypvf1d2zn3p6yd93wzss"))))
    (properties `((upstream-name . "puzzle")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyverse
                             r-sqldf
                             r-reshape2
                             r-reshape
                             r-readxl
                             r-readr
                             r-plyr
                             r-lubridate
                             r-kableextra
                             r-dplyr))
    (home-page "https://github.com/syneoshealth/puzzle")
    (synopsis "Assembling Data Sets for Non-Linear Mixed Effects Modeling")
    (description
     "To Simplify the time consuming and error prone task of assembling complex data
sets for non-linear mixed effects modeling.  Users are able to select from
different absorption processes such as zero and first order, or a combination of
both.  Furthermore, data sets containing data from several entities, responses,
and covariates can be simultaneously assembled.")
    (license license:gpl3)))

(define-public r-pushoverr
  (package
    (name "r-pushoverr")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pushoverr" version))
       (sha256
        (base32 "05ma76jhk00m9872f7gb0vwmk2q3l79r1ddaf1slbhgd57l1avk2"))))
    (properties `((upstream-name . "pushoverr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-httr r-glue r-cli r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://briandconnelly.github.io/pushoverr/")
    (synopsis "Send Push Notifications using 'Pushover'")
    (description
     "Send push notifications to mobile devices or the desktop using Pushover
<https://pushover.net>.  These notifications can display things such as results,
job status, plots, or any other text or numeric data.")
    (license license:bsd-2)))

(define-public r-pushbar
  (package
    (name "r-pushbar")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pushbar" version))
       (sha256
        (base32 "1gxarv6x32y6ssrhsq3pyihzcvpnnj7bg0jf2cpcr99da78n1d9b"))))
    (properties `((upstream-name . "pushbar")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-jsonlite))
    (home-page "https://github.com/JohnCoene/pushbar")
    (synopsis "Create Sliders for 'Shiny'")
    (description
     "Create sliders from left, right, top and bottom which may include any html or
Shiny input or output.")
    (license license:expat)))

(define-public r-pursuit
  (package
    (name "r-pursuit")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Pursuit" version))
       (sha256
        (base32 "1wg1ix466zcbcgr2qqa6sbh9blx5g8hjqmagf9z3ig06qgxf1zrr"))))
    (properties `((upstream-name . "Pursuit")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=Pursuit")
    (synopsis "Projection Pursuit")
    (description
     "Projection pursuit (PP) with 17 methods and grand tour with 3 methods.  Being
that projection pursuit searches for low-dimensional linear projections in
high-dimensional data structures, while grand tour is a technique used to
explore multivariate statistical data through animation.")
    (license license:gpl3)))

(define-public r-purrrlyr
  (package
    (name "r-purrrlyr")
    (version "0.0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "purrrlyr" version))
       (sha256
        (base32 "0ql08dka07wi1cxjcn12pxj00j0cm112074l7byj9vmi3rlqynh8"))))
    (properties `((upstream-name . "purrrlyr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-purrr r-magrittr r-dplyr))
    (home-page "https://github.com/hadley/purrrlyr")
    (synopsis "Tools at the Intersection of 'purrr' and 'dplyr'")
    (description
     "Some functions at the intersection of dplyr and purrr that formerly lived in
purrr'.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-purpleair
  (package
    (name "r-purpleair")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PurpleAir" version))
       (sha256
        (base32 "17ir79f5r8c9lv4gx9mr9iv26xapygd2azgw6gdm3lcl99r36cgm"))))
    (properties `((upstream-name . "PurpleAir")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-rlang
                             r-purrr
                             r-httr2
                             r-glue
                             r-dplyr
                             r-cli))
    (home-page "https://github.com/cole-brokamp/PurpleAir")
    (synopsis "Query the 'PurpleAir' Application Programming Interface")
    (description
     "Send requests to the @code{PurpleAir} Application Programming Interface (API;
<https://community.purpleair.com/c/data/api/18>).  Check a @code{PurpleAir} API
key and get information about the related organization.  Download real-time data
from a single @code{PurpleAir} sensor or many sensors by sensor identifier,
geographical bounding box, or time since modified.  Download historical data
from a single sensor.")
    (license license:expat)))

(define-public r-purging
  (package
    (name "r-purging")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "purging" version))
       (sha256
        (base32 "1b8f87jn6wyh4fp6b1660bd484wcf7xiajdg9dz2594aj1r94qsr"))))
    (properties `((upstream-name . "purging")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=purging")
    (synopsis
     "Simple Method for Purging Mediation Effects among Independent Variables")
    (description
     "Simple method of purging independent variables of mediating effects.  First,
regress the direct variable on the indirect variable.  Then, used the stored
residuals as the new purged (direct) variable in the updated specification.
This purging process allows for use of a new direct variable uncorrelated with
the indirect variable.  Please cite the method and/or package using Waggoner,
Philip D. (2018) <doi:10.1177/1532673X18759644>.")
    (license license:expat)))

(define-public r-purger
  (package
    (name "r-purger")
    (version "1.8.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "purgeR" version))
       (sha256
        (base32 "0w3mcij7q5aqbs5phiyqprgr3hvs77fnjmbayp2rwn9qm1sx160a"))))
    (properties `((upstream-name . "purgeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppprogress r-rcpp r-progress r-foreach
                             r-dosnow))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://gitlab.com/elcortegano/purgeR/")
    (synopsis "Inbreeding-Purging Estimation in Pedigreed Populations")
    (description
     "Inbreeding-purging analysis of pedigreed populations, including the computation
of the inbreeding coefficient, partial, ancestral and purged inbreeding
coefficients, and measures of the opportunity of purging related to the
individual reduction of inbreeding load.  In addition, functions to calculate
the effective population size and other parameters relevant to population
genetics are included.  See LÃ³pez-Cortegano E. (2021)
<doi:10.1093/bioinformatics/btab599>.")
    (license license:gpl2)))

(define-public r-purge
  (package
    (name "r-purge")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "purge" version))
       (sha256
        (base32 "1faf8mkaxsnj63wnig5rs50hd3j6vzaj0xkdz8kn0j7y2vvshp9p"))))
    (properties `((upstream-name . "purge")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=purge")
    (synopsis "Purge Training Data from Models")
    (description
     "Enables the removal of training data from fitted R models while retaining
predict functionality.  The purged models are more portable as their memory
footprints do not scale with the training sample size.")
    (license license:expat)))

(define-public r-pureseqtmr
  (package
    (name "r-pureseqtmr")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pureseqtmr" version))
       (sha256
        (base32 "105v0ydl458xzgkdas7m8llf0672s9klv816sjl27dy0a3k8163f"))))
    (properties `((upstream-name . "pureseqtmr")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-readr
                             r-rcpp
                             r-rappdirs
                             r-plyr
                             r-peptides
                             r-ggplot2
                             r-dplyr
                             r-devtools
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/richelbilderbeek/pureseqtmr/")
    (synopsis "Predict Transmembrane Protein Topology")
    (description
     "Proteins reside in either the cell plasma or in the cell membrane.  A membrane
protein goes through the membrane at least once.  Given the amino acid sequence
of a membrane protein, the tool @code{PureseqTM}
(<https://github.com/@code{PureseqTM/pureseqTM_package>}, as described in
\"Efficient And Accurate Prediction Of Transmembrane Topology From Amino acid
sequence only.\", Wang, Qing, et al (2019), <doi:10.1101/627307>), can predict
the topology of a membrane protein.  This package allows one to use
@code{PureseqTM} from R.")
    (license license:gpl3)))

(define-public r-puremoe
  (package
    (name "r-puremoe")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "puremoe" version))
       (sha256
        (base32 "119pndm7s343vmgac3jx64f66ax1gk4831svc9b8f54bgk2gqvdx"))))
    (properties `((upstream-name . "puremoe")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-textshape
                             r-rentrez
                             r-rappdirs
                             r-pbapply
                             r-jsonlite
                             r-httr
                             r-data-table))
    (home-page "https://github.com/jaytimm/puremoe")
    (synopsis "Pubmed Unified REtrieval for Multi-Output Exploration")
    (description
     "Access a variety of @code{PubMed} data through a single, user-friendly
interface, including abstracts <https://pubmed.ncbi.nlm.nih.gov/>, bibliometrics
from @code{iCite} <https://icite.od.nih.gov/>, pubtations from @code{PubTator3}
<https://www.ncbi.nlm.nih.gov/research/pubtator3/>, and full-text records from
PMC <https://www.ncbi.nlm.nih.gov/pmc/>.")
    (license license:expat)))

(define-public r-pupmsi
  (package
    (name "r-pupmsi")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PUPMSI" version))
       (sha256
        (base32 "04w2c783bbkfdqkxvwvrf3fk09hzswbkq7najcn456cz22241w9l"))))
    (properties `((upstream-name . "PUPMSI")))
    (build-system r-build-system)
    (propagated-inputs (list r-nls2 r-minpack-lm r-metrics r-ggplot2))
    (home-page "https://cran.r-project.org/package=PUPMSI")
    (synopsis "Moisture Sorption Isotherm Modeling Program")
    (description
     "This package contains sixteen moisture sorption isotherm models, which evaluate
the fitness of adsorption and desorption curves for further understanding of the
relationship between moisture content and water activity.  Fitness evaluation is
conducted through parameter estimation and error analysis.  Moreover, graphical
representation, hysteresis area estimation, and isotherm classification through
the equation of Blahovec & Yanniotis (2009) <doi:10.1016/j.jfoodeng.2008.08.007>
which is based on the classification system introduced by Brunauer et.  al.
(1940) <doi:10.1021/ja01864a025> are also included for the visualization of
models and hysteresis.")
    (license license:gpl2)))

(define-public r-pupmcr
  (package
    (name "r-pupmcr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PUPMCR" version))
       (sha256
        (base32 "00dcvpy3ai613addgw45q8p3zmh6jf32wfnqg07kxlqamv8rw8ff"))))
    (properties `((upstream-name . "PUPMCR")))
    (build-system r-build-system)
    (propagated-inputs (list r-readxl r-colordistance))
    (home-page "https://cran.r-project.org/package=PUPMCR")
    (synopsis
     "Image-Based Identification of Color Based on Rayner (1970) Terminology and Known Fungal Pigments")
    (description
     "Image-based color matching using the \"Mycological Colour Chart\" by Rayner (1970,
ISBN:9780851980263) and its associated fungal pigments.  This package will
assist mycologists in identifying color during morphological analysis.")
    (license license:gpl2)))

(define-public r-pupilpre
  (package
    (name "r-pupilpre")
    (version "0.6.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PupilPre" version))
       (sha256
        (base32 "123m1zqld0jl26vqz41iic7vxzyfs1aw8zi5ki2cy6wzw9ss87m6"))))
    (properties `((upstream-name . "PupilPre")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-vwpre
                             r-tidyr
                             r-signal
                             r-shiny
                             r-robustbase
                             r-rlang
                             r-mgcv
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PupilPre")
    (synopsis "Preprocessing Pupil Size Data")
    (description
     "Pupillometric data collected using SR Research Eyelink eye trackers requires
significant preprocessing.  This package contains functions for preparing pupil
dilation data for visualization and statistical analysis.  Specifically, it
provides a pipeline of functions which aid in data validation, the removal of
blinks/artifacts, downsampling, and baselining, among others.  Additionally,
plotting functions for creating grand average and conditional average plots are
provided.  See the vignette for samples of the functionality.  The package is
designed for handling data collected with SR Research Eyelink eye trackers using
Sample Reports created in SR Research Data Viewer.")
    (license license:gpl3)))

(define-public r-pupak
  (package
    (name "r-pupak")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PUPAK" version))
       (sha256
        (base32 "1274z53qclddww90qgnl9vwdnxv2p66lclmq9fixchnpgm9xc6qw"))))
    (properties `((upstream-name . "PUPAK")))
    (build-system r-build-system)
    (propagated-inputs (list r-segmented r-nls2 r-metrics r-ggplot2))
    (home-page "https://cran.r-project.org/package=PUPAK")
    (synopsis
     "Parameter Estimation, and Plot Visualization of Adsorption Kinetic Models")
    (description
     "This package contains model fitting functions for linear and non-linear
adsorption kinetic and diffusion models.  Adsorption kinetics is used for
characterizing the rate of solute adsorption and the time necessary for the
adsorption process.  Adsorption kinetics offers vital information on adsorption
rate, adsorbent performance in response time, and mass transfer processes.  In
addition, diffusion models are included in the package as solute diffusion
affects the adsorption kinetic experiments.  This package consists of 20
adsorption and diffusion models, including Pseudo First Order (PFO), Pseudo
Second Order (PSO), Elovich, and Weber-Morris model (commonly called the
intraparticle model) stated by Plazinski et al. (2009)
<doi:10.1016/j.cis.2009.07.009>.  This package also contains a summary function
where the statistical errors of each model are ranked for a more straightforward
determination of the best fit model.")
    (license license:gpl2)))

(define-public r-pump
  (package
    (name "r-pump")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PUMP" version))
       (sha256
        (base32 "1s1aqma0p32y9db3xf9yrfgg12i3skdgzcvw3kg9s5z1msx4gax7"))))
    (properties `((upstream-name . "PUMP")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-readr
                             r-randomizr
                             r-purrr
                             r-mvtnorm
                             r-magrittr
                             r-lme4
                             r-glue
                             r-ggpubr
                             r-ggplot2
                             r-future
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MDRCNY/PUMP")
    (synopsis "Power Under Multiplicity Project")
    (description
     "Estimates power, minimum detectable effect size (MDES) and sample size
requirements.  The context is multilevel randomized experiments with multiple
outcomes.  The estimation takes into account the use of multiple testing
procedures.  Development of this package was supported by a grant from the
Institute of Education Sciences (R305D170030).  For a full package description,
including a detailed technical appendix, see <doi:10.18637/jss.v108.i06>.")
    (license license:gpl3)))

(define-public r-pumilior
  (package
    (name "r-pumilior")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pumilioR" version))
       (sha256
        (base32 "1791r5lkr6hafd2rz9f9nfy5vc03c4833f292khvd2cg43sb0bfc"))))
    (properties `((upstream-name . "pumilioR")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml r-rcurl))
    (native-inputs (list r-knitr))
    (home-page "http://ljvillanueva.github.io/pumilioR/")
    (synopsis "Pumilio in R")
    (description
     "R package to query and get data out of a Pumilio sound archive system
(http://ljvillanueva.github.io/pumilio/).")
    (license license:gpl3)))

(define-public r-pulsar
  (package
    (name "r-pulsar")
    (version "0.3.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pulsar" version))
       (sha256
        (base32 "14wkqxgi8vi5szd01j5q5vis07zav22wljy32g3vljxyn8jyz0pf"))))
    (properties `((upstream-name . "pulsar")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/zdk123/pulsar")
    (synopsis
     "Parallel Utilities for Lambda Selection along a Regularization Path")
    (description
     "Model selection for penalized graphical models using the Stability Approach to
Regularization Selection ('@code{StARS}'), with options for speed-ups including
Bounded @code{StARS} (B-@code{StARS}), batch computing, and other stability
metrics (e.g., graphlet stability G-@code{StARS}).  Christian L. MÃ¼ller,
Richard Bonneau, Zachary Kurtz (2016) <@code{arXiv:1605.07072>}.")
    (license license:gpl3+)))

(define-public r-puls
  (package
    (name "r-puls")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "puls" version))
       (sha256
        (base32 "0cdgcm4fvakk2mdjmlsnin3d0bf56wrm23p9f4bhh5j1ib68lsh5"))))
    (properties `((upstream-name . "puls")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-monoclust
                             r-ggplot2
                             r-fda-usc
                             r-fda
                             r-dplyr
                             r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://vinhtantran.github.io/puls/")
    (synopsis "Partitioning Using Local Subregions")
    (description
     "This package provides a method of clustering functional data using subregion
information of the curves.  It is intended to supplement the fda and fda.usc
packages in functional data object clustering.  It also facilitates the printing
and plotting of the results in a tree format and limits the partitioning
candidates into a specific set of subregions.")
    (license license:gpl2+)))

(define-public r-pullword
  (package
    (name "r-pullword")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pullword" version))
       (sha256
        (base32 "125d091ba9h8h61y0m1bnflklmz898sp9w6ibca50vwz3d2qy5rz"))))
    (properties `((upstream-name . "pullword")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcurl))
    (home-page "https://cran.r-project.org/package=pullword")
    (synopsis "R Interface to Pullword Service")
    (description
     "R Interface to Pullword Service for natural language processing in Chinese.  It
enables users to extract valuable words from text by deep learning models.  For
more details please visit the official site (in Chinese)
<http://www.pullword.com/>.")
    (license license:lgpl2.0+)))

(define-public r-pulasso
  (package
    (name "r-pulasso")
    (version "3.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PUlasso" version))
       (sha256
        (base32 "1lyf7x4sixpl74alns3kmr2iz10qmjz444gyqxgj5k4xqswgaz15"))))
    (properties `((upstream-name . "PUlasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen
                             r-rcpp
                             r-matrix
                             r-ggplot2
                             r-foreach
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://arxiv.org/abs/1711.08129")
    (synopsis "High-Dimensional Variable Selection with Presence-Only Data")
    (description
     "Efficient algorithm for solving PU (Positive and Unlabeled) problem in low or
high dimensional setting with lasso or group lasso penalty.  The algorithm uses
Maximization-Minorization and (block) coordinate descent.  Sparse calculation
and parallel computing are supported for the computational speed-up.  See Hyebin
Song, Garvesh Raskutti (2018) <@code{arXiv:1711.08129>}.")
    (license license:gpl2)))

(define-public r-pugmm
  (package
    (name "r-pugmm")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PUGMM" version))
       (sha256
        (base32 "03bwg4mb0kc708plqzym0xw5giai45nk601fs53gfnad57y70wdp"))))
    (properties `((upstream-name . "PUGMM")))
    (build-system r-build-system)
    (propagated-inputs (list r-ppclust
                             r-mcompanion
                             r-mclust
                             r-matrix
                             r-mass
                             r-igraph
                             r-foreach
                             r-doparallel
                             r-clusterr))
    (home-page "https://github.com/giorgiazaccaria/PUGMM")
    (synopsis "Parsimonious Ultrametric Gaussian Mixture Models")
    (description
     "Finite Gaussian mixture models with parsimonious extended ultrametric covariance
structures estimated via a grouped coordinate ascent algorithm, which is
equivalent to the Expectation-Maximization algorithm.  The thirteen ultrametric
covariance structures implemented allow for the inspection of different
hierarchical relationships among variables.  The estimation of an ultrametric
correlation matrix is included as a function.  The methodologies are described
in Cavicchia, Vichi, Zaccaria (2024) <doi:10.1007/s11222-024-10405-9>,
Cavicchia, Vichi, Zaccaria (2022) <doi:10.1007/s11634-021-00488-x> and
Cavicchia, Vichi, Zaccaria (2020) <doi:10.1007/s11634-020-00400-z>.")
    (license license:expat)))

(define-public r-pubtatordb
  (package
    (name "r-pubtatordb")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pubtatordb" version))
       (sha256
        (base32 "0i5azbf75bwxxzpsr5fc2hh22pbwix00hz9dcdpn5jgp2957f7wx"))))
    (properties `((upstream-name . "pubtatordb")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsqlite
                             r-readr
                             r-r-utils
                             r-dplyr
                             r-dbi
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pubtatordb")
    (synopsis "Create and Query a Local 'PubTator' Database")
    (description
     "@code{PubTator}
<https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/@code{PubTator/>} is a
National Center for Biotechnology Information (NCBI) tool that enhances the
annotation of articles on @code{PubMed} <https://www.ncbi.nlm.nih.gov/pubmed/>.
It makes it possible to rapidly identify potential relationships between genes
or proteins using text mining techniques.  In contrast, manually searching for
and reading the annotated articles would be very time consuming. @code{PubTator}
offers both an online interface and a RESTful API, however, neither of these
approaches are well suited for frequent, high-throughput analyses.  The package
pubtatordb provides a set of functions that make it easy for the average R user
to download @code{PubTator} annotations, create, and then query a local version
of the database.")
    (license license:expat)))

(define-public r-pubmedwordcloud
  (package
    (name "r-pubmedwordcloud")
    (version "0.3.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PubMedWordcloud" version))
       (sha256
        (base32 "1wfp1fpr4an7psrwidx38brvhvghmvfsgjy4gdx81p1bdsdn2kw2"))))
    (properties `((upstream-name . "PubMedWordcloud")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml
                             r-wordcloud
                             r-tm
                             r-stringr
                             r-rcurl
                             r-rcolorbrewer))
    (home-page "http://felixfan.github.io/PubMedWordcloud/")
    (synopsis "'Pubmed' Word Clouds")
    (description
     "Create a word cloud using the abstract of publications from Pubmed'.")
    (license license:gpl2+)))

(define-public r-pubmedtk
  (package
    (name "r-pubmedtk")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pubmedtk" version))
       (sha256
        (base32 "176idn5q7aac9bsvw0qbxq8qdx9axg5prsvv2rjrwx6hhf2gz20a"))))
    (properties `((upstream-name . "pubmedtk")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-stringr
                             r-rlang
                             r-magrittr
                             r-jsonlite
                             r-httr
                             r-dplyr
                             r-assertthat))
    (home-page "https://github.com/bgcarlisle/pubmedtk")
    (synopsis "'Pubmed' Toolkit")
    (description
     "This package provides various functions for retrieving and interpreting
information from Pubmed via the API,
<https://www.ncbi.nlm.nih.gov/home/develop/api/>.")
    (license license:agpl3+)))

(define-public r-pubmedr
  (package
    (name "r-pubmedr")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pubmedR" version))
       (sha256
        (base32 "153br94iqayrvvgzrzx0r3ilcrmnznfdnzk8yaii01s8i1g1wxcd"))))
    (properties `((upstream-name . "pubmedR")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml r-rentrez))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/massimoaria/pubmedR")
    (synopsis
     "Gathering Metadata About Publications, Grants, Clinical Trials from 'PubMed' Database")
    (description
     "This package provides a set of tools to extract bibliographic content from
@code{PubMed} database using NCBI REST API
<https://www.ncbi.nlm.nih.gov/home/develop/api/>.")
    (license license:gpl3)))

(define-public r-pubmedmining
  (package
    (name "r-pubmedmining")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PubMedMining" version))
       (sha256
        (base32 "03lhml5jk69racmhlh6xa8j2rv8xb87vg6hr1caiqpd4kqg16n1q"))))
    (properties `((upstream-name . "PubMedMining")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-easypubmed))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PubMedMining")
    (synopsis "Text-Mining of the 'PubMed' Repository")
    (description
     "Easy function for text-mining the @code{PubMed} repository based on defined sets
of terms.  The relationship between fix-terms (related to your research topic)
and pub-terms (terms which pivot around your research focus) is calculated using
the pointwise mutual information algorithm ('PMI').  Church, Kenneth Ward and
Hanks, Patrick (1990) <https://www.aclweb.org/anthology/J90-1003/> A text file
is generated with the PMI'-scores for each fix-term.  Then for each collocation
pairs (a fix-term + a pub-term), a text file is generated with related article
titles and publishing years.  Additional Author section will follow in the next
version updates.")
    (license license:expat)))

(define-public r-pubmed-miner
  (package
    (name "r-pubmed-miner")
    (version "1.0.21")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pubmed.mineR" version))
       (sha256
        (base32 "1dwcpzxqx5672yq7pf7jd692w0pj4737sjdkh395nwpqsllp0sw9"))))
    (properties `((upstream-name . "pubmed.mineR")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml r-rjsonio r-rcurl r-r2html r-boot))
    (home-page "https://cran.r-project.org/package=pubmed.mineR")
    (synopsis "Text Mining of PubMed Abstracts")
    (description "Text mining of @code{PubMed} Abstracts (text and XML) from
<https://pubmed.ncbi.nlm.nih.gov/>.")
    (license license:gpl3)))

(define-public r-publish
  (package
    (name "r-publish")
    (version "2023.01.17")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Publish" version))
       (sha256
        (base32 "0lsr27014zm389xzyddklkql05zh4x9d7jcjz3yv78fwbjzw4v23"))))
    (properties `((upstream-name . "Publish")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-prodlim r-multcomp r-lava
                             r-data-table))
    (home-page "https://cran.r-project.org/package=Publish")
    (synopsis
     "Format Output of Various Routines in a Suitable Way for Reports and Publication")
    (description
     "This package provides a bunch of convenience functions that transform the
results of some basic statistical analyses into table format nearly ready for
publication.  This includes descriptive tables, tables of logistic regression
and Cox regression results as well as forest plots.")
    (license license:gpl2+)))

(define-public r-publipha
  (package
    (name "r-publipha")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "publipha" version))
       (sha256
        (base32 "03fpwxh5fyiq84wvmp5yyvsvqnznxyc4ncffn0nfhdnj5giymqvh"))))
    (properties `((upstream-name . "publipha")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-loo
                             r-bh))
    (home-page "https://cran.r-project.org/package=publipha")
    (synopsis "Bayesian Meta-Analysis with Publications Bias and P-Hacking")
    (description
     "This package provides tools for Bayesian estimation of meta-analysis models that
account for publications bias or p-hacking.  For publication bias, this package
implements a variant of the p-value based selection model of Hedges (1992)
<doi:10.1214/ss/1177011364> with discrete selection probabilities.  It also
implements the mixture of truncated normals model for p-hacking described in
Moss and De Bin (2019) <@code{arXiv:1911.12445>}.")
    (license license:gpl3)))

(define-public r-publicationbias
  (package
    (name "r-publicationbias")
    (version "2.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PublicationBias" version))
       (sha256
        (base32 "1nwkp5czv31v57qzaahj18lkw557a48qcnwrz6lmz9hw74b52qm6"))))
    (properties `((upstream-name . "PublicationBias")))
    (build-system r-build-system)
    (propagated-inputs (list r-robumeta
                             r-rlang
                             r-rdpack
                             r-metafor
                             r-metabias
                             r-lifecycle
                             r-glue
                             r-ggplot2
                             r-dplyr))
    (home-page "https://github.com/mathurlabstanford/PublicationBias")
    (synopsis "Sensitivity Analysis for Publication Bias in Meta-Analyses")
    (description
     "This package performs sensitivity analysis for publication bias in meta-analyses
(per Mathur & @code{VanderWeele}, 2020 [<doi:10.31219/osf.io/s9dp6>]).  These
analyses enable statements such as: \"For publication bias to shift the observed
point estimate to the null, significant results would need to be at least
30-fold more likely to be published than negative or nonsignificant results.\"
Comparable statements can be made regarding shifting to a chosen non-null value
or shifting the confidence interval.  Provides a worst-case meta-analytic point
estimate under maximal publication bias obtained simply by conducting a standard
meta-analysis of only the negative and \"nonsignificant\" studies.")
    (license license:gpl2)))

(define-public r-public-ctn0094extra
  (package
    (name "r-public-ctn0094extra")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "public.ctn0094extra" version))
       (sha256
        (base32 "0zmf0nx6skzpbdy8niqm2d7an1q4wy6y30wim53i18mzpm84wys4"))))
    (properties `((upstream-name . "public.ctn0094extra")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-purrr
                             r-public-ctn0094data
                             r-magrittr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://ctn-0094.github.io/public.ctn0094extra/")
    (synopsis "Helper Files for the CTN-0094 Relational Database")
    (description
     "Engineered features and \"helper\" functions ancillary to the public.ctn0094data
package, extending this package for ease of use (see
<https://CRAN.R-project.org/package=public.ctn0094data>).  This
public.ctn0094data package contains harmonized datasets from some of the
National Institute of Drug Abuse's Clinical Trials Network (NIDA's CTN)
projects.  Specifically, the CTN-0094 project is to harmonize and de-identify
clinical trials data from the CTN-0027, CTN-0030, and CTN-51 studies for opioid
use disorder.  This current version is built from public.ctn0094data v.  1.0.6.")
    (license license:expat)))

(define-public r-public-ctn0094data
  (package
    (name "r-public-ctn0094data")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "public.ctn0094data" version))
       (sha256
        (base32 "105v4if9fqbqfrgkvcc6ws2cggn67fp3nh9y41absd7jvmzsb49h"))))
    (properties `((upstream-name . "public.ctn0094data")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://ctn-0094.github.io/public.ctn0094data/")
    (synopsis "De-Identified Data from CTN-0094")
    (description
     "These are harmonized datasets produced as part of the Clinical Trials Network
(CTN) protocol number 0094.  This is a US National Institute of Drug Abuse
(NIDA) funded project; to learn more go to
<https://ctnlibrary.org/protocol/ctn0094/>.  These are datasets which have the
data harmonized from CTN-0027 (<https://ctnlibrary.org/protocol/ctn0027/>),
CTN-0030 (<https://ctnlibrary.org/protocol/ctn0030/>), and CTN-0051
(<https://ctnlibrary.org/protocol/ctn0051/>).")
    (license license:expat)))

(define-public r-pubh
  (package
    (name "r-pubh")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pubh" version))
       (sha256
        (base32 "1z575vb9b4774p42dyqbbf14dynnym0377zr5i0c6whr67dcz99q"))))
    (properties `((upstream-name . "pubh")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tibble
                             r-survival
                             r-sjmisc
                             r-sjlabelled
                             r-sandwich
                             r-performance
                             r-lmtest
                             r-jtools
                             r-ggplot2
                             r-ggformula
                             r-epitools
                             r-epi
                             r-emmeans
                             r-dplyr
                             r-car))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pubh")
    (synopsis "Toolbox for Public Health and Epidemiology")
    (description
     "This package provides a toolbox for making R functions and capabilities more
accessible to students and professionals from Epidemiology and Public Health
related disciplines.  Includes a function to report coefficients and confidence
intervals from models using robust standard errors (when available), functions
that expand ggplot2 plots and functions relevant for introductory papers in
Epidemiology or Public Health.  Please note that use of the provided data sets
is for educational purposes only.")
    (license license:gpl2)))

(define-public r-pubchemr
  (package
    (name "r-pubchemr")
    (version "2.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PubChemR" version))
       (sha256
        (base32 "0gsxw00v5qfgbwdxksp7fxnsdp9h899pz9wn4wskvy4xy4cg41pm"))))
    (properties `((upstream-name . "PubChemR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-testthat
                             r-stringr
                             r-rsvg
                             r-rjsonio
                             r-rcurl
                             r-png
                             r-magrittr
                             r-magick
                             r-httr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/selcukorkmaz/PubChemR")
    (synopsis
     "Interface to the 'PubChem' Database for Chemical Data Retrieval")
    (description
     "This package provides an interface to the @code{PubChem} database via the PUG
REST <https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest> and PUG View
<https://pubchem.ncbi.nlm.nih.gov/docs/pug-view> services.  This package allows
users to automatically access chemical and biological data from @code{PubChem}',
including compounds, substances, assays, and various other data types.
Functions are available to retrieve data in different formats, perform searches,
and access detailed annotations.")
    (license license:gpl2+)))

(define-public r-ptycho
  (package
    (name "r-ptycho")
    (version "1.1-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ptycho" version))
       (sha256
        (base32 "1llk3rpk0lf80vwvs23d6dqhgyic3a6sfjc393csj69hh01nrdvc"))))
    (properties `((upstream-name . "ptycho")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-plyr r-coda))
    (home-page "web.stanford.edu/~lstell/ptycho/")
    (synopsis "Bayesian Variable Selection with Hierarchical Priors")
    (description
     "Bayesian variable selection for linear regression models using hierarchical
priors.  There is a prior that combines information across responses and one
that combines information across covariates, as well as a standard spike and
slab prior for comparison.  An MCMC samples from the marginal posterior
distribution for the 0-1 variables indicating if each covariate belongs to the
model for each response.")
    (license license:gpl2+)))

(define-public r-ptxqc
  (package
    (name "r-ptxqc")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PTXQC" version))
       (sha256
        (base32 "0855xir9b604lhhymxf3rhin2sikxvg25zbgd5gvwgc5n3114d3m"))))
    (properties `((upstream-name . "PTXQC")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-xml2
                             r-upsetr
                             r-seqinr
                             r-rmzqc
                             r-rmarkdown
                             r-reshape2
                             r-rcolorbrewer
                             r-r6p
                             r-r6
                             r-plyr
                             r-magrittr
                             r-knitr
                             r-htmltable
                             r-gtable
                             r-gridextra
                             r-ggplot2
                             r-ggdendro
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/cbielow/PTXQC")
    (synopsis "Quality Report Generation for MaxQuant and mzTab Results")
    (description
     "Generates Proteomics (PTX) quality control (QC) reports for shotgun LC-MS data
analyzed with the @code{MaxQuant} software suite (from .txt files) or
@code{mzTab} files (ideally from @code{OpenMS} @code{QualityControl} tool).
Reports are customizable (target thresholds, subsetting) and available in HTML
or PDF format.  Published in J. Proteome Res., Proteomics Quality Control:
Quality Control Software for @code{MaxQuant} Results (2015)
<doi:10.1021/acs.jproteome.5b00780>.")
    (license license:bsd-3)))

(define-public r-ptwins
  (package
    (name "r-ptwins")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PTwins" version))
       (sha256
        (base32 "166mc7w52n9d3n72h5jvvhbki87prkils3yigdf57dn9pyixrd66"))))
    (properties `((upstream-name . "PTwins")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PTwins")
    (synopsis
     "Percentile Estimation of Fetal Weight for Twins by Chorionicity")
    (description
     "Package to Percentile estimation of fetal weight for twins by chorionicity
(dichorionic-diamniotic or monochorionic-diamniotic).")
    (license license:gpl3)))

(define-public r-ptwikiwords
  (package
    (name "r-ptwikiwords")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ptwikiwords" version))
       (sha256
        (base32 "129dad1vy52sf97dqrkwa49vjhv2kvs4pmd5zvq8pxd51hqm6wy9"))))
    (properties `((upstream-name . "ptwikiwords")))
    (build-system r-build-system)
    (home-page "https://github.com/dfalbel/ptwikiwords")
    (synopsis "Words Used in Portuguese Wikipedia")
    (description
     "This package contains a dataset of words used in 15.000 randomly extracted pages
from the Portuguese Wikipedia (<https://pt.wikipedia.org/>).")
    (license license:expat)))

(define-public r-ptvapi
  (package
    (name "r-ptvapi")
    (version "2.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ptvapi" version))
       (sha256
        (base32 "1if28cczy7vj18q4cy9dnynjdka7dgzpy5p2jxh1b3kklxbdwgcx"))))
    (properties `((upstream-name . "ptvapi")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-purrr
                             r-jsonlite
                             r-httr
                             r-glue
                             r-digest
                             r-assertthat))
    (home-page "https://github.com/mdneuzerling/ptvapi")
    (synopsis "Access the 'Public Transport Victoria' Timetable API")
    (description
     "Access the Public Transport Victoria Timetable API
<https://www.ptv.vic.gov.au/footer/data-and-reporting/datasets/ptv-timetable-api/>,
with results returned as familiar R data structures.  Retrieve information on
stops, routes, disruptions, departures, and more.")
    (license license:expat)))

(define-public r-pttstability
  (package
    (name "r-pttstability")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pttstability" version))
       (sha256
        (base32 "0pyja52dz5d6nj5mq7a4ba6grvvkvag76fmy7d8qj9z14dfw7sln"))))
    (properties `((upstream-name . "pttstability")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pttstability")
    (synopsis "Particle-Takens Stability")
    (description
     "Includes a collection of functions presented in \"Measuring stability in
ecological systems without static equilibria\" by Clark et al. (2022)
<doi:10.1002/ecs2.4328> in Ecosphere.  These can be used to estimate the
parameters of a stochastic state space model (i.e.  a model where a time series
is observed with error).  The goal of this package is to estimate the
variability around a deterministic process, both in terms of observation error -
i.e.  variability due to imperfect observations that does not influence system
state - and in terms of process noise - i.e.  stochastic variation in the actual
state of the process.  Unlike classical methods for estimating variability, this
package does not necessarily assume that the deterministic state is fixed (i.e.
a fixed-point equilibrium), meaning that variability around a dynamic trajectory
can be estimated (e.g. stochastic fluctuations during predator-prey dynamics).")
    (license license:gpl3)))

(define-public r-ptsr
  (package
    (name "r-ptsr")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PTSR" version))
       (sha256
        (base32 "0zwqw8zdp0p05nm64yg658iqfbj26n3v2py8r5m6g1sjispm5whf"))))
    (properties `((upstream-name . "PTSR")))
    (build-system r-build-system)
    (propagated-inputs (list r-suppdists r-numderiv r-extradistr r-actuar))
    (home-page "https://cran.r-project.org/package=PTSR")
    (synopsis "Positive Time Series Regression")
    (description
     "This package provides a collection of functions to simulate, estimate and
forecast a wide range of regression based dynamic models for positive time
series.  This package implements the results presented in Prass, T.S.; Carlos,
J.H.; Taufemback, C.G. and Pumi, G. (2022). \"Positive Time Series Regression\"
<@code{arXiv:2201.03667>}.")
    (license license:gpl3+)))

(define-public r-ptspotter
  (package
    (name "r-ptspotter")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ptspotter" version))
       (sha256
        (base32 "0mw9s2h6bhyjrcw9f0ayzcwg30inwyr7dqfk43qc24ppkclh85wh"))))
    (properties `((upstream-name . "ptspotter")))
    (build-system r-build-system)
    (propagated-inputs (list r-this-path r-stringr r-pryr r-log4r r-beepr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/r-leyshon/ptspotter")
    (synopsis "Helper Functions for Use with \"ProjectTemplate\"")
    (description
     "Utility functions produced specifically for (but not limited to) working with
@code{ProjectTemplate} data pipelines.  This package helps to quickly create and
manage sequentially numbered scripts, quickly set up logging with log4r and
functions to help debug and monitor procedures.")
    (license license:expat)))

(define-public r-ptprocess
  (package
    (name "r-ptprocess")
    (version "3.3-16")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PtProcess" version))
       (sha256
        (base32 "1d5i847jy9i1llbfsdkqcjmiids3kjxnqscinrwqyv7mwb9ijk2z"))))
    (properties `((upstream-name . "PtProcess")))
    (build-system r-build-system)
    (home-page "https://www.statsresearch.co.nz/dsh/sslib/")
    (synopsis "Time Dependent Point Process Modelling")
    (description
     "Fits and analyses time dependent marked point process models with an emphasis on
earthquake modelling.  For a more detailed introduction to the package, see the
topic \"@code{PtProcess}\".  A list of recent changes can be found in the topic
\"Change Log\".")
    (license license:gpl2+)))

(define-public r-ptools
  (package
    (name "r-ptools")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ptools" version))
       (sha256
        (base32 "12k47f2nwf5r0d4r1n3qksdsd1gk3ax519hqz6803hynwlcqx2a3"))))
    (properties `((upstream-name . "ptools")))
    (build-system r-build-system)
    (propagated-inputs (list r-spatstat-utils
                             r-spatstat-geom
                             r-sp
                             r-sf
                             r-raster
                             r-rann
                             r-partitions
                             r-igraph))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/apwheele/ptools")
    (synopsis "Tools for Poisson Data")
    (description
     "This package provides functions used for analyzing count data, mostly crime
counts.  Includes checking difference in two Poisson counts (e-test), checking
the fit for a Poisson distribution, small sample tests for counts in bins,
Weighted Displacement Difference test (Wheeler and Ratcliffe, 2018)
<doi:10.1186/s40163-018-0085-5>, to evaluate crime changes over time in
treated/control areas.  Additionally includes functions for aggregating spatial
data and spatial feature engineering.")
    (license license:expat)))

(define-public r-ptmixed
  (package
    (name "r-ptmixed")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ptmixed" version))
       (sha256
        (base32 "1wdasnmddb2k8n7vzsjqw6kjg6hgh8bjn6m8fgrhxdrpp2pgrqx4"))))
    (properties `((upstream-name . "ptmixed")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv
                             r-mvtnorm
                             r-moments
                             r-matrixcalc
                             r-lme4
                             r-glmmadaptive))
    (native-inputs (list r-knitr))
    (home-page "https://mirkosignorelli.github.io/r")
    (synopsis "Poisson-Tweedie Generalized Linear Mixed Model")
    (description
     "Fits the Poisson-Tweedie generalized linear mixed model described in Signorelli
et al. (2021, <doi:10.1177/1471082X20936017>).  Likelihood approximation based
on adaptive Gauss Hermite quadrature rule.")
    (license license:gpl3)))

(define-public r-ptm
  (package
    (name "r-ptm")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ptm" version))
       (sha256
        (base32 "1g5yfck6dnlb5bc3cqjch9x7k9z4p84q84nnakjnf56c3ddvwrjr"))))
    (properties `((upstream-name . "ptm")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-httr r-curl r-bio3d))
    (home-page "https://bitbucket.org/jcaledo/ptm")
    (synopsis "Analyses of Protein Post-Translational Modifications")
    (description
     "This package contains utilities for the analysis of post-translational
modifications (PTMs) in proteins, with particular emphasis on the sulfoxidation
of methionine residues.  Features include the ability to download, filter and
analyze data from the sulfoxidation database @code{MetOSite}'.  Utilities to
search and characterize S-aromatic motifs in proteins are also provided.  In
addition, functions to analyze sequence environments around modifiable residues
in proteins can be found.  For instance, ptm allows to search for amino acids
either overrepresented or avoided around the modifiable residues from the
proteins of interest.  Functions tailored to test statistical hypothesis related
to these differential sequence environments are also implemented.  Further and
detailed information regarding the methods in this package can be found in
(Aledo (2020) <https://metositeptm.com>).")
    (license license:gpl2+)))

(define-public r-ptitan2
  (package
    (name "r-ptitan2")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pTITAN2" version))
       (sha256
        (base32 "1mwx7k8ql4l572xvwb5gns9n4kqk779wwm4sg7nbgzz8xvcdh665"))))
    (properties `((upstream-name . "pTITAN2")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/USEPA/pTITAN2")
    (synopsis "Permutations of Treatment Labels and TITAN2 Analysis")
    (description
     "Permute treatment labels for taxa and environmental gradients to generate an
empirical distribution of change points.  This is an extension for the TITAN2
package <https://cran.r-project.org/package=TITAN2>.")
    (license license:expat)))

(define-public r-ptinpoly
  (package
    (name "r-ptinpoly")
    (version "2.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ptinpoly" version))
       (sha256
        (base32 "126chp92ha48agx5i819bzdij79zbgj32msijvaid3zhxjx7qy1j"))))
    (properties `((upstream-name . "ptinpoly")))
    (build-system r-build-system)
    (propagated-inputs (list r-misc3d))
    (home-page "http://ptinpoly.pbworks.com")
    (synopsis "Point-in-Polyhedron Test (2D and 3D)")
    (description
     "Function pip3d() tests whether a point in 3D space is within, exactly on, or
outside an enclosed surface defined by a triangular mesh.  Function pip2d()
tests whether a point in 2D space is within, exactly on, or outside a polygon.
For a reference, see: Liu et al., A new point containment test algorithm based
on preprocessing and determining triangles, Computer-Aided Design
42(12):1143-1150.")
    (license license:gpl2)))

(define-public r-pterp
  (package
    (name "r-pterp")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PTERP" version))
       (sha256
        (base32 "1mzb3lm1wrbya5lmgndrkz5arwpz5zr4gfl5bnvcbs0g74xlsa44"))))
    (properties `((upstream-name . "PTERP")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mvtnorm r-mass))
    (home-page "https://cran.r-project.org/package=PTERP")
    (synopsis "PTE and RP for Optimally-Transformed Surrogate")
    (description
     "Evaluates the strength of a surrogate marker by estimating the proportion of
treatment effect explained (PTE) and relative power(RP) for the
optimally-transformed version of the surrogate.  Details available in Wang et al
(2022) <@code{arXiv:2209.08414>}.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-pte
  (package
    (name "r-pte")
    (version "1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PTE" version))
       (sha256
        (base32 "1azkrij4kfmvp03lnzm88gwgvz8g54jq94j0qg8ynxvyjzrppc98"))))
    (properties `((upstream-name . "PTE")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-foreach r-doparallel))
    (home-page "https://cran.r-project.org/package=PTE")
    (synopsis "Personalized Treatment Evaluator")
    (description
     "We provide inference for personalized medicine models.  Namely, we answer the
questions: (1) how much better does a purported personalized recommendation
engine for treatments do over a business-as-usual approach and (2) is that
difference statistically significant?")
    (license license:gpl3)))

(define-public r-ptak
  (package
    (name "r-ptak")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PTAk" version))
       (sha256
        (base32 "1xnjils6i36mpzzg7lpi1bpq90mm4gwp4szk6fcg9i4dvkrznfki"))))
    (properties `((upstream-name . "PTAk")))
    (build-system r-build-system)
    (propagated-inputs (list r-tensor))
    (home-page "https://www.GeotRYcs.com")
    (synopsis "Principal Tensor Analysis on k Modes")
    (description
     "This package provides a multiway method to decompose a tensor (array) of any
order, as a generalisation of SVD also supporting non-identity metrics and
penalisations.  2-way SVD with these extensions is also available.  The package
includes also some other multiway methods: PCAn (Tucker-n) and PARAFAC/CANDECOMP
with these extensions.")
    (license license:gpl2+)))

(define-public r-ptable
  (package
    (name "r-ptable")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ptable" version))
       (sha256
        (base32 "142nw1rq9nm9fc6amcsdfy4qnv89p5yz90cxxbw1r4bylm86grs2"))))
    (properties `((upstream-name . "ptable")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmarkdown
                             r-rlang
                             r-rcolorbrewer
                             r-nloptr
                             r-ggplot2
                             r-flexdashboard
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sdcTools/ptable")
    (synopsis "Generation of Perturbation Tables for the Cell-Key Method")
    (description
     "Tabular data from statistical institutes and agencies are mostly confidential
and must be protected prior to publications.  The cell-key method is a
post-tabular Statistical Disclosure Control perturbation technique that adds
random noise to tabular data.  The statistical properties of the perturbations
are defined by some noise probability distributions - also referred to as
perturbation tables.  This tool can be used to create the perturbation tables
based on a maximum entropy approach as described for example in Giessing (2016)
<doi:10.1007/978-3-319-45381-1_18>.  The perturbation tables created can finally
be used to apply a cell-key method to frequency count or magnitude tables.")
    (license (license:fsdg-compatible "EUPL"))))

(define-public r-psyverse
  (package
    (name "r-psyverse")
    (version "0.2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psyverse" version))
       (sha256
        (base32 "1ry7n2nsj3vq8j5f10iqcldg3bas9mr1995d7hm1ih6dc4gbg934"))))
    (properties `((upstream-name . "psyverse")))
    (build-system r-build-system)
    (propagated-inputs (list r-yum r-yaml))
    (native-inputs (list r-knitr))
    (home-page "https://psyverse.one")
    (synopsis "Decentralized Unequivocality in Psychological Science")
    (description
     "The constructs used to study the human psychology have many definitions and
corresponding instructions for eliciting and coding qualitative data pertaining
to constructs content and for measuring the constructs.  This plethora of
definitions and instructions necessitates unequivocal reference to specific
definitions and instructions in empirical and secondary research.  This package
implements a human- and machine-readable standard for specifying construct
definitions and instructions for measurement and qualitative research based on
YAML'.  This standard facilitates systematic unequivocal reference to specific
construct definitions and corresponding instructions in a decentralized manner
(i.e.  without requiring central curation; Peters (2020)
<doi:10.31234/osf.io/xebhn>).")
    (license license:gpl3+)))

(define-public r-psyphy
  (package
    (name "r-psyphy")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psyphy" version))
       (sha256
        (base32 "0s3bdzbjbdapfgg44qlk4dp2m3h3hhcqy93vh32170rzkxj82p4p"))))
    (properties `((upstream-name . "psyphy")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=psyphy")
    (synopsis "Functions for Analyzing Psychophysical Data in R")
    (description
     "An assortment of functions that could be useful in analyzing data from
psychophysical experiments.  It includes functions for calculating d from
several different experimental designs, links for m-alternative forced-choice
(mafc) data to be used with the binomial family in glm (and possibly other
contexts) and self-Start functions for estimating gamma values for CRT screen
calibrations.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-psyntur
  (package
    (name "r-psyntur")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psyntur" version))
       (sha256
        (base32 "0dh7vdj3ynsxibcf0wmh5h4yknijg78azjzlma25glihpgd2nbbw"))))
    (properties `((upstream-name . "psyntur")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-rlang
                             r-purrr
                             r-psych
                             r-plyr
                             r-magrittr
                             r-ggthemes
                             r-ggplot2
                             r-ggally
                             r-formula-tools
                             r-fastdummies
                             r-ez
                             r-effsize
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=psyntur")
    (synopsis "Helper Tools for Teaching Statistical Data Analysis")
    (description
     "This package provides functions and data-sets that are helpful for teaching
statistics and data analysis.  It was originally designed for use when teaching
students in the Psychology Department at Nottingham Trent University.")
    (license license:gpl3)))

(define-public r-psymetadata
  (package
    (name "r-psymetadata")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psymetadata" version))
       (sha256
        (base32 "1m1b1fhiv31hjc7iy20c065w25mgzmsbp0vlh2vlzrlqmfw0qpwv"))))
    (properties `((upstream-name . "psymetadata")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack))
    (home-page "https://cran.r-project.org/package=psymetadata")
    (synopsis "Open Datasets from Meta-Analyses in Psychology Research")
    (description
     "Data and examples from meta-analyses in psychology research.")
    (license license:gpl2+)))

(define-public r-psycontrol
  (package
    (name "r-psycontrol")
    (version "1.0.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PsyControl" version))
       (sha256
        (base32 "1c5ml6s3c8kzf7p0mig84bmq40b0491ah713bm6jpy2s3nwnmafs"))))
    (properties `((upstream-name . "PsyControl")))
    (build-system r-build-system)
    (propagated-inputs (list r-ltm r-irtoys))
    (home-page "https://cran.r-project.org/package=PsyControl")
    (synopsis "CUSUM Person Fit Statistics")
    (description
     "Person fit statistics based on Quality Control measures are provided for
questionnaires and tests given a specified IRT model.  Statistics based on
Cumulative Sum (CUSUM) charts are provided.  Options are given for banks with
polytomous and dichotomous data.")
    (license license:gpl2)))

(define-public r-psycmodel
  (package
    (name "r-psycmodel")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psycModel" version))
       (sha256
        (base32 "08n2vc5mjzv28qxayzw7b20y5ci4vgh0ppks4mpyx0a97hnnz24r"))))
    (properties `((upstream-name . "psycModel")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-psych
                             r-performance
                             r-patchwork
                             r-parameters
                             r-lmertest
                             r-lme4
                             r-lifecycle
                             r-lavaan
                             r-insight
                             r-glue
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jasonmoy28/psycModel")
    (synopsis
     "Integrated Toolkit for Psychological Analysis and Modeling in R")
    (description
     "This package provides a beginner-friendly R package for modeling in psychology
or related field.  It allows fitting models, plotting, checking goodness of fit,
and model assumption violations all in one place.  It also produces beautiful
and easy-to-read output.")
    (license license:gpl3+)))

(define-public r-psychwordvec
  (package
    (name "r-psychwordvec")
    (version "2023.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PsychWordVec" version))
       (sha256
        (base32 "04dp4k0x2nmcfiqbyzirhr595lgishxh0qbymy210g96sghbrpf3"))))
    (properties `((upstream-name . "PsychWordVec")))
    (build-system r-build-system)
    (propagated-inputs (list r-word2vec
                             r-vroom
                             r-text2vec
                             r-text
                             r-stringr
                             r-rtsne
                             r-rsparse
                             r-rgl
                             r-reticulate
                             r-qgraph
                             r-purrr
                             r-psych
                             r-ggrepel
                             r-ggplot2
                             r-fasttextr
                             r-dplyr
                             r-data-table
                             r-corrplot
                             r-cli
                             r-brucer))
    (home-page "https://psychbruce.github.io/PsychWordVec/")
    (synopsis "Word Embedding Research Framework for Psychological Science")
    (description
     "An integrative toolbox of word embedding research that provides: (1) a
collection of pre-trained static word vectors in the .RData compressed format
<https://psychbruce.github.io/@code{WordVector_RData.pdf>}; (2) a series of
functions to process, analyze, and visualize word vectors; (3) a range of tests
to examine conceptual associations, including the Word Embedding Association
Test <doi:10.1126/science.aal4230> and the Relative Norm Distance
<doi:10.1073/pnas.1720347115>, with permutation test of significance; (4) a set
of training methods to locally train (static) word vectors from text corpora,
including Word2Vec <@code{arXiv:1301.3781>}, @code{GloVe}
<doi:10.3115/v1/D14-1162>, and @code{FastText} <@code{arXiv:1607.04606>}; (5) a
group of functions to download pre-trained language models (e.g., GPT', BERT')
and extract contextualized (dynamic) word vectors (based on the R package
text').")
    (license license:gpl3)))

(define-public r-psychtools
  (package
    (name "r-psychtools")
    (version "2.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psychTools" version))
       (sha256
        (base32 "14hk7f714cjq6grrzqi6l0r5f7jp2j0sns8acprqyqw36siqck78"))))
    (properties `((upstream-name . "psychTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-rtf r-psych r-foreign))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=psychTools")
    (synopsis
     "Tools to Accompany the 'psych' Package for Psychological Research")
    (description
     "Support functions, data sets, and vignettes for the psych package.  Contains
several of the biggest data sets for the psych package as well as four
vignettes.  A few helper functions for file manipulation are included as well.
For more information, see the <https://personality-project.org/r/> web page.")
    (license license:gpl2+)))

(define-public r-psychrolib
  (package
    (name "r-psychrolib")
    (version "2.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psychrolib" version))
       (sha256
        (base32 "0d43zhpl5ik90hqpdy62x7rwmxag3z1mdrn3cl1whr04rmymhk20"))))
    (properties `((upstream-name . "psychrolib")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://github.com/psychrometrics/psychrolib")
    (synopsis "Psychrometric Properties of Moist and Dry Air")
    (description
     "Implementation of @code{PsychroLib}
<https://github.com/psychrometrics/psychrolib> library which contains functions
to enable the calculation properties of moist and dry air in both metric (SI)
and imperial (IP) systems of units.  References: Meyer, D. and Thevenard, D
(2019) <doi:10.21105/joss.01137>.")
    (license license:expat)))

(define-public r-psychreport
  (package
    (name "r-psychreport")
    (version "3.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psychReport" version))
       (sha256
        (base32 "193y07hmx630wn83ss47i2rssl1pswgc87z2ygpkaw8y8vv46wk9"))))
    (properties `((upstream-name . "psychReport")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable r-ez r-dplyr r-cli r-broom))
    (home-page "https://cran.r-project.org/package=psychReport")
    (synopsis "Reproducible Reports in Psychology")
    (description
     "Helper functions for producing reports in Psychology (Reproducible Research).
Provides required formatted strings (APA style) for use in Knitr'/'Latex
integration within *.Rnw files.")
    (license license:expat)))

(define-public r-psychotree
  (package
    (name "r-psychotree")
    (version "0.16-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psychotree" version))
       (sha256
        (base32 "04ipl6kadfvyl28wx8jbpisb4pcswrwq1qiqm90h47ldbnmha932"))))
    (properties `((upstream-name . "psychotree")))
    (build-system r-build-system)
    (propagated-inputs (list r-psychotools r-partykit r-formula))
    (home-page "https://cran.r-project.org/package=psychotree")
    (synopsis "Recursive Partitioning Based on Psychometric Models")
    (description
     "Recursive partitioning based on psychometric models, employing the general MOB
algorithm (from package partykit) to obtain Bradley-Terry trees, Rasch trees,
rating scale and partial credit trees, and MPT trees, trees for 1PL, 2PL, 3PL
and 4PL models and generalized partial credit models.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-psychotools
  (package
    (name "r-psychotools")
    (version "0.7-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psychotools" version))
       (sha256
        (base32 "197hym0fxfsyg6kf0pf9g5wbsa1kkicd4lqrxfy2g3c7aczad9zy"))))
    (properties `((upstream-name . "psychotools")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=psychotools")
    (synopsis "Psychometric Modeling Infrastructure")
    (description
     "Infrastructure for psychometric modeling such as data classes (for item response
data and paired comparisons), basic model fitting functions (for Bradley-Terry,
Rasch, parametric logistic IRT, generalized partial credit, rating scale,
multinomial processing tree models), extractor functions for different types of
parameters (item, person, threshold, discrimination, guessing, upper
asymptotes), unified inference and visualizations, and various datasets for
illustration.  Intended as a common lightweight and efficient toolbox for
psychometric modeling and a common building block for fitting psychometric
mixture models in package \"psychomix\" and trees based on psychometric models in
package \"psychotree\".")
    (license (list license:gpl2 license:gpl3))))

(define-public r-psychonetrics
  (package
    (name "r-psychonetrics")
    (version "0.13")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psychonetrics" version))
       (sha256
        (base32 "0yrpw1dcz8mzfydv48dk4liqhfcwqh3s7c9xpfcniisab5hvq3m6"))))
    (properties `((upstream-name . "psychonetrics")))
    (build-system r-build-system)
    (propagated-inputs (list r-vca
                             r-tidyr
                             r-roptim
                             r-rlang
                             r-rcpparmadillo
                             r-rcpp
                             r-qgraph
                             r-psych
                             r-pbv
                             r-pbapply
                             r-optimx
                             r-numderiv
                             r-mgcv
                             r-matrix
                             r-magrittr
                             r-lavaan
                             r-isingsampler
                             r-glasso
                             r-ga
                             r-dplyr
                             r-corpcor
                             r-combinat
                             r-abind))
    (home-page "http://psychonetrics.org/")
    (synopsis "Structural Equation Modeling and Confirmatory Network Analysis")
    (description
     "Multi-group (dynamical) structural equation models in combination with
confirmatory network models from cross-sectional, time-series and panel data
<doi:10.31234/osf.io/8ha93>.  Allows for confirmatory testing and fit as well as
exploratory model search.")
    (license license:gpl2)))

(define-public r-psychomix
  (package
    (name "r-psychomix")
    (version "1.1-9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psychomix" version))
       (sha256
        (base32 "0mr9ibzzn203r8hxvgf3927r7ml30ikifv6ghc0apj5v50phjhvv"))))
    (properties `((upstream-name . "psychomix")))
    (build-system r-build-system)
    (propagated-inputs (list r-psychotools r-modeltools r-lattice r-formula
                             r-flexmix))
    (home-page "https://cran.r-project.org/package=psychomix")
    (synopsis "Psychometric Mixture Models")
    (description
     "Psychometric mixture models based on flexmix infrastructure.  At the moment
Rasch mixture models with different parameterizations of the score distribution
(saturated vs. mean/variance specification), Bradley-Terry mixture models, and
MPT mixture models are implemented.  These mixture models can be estimated with
or without concomitant variables.  See Frick et al. (2012)
<doi:10.18637/jss.v048.i07> and Frick et al. (2015)
<doi:10.1177/0013164414536183> for details on the Rasch mixture models.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-psychometric
  (package
    (name "r-psychometric")
    (version "2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psychometric" version))
       (sha256
        (base32 "1mlggzw80pb7kfykva1s4ic726l8dckrgik2519njh32ycvk9x1s"))))
    (properties `((upstream-name . "psychometric")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr r-nlme r-multilevel r-dplyr))
    (home-page "https://cran.r-project.org/package=psychometric")
    (synopsis "Applied Psychometric Theory")
    (description
     "This package contains functions useful for correlation theory, meta-analysis
(validity-generalization), reliability, item analysis, inter-rater reliability,
and classical utility.")
    (license license:gpl2+)))

(define-public r-psycho
  (package
    (name "r-psycho")
    (version "0.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psycho" version))
       (sha256
        (base32 "1a3qccr9jwrbwkfbrvckkhhk9qygrc5n4mq80kpzx0zy5bylxi0a"))))
    (properties `((upstream-name . "psycho")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-scales
                             r-parameters
                             r-insight
                             r-ggplot2
                             r-effectsize
                             r-dplyr
                             r-bayestestr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/neuropsychology/psycho.R")
    (synopsis
     "Efficient and Publishing-Oriented Workflow for Psychological Science")
    (description
     "The main goal of the psycho package is to provide tools for psychologists,
neuropsychologists and neuroscientists, to facilitate and speed up the time
spent on data analysis.  It aims at supporting best practices and tools to
format the output of statistical methods to directly paste them into a
manuscript, ensuring statistical reporting standardization and conformity.")
    (license license:expat)))

(define-public r-psychmeta
  (package
    (name "r-psychmeta")
    (version "2.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psychmeta" version))
       (sha256
        (base32 "1705zj755sw6pigyqrh62z84cpi3yvgmqfzn8gl7bpgc1fjyryck"))))
    (properties `((upstream-name . "psychmeta")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-progress
                             r-metafor
                             r-ggplot2
                             r-dplyr
                             r-curl
                             r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=psychmeta")
    (synopsis "Psychometric Meta-Analysis Toolkit")
    (description
     "This package provides tools for computing bare-bones and psychometric
meta-analyses and for generating psychometric data for use in meta-analysis
simulations.  Supports bare-bones, individual-correction, and
artifact-distribution methods for meta-analyzing correlations and d values.
Includes tools for converting effect sizes, computing sporadic artifact
corrections, reshaping meta-analytic databases, computing multivariate
corrections for range variation, and more.  Bugs can be reported to
<https://github.com/psychmeta/psychmeta/issues> or <issues@@psychmeta.com>.")
    (license license:gpl3+)))

(define-public r-psyccleaning
  (package
    (name "r-psyccleaning")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psycCleaning" version))
       (sha256
        (base32 "1xpywbf77ayx56mvcgxjch1fm6cw0al1sr7vqn3di5c56gmxn7p3"))))
    (properties `((upstream-name . "psycCleaning")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-tibble r-rlang r-dplyr r-data-table))
    (home-page "https://jasonmoy28.github.io/psycCleaning/")
    (synopsis "Data Cleaning for Psychological Analyses")
    (description
     "Useful for preparing and cleaning data.  It includes functions to center data,
reverse coding, dummy code and effect code data, and more.")
    (license license:gpl3+)))

(define-public r-psy
  (package
    (name "r-psy")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psy" version))
       (sha256
        (base32 "1cps0lc31l2zchlmrmbwhyz9zjxkl4rcjja838cnqci7plcav28l"))))
    (properties `((upstream-name . "psy")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=psy")
    (synopsis "Various Procedures Used in Psychometrics")
    (description
     "Kappa, ICC, reliability coefficient, parallel analysis, multi-traits
multi-methods, spherical representation of a correlation matrix.")
    (license license:gpl2+)))

(define-public r-psweight
  (package
    (name "r-psweight")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PSweight" version))
       (sha256
        (base32 "1p0iq95jwhh6j3gw3zd8m58rryy8j33k2lrzypb5qzhwrgx2i41v"))))
    (properties `((upstream-name . "PSweight")))
    (build-system r-build-system)
    (propagated-inputs (list r-superlearner
                             r-numderiv
                             r-nnet
                             r-mass
                             r-lme4
                             r-ggplot2
                             r-gbm))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/thuizhou/PSweight")
    (synopsis
     "Propensity Score Weighting for Causal Inference with Observational Studies and Randomized Trials")
    (description
     "Supports propensity score weighting analysis of observational studies and
randomized trials.  Enables the estimation and inference of average causal
effects with binary and multiple treatments using overlap weights (ATO), inverse
probability of treatment weights (ATE), average treatment effect among the
treated weights (ATT), matching weights (ATM) and entropy weights (ATEN), with
and without propensity score trimming.  These weights are members of the family
of balancing weights introduced in Li, Morgan and Zaslavsky (2018)
<doi:10.1080/01621459.2016.1260466> and Li and Li (2019)
<doi:10.1214/19-AOAS1282>.")
    (license license:gpl2+)))

(define-public r-psw
  (package
    (name "r-psw")
    (version "1.1-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PSW" version))
       (sha256
        (base32 "0ahm7rp795d7j88n15b3q0gl573p3g0krc7jd1zv87g4bsdc9b5x"))))
    (properties `((upstream-name . "PSW")))
    (build-system r-build-system)
    (propagated-inputs (list r-hmisc r-gtools))
    (home-page "https://cran.r-project.org/package=PSW")
    (synopsis "Propensity Score Weighting Methods for Dichotomous Treatments")
    (description
     "This package provides propensity score weighting methods to control for
confounding in causal inference with dichotomous treatments and
continuous/binary outcomes.  It includes the following functional modules: (1)
visualization of the propensity score distribution in both treatment groups with
mirror histogram, (2) covariate balance diagnosis, (3) propensity score model
specification test, (4) weighted estimation of treatment effect, and (5)
augmented estimation of treatment effect with outcome regression.  The weighting
methods include the inverse probability weight (IPW) for estimating the average
treatment effect (ATE), the IPW for average treatment effect of the treated
(ATT), the IPW for the average treatment effect of the controls (ATC), the
matching weight (MW), the overlap weight (OVERLAP), and the trapezoidal weight
(TRAPEZOIDAL).  Sandwich variance estimation is provided to adjust for the
sampling variability of the estimated propensity score.  These methods are
discussed by Hirano et al (2003) <DOI:10.1111/1468-0262.00442>, Lunceford and
Davidian (2004) <DOI:10.1002/sim.1903>, Li and Greene (2013)
<DOI:10.1515/ijb-2012-0030>, and Li et al (2016)
<DOI:10.1080/01621459.2016.1260466>.")
    (license license:gpl2+)))

(define-public r-psvmsdr
  (package
    (name "r-psvmsdr")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psvmSDR" version))
       (sha256
        (base32 "1mlxrlq3h3q6230xkb15cn7pyccvi7flbkm8q7dsp812dadsf449"))))
    (properties `((upstream-name . "psvmSDR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=psvmSDR")
    (synopsis "Unified Principal Sufficient Dimension Reduction Package")
    (description
     "This package provides a unified and user-friendly framework for applying the
principal sufficient dimension reduction methods for both linear and nonlinear
cases.  The package has an extendable power by varying loss functions for the
support vector machine, even for an user-defined arbitrary function, unless
those are convex and differentiable everywhere over the support (Li et al.
(2011) <doi:10.1214/11-AOS932>).  Also, it provides a real-time sufficient
dimension reduction update procedure using the principal least squares support
vector machine (Artemiou et al. (2021) <doi:10.1016/j.patcog.2020.107768>).")
    (license license:gpl2)))

(define-public r-pstrata
  (package
    (name "r-pstrata")
    (version "0.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PStrata" version))
       (sha256
        (base32 "1nzvspzihxwra0hzyc8cfnml19xz26dam23y2q09794dpg5a7pcf"))))
    (properties `((upstream-name . "PStrata")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rstan
                             r-purrr
                             r-lme4
                             r-ggplot2
                             r-dplyr
                             r-abind))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=PStrata")
    (synopsis "Principal Stratification Analysis in R")
    (description
     "Estimating causal effects in the presence of post-treatment confounding using
principal stratification.  PStrata allows for customized monotonicity
assumptions and exclusion restriction assumptions, with automatic full Bayesian
inference supported by Stan'.  The main function to use in this package is
PStrata(), which provides posterior estimates of principal causal effect with
uncertainty quantification.  Visualization tools are also provided for diagnosis
and interpretation.  See Liu and Li (2023) <@code{arXiv:2304.02740>} for
details.")
    (license license:gpl2+)))

(define-public r-pstest
  (package
    (name "r-pstest")
    (version "0.1.3.900")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pstest" version))
       (sha256
        (base32 "1rhd4vgqpr9yq0pc3xql3nfgl30v20ldywapp9n967ky2dja4k0z"))))
    (properties `((upstream-name . "pstest")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-glmx))
    (home-page "https://github.com/pedrohcgs/pstest")
    (synopsis "Specification Tests for Parametric Propensity Score Models")
    (description
     "The propensity score is one of the most widely used tools in studying the causal
effect of a treatment, intervention, or policy.  Given that the propensity score
is usually unknown, it has to be estimated, implying that the reliability of
many treatment effect estimators depends on the correct specification of the
(parametric) propensity score.  This package implements the data-driven
nonparametric diagnostic tools for detecting propensity score misspecification
proposed by Sant'Anna and Song (2019) <doi:10.1016/j.jeconom.2019.02.002>.")
    (license license:gpl2)))

(define-public r-pstat
  (package
    (name "r-pstat")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Pstat" version))
       (sha256
        (base32 "17bqkaf9590jhiaamaafmvkqmy2aqkcfqhk8m4w35g58j48yk4yx"))))
    (properties `((upstream-name . "Pstat")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=Pstat")
    (synopsis "Assessing Pst Statistics")
    (description
     "Calculating Pst values to assess differentiation among populations from a set of
quantitative traits is the primary purpose of such a package.  The bootstrap
method provides confidence intervals and distribution histograms of Pst.
Variations of Pst in function of the parameter c/h^2 are studied as well.
Finally, the package proposes different transformations especially to eliminate
any variation resulting from allometric growth (calculation of residuals from
linear regressions, Reist standardizations or Aitchison transformation).")
    (license license:gpl2)))

(define-public r-pst
  (package
    (name "r-pst")
    (version "0.94.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PST" version))
       (sha256
        (base32 "0rd3mswcckgh5vfb1bpid9dki0yq0ph098jp2awnm21qzvx72va4"))))
    (properties `((upstream-name . "PST")))
    (build-system r-build-system)
    (propagated-inputs (list r-traminer r-rcolorbrewer))
    (home-page "https://r-forge.r-project.org/projects/pst")
    (synopsis "Probabilistic Suffix Trees and Variable Length Markov Chains")
    (description
     "This package provides a framework for analysing state sequences with
probabilistic suffix trees (PST), the construction that stores variable length
Markov chains (VLMC).  Besides functions for learning and optimizing VLMC
models, the PST library includes many additional tools to analyse sequence data
with these models: visualization tools, functions for sequence prediction and
artificial sequences generation, as well as for context and pattern mining.  The
package is specifically adapted to the field of social sciences by allowing to
learn VLMC models from sets of individual sequences possibly containing missing
values, and by accounting for case weights.  The library also allows to compute
probabilistic divergence between two models, and to fit segmented VLMC, where
sub-models fitted to distinct strata of the learning sample are stored in a
single PST. This software results from research work executed within the
framework of the Swiss National Centre of Competence in Research LIVES, which is
financed by the Swiss National Science Foundation.  The authors are grateful to
the Swiss National Science Foundation for its financial support.")
    (license license:gpl2+)))

(define-public r-pssubpathway
  (package
    (name "r-pssubpathway")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psSubpathway" version))
       (sha256
        (base32 "044midk0smvf0lmpv7gy7gls9gab06jrrb53cjgj58mdijm6db7x"))))
    (properties `((upstream-name . "psSubpathway")))
    (build-system r-build-system)
    (propagated-inputs (list r-pheatmap r-mpmi r-igraph r-gsva))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=psSubpathway")
    (synopsis "Flexible Identification of Phenotype-Specific Subpathways")
    (description
     "This package provides a network-based systems biology tool for flexible
identification of phenotype-specific subpathways in the cancer gene expression
data with multiple categories (such as multiple subtype or developmental stages
of cancer).  Subtype Set Enrichment Analysis (@code{SubSEA}) and Dynamic Changed
Subpathway Analysis (DCSA) are developed to flexible identify subtype specific
and dynamic changed subpathways respectively.  The operation modes include
extraction of subpathways from biological pathways, inference of subpathway
activities in the context of gene expression data, identification of subtype
specific subpathways with @code{SubSEA}, identification of dynamic changed
subpathways associated with the cancer developmental stage with DCSA, and
visualization of the activities of resulting subpathways by using box plots and
heat maps.  Its capabilities render the tool could find the specific abnormal
subpathways in the cancer dataset with multi-phenotype samples.")
    (license license:gpl2+)))

(define-public r-pssmooth
  (package
    (name "r-pssmooth")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pssmooth" version))
       (sha256
        (base32 "08syh52gjaf72kq01v6b5dljmwi29nvmjmkkgmjb97zsi15z1wd3"))))
    (properties `((upstream-name . "pssmooth")))
    (build-system r-build-system)
    (propagated-inputs (list r-osdesign r-np r-mass r-chngpt))
    (home-page "https://github.com/mjuraska/pssmooth")
    (synopsis
     "Flexible and Efficient Evaluation of Principal Surrogates/Treatment Effect Modifiers")
    (description
     "This package implements estimation and testing procedures for evaluating an
intermediate biomarker response as a principal surrogate of a clinical response
to treatment (i.e., principal stratification effect modification analysis), as
described in Juraska M, Huang Y, and Gilbert PB (2020), Inference on treatment
effect modification by biomarker response in a three-phase sampling design,
Biostatistics, 21(3): 545-560 <doi:10.1093/biostatistics/kxy074>.  The methods
avoid the restrictive placebo structural risk modeling assumption common to past
methods and further improve robustness by the use of nonparametric kernel
smoothing for biomarker density estimation.  A randomized controlled two-group
clinical efficacy trial is assumed with an ordered categorical or continuous
univariate biomarker response measured at a fixed timepoint post-randomization
and with a univariate baseline surrogate measure allowed to be observed in only
a subset of trial participants with an observed biomarker response (see the
flexible three-phase sampling design in the paper for details).  Bootstrap-based
procedures are available for pointwise and simultaneous confidence intervals and
testing of four relevant hypotheses.  Summary and plotting functions are
provided for estimation results.")
    (license license:gpl2)))

(define-public r-pssmcool
  (package
    (name "r-pssmcool")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PSSMCOOL" version))
       (sha256
        (base32 "0iyhs2sas08kjl6hd0y4n4rz4lvvvlrz3w3izl3may6mscykrciy"))))
    (properties `((upstream-name . "PSSMCOOL")))
    (build-system r-build-system)
    (propagated-inputs (list r-phontools r-infotheo r-gtools r-dtt))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/BioCool-Lab/PSSMCOOL")
    (synopsis
     "Features Extracted from Position Specific Scoring Matrix (PSSM)")
    (description
     "Returns almost all features that has been extracted from Position Specific
Scoring Matrix (PSSM) so far, which is a matrix of L rows (L is protein length)
and 20 columns produced by PSI-BLAST which is a program to produce PSSM Matrix
from multiple sequence alignment of proteins see
<https://www.ncbi.nlm.nih.gov/books/NBK2590/> for mor details.  some of these
features are described in Zahiri, J., et al.(2013)
<DOI:10.1016/j.ygeno.2013.05.006>, Saini, H., et al.(2016)
<DOI:10.17706/jsw.11.8.756-767>, Ding, S., et al.(2014)
<DOI:10.1016/j.biochi.2013.09.013>, Cheng, C.W., et al.(2008)
<DOI:10.1186/1471-2105-9-S12-S6>, Juan, E.Y., et al.(2009)
<DOI:10.1109/CISIS.2009.194>.")
    (license license:gpl3)))

(define-public r-pssim
  (package
    (name "r-pssim")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PSSIM" version))
       (sha256
        (base32 "144lh31b1c4ycq0wrgf9745q0c8b5sfnmrd0f7vwx95i86bzr3mj"))))
    (properties `((upstream-name . "PSSIM")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PSSIM")
    (synopsis
     "Test of Independence & Image Structural Similarity Measure PSSIM")
    (description
     "Test-based Image structural similarity measure and test of independence.  This
package implements the key functions of two tasks: (1) computing image
structural similarity measure PSSIM of Wang, Maldonado and Silwal (2011)
<DOI:10.1016/j.csda.2011.04.021>; and (2) test of independence between a
response and a covariate in presence of heteroscedastic treatment effects
proposed by Wang, Tolos, and Wang (2010) <DOI:10.1002/cjs.10068>.")
    (license license:gpl2)))

(define-public r-pss-health
  (package
    (name "r-pss-health")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PSS.Health" version))
       (sha256
        (base32 "0vkg727rhh267vvcf8zkf5k6wm0k54vqysrsm4zc3sin3w1chnaf"))))
    (properties `((upstream-name . "PSS.Health")))
    (build-system r-build-system)
    (propagated-inputs (list r-writexl
                             r-shinyhelper
                             r-shinyfeedback
                             r-shinycssloaders
                             r-shiny
                             r-pwr2
                             r-pwr
                             r-proc
                             r-presize
                             r-powersurvepi
                             r-powermediation
                             r-plotly
                             r-longpower
                             r-kappasize
                             r-icc-sample-size
                             r-ggplot2
                             r-epir
                             r-envstats
                             r-easypower
                             r-dt
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://hcpa-unidade-bioestatistica.shinyapps.io/PSS_Health/")
    (synopsis "Power and Sample Size for Health Researchers via Shiny")
    (description
     "Power and Sample Size for Health Researchers is a Shiny application that brings
together a series of functions related to sample size and power calculations for
common analysis in the healthcare field.  There are functionalities to calculate
the power, sample size to estimate or test hypotheses for means and proportions
(including test for correlated groups, equivalence, non-inferiority and
superiority), association, correlations coefficients, regression coefficients
(linear, logistic, gamma, and Cox), linear mixed model, Cronbach's alpha,
interobserver agreement, intraclass correlation coefficients, limit of agreement
on Bland-Altman plots, area under the curve, sensitivity and specificity
incorporating the prevalence of disease.  You can also use the online version at
<https://hcpa-unidade-bioestatistica.shinyapps.io/PSS_Health/>.")
    (license license:gpl2+)))

(define-public r-psqn
  (package
    (name "r-psqn")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psqn" version))
       (sha256
        (base32 "0zy5h7v9fh07kjqjk5vc1d4354xk56227gkvq9jn8cy8igdb6lyg"))))
    (properties `((upstream-name . "psqn")))
    (build-system r-build-system)
    (propagated-inputs (list r-testthat r-rcppeigen r-rcpp r-matrix))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/boennecd/psqn")
    (synopsis "Partially Separable Quasi-Newton")
    (description
     "This package provides quasi-Newton methods to minimize partially separable
functions.  The methods are largely described by Nocedal and Wright (2006)
<doi:10.1007/978-0-387-40065-5>.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-pspmanalysis
  (package
    (name "r-pspmanalysis")
    (version "0.3.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PSPManalysis" version))
       (sha256
        (base32 "0wc4rkkwqwjhx54n05bjc2q370bb3ca1c7annxg89bmr9msf8zd1"))))
    (properties `((upstream-name . "PSPManalysis")))
    (build-system r-build-system)
    (propagated-inputs (list r-rstudioapi r-pkgbuild))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=PSPManalysis")
    (synopsis "Analysis of Physiologically Structured Population Models")
    (description
     "This package performs demographic, bifurcation and evolutionary analysis of
physiologically structured population models, which is a class of models that
consistently translates continuous-time models of individual life history to the
population level.  A model of individual life history has to be implemented
specifying the individual-level functions that determine the life history, such
as development and mortality rates and fecundity.  M.A. Kirkilionis, O.
Diekmann, B. Lisser, M. Nool, B. Sommeijer & A.M. de Roos (2001)
<doi:10.1142/S0218202501001264>.  O.Diekmann, M.Gyllenberg & J.A.J.Metz (2003)
<doi:10.1016/S0040-5809(02)00058-8>.  A.M. de Roos (2008)
<doi:10.1111/j.1461-0248.2007.01121.x>.")
    (license license:gpl3)))

(define-public r-pspline
  (package
    (name "r-pspline")
    (version "1.0-20")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pspline" version))
       (sha256
        (base32 "12pbjdxvb92qwzxrdag3cgaa12v9psy5whvw8p7i0p8dhydwv9za"))))
    (properties `((upstream-name . "pspline")))
    (build-system r-build-system)
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=pspline")
    (synopsis "Penalized Smoothing Splines")
    (description "Smoothing splines with penalties on order m derivatives.")
    (license (license:fsdg-compatible "Unlimited"))))

(define-public r-pspearman
  (package
    (name "r-pspearman")
    (version "0.3-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pspearman" version))
       (sha256
        (base32 "09l3knwaj4xs9dfz1xcj4mbynd4w1vxcgmzfqxz97czzbf7ingwh"))))
    (properties `((upstream-name . "pspearman")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pspearman")
    (synopsis "Spearman's Rank Correlation Test")
    (description
     "Spearman's rank correlation test with precomputed exact null distribution for n
<= 22.")
    (license license:gpl3)))

(define-public r-pspatreg
  (package
    (name "r-pspatreg")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pspatreg" version))
       (sha256
        (base32 "0giaxz7zdwik6qw9xlgipxwksdia044y2h64ik2bz03hi21w5n2i"))))
    (properties `((upstream-name . "pspatreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-spdep
                             r-spatialreg
                             r-sf
                             r-rdpack
                             r-plm
                             r-numderiv
                             r-minqa
                             r-mba
                             r-matrix
                             r-mass
                             r-ggplot2
                             r-fields
                             r-dplyr
                             r-ameshousing))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rominsal/pspatreg")
    (synopsis
     "Spatial and Spatio-Temporal Semiparametric Regression Models with Spatial Lags")
    (description
     "Estimation and inference of spatial and spatio-temporal semiparametric models
including spatial or spatio-temporal non-parametric trends, parametric and
non-parametric covariates and, possibly, a spatial lag for the dependent
variable and temporal correlation in the noise.  The spatio-temporal trend can
be decomposed in ANOVA way including main and interaction functional terms.  Use
of SAP algorithm to estimate the spatial or spatio-temporal trend and
non-parametric covariates.  The methodology of these models can be found in next
references Basile, R. et al. (2014), <doi:10.1016/j.jedc.2014.06.011>;
Rodriguez-Alvarez, M.X. et al. (2015) <doi:10.1007/s11222-014-9464-2> and,
particularly referred to the focus of the package, Minguez, R., Basile, R. and
Durban, M. (2020) <doi:10.1007/s10260-019-00492-8>.")
    (license license:gpl3)))

(define-public r-psp
  (package
    (name "r-psp")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psp" version))
       (sha256
        (base32 "10b93c3x39304j9wz2fqlmx1x417vpzdi9b11bjil8brdg5qipk6"))))
    (properties `((upstream-name . "psp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-data-table))
    (home-page "https://github.com/lenarddome/psp")
    (synopsis "Parameter Space Partitioning MCMC for Global Model Evaluation")
    (description
     "This package implements an n-dimensional parameter space partitioning algorithm
for evaluating the global behaviour of formal computational models as described
by Pitt, Kim, Navarro and Myung (2006) <doi:10.1037/0033-295X.113.1.57>.")
    (license license:gpl3+)))

(define-public r-psoptim
  (package
    (name "r-psoptim")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psoptim" version))
       (sha256
        (base32 "1yziabkd3h05cfl5jy5l8ji2y3w21acvxsq3inxyh0iwyr8qdkkl"))))
    (properties `((upstream-name . "psoptim")))
    (build-system r-build-system)
    (home-page "https://www.r-project.org")
    (synopsis "Particle Swarm Optimization")
    (description "Particle swarm optimization - a basic variant.")
    (license license:gpl2+)))

(define-public r-pso
  (package
    (name "r-pso")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pso" version))
       (sha256
        (base32 "0dq02pv86iwxpvd8wfbryxfsxqrrw4991iim60nyh8mn02gflkmi"))))
    (properties `((upstream-name . "pso")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pso")
    (synopsis "Particle Swarm Optimization")
    (description
     "This package provides an implementation of particle swarm optimisation
consistent with the standard PSO 2007/2011 by Maurice Clerc.  Additionally a
number of ancillary routines are provided for easy testing and graphics.")
    (license license:lgpl3)))

(define-public r-psminer
  (package
    (name "r-psminer")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psmineR" version))
       (sha256
        (base32 "0fc30hsqkbfjj7wz2s7yn1w3hrr80k8bmj6ah8diswrq1ms12k3i"))))
    (properties `((upstream-name . "psmineR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringi
                             r-rlang
                             r-glue
                             r-ggplot2
                             r-forcats
                             r-dplyr
                             r-data-table
                             r-cli
                             r-bupar))
    (home-page "https://bupar.net/")
    (synopsis "Performance Spectrum Miner for Event Data")
    (description
     "Compute detailed and aggregated performance spectrum for event data.  The
detailed performance spectrum describes the event data in terms of segments,
where the performance of each segment is measured and plotted for any
occurrences of this segment over time and can be classified, e.g., regarding the
overall population.  The aggregated performance spectrum visualises the amount
of cases of particular performance over time.  Denisov, V., Fahland, D., & van
der Aalst, W. M. P. (2018) <doi:10.1007/978-3-319-98648-7_9>.")
    (license license:expat)))

(define-public r-psm3mkv
  (package
    (name "r-psm3mkv")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psm3mkv" version))
       (sha256
        (base32 "0fbqyqgmjzqncx68x1cydfa9cm5k00k57d7hbb0iffcq08c0ya36"))))
    (properties `((upstream-name . "psm3mkv")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-survival
                             r-stringr
                             r-simplicialcubature
                             r-rlang
                             r-purrr
                             r-pharmaverseadam
                             r-ggplot2
                             r-flexsurv
                             r-dplyr
                             r-admiral))
    (native-inputs (list r-knitr))
    (home-page "https://merck.github.io/psm3mkv/")
    (synopsis "Evaluate Partitioned Survival and State Transition Models")
    (description
     "Fits and evaluates three-state partitioned survival analyses (@code{PartSAs})
and Markov models (clock forward or clock reset) to progression and overall
survival data typically collected in oncology clinical trials.  These model
structures are typically considered in cost-effectiveness modeling in
advanced/metastatic cancer indications.  Muston (2024). \"Informing structural
assumptions for three state oncology cost-effectiveness models through model
efficiency and fit\".  Applied Health Economics and Health Policy.")
    (license license:gpl3+)))

(define-public r-pslm2015
  (package
    (name "r-pslm2015")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PSLM2015" version))
       (sha256
        (base32 "1f8kzlqil2ac8a9fbj9fhdni2narh2yaaz044mlx7gsv2ns3ai19"))))
    (properties `((upstream-name . "PSLM2015")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr r-ggplot2 r-dplyr))
    (home-page "https://github.com/MYaseen208/PSLM2015")
    (synopsis
     "Pakistan Social and Living Standards Measurement Survey 2014-15")
    (description
     "Data and statistics of Pakistan Social and Living Standards Measurement (PSLM)
survey 2014-15 from Pakistan Bureau of Statistics (<http://www.pbs.gov.pk/>).")
    (license license:gpl2)))

(define-public r-psinference
  (package
    (name "r-psinference")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PSinference" version))
       (sha256
        (base32 "0smhh9gnaiwhjxmkzwd63c30l9frs8svkfaz4kw9lxn9i906py77"))))
    (properties `((upstream-name . "PSinference")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://github.com/ricardomourarpm/PSinference")
    (synopsis
     "Inference for Released Plug-in Sampling Single Synthetic Dataset")
    (description
     "Considering the singly imputed synthetic data generated via plug-in sampling
under the multivariate normal model, draws inference procedures including the
generalized variance, the sphericity test, the test for independence between two
subsets of variables, and the test for the regression of one set of variables on
the other.  For more details see Klein et al. (2021)
<doi:10.1007/s13571-019-00215-9>.")
    (license license:gpl2+)))

(define-public r-psindependencetest
  (package
    (name "r-psindependencetest")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PSIndependenceTest" version))
       (sha256
        (base32 "0nsg9c08j8ag66nlx2sjcddrpv2zkv1fm624afr50qyyyvr04g7z"))))
    (properties `((upstream-name . "PSIndependenceTest")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PSIndependenceTest")
    (synopsis
     "Independence Tests for Two-Way, Three-Way and Four-Way Contingency Tables")
    (description
     "Presentation two independence tests for two-way, three-way and four-way
contingency tables.  These tests are: the modular test and the logarithmic
minimum test.  For details on this method see: Sulewski (2017)
<doi:10.18778/0208-6018.330.04>, Sulewski (2018)
<doi:10.1080/02664763.2018.1424122>, Sulewski (2019)
<doi:10.2478/bile-2019-0003>, Sulewski (2021)
<doi:10.1080/00949655.2021.1908286>.")
    (license license:gpl3)))

(define-public r-psim
  (package
    (name "r-psim")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PSIM" version))
       (sha256
        (base32 "0d36yvihc24bb3djzsgc5hsbdmjxnpfn4jx513l1kscq1r6hcidd"))))
    (properties `((upstream-name . "PSIM")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyverse r-matrixstats r-magrittr r-dplyr))
    (home-page "https://github.com/luana1909/PSIM")
    (synopsis "Preference Selection Index Method (PSIM)")
    (description
     "The Preference Selection Index Method was created in (2010) and provides an
innovative approach to determining the relative importance of criteria without
pairwise comparisons, unlike the Analytic Hierarchy Process.  The Preference
Selection Index Method uses statistical methods to calculate the criteria
weights and reflects their relative importance in the final decision-making
process, offering an objective and non-subjective solution.  This method is
beneficial in multi-criteria decision analysis.  The PSIM package provides a
practical and accessible tool for implementing the Preference Selection Index
Method in R. It calculates the weights of criteria and makes the method
available to researchers, analysts, and professionals without the need to
develop complex calculations manually.  More details about the Preference
Selection Index Method can be found in Maniya K. and Bhatt M. G.(2010)
<doi:10.1016/j.matdes.2009.11.020>.")
    (license license:gpl3)))

(define-public r-psidread
  (package
    (name "r-psidread")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psidread" version))
       (sha256
        (base32 "0k1rk5bsdhf9x7znmq99snw8p5qfnysz7hlh7s7adg7hn79rifx4"))))
    (properties `((upstream-name . "psidread")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-stringr r-dplyr r-asciisetupreader))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Qcrates/psidread")
    (synopsis
     "Streamline Building Panel Data from Panel Study of Income Dynamics ('PSID') Raw Files")
    (description
     "Streamline the management, creation, and formatting of panel data from the Panel
Study of Income Dynamics ('PSID') <https://psidonline.isr.umich.edu> using this
user-friendly tool.  Simply define variable names and input code book details
directly from the PSID official website, and this toolbox will efficiently
facilitate the data preparation process, transforming raw PSID files into a
well-organized format ready for further analysis.")
    (license license:gpl3+)))

(define-public r-psica
  (package
    (name "r-psica")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psica" version))
       (sha256
        (base32 "1cnrd1p8ikv0hl97jwypw8np1ypa0d8k2mizwd1clkv7inbb264g"))))
    (properties `((upstream-name . "psica")))
    (build-system r-build-system)
    (propagated-inputs (list r-rpart
                             r-rdpack
                             r-randomforest
                             r-partykit
                             r-party
                             r-gridbase
                             r-bayestree))
    (home-page "https://cran.r-project.org/package=psica")
    (synopsis
     "Decision Tree Analysis for Probabilistic Subgroup Identification with Multiple Treatments")
    (description
     "In the situation when multiple alternative treatments or interventions
available, different population groups may respond differently to different
treatments.  This package implements a method that discovers the population
subgroups in which a certain treatment has a better effect than the other
alternative treatments.  This is done by first estimating the treatment effect
for a given treatment and its uncertainty by computing random forests, and the
resulting model is summarized by a decision tree in which the probabilities that
the given treatment is best for a given subgroup is shown in the corresponding
terminal node of the tree.")
    (license license:gpl2+)))

(define-public r-psgp
  (package
    (name "r-psgp")
    (version "0.3-21")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psgp" version))
       (sha256
        (base32 "1pcg6q1g5ipial8kyk20n15zzls4ig0m0sxcc7d3ngryir89cyzs"))))
    (properties `((upstream-name . "psgp")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-rcpparmadillo
                             r-rcpp
                             r-intamap
                             r-gstat
                             r-foreach
                             r-doparallel
                             r-automap))
    (home-page "https://cran.r-project.org/package=psgp")
    (synopsis "Projected Spatial Gaussian Process Methods")
    (description
     "This package implements projected sparse Gaussian process Kriging (Ingram et.
al.', 2008, <doi:10.1007/s00477-007-0163-9>) as an additional method for the
intamap package.  More details on implementation (Barillec et.  al.', 2010,
<doi:10.1016/j.cageo.2010.05.008>).")
    (license license:gpl2+)))

(define-public r-psgoft
  (package
    (name "r-psgoft")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PSGoft" version))
       (sha256
        (base32 "177znpqd6wix76bk59w452ki8qa6rmvwiwddflgqi6chybbn6cpr"))))
    (properties `((upstream-name . "PSGoft")))
    (build-system r-build-system)
    (propagated-inputs (list r-moments))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PSGoft")
    (synopsis "Modified Lilliefors Goodness-of-Fit Normality Test")
    (description
     "Presentation of a new goodness-of-fit normality test based on the Lilliefors
method.  For details on this method see: Sulewski (2019)
<doi:10.1080/03610918.2019.1664580>.")
    (license license:gpl3)))

(define-public r-psgd
  (package
    (name "r-psgd")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PSGD" version))
       (sha256
        (base32 "00013hw99kwk6jlypyb38fly0smyyk6gzxvcfd33n2cbv14jx4gk"))))
    (properties `((upstream-name . "PSGD")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=PSGD")
    (synopsis "Projected Subset Gradient Descent")
    (description
     "This package provides functions to generate ensembles of generalized linear
models using a greedy projected subset gradient descent algorithm.  The sparsity
and diversity tuning parameters are selected by cross-validation.")
    (license license:gpl2+)))

(define-public r-psfmi
  (package
    (name "r-psfmi")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psfmi" version))
       (sha256
        (base32 "1nda2vzmq7cz04g9irl186vkvi3dxpmcsm9dxhjiql3mrax82r8c"))))
    (properties `((upstream-name . "psfmi")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-survival
                             r-stringr
                             r-rsample
                             r-rms
                             r-purrr
                             r-proc
                             r-norm
                             r-mitools
                             r-mitml
                             r-mice
                             r-magrittr
                             r-lme4
                             r-ggplot2
                             r-dplyr
                             r-cvauc
                             r-car))
    (native-inputs (list r-knitr))
    (home-page "https://mwheymans.github.io/psfmi/")
    (synopsis
     "Prediction Model Pooling, Selection and Performance Evaluation Across Multiply Imputed Datasets")
    (description
     "Pooling, backward and forward selection of linear, logistic and Cox regression
models in multiply imputed datasets.  Backward and forward selection can be done
from the pooled model using Rubin's Rules (RR), the D1, D2, D3, D4 and the
median p-values method.  This is also possible for Mixed models.  The models can
contain continuous, dichotomous, categorical and restricted cubic spline
predictors and interaction terms between	all these type of predictors.  The
stability of the models	can be evaluated using (cluster) bootstrapping.  The
package further contains functions to pool model performance measures as
ROC/AUC, Reclassification, R-squared, scaled Brier score, H&L test and
calibration	plots for logistic regression models.  Internal validation can be
done across multiply imputed datasets with cross-validation or bootstrapping.
The adjusted intercept after shrinkage of pooled regression coefficients can be
obtained.  Backward and forward selection as part of internal validation is
possible.  A function to externally validate logistic prediction models in
multiple imputed datasets is available and a function to compare models.  For
Cox models a strata variable can be included.  Eekhout (2017)
<doi:10.1186/s12874-017-0404-7>.  Wiel (2009)
<doi:10.1093/biostatistics/kxp011>.  Marshall (2009)
<doi:10.1186/1471-2288-9-57>.")
    (license license:gpl2+)))

(define-public r-psf
  (package
    (name "r-psf")
    (version "0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PSF" version))
       (sha256
        (base32 "00zwbw9bn5bs181m831nb08wwrmp2jpmqmriy8j52njq289hnc32"))))
    (properties `((upstream-name . "PSF")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://www.neerajbokde.in/viggnette/2021-10-13-PSF/")
    (synopsis
     "Forecasting of Univariate Time Series Using the Pattern Sequence-Based Forecasting (PSF) Algorithm")
    (description
     "Pattern Sequence Based Forecasting (PSF) takes univariate time series data as
input and assist to forecast its future values.  This algorithm forecasts the
behavior of time series based on similarity of pattern sequences.  Initially,
clustering is done with the labeling of samples from database.  The labels
associated with samples are then used for forecasting the future behaviour of
time series data.  The further technical details and references regarding PSF
are discussed in Vignette.")
    (license license:gpl2+)))

(define-public r-pseval
  (package
    (name "r-pseval")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pseval" version))
       (sha256
        (base32 "1jgnv1l9adhwrmkmp6wkzz7jf7w1hyqy47ajr29l21p4g037py45"))))
    (properties `((upstream-name . "pseval")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pseval")
    (synopsis
     "Methods for Evaluating Principal Surrogates of Treatment Response")
    (description
     "This package contains the core methods for the evaluation of principal
surrogates in a single clinical trial.  Provides a flexible interface for
defining models for the risk given treatment and the surrogate, the models for
integration over the missing counterfactual surrogate responses, and the
estimation methods.  Estimated maximum likelihood and pseudo-score can be used
for estimation, and the bootstrap for inference.  A variety of post-estimation
summary methods are provided, including print, summary, plot, and testing.")
    (license license:expat)))

(define-public r-pseudohouseholds
  (package
    (name "r-pseudohouseholds")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pseudohouseholds" version))
       (sha256
        (base32 "102kc3j106bw6dlarmvwyh4mg68imbyxzc5j6pkzn6xpnvils3fm"))))
    (properties `((upstream-name . "pseudohouseholds")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-furrr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/chris31415926535/pseudohouseholds")
    (synopsis "Generate Pseudohouseholds on Road Networks in Regions")
    (description
     "Given an arbitrary set of spatial regions and road networks, generate a set of
representative points, or pseudohouseholds, that can be used for travel burden
analysis.  Parallel processing is supported.")
    (license license:expat)))

(define-public r-pseudo
  (package
    (name "r-pseudo")
    (version "1.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pseudo" version))
       (sha256
        (base32 "0ccf3gz2g7g5y4acpj2qnb39hrghhdganizlddg6rx7al869fffs"))))
    (properties `((upstream-name . "pseudo")))
    (build-system r-build-system)
    (propagated-inputs (list r-kmsurv r-geepack))
    (home-page "https://cran.r-project.org/package=pseudo")
    (synopsis "Computes Pseudo-Observations for Modeling")
    (description
     "Various functions for computing pseudo-observations for censored data
regression.  Computes pseudo-observations for modeling: competing risks based on
the cumulative incidence function, survival function based on the restricted
mean, survival function based on the Kaplan-Meier estimator see Klein et al.
(2008) <doi:10.1016/j.cmpb.2007.11.017>.")
    (license license:gpl2)))

(define-public r-psdr
  (package
    (name "r-psdr")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psdr" version))
       (sha256
        (base32 "02ypgi1gxnp0mn9p1w2j34ypnpbkrwp9i0ma0i7657zddcfnf391"))))
    (properties `((upstream-name . "psdr")))
    (build-system r-build-system)
    (propagated-inputs (list r-qpdf r-ggplot2 r-devtools))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=psdr")
    (synopsis "Use Time Series to Generate and Compare Power Spectral Density")
    (description
     "This package provides functions that allow you to generate and compare power
spectral density (PSD) plots given time series data.  Fast Fourier Transform
(FFT) is used to take a time series data, analyze the oscillations, and then
output the frequencies of these oscillations in the time series in the form of a
PSD plot.Thus given a time series, the dominant frequencies in the time series
can be identified.  Additional functions in this package allow the dominant
frequencies of multiple groups of time series to be compared with each other.
To see example usage with the main functions of this package, please visit this
site: <https://yhhc2.github.io/psdr/articles/Introduction.html>.  The
mathematical operations used to generate the PSDs are described in these sites:
<https://www.mathworks.com/help/matlab/ref/fft.html>.
<https://www.mathworks.com/help/signal/ug/power-spectral-density-estimates-using-fft.html>.")
    (license (list license:gpl3+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-psdistr
  (package
    (name "r-psdistr")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PSDistr" version))
       (sha256
        (base32 "1djhpc9p5q9lqnx7pn2jcskmq28iriq42yyid70sc5r917c357x8"))))
    (properties `((upstream-name . "PSDistr")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PSDistr")
    (synopsis "Distributions Derived from Normal Distribution")
    (description
     "Presentation of distributions such as: two-piece power normal (TPPN),
plasticizing component (PC), DS normal (DSN), expnormal (EN), Sulewski
plasticizing component (SPC), easily changeable kurtosis (ECK) distributions.
Density, distribution function, quantile function and random generation are
presented.  For details on this method see: Sulewski (2019)
<doi:10.1080/03610926.2019.1674871>, Sulewski (2021)
<doi:10.1080/03610926.2020.1837881>, Sulewski (2021)
<doi:10.1134/S1995080221120337>, Sulewski (2022) <\"New members of the Johnson
family of probability dis-tributions: properties and application\">, Sulewski,
Volodin (2022) <doi:10.1134/S1995080222110270>, Sulewski (2023)
<doi:10.17713/ajs.v52i3.1434>.")
    (license license:gpl3)))

(define-public r-psdata
  (package
    (name "r-psdata")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psData" version))
       (sha256
        (base32 "013hb6lk9rm2w08m5jbw90ndrcd4wyy2h125jx07c9bs60wh4mp4"))))
    (properties `((upstream-name . "psData")))
    (build-system r-build-system)
    (propagated-inputs (list r-xlsx r-rio r-reshape2 r-datacombine
                             r-countrycode))
    (home-page "http://cran.r-project.org/package=psData")
    (synopsis "Download Regularly Maintained Political Science Data Sets")
    (description
     "This R package includes functions for gathering commonly used and regularly
maintained data set in political science.  It also includes functions for
combining components from these data sets into variables that have been
suggested in the literature, but are not regularly maintained.")
    (license license:gpl3+)))

(define-public r-psd
  (package
    (name "r-psd")
    (version "2.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psd" version))
       (sha256
        (base32 "1c9kq5g2p4iy1jdc9fhfs1v0hcnc8cbrpvafdpy3l4n9m66hahky"))))
    (properties `((upstream-name . "psd")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-rcolorbrewer))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/abarbour/psd")
    (synopsis
     "Adaptive, Sine-Multitaper Power Spectral Density and Cross Spectrum Estimation")
    (description
     "This package produces power spectral density estimates through iterative
refinement of the optimal number of sine-tapers at each frequency.  This
optimization procedure is based on the method of Riedel and Sidorenko (1995),
which minimizes the Mean Square Error (sum of variance and bias) at each
frequency, but modified for computational stability.  The same procedure can now
be used to calculate the cross spectrum (multivariate analyses).")
    (license license:gpl2+)))

(define-public r-pscr
  (package
    (name "r-pscr")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PScr" version))
       (sha256
        (base32 "0cbmb1z4iwxw82m3f3y5iyr2hd1mgqsrvn7ip4rmsqy3zakyh06s"))))
    (properties `((upstream-name . "PScr")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam r-survival r-pracma))
    (home-page "https://cran.r-project.org/package=PScr")
    (synopsis "Estimation for the Power Series Cure Rate Model")
    (description
     "Provide estimation for particular cases of the power series cure rate model
<doi:10.1080/03610918.2011.639971>.  For the distribution of the concurrent
causes the alternative models are the Poisson, logarithmic, negative binomial
and Bernoulli (which are includes in the original work), the polylogarithm model
<doi:10.1080/00949655.2018.1451850> and the Flory-Schulz
<doi:10.3390/math10244643>.  The estimation procedure is based on the EM
algorithm discussed in <doi:10.1080/03610918.2016.1202276>.  For the
distribution of the time-to-event the alternative models are slash half-normal,
Weibull, gamma and Birnbaum-Saunders distributions.")
    (license license:gpl2+)))

(define-public r-pscore
  (package
    (name "r-pscore")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pscore" version))
       (sha256
        (base32 "0flzqr9x0z2mjnbm5cjm8hqndkbv2yp11agkx89mbaafsg0hmjn1"))))
    (properties `((upstream-name . "pscore")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-lavaan r-jwileymisc r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://score-project.org")
    (synopsis "Standardizing Physiological Composite Risk Endpoints")
    (description
     "This package provides a number of functions to simplify and automate the
scoring, comparison, and evaluation of different ways of creating composites of
data.  It is particularly aimed at facilitating the creation of physiological
composites of metabolic syndrome symptom score (@code{MetSSS}) and allostatic
load (AL).  Provides a wrapper to calculate the @code{MetSSS} on new data using
the Healthy Hearts formula.")
    (license license:lgpl3)))

(define-public r-psborrow2
  (package
    (name "r-psborrow2")
    (version "0.0.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psborrow2" version))
       (sha256
        (base32 "05mcfbgkdlx6yfiysvh3yk75c8rdcci7ql89n76vlm9h17ff5fm1"))))
    (properties `((upstream-name . "psborrow2")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-simsurv
                             r-posterior
                             r-mvtnorm
                             r-matrix
                             r-glue
                             r-generics
                             r-future
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Genentech/psborrow2")
    (synopsis "Bayesian Dynamic Borrowing Analysis and Simulation")
    (description
     "Bayesian dynamic borrowing is an approach to incorporating external data to
supplement a randomized, controlled trial analysis in which external data are
incorporated in a dynamic way (e.g., based on similarity of outcomes); see Viele
2013 <doi:10.1002/pst.1589> for an overview.  This package implements the
hierarchical commensurate prior approach to dynamic borrowing as described in
Hobbes 2011 <doi:10.1111/j.1541-0420.2011.01564.x>.  There are three main
functionalities.  First, psborrow2 provides a user-friendly interface for
applying dynamic borrowing on the study results handles the Markov Chain Monte
Carlo sampling on behalf of the user.  Second, psborrow2 provides a simulation
framework to compare different borrowing parameters (e.g. full borrowing, no
borrowing, dynamic borrowing) and other trial and borrowing characteristics
(e.g. sample size, covariates) in a unified way.  Third, psborrow2 provides a
set of functions to generate data for simulation studies, and also allows the
user to specify their own data generation process.  This package is designed to
use the sampling functions from cmdstanr which can be installed from
<https://mc-stan.org/r-packages/>.")
    (license license:asl2.0)))

(define-public r-psborrow
  (package
    (name "r-psborrow")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psborrow" version))
       (sha256
        (base32 "0m2jcf5awlwkzscsps0fczpaflxzi0w6m9qxlzm7abip277lhgpd"))))
    (properties `((upstream-name . "psborrow")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rjags
                             r-mvtnorm
                             r-matchit
                             r-ggplot2
                             r-futile-logger
                             r-foreach
                             r-dplyr
                             r-doparallel
                             r-data-table))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=psborrow")
    (synopsis "Bayesian Dynamic Borrowing with Propensity Score")
    (description
     "This package provides a tool which aims to help evaluate the effect of external
borrowing using an integrated approach described in Lewis et al., (2019)
<doi:10.1080/19466315.2018.1497533> that combines propensity score and Bayesian
dynamic borrowing methods.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-psbcspeedup
  (package
    (name "r-psbcspeedup")
    (version "2.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psbcSpeedUp" version))
       (sha256
        (base32 "12nzqg67wpycalv31d04h8qq519c2i2rkxpqdnf1vb6w84c6nknv"))))
    (properties `((upstream-name . "psbcSpeedUp")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-survival
                             r-riskregression
                             r-rcpparmadillo
                             r-rcpp
                             r-mass
                             r-ggplot2
                             r-ggally))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ocbe-uio/psbcSpeedUp")
    (synopsis "Penalized Semiparametric Bayesian Cox Models")
    (description
     "Algorithms to speed up the Bayesian Lasso Cox model (Lee et al., Int J Biostat,
2011 <doi:10.2202/1557-4679.1301>) and the Bayesian Lasso Cox with mandatory
variables (Zucknick et al.  Biometrical J, 2015 <doi:10.1002/bimj.201400160>).")
    (license license:gpl3)))

(define-public r-psbcgroup
  (package
    (name "r-psbcgroup")
    (version "1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psbcGroup" version))
       (sha256
        (base32 "08gabhcpjsm92xrld3h14y092x05apz4b1znf10d6j3n1880fbl4"))))
    (properties `((upstream-name . "psbcGroup")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-suppdists r-mvtnorm r-learnbayes))
    (home-page "https://cran.r-project.org/package=psbcGroup")
    (synopsis
     "Penalized Parametric and Semiparametric Bayesian Survival Models with Shrinkage and Grouping Priors")
    (description
     "Algorithms to implement various Bayesian penalized survival regression models
including: semiparametric proportional hazards models with lasso priors (Lee et
al., Int J Biostat, 2011 <doi:10.2202/1557-4679.1301>) and three other shrinkage
and group priors (Lee et al., Stat Anal Data Min, 2015 <doi:10.1002/sam.11266>);
parametric accelerated failure time models with group/ordinary lasso prior (Lee
et al.  Comput Stat Data Anal, 2017 <doi:10.1016/j.csda.2017.02.014>).")
    (license license:gpl2+)))

(define-public r-psbayesborrow
  (package
    (name "r-psbayesborrow")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "psBayesborrow" version))
       (sha256
        (base32 "1kxwb9kvxykapw8mmihazx1wbd4z3yqwyj09dli6h82zcq2w220n"))))
    (properties `((upstream-name . "psBayesborrow")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-overlapping
                             r-optmatch
                             r-matchit
                             r-e1071
                             r-copula
                             r-boot
                             r-bh))
    (home-page "https://cran.r-project.org/package=psBayesborrow")
    (synopsis "Bayesian Information Borrowing with Propensity Score Matching")
    (description
     "Hybrid control design is a way to borrow information from external controls to
augment concurrent controls in a randomized controlled trial and is expected to
overcome the feasibility issue when adequate randomized controlled trials cannot
be conducted.  A major challenge in the hybrid control design is its inability
to eliminate a prior-data conflict caused by systematic imbalances in measured
or unmeasured confounding factors between patients in the concurrent
treatment/control group and external controls.  To prevent the prior-data
conflict, a combined use of propensity score matching and Bayesian commensurate
prior has been proposed in the context of hybrid control design.  The propensity
score matching is first performed to guarantee the balance in baseline
characteristics, and then the Bayesian commensurate prior is constructed while
discounting the information based on the similarity in outcomes between the
concurrent and external controls. @code{psBayesborrow} is a package to implement
the propensity score matching and the Bayesian analysis with commensurate prior,
as well as to conduct a simulation study to assess operating characteristics of
the hybrid control design, where users can choose design parameters in flexible
and straightforward ways depending on their own application.")
    (license license:expat)))

(define-public r-psawr
  (package
    (name "r-psawr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PSAWR" version))
       (sha256
        (base32 "0qaaw6bxb4p9h1y6fykgn5g7bllr1x1zdp3p1idm3sk30zfpvp94"))))
    (properties `((upstream-name . "PSAWR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-httr r-dplyr))
    (home-page "https://github.com/schochastics/PSAWR/")
    (synopsis
     "'Pushshift' API Wrapper for 'Reddit' Submission and Comment Search")
    (description
     "Connects to the API of <https://pushshift.io/> to search for Reddit comments and
submissions.")
    (license license:expat)))

(define-public r-psagraphics
  (package
    (name "r-psagraphics")
    (version "2.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PSAgraphics" version))
       (sha256
        (base32 "1fa11imkca782cd6jh3y41yxpy9xrdm7azq62al1k7i5jsq3chqd"))))
    (properties `((upstream-name . "PSAgraphics")))
    (build-system r-build-system)
    (propagated-inputs (list r-rpart))
    (home-page "https://jbryer.github.io/PSAgraphics/")
    (synopsis "Propensity Score Analysis Graphics")
    (description
     "This package provides a collection of functions that primarily produce graphics
to aid in a Propensity Score Analysis (PSA).  Functions include: cat.psa and
box.psa to test balance within strata of categorical and quantitative
covariates, circ.psa for a representation of the estimated effect size by
stratum, loess.psa that provides a graphic and loess based effect size estimate,
and various balance functions that provide measures of the balance achieved via
a PSA in a categorical covariate.")
    (license license:gpl2+)))

(define-public r-psaboot
  (package
    (name "r-psaboot")
    (version "1.3.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PSAboot" version))
       (sha256
        (base32 "1y0d5gglmzcnd0yvq33k1canjzhl62df9abgw1w08azm94arfh1q"))))
    (properties `((upstream-name . "PSAboot")))
    (build-system r-build-system)
    (propagated-inputs (list r-trimatch
                             r-rpart
                             r-reshape2
                             r-psych
                             r-psagraphics
                             r-party
                             r-modeltools
                             r-matchit
                             r-matching
                             r-ggthemes
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jbryer/PSAboot")
    (synopsis "Bootstrapping for Propensity Score Analysis")
    (description
     "It is often advantageous to test a hypothesis more than once in the context of
propensity score analysis (Rosenbaum, 2012) <doi:10.1093/biomet/ass032>.  The
functions in this package facilitate bootstrapping for propensity score analysis
(PSA).  By default, bootstrapping using two classification tree methods (using
rpart and ctree functions), two matching methods (using Matching and
@code{MatchIt} packages), and stratification with logistic regression.  A
framework is described for users to implement additional propensity score
methods.  Visualizations are emphasized for diagnosing balance; exploring the
correlation relationships between bootstrap samples and methods; and to
summarize results.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-przewodnik
  (package
    (name "r-przewodnik")
    (version "0.16.12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Przewodnik" version))
       (sha256
        (base32 "1aq80aqflfm1ypqa51h10mc3g0qdr651j6g1b7008k7h0ryq42gv"))))
    (properties `((upstream-name . "Przewodnik")))
    (build-system r-build-system)
    (propagated-inputs (list r-pogromcydanych r-pbimisc))
    (home-page "https://cran.r-project.org/package=Przewodnik")
    (synopsis
     "Datasets and Functions Used in the Book 'Przewodnik po Pakiecie R'")
    (description
     "Data sets and functions used in the polish book \"Przewodnik po pakiecie R\" (The
Hitchhiker's Guide to the R).  See more at <http://biecek.pl/R>.  Among others
you will find here data about housing prices, cancer patients, running times and
many others.")
    (license license:gpl2)))

(define-public r-prwarp
  (package
    (name "r-prwarp")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prWarp" version))
       (sha256
        (base32 "1iibz1gjx5r51jvv1g7csir871ccl74p0jcqnx6cs78i7xg02wf6"))))
    (properties `((upstream-name . "prWarp")))
    (build-system r-build-system)
    (propagated-inputs (list r-morpho))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=prWarp")
    (synopsis "Warping Landmark Configurations")
    (description
     "Compute bending energies, principal warps, partial warp scores, and the
non-affine component of shape variation for 2D landmark configurations, as well
as Mardia-Dryden distributions and self-similar distributions of landmarks, as
described in Mitteroecker et al. (2020) <doi:10.1093/sysbio/syaa007>.  Working
examples to decompose shape variation into small-scale and large-scale
components, and to decompose the total shape variation into outline and residual
shape components are provided.  Two landmark datasets are provided, that
quantify skull morphology in humans and papionin primates, respectively from
Mitteroecker et al. (2020) <doi:10.5061/dryad.j6q573n8s> and Grunstra et al.
(2020) <doi:10.5061/dryad.zkh189373>.")
    (license license:gpl3)))

(define-public r-prtree
  (package
    (name "r-prtree")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PRTree" version))
       (sha256
        (base32 "1m1npdajyrc6hcdfl8qpj0zi5wa4sh4mxsi37yz0i9v24ygdw5r8"))))
    (properties `((upstream-name . "PRTree")))
    (build-system r-build-system)
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=PRTree")
    (synopsis "Probabilistic Regression Trees")
    (description
     "Probabilistic Regression Trees (PRTree).  Functions for fitting and predicting
PRTree models with some adaptations to handle missing values.  The main
calculations are performed in FORTRAN', resulting in highly efficient
algorithms.  This package's implementation is based on the PRTree methodology
described in Alkhoury, S.; Devijver, E.; Clausel, M.; Tami, M.; Gaussier, E.;
Oppenheim, G. (2020) - \"Smooth And Consistent Probabilistic Regression Trees\"
<https://proceedings.neurips.cc/paper_files/paper/2020/file/8289889263db4a40463e3f358bb7c7a1-Paper.pdf>.")
    (license license:gpl3+)))

(define-public r-prt
  (package
    (name "r-prt")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prt" version))
       (sha256
        (base32 "02f75a9ici0a7fm08r0dhykf73jpixg5cm8ym2z18dhghas4zly4"))))
    (properties `((upstream-name . "prt")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tibble
                             r-rlang
                             r-pillar
                             r-fst
                             r-data-table
                             r-crayon
                             r-cli
                             r-backports
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://nbenn.github.io/prt/")
    (synopsis "Tabular Data Backed by Partitioned 'fst' Files")
    (description
     "Intended for larger-than-memory tabular data, prt objects provide an interface
to read row and/or column subsets into memory as data.table objects.  Data
queries, constructed as R expressions, are evaluated using the non-standard
evaluation framework provided by rlang and file-backing is powered by the fast
and efficient fst package.")
    (license license:gpl3)))

(define-public r-prsr
  (package
    (name "r-prsr")
    (version "3.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pRSR" version))
       (sha256
        (base32 "1irx95b7cwvx3gpn9brjjn4k947m8frz542r18nilc9f1159mb3s"))))
    (properties `((upstream-name . "pRSR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pRSR")
    (synopsis "Test of Periodicity using Response Surface Regression")
    (description
     "Tests periodicity in short time series using response surface regression.")
    (license license:gpl2+)))

(define-public r-prspgx
  (package
    (name "r-prspgx")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PRSPGx" version))
       (sha256
        (base32 "1vvclrp6h63l471qvgwkdhs52lrkapz0v90gwld89zwim6pnh7av"))))
    (properties `((upstream-name . "PRSPGx")))
    (build-system r-build-system)
    (propagated-inputs (list r-sgl
                             r-rfast
                             r-propagate
                             r-mvtnorm
                             r-mcmcpack
                             r-matrixcalc
                             r-matrix
                             r-lmtest
                             r-glmnet
                             r-gigrvg
                             r-gglasso
                             r-bigstatsr
                             r-bigsparser
                             r-bigsnpr
                             r-bigparallelr
                             r-bdsmatrix))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PRSPGx")
    (synopsis "Construct PGx PRS")
    (description
     "Construct pharmacogenomics (PGx) polygenic risk score (PRS) with PRS-PGx-Unadj
(unadjusted), PRS-PGx-CT (clumping and thresholding), PRS-PGx-L, -GL, -SGL
(penalized regression), PRS-PGx-Bayes (Bayesian regression).  Package is based
on Pharmacogenomics Polyenic Risk Score for Drug Response Prediction Using
PRS-PGx Methods by Zhai, S., Zhang, H., Mehrotra, D.V., and Shen, J., 2021
(submitted).")
    (license license:gpl2+)))

(define-public r-prsim
  (package
    (name "r-prsim")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PRSim" version))
       (sha256
        (base32 "1imy9blzdk2y4xzmsb86g49zzm59cpi2xrh3bgpw20611a03505d"))))
    (properties `((upstream-name . "PRSim")))
    (build-system r-build-system)
    (propagated-inputs (list r-wavscalogram r-splus2r r-mev r-lmomco r-goftest))
    (home-page "https://git.math.uzh.ch/reinhard.furrer/PRSim-devel")
    (synopsis
     "Stochastic Simulation of Streamflow Time Series using Phase Randomization")
    (description
     "This package provides a simulation framework to simulate streamflow time series
with similar main characteristics as observed data.  These characteristics
include the distribution of daily streamflow values and their temporal
correlation as expressed by short- and long-range dependence.  The approach is
based on the randomization of the phases of the Fourier transform or the phases
of the wavelet transform.  The function prsim() is applicable to single site
simulation and uses the Fourier transform.  The function prsim.wave() extends
the approach to multiple sites and is based on the complex wavelet transform.
The function prsim.weather() extends the approach to multiple variables for
weather generation.  We further use the flexible four-parameter Kappa
distribution, which allows for the extrapolation to yet unobserved low and high
flows.  Alternatively, the empirical or any other distribution can be used.  A
detailed description of the simulation approach for single sites and an
application example can be found in Brunner et al. (2019)
<doi:10.5194/hess-23-3175-2019>.  A detailed description and evaluation of the
wavelet-based multi-site approach can be found in Brunner and Gilleland (2020)
<doi:10.5194/hess-24-3967-2020>.  A detailed description and evaluation of the
multi-variable and multi-site weather generator can be found in Brunner et al.
(2021) <doi:10.5194/esd-12-621-2021>.  A detailed description and evaluation of
the non-stationary streamflow generator can be found in Brunner and Gilleland
(2024) <doi:10.1029/2023EF004238>.")
    (license license:gpl3)))

(define-public r-prrd
  (package
    (name "r-prrd")
    (version "0.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prrd" version))
       (sha256
        (base32 "1wr469jvzgsic38x8xr72lx7px0k6l957z4hl63bgj7w2dczwyqz"))))
    (properties `((upstream-name . "prrd")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsqlite
                             r-liteq
                             r-dbi
                             r-data-table
                             r-crayon
                             r-config))
    (home-page "https://github.com/eddelbuettel/prrd")
    (synopsis "Parallel Runs of Reverse Depends")
    (description
     "Reverse depends for a given package are queued such that multiple workers can
run the reverse-dependency tests in parallel.")
    (license license:gpl2+)))

(define-public r-prqlr
  (package
    (name "r-prqlr")
    (version "0.9.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prqlr" version))
       (sha256
        (base32 "05k2wj5dyqrbscc3dg1400i5zg0fvx1hgxq9djm9wiskmha2scj5"))))
    (properties `((upstream-name . "prqlr")))
    (build-system r-build-system)
    (inputs (list))
    (native-inputs (list r-knitr))
    (home-page "https://prql.github.io/prqlc-r/")
    (synopsis "R Bindings for the 'prqlc' Rust Library")
    (description
     "This package provides a function to convert PRQL strings to SQL strings.
Combined with other R functions that take SQL as an argument, PRQL can be used
on R.")
    (license license:expat)))

(define-public r-prp
  (package
    (name "r-prp")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PRP" version))
       (sha256
        (base32 "1q4idlk2crxsd841b203hkcyp9glqhsg83liddqlxixxih7rkzp1"))))
    (properties `((upstream-name . "PRP")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://cran.r-project.org/package=PRP")
    (synopsis "Bayesian Prior and Posterior Predictive Replication Assessment")
    (description
     "Utilize the Bayesian prior and posterior predictive checking approach to provide
a statistical assessment of replication success and failure.  The package is
based on the methods proposed in Zhao,Y., Wen X.(2021)
<@code{arXiv:2105.03993>}.")
    (license license:gpl2)))

(define-public r-prozor
  (package
    (name "r-prozor")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prozor" version))
       (sha256
        (base32 "0gcjfjsvj2pjp9ih7nxvs9gfj5b94g8d3ziaz16wfbnjqm4a38lp"))))
    (properties `((upstream-name . "prozor")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-seqinr
                             r-rlang
                             r-readr
                             r-purrr
                             r-matrix
                             r-dplyr
                             r-docopt
                             r-ahocorasicktrie))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/protviz/prozor")
    (synopsis "Minimal Protein Set Explaining Peptide Spectrum Matches")
    (description
     "Determine minimal protein set explaining peptide spectrum matches.  Utility
functions for creating fasta amino acid databases with decoys and contaminants.
Peptide false discovery rate estimation for target decoy search results on psm,
precursor, peptide and protein level.  Computing dynamic swath window sizes
based on MS1 or MS2 signal distributions.")
    (license license:gpl3)))

(define-public r-proxirr
  (package
    (name "r-proxirr")
    (version "0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "proxirr" version))
       (sha256
        (base32 "0fknabfdvcmcpxai9sasnqay9ymy1vm9bvghiw9awx1lvn85024r"))))
    (properties `((upstream-name . "proxirr")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=proxirr")
    (synopsis "Alpha and Beta Proximity to Irreplaceability")
    (description
     "This package provides functions to measure Alpha and Beta Proximity to
Irreplaceability.  The methods for Alpha and Beta irreplaceability were first
described in: Baisero D., Schuster R. & Plumptre A.J. Redefining and Mapping
Global Irreplaceability.  Conservation Biology 2021;1-11.
<doi:10.1111/cobi.13806>.")
    (license license:expat)))

(define-public r-provviz
  (package
    (name "r-provviz")
    (version "1.0.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "provViz" version))
       (sha256
        (base32 "0hwd9x02xcldrmmnpknsycqlb78ngdsphs7n141rcxsv167gbmcq"))))
    (properties `((upstream-name . "provViz")))
    (build-system r-build-system)
    (home-page "https://github.com/ProvTools/provViz")
    (synopsis "Provenance Visualizer")
    (description
     "Displays provenance graphically for provenance collected by the rdt or
@code{rdtLite} packages, or other tools providing compatible PROV JSON output.
The exact format of the JSON created by rdt and @code{rdtLite} is described in
<https://github.com/End-to-end-provenance/@code{ExtendedProvJson>}.  More
information about @code{rdtLite} and associated tools is available at
<https://github.com/End-to-end-provenance/> and Barbara Lerner, Emery Boose, and
Luis Perez (2018), Using Introspection to Collect Provenance in R, Informatics,
<doi: 10.3390/informatics5010012>.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-provtracer
  (package
    (name "r-provtracer")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "provTraceR" version))
       (sha256
        (base32 "19iillyh5zw64mmr6ik4ci1n2p651xqzxa4iq5bwa70imbzyy5ip"))))
    (properties `((upstream-name . "provTraceR")))
    (build-system r-build-system)
    (propagated-inputs (list r-provparser))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/End-to-end-provenance")
    (synopsis
     "Uses Provenance to Trace File Lineage for One or more R Scripts")
    (description
     "Uses provenance collected by @code{rdtLite} package or comparable tool to
display information about input files, output files, and exchanged files for a
single R script or a series of R scripts.")
    (license license:gpl3)))

(define-public r-provsummarizer
  (package
    (name "r-provsummarizer")
    (version "1.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "provSummarizeR" version))
       (sha256
        (base32 "1sab34fhyzzzj600ks2xwyv2y1657ir5k2dzkjry8y1mwvag796j"))))
    (properties `((upstream-name . "provSummarizeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-provparser))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/End-to-end-provenance")
    (synopsis
     "Summarizes Provenance Related to Inputs and Outputs of a Script or Console Commands")
    (description
     "Reads the provenance collected by the @code{rdtLite} or rdt packages, or other
tools providing compatible PROV JSON output, created by the execution of a
script or a console session, and provides a human-readable summary identifying
the input and output files, the scripts used (if any), errors and warnings
produced, and the environment in which it was executed.  It can also optionally
package all the files into a zip file.  The exact format of the PROV JSON file
created by @code{rdtLite} and rdt is described in
<https://github.com/End-to-end-provenance/@code{ExtendedProvJson>}.  More
information about @code{rdtLite} and associated tools is available at
<https://github.com/End-to-end-provenance/> and Lerner, Boose, and Perez (2018),
Using Introspection to Collect Provenance in R, Informatics, <doi:
10.3390/informatics5010012>.")
    (license license:gpl3)))

(define-public r-provparser
  (package
    (name "r-provparser")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "provParseR" version))
       (sha256
        (base32 "1mzhpl2zaaj339f4vy9dgzbg0j434cysy53sliswq100hsfxgd18"))))
    (properties `((upstream-name . "provParseR")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite))
    (home-page "https://github.com/End-to-end-provenance")
    (synopsis "Pulls Information from Prov.Json Files")
    (description
     "R functions to access provenance information collected by rdt or
@code{rdtLite}'.  The information is stored inside a @code{ProvInfo} object and
can be accessed through a collection of functions that will return the requested
data.  The exact format of the JSON created by rdt and @code{rdtLite} is
described in <https://github.com/End-to-end-provenance/@code{ExtendedProvJson>}.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-provgraphr
  (package
    (name "r-provgraphr")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "provGraphR" version))
       (sha256
        (base32 "0flq20i2m2dhkjjnipb4fash32db5pnlrwswqw1v1028vgv4yfnq"))))
    (properties `((upstream-name . "provGraphR")))
    (build-system r-build-system)
    (propagated-inputs (list r-provparser r-matrix r-igraph))
    (home-page "https://cran.r-project.org/package=provGraphR")
    (synopsis "Creates Adjacency Matrices for Lineage Searches")
    (description
     "This package creates and manages a provenance graph corresponding to the
provenance created by the @code{rdtLite} package, which collects provenance from
R scripts. @code{rdtLite} is available on CRAN. The provenance format is an
extension of the W3C PROV JSON format
(<https://www.w3.org/Submission/2013/SUBM-prov-json-20130424/>).  The extended
JSON provenance format is described in
<https://github.com/End-to-end-provenance/@code{ExtendedProvJson>}.")
    (license license:gpl3)))

(define-public r-provexplainr
  (package
    (name "r-provexplainr")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "provExplainR" version))
       (sha256
        (base32 "1bkwdz6zm3wq8j16kyqiqmz5mqzizyacgh5rmhfa94rvsb9vh008"))))
    (properties `((upstream-name . "provExplainR")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-provparser r-dplyr r-digest r-diffobj))
    (home-page "https://github.com/End-to-end-provenance")
    (synopsis
     "Compare Provenance Collections to Explain Changed Script Outputs")
    (description
     "Inspects provenance collected by the rdt or @code{rdtLite} packages, or other
tools providing compatible PROV JSON output created by the execution of a
script, and find differences between two provenance collections.  Factors under
examination included the hardware and software used to execute the script,
versions of attached libraries, use of global variables, modified inputs and
outputs, and changes in main and sourced scripts.  Based on detected changes,
@code{provExplainR} can be used to study how these factors affect the behavior
of the script and generate a promising diagnosis of the causes of different
script results.  More information about @code{rdtLite} and associated tools is
available at <https://github.com/End-to-end-provenance/> and Barbara Lerner,
Emery Boose, and Luis Perez (2018), Using Introspection to Collect Provenance in
R, Informatics, <doi:10.3390/informatics5010012>.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-proverbs
  (package
    (name "r-proverbs")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "proverbs" version))
       (sha256
        (base32 "1ilfzkpw3m8q4cr1ndygzjz363ghv0f1j5n7sw4whrzafhnlbdvg"))))
    (properties `((upstream-name . "proverbs")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rvest
                             r-purrr
                             r-lubridate
                             r-httr2
                             r-glue
                             r-crayon
                             r-cli
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bradlindblad/proverbs")
    (synopsis "Print a Daily Bible Proverb to Console")
    (description
     "This package provides a simple package to grab a Bible proverb corresponding to
the day of the month.")
    (license license:expat)))

(define-public r-provenance
  (package
    (name "r-provenance")
    (version "4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "provenance" version))
       (sha256
        (base32 "07dhpbwkn5x876i2zjrmc8rb5463956lqx6snddcraywc3jplgvb"))))
    (properties `((upstream-name . "provenance")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-isoplotr))
    (home-page "https://www.ucl.ac.uk/~ucfbpve/provenance/")
    (synopsis "Statistical Toolbox for Sedimentary Provenance Analysis")
    (description
     "Bundles a number of established statistical methods to facilitate the visual
interpretation of large datasets in sedimentary geology.  Includes functionality
for adaptive kernel density estimation, principal component analysis,
correspondence analysis, multidimensional scaling, generalised procrustes
analysis and individual differences scaling using a variety of dissimilarity
measures.  Univariate provenance proxies, such as single-grain ages or
(isotopic) compositions are compared with the Kolmogorov-Smirnov, Kuiper,
Wasserstein-2 or Sircombe-Hazelton L2 distances.  Categorical provenance proxies
such as chemical compositions are compared with the Aitchison and Bray-Curtis
distances,and count data with the chi-square distance.  Varietal data can either
be converted to one or more distributional datasets, or directly compared using
the multivariate Wasserstein distance.  Also included are tools to plot
compositional and count data on ternary diagrams and point-counting data on
radial plots, to calculate the sample size required for specified levels of
statistical precision, and to assess the effects of hydraulic sorting on
detrital compositions.  Includes an intuitive query-based user interface for
users who are not proficient in R.")
    (license license:gpl2)))

(define-public r-provdebugr
  (package
    (name "r-provdebugr")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "provDebugR" version))
       (sha256
        (base32 "0ynz5m1gkaj1wks5l71r3iz7npf3mz0r04jsa8zwg670zsv2x7rf"))))
    (properties `((upstream-name . "provDebugR")))
    (build-system r-build-system)
    (propagated-inputs (list r-textutils r-provparser r-provgraphr r-jsonlite
                             r-httr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=provDebugR")
    (synopsis "Time-Travelling Debugger")
    (description
     "Uses provenance post-execution to help the user understand and debug their
script by providing functions to look at intermediate steps and data values,
their forwards and backwards lineage, and to understand the steps leading up to
warning and error messages. @code{provDebugR} uses provenance produced by
@code{rdtLite} (available on CRAN), stored in PROV-JSON format.")
    (license license:gpl3)))

(define-public r-proustr
  (package
    (name "r-proustr")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "proustr" version))
       (sha256
        (base32 "1qp4v4vb1qh137qn7zi1d2g999z17kq6kpyxy9355mj8c49iy8pn"))))
    (properties `((upstream-name . "proustr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tokenizers
                             r-tidyr
                             r-stringr
                             r-snowballc
                             r-rlang
                             r-attempt))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ColinFay/proustr")
    (synopsis "Tools for Natural Language Processing in French")
    (description
     "This package provides tools for Natural Language Processing in French and texts
from Marcel Proust's collection \"A La Recherche Du Temps Perdu\".  The novels
contained in this collection are \"Du cote de chez Swann \", \"A l'ombre des jeunes
filles en fleurs\",\"Le Cote de Guermantes\", \"Sodome et Gomorrhe I et II\", \"La
Prisonniere\", \"Albertine disparue\", and \"Le Temps retrouve\".")
    (license license:expat)))

(define-public r-protti
  (package
    (name "r-protti")
    (version "0.9.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "protti" version))
       (sha256
        (base32 "1lnlikkgw0cvyw6sh49fzzwin31vya3dgq6zyash51vf6i3nc992"))))
    (properties `((upstream-name . "protti")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-readr
                             r-r-utils
                             r-purrr
                             r-progress
                             r-plotly
                             r-magrittr
                             r-lifecycle
                             r-janitor
                             r-httr
                             r-ggrepel
                             r-ggplot2
                             r-forcats
                             r-dplyr
                             r-data-table
                             r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jpquast/protti")
    (synopsis
     "Bottom-Up Proteomics and LiP-MS Quality Control and Data Analysis Tools")
    (description
     "Useful functions and workflows for proteomics quality control and data analysis
of both limited proteolysis-coupled mass spectrometry (@code{LiP-MS}) (Feng et.
al. (2014) <doi:10.1038/nbt.2999>) and regular bottom-up proteomics experiments.
 Data generated with search tools such as Spectronaut', @code{MaxQuant} and
Proteome Discover can be easily used due to flexibility of functions.")
    (license license:expat)))

(define-public r-protrackr
  (package
    (name "r-protrackr")
    (version "0.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProTrackR" version))
       (sha256
        (base32 "08c53ndrfgaw8hxvrw13ypcbm62nlxgfnip7p9pivvlrjpiiryvj"))))
    (properties `((upstream-name . "ProTrackR")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml r-tuner r-signal r-lattice r-audio))
    (home-page "https://pepijn-devries.github.io/ProTrackR/")
    (synopsis "Manipulate and Play 'ProTracker' Modules")
    (description
     "@code{ProTracker} is a popular music tracker to sequence music on a Commodore
Amiga machine.  This package offers the opportunity to import, export,
manipulate and play @code{ProTracker} module files.  Even though the file format
could be considered archaic, it still remains popular to this date.  This
package intends to contribute to this popularity and therewith keeping the
legacy of @code{ProTracker} and the Commodore Amiga alive.")
    (license license:gpl3+)))

(define-public r-protr
  (package
    (name "r-protr")
    (version "1.7-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "protr" version))
       (sha256
        (base32 "0r7ccnj34m26r91pgcvwpvdm0n7k6yd4cbkl8d5zdvghgj7md4ld"))))
    (properties `((upstream-name . "protr")))
    (build-system r-build-system)
    (inputs (list))
    (native-inputs (list r-knitr))
    (home-page "https://nanx.me/protr/")
    (synopsis
     "Generating Various Numerical Representation Schemes for Protein Sequences")
    (description
     "Comprehensive toolkit for generating various numerical features of protein
sequences described in Xiao et al. (2015) <DOI:10.1093/bioinformatics/btv042>.
For full functionality, the software ncbi-blast+ is needed, see
<https://blast.ncbi.nlm.nih.gov/doc/blast-help/downloadblastdata.html> for more
information.")
    (license license:bsd-3)))

(define-public r-prototest
  (package
    (name "r-prototest")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prototest" version))
       (sha256
        (base32 "07g58hq2qdpczqhjsv6dq1bya9rs958r103n91icw5yc19bvyhi2"))))
    (properties `((upstream-name . "prototest")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mass r-intervals
                             r-glmnet))
    (home-page "http://arxiv.org/abs/1511.07839")
    (synopsis "Inference on Prototypes from Clusters of Features")
    (description
     "Procedures for testing for group-wide signal in clusters of variables.  Tests
can be performed for single groups in isolation (univariate) or multiple groups
together (multivariate).  Specific tests include the exact and approximate
(un)selective likelihood ratio tests described in Reid et al (2015), the
selective F test and marginal screening prototype test of Reid and Tibshirani
(2015).  User may pre-specify columns to be included in prototype formation, or
allow the function to select them itself.  A mixture of these two is also
possible.  Any variable selection is accounted for using the selective inference
framework.  Options for non-sampling and hit-and-run null reference
distributions.")
    (license license:gpl2+)))

(define-public r-protoshiny
  (package
    (name "r-protoshiny")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "protoshiny" version))
       (sha256
        (base32 "0ms8sy8p5cd8vg82m7pny6jcg5mis1l7548m2g392gjcf3hf1sf7"))))
    (properties `((upstream-name . "protoshiny")))
    (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-shinythemes
                             r-shinycssloaders
                             r-shiny
                             r-rare
                             r-protoclust
                             r-dynamictreecut
                             r-dt))
    (native-inputs (list esbuild))
    (home-page "https://cran.r-project.org/package=protoshiny")
    (synopsis
     "Interactive Dendrograms for Visualizing Hierarchical Clusters with Prototypes")
    (description
     "Shiny app to interactively visualize hierarchical clustering with prototypes.
For details on hierarchical clustering with prototypes, see Bien and Tibshirani
(2011) <doi:10.1198/jasa.2011.tm10183>.  This package currently launches the
application.")
    (license license:gpl3)))

(define-public r-proton
  (package
    (name "r-proton")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "proton" version))
       (sha256
        (base32 "1mgaw54is8l6ac1rf8s70rj7kv9xgsfdrlvjz01ggfwg7c6pyr3s"))))
    (properties `((upstream-name . "proton")))
    (build-system r-build-system)
    (propagated-inputs (list r-digest))
    (home-page "https://cran.r-project.org/package=proton")
    (synopsis "The Proton Game")
    (description
     "The Proton Game is a console-based data-crunching game for younger and older
data scientists.  Act as a data-hacker and find Slawomir Pietraszko's
credentials to the Proton server.  You have to solve four data-based puzzles to
find the login and password.  There are many ways to solve these puzzles.  You
may use loops, data filtering, ordering, aggregation or other tools.  Only
basics knowledge of R is required to play the game, yet the more functions you
know, the more approaches you can try.  The knowledge of dplyr is not required
but may be very helpful.  This game is linked with the ,,Pietraszko's Cave story
available at http://biecek.pl/@code{BetaBit/Warsaw}.  It's a part of Beta and
Bit series.  You will find more about the Beta and Bit series at
http://biecek.pl/@code{BetaBit}.")
    (license license:gpl2)))

(define-public r-protoclust
  (package
    (name "r-protoclust")
    (version "1.6.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "protoclust" version))
       (sha256
        (base32 "0cbl9jxdc2j4z5viczl70zcd9z83qlipvkqi5f2bpypyvi2s0xs1"))))
    (properties `((upstream-name . "protoclust")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=protoclust")
    (synopsis "Hierarchical Clustering with Prototypes")
    (description
     "This package performs minimax linkage hierarchical clustering.  Every cluster
has an associated prototype element that represents that cluster as described in
Bien, J., and Tibshirani, R. (2011), \"Hierarchical Clustering with Prototypes
via Minimax Linkage,\" The Journal of the American Statistical Association,
106(495), 1075-1084.")
    (license license:gpl2)))

(define-public r-prothmm
  (package
    (name "r-prothmm")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "protHMM" version))
       (sha256
        (base32 "0j4n24dfvg6qqmyf5qljx34jr9p073hqflmzj57gyb8vwfzq6psr"))))
    (properties `((upstream-name . "protHMM")))
    (build-system r-build-system)
    (propagated-inputs (list r-phontools r-gtools))
    (native-inputs (list r-knitr))
    (home-page "https://semran9.github.io/protHMM/")
    (synopsis "Protein Feature Extraction from Profile Hidden Markov Models")
    (description
     "Calculates a comprehensive list of features from profile hidden Markov models
(HMMs) of proteins.  Adapts and ports features for use with HMMs instead of
Position Specific Scoring Matrices, in order to take advantage of more accurate
multiple sequence alignment by programs such as HHBlits Remmert et al. (2012)
<DOI:10.1038/nmeth.1818> and HMMer Eddy (2011)
<DOI:10.1371/journal.pcbi.1002195>.  Features calculated by this package can be
used for protein fold classification, protein structural class prediction,
sub-cellular localization and protein-protein interaction, among other tasks.
Some examples of features extracted are found in Song et al. (2018)
<DOI:10.3390/app8010089>, Jin & Zhu (2021) <DOI:10.1155/2021/8629776>, Lyons et
al. (2015) <DOI:10.1109/tnb.2015.2457906> and Saini et al. (2015)
<DOI:10.1016/j.jtbi.2015.05.030>.")
    (license license:gpl3+)))

(define-public r-proteus
  (package
    (name "r-proteus")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "proteus" version))
       (sha256
        (base32 "1i8h07k5pdsywpw5g8d06xgl5gk06g5q93jimfwkbry9ykagh1cw"))))
    (properties `((upstream-name . "proteus")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam
                             r-torch
                             r-tictoc
                             r-stringr
                             r-sn
                             r-scales
                             r-readr
                             r-purrr
                             r-narray
                             r-moments
                             r-modeest
                             r-lubridate
                             r-imputets
                             r-greybox
                             r-ggthemes
                             r-ggplot2
                             r-future
                             r-furrr
                             r-fancova
                             r-dplyr
                             r-actuar
                             r-abind))
    (home-page "https://rpubs.com/giancarlo_vercellino/proteus")
    (synopsis "Multiform Seq2Seq Model for Time-Feature Analysis")
    (description
     "Seq2seq time-feature analysis based on variational model, with a wide range of
distributions available for the latent variable.")
    (license license:gpl3)))

(define-public r-proteomicscv
  (package
    (name "r-proteomicscv")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "proteomicsCV" version))
       (sha256
        (base32 "0gvjl47x1wca2y386if2fgzp8k7a1xn9qxlq82ml66n193bp4k6a"))))
    (properties `((upstream-name . "proteomicsCV")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=proteomicsCV")
    (synopsis
     "Calculates the Percentage CV for Mass Spectrometry-Based Proteomics Data")
    (description
     "Calculates the percentage coefficient of variation (CV) for mass
spectrometry-based proteomic data.  The CV can be calculated with the
traditional formula for raw (non log transformed) intensity data, or log
transformed data.  This package currently does not reference any academic
publication.")
    (license license:expat)))

(define-public r-proteobayes
  (package
    (name "r-proteobayes")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProteoBayes" version))
       (sha256
        (base32 "0cxfzr1wrz0n3v2avwq285kp39jgznmylfdinmx5rj69454r630h"))))
    (properties `((upstream-name . "ProteoBayes")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rlang
                             r-mvtnorm
                             r-magrittr
                             r-ggplot2
                             r-extradistr
                             r-dplyr))
    (home-page "https://mariechion.github.io/ProteoBayes/")
    (synopsis "Bayesian Statistical Tools for Quantitative Proteomics")
    (description
     "Bayesian toolbox for quantitative proteomics.  In particular, this package
provides functions to generate synthetic datasets, execute Bayesian differential
analysis methods, and display results as, described in the associated article
Marie Chion and Arthur Leroy (2023) <@code{arXiv:2307.08975>}.")
    (license license:expat)))

(define-public r-protein8k
  (package
    (name "r-protein8k")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "protein8k" version))
       (sha256
        (base32 "11qj4sadhcsp1kk5kjmvzj2jnd3srsm5mp0q9h9khp1ps6dk35yw"))))
    (properties `((upstream-name . "protein8k")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny
                             r-rlang
                             r-rjson
                             r-pryr
                             r-magick
                             r-lattice
                             r-gridextra
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=protein8k")
    (synopsis "Perform Analysis and Create Visualizations of Proteins")
    (description
     "Read Protein Data Bank (PDB) files, performs its analysis, and presents the
result using different visualization types including 3D. The package also has
additional capability for handling Virus Report data from the National Center
for Biotechnology Information (NCBI) database.  Nature Structural Biology 10,
980 (2003) <doi:10.1038/nsb1203-980>.  US National Library of Medicine (2021)
<https://www.ncbi.nlm.nih.gov/datasets/docs/reference-docs/data-reports/virus/>.")
    (license license:cc0)))

(define-public r-protag
  (package
    (name "r-protag")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "protag" version))
       (sha256
        (base32 "1iaw2w1d7hdvlg5vplpqc49wq08dgmm0ifvp2v0ggqb8invvdnw8"))))
    (properties `((upstream-name . "protag")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer r-ggplot2 r-dplyr))
    (home-page "https://cran.r-project.org/package=protag")
    (synopsis "Search Tagged Peptides & Draw Highlighted Mass Spectra")
    (description
     "In a typical protein labelling procedure, proteins are chemically tagged with a
functional group, usually at specific sites, then digested into peptides, which
are then analyzed using matrix-assisted laser desorption ionization - time of
flight mass spectrometry (MALDI-TOF MS) to generate peptide fingerprint.
Relative to the control, peptides that are heavier by the mass of the labelling
group are informative for sequence determination.  Searching for peptides with
such mass shifts, however, can be difficult.  This package, designed to tackle
this inconvenience, takes as input the mass list of two or multiple MALDI-TOF MS
mass lists, and makes pairwise comparisons between the labeled groups vs.
control, and restores centroid mass spectra with highlighted peaks of interest
for easier visual examination.  Particularly, peaks differentiated by the mass
of the labelling group are defined as a âpairâ, those with equal masses as a
âmatchâ, and all the other peaks as a âmismatchâ.For more bioanalytical
background information, refer to following publications: Jingjing Deng (2015)
<doi:10.1007/978-1-4939-2550-6_19>; Elizabeth Chang (2016)
<doi:10.7171/jbt.16-2702-002>.")
    (license license:gpl2)))

(define-public r-prosportsdraftdata
  (package
    (name "r-prosportsdraftdata")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProSportsDraftData" version))
       (sha256
        (base32 "0x5f0gz8cigrlpla3bf7vpwn8ndmmd49crb2m3g27zv2hn5b850r"))))
    (properties `((upstream-name . "ProSportsDraftData")))
    (build-system r-build-system)
    (propagated-inputs (list r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Ginsburg1/ProSportsDraftData")
    (synopsis "Professional Sports Draft Data")
    (description
     "We provide comprehensive draft data for major professional sports leagues,
including the National Football League (NFL), National Basketball Association
(NBA), and National Hockey League (NHL).  It offers access to both historical
and current draft data, allowing for detailed analysis and research on player
biases and player performance.  The package is useful for sports fans and
researchers interested in identifying biases and trends within scouting reports.
 Created by web scraping data from leading websites that cover professional
sports player scouting reports, the package allows users to filter and summarize
data for analytical purposes.  For further details on the methods used, please
refer to Wickham (2022) \"rvest: Easily Harvest (Scrape) Web Pages\"
<https://CRAN.R-project.org/package=rvest> and Harrison (2023) \"RSelenium: R
Bindings for Selenium @code{WebDriver}\"
<https://CRAN.R-project.org/package=RSelenium>.")
    (license license:gpl3)))

(define-public r-prosper
  (package
    (name "r-prosper")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PROSPER" version))
       (sha256
        (base32 "1wdrdqii7d4yfky6706n9xjmabnz99jjja1c80nbj3fp8idsbli5"))))
    (properties `((upstream-name . "PROSPER")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table))
    (home-page "https://cran.r-project.org/package=PROSPER")
    (synopsis "Simulation of Weed Population Dynamics")
    (description
     "An environment to simulate the development of annual plant populations with
regard to population dynamics and genetics, especially herbicide resistance.  It
combines genetics on the individual level (Renton et al.  2011) with a
stochastic development on the population level (Daedlow, 2015).  Renton, M,
Diggle, A, Manalil, S and Powles, S (2011) <doi:10.1016/j.jtbi.2011.05.010>
Daedlow, Daniel (2015, doctoral dissertation: University of Rostock, Faculty of
Agriculture and Environmental Sciences.).")
    (license license:gpl3)))

(define-public r-prosgpv
  (package
    (name "r-prosgpv")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProSGPV" version))
       (sha256
        (base32 "1837p4vg1g4225rv55gaphma429j6pnfh078cxpv5ha2n0h1sfz3"))))
    (properties `((upstream-name . "ProSGPV")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mass r-glmnet r-brglm2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/zuoyi93/ProSGPV")
    (synopsis "Penalized Regression with Second-Generation P-Values")
    (description
     "Implementation of penalized regression with second-generation p-values for
variable selection.  The algorithm can handle linear regression, GLM, and Cox
regression.  S3 methods print(), summary(), coef(), predict(), and plot() are
available for the algorithm.  Technical details can be found at Zuo et al.
(2021) <doi:10.1080/00031305.2021.1946150>.")
    (license license:gpl3)))

(define-public r-prosetta
  (package
    (name "r-prosetta")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PROsetta" version))
       (sha256
        (base32 "1b013cwng8k17yfm4zrwjg7vc4papr2wmqq3a5kawl7bfy4ibaqw"))))
    (properties `((upstream-name . "PROsetta")))
    (build-system r-build-system)
    (propagated-inputs (list r-testdesign
                             r-rcpparmadillo
                             r-rcpp
                             r-psych
                             r-plink
                             r-mvnfast
                             r-mirt
                             r-lavaan
                             r-equate))
    (native-inputs (list r-knitr))
    (home-page "https://www.prosettastone.org/")
    (synopsis "Linking Patient-Reported Outcomes Measures")
    (description
     "Perform scale linking to establish relationships between instruments that
measure similar constructs according to the PROsetta Stone methodology, as in
Choi, Schalet, Cook, & Cella (2014) <doi:10.1037/a0035768>.")
    (license license:gpl3)))

(define-public r-proscorertools
  (package
    (name "r-proscorertools")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PROscorerTools" version))
       (sha256
        (base32 "0d4igzviz7j62cmnpv6xk01zw2f4yqj532y8i0h22ibdx950qj60"))))
    (properties `((upstream-name . "PROscorerTools")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MSKCC-Epi-Bio/PROscorerTools")
    (synopsis
     "Tools to Score Patient-Reported Outcome (PRO) and Other Psychometric Measures")
    (description
     "This package provides a reliable and flexible toolbox to score patient-reported
outcome (PRO), Quality of Life (QOL), and other psychometric measures.  The
guiding philosophy is that scoring errors can be eliminated by using a limited
number of well-tested, well-behaved functions to score PRO-like measures.  The
workhorse of the package is the @code{scoreScale} function, which can be used to
score most single-scale measures.  It can reverse code items that need to be
reversed before scoring and pro-rate scores for missing item data.  Currently,
three different types of scores can be output: summed item scores, mean item
scores, and scores scaled to range from 0 to 100.  The PR@code{OscorerTools}
functions can be used to write new functions that score more complex measures.
In fact, PR@code{OscorerTools} functions are the building blocks of the scoring
functions in the PROscorer package (which is a repository of functions that
score specific commonly-used instruments).  Users are encouraged to use
PR@code{OscorerTools} to write scoring functions for their favorite PRO-like
instruments, and to submit these functions for inclusion in PROscorer (a
tutorial vignette will be added soon).  The long-term vision for the
PR@code{OscorerTools} and PROscorer packages is to provide an easy-to-use system
to facilitate the incorporation of PRO measures into research studies in a
scientifically rigorous and reproducible manner.  These packages and their
vignettes are intended to help establish and promote \"best practices\" for
scoring and describing PRO-like measures in research.")
    (license license:expat)))

(define-public r-proscorer
  (package
    (name "r-proscorer")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PROscorer" version))
       (sha256
        (base32 "0l88hsyg5rmw8vyyqa959aa7zgw4brc4gxyx13qqdkxisxy18aqz"))))
    (properties `((upstream-name . "PROscorer")))
    (build-system r-build-system)
    (propagated-inputs (list r-proscorertools))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/raybaser/PROscorer")
    (synopsis
     "Functions to Score Commonly-Used Patient-Reported Outcome (PRO) Measures and Other Psychometric Instruments")
    (description
     "An extensible repository of accurate, up-to-date functions to score commonly
used patient-reported outcome (PRO), quality of life (QOL), and other
psychometric and psychological measures.  PROscorer', together with the
PR@code{OscorerTools} package, is a system to facilitate the incorporation of
PRO measures into research studies and clinical settings in a scientifically
rigorous and reproducible manner.  These packages and their vignettes are
intended to help establish and promote best practices for scoring PRO and
PRO-like measures in research.  The PROscorer Instrument Descriptions vignette
contains descriptions of each instrument scored by PROscorer', complete with
references.  These instrument descriptions are suitable for inclusion in formal
study protocol documents, grant proposals, and manuscript Method sections.  Each
PROscorer function is composed of helper functions from the
PR@code{OscorerTools} package, and users are encouraged to contribute new
functions to PROscorer'.  More scoring functions are currently in development
and will be added in future updates.")
    (license license:expat)))

(define-public r-proreg
  (package
    (name "r-proreg")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PROreg" version))
       (sha256
        (base32 "0x3gaadd9nd5mcs6l2jv0874afps13qgq81bqwcg1hhgjzy2k4mm"))))
    (properties `((upstream-name . "PROreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-rootsolve
                             r-rcolorbrewer
                             r-numderiv
                             r-matrixcalc
                             r-matrix
                             r-fmsb
                             r-car))
    (home-page "https://cran.r-project.org/package=PROreg")
    (synopsis "Patient Reported Outcomes Regression Analysis")
    (description
     "It offers a wide variety of techniques, such as graphics, recoding, or
regression models, for a comprehensive analysis of patient-reported outcomes
(PRO).  Especially novel is the broad range of regression models based on the
beta-binomial distribution useful for analyzing binomial data with
over-dispersion in cross-sectional, longitudinal, or multidimensional response
studies (see Najera-Zuloaga J., Lee D.-J. and Arostegui I. (2019)
<doi:10.1002/bimj.201700251>).")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-propublicar
  (package
    (name "r-propublicar")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProPublicaR" version))
       (sha256
        (base32 "0rgfblkzx7rgv6q6k1vq47fxxdaa46hq5wv6vxal2bjj1v2qflav"))))
    (properties `((upstream-name . "ProPublicaR")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-lubridate r-httr r-dplyr r-config))
    (home-page "https://cran.r-project.org/package=ProPublicaR")
    (synopsis "Access Functions for ProPublica's APIs")
    (description
     "This package provides wrapper functions to access the @code{ProPublica's}
Congress and Campaign Finance APIs.  The Congress API provides near real-time
access to legislative data from the House of Representatives, the Senate and the
Library of Congress.  The Campaign Finance API provides data from United States
Federal Election Commission filings and other sources.  The API covers summary
information for candidates and committees, as well as certain types of itemized
data.  For more information about these APIs go to:
<https://www.propublica.org/datastore/apis>.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-propubbills
  (package
    (name "r-propubbills")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "proPubBills" version))
       (sha256
        (base32 "1fpn9x31jjahdyk0f30mbb3ijj4dsghrq9q94r04pjsgr2jw23zx"))))
    (properties `((upstream-name . "proPubBills")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-httr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=proPubBills")
    (synopsis "'ProPublica' U.S. Congress Bills API Wrapper")
    (description
     "An API wrapper around the @code{ProPublica} API
<https://projects.propublica.org/api-docs/congress-api/> for U.S. Congressional
Bills.  Users can include their API key, U.S. Congress, branch, and offset
ranges, to return a dataframe of all results within those parameters.  This
package is different from the RPublica package because it is for the
@code{ProPublica} U.S. Congress data API, and the RPublica package is for the
Nonprofit Explorer, Forensics, and Free the Files data APIs.")
    (license license:expat)))

(define-public r-propscrrand
  (package
    (name "r-propscrrand")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PropScrRand" version))
       (sha256
        (base32 "1710mw0nrr9x8r39i400l74inz0g8pcqdavqzlagvj27p8b1akzq"))))
    (properties `((upstream-name . "PropScrRand")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PropScrRand")
    (synopsis
     "Propensity Score Methods for Assigning Treatment in Randomized Trials")
    (description
     "This package contains functions to run propensity-biased allocation to balance
covariate distributions in sequential trials and propensity-constrained
randomization to balance covariate distributions in trials with known baseline
covariates at time of randomization.  Currently only supports trials comparing
two groups.")
    (license license:gpl3)))

(define-public r-propoverlap
  (package
    (name "r-propoverlap")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "propOverlap" version))
       (sha256
        (base32 "0q72z9vbkpll4i3wy3fq06rz97in2cm3jjnvl6p9w8qc44zjlcyl"))))
    (properties `((upstream-name . "propOverlap")))
    (build-system r-build-system)
    (propagated-inputs (list r-biobase))
    (home-page "https://cran.r-project.org/package=propOverlap")
    (synopsis
     "Feature (gene) selection based on the Proportional Overlapping Scores")
    (description
     "This package provides a package for selecting the most relevant features (genes)
in the high-dimensional binary classification problems.  The discriminative
features are identified using analyzing the overlap between the expression
values across both classes.  The package includes functions for measuring the
proportional overlapping score for each gene avoiding the outliers effect.  The
used measure for the overlap is the one defined in the \"Proportional Overlapping
Score (POS)\" technique for feature selection.  A gene mask which represents a
gene's classification power can also be produced for each gene (feature).  The
set size of the selected genes might be set by the user.  The minimum set of
genes that correctly classify the maximum number of the given tissue samples
(observations) can be also produced.")
    (license license:gpl2+)))

(define-public r-proporz
  (package
    (name "r-proporz")
    (version "1.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "proporz" version))
       (sha256
        (base32 "0yvrmrbvv9dmcl6ry42whr40dlirirrr5xdn591qim5lkwxc17y1"))))
    (properties `((upstream-name . "proporz")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/polettif/proporz")
    (synopsis "Proportional Apportionment")
    (description
     "Calculate seat apportionment for legislative bodies with various methods.  The
algorithms include divisor or highest averages methods (e.g. Jefferson, Webster
or Adams), largest remainder methods and biproportional apportionment.  Gaffke,
N. & Pukelsheim, F. (2008) <doi:10.1016/j.mathsocsci.2008.01.004> Oelbermann, K.
F. (2016) <doi:10.1016/j.mathsocsci.2016.02.003>.")
    (license license:gpl3+)))

(define-public r-prophet
  (package
    (name "r-prophet")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prophet" version))
       (sha256
        (base32 "06mryavky9xj54ig4scqjgpx7i56nwhs53g0zqsbs9f6qxja7gll"))))
    (properties `((upstream-name . "prophet")))
    (build-system r-build-system)
    (propagated-inputs (list r-xts
                             r-tidyr
                             r-stanheaders
                             r-scales
                             r-rstantools
                             r-rstan
                             r-rlang
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-lubridate
                             r-ggplot2
                             r-extradistr
                             r-dygraphs
                             r-dplyr
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/facebook/prophet")
    (synopsis "Automatic Forecasting Procedure")
    (description
     "This package implements a procedure for forecasting time series data based on an
additive model where non-linear trends are fit with yearly, weekly, and daily
seasonality, plus holiday effects.  It works best with time series that have
strong seasonal effects and several seasons of historical data.  Prophet is
robust to missing data and shifts in the trend, and typically handles outliers
well.")
    (license license:expat)))

(define-public r-properties
  (package
    (name "r-properties")
    (version "0.0-9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "properties" version))
       (sha256
        (base32 "1zd66cg31yd53rqsc0cwxhlpfn7kvc67qdh3vyj9qib8kwxaw1zl"))))
    (properties `((upstream-name . "properties")))
    (build-system r-build-system)
    (home-page "https://www.rservicebus.io/")
    (synopsis "Parse 'Java' Properties Files for 'R Service Bus' Applications")
    (description
     "Allows to parse Java properties files in the context of R Service Bus
applications.")
    (license license:gpl2)))

(define-public r-propclust
  (package
    (name "r-propclust")
    (version "1.4-7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PropClust" version))
       (sha256
        (base32 "1xwm8a6m5j5rdry4gqjl2c6dx2kkzjz29nzmg4bjnk3y9xpcn8xn"))))
    (properties `((upstream-name . "PropClust")))
    (build-system r-build-system)
    (propagated-inputs (list r-fastcluster r-dynamictreecut))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=PropClust")
    (synopsis "Propensity Clustering and Decomposition")
    (description
     "Implementation of propensity clustering and decomposition as described in Ranola
et al. (2013) <doi:10.1186/1752-0509-7-21>.  Propensity decomposition can be
viewed on the one hand as a generalization of the eigenvector-based
approximation of correlation networks, and on the other hand as a generalization
of random multigraph models and conformity-based decompositions.")
    (license license:gpl2+)))

(define-public r-propcis
  (package
    (name "r-propcis")
    (version "0.3-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PropCIs" version))
       (sha256
        (base32 "1ywzi88igzaj2wnbrqa08cy7pw886z2m0x3j7rk45rin9mgpfdfd"))))
    (properties `((upstream-name . "PropCIs")))
    (build-system r-build-system)
    (home-page "https://github.com/shearer/PropCIs")
    (synopsis "Various Confidence Interval Methods for Proportions")
    (description
     "Computes two-sample confidence intervals for single, paired and independent
proportions.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-propagate
  (package
    (name "r-propagate")
    (version "1.0-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "propagate" version))
       (sha256
        (base32 "17jinz1jramlsf87i7vyn2qp82rc1dacljfaxxplrk3crhw3bhs7"))))
    (properties `((upstream-name . "propagate")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmvtnorm r-rcpp r-minpack-lm r-mass r-ff))
    (home-page "https://cran.r-project.org/package=propagate")
    (synopsis "Propagation of Uncertainty")
    (description
     "Propagation of uncertainty using higher-order Taylor expansion and Monte Carlo
simulation.")
    (license license:gpl2+)))

(define-public r-prop-comb-rr
  (package
    (name "r-prop-comb-rr")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prop.comb.RR" version))
       (sha256
        (base32 "10s52fkq7rv6v4gw1yd46f0fbg89ksim0qhgb5jbglzrqh585ny5"))))
    (properties `((upstream-name . "prop.comb.RR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rootsolve))
    (home-page "https://cran.r-project.org/package=prop.comb.RR")
    (synopsis "Analyzing Combination of Proportions and Relative Risk")
    (description
     "Carrying out inferences about any linear combination of proportions and the
ratio of two proportions.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-promr
  (package
    (name "r-promr")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "promr" version))
       (sha256
        (base32 "0b737gyg828s2m30l77qc59caazdsp84cg8a72arg0fqs70hk580"))))
    (properties `((upstream-name . "promr")))
    (build-system r-build-system)
    (propagated-inputs (list r-urltools r-tibble r-httr))
    (home-page "https://github.com/domodwyer/promr")
    (synopsis "Prometheus 'PromQL' Query Client for 'R'")
    (description
     "This package provides a native R client library for querying the Prometheus
time-series database, using the @code{PromQL} query language.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-promptr
  (package
    (name "r-promptr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "promptr" version))
       (sha256
        (base32 "17yi549a2xx9dmsaqwckbyagjgymjrc1d8nzffmr24pk9sh2z3fj"))))
    (properties `((upstream-name . "promptr")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-jsonlite
                             r-httr2
                             r-glue
                             r-dplyr
                             r-curl))
    (home-page "https://github.com/joeornstein/promptr")
    (synopsis "Format and Complete Few-Shot LLM Prompts")
    (description
     "Format and submit few-shot prompts to @code{OpenAI's} Large Language Models
(LLMs).  Designed to be particularly useful for text classification problems in
the social sciences.  Methods are described in Ornstein, Blasingame, and
Truscott (2024)
<https://joeornstein.github.io/publications/ornstein-blasingame-truscott.pdf>.")
    (license license:expat)))

(define-public r-prompter
  (package
    (name "r-prompter")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prompter" version))
       (sha256
        (base32 "18bbgcirw6z1vwna4bad4f4s4wnfq62bf9mzkakzlrn59kq9rxgc"))))
    (properties `((upstream-name . "prompter")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny))
    (home-page "https://prompter.etiennebacher.com")
    (synopsis "Add Tooltips in 'Shiny' Apps with 'Hint.css'")
    (description
     "In Shiny apps, it is sometimes useful to store information on a particular item
in a tooltip.  Prompter allows you to easily create such tooltips, using
Hint.css'.")
    (license license:expat)))

(define-public r-prompt
  (package
    (name "r-prompt")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prompt" version))
       (sha256
        (base32 "12fqdz906b96h4iaa1hpfwamp94c9am5287l57dsh2g4dpqvw91l"))))
    (properties `((upstream-name . "prompt")))
    (build-system r-build-system)
    (propagated-inputs (list r-cli))
    (home-page "https://github.com/gaborcsardi/prompt")
    (synopsis "Dynamic 'R' Prompt")
    (description
     "Set the R prompt dynamically, from a function.  The package contains some
examples to include various useful dynamic information in the prompt: the status
of the last command (success or failure); the amount of memory allocated by the
current R process; the name of the R package(s) loaded by pkgload and/or
devtools'; various git information: the name of the active branch, whether it is
dirty, if it needs pushes pulls.  You can also create your own prompt if you
don't like the predefined examples.")
    (license license:expat)))

(define-public r-promotionimpact
  (package
    (name "r-promotionimpact")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "promotionImpact" version))
       (sha256
        (base32 "18iq2r5zirddlfx20alm6qnxf9qv3ii3c9bcm789q0q18d2hyj2q"))))
    (properties `((upstream-name . "promotionImpact")))
    (build-system r-build-system)
    (propagated-inputs (list r-strucchange
                             r-stringr
                             r-scales
                             r-reshape2
                             r-rcpp
                             r-prophet
                             r-lmtest
                             r-kernsmooth
                             r-ggpubr
                             r-ggplot2
                             r-dplyr
                             r-crayon))
    (home-page "https://github.com/ncsoft/promotionImpact")
    (synopsis "Analysis & Measurement of Promotion Effectiveness")
    (description
     "Analysis and measurement of promotion effectiveness on a given target variable
(e.g. daily sales).  After converting promotion schedule into dummy or smoothed
predictor variables, the package estimates the effects of these variables
controlled for trend/periodicity/structural change using prophet by Taylor and
Letham (2017) <doi:10.7287/peerj.preprints.3190v2> and some prespecified
variables (e.g. start of a month).")
    (license license:bsd-3)))

(define-public r-promote
  (package
    (name "r-promote")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "promote" version))
       (sha256
        (base32 "1cdz4xqinyzpbbgkxxd4f86n1h8zj88vp5z6w01cxd5ykyjf5411"))))
    (properties `((upstream-name . "promote")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-jsonlite r-httr))
    (home-page "https://github.com/alteryx/promote-r-client")
    (synopsis "Client for the 'Alteryx Promote' API")
    (description
     "Deploy, maintain, and invoke predictive models using the Alteryx Promote REST
API. Alteryx Promote is available at the URL:
<https://www.alteryx.com/products/alteryx-promote>.")
    (license (license:fsdg-compatible "FreeBSD"))))

(define-public r-promor
  (package
    (name "r-promor")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "promor" version))
       (sha256
        (base32 "0cpf6946522lhnsr6fxm8ppvq69vjpkx7p7p0chx7kibc9ivb1ia"))))
    (properties `((upstream-name . "promor")))
    (build-system r-build-system)
    (propagated-inputs (list r-xgboost
                             r-viridis
                             r-vim
                             r-statmod
                             r-reshape2
                             r-proc
                             r-pcamethods
                             r-naivebayes
                             r-missforest
                             r-limma
                             r-kernlab
                             r-gridextra
                             r-ggrepel
                             r-ggplot2
                             r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/caranathunge/promor")
    (synopsis "Proteomics Data Analysis and Modeling Tools")
    (description
     "This package provides a comprehensive, user-friendly package for label-free
proteomics data analysis and machine learning-based modeling.  Data generated
from @code{MaxQuant} can be easily used to conduct differential expression
analysis, build predictive models with top protein candidates, and assess model
performance.  promor includes a suite of tools for quality control,
visualization, missing data imputation (Lazar et.  al. (2016)
<doi:10.1021/acs.jproteome.5b00981>), differential expression analysis (Ritchie
et.  al. (2015) <doi:10.1093/nar/gkv007>), and machine learning-based modeling
(Kuhn (2008) <doi:10.18637/jss.v028.i05>).")
    (license license:lgpl2.1+)))

(define-public r-prometheetools
  (package
    (name "r-prometheetools")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PrometheeTools" version))
       (sha256
        (base32 "09hrpybagl58vm22bhvpwb07s542hi4j15mqwwwyhi3dw0sn9xfy"))))
    (properties `((upstream-name . "PrometheeTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://github.com/ifelipebj/PrometheeTools")
    (synopsis "PROMETHEE and GLNF for Ranking and Sorting Problems")
    (description
     "PROMETHEE (Preference Ranking Organisation METHod for Enrichment of Evaluations)
based method assesses alternatives to obtain partial and complete rankings.  The
package also provides the GLNF (Global Local Net Flow) sorting algorithm to
classify alternatives into ordered categories, as well as an index function to
measure the classification quality.  Barrera, F., Segura, M., & Maroto, C.
(2023) <doi:10.1111/itor.13288>.  Brans, J.P.; De Smet, Y., (2016)
<doi:10.1007/978-1-4939-3094-4_6>.")
    (license license:gpl3+)))

(define-public r-promethee123
  (package
    (name "r-promethee123")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "promethee123" version))
       (sha256
        (base32 "0s1wkcrvm32dpqg9ahn10kni1xv3ll7akqz4y6yn1d36km81bf21"))))
    (properties `((upstream-name . "promethee123")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-cowplot))
    (home-page "https://cran.r-project.org/package=promethee123")
    (synopsis "PROMETHEE I, II, and III Methods")
    (description
     "The PROMETHEE method is a multi-criteria decision-making method addressing with
outranking problems.  The method establishes a preference structure between the
alternatives, having a preference function for each criterion.  IN this context,
three variants of the method is carried out: PROMETHEE I (Partial Outranking),
PROMETHEE II (Total Outranking), and PROMETHEE III (Outranking by Intervals).")
    (license license:gpl3)))

(define-public r-promethee
  (package
    (name "r-promethee")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PROMETHEE" version))
       (sha256
        (base32 "0r53mb9ndwm4yli3h50cfamsjzpq6b67g72mszm7lpbqpj58wqw6"))))
    (properties `((upstream-name . "PROMETHEE")))
    (build-system r-build-system)
    (propagated-inputs (list r-lpsolve))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=PROMETHEE")
    (synopsis
     "Preference Ranking Organization METHod for Enrichment of Evaluations")
    (description
     "This package provides functions which can be used to support the Multicriteria
Decision Analysis (MCDA) process involving multiple criteria, by PROMETHEE
(Preference Ranking Organization METHod for Enrichment of Evaluations).")
    (license license:gpl3)))

(define-public r-prome
  (package
    (name "r-prome")
    (version "1.9.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prome" version))
       (sha256
        (base32 "134nc90av9js4qdq7dxy7z93h621i6w1vbgly38minprfam0wwm0"))))
    (properties `((upstream-name . "prome")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-bh))
    (home-page "https://cran.r-project.org/package=prome")
    (synopsis "Patient-Reported Outcome Data Analysis with Stan")
    (description
     "Algorithms and subroutines for patient-reported outcome data analysis.")
    (license license:expat)))

(define-public r-prolsirm
  (package
    (name "r-prolsirm")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prolsirm" version))
       (sha256
        (base32 "1nx4bal130r3c7hb0x5j22rh7988p62qll3rm0j8wkiqdnqkyhay"))))
    (properties `((upstream-name . "prolsirm")))
    (build-system r-build-system)
    (propagated-inputs (list r-mcmcpack))
    (home-page "https://cran.r-project.org/package=prolsirm")
    (synopsis "Procrustes Matching for Latent Space Item Response Model")
    (description
     "Procrustes matching of the posterior samples of person and item latent positions
from latent space item response models.  The methods implemented in this package
are based on work by Borg, I., Groenen, P. (1997, ISBN:978-0-387-94845-4), Jeon,
M., Jin, I. H., Schweinberger, M., Baugh, S. (2021)
<doi:10.1007/s11336-021-09762-5>, and Andrew, D. M., Kevin M. Q., Jong Hee Park.
(2011) <doi:10.18637/jss.v042.i09>.")
    (license license:gpl3+)))

(define-public r-prolific-api
  (package
    (name "r-prolific-api")
    (version "0.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prolific.api" version))
       (sha256
        (base32 "1xg1xgkj53zls6zh5nil5njb783ymks9hgfpsxcdnvhnhgsb50k2"))))
    (properties `((upstream-name . "prolific.api")))
    (build-system r-build-system)
    (inputs (list curl))
    (propagated-inputs (list r-jsonlite r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=prolific.api")
    (synopsis "User-Friendly Interface for Accessing the Prolific API")
    (description
     "This package provides a user-friendly interface for creating and managing
empirical crowd-sourcing studies via API access to <https://www.prolific.co>.")
    (license license:gpl3+)))

(define-public r-proliferativeindex
  (package
    (name "r-proliferativeindex")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProliferativeIndex" version))
       (sha256
        (base32 "03ipsbs8pfwr8wsx7j2y9c67ic4qcady7xpa47l8dr14ff63cfk7"))))
    (properties `((upstream-name . "ProliferativeIndex")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ProliferativeIndex")
    (synopsis "Calculates and Analyzes the Proliferative Index")
    (description
     "This package provides functions for calculating and analyzing the proliferative
index (PI) from an RNA-seq dataset.  As described in Ramaker & Lasseigne, et al.
@code{bioRxiv}, 2016 <doi:10.1101/063057>.")
    (license license:expat)))

(define-public r-projmgr
  (package
    (name "r-projmgr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "projmgr" version))
       (sha256
        (base32 "0wj96zvvhpnh6cn2sm3n4nv746v76ybv7blx0lsq98xwk4abqp8k"))))
    (properties `((upstream-name . "projmgr")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr r-gh))
    (home-page "https://github.com/emilyriederer/projmgr")
    (synopsis "Task Tracking and Project Management with GitHub")
    (description
     "This package provides programmatic access to @code{GitHub} API with a focus on
project management.  Key functionality includes setting up issues and milestones
from R objects or YAML configurations, querying outstanding or completed tasks,
and generating progress updates in tables, charts, and RMarkdown reports.
Useful for those using @code{GitHub} in personal, professional, or academic
settings with an emphasis on streamlining the workflow of data analysis
projects.")
    (license license:expat)))

(define-public r-projecttemplate
  (package
    (name "r-projecttemplate")
    (version "0.11.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProjectTemplate" version))
       (sha256
        (base32 "1898dfrfrilpa9gackifgnyf5m5j6r9359c7m8avcxn0qsygiviz"))))
    (properties `((upstream-name . "ProjectTemplate")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-digest))
    (home-page "http://projecttemplate.net")
    (synopsis "Automates the Creation of New Statistical Analysis Projects")
    (description
     "This package provides functions to automatically build a directory structure for
a new R project.  Using this structure, @code{ProjectTemplate} automates data
loading, preprocessing, library importing and unit testing.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-projects
  (package
    (name "r-projects")
    (version "2.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "projects" version))
       (sha256
        (base32 "0ljrhswr5sfq051x0aicl9yvwakw9d9drgrmcz9d8qhgw1if0mky"))))
    (properties `((upstream-name . "projects")))
    (build-system r-build-system)
    (propagated-inputs (list r-zip
                             r-vctrs
                             r-tibble
                             r-stringr
                             r-sessioninfo
                             r-rstudioapi
                             r-rlang
                             r-readr
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-fs
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=projects")
    (synopsis "Project Infrastructure for Researchers")
    (description
     "This package provides a project infrastructure with a focus on manuscript
creation.  Creates a project folder with a single command, containing
subdirectories for specific components, templates for manuscripts, and so on.")
    (license license:expat)))

(define-public r-projectmanagement
  (package
    (name "r-projectmanagement")
    (version "1.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProjectManagement" version))
       (sha256
        (base32 "04i1rz5dwgkgy6da12v3vhq3skqmnqlb3dry99hycjrfql0v4wsr"))))
    (properties `((upstream-name . "ProjectManagement")))
    (build-system r-build-system)
    (propagated-inputs (list r-triangle r-plotly r-lpsolveapi r-kappalab
                             r-igraph))
    (home-page "https://cran.r-project.org/package=ProjectManagement")
    (synopsis "Management of Deterministic and Stochastic Projects")
    (description
     "Management problems of deterministic and stochastic projects.  It obtains the
duration of a project and the appropriate slack for each activity in a
deterministic context.  In addition it obtains a schedule of activities time
(Castro, GÃ³mez & Tejada (2007) <doi:10.1016/j.orl.2007.01.003>).  It also
allows the management of resources.  When the project is done, and the actual
duration for each activity is known, then it can know how long the project is
delayed and make a fair delivery of the delay between each activity
(BergantiÃ±os, Valencia-Toledo & Vidal-Puga (2018)
<doi:10.1016/j.dam.2017.08.012>).  In a stochastic context it can estimate the
average duration of the project and plot the density of this duration, as well
as, the density of the early and last times of the chosen activities.  As in the
deterministic case, it can make a distribution of the delay generated by
observing the project already carried out.")
    (license license:gpl2+)))

(define-public r-projections
  (package
    (name "r-projections")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "projections" version))
       (sha256
        (base32 "0wpqklw5aif9vgcp6hyc7j5k8ifxwx5bhhd50llwgrh5xiy9yxyq"))))
    (properties `((upstream-name . "projections")))
    (build-system r-build-system)
    (propagated-inputs (list r-incidence r-ggplot2))
    (home-page "https://www.repidemicsconsortium.org/projections/")
    (synopsis "Project Future Case Incidence")
    (description
     "This package provides functions and graphics for projecting daily incidence
based on past incidence, and estimates of the serial interval and reproduction
number.  Projections are based on a branching process using a
Poisson-distributed number of new cases per day, similar to the model used for
estimating R in @code{EpiEstim} or in @code{earlyR}', and described by Nouvellet
et al. (2017) <doi:10.1016/j.epidem.2017.02.012>.  The package provides the S3
class projections which extends matrix', with accessors and additional helpers
for handling, subsetting, merging, or adding these objects, as well as dedicated
printing and plotting methods.")
    (license license:expat)))

(define-public r-projectionbasedclustering
  (package
    (name "r-projectionbasedclustering")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProjectionBasedClustering" version))
       (sha256
        (base32 "0v3w5bfmvc36bf19p1lp13p574xlbjq0gg43mwm598cnyqsw12xq"))))
    (properties `((upstream-name . "ProjectionBasedClustering")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-shinythemes
                             r-shinyjs
                             r-shiny
                             r-rcpp
                             r-plotly
                             r-ggplot2
                             r-geometry
                             r-generalizedumatrix
                             r-deldir))
    (home-page "https://www.deepbionics.org")
    (synopsis "Projection Based Clustering")
    (description
     "This package provides a clustering approach applicable to every projection
method is proposed here.  The two-dimensional scatter plot of any projection
method can construct a topographic map which displays unapparent data structures
by using distance and density information of the data.  The generalized
U*-matrix renders this visualization in the form of a topographic map, which can
be used to automatically define the clusters of high-dimensional data.  The
whole system is based on Thrun and Ultsch, \"Using Projection based Clustering to
Find Distance and Density based Clusters in High-Dimensional Data\"
<DOI:10.1007/s00357-020-09373-2>.  Selecting the correct projection method will
result in a visualization in which mountains surround each cluster.  The number
of clusters can be determined by counting valleys on the topographic map.  Most
projection methods are wrappers for already available methods in R. By contrast,
the neighbor retrieval visualizer (@code{NeRV}) is based on C++ source code of
the dredviz software package, and the Curvilinear Component Analysis (CCA) is
translated from MATLAB ('SOM Toolbox 2.0) to R.")
    (license license:gpl3)))

(define-public r-proj
  (package
    (name "r-proj")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PROJ" version))
       (sha256
        (base32 "16wcr1p2rp9d5y14n9km0d24zhvja3qj5d34lx9mv9l969licqzs"))))
    (properties `((upstream-name . "PROJ")))
    (build-system r-build-system)
    (inputs (list zlib proj openssl openssh curl))
    (propagated-inputs (list r-wk r-lifecycle))
    (native-inputs (list pkg-config r-knitr))
    (home-page "https://github.com/hypertidy/PROJ")
    (synopsis "Generic Coordinate System Transformations Using 'PROJ'")
    (description
     "This package provides a wrapper around the generic coordinate transformation
software PROJ that transforms coordinates from one coordinate reference system
('CRS') to another.  This includes cartographic projections as well as geodetic
transformations.  The intention is for this package to be used by user-packages
such as reproj', and that the older PROJ.4 and version 5 pathways be provided by
the proj4 package.")
    (license license:gpl3)))

(define-public r-progmodule
  (package
    (name "r-progmodule")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProgModule" version))
       (sha256
        (base32 "16im1ymprfak06vv0m8z0wccvszknzgg5br14q1048g8975rdj1h"))))
    (properties `((upstream-name . "ProgModule")))
    (build-system r-build-system)
    (propagated-inputs (list r-survminer
                             r-survival
                             r-pathwaytmb
                             r-patchwork
                             r-maftools
                             r-infotheo
                             r-igraph))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ProgModule")
    (synopsis "Identification of Prognosis-Related Mutually Exclusive Modules")
    (description
     "This package provides a novel tool to identify candidate driver modules for
predicting the prognosis of patients by integrating exclusive coverage of
mutations with clinical characteristics in cancer.")
    (license license:gpl2+)))

(define-public r-progenyclust
  (package
    (name "r-progenyclust")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "progenyClust" version))
       (sha256
        (base32 "0azp5pvk316s8xbawcqwqfd80fxb4xn8hc6aq87xwksc6fhwp94l"))))
    (properties `((upstream-name . "progenyClust")))
    (build-system r-build-system)
    (propagated-inputs (list r-hmisc))
    (home-page "https://cran.r-project.org/package=progenyClust")
    (synopsis "Finding the Optimal Cluster Number Using Progeny Clustering")
    (description
     "Implementing the Progeny Clustering algorithm, the @code{progenyClust} package
assesses the clustering stability and identifies the optimal clustering number
for a given data matrix.  It uses k-means clustering as a default, provides a
tailored hierarchical clustering function, and can be customized to work with
other clustering algorithms and different parameter settings.  The package
includes a main function @code{progenyClust}(), plot and summary methods for
@code{progenyClust} object, a function hclust.@code{progenyClust}() for
hierarchical clustering, and two example datasets (test and cell) for testing.")
    (license license:agpl3)))

(define-public r-proftools
  (package
    (name "r-proftools")
    (version "0.99-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "proftools" version))
       (sha256
        (base32 "095pxyyv6k818qwcjygbz1h24k0xkqi1agns8cqh2m5g64aynd70"))))
    (properties `((upstream-name . "proftools")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=proftools")
    (synopsis "Profile Output Processing Tools for R")
    (description
     "This package provides tools for examining Rprof profile output.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-profrep
  (package
    (name "r-profrep")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "profrep" version))
       (sha256
        (base32 "0p2sd95w8n4l2mxyzjxppynfdqbg6znd69360mgyci3r61za2z6z"))))
    (properties `((upstream-name . "profrep")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://ubeattie.github.io/profrep/")
    (synopsis "Profile Repeatability")
    (description
     "Calculates profile repeatability for replicate stress response curves, or
similar time-series data.  Profile repeatability is an individual repeatability
metric that uses the variances at each timepoint, the maximum variance, the
number of crossings (lines that cross over each other), and the number of
replicates to compute the repeatability score.  For more information see Reed et
al. (2019) <doi:10.1016/j.ygcen.2018.09.015>.")
    (license license:expat)))

(define-public r-profr
  (package
    (name "r-profr")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "profr" version))
       (sha256
        (base32 "00bv1q0qn46nz4zjfs0l2yfh3hk71rajlp2rrlwg442z11k79ap9"))))
    (properties `((upstream-name . "profr")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-plyr))
    (home-page "https://github.com/hadley/profr")
    (synopsis "An Alternative Display for Profiling Information")
    (description
     "An alternative data structure and visual rendering for the profiling information
generated by Rprof.")
    (license license:expat)))

(define-public r-profounddata
  (package
    (name "r-profounddata")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProfoundData" version))
       (sha256
        (base32 "1zqg821pv3i5nf76s7yppnmy13y14pj0l7rjhk50wlxrxdb53ibz"))))
    (properties `((upstream-name . "ProfoundData")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-sqldf
                             r-settings
                             r-rsqlite
                             r-rnetcdf
                             r-dbi))
    (native-inputs (list r-r-rsp))
    (home-page "https://cost-fp1304-profound.github.io/ProfoundData/")
    (synopsis "Downloading and Exploring Data from the PROFOUND Database")
    (description
     "This package provides an R interface for the PROFOUND database
<doi:10.5880/PIK.2019.008>.  The PROFOUND database contains a wide range of data
to evaluate vegetation models and simulate climate impacts at the forest stand
scale.  It includes 9 forest sites across Europe, and provides for them a site
description as well as soil, climate, CO2, Nitrogen deposition, tree-level,
forest stand-level and remote sensing data.  Moreover, for a subset of 5 sites,
also time series of carbon fluxes, energy balances and soil water are available.")
    (license license:gpl3)))

(define-public r-profoc
  (package
    (name "r-profoc")
    (version "1.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "profoc" version))
       (sha256
        (base32 "0iknp4mjz3q2nzfy3ysnhxwrf3qbylzggl5jp6azc488gln2wfhq"))))
    (properties `((upstream-name . "profoc")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-splines2
                             r-rcpptimer
                             r-rcppprogress
                             r-rcpparmadillo
                             r-rcpp
                             r-matrix
                             r-lifecycle
                             r-ggplot2
                             r-generics
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://profoc.berrisch.biz")
    (synopsis "Probabilistic Forecast Combination Using CRPS Learning")
    (description
     "Combine probabilistic forecasts using CRPS learning algorithms proposed in
Berrisch, Ziel (2021) <doi:10.48550/@code{arXiv.2102.00968>}
<doi:10.1016/j.jeconom.2021.11.008>.  The package implements multiple online
learning algorithms like Bernstein online aggregation; see Wintenberger (2014)
<doi:10.48550/@code{arXiv.1404.1356>}.  Quantile regression is also implemented
for comparison purposes.  Model parameters can be tuned automatically with
respect to the loss of the forecast combination.  Methods like predict(),
update(), plot() and print() are available for convenience.  This package
utilizes the optim C++ library for numeric optimization
<https://github.com/kthohr/optim>.")
    (license license:gpl3+)))

(define-public r-profiplots
  (package
    (name "r-profiplots")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "profiplots" version))
       (sha256
        (base32 "0b2i1m9x7aqhlizv059q1gv8m15myim4q0s81gaskqf9rid83apz"))))
    (properties `((upstream-name . "profiplots")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://profinit.eu")
    (synopsis "Profinit Plotting Theme")
    (description
     "Help unify visual output of R analyses in the Profinit EU company.  So far,
there are color and fill scales for ggplot2', plotting theme for ggplot2', color
palettes and utils to make the tools default choices.")
    (license license:expat)))

(define-public r-profiler
  (package
    (name "r-profiler")
    (version "0.3-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "profileR" version))
       (sha256
        (base32 "0fgc7585a7194c67irafjnx8g2j6mn3qhcfn76c6dh28clp20qp9"))))
    (properties `((upstream-name . "profileR")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape r-rcolorbrewer r-lavaan r-ggplot2))
    (home-page "https://cran.r-project.org/package=profileR")
    (synopsis "Profile Analysis of Multivariate Data in R")
    (description
     "This package provides a suite of multivariate methods and data visualization
tools to implement profile analysis and cross-validation techniques described in
Davison & Davenport (2002) <DOI: 10.1037/1082-989X.7.4.468>, Bulut (2013), and
other published and unpublished resources.  The package includes routines to
perform criterion-related profile analysis, profile analysis via
multidimensional scaling, moderated profile analysis, profile analysis by group,
and a within-person factor model to derive score profiles.")
    (license license:gpl2+)))

(define-public r-profilelikelihood
  (package
    (name "r-profilelikelihood")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProfileLikelihood" version))
       (sha256
        (base32 "0yxcrxw3jb8bnwqh5f5d8bmm5jlkyxfld12zfyw3b1031zj1vh6i"))))
    (properties `((upstream-name . "ProfileLikelihood")))
    (build-system r-build-system)
    (propagated-inputs (list r-nlme r-mass))
    (home-page "https://cran.r-project.org/package=ProfileLikelihood")
    (synopsis
     "Profile Likelihood for a Parameter in Commonly Used Statistical Models")
    (description
     "This package provides profile likelihoods for a parameter of interest in
commonly used statistical models.  The models include linear models, generalized
linear models, proportional odds models, linear mixed-effects models, and linear
models for longitudinal responses fitted by generalized least squares.  The
package also provides plots for normalized profile likelihoods as well as the
maximum profile likelihood estimates and the kth likelihood support intervals.")
    (license license:gpl3+)))

(define-public r-profile
  (package
    (name "r-profile")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "profile" version))
       (sha256
        (base32 "1pzgmsn961aq6nxyi7f884hazxvf0d9szqiyv7945ap7kc9knyqd"))))
    (properties `((upstream-name . "profile")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr r-vctrs r-tibble r-rlang))
    (home-page "https://r-prof.github.io/profile/")
    (synopsis "Read, Manipulate, and Write Profiler Data")
    (description
     "Defines a data structure for profiler data, and methods to read and write from
the Rprof and pprof file formats.")
    (license license:expat)))

(define-public r-proffer
  (package
    (name "r-proffer")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "proffer" version))
       (sha256
        (base32 "1qg3yx6dhlsvvqdvcbypngfjb59zja0k32d824vgphsciz1bycia"))))
    (properties `((upstream-name . "proffer")))
    (build-system r-build-system)
    (inputs (list graphviz))
    (propagated-inputs (list r-withr
                             r-rprotobuf
                             r-profile
                             r-processx
                             r-pingr
                             r-parallelly
                             r-cli))
    (home-page "https://github.com/r-prof/proffer")
    (synopsis "Profile R Code and Visualize with 'Pprof'")
    (description
     "Like similar profiling tools, the proffer package automatically detects sources
of slowness in R code.  The distinguishing feature of proffer is its utilization
of pprof', which supplies interactive visualizations that are efficient and easy
to interpret.  Behind the scenes, the profile package converts native Rprof()
data to a protocol buffer that pprof understands.  For the documentation of
proffer', visit <https://r-prof.github.io/proffer/>.  To learn about the
implementations and methodologies of pprof', profile', and protocol buffers,
visit <https://github.com/google/pprof>. <https://protobuf.dev>, and
<https://github.com/r-prof/profile>, respectively.")
    (license license:expat)))

(define-public r-profextrema
  (package
    (name "r-profextrema")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "profExtrema" version))
       (sha256
        (base32 "0gdhamnngh1yh3nb7l656qhdr61s0zn6h1hagh22k2fn11z3xhsl"))))
    (properties `((upstream-name . "profExtrema")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer
                             r-rcdd
                             r-quantreg
                             r-pgpx
                             r-microbenchmark
                             r-mass
                             r-lhs
                             r-kriginv
                             r-dicekriging))
    (home-page "https://cran.r-project.org/package=profExtrema")
    (synopsis "Compute and Visualize Profile Extrema Functions")
    (description
     "Computes profile extrema functions for arbitrary functions.  If the function is
expensive-to-evaluate it computes profile extrema by emulating the function with
a Gaussian process (using package @code{DiceKriging}').  In this case
uncertainty quantification on the profile extrema can also be computed.  The
different plotting functions for profile extrema give the user a tool to better
locate excursion sets.")
    (license license:gpl3)))

(define-public r-professr
  (package
    (name "r-professr")
    (version "2.4-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProfessR" version))
       (sha256
        (base32 "0df7ll7zv3kh5ygdcq9klzjy4g1knd9z0zv3whw5x3a19kkaaili"))))
    (properties `((upstream-name . "ProfessR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rpmg))
    (home-page "https://cran.r-project.org/package=ProfessR")
    (synopsis "Grades Setting and Exam Maker")
    (description
     "Programs to determine student grades and create examinations from Question
banks.  Programs will create numerous multiple choice exams, randomly shuffled,
for different versions of same question list.")
    (license license:gpl2+)))

(define-public r-profast
  (package
    (name "r-profast")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProFAST" version))
       (sha256
        (base32 "06643m0rr54v42sg16vp3dlfkvkjflqlmv2kv87c2n668gifxaw7"))))
    (properties `((upstream-name . "ProFAST")))
    (build-system r-build-system)
    (propagated-inputs (list r-seurat
                             r-rcpparmadillo
                             r-rcpp
                             r-precast
                             r-pbapply
                             r-org-mm-eg-db
                             r-org-hs-eg-db
                             r-mclust
                             r-matrix
                             r-irlba
                             r-harmony
                             r-gtools
                             r-ggplot2
                             r-future
                             r-furrr
                             r-dr-sc
                             r-biomart
                             r-annotationdbi))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/feiyoung/ProFAST")
    (synopsis
     "Probabilistic Factor Analysis for Spatially-Aware Dimension Reduction")
    (description
     "Probabilistic factor analysis for spatially-aware dimension reduction across
multi-section spatial transcriptomics data with millions of spatial locations.
More details can be referred to Wei Liu, et al. (2023)
<doi:10.1101/2023.07.11.548486>.")
    (license license:gpl3)))

(define-public r-productivity
  (package
    (name "r-productivity")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "productivity" version))
       (sha256
        (base32 "1g8b4vlyqq3xyzd4dki01glhc3g9j3ywgwpxylkmzp0ivqic53yc"))))
    (properties `((upstream-name . "productivity")))
    (build-system r-build-system)
    (propagated-inputs (list r-lpsolveapi r-iterators r-foreach r-doparallel))
    (home-page "https://cran.r-project.org/package=productivity")
    (synopsis "Indices of Productivity Using Data Envelopment Analysis (DEA)")
    (description
     "Levels and changes of productivity and profitability are measured with various
indices.  The package contains the multiplicatively complete FÃ¤re-Primont,
Fisher, Hicks-Moorsteen, Laspeyres, Lowe, and Paasche indices, as well as the
classic Malmquist productivity index.  FÃ¤re-Primont and Lowe indices verify the
transitivity property and can therefore be used for multilateral or
multitemporal comparison.  Fisher, Hicks-Moorsteen, Laspeyres, Malmquist, and
Paasche indices are not transitive and are only to be used for binary
comparison.  All indices can also be decomposed into different components,
providing insightful information on the sources of productivity and
profitability changes.  In the use of Malmquist productivity index, the
technological change index can be further decomposed into bias technological
change components.  The package also allows to prohibit technological regression
(negative technological change).  In the case of the Fisher, Hicks-Moorsteen,
Laspeyres, Paasche and the transitive FÃ¤re-Primont and Lowe indices, it is
furthermore possible to rule out technological change.  Deflated shadow prices
can also be obtained.  Besides, the package allows parallel computing as an
option, depending on the user's computer configuration.  All computations are
carried out with the nonparametric Data Envelopment Analysis (DEA), and several
assumptions regarding returns to scale are available.  All DEA linear programs
are implemented using lp_solve'.")
    (license license:gpl3)))

(define-public r-prodigenr
  (package
    (name "r-prodigenr")
    (version "0.6.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prodigenr" version))
       (sha256
        (base32 "07r6xiqany2qzarfqphs90pcwx7zbki0z7mp329l7szfz01wxiqr"))))
    (properties `((upstream-name . "prodigenr")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-whisker
                             r-rprojroot
                             r-rmarkdown
                             r-rlang
                             r-fs
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rostools/prodigenr")
    (synopsis "Research Project Directory Generator")
    (description
     "Create a project directory structure, along with typical files for that project.
 This allows projects to be quickly and easily created, as well as for them to
be standardized.  Designed specifically with scientists in mind (mainly
bio-medical researchers, but likely applies to other fields).")
    (license license:expat)))

(define-public r-prodest
  (package
    (name "r-prodest")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prodest" version))
       (sha256
        (base32 "16nqgd9wrjfxymwbxrnlak54cagbv3fxgbql0w5bxnmyrbbqy509"))))
    (properties `((upstream-name . "prodest")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsolnp r-matrix r-dplyr r-deoptim r-aer))
    (home-page
     "https://github.com/GabrieleRovigatti/prodest/tree/master/prodest")
    (synopsis "Production Function Estimation")
    (description
     "This package implements the methods proposed by Olley, G.S. and Pakes, A. (1996)
<doi:10.2307/2171831>, Levinsohn, J. and Petrin, A. (2003)
<doi:10.1111/1467-937X.00246>, Ackerberg, D.A. and Caves, K. and Frazer, G.
(2015) <doi:10.3982/ECTA13408> and Wooldridge, J.M. (2009)
<doi:10.1016/j.econlet.2009.04.026> for structural productivity estimation .")
    (license license:gpl3)))

(define-public r-prodenica
  (package
    (name "r-prodenica")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProDenICA" version))
       (sha256
        (base32 "0g1ldrb6gsrgm09xjl0dynqaargkj1in8k39rnpanf4zzyrj5181"))))
    (properties `((upstream-name . "ProDenICA")))
    (build-system r-build-system)
    (propagated-inputs (list r-gam))
    (home-page
     "https://hastie.su.domains/ElemStatLearn/printings/ESLII_print12_toc.pdf")
    (synopsis
     "Product Density Estimation for ICA using Tilted Gaussian Density Estimates")
    (description
     "This package provides a direct and flexible method for estimating an ICA model.
This approach estimates the densities for each component directly via a tilted
Gaussian.  The tilt functions are estimated via a GAM Poisson model.  Details
can be found in \"Elements of Statistical Learning (2nd Edition)\" in Section
14.7.4.")
    (license license:gpl2)))

(define-public r-procs
  (package
    (name "r-procs")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "procs" version))
       (sha256
        (base32 "06hsdzw23854pc9zirbbfzm451ybllrp5gx6b4bk0pd8wv1iza01"))))
    (properties `((upstream-name . "procs")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-saslm r-reporter r-fmtr r-common))
    (native-inputs (list r-knitr))
    (home-page "https://procs.r-sassy.org")
    (synopsis "Recreates Some 'SASÂ®' Procedures in 'R'")
    (description
     "This package contains functions to simulate the most commonly used SASÂ®
procedures.  Specifically, the package aims to simulate the functionality of
proc freq', proc means', proc ttest', proc reg', proc transpose', proc sort',
and proc print'.  The simulation will include recreating all statistics with the
highest fidelity possible.")
    (license license:cc0)))

(define-public r-procmod
  (package
    (name "r-procmod")
    (version "1.0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProcMod" version))
       (sha256
        (base32 "0rxh135gs9gq9sag1cmvywdgnax7n989gmffbxil71fy49kxpwdg"))))
    (properties `((upstream-name . "ProcMod")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-permute r-matrix r-mass r-foreach))
    (home-page "https://cran.r-project.org/package=ProcMod")
    (synopsis "Informative Procrustean Matrix Correlation")
    (description
     "Estimates corrected Procrustean correlation between matrices for removing
overfitting effect.  Coissac Eric and Gonindard-Melodelima Christelle (2019)
<doi:10.1101/842070>.")
    (license (license:fsdg-compatible "CeCILL-2"))))

(define-public r-procmaps
  (package
    (name "r-procmaps")
    (version "0.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "procmaps" version))
       (sha256
        (base32 "0c5cby1942s9vdl0a6nyiih6x7kxii02w2l4fp8n9z07h7j5jb69"))))
    (properties `((upstream-name . "procmaps")))
    (build-system r-build-system)
    (home-page "https://r-prof.github.io/procmaps/")
    (synopsis "Portable Address Space Mapping")
    (description
     "Portable /proc/self/maps as a data frame.  Determine which library or other
region is mapped to a specific address of a process. -- R packages can contain
native code, compiled to shared libraries at build or installation time.  When
loaded, each shared library occupies a portion of the address space of the main
process.  When only a machine instruction pointer is available (e.g. from a
backtrace during error inspection or profiling), the address space map
determines which library this instruction pointer corresponds to.")
    (license license:gpl3)))

(define-public r-proclhmm
  (package
    (name "r-proclhmm")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "proclhmm" version))
       (sha256
        (base32 "05pwa09bkclzshyk3jsnhsj43vdp89miw3zznj082zj9484vfhzj"))))
    (properties `((upstream-name . "proclhmm")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod r-rcpp))
    (home-page "https://cran.r-project.org/package=proclhmm")
    (synopsis "Latent Hidden Markov Models for Response Process Data")
    (description
     "This package provides functions for simulating from and fitting the latent
hidden Markov models for response process data (Tang, 2024)
<doi:10.1007/s11336-023-09938-1>.  It also includes functions for simulating
from and fitting ordinary hidden Markov models.")
    (license license:gpl3+)))

(define-public r-processpredictr
  (package
    (name "r-processpredictr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "processpredictR" version))
       (sha256
        (base32 "161gbyq21j5yip5p8p235mvcffchm7c8ksks1z7q5cwbrilcx380"))))
    (properties `((upstream-name . "processpredictR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tensorflow
                             r-stringr
                             r-rlang
                             r-reticulate
                             r-purrr
                             r-progress
                             r-plotly
                             r-mltools
                             r-magrittr
                             r-keras
                             r-glue
                             r-ggplot2
                             r-forcats
                             r-edear
                             r-dplyr
                             r-data-table
                             r-cli
                             r-bupar))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=processpredictR")
    (synopsis "Process Prediction")
    (description
     "Means to predict process flow, such as process outcome, next activity, next
time, remaining time, and remaining trace.  Off-the-shelf predictive models
based on the concept of Transformers are provided, as well as multiple ways to
customize the models.  This package is partly based on work described in Zaharah
A. Bukhsh, Aaqib Saeed, & Remco M. Dijkman. (2021). \"@code{ProcessTransformer}:
Predictive Business Process Monitoring with Transformer Network\"
<@code{arXiv:2104.00721>}.")
    (license license:expat)))

(define-public r-processmonitr
  (package
    (name "r-processmonitr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "processmonitR" version))
       (sha256
        (base32 "1ckr438yifpsh095j6bb65dq3rdm7cfm8k7qsjmcqwjkww4j74z8"))))
    (properties `((upstream-name . "processmonitR")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny
                             r-miniui
                             r-magrittr
                             r-ggplot2
                             r-edear
                             r-dplyr
                             r-bupar))
    (native-inputs (list r-knitr))
    (home-page "https://www.bupar.net")
    (synopsis "Building Process Monitoring Dashboards")
    (description
     "This package provides functions for constructing dashboards for business process
monitoring.  Building on the event log objects class from package @code{bupaR}'.
 Allows the use to assemble custom shiny dashboards based on process data.")
    (license license:expat)))

(define-public r-processmapr
  (package
    (name "r-processmapr")
    (version "0.5.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "processmapR" version))
       (sha256
        (base32 "1bih54laq2njk4l51i45j43b4qwgp9qbx3yz04jb7miz5v6zqb8j"))))
    (properties `((upstream-name . "processmapR")))
    (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-scales
                             r-rlang
                             r-rcpp
                             r-purrr
                             r-plotly
                             r-miniui
                             r-lifecycle
                             r-htmlwidgets
                             r-htmltools
                             r-hms
                             r-glue
                             r-ggplot2
                             r-forcats
                             r-edear
                             r-dplyr
                             r-diagrammer
                             r-data-table
                             r-cli
                             r-bupar
                             r-bh))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://bupar.net/")
    (synopsis "Construct Process Maps Using Event Data")
    (description
     "Visualize event logs using directed graphs, i.e.  process maps.  Part of the
@code{bupaR} framework.")
    (license license:expat)))

(define-public r-processcheckr
  (package
    (name "r-processcheckr")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "processcheckR" version))
       (sha256
        (base32 "1qsmklvlldjw29f10pxn5455xp6nql3qkxm2yak367q4a3z069x6"))))
    (properties `((upstream-name . "processcheckR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-stringi
                             r-rlang
                             r-lifecycle
                             r-glue
                             r-edear
                             r-dplyr
                             r-bupar))
    (native-inputs (list r-knitr))
    (home-page "https://bupar.net/")
    (synopsis "Rule-Based Conformance Checking of Business Process Event Data")
    (description
     "Check compliance of event-data from (business) processes with respect to
specified rules.  Rules supported are of three types: frequency (activities that
should (not) happen x number of times), order (succession between activities)
and exclusiveness (and and exclusive choice between activities).")
    (license license:expat)))

(define-public r-processanimater
  (package
    (name "r-processanimater")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "processanimateR" version))
       (sha256
        (base32 "054m578ifb4hhlalijkdmjxifn36vy61sdzjgcr1gg4yxfi2fbx3"))))
    (properties `((upstream-name . "processanimateR")))
    (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-rlang
                             r-processmapr
                             r-magrittr
                             r-htmlwidgets
                             r-htmltools
                             r-dplyr
                             r-diagrammer
                             r-bupar))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://github.com/bupaverse/processanimateR/")
    (synopsis "Process Map Token Replay Animation")
    (description
     "This package provides animated process maps based on the @code{procesmapR}
package.  Cases stored in event logs created with with @code{bupaR} S3 class
eventlog() are rendered as tokens (SVG shapes) and animated according to their
occurrence times on top of the process map.  For rendering SVG animations
('SMIL') and the htmlwidget package are used.")
    (license license:expat)))

(define-public r-proceduralnames
  (package
    (name "r-proceduralnames")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "proceduralnames" version))
       (sha256
        (base32 "1jdaizvsmw1ipbjm2qbgfvnkaz68zga7i8lp7yjf5rfzjvfi1hwq"))))
    (properties `((upstream-name . "proceduralnames")))
    (build-system r-build-system)
    (home-page "https://mikemahoney218.github.io/proceduralnames/")
    (synopsis "Several Methods for Procedural Name Generation")
    (description
     "This package provides a small, dependency-free way to generate random names.
Methods provided include the adjective-surname approach of Docker containers
('<https://github.com/moby/moby/blob/master/pkg/namesgenerator/names-generator.go>'),
and combinations of common English or Spanish words.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-procdata
  (package
    (name "r-procdata")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProcData" version))
       (sha256
        (base32 "1a0rbj57rbxf5axfyd1j9j1mh7ara3rlq31lysaprwaymig94mml"))))
    (properties `((upstream-name . "ProcData")))
    (build-system r-build-system)
    (inputs (list tensorflow python))
    (propagated-inputs (list r-rcpp r-keras))
    (home-page "https://cran.r-project.org/package=ProcData")
    (synopsis "Process Data Analysis")
    (description
     "This package provides tools for exploratory process data analysis.  Process data
refers to the data describing participants problem-solving processes in
computer-based assessments.  It is often recorded in computer log files.  This
package provides functions to read, process, and write process data.  It also
implements two feature extraction methods to compress the information stored in
process data into standard numerical vectors.  This package also provides
recurrent neural network based models that relate response processes with other
binary or scale variables of interest.  The functions that involve training and
evaluating neural networks are wrappers of functions in keras'.")
    (license license:gpl2+)))

(define-public r-proccalibrad
  (package
    (name "r-proccalibrad")
    (version "0.14")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "proccalibrad" version))
       (sha256
        (base32 "0vr55rpcqh9wvy3bklssrimp42x85w3sanjgshfij657v68vmx46"))))
    (properties `((upstream-name . "proccalibrad")))
    (build-system r-build-system)
    (inputs (list))
    (home-page "https://cran.r-project.org/package=proccalibrad")
    (synopsis "Extraction of Bands from MODIS Calibrated Radiances MOD02 NRT")
    (description
     "Package for processing downloaded MODIS Calibrated radiances Product HDF files.
Specifically, MOD02 calibrated radiance product files, and the associated MOD03
geolocation files (for MODIS-TERRA).  The package will be most effective if the
user installs MRTSwath (MODIS Reprojection Tool for swath products;
<https://lpdaac.usgs.gov/tools/modis_reprojection_tool_swath>, and adds the
directory with the MRTSwath executable to the default R PATH by editing
~/.Rprofile.")
    (license license:gpl2+)))

(define-public r-probyx
  (package
    (name "r-probyx")
    (version "1.1-0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProbYX" version))
       (sha256
        (base32 "1arxk01c3aff7hfqjy1a398548jmz15hs70shh4a40qw2f9f995m"))))
    (properties `((upstream-name . "ProbYX")))
    (build-system r-build-system)
    (propagated-inputs (list r-rootsolve))
    (home-page "https://cran.r-project.org/package=ProbYX")
    (synopsis "Inference for the Stress-Strength Model R = P(Y<X)")
    (description
     "Confidence intervals and point estimation for R under various parametric model
assumptions; likelihood inference based on classical first-order approximations
and higher-order asymptotic procedures.")
    (license license:gpl2)))

(define-public r-probstats4econ
  (package
    (name "r-probstats4econ")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "probstats4econ" version))
       (sha256
        (base32 "1by0g4jvilxygfhgz9mzkxz6jlvjy0dsik7faa2b4awfckhb15nh"))))
    (properties `((upstream-name . "probstats4econ")))
    (build-system r-build-system)
    (home-page "https://probstats4econ.com/package.html")
    (synopsis
     "Companion Package to Probability and Statistics for Economics and Business")
    (description
     "Utilities for multiple hypothesis testing, companion datasets from \"Probability
and Statistics for Economics and Business: An Introduction Using R\" by Jason
Abrevaya (MIT Press, under contract).")
    (license license:gpl3+)))

(define-public r-probshiny
  (package
    (name "r-probshiny")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PROBShiny" version))
       (sha256
        (base32 "17p6pb8nwq36ha697f2lf8nwzj5cylmh8rdzyag49ks5hi9painb"))))
    (properties `((upstream-name . "PROBShiny")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinymatrix
                             r-shiny
                             r-rpivottable
                             r-rmarkdown
                             r-laplacesdemon
                             r-epitools))
    (home-page "https://cran.r-project.org/package=PROBShiny")
    (synopsis "Interactive Document for Working with Basic Probability")
    (description
     "An interactive document on the topic of basic probability using rmarkdown and
shiny packages.  Runtime examples are provided in the package function as well
as at <https://analyticmodels.shinyapps.io/@code{BayesShiny/>}.")
    (license license:gpl2)))

(define-public r-probsamplingi
  (package
    (name "r-probsamplingi")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProbSamplingI" version))
       (sha256
        (base32 "0hmrl7p89rrzjhxrv088yhs8yqw32iwjnfplpcrm67gmmsj87cj3"))))
    (properties `((upstream-name . "ProbSamplingI")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=ProbSamplingI")
    (synopsis "Probabilistic Sampling Design and Strategies")
    (description
     "It allows the user to determine sample sizes, select probabilistic samples, make
estimates of different parameters for the total finite population and in studio
domains, using the main design drawings.")
    (license license:gpl2+)))

(define-public r-probs
  (package
    (name "r-probs")
    (version "0.9.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "probs" version))
       (sha256
        (base32 "0gbivss3ydnjy1acjczf5khp40l4sk1r53pbx151bxrdq74q8a3i"))))
    (properties `((upstream-name . "probs")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape r-mass r-combinat))
    (home-page "https://cran.r-project.org/package=probs")
    (synopsis "Elementary Probability on Finite Sample Spaces")
    (description
     "This package performs elementary probability calculations on finite sample
spaces, which may be represented by data frames or lists.  This package is meant
to rescue some widely used functions from the archived prob package (see
<https://cran.r-project.org/src/contrib/Archive/prob/>).  Functionality includes
setting up sample spaces, counting tools, defining probability spaces,
performing set algebra, calculating probability and conditional probability,
tools for simulation and checking the law of large numbers, adding random
variables, and finding marginal distributions.  Characteristic functions for all
base R distributions are included.")
    (license license:gpl3+)))

(define-public r-probout
  (package
    (name "r-probout")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "probout" version))
       (sha256
        (base32 "1x7q878xzw1b1c6jfx856akxzcxrwanv67jji2ifkcdx2hgmgvpx"))))
    (properties `((upstream-name . "probout")))
    (build-system r-build-system)
    (propagated-inputs (list r-mclust r-mass r-fnn))
    (native-inputs (list r-knitr gfortran))
    (home-page "https://www.r-project.org")
    (synopsis
     "Unsupervised Multivariate Outlier Probabilities for Large Datasets")
    (description
     "Estimates unsupervised outlier probabilities for multivariate numeric data with
many observations from a nonparametric outlier statistic.")
    (license license:expat)))

(define-public r-probmarg
  (package
    (name "r-probmarg")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProbMarg" version))
       (sha256
        (base32 "02qc6c08vfg3inh14d3dzq28zgsgk9p7kb2ni9d14pcx5w9q6sji"))))
    (properties `((upstream-name . "ProbMarg")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=ProbMarg")
    (synopsis
     "Computing Logit & Probit Predicted Probabilities & Marginal Effects")
    (description
     "Computes predicted probabilities and marginal effects for binary & ordinal logit
and probit, (partial) generalized ordinal & multinomial logit models estimated
with the glm(), clm() (in the ordinal package), and vglm() (in the VGAM package)
functions.")
    (license license:gpl3)))

(define-public r-probitspatial
  (package
    (name "r-probitspatial")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProbitSpatial" version))
       (sha256
        (base32 "1wa020hb02zjf4shzb345nyjkxm38wf67knvnl9iyh6n2sc34975"))))
    (properties `((upstream-name . "ProbitSpatial")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp r-rann r-numderiv r-matrix))
    (home-page "https://cran.r-project.org/package=ProbitSpatial")
    (synopsis "Probit with Spatial Dependence, SAR, SEM and SARAR Models")
    (description
     "Fast estimation of binomial spatial probit regression models with spatial
autocorrelation for big datasets.")
    (license license:gpl2+)))

(define-public r-probedeveloper
  (package
    (name "r-probedeveloper")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProbeDeveloper" version))
       (sha256
        (base32 "0ixmxa8sndd16062xd4vivagzd0ghb8d3n153i2wg7rq7g85c1dg"))))
    (properties `((upstream-name . "ProbeDeveloper")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmcalculator r-biostrings))
    (home-page "https://cran.r-project.org/package=ProbeDeveloper")
    (synopsis "Develop Hybridization Probes")
    (description
     "Hybridization probes for target sequences can be made based on melting
temperature value calculated by R package @code{TmCalculator}
<https://CRAN.R-project.org/package=@code{TmCalculator>} and methods extended
from Beliveau, B. J.,(2018) <doi:10.1073/pnas.1714530115>, and those
hybridization probes can be used to capture specific target regions in
fluorescence in situ hybridization and next generation sequence experiments.")
    (license license:gpl2+)))

(define-public r-probe
  (package
    (name "r-probe")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "probe" version))
       (sha256
        (base32 "1bgkbsx8aa0r5dpnr1nv2q9p6af40dfzl3g333wmdg1dd6d2zp4n"))))
    (properties `((upstream-name . "probe")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-glmnet))
    (home-page "https://cran.r-project.org/package=probe")
    (synopsis "Sparse High-Dimensional Linear Regression with PROBE")
    (description
     "This package implements an efficient and powerful Bayesian approach for sparse
high-dimensional linear regression.  It uses minimal prior assumptions on the
parameters through plug-in empirical Bayes estimates of hyperparameters.  An
efficient Parameter-Expanded Expectation-Conditional-Maximization (PX-ECM)
algorithm estimates maximum a posteriori (MAP) values of regression parameters
and variable selection probabilities.  The PX-ECM results in a robust
computationally efficient coordinate-wise optimization, which adjusts for the
impact of other predictor variables.  The E-step is motivated by the popular
two-group approach to multiple testing.  The result is a @code{PaRtitiOned}
empirical Bayes Ecm (PROBE) algorithm applied to sparse high-dimensional linear
regression, implemented using one-at-a-time or all-at-once type optimization.
More information can be found in @code{McLain}, Zgodic, and Bondell (2022)
<@code{arXiv:2209.08139>}.")
    (license license:gpl2+)))

(define-public r-probbreed
  (package
    (name "r-probbreed")
    (version "1.0.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProbBreed" version))
       (sha256
        (base32 "05rzc77swaig5k9nyzgswm6nn8p9ifhhfycwwla9lm6rrlxjzdmx"))))
    (properties `((upstream-name . "ProbBreed")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rlang
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-lifecycle
                             r-ggplot2
                             r-bh))
    (home-page "https://github.com/saulo-chaves/ProbBreed")
    (synopsis "Probability Theory for Selecting Candidates in Plant Breeding")
    (description
     "Use probability theory under the Bayesian framework for calculating the risk of
selecting candidates in a multi-environment context.  Contained are functions
used to fit a Bayesian multi-environment model (based on the available presets),
extract posterior values and maximum posterior values, compute the variance
components, check the modelâs convergence, and calculate the probabilities.
For both across and within-environments scopes, the package computes the
probability of superior performance and the pairwise probability of superior
performance.  Furthermore, the probability of superior stability and the
pairwise probability of superior stability across environments is estimated.  A
joint probability of superior performance and stability is also provided.")
    (license license:agpl3+)))

(define-public r-probbayes
  (package
    (name "r-probbayes")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProbBayes" version))
       (sha256
        (base32 "0bkbqyrmpy9swaxsl91w6iiw81znjf1lk4b0vqgn6wdnrir6gkmn"))))
    (properties `((upstream-name . "ProbBayes")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-learnbayes r-gridextra r-ggplot2))
    (home-page "https://github.com/bayesball/ProbBayes")
    (synopsis "Probability and Bayesian Modeling")
    (description
     "This package provides functions and datasets to accompany J. Albert and J. Hu,
\"Probability and Bayesian Modeling\", CRC Press, (2019, ISBN: 1138492566).")
    (license license:gpl2+)))

(define-public r-probably
  (package
    (name "r-probably")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "probably" version))
       (sha256
        (base32 "1ck84r63n72nf8w4v5kfyvhk7b8r6j1hq3sdcclar1k965yphl8n"))))
    (properties `((upstream-name . "probably")))
    (build-system r-build-system)
    (propagated-inputs (list r-yardstick
                             r-workflows
                             r-withr
                             r-vctrs
                             r-tune
                             r-tidyselect
                             r-tidyr
                             r-rlang
                             r-purrr
                             r-pillar
                             r-hardhat
                             r-ggplot2
                             r-generics
                             r-furrr
                             r-dplyr
                             r-cli
                             r-butcher))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/tidymodels/probably")
    (synopsis "Tools for Post-Processing Predicted Values")
    (description
     "Models can be improved by post-processing class probabilities, by:
recalibration, conversion to hard probabilities, assessment of equivocal zones,
and other activities.  probably contains tools for conducting these operations
as well as calibration tools and conformal inference techniques for regression
models.")
    (license license:expat)))

(define-public r-proae
  (package
    (name "r-proae")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ProAE" version))
       (sha256
        (base32 "1sgd5ws1jpnjsnx1daywsn3ygl6lmg048fgvc7i4nkkryq7zls6b"))))
    (properties `((upstream-name . "ProAE")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr
                             r-kableextra
                             r-hmisc
                             r-gridextra
                             r-ggtext
                             r-ggplot2
                             r-ggpattern
                             r-ggnewscale
                             r-dplyr
                             r-desctools))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ProAE")
    (synopsis "PRO-CTCAE Scoring, Analysis, and Graphical Tools")
    (description
     "This package provides a collection of tools to facilitate standardized analysis
and graphical procedures when using the National Cancer Instituteâs
Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse
Events (PRO-CTCAE) and other PRO measurements.")
    (license license:gpl3)))

(define-public r-pro
  (package
    (name "r-pro")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pro" version))
       (sha256
        (base32 "0f0iliq7bhf313hi0jbwavljic4laxfc0n3gac5y6hzm39gvvgag"))))
    (properties `((upstream-name . "pro")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pro")
    (synopsis "Point-Process Response Model for Optogenetics")
    (description
     "Optogenetics is a new tool to study neuronal circuits that have been genetically
modified to allow stimulation by flashes of light.  This package implements the
methodological framework, Point-process Response model for Optogenetics (PRO),
for analyzing data from these experiments.  This method provides explicit
nonlinear transformations to link the flash point-process with the spiking
point-process.  Such response functions can be used to provide important and
interpretable scientific insights into the properties of the biophysical process
that governs neural spiking in response to optogenetic stimulation.")
    (license license:gpl2)))

(define-public r-prnsamplr
  (package
    (name "r-prnsamplr")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prnsamplr" version))
       (sha256
        (base32 "1qhrk6am6irp5m0gh61vv6612738p0kwjcvgdnza50731rias8v7"))))
    (properties `((upstream-name . "prnsamplr")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=prnsamplr")
    (synopsis "Permanent Random Number Sampling")
    (description
     "Survey sampling using permanent random numbers (PRN's).  A solution to the
problem of unknown overlap between survey samples, which leads to a low
precision in estimates when the survey is repeated or combined with other
surveys.  The PRN solution is to supply the U(0, 1) random numbers to the
sampling procedure, instead of having the sampling procedure generate them.  In
Lindblom (2014) <doi:10.2478/jos-2014-0047>, and therein cited articles, it is
shown how this is carried out and how it improves the estimates.  This package
supports two common fixed-size sampling procedures (simple random sampling and
probability-proportional-to-size sampling) and includes a function for
transforming the PRN's in order to control the sample overlap.")
    (license license:expat)))

(define-public r-prng
  (package
    (name "r-prng")
    (version "0.0.2.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PRNG" version))
       (sha256
        (base32 "0dildm11z7p5m0lxcqiwg1bm25hsczyba68cnkdcj1bankd5gcwx"))))
    (properties `((upstream-name . "PRNG")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PRNG")
    (synopsis "Pseudo-Random Number Generator")
    (description
     "This package provides functions for generating pseudo-random numbers that follow
a uniform distribution [0,1].  Randomness tests were conducted using the
National Institute of Standards and Technology test
suite<https://csrc.nist.gov/pubs/sp/800/22/r1/upd1/final>, along with additional
tests.  The sequence generated depends on the initial values and parameters.
The package includes a linear congruence map as the decision map and three
chaotic maps to generate the pseudo-random sequence, which follow a uniform
distribution.  Other distributions can be generated from the uniform
distribution using the Inversion Principle Method and BOX-Muller transformation.
 Small perturbations in seed values result in entirely different sequences of
numbers due to the sensitive nature of the maps being used.  The chaotic nature
of the maps helps achieve randomness in the generator.  Additionally, the
generator is capable of producing random bits.")
    (license license:expat)))

(define-public r-privatelr
  (package
    (name "r-privatelr")
    (version "1.2-22")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PrivateLR" version))
       (sha256
        (base32 "0d142fa3wk7yadvs8jszajs6hq9m03p0j6h5r4pbw7j0d1l72hgc"))))
    (properties `((upstream-name . "PrivateLR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PrivateLR")
    (synopsis "Differentially Private Regularized Logistic Regression")
    (description
     "This package implements two differentially private algorithms for estimating
L2-regularized logistic regression coefficients.  A randomized algorithm F is
epsilon-differentially private (C. Dwork, Differential Privacy, ICALP 2006
<DOI:10.1007/11681878_14>), if |log(P(F(D) in S)) - log(P(F(D') in S))| <=
epsilon for any pair D, D of datasets that differ in exactly one record, any
measurable set S, and the randomness is taken over the choices F makes.")
    (license license:gpl2+)))

(define-public r-prithulib
  (package
    (name "r-prithulib")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prithulib" version))
       (sha256
        (base32 "12ybksrvxzc5gjz8m38cs35b7r3kd8m0zqm2c67pqw38vq82sjxm"))))
    (properties `((upstream-name . "prithulib")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=prithulib")
    (synopsis "Perform Random Experiments")
    (description
     "Enables user to perform the following: 1.  Roll n number of die/dice (roll()).
2.  Toss n number of coin(s) (toss()).  3.  Play the game of Rock, Paper,
Scissors.  4.  Choose n number of card(s) from a pack of 52 playing cards (Joker
optional).")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-prisonbrief
  (package
    (name "r-prisonbrief")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prisonbrief" version))
       (sha256
        (base32 "06550fnnffpcnv4q2nicrm7p573313lvnj6hkdrf7979ygrsmmfi"))))
    (properties `((upstream-name . "prisonbrief")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-sf
                             r-rvest
                             r-rnaturalearthdata
                             r-rnaturalearth
                             r-passport
                             r-magrittr
                             r-httr
                             r-dplyr))
    (home-page "https://danilofreire.github.io/prisonbrief/")
    (synopsis "Downloads and Parses World Prison Brief Data")
    (description
     "Download, parses and tidies information from the World Prison Brief project
<http://www.prisonstudies.org/>.")
    (license license:expat)))

(define-public r-prismjs
  (package
    (name "r-prismjs")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prismjs" version))
       (sha256
        (base32 "0m655x662r29c5s5yzb6p268lp60rp5y3lxkiar5mrrg96j6g44i"))))
    (properties `((upstream-name . "prismjs")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-v8))
    (home-page "https://cran.r-project.org/package=prismjs")
    (synopsis "Server-Side Syntax Highlighting")
    (description
     "Prism <https://prismjs.com/> is a lightweight, extensible syntax highlighter,
built with modern web standards in mind.  This package provides server-side
rendering in R using V8 such that no @code{JavaScript} library is required in
the resulting HTML documents.  Over 400 languages are supported.")
    (license license:expat)))

(define-public r-prismastatement
  (package
    (name "r-prismastatement")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PRISMAstatement" version))
       (sha256
        (base32 "1vvab8h71g8c910s2h4d12qafqdncfzmsh5f1b4m79plpqi3s49z"))))
    (properties `((upstream-name . "PRISMAstatement")))
    (build-system r-build-system)
    (propagated-inputs (list r-diagrammer))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jackwasey/PRISMAstatement")
    (synopsis "Plot Flow Charts According to the \"PRISMA\" Statement")
    (description
     "Plot a PRISMA <http://prisma-statement.org/> flow chart describing the
identification, screening, eligibility and inclusion or studies in systematic
reviews.  The PRISMA statement defines an evidence-based, minimal set of items
for reporting in systematic reviews and meta-analyses.  PRISMA should be used
for the reporting of studies evaluating randomized clinical trials (RCT), and is
also for reporting on systematic reviews of other types of research.  There is
also a function to generate flow charts describing exclusions and inclusions for
any kind of study.")
    (license license:gpl3)))

(define-public r-prismadiagramr
  (package
    (name "r-prismadiagramr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prismadiagramR" version))
       (sha256
        (base32 "13x3y4rv9b2d013jd6853zkq8rbxq88wlffmdnjs1rca0fnfrn4f"))))
    (properties `((upstream-name . "prismadiagramR")))
    (build-system r-build-system)
    (propagated-inputs (list r-dplyr r-diagrammer))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ltrainstg/prismadiagramR")
    (synopsis "Creates a Prisma Diagram using 'DiagrammeR'")
    (description
     "This package creates PRISMA <http://prisma-statement.org/> diagram from a
minimal dataset of included and excluded studies and allows for more custom
diagrams.  PRISMA diagrams are used to track the identification, screening,
eligibility, and inclusion of studies in a systematic review.")
    (license license:expat)))

(define-public r-prisma2020
  (package
    (name "r-prisma2020")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PRISMA2020" version))
       (sha256
        (base32 "0jhf1kgcc29b8gsb42b3d5hyfzsa13lz9qgs104ynrzld94kqqwf"))))
    (properties `((upstream-name . "PRISMA2020")))
    (build-system r-build-system)
    (propagated-inputs (list r-zip
                             r-xml2
                             r-webp
                             r-stringr
                             r-shinyjs
                             r-shiny
                             r-scales
                             r-rsvg
                             r-rio
                             r-htmlwidgets
                             r-htmltools
                             r-dt
                             r-diagrammersvg
                             r-diagrammer))
    (home-page "https://cran.r-project.org/package=PRISMA2020")
    (synopsis "Make Interactive 'PRISMA' Flow Diagrams")
    (description
     "Systematic reviews should be described in a high degree of methodological
detail.  The PRISMA Statement calls for a high level of reporting detail in
systematic reviews and meta-analyses.  An integral part of the methodological
description of a review is a flow diagram.  This package produces an interactive
flow diagram that conforms to the PRISMA2020 preprint.  When made interactive,
the reader/user can click on each box and be directed to another website or file
online (e.g. a detailed description of the screening methods, or a list of
excluded full texts), with a mouse-over tool tip that describes the information
linked to in more detail.  Interactive versions can be saved as HTML files,
whilst static versions for inclusion in manuscripts can be saved as HTML, PDF,
PNG, SVG, PS or WEBP files.")
    (license license:expat)))

(define-public r-prisma
  (package
    (name "r-prisma")
    (version "0.2-7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PRISMA" version))
       (sha256
        (base32 "0sk2d07m8rhipa0kdbaf8ngryda6f1ihp5akpnyw2llajf0x8as5"))))
    (properties `((upstream-name . "PRISMA")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-gplots r-ggplot2))
    (home-page "https://cran.r-project.org/package=PRISMA")
    (synopsis "Protocol Inspection and State Machine Analysis")
    (description
     "Loads and processes huge text corpora processed with the sally toolbox
(<http://www.mlsec.org/sally/>).  sally acts as a very fast preprocessor which
splits the text files into tokens or n-grams.  These output files can then be
read with the PRISMA package which applies testing-based token selection and has
some replicate-aware, highly tuned non-negative matrix factorization and
principal component analysis implementation which allows the processing of very
big data sets even on desktop machines.")
    (license license:gpl2+)))

(define-public r-prism-forecast
  (package
    (name "r-prism-forecast")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PRISM.forecast" version))
       (sha256
        (base32 "07kq6fprbj8zccs6cx1aiy5mrcg77b0fh42zvg2pimbvr8cqhy31"))))
    (properties `((upstream-name . "PRISM.forecast")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-xts r-glmnet))
    (home-page "https://github.com/ryanddyi/prism")
    (synopsis
     "Penalized Regression with Inferred Seasonality Module - Forecasting Unemployment Initial Claims using 'Google Trends' Data")
    (description
     "This package implements Penalized Regression with Inferred Seasonality Module
(PRISM) to generate forecast estimation of weekly unemployment initial claims
using Google Trends data.  It includes required data and tools for backtesting
the performance in 2007-2020.")
    (license license:gpl2)))

(define-public r-prism
  (package
    (name "r-prism")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prism" version))
       (sha256
        (base32 "1fgmv58igvkp11n8jgqvxd2qpccpv3s6xwzm2678ikrgc3hynj0x"))))
    (properties `((upstream-name . "prism")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-readr
                             r-raster
                             r-magrittr
                             r-httr
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://docs.ropensci.org/prism/")
    (synopsis "Access Data from the Oregon State Prism Climate Project")
    (description
     "Allows users to access the Oregon State Prism climate data
(<https://prism.nacse.org/>).  Using the web service API data can easily
downloaded in bulk and loaded into R for spatial analysis.  Some user friendly
visualizations are also provided.")
    (license license:expat)))

(define-public r-priorsense
  (package
    (name "r-priorsense")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "priorsense" version))
       (sha256
        (base32 "1lmqwby7j8x5y2g7pmrd5770drck4xhwx8ajn1kxw23b9hbx5wxs"))))
    (properties `((upstream-name . "priorsense")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-rlang
                             r-posterior
                             r-matrixstats
                             r-ggplot2
                             r-ggh4x
                             r-ggdist
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://n-kall.github.io/priorsense/")
    (synopsis "Prior Diagnostics and Sensitivity Analysis")
    (description
     "This package provides functions for prior and likelihood sensitivity analysis in
Bayesian models.  Currently it implements methods to determine the sensitivity
of the posterior to power-scaling perturbations of the prior and likelihood.")
    (license license:gpl3+)))

(define-public r-prioritylasso
  (package
    (name "r-prioritylasso")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prioritylasso" version))
       (sha256
        (base32 "1vn4i7rg1nsir98lgaxnw4851rwyv8kx25z8nc942qsr3r7cvdf6"))))
    (properties `((upstream-name . "prioritylasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-glmnet r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=prioritylasso")
    (synopsis "Analyzing Multiple Omics Data with an Offset Approach")
    (description
     "Fits successive Lasso models for several blocks of (omics) data with different
priorities and takes the predicted values as an offset for the next block.  Also
offers options to deal with block-wise missingness in multi-omics data.")
    (license license:gpl2)))

(define-public r-prioritizrdata
  (package
    (name "r-prioritizrdata")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prioritizrdata" version))
       (sha256
        (base32 "0dvxh43dk9mhsqdj89zilh3r91cqngl07ggw5r5rj761xl3km10w"))))
    (properties `((upstream-name . "prioritizrdata")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-terra r-sf))
    (home-page "https://prioritizr.github.io/prioritizrdata/")
    (synopsis "Conservation Planning Datasets")
    (description
     "Conservation planning datasets for learning how to use the prioritizr package
<https://CRAN.R-project.org/package=prioritizr>.")
    (license license:gpl3)))

(define-public r-prioritizr
  (package
    (name "r-prioritizr")
    (version "8.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prioritizr" version))
       (sha256
        (base32 "18z5zm34ry45kapp0a146gvwij41wyhhxxyzwiz7ly79l6lwgs9s"))))
    (properties `((upstream-name . "prioritizr")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-tibble
                             r-terra
                             r-slam
                             r-sf
                             r-rlang
                             r-rcpparmadillo
                             r-rcpp
                             r-raster
                             r-r6
                             r-matrix
                             r-magrittr
                             r-igraph
                             r-exactextractr
                             r-cli
                             r-bh
                             r-assertthat
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://prioritizr.net")
    (synopsis "Systematic Conservation Prioritization in R")
    (description
     "Systematic conservation prioritization using mixed integer linear programming
(MILP).  It provides a flexible interface for building and solving conservation
planning problems.  Once built, conservation planning problems can be solved
using a variety of commercial and open-source exact algorithm solvers.  By using
exact algorithm solvers, solutions can be generated that are guaranteed to be
optimal (or within a pre-specified optimality gap).  Furthermore, conservation
problems can be constructed to optimize the spatial allocation of different
management actions or zones, meaning that conservation practitioners can
identify solutions that benefit multiple stakeholders.  To solve large-scale or
complex conservation planning problems, users should install the Gurobi
optimization software (available from <https://www.gurobi.com/>) and the gurobi
R package (see Gurobi Installation Guide vignette for details).  Users can also
install the IBM CPLEX software
(<https://www.ibm.com/products/ilog-cplex-optimization-studio/cplex-optimizer>)
and the @code{cplexAPI} R package (available at
<https://github.com/cran/@code{cplexAPI>}).  Additionally, the rcbc R package
(available at <https://github.com/dirkschumacher/rcbc>) can be used to generate
solutions using the CBC optimization software
(<https://github.com/coin-or/Cbc>).")
    (license license:gpl3)))

(define-public r-prioriactions
  (package
    (name "r-prioriactions")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prioriactions" version))
       (sha256
        (base32 "0k0vaqpdj9rlxcmfn6qb1bj8ssbazj33qa4rg4l8agpf9x83n9f8"))))
    (properties `((upstream-name . "prioriactions")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rlang
                             r-rcpparmadillo
                             r-rcpp
                             r-proto
                             r-matrix
                             r-magrittr
                             r-dplyr
                             r-bh
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://prioriactions.github.io/prioriactions/")
    (synopsis "Multi-Action Conservation Planning")
    (description
     "This uses a mixed integer mathematical programming (MIP) approach for building
and solving multi-action planning problems, where the goal is to find an optimal
combination of management actions that abate threats, in an efficient way while
accounting for spatial aspects.  Thus, optimizing the connectivity and
conservation effectiveness of the prioritized units and of the deployed actions.
 The package is capable of handling different commercial (gurobi, CPLEX) and
non-commercial (symphony, CBC) MIP solvers.  Gurobi optimization solver can be
installed using comprehensive instructions in the gurobi installation vignette
of the prioritizr package (available in
<https://prioritizr.net/articles/gurobi_installation_guide.html>).  Instead,
CPLEX optimization solver can be obtain from IBM CPLEX web page (available here
<https://www.ibm.com/es-es/products/ilog-cplex-optimization-studio>).
Additionally, the rcbc R package (available at
<https://github.com/dirkschumacher/rcbc>) can be used to obtain solutions using
the CBC optimization software (<https://github.com/coin-or/Cbc>).  Methods used
in the package refers to Salgado-Rojas et al. (2020)
<doi:10.1016/j.ecolmodel.2019.108901>, Beyer et al. (2016)
<doi:10.1016/j.ecolmodel.2016.02.005>, Cattarino et al. (2015)
<doi:10.1371/journal.pone.0128027> and Watts et al. (2009)
<doi:10.1016/j.envsoft.2009.06.005>.  See the prioriactions website for more
information, documentations and examples.")
    (license license:gpl2+)))

(define-public r-priorgen
  (package
    (name "r-priorgen")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PriorGen" version))
       (sha256
        (base32 "0fa5f8m6sqcljfm5qla7spwvkpa2brp5l4kmrw2qa3xb4hn34l4j"))))
    (properties `((upstream-name . "PriorGen")))
    (build-system r-build-system)
    (propagated-inputs (list r-rootsolve r-nleqslv))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PriorGen")
    (synopsis "Generates Prior Distributions for Proportions")
    (description
     "Translates beliefs into prior information in the form of Beta and Gamma
distributions.  It can be used for the generation of priors on the prevalence of
disease and the sensitivity/specificity of diagnostic tests and any other
binomial experiment.")
    (license license:gpl3+)))

(define-public r-priorcon
  (package
    (name "r-priorcon")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "priorCON" version))
       (sha256
        (base32 "1dy0n57hlii7p5gna3jbr0xq7yacvcc3qxvngq00m21s6vxza1ic"))))
    (properties `((upstream-name . "priorCON")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmap
                             r-terra
                             r-sf
                             r-prioritizr
                             r-igraph
                             r-highs
                             r-braingraph))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/cadam00/priorCON")
    (synopsis
     "Graph Community Detection Methods into Systematic Conservation Planning")
    (description
     "An innovative tool-set that incorporates graph community detection methods into
systematic conservation planning.  It is designed to enhance spatial
prioritization by focusing on the protection of areas with high ecological
connectivity.  Unlike traditional approaches that prioritize individual planning
units, @code{priorCON} focuses on clusters of features that exhibit strong
ecological linkages.  The @code{priorCON} package is built upon the prioritizr
package <doi:10.32614/CRAN.package.prioritizr>, using commercial and open-source
exact algorithm solvers that ensure optimal solutions to prioritization
problems.")
    (license license:gpl3)))

(define-public r-prior3d
  (package
    (name "r-prior3d")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prior3D" version))
       (sha256
        (base32 "1l3ikdkgzlgqqk48hkfz5z37jnf54ws7b0zfcdl51zcmv43yra19"))))
    (properties `((upstream-name . "prior3D")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-terra
                             r-readxl
                             r-rasterdiv
                             r-prioritizr
                             r-maps
                             r-highs
                             r-geodiv))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/cadam00/prior3D")
    (synopsis "3D Prioritization Algorithm")
    (description
     "Three-dimensional systematic conservation planning, conducting nested
prioritization analyses across multiple depth levels and ensuring efficient
resource allocation throughout the water column (Doxa et al.  2024
<doi:10.1111/gcb.16268>).  It provides a structured workflow designed to address
biodiversity conservation and management challenges in the 3 dimensions, while
facilitating usersâ choices and parameterization.")
    (license license:gpl3)))

(define-public r-priogene
  (package
    (name "r-priogene")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prioGene" version))
       (sha256
        (base32 "0k1rlvmfhj1m2zp5zaac8wcwjypd6f4dd8fxxsyrzsafp9aw189l"))))
    (properties `((upstream-name . "prioGene")))
    (build-system r-build-system)
    (propagated-inputs (list r-org-hs-eg-db r-annotationdbi))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=prioGene")
    (synopsis
     "Candidate Gene Prioritization for Non-Communicable Diseases Based on Functional Information")
    (description
     "In gene sequencing methods, the topological features of protein-protein
interaction (PPI) networks are often used, such as @code{ToppNet}
<https://toppgene.cchmc.org>.  In this study, a candidate gene prioritization
method was proposed for non-communicable diseases considering disease risks
transferred between genes in weighted disease PPI networks with weights for
nodes and edges based on functional information.")
    (license license:artistic2.0)))

(define-public r-prinvars
  (package
    (name "r-prinvars")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prinvars" version))
       (sha256
        (base32 "10cxp5dqjmi0kbx8g2qvpvwv2lcdg4sd2njg81l9g8dphx0c662f"))))
    (properties `((upstream-name . "prinvars")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-pma r-elasticnet))
    (home-page "https://github.com/Ronho/prinvars")
    (synopsis "Principal Variables")
    (description
     "This package provides methods for reducing the number of features within a data
set.  See Bauer JO (2021) <doi:10.1145/3475827.3475832> and Bauer JO, Drabant B
(2021) <doi:10.1016/j.jmva.2021.104754> for more information on principal
loading analysis.")
    (license license:expat)))

(define-public r-printr
  (package
    (name "r-printr")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "printr" version))
       (sha256
        (base32 "1xqykwfpv78pg630vw4s8idbayrl52pfp9j4ddhwr33nmvxnkiix"))))
    (properties `((upstream-name . "printr")))
    (build-system r-build-system)
    (propagated-inputs (list r-knitr))
    (native-inputs (list r-knitr))
    (home-page "https://yihui.org/printr/")
    (synopsis
     "Automatically Print R Objects to Appropriate Formats According to the 'knitr' Output Format")
    (description
     "Extends the S3 generic function knit_print() in knitr to automatically print
some objects using an appropriate format such as Markdown or @code{LaTeX}.  For
example, data frames are automatically printed as tables, and the help() pages
can also be rendered in knitr documents.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-prindt
  (package
    (name "r-prindt")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PrInDT" version))
       (sha256
        (base32 "0z03d87bnp2cf2k4m25il2wqglj53g5mdz7wpjzvsklnzsmxdbmy"))))
    (properties `((upstream-name . "PrInDT")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-splitstackshape r-party r-mass))
    (home-page "https://cran.r-project.org/package=PrInDT")
    (synopsis
     "Prediction and Interpretation in Decision Trees for Classification and Regression")
    (description
     "Optimization of conditional inference trees from the package party for
classification and regression.  For optimization, the model space is searched
for the best tree on the full sample by means of repeated subsampling.
Restrictions are allowed so that only trees are accepted which do not include
pre-specified uninterpretable split results (cf.  Weihs & Buschfeld, 2021a).
The function @code{PrInDT}() represents the basic resampling loop for 2-class
classification (cf.  Weihs & Buschfeld, 2021a).  The function @code{RePrInDT}()
(repeated @code{PrInDT}()) allows for repeated applications of @code{PrInDT}()
for different percentages of the observations of the large and the small classes
(cf.  Weihs & Buschfeld, 2021c).  The function @code{NesPrInDT}() (nested
@code{PrInDT}()) allows for an extra layer of subsampling for a specific factor
variable (cf.  Weihs & Buschfeld, 2021b).  The functions @code{PrInDTMulev}()
and @code{PrInDTMulab}() deal with multilevel and multilabel classification.  In
addition to these @code{PrInDT}() variants for classification, the function
@code{PrInDTreg}() has been developed for regression problems.  Finally, the
function @code{PostPrInDT}() allows for a posterior analysis of the distribution
of a specified variable in the terminal nodes of a given tree.  References are:
-- Weihs, C., Buschfeld, S. (2021a) \"Combining Prediction and Interpretation in
Decision Trees (@code{PrInDT}) - a Linguistic Example\"
<@code{arXiv:2103.02336>}; -- Weihs, C., Buschfeld, S. (2021b)
\"@code{NesPrInDT}: Nested undersampling in @code{PrInDT}\"
<@code{arXiv:2103.14931>}; -- Weihs, C., Buschfeld, S. (2021c) \"Repeated
undersampling in @code{PrInDT} (@code{RePrInDT}): Variation in undersampling and
prediction, and ranking of predictors in ensembles\" <@code{arXiv:2108.05129>}.")
    (license license:gpl2)))

(define-public r-primme
  (package
    (name "r-primme")
    (version "3.2-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PRIMME" version))
       (sha256
        (base32 "0r3wxpi6prn8z8bb5jqbdba18qlnkvkasx0qp0b5r41jqznkg1m0"))))
    (properties `((upstream-name . "PRIMME")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-matrix))
    (home-page "https://cran.r-project.org/package=PRIMME")
    (synopsis
     "Eigenvalues and Singular Values and Vectors from Large Matrices")
    (description
     "R interface to PRIMME <https://www.cs.wm.edu/~andreas/software/>, a C library
for computing a few eigenvalues and their corresponding eigenvectors of a real
symmetric or complex Hermitian matrix, or generalized Hermitian eigenproblem.
It can also compute singular values and vectors of a square or rectangular
matrix.  PRIMME finds largest, smallest, or interior singular/eigenvalues and
can use preconditioning to accelerate convergence.  General description of the
methods are provided in the papers Stathopoulos (2010,
<doi:10.1145/1731022.1731031>) and Wu (2017, <doi:10.1137/16M1082214>).  See
citation(\"PRIMME\") for details.")
    (license license:gpl3)))

(define-public r-primes
  (package
    (name "r-primes")
    (version "1.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "primes" version))
       (sha256
        (base32 "1jpj7hq2hsv6x3qp409whim7480hswvmhd327b2yml8f734nv45d"))))
    (properties `((upstream-name . "primes")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://github.com/ironholds/primes")
    (synopsis "Fast Functions for Prime Numbers")
    (description
     "Fast functions for dealing with prime numbers, such as testing whether a number
is prime and generating a sequence prime numbers.  Additional functions include
finding prime factors and Ruth-Aaron pairs, finding next and previous prime
numbers in the series, finding or estimating the nth prime, estimating the
number of primes less than or equal to an arbitrary number, computing
primorials, prime k-tuples (e.g., twin primes), finding the greatest common
divisor and smallest (least) common multiple, testing whether two numbers are
coprime, and computing Euler's totient function.  Most functions are vectorized
for speed and convenience.")
    (license license:expat)))

(define-public r-primertree
  (package
    (name "r-primertree")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "primerTree" version))
       (sha256
        (base32 "1w2xmmpqsp8plwsacfknkjkgs4x1fksrks614jwlg6fll4zxh42n"))))
    (properties `((upstream-name . "primerTree")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml
                             r-stringr
                             r-scales
                             r-reshape2
                             r-plyr
                             r-lubridate
                             r-httr
                             r-gridextra
                             r-ggplot2
                             r-foreach
                             r-directlabels
                             r-ape))
    (home-page "https://cran.r-project.org/package=primerTree")
    (synopsis
     "Visually Assessing the Specificity and Informativeness of Primer Pairs")
    (description
     "Identifies potential target sequences for a given set of primers and generates
phylogenetic trees annotated with the taxonomies of the predicted amplification
products.")
    (license license:gpl2)))

(define-public r-primer
  (package
    (name "r-primer")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "primer" version))
       (sha256
        (base32 "0qs4bzh80a6058v3s25z0w64wqpscgcciqbyqdifixl51z2qadf4"))))
    (properties `((upstream-name . "primer")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-ggplot2 r-desolve))
    (home-page "https://cran.r-project.org/package=primer")
    (synopsis "Functions and Data for the Book, a Primer of Ecology with R")
    (description
     "This package provides functions are primarily functions for systems of ordinary
differential equations, difference equations, and eigenanalysis and projection
of demographic matrices; data are for examples.")
    (license license:gpl3)))

(define-public r-primeplus
  (package
    (name "r-primeplus")
    (version "1.0.16")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PRIMEplus" version))
       (sha256
        (base32 "14g3m759gyc41hm4rijvmfzky3g4xwmdjmyfi9s5rx096xnyhn7g"))))
    (properties `((upstream-name . "PRIMEplus")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-msm))
    (home-page "https://cran.r-project.org/package=PRIMEplus")
    (synopsis "Study Design for Immunotherapy Clinical Trials")
    (description
     "Perform sample size, power calculation and subsequent analysis for
Immuno-oncology (IO) trials composed of responders and non-responders.")
    (license license:gpl2)))

(define-public r-primepca
  (package
    (name "r-primepca")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "primePCA" version))
       (sha256
        (base32 "0w3w50x17q7gwji19m6dx5vq46h8p62alf3axd10mycl609jlbcb"))))
    (properties `((upstream-name . "primePCA")))
    (build-system r-build-system)
    (propagated-inputs (list r-softimpute r-matrix r-mass))
    (home-page "https://cran.r-project.org/package=primePCA")
    (synopsis "Projected Refinement for Imputation of Missing Entries in PCA")
    (description
     "This package implements the @code{primePCA} algorithm, developed and analysed in
Zhu, Z., Wang, T. and Samworth, R. J. (2019) High-dimensional principal
component analysis with heterogeneous missingness. <@code{arXiv:1906.12125>}.")
    (license license:gpl3)))

(define-public r-primefactr
  (package
    (name "r-primefactr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "primefactr" version))
       (sha256
        (base32 "11hi75jwjxrl9p5m8yjwjb20pc9y9vkhi8sjrnisdq2hm53m483w"))))
    (properties `((upstream-name . "primefactr")))
    (build-system r-build-system)
    (home-page "https://github.com/privefl/primefactr")
    (synopsis "Use Prime Factorization for Computations")
    (description
     "Use Prime Factorization for simplifying computations, for instance for ratios of
large factorials.")
    (license license:gpl3)))

(define-public r-primate
  (package
    (name "r-primate")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "primate" version))
       (sha256
        (base32 "0agrz57xvk8azvb8af299l0g0iwamxy0l0m5y41rlsi03szszlii"))))
    (properties `((upstream-name . "primate")))
    (build-system r-build-system)
    (propagated-inputs (list r-caroline))
    (home-page "https://cran.r-project.org/package=primate")
    (synopsis "Tools and Methods for Primatological Data Science")
    (description
     "Data from All the World's Primates relational SQL database and other tabular
datasets are made available via drivers and connection functions.  Additionally
we provide several functions and examples to facilitate the merging and
aggregation of these tabular inputs.")
    (license (license:fsdg-compatible "Apache License"))))

(define-public r-primal
  (package
    (name "r-primal")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PRIMAL" version))
       (sha256
        (base32 "04fn0c10v9ai7vrrd4qr84rid3x42wq749rn5p1i6nkvmjv3dnhs"))))
    (properties `((upstream-name . "PRIMAL")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp r-matrix))
    (home-page "https://cran.r-project.org/package=PRIMAL")
    (synopsis "Parametric Simplex Method for Sparse Learning")
    (description
     "This package implements a unified framework of parametric simplex method for a
variety of sparse learning problems (e.g., Dantzig selector (for linear
regression), sparse quantile regression, sparse support vector machines, and
compressive sensing) combined with efficient hyper-parameter selection
strategies.  The core algorithm is implemented in C++ with Eigen3 support for
portable high performance linear algebra.  For more details about parametric
simplex method, see Haotian Pang (2017)
<https://papers.nips.cc/paper/6623-parametric-simplex-method-for-sparse-learning.pdf>.")
    (license license:gpl2+)))

(define-public r-prim
  (package
    (name "r-prim")
    (version "1.0.21")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prim" version))
       (sha256
        (base32 "085dwmflag45qg0p4w2f9v000zj2kfaijp6jm11dj85swxmx7vjm"))))
    (properties `((upstream-name . "prim")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-plot3d))
    (native-inputs (list r-knitr))
    (home-page "https://www.mvstat.net/tduong/")
    (synopsis "Patient Rule Induction Method (PRIM)")
    (description
     "Patient Rule Induction Method (PRIM) for bump hunting in high-dimensional data.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-pricesensitivitymeter
  (package
    (name "r-pricesensitivitymeter")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pricesensitivitymeter" version))
       (sha256
        (base32 "136y3dfm6chznp8bnyw6xd9wkv265j3ykqicxib1vwgwaprg6zdm"))))
    (properties `((upstream-name . "pricesensitivitymeter")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey r-rlang r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://max-alletsee.github.io/pricesensitivitymeter/")
    (synopsis "Van Westendorp Price Sensitivity Meter Analysis")
    (description
     "An implementation of the van Westendorp Price Sensitivity Meter in R, which is a
survey-based approach to analyze consumer price preferences and sensitivity (van
Westendorp 1976, isbn:9789283100386).")
    (license license:expat)))

(define-public r-pricer
  (package
    (name "r-pricer")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "priceR" version))
       (sha256
        (base32 "08s3sx3cmvvr7ibrybbw35pkvizhc8yqdikwhv9c0ljmv5q3qww9"))))
    (properties `((upstream-name . "priceR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-stringi
                             r-purrr
                             r-lubridate
                             r-jsonlite
                             r-gsubfn
                             r-dplyr))
    (home-page "https://github.com/stevecondylios/priceR")
    (synopsis "Economics and Pricing Tools")
    (description
     "This package provides functions to aid in micro and macro economic analysis and
handling of price and currency data.  Includes extraction of relevant inflation
and exchange rate data from World Bank API, data cleaning/parsing, and
standardisation.  Inflation adjustment calculations as found in Principles of
Macroeconomics by Gregory Mankiw et al (2014).  Current and historical end of
day exchange rates for 171 currencies from the European Central Bank Statistical
Data Warehouse (2020) <https://sdw.ecb.europa.eu/@code{curConverter.do>}.")
    (license license:expat)))

(define-public r-pricelevels
  (package
    (name "r-pricelevels")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pricelevels" version))
       (sha256
        (base32 "1mi949l7fczcsha81qlviaq14882062v7wr4raxavhb5hg1m057r"))))
    (properties `((upstream-name . "pricelevels")))
    (build-system r-build-system)
    (propagated-inputs (list r-minpack-lm r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sweinand/pricelevels")
    (synopsis "Spatial Price Level Comparisons")
    (description
     "Price comparisons within or between countries provide an overall measure of the
relative difference in prices, often denoted as price levels.  This package
provides index number methods for such price comparisons (e.g., The World Bank,
2011, <doi:10.1596/978-0-8213-9728-2>).  Moreover, it contains functions for
sampling and characterizing price data.")
    (license (license:fsdg-compatible "EUPL"))))

(define-public r-priceindices
  (package
    (name "r-priceindices")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PriceIndices" version))
       (sha256
        (base32 "0f4d5lq1jm3fyafrwypgpp637kizqnkkh6sx0y6sncd610iv0jfg"))))
    (properties `((upstream-name . "PriceIndices")))
    (build-system r-build-system)
    (propagated-inputs (list r-xgboost
                             r-stringr
                             r-strex
                             r-reshape
                             r-reclin2
                             r-lubridate
                             r-ggplot2
                             r-dplyr
                             r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PriceIndices")
    (synopsis "Calculating Bilateral and Multilateral Price Indexes")
    (description
     "Preparing a scanner data set for price dynamics calculations (data selecting,
data classification, data matching, data filtering).  Computing bilateral and
multilateral indexes.  For details on these methods see: Diewert and Fox (2020)
<doi:10.1080/07350015.2020.1816176>, BiaÅek (2019) <doi:10.2478/jos-2019-0014>
or BiaÅek (2020) <doi:10.2478/jos-2020-0037>.")
    (license license:gpl3)))

(define-public r-prf
  (package
    (name "r-prf")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pRF" version))
       (sha256
        (base32 "17srabk7mam16rdzc5g9ggdrhjjk8wibny40gxvgzkv7qgq7m80x"))))
    (properties `((upstream-name . "pRF")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2
                             r-randomforest
                             r-permute
                             r-multtest
                             r-ggplot2
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=pRF")
    (synopsis "Permutation Significance for Random Forests")
    (description
     "Estimate False Discovery Rates (FDRs) for importance metrics from random forest
runs.")
    (license license:gpl3)))

(define-public r-prevtoinc
  (package
    (name "r-prevtoinc")
    (version "0.12.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prevtoinc" version))
       (sha256
        (base32 "0ypdxcx9gh87a7ih3cw3yai47rzv41qwh4kpk6007ai8jkjv4n9w"))))
    (properties `((upstream-name . "prevtoinc")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-rlang r-purrr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=prevtoinc")
    (synopsis
     "Prevalence to Incidence Calculations for Point-Prevalence Studies in a Nosocomial Setting")
    (description
     "This package provides functions to simulate point prevalence studies (PPSs) of
healthcare-associated infections (HAIs) and to convert prevalence to incidence
in steady state setups.  Companion package to the preprint Willrich et al., From
prevalence to incidence - a new approach in the hospital setting;
<doi:10.1101/554725> , where methods are explained in detail.")
    (license license:expat)))

(define-public r-prevr
  (package
    (name "r-prevr")
    (version "5.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prevR" version))
       (sha256
        (base32 "17sygcfj29cvycsi1d2870gwcqq1ij6gppsqj46phc04r8q5hs5v"))))
    (properties `((upstream-name . "prevR")))
    (build-system r-build-system)
    (propagated-inputs (list r-stars
                             r-sf
                             r-kernsmooth
                             r-gstat
                             r-ggplot2
                             r-foreign
                             r-fields
                             r-directlabels))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/larmarange/prevR/")
    (synopsis
     "Estimating Regional Trends of a Prevalence from a DHS and Similar Surveys")
    (description
     "Spatial estimation of a prevalence surface or a relative risks surface, using
data from a Demographic and Health Survey (DHS) or an analog survey, see
Larmarange et al. (2011) <doi:10.4000/cybergeo.24606>.")
    (license license:cecill)))

(define-public r-prevmap
  (package
    (name "r-prevmap")
    (version "1.5.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PrevMap" version))
       (sha256
        (base32 "12cw4dmbsnx9c4y5l5k0paqn1wwbxy9ciw46r254n5rd275fhksh"))))
    (properties `((upstream-name . "PrevMap")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm
                             r-splancs
                             r-raster
                             r-pdist
                             r-numderiv
                             r-maxlik
                             r-matrix
                             r-lme4))
    (home-page "https://cran.r-project.org/package=PrevMap")
    (synopsis
     "Geostatistical Modelling of Spatially Referenced Prevalence Data")
    (description
     "This package provides functions for both likelihood-based and Bayesian analysis
of spatially referenced prevalence data.  For a tutorial on the use of the R
package, see Giorgi and Diggle (2017) <doi:10.18637/jss.v078.i08>.")
    (license license:gpl2+)))

(define-public r-previsionio
  (package
    (name "r-previsionio")
    (version "11.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "previsionio" version))
       (sha256
        (base32 "08hrklr3ngphdhp50lkz6ls9nn1bzdvs6xj583spsw4vm7j44yfr"))))
    (properties `((upstream-name . "previsionio")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml
                             r-plotly
                             r-metrics
                             r-magrittr
                             r-jsonlite
                             r-httr
                             r-futile-logger
                             r-data-table))
    (home-page "https://cran.r-project.org/package=previsionio")
    (synopsis "'Prevision.io' R SDK")
    (description
     "For working with the Prevision.io AI model management platform's API
<https://prevision.io/>.")
    (license license:expat)))

(define-public r-preventr
  (package
    (name "r-preventr")
    (version "0.10.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "preventr" version))
       (sha256
        (base32 "1m7g2p38wb95m3rci29b4x65ks1m4cwlawxwkqqzshcwnknxm57g"))))
    (properties `((upstream-name . "preventr")))
    (build-system r-build-system)
    (propagated-inputs (list r-dplyr))
    (home-page "https://martingmayer.com/preventr")
    (synopsis "An Implementation of the AHA PREVENT Equations")
    (description
     "This package implements the American Heart Association (AHA) Predicting Risk of
cardiovascular disease EVENTs (PREVENT) equations from Khan SS, Matsushita K,
Sang Y, and colleagues (2024) <doi:10.1161/CIRCULATIONAHA.123.067626>.")
    (license license:expat)))

(define-public r-prevederer
  (package
    (name "r-prevederer")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prevederer" version))
       (sha256
        (base32 "0vkx9nrlgp36xbl95lrb5sb5yivxlcw4yvbs8hgbm31kh0vbz32f"))))
    (properties `((upstream-name . "prevederer")))
    (build-system r-build-system)
    (propagated-inputs (list r-httr))
    (home-page "https://github.com/wkdavis/prevederer")
    (synopsis "Wrapper for the 'Prevedere' API")
    (description
     "Easy and efficient access to the API provided by Prevedere', an industry
insights and predictive analytics company.  Query and download indicators,
models and workbenches built with Prevedere for further analysis and reporting
<https://www.prevedere.com/>.")
    (license license:expat)))

(define-public r-prevalence
  (package
    (name "r-prevalence")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prevalence" version))
       (sha256
        (base32 "0llxcz7bg62l6qq4xaf2xwmc2k0y2l3zw5nb6bq0a9qnp3anj8ah"))))
    (properties `((upstream-name . "prevalence")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-rjags r-coda))
    (home-page "http://prevalence.cbra.be/")
    (synopsis "Tools for Prevalence Assessment Studies")
    (description
     "The prevalence package provides Frequentist and Bayesian methods for prevalence
assessment studies.  IMPORTANT: the @code{truePrev} functions in the prevalence
package call on JAGS (Just Another Gibbs Sampler), which therefore has to be
available on the user's system.  JAGS can be downloaded from
<https://mcmc-jags.sourceforge.io/>.")
    (license license:gpl2+)))

(define-public r-prettyr
  (package
    (name "r-prettyr")
    (version "2.2-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prettyR" version))
       (sha256
        (base32 "1rs90cmr5dyry724php90mp41qwzzk1z3gxfwcj1k157qawacyhr"))))
    (properties `((upstream-name . "prettyR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=prettyR")
    (synopsis "Pretty Descriptive Stats")
    (description
     "This package provides functions for conventionally formatting descriptive stats,
reshaping data frames and formatting R output as HTML.")
    (license license:gpl2+)))

(define-public r-prettymapr
  (package
    (name "r-prettymapr")
    (version "0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prettymapr" version))
       (sha256
        (base32 "03dlycmbd7hd3jq1n7x3nz87qwhxxj1nc4ai6yxmbgxbdasgs4y0"))))
    (properties `((upstream-name . "prettymapr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjson r-plyr r-httr r-digest))
    (home-page "https://github.com/paleolimbot/prettymapr")
    (synopsis "Scale Bar, North Arrow, and Pretty Margins in R")
    (description
     "Automates the process of creating a scale bar and north arrow in any package
that uses base graphics to plot in R. Bounding box tools help find and
manipulate extents.  Finally, there is a function to automate the process of
setting margins, plotting the map, scale bar, and north arrow, and resetting
graphic parameters upon completion.")
    (license license:gpl2)))

(define-public r-prettyglm
  (package
    (name "r-prettyglm")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prettyglm" version))
       (sha256
        (base32 "1pivjpnspis7zshdj7kasncb8qpy40ld9f40fwlb3x14snrvbqix"))))
    (properties `((upstream-name . "prettyglm")))
    (build-system r-build-system)
    (propagated-inputs (list r-vip
                             r-tidyselect
                             r-tidyr
                             r-tidycat
                             r-tibble
                             r-stringr
                             r-rcolorbrewer
                             r-plotly
                             r-knitr
                             r-kableextra
                             r-forcats
                             r-dplyr
                             r-car
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://jared-fowler.github.io/prettyglm/")
    (synopsis "Pretty Summaries of Generalized Linear Model Coefficients")
    (description
     "One of the main advantages of using Generalised Linear Models is their
interpretability.  The goal of prettyglm is to provide a set of functions which
easily create beautiful coefficient summaries which can readily be shared and
explained.  prettyglm helps users create coefficient summaries which include
categorical base levels, variable importance and type III p.values.  prettyglm
also creates beautiful relativity plots for categorical, continuous and splined
coefficients.")
    (license license:gpl3)))

(define-public r-prettycols
  (package
    (name "r-prettycols")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PrettyCols" version))
       (sha256
        (base32 "16r3bjrhdqpfbwlmiqp474wyj2pg6fnhql05carz1ayqga08pgwb"))))
    (properties `((upstream-name . "PrettyCols")))
    (build-system r-build-system)
    (propagated-inputs (list r-lifecycle r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://nrennie.rbind.io/PrettyCols/")
    (synopsis "Pretty Colour Palettes")
    (description "Defines aesthetically pleasing colour palettes.")
    (license license:cc0)))

(define-public r-prettycode
  (package
    (name "r-prettycode")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prettycode" version))
       (sha256
        (base32 "1ifh9h73szc23p1ii77z0r68n1gghndqqdm07wd7gxsg5pppp1sg"))))
    (properties `((upstream-name . "prettycode")))
    (build-system r-build-system)
    (propagated-inputs (list r-crayon))
    (home-page "https://github.com/r-lib/prettycode#readme")
    (synopsis "Pretty Print R Code in the Terminal")
    (description
     "Replace the standard print method for functions with one that performs syntax
highlighting, using ANSI colors, if the terminal supports them.")
    (license license:expat)))

(define-public r-prettyb
  (package
    (name "r-prettyb")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prettyB" version))
       (sha256
        (base32 "1wszcmn5jnk02iyvjld42291m1irfp9m0b3lcdyyp41iq3icm67g"))))
    (properties `((upstream-name . "prettyB")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jumpingrivers/prettyB/")
    (synopsis "Pretty Base Graphics")
    (description
     "Drop-in replacements for standard base graphics functions.  The replacements are
prettier versions of the originals.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-prettifyaddins
  (package
    (name "r-prettifyaddins")
    (version "2.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prettifyAddins" version))
       (sha256
        (base32 "0ncj10j1ygc1dhlqdg5vklzf258bjbg6mry8i8vqqh1dxvl2djwr"))))
    (properties `((upstream-name . "prettifyAddins")))
    (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-xrjulia
                             r-xml2
                             r-webdriver
                             r-shiny
                             r-rstudioapi
                             r-httr
                             r-chromote))
    (native-inputs (list esbuild))
    (home-page "https://github.com/stla/prettifyAddins")
    (synopsis
     "'RStudio' Addins to Prettify 'JavaScript', 'C++', 'Python', and More")
    (description
     "This package provides RStudio addins to prettify HTML', CSS', SCSS',
@code{JavaScript}', JSX', Markdown', C(++)', @code{LaTeX}', Python', Julia',
XML', Java', JSON', Ruby', and to reindent C(++)', Fortran', Java', Julia',
Python', SAS', Scala', Shell', SQL and \"@code{TypeScript}\".  Two kinds of addins
are provided: Prettify and Indent'.  The Indent addins only reindent the code,
while the Prettify addins also modify the code, e.g. trailing semi-colons are
added to @code{JavaScript} code when they are missing.")
    (license license:gpl3)))

(define-public r-pretest
  (package
    (name "r-pretest")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pretest" version))
       (sha256
        (base32 "11q90wf2sis0km20xnpx5wr7h7p80vl2bpah5m1a56qixswql5cm"))))
    (properties `((upstream-name . "pretest")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pretest")
    (synopsis
     "Novel Approach to Predictive Accuracy Testing in Nested Environments")
    (description
     "This repository contains the codes for using the predictive accuracy comparison
tests developed in Pitarakis, J. (2023) <doi:10.1017/S0266466623000154>.")
    (license license:gpl3)))

(define-public r-pressure
  (package
    (name "r-pressure")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pressuRe" version))
       (sha256
        (base32 "14cidhyfngzimpxj7cgb1588dzr363dqbngh2rahhcg5rhivz9zc"))))
    (properties `((upstream-name . "pressuRe")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-stringr
                             r-sf
                             r-scales
                             r-readxl
                             r-raster
                             r-pracma
                             r-magrittr
                             r-magick
                             r-ggplot2
                             r-ggmap
                             r-gdistance
                             r-dplyr
                             r-abind))
    (home-page "https://github.com/Telfer/pressuRe")
    (synopsis "Imports, Processes, and Visualizes Biomechanical Pressure Data")
    (description
     "Allows biomechanical pressure data from a range of systems to be imported and
processed in a reproducible manner.  Automatic and manual tools are included to
let the user define regions (masks) to be analyzed.  Also includes functions for
visualizing and animating pressure data.  Example methods are described in Shi
et al., (2022) <doi:10.1038/s41598-022-19814-0>, Lee et al., (2014)
<doi:10.1186/1757-1146-7-18>, van der Zward et al., (2014)
<doi:10.1186/1757-1146-7-20>, Najafi et al., (2010)
<doi:10.1016/j.gaitpost.2009.09.003>, Cavanagh and Rodgers (1987)
<doi:10.1016/0021-9290(87)90255-7>.")
    (license license:expat)))

(define-public r-presspurt
  (package
    (name "r-presspurt")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PressPurt" version))
       (sha256
        (base32 "05kca0rlhlvhgnw3r3p4sh42zwvhrhcjdwz65ykp14jxybfhc8in"))))
    (properties `((upstream-name . "PressPurt")))
    (build-system r-build-system)
    (propagated-inputs (list r-reticulate r-gridextra r-ggplot2 r-data-table))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/dkoslicki/PressPurt")
    (synopsis "Indeterminacy of Networks via Press Perturbations")
    (description
     "This is a computational package designed to identify the most sensitive
interactions within a network which must be estimated most accurately in order
to produce qualitatively robust predictions to a press perturbation.  This is
accomplished by enumerating the number of sign switches (and their magnitude) in
the net effects matrix when an edge experiences uncertainty.  The package
produces data and visualizations when uncertainty is associated to one or more
edges in the network and according to a variety of distributions.  The software
requires the network to be described by a system of differential equations but
only requires as input a numerical Jacobian matrix evaluated at an equilibrium
point.  This package is based on Koslicki, D., & Novak, M. (2017)
<doi:10.1007/s00285-017-1163-0>.")
    (license license:expat)))

(define-public r-presmtp
  (package
    (name "r-presmtp")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "presmTP" version))
       (sha256
        (base32 "134p58zzk8i0whscvl5qdfn4niksypyxavq1m15cvw4zzk4dk6fw"))))
    (properties `((upstream-name . "presmTP")))
    (build-system r-build-system)
    (propagated-inputs (list r-survpresmooth r-mgcv))
    (home-page "https://cran.r-project.org/package=presmTP")
    (synopsis "Methods for Transition Probabilities")
    (description
     "This package provides a function for estimating the transition probabilities in
an illness-death model.  The transition probabilities can be estimated from the
unsmoothed landmark estimators developed by de Una-Alvarez and Meira-Machado
(2015) <doi:10.1111/biom.12288>.  Presmoothed estimates can also be obtained
through the use of a parametric family of binary regression curves, such as
logit, probit or cauchit.  The additive logistic regression model and
nonparametric regression are also alternatives which have been implemented.  The
idea behind the presmoothed landmark estimators is to use the presmoothing
techniques developed by Cao et al. (2005) <doi:10.1007/s00180-007-0076-6> in the
landmark estimation of the transition probabilities.")
    (license license:gpl3)))

(define-public r-presize
  (package
    (name "r-presize")
    (version "0.3.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "presize" version))
       (sha256
        (base32 "1qabv7lsrd0rrjkg7icr7qxj4jrx78s6rh1n961s6qrw6gldq4ga"))))
    (properties `((upstream-name . "presize")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-kappasize))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/CTU-Bern/presize")
    (synopsis "Precision Based Sample Size Calculation")
    (description
     "Bland (2009) <doi:10.1136/bmj.b3985> recommended to base study sizes on the
width of the confidence interval rather the power of a statistical test.  The
goal of presize is to provide functions for such precision based sample size
calculations.  For a given sample size, the functions will return the precision
(width of the confidence interval), and vice versa.")
    (license license:gpl3)))

(define-public r-presiduals
  (package
    (name "r-presiduals")
    (version "1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PResiduals" version))
       (sha256
        (base32 "0mq16cs07hw5c9jj08vpgbb15xvjvz9h0npsbqphiibsb6ca5fwb"))))
    (properties `((upstream-name . "PResiduals")))
    (build-system r-build-system)
    (propagated-inputs (list r-sparsem r-rms r-mass r-formula))
    (home-page "https://cran.r-project.org/package=PResiduals")
    (synopsis "Probability-Scale Residuals and Residual Correlations")
    (description
     "Computes probability-scale residuals and residual correlations for continuous,
ordinal, binary, count, and time-to-event data <doi:10.18637/jss.v094.i12>.")
    (license license:gpl2+)))

(define-public r-presentes
  (package
    (name "r-presentes")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "presentes" version))
       (sha256
        (base32 "0d6zhishc7wxcvvglgw44pc6c172qpxb8i4iw3lqbmji7ash5zpx"))))
    (properties `((upstream-name . "presentes")))
    (build-system r-build-system)
    (home-page "https://diegokoz.github.io/presentes/")
    (synopsis "Registry of Victims of State Terrorism in Argentina")
    (description
     "Compilation and digitalization of the official registry of victims of state
terrorism in Argentina during the last military coup.  The original data comes
from RUVTE-ILID (2019)
<https://www.argentina.gob.ar/sitiosdememoria/ruvte/informe> and
<http://basededatos.parquedelamemoria.org.ar/registros/>.  The title, presentes,
comes from present in spanish.")
    (license license:expat)))

(define-public r-presenter
  (package
    (name "r-presenter")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "presenter" version))
       (sha256
        (base32 "1mfwpp8hd48yvadvrxz7w7spx76hb79yld7mhl6fw0mirbys98cj"))))
    (properties `((upstream-name . "presenter")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-stringi
                             r-rvg
                             r-rlang
                             r-randomcolor
                             r-purrr
                             r-openxlsx
                             r-officer
                             r-magrittr
                             r-lubridate
                             r-janitor
                             r-framecleaner
                             r-formattable
                             r-flextable
                             r-dplyr
                             r-berryfunctions))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Harrison4192/presenter")
    (synopsis "Present Data with Style")
    (description
     "Consists of custom wrapper functions using packages openxlsx', flextable', and
officer to create highly formatted MS office friendly output of your data
frames.  These viewer friendly outputs are intended to match expectations of
professional looking presentations in business and consulting scenarios.  The
functions are opinionated in the sense that they expect the input data frame to
have certain properties in order to take advantage of the automated formatting.")
    (license license:expat)))

(define-public r-presens
  (package
    (name "r-presens")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "presens" version))
       (sha256
        (base32 "175nr9pqn3m6kh9bcc1gxqmqv05xdsqdw9lx385lmm1g947d2159"))))
    (properties `((upstream-name . "presens")))
    (build-system r-build-system)
    (propagated-inputs (list r-measurements r-marelac))
    (home-page "https://cran.r-project.org/package=presens")
    (synopsis "Interface for PreSens Fiber Optic Data")
    (description
     "Makes output files from select @code{PreSens} Fiber Optic Oxygen Transmitters
easier to work with in R. See <http://www.presens.de> for more information about
@code{PreSens} (Precision Sensing @code{GmbH}).  Note: this package is neither
created nor maintained by @code{PreSens}.")
    (license license:gpl3)))

(define-public r-presenceabsence
  (package
    (name "r-presenceabsence")
    (version "1.1.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PresenceAbsence" version))
       (sha256
        (base32 "1dz4psinvj5zizj663rfigfr14z9d6khj2jqkk3bfr9qg19n8fn6"))))
    (properties `((upstream-name . "PresenceAbsence")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PresenceAbsence")
    (synopsis "Presence-Absence Model Evaluation")
    (description
     "This package provides a set of functions useful when evaluating the results of
presence-absence models.  Package includes functions for calculating threshold
dependent measures such as confusion matrices, pcc, sensitivity, specificity,
and Kappa, and produces plots of each measure as the threshold is varied.  It
will calculate optimal threshold choice according to a choice of optimization
criteria.  It also includes functions to plot the threshold independent ROC
curves along with the associated AUC (area under the curve).")
    (license (license:fsdg-compatible "Unlimited"))))

(define-public r-preregr
  (package
    (name "r-preregr")
    (version "0.2.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "preregr" version))
       (sha256
        (base32 "011fplx40gnakallq24ak9sqvlxncpmihg474wplzppyv103pa50"))))
    (properties `((upstream-name . "preregr")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml r-rmdpartials r-jsonlite r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://preregr.opens.science")
    (synopsis
     "Specify (Pre)Registrations and Export Them Human- And Machine-Readably")
    (description
     "Preregistrations, or more generally, registrations, enable explicit timestamped
and (often but not necessarily publicly) frozen documentation of plans and
expectations as well as decisions and justifications.  In research,
preregistrations are commonly used to clearly document plans and facilitate
justifications of deviations from those plans, as well as decreasing the effects
of publication bias by enabling identification of research that was conducted
but not published.  Like reporting guidelines, (pre)registration forms often
have specific structures that facilitate systematic reporting of important
items.  The preregr package facilitates specifying (pre)registrations in R and
exporting them to a human-readable format (using R Markdown partials or
exporting to an HTML file) as well as human-readable embedded data (using
JSON'), as well as importing such exported (pre)registration specifications from
such embedded JSON'.")
    (license license:gpl3+)))

(define-public r-preputils
  (package
    (name "r-preputils")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "preputils" version))
       (sha256
        (base32 "1ryrxmcm9vxhblk9r1vaay8ihff6nnd87dzd3375lfcla2fr9jzv"))))
    (properties `((upstream-name . "preputils")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table))
    (home-page "https://cran.r-project.org/package=preputils")
    (synopsis "Utilities for Preparation of Data Analysis")
    (description
     "Miscellaneous small utilities are provided to mitigate issues with messy,
inconsistent or high dimensional data and help for preprocessing and preparing
analyses.")
    (license license:gpl3)))

(define-public r-prepshiny
  (package
    (name "r-prepshiny")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PREPShiny" version))
       (sha256
        (base32 "0zsg07y6nhp2lma3pdz8xd981y3mh8k21il5jv0inma1d1xr4g3f"))))
    (properties `((upstream-name . "PREPShiny")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-rmarkdown r-psycho r-dplyr))
    (home-page "https://cran.r-project.org/package=PREPShiny")
    (synopsis "Interactive Document for Preprocessing the Dataset")
    (description
     "An interactive document for preprocessing the dataset using rmarkdown and shiny
packages.  Runtime examples are provided in the package function as well as at
<https://analyticmodels.shinyapps.io/PREPShiny/>.")
    (license license:gpl2)))

(define-public r-preprocessrecordlinkage
  (package
    (name "r-preprocessrecordlinkage")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PreProcessRecordLinkage" version))
       (sha256
        (base32 "05j3nbilkw8iqhdazkjz58sgnnwan2nh28b2sqi0gphzdbwks7mi"))))
    (properties `((upstream-name . "PreProcessRecordLinkage")))
    (build-system r-build-system)
    (propagated-inputs (list r-tm r-syn r-stringr r-recordlinkage r-data-table))
    (home-page "https://cran.r-project.org/package=PreProcessRecordLinkage")
    (synopsis "Preprocessing Record Linkage")
    (description
     "In this record linkage package, data preprocessing has been meticulously
executed to cover a wide range of datasets, ensuring that variable names are
standardized using synonyms.  This approach facilitates seamless data
integration and analysis across various datasets.  While users have the
flexibility to modify variable names, the system intelligently ensures that
changes are only permitted when they do not compromise data consistency or
essential variable essence.")
    (license license:gpl3)))

(define-public r-preprocessing
  (package
    (name "r-preprocessing")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PreProcessing" version))
       (sha256
        (base32 "08wrdg3q7r8h7lqn3yppl3qqlhnaak81vd9ymqlkzw92981rxxzx"))))
    (properties `((upstream-name . "PreProcessing")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://cran.r-project.org/package=PreProcessing")
    (synopsis "Various Preprocessing Transformations of Numeric Data Matrices")
    (description
     "Preprocess numeric data matrices into desired transformed representations.
Standardization, Unitization, Cubitization and adaptive intervals are offered.")
    (license license:gpl3)))

(define-public r-preprocess
  (package
    (name "r-preprocess")
    (version "3.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PreProcess" version))
       (sha256
        (base32 "1fxzkmrj76mc94xdj7a0nq450021i1jxkigmh5jhrmamkp5581jf"))))
    (properties `((upstream-name . "PreProcess")))
    (build-system r-build-system)
    (propagated-inputs (list r-oompabase))
    (home-page "http://oompa.r-forge.r-project.org/")
    (synopsis "Basic Functions for Pre-Processing Microarrays")
    (description
     "This package provides classes to pre-process microarray gene expression data as
part of the OOMPA collection of packages described at
<http://oompa.r-forge.r-project.org/>.")
    (license license:asl2.0)))

(define-public r-prepplot
  (package
    (name "r-prepplot")
    (version "1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prepplot" version))
       (sha256
        (base32 "1x6znfhmrsb2zj2cfygsw1fdszcp4xdpaip4b4d5cdl2rykb3bjn"))))
    (properties `((upstream-name . "prepplot")))
    (build-system r-build-system)
    (propagated-inputs (list r-shape r-plotrix))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=prepplot")
    (synopsis "Prepare Figure Region for Base Graphics")
    (description
     "This package provides a figure region is prepared, creating a plot region with
suitable background color, grid lines or shadings, and providing axes and
labeling if not suppressed.  Subsequently, information carrying graphics
elements can be added (points, lines, barplot with add=TRUE and so forth).")
    (license license:gpl2+)))

(define-public r-prepdesigns
  (package
    (name "r-prepdesigns")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pRepDesigns" version))
       (sha256
        (base32 "1n8yzn4k3gsldrxr7fs7qmys854lfl54cwssd47w1k7kr259gvdg"))))
    (properties `((upstream-name . "pRepDesigns")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pRepDesigns")
    (synopsis "Partially Replicated (p-Rep) Designs")
    (description
     "Early generation breeding trials are to be conducted in multiple environments
where it may not be possible to replicate all the lines in each environment due
to scarcity of resources.  For such situations, partially replicated (p-Rep)
designs have wide application potential as only a proportion of the test lines
are replicated at each environment.  A collection of several utility functions
related to p-Rep designs have been developed.  Here, the package contains six
functions for a complete stepwise analytical study of these designs.  Five
functions @code{pRep1}(), @code{pRep2}(), @code{pRep3}(), @code{pRep4}() and
@code{pRep5}(), are used to generate five new series of p-Rep designs and also
compute average variance factors and canonical efficiency factors of generated
designs.  A fourth function NCEV() is used to generate incidence matrix (N),
information matrix (C), canonical efficiency factor (E) and average variance
factor (V).  This function is general in nature and can be used for studying the
characterization properties of any block design.  A construction procedure for
p-Rep designs was given by Williams et al.(2011) <doi:10.1002/bimj.201000102>
which was tedious and time consuming.  Here, in this package, five different
methods have been given to generate p-Rep designs easily.")
    (license license:gpl2+)))

(define-public r-prepdat
  (package
    (name "r-prepdat")
    (version "1.0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prepdat" version))
       (sha256
        (base32 "19rxyss96cmjhzm69f6l1cd3vkw3wskzhxnbmrz4546x00gf9g5v"))))
    (properties `((upstream-name . "prepdat")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-psych r-dplyr))
    (home-page "http://github.com/ayalaallon/prepdat")
    (synopsis "Preparing Experimental Data for Statistical Analysis")
    (description
     "Prepares data for statistical analysis (e.g., analysis of variance ;ANOVA) by
enabling the user to easily and quickly merge (using the file_merge() function)
raw data files into one merged table and then aggregate the merged table (using
the prep() function) into a finalized table while keeping track and summarizing
every step of the preparation.  The finalized table contains several
possibilities for dependent measures of the dependent variable.  Most suitable
when measuring variables in an interval or ratio scale (e.g., reaction-times)
and/or discrete values such as accuracy.  Main functions included are
file_merge() and prep().  The file_merge() function vertically merges individual
data files (in a long format) in which each line is a single observation to one
single dataset.  The prep() function aggregates the single dataset according to
any combination of grouping variables (i.e., between-subjects and
within-subjects independent variables, respectively), and returns a data frame
with a number of dependent measures for further analysis for each cell according
to the combination of provided grouping variables.  Dependent measures for each
cell include among others means before and after rejecting all values according
to a flexible standard deviation criteria, number of rejected values according
to the flexible standard deviation criteria, proportions of rejected values
according to the flexible standard deviation criteria, number of values before
rejection, means after rejecting values according to procedures described in Van
Selst & Jolicoeur (1994; suitable when measuring reaction-times), standard
deviations, medians, means according to any percentile (e.g., 0.05, 0.25, 0.75,
0.95) and harmonic means.  The data frame prep() returns can also be exported as
a txt file to be used for statistical analysis in other statistical programs.")
    (license license:gpl3)))

(define-public r-prenoms
  (package
    (name "r-prenoms")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prenoms" version))
       (sha256
        (base32 "0r642akcc5fxg736b9d6i53spywk2f7kingni0akgr0gdxc7s6aj"))))
    (properties `((upstream-name . "prenoms")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble))
    (home-page "<https://github.com/desautm/prenoms>")
    (synopsis "Names Given to Babies in Quebec Between 1980 and 2020")
    (description
     "This package provides a database containing the names of the babies born in
Quebec between 1980 and 2020.")
    (license license:expat)))

(define-public r-premium
  (package
    (name "r-premium")
    (version "3.2.13")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PReMiuM" version))
       (sha256
        (base32 "1qamq7glc0dag1ps0dr4f931ij9jk91lj5h1k2g0yd0h3kqamawn"))))
    (properties `((upstream-name . "PReMiuM")))
    (build-system r-build-system)
    (propagated-inputs (list r-spdep
                             r-sf
                             r-rcppeigen
                             r-rcpp
                             r-plotrix
                             r-ggplot2
                             r-gamlss-dist
                             r-data-table
                             r-cluster
                             r-bh))
    (home-page "https://www.silvialiverani.com/software/")
    (synopsis "Dirichlet Process Bayesian Clustering, Profile Regression")
    (description
     "Bayesian clustering using a Dirichlet process mixture model.  This model is an
alternative to regression models, non-parametrically linking a response vector
to covariate data through cluster membership.  The package allows Bernoulli,
Binomial, Poisson, Normal, survival and categorical response, as well as Normal
and discrete covariates.  It also allows for fixed effects in the response
model, where a spatial CAR (conditional autoregressive) term can be also
included.  Additionally, predictions may be made for the response, and missing
values for the covariates are handled.  Several samplers and label switching
moves are implemented along with diagnostic tools to assess convergence.  A
number of R functions for post-processing of the output are also provided.  In
addition to fitting mixtures, it may additionally be of interest to determine
which covariates actively drive the mixture components.  This is implemented in
the package as variable selection.  The main reference for the package is
Liverani, Hastie, Azizi, Papathomas and Richardson (2015)
<doi:10.18637/jss.v064.i07>.")
    (license license:gpl2)))

(define-public r-preknitposthtmlrender
  (package
    (name "r-preknitposthtmlrender")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PreKnitPostHTMLRender" version))
       (sha256
        (base32 "1749cd734f4bf0n4068s2m617k6as8h9bwp44mm3la2xy3vjf5g1"))))
    (properties `((upstream-name . "PreKnitPostHTMLRender")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml r-rmarkdown r-knitr))
    (home-page "https://github.com/chinsoon12/PreKnitPostHTMLRender")
    (synopsis "Pre-Knitting Processing and Post HTML-Rendering Processing")
    (description
     "Dynamize headers or R code within Rmd files to prevent proliferation of Rmd
files for similar reports.  Add in external HTML document within rmarkdown
rendered HTML doc.")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-prefmod
  (package
    (name "r-prefmod")
    (version "0.8-36")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prefmod" version))
       (sha256
        (base32 "1xac3x07yrhrk5j4aq798yk3hf0ig5p84n40cw85i75k3qxddk6f"))))
    (properties `((upstream-name . "prefmod")))
    (build-system r-build-system)
    (propagated-inputs (list r-gnm r-colorspace))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=prefmod")
    (synopsis "Utilities to Fit Paired Comparison Models for Preferences")
    (description
     "Generates design matrix for analysing real paired comparisons and derived paired
comparison data (Likert type items/ratings or rankings) using a loglinear
approach.  Fits loglinear Bradley-Terry model (LLBT) exploiting an eliminate
feature.  Computes pattern models for paired comparisons, rankings, and ratings.
 Some treatment of missing values (MCAR and MNAR).  Fits latent class (mixture)
models for paired comparison, rating and ranking patterns using a non-parametric
ML approach.")
    (license license:gpl2+)))

(define-public r-prefio
  (package
    (name "r-prefio")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prefio" version))
       (sha256
        (base32 "0zc8j1hj47iivx40i1wzilcn3456smbrzz2g6s1khqsx366mw9qr"))))
    (properties `((upstream-name . "prefio")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-magrittr r-dplyr))
    (home-page "https://github.com/fleverest/prefio/")
    (synopsis "Structures for Preference Data")
    (description
     "Convenient structures for creating, sourcing, reading, writing and manipulating
ordinal preference data.  Methods for writing to/from @code{PrefLib} formats.
See Nicholas Mattei and Toby Walsh \"@code{PrefLib}: A Library of Preference
Data\" (2013) <doi:10.1007/978-3-642-41575-3_20>.")
    (license license:gpl3)))

(define-public r-preference
  (package
    (name "r-preference")
    (version "1.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "preference" version))
       (sha256
        (base32 "12gpiwd69ik4r2l67fvad0jmp78w919nhly4drxhfiizl3mw4jdn"))))
    (properties `((upstream-name . "preference")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-ggplot2))
    (home-page "https://github.com/kaneplusplus/preference")
    (synopsis "2-Stage Preference Trial Design and Analysis")
    (description
     "Design and analyze two-stage randomized trials with a continuous outcome
measure.  The package contains functions to compute the required sample size
needed to detect a given preference, treatment, and selection effect;
alternatively, the package contains functions that can report the study power
given a fixed sample size.  Finally, analysis functions are provided to test
each effect using either summary data (i.e.  means, variances) or raw study data
<doi:10.18637/jss.v094.c02>.")
    (license license:lgpl2.0)))

(define-public r-preferably
  (package
    (name "r-preferably")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "preferably" version))
       (sha256
        (base32 "11xvh0a8m2lpmrrrrn95axqgz9rdipn63zd7c505a8spzwf6dvm5"))))
    (properties `((upstream-name . "preferably")))
    (build-system r-build-system)
    (propagated-inputs (list r-pkgdown r-knitr))
    (native-inputs (list r-knitr))
    (home-page "https://preferably.amirmasoudabdol.name")
    (synopsis "'pkgdown' Template")
    (description
     "This is an accessible template for pkgdown'.  It uses two bootstrap themes,
Flatly and Darkly and utilizes the prefers-color-scheme CSS variable to
automatically serve either of the two based on userâs operating system
setting, or allowing them to manually toggle between them.")
    (license license:expat)))

(define-public r-prefer
  (package
    (name "r-prefer")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prefeR" version))
       (sha256
        (base32 "0n9ns715f274bcr3mbh7yjyvx3ajz949f7yyyq38f54cs2anwwrd"))))
    (properties `((upstream-name . "prefeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-mcmc r-entropy))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jlepird/prefeR")
    (synopsis "R Package for Pairwise Preference Elicitation")
    (description
     "Allows users to derive multi-objective weights from pairwise comparisons, which
research shows is more repeatable, transparent, and intuitive other techniques.
These weights can be rank existing alternatives or to define a multi-objective
utility function for optimization.")
    (license license:expat)))

(define-public r-pref
  (package
    (name "r-pref")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pref" version))
       (sha256
        (base32 "1cxq5cdif2w4syb46vaa58jf06lw7hqqsn6isib5i28di096fp8g"))))
    (properties `((upstream-name . "pref")))
    (build-system r-build-system)
    (propagated-inputs (list r-jpeg))
    (home-page "https://github.com/denismollison/pref")
    (synopsis "Preference Voting with Explanatory Graphics")
    (description
     "This package implements the Single Transferable Vote (STV) electoral system,
with clear explanatory graphics.  The core function stv() uses Meek's method,
the purest expression of the simple principles of STV, but which requires
electronic counting.  It can handle votes expressing equal preferences for
subsets of the candidates.  A function stv.wig() implementing the Weighted
Inclusive Gregory method, as used in Scottish council elections, is also
provided, and with the same options, as described in the manual.  The required
vote data format is as an R list: a function pref.data() is provided to
transform some commonly used data formats into this format.  References for
methodology: Hill, Wichmann and Woodall (1987) <doi:10.1093/comjnl/30.3.277>,
Hill, David (2006) <https://www.votingmatters.org.uk/ISSUE22/I22P2.pdf>,
Mollison, Denis (2023) <@code{arXiv:2303.15310>}, (see also the package manual
pref_pkg_manual.pdf).")
    (license license:expat)))

(define-public r-predtoolsts
  (package
    (name "r-predtoolsts")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "predtoolsTS" version))
       (sha256
        (base32 "0f9fapw9yjdzj6pwcskbyfwjsvg09ahn20vrv4bgi0dx8am291rl"))))
    (properties `((upstream-name . "predtoolsTS")))
    (build-system r-build-system)
    (propagated-inputs (list r-tspred r-tseries r-metrics r-forecast r-caret))
    (home-page "https://github.com/avm00016/predtoolsTS")
    (synopsis "Time Series Prediction Tools")
    (description
     "Makes the time series prediction easier by automatizing this process using four
main functions: prep(), modl(), pred() and postp().  Features different
preprocessing methods to homogenize variance and to remove trend and
seasonality.  Also has the potential to bring together different predictive
models to make comparatives.  Features ARIMA and Data Mining Regression models
(using caret).")
    (license license:gpl2+)))

(define-public r-predtools
  (package
    (name "r-predtools")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "predtools" version))
       (sha256
        (base32 "150jlfbjvhnvvvbbjsr3hc0v778p9lyj8ml167inzbw4cynk2g9j"))))
    (properties `((upstream-name . "predtools")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp
                             r-rconics
                             r-proc
                             r-mvtnorm
                             r-magrittr
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/resplab/predtools")
    (synopsis "Prediction Model Tools")
    (description
     "This package provides additional functions for evaluating predictive models,
including plotting calibration curves and model-based Receiver Operating
Characteristic (@code{mROC}) based on Sadatsafavi et al (2021)
<@code{arXiv:2003.00316>}.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-predtest
  (package
    (name "r-predtest")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PredTest" version))
       (sha256
        (base32 "0f199gyhr91kddgkg84yplmfv865dq3lcb4ikvlsk559vlv3pxj9"))))
    (properties `((upstream-name . "PredTest")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PredTest")
    (synopsis "Preparing Data For, and Calculating the Prediction Test")
    (description
     "Global hypothesis tests combine information across multiple endpoints to test a
single hypothesis.  The prediction test is a recently proposed global hypothesis
test with good performance for small sample sizes and many endpoints of
interest.  The test is also flexible in the types and combinations of expected
results across the individual endpoints.  This package provides functions for
data processing and calculation of the prediction test.")
    (license license:expat)))

(define-public r-predrupdate
  (package
    (name "r-predrupdate")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "predRupdate" version))
       (sha256
        (base32 "0wy5yrzsp36gyb6za4965xnk1pkii895a510zkghybarxnsxcb1h"))))
    (properties `((upstream-name . "predRupdate")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rlang r-proc r-ggpubr r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/GlenMartin31/predRupdate")
    (synopsis "Prediction Model Validation and Updating")
    (description
     "Evaluate the predictive performance of an existing (i.e.  previously developed)
prediction/ prognostic model given relevant information about the existing
prediction model (e.g. coefficients) and a new dataset.  Provides a range of
model updating methods that help tailor the existing model to the new dataset;
see Su et al. (2018) <doi:10.1177/0962280215626466>.  Techniques to aggregate
multiple existing prediction models on the new data are also provided; see
Debray et al. (2014) <doi:10.1002/sim.6080> and Martin et al. (2018)
<doi:10.1002/sim.7586>).")
    (license license:expat)))

(define-public r-predreliability
  (package
    (name "r-predreliability")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "predReliability" version))
       (sha256
        (base32 "18s9ijg3fcg6qb0y6pnwngmv69ajm93z0xjdzkq3fp5bxk1qylyb"))))
    (properties `((upstream-name . "predReliability")))
    (build-system r-build-system)
    (propagated-inputs (list r-rpart r-cluster))
    (home-page "https://cran.r-project.org/package=predReliability")
    (synopsis
     "Estimates Reliability of Individual Supervised Learning Predictions")
    (description
     "An implementation of reliability estimation methods described in the paper
(Bosnic, Z., & Kononenko, I. (2008) <doi:10.1007/s10489-007-0084-9>), which
allows you to test the reliability of a single predicted instance made by your
model and prediction function.  It also allows you to make a correlation test to
estimate which reliability estimate is the most accurate for your model.")
    (license license:gpl3)))

(define-public r-predpsych
  (package
    (name "r-predpsych")
    (version "0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PredPsych" version))
       (sha256
        (base32 "00h4p1szqzh4a2kjvlriyinh330cfwxsrvs6fi089mr207q890vj"))))
    (properties `((upstream-name . "PredPsych")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod
                             r-rpart
                             r-randomforest
                             r-plyr
                             r-party
                             r-mclust
                             r-mass
                             r-ggplot2
                             r-e1071
                             r-caret))
    (home-page "https://cran.r-project.org/package=PredPsych")
    (synopsis "Predictive Approaches in Psychology")
    (description
     "Recent years have seen an increased interest in novel methods for analyzing
quantitative data from experimental psychology.  Currently, however, they lack
an established and accessible software framework.  Many existing implementations
provide no guidelines, consisting of small code snippets, or sets of packages.
In addition, the use of existing packages often requires advanced programming
experience. @code{PredPsych} is a user-friendly toolbox based on machine
learning predictive algorithms.  It comprises of multiple functionalities for
multivariate analyses of quantitative behavioral data based on machine learning
models.")
    (license license:gpl3)))

(define-public r-predkmeans
  (package
    (name "r-predkmeans")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "predkmeans" version))
       (sha256
        (base32 "0352c50zlyzn8fczrkinayllj9jp978njjl7dazzkzypx0p20va5"))))
    (properties `((upstream-name . "predkmeans")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mgcv r-maxlik r-e1071))
    (home-page "https://cran.r-project.org/package=predkmeans")
    (synopsis "Covariate Adaptive Clustering")
    (description
     "This package implements the predictive k-means method for clustering
observations, using a mixture of experts model to allow covariates to influence
cluster centers.  Motivated by air pollution epidemiology settings, where
cluster membership needs to be predicted across space.  Includes functions for
predicting cluster membership using spatial splines and principal component
analysis (PCA) scores using either multinomial logistic regression or support
vector machines (SVMs).  For method details see Keller et al. (2017)
<doi:10.1214/16-AOAS992>.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-predint
  (package
    (name "r-predint")
    (version "2.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "predint" version))
       (sha256
        (base32 "1db5nzwhj2zjzqpdrp0phcwxxgn4pdphb05hjlyc9r58hspg8k11"))))
    (properties `((upstream-name . "predint")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-lme4 r-ggplot2))
    (home-page "https://github.com/MaxMenssen/predint")
    (synopsis "Prediction Intervals")
    (description
     "An implementation of prediction intervals for overdispersed count data, for
overdispersed binomial data and for linear random effects models.")
    (license license:gpl2+)))

(define-public r-predieval
  (package
    (name "r-predieval")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "predieval" version))
       (sha256
        (base32 "096zfb94iixza0lfzr4p21gkcs9hl7h0d5jhbzdi3hhyz80zymrj"))))
    (properties `((upstream-name . "predieval")))
    (build-system r-build-system)
    (propagated-inputs (list r-matching r-mass r-hmisc r-ggplot2))
    (home-page "https://github.com/esm-ispm-unibe-ch/predieval")
    (synopsis
     "Assessing Performance of Prediction Models for Predicting Patient-Level Treatment Benefit")
    (description
     "This package provides methods for assessing the performance of a prediction
model with respect to identifying patient-level treatment benefit.  All methods
are applicable for continuous and binary outcomes, and for any type of
statistical or machine-learning prediction model as long as it uses baseline
covariates to predict outcomes under treatment and control.")
    (license license:gpl2+)))

(define-public r-predicts
  (package
    (name "r-predicts")
    (version "0.1-16")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "predicts" version))
       (sha256
        (base32 "0fnmqb60cgyfwlybydd36lbg3yv5k9bhz4byiwp4sh7lrw02klvg"))))
    (properties `((upstream-name . "predicts")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra))
    (home-page "https://rspatial.org/sdm/")
    (synopsis "Spatial Prediction Tools")
    (description
     "This package provides methods for spatial predictive modeling, especially for
spatial distribution models.  This includes algorithms for model fitting and
prediction, as well as methods for model evaluation.")
    (license license:gpl3+)))

(define-public r-predictrace
  (package
    (name "r-predictrace")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "predictrace" version))
       (sha256
        (base32 "0xy1vkxgqq9faa62shbdkg3fgwwm8khybbhagqwc6a01ibm32mnk"))))
    (properties `((upstream-name . "predictrace")))
    (build-system r-build-system)
    (propagated-inputs (list r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jacobkap/predictrace")
    (synopsis
     "Predict the Race and Gender of a Given Name Using Census and Social Security Administration Data")
    (description
     "Predicts the most common race of a surname and based on U.S. Census data, and
the most common first named based on U.S. Social Security Administration data.")
    (license license:expat)))

(define-public r-predictorselect
  (package
    (name "r-predictorselect")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PredictorSelect" version))
       (sha256
        (base32 "1iibvh9fifjg962hirwnnmksc81yjpv5jb13yjhb4sjpi7fd6482"))))
    (properties `((upstream-name . "PredictorSelect")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PredictorSelect")
    (synopsis
     "Out-of-Sample Predictability in Predictive Regressions with Many Predictor Candidates")
    (description
     "Consider a linear predictive regression setting with a potentially large set of
candidate predictors.  This work is concerned with detecting the presence of out
of sample predictability based on out of sample mean squared error comparisons
given in Gonzalo and Pitarakis (2023) <doi:10.1016/j.ijforecast.2023.10.005>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-predictor
  (package
    (name "r-predictor")
    (version "3.0.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "predictoR" version))
       (sha256
        (base32 "0lj13rn9dpx0rqxb03abisa06ihphmrq5shpv6q2p0qb857pgmkf"))))
    (properties `((upstream-name . "predictoR")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-xgboost
                             r-trainer
                             r-shinyjs
                             r-shinydashboardplus
                             r-shinydashboard
                             r-shinycustomloader
                             r-shinyace
                             r-shiny
                             r-rpart-plot
                             r-rlang
                             r-loader
                             r-htmltools
                             r-golem
                             r-glmnet
                             r-echarts4r
                             r-dt
                             r-dplyr
                             r-config
                             r-colourpicker))
    (home-page "https://promidat.website/")
    (synopsis "Predictive Data Analysis System")
    (description
     "Perform a supervised data analysis on a database through a shiny graphical
interface.  It includes methods such as K-Nearest Neighbors, Decision Trees, ADA
Boosting, Extreme Gradient Boosting, Random Forest, Neural Networks, Deep
Learning, Support Vector Machines and Bayesian Methods.")
    (license license:gpl2+)))

(define-public r-predictnmb
  (package
    (name "r-predictnmb")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "predictNMB" version))
       (sha256
        (base32 "1g4mak6n3ris9hdk6pl0zb6jmh5ziicqbfv7y6p2i304af768pgy"))))
    (properties `((upstream-name . "predictNMB")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-scales
                             r-rlang
                             r-pmsampsize
                             r-magrittr
                             r-ggplot2
                             r-dplyr
                             r-cutpointr
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://docs.ropensci.org/predictNMB/")
    (synopsis "Evaluate Clinical Prediction Models by Net Monetary Benefit")
    (description
     "Estimates when and where a model-guided treatment strategy may outperform a
treat-all or treat-none approach by Monte Carlo simulation and evaluation of the
Net Monetary Benefit.  Details can be viewed in Parsons et al. (2023)
<doi:10.21105/joss.05328>.")
    (license license:gpl3+)))

(define-public r-predictmeans
  (package
    (name "r-predictmeans")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "predictmeans" version))
       (sha256
        (base32 "1010s79c55vndv3v4my0xrmdxvgpy7vhp46j1anxvr3xzgx1279q"))))
    (properties `((upstream-name . "predictmeans")))
    (build-system r-build-system)
    (propagated-inputs (list r-splines2
                             r-plyr
                             r-plotly
                             r-pbkrtest
                             r-numderiv
                             r-nlme
                             r-matrix
                             r-mass
                             r-lmesplines
                             r-lmertest
                             r-lmeinfo
                             r-lme4
                             r-hrw
                             r-glmmtmb
                             r-ggplot2
                             r-car))
    (home-page "https://CRAN.R-project.org/package=predictmeans")
    (synopsis "Predicted Means for Linear and Semiparametric Models")
    (description
     "Providing functions to diagnose and make inferences from various linear models,
such as those obtained from aov', lm', glm', gls', lme', lmer', @code{glmmTMB}
and semireg'.  Inferences include predicted means and standard errors,
contrasts, multiple comparisons, permutation tests, adjusted R-square and
graphs.")
    (license license:gpl2+)))

(define-public r-predictme
  (package
    (name "r-predictme")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "predictMe" version))
       (sha256
        (base32 "0n4sv9ahyvly17grlilybb7vwq9pzyn6mvhcn8z9cyswqa7svs49"))))
    (properties `((upstream-name . "predictMe")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-rdpack r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mmiche/predictMe")
    (synopsis "Visualize Individual Prediction Performance")
    (description
     "Enables researchers to visualize the prediction performance of any algorithm on
the individual level (or close to it), given that the predicted outcome is
either binary or continuous.  Visual results are instantly comprehensible.")
    (license license:expat)))

(define-public r-predictionr
  (package
    (name "r-predictionr")
    (version "1.0-12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PredictionR" version))
       (sha256
        (base32 "0kgc8rjdqskasdaxsbir9gwvm55rw5cpdg3y9nsj7ccg9fc1ji19"))))
    (properties `((upstream-name . "PredictionR")))
    (build-system r-build-system)
    (propagated-inputs (list r-renext r-fitdistrplus))
    (home-page "https://cran.r-project.org/package=PredictionR")
    (synopsis "Prediction for Future Data from any Continuous Distribution")
    (description
     "This package provides functions to get prediction intervals and prediction
points of future observations from any continuous distribution.")
    (license license:gpl2+)))

(define-public r-predictioninterval
  (package
    (name "r-predictioninterval")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "predictionInterval" version))
       (sha256
        (base32 "029hc57cblfcbqckrghf95l2rkn7acgb4yr36da01bx8bmpww0bn"))))
    (properties `((upstream-name . "predictionInterval")))
    (build-system r-build-system)
    (propagated-inputs (list r-pbapply r-mbess r-mass r-ggplot2))
    (home-page "https://cran.r-project.org/package=predictionInterval")
    (synopsis
     "Prediction Interval Functions for Assessing Replication Study Results")
    (description
     "This package provides a common problem faced by journal reviewers and authors is
the question of whether the results of a replication study are consistent with
the original published study.  One solution to this problem is to examine the
effect size from the original study and generate the range of effect sizes that
could reasonably be obtained (due to random sampling) in a replication attempt
(i.e., calculate a prediction interval).  This package has functions that
calculate the prediction interval for the correlation (i.e., r), standardized
mean difference (i.e., d-value), and mean.")
    (license (license:fsdg-compatible "MIT License + file LICENSE"))))

(define-public r-predictabel
  (package
    (name "r-predictabel")
    (version "1.2-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PredictABEL" version))
       (sha256
        (base32 "144xmfsp62ssmg501f4y173jrqyv8vps36j4yrwg6ghcmyc01wf8"))))
    (properties `((upstream-name . "PredictABEL")))
    (build-system r-build-system)
    (propagated-inputs (list r-rocr r-pbsmodelling r-lazyeval r-hmisc))
    (home-page "https://cran.r-project.org/package=PredictABEL")
    (synopsis "Assessment of Risk Prediction Models")
    (description
     "We included functions to assess the performance of risk models.  The package
contains functions for the various measures that are used in empirical studies,
including univariate and multivariate odds ratios (OR) of the predictors, the
c-statistic (or area under the receiver operating characteristic (ROC) curve
(AUC)), Hosmer-Lemeshow goodness of fit test, reclassification table, net
reclassification improvement (NRI) and integrated discrimination improvement
(IDI).  Also included are functions to create plots, such as risk distributions,
ROC curves, calibration plot, discrimination box plot and predictiveness curves.
 In addition to functions to assess the performance of risk models, the package
includes functions to obtain weighted and unweighted risk scores as well as
predicted risks using logistic regression analysis.  These logistic regression
functions are specifically written for models that include genetic variables,
but they can also be applied to models that are based on non-genetic risk
factors only.  Finally, the package includes function to construct a simulated
dataset with genotypes, genetic risks, and disease status for a hypothetical
population, which is used for the evaluation of genetic risk models.")
    (license license:gpl2+)))

(define-public r-predict3d
  (package
    (name "r-predict3d")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "predict3d" version))
       (sha256
        (base32 "1qfr9ixdzcr2v030fds2h3fmyx4xhc5lbbg44i4imzg96w1z3lxy"))))
    (properties `((upstream-name . "predict3d")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-rlang
                             r-rgl
                             r-reshape2
                             r-purrr
                             r-plyr
                             r-modelr
                             r-magrittr
                             r-ggplot2
                             r-ggiraphextra
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/cardiomoon/predict3d")
    (synopsis "Draw Three Dimensional Predict Plot Using Package 'rgl'")
    (description
     "Draw 2 dimensional and three dimensional plot for multiple regression models
using package ggplot2 and rgl'.  Supports linear models (lm), generalized linear
models (glm) and local polynomial regression fittings (loess).")
    (license license:gpl2)))

(define-public r-predhy-gui
  (package
    (name "r-predhy-gui")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "predhy.GUI" version))
       (sha256
        (base32 "04grfxb18vqhdh3ylq7dm3z3kal21nkr33pgj9698lzy1gn6ki22"))))
    (properties `((upstream-name . "predhy.GUI")))
    (build-system r-build-system)
    (propagated-inputs (list r-xgboost
                             r-shiny
                             r-predhy
                             r-pls
                             r-lightgbm
                             r-htmltools
                             r-glmnet
                             r-foreach
                             r-dt
                             r-doparallel
                             r-data-table
                             r-bglr))
    (home-page "https://cran.r-project.org/package=predhy.GUI")
    (synopsis
     "Genomic Prediction of Hybrid Performance with Graphical User Interface")
    (description
     "This package performs genomic prediction of hybrid performance using eight GS
methods including GBLUP, @code{BayesB}, RKHS, PLS, LASSO, Elastic net, XGBoost
and @code{LightGBM}.  GBLUP: genomic best liner unbiased prediction, RKHS:
reproducing kernel Hilbert space, PLS: partial least squares regression, LASSO:
least absolute shrinkage and selection operator, XGBoost: extreme gradient
boosting, @code{LightGBM}: light gradient boosting machine.  It also provides
fast cross-validation and mating design scheme for training population (Xu S et
al (2016) <doi:10.1111/tpj.13242>; Xu S (2017) <doi:10.1534/g3.116.038059>).")
    (license license:gpl3)))

(define-public r-predhy
  (package
    (name "r-predhy")
    (version "2.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "predhy" version))
       (sha256
        (base32 "0s38gzbynvllf4fi1mrjd2yj24sc091gi5cd8vbj63pl8qkbfjjc"))))
    (properties `((upstream-name . "predhy")))
    (build-system r-build-system)
    (propagated-inputs (list r-xgboost
                             r-pls
                             r-lightgbm
                             r-glmnet
                             r-foreach
                             r-doparallel
                             r-bglr))
    (home-page "https://cran.r-project.org/package=predhy")
    (synopsis "Genomic Prediction of Hybrid Performance")
    (description
     "This package performs genomic prediction of hybrid performance using eight GS
methods including GBLUP, @code{BayesB}, RKHS, PLS, LASSO, Elastic net,
@code{LightGBM} and XGBoost.  It also provides fast cross-validation and mating
design scheme for training population (Xu S et al (2016)
<doi:10.1111/tpj.13242>; Xu S (2017) <doi:10.1534/g3.116.038059>).")
    (license license:gpl3)))

(define-public r-predfairness
  (package
    (name "r-predfairness")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "predfairness" version))
       (sha256
        (base32 "0b9d6a2rk6vw4y5hnh721gmirnm419yynbfg7arjb96558l282kd"))))
    (properties `((upstream-name . "predfairness")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=predfairness")
    (synopsis "Discrimination Mitigation for Machine Learning Models")
    (description
     "Based on different statistical definitions of discrimination, several methods
have been proposed to detect and mitigate social inequality in machine learning
models.  This package aims to provide an alternative to fairness treatment in
predictive models.  The ROC method implemented in this package is described by
Kamiran, Karim and Zhang (2012) <https://ieeexplore.ieee.org/document/6413831/>.")
    (license license:gpl2+)))

(define-public r-predcrg
  (package
    (name "r-predcrg")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PredCRG" version))
       (sha256
        (base32 "0z84p8l2q66rapb9lhx8m8iadz3q989q5qkvcp8ylv80cz6qkibx"))))
    (properties `((upstream-name . "PredCRG")))
    (build-system r-build-system)
    (propagated-inputs (list r-protr r-peptides r-kernlab r-e1071 r-biostrings))
    (home-page "https://cran.r-project.org/package=PredCRG")
    (synopsis
     "Computational Prediction of Proteins Encoded by Circadian Genes")
    (description
     "This package provides a computational model for predicting proteins encoded by
circadian genes.  The support vector machine has been employed with Laplace
kernel for prediction of circadian proteins, where compositional, transitional
and physico-chemical features were utilized as numeric features.  User can
predict for the test dataset using the proposed computational model.  Besides,
the user can also build their own training model using their training dataset,
followed by prediction for the test set.")
    (license license:gpl2+)))

(define-public r-precondition
  (package
    (name "r-precondition")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "precondition" version))
       (sha256
        (base32 "0srmr33q56dimkn0m7aad19ddrp28a0dzrpnyh4hglwy9zxfqz9j"))))
    (properties `((upstream-name . "precondition")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang))
    (home-page "https://cran.r-project.org/package=precondition")
    (synopsis "Lightweight Precondition, Postcondition, and Sanity Checks")
    (description
     "This package implements fast, safe, and customizable assertions routines, which
can be used in place of base::stopifnot().")
    (license license:expat)))

(define-public r-precommit
  (package
    (name "r-precommit")
    (version "0.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "precommit" version))
       (sha256
        (base32 "0rh5vrr6q3z4pqm4548v3mxfcf681kpamkq8q54w49hhlp73dgmi"))))
    (properties `((upstream-name . "precommit")))
    (build-system r-build-system)
    (inputs (list git))
    (propagated-inputs (list r-yaml
                             r-withr
                             r-rprojroot
                             r-rlang
                             r-r-cache
                             r-purrr
                             r-magrittr
                             r-here
                             r-fs
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://lorenzwalthert.github.io/precommit/")
    (synopsis "Pre-Commit Hooks")
    (description
     "Useful git hooks for R building on top of the multi-language framework
pre-commit for hook management.  This package provides git hooks for common
tasks like formatting files with styler or spell checking as well as wrapper
functions to access the pre-commit executable.")
    (license license:gpl3)))

(define-public r-precmed
  (package
    (name "r-precmed")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "precmed" version))
       (sha256
        (base32 "0clla6wpkclmx2v3mwzcwz95jm7i2802pc6v001g5gjgqzvzyp9q"))))
    (properties `((upstream-name . "precmed")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-survival
                             r-stringr
                             r-rlang
                             r-randomforestsrc
                             r-mgcv
                             r-mass
                             r-glmnet
                             r-ggplot2
                             r-gbm
                             r-gam
                             r-dplyr))
    (home-page "https://github.com/smartdata-analysis-and-statistics/precmed")
    (synopsis "Precision Medicine")
    (description
     "This package provides a doubly robust precision medicine approach to fit,
cross-validate and visualize prediction models for the conditional average
treatment effect (CATE).  It implements doubly robust estimation and
semiparametric modeling approach of treatment-covariate interactions as proposed
by Yadlowsky et al. (2020) <doi:10.1080/01621459.2020.1772080>.")
    (license license:asl2.0)))

(define-public r-precisesums
  (package
    (name "r-precisesums")
    (version "0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PreciseSums" version))
       (sha256
        (base32 "10fnr0b159ym001hdnkr7cir5vaaglcxrbyrxmky430f5nhhcv0f"))))
    (properties `((upstream-name . "PreciseSums")))
    (build-system r-build-system)
    (home-page "https://github.com/nlmixr2/PreciseSums")
    (synopsis "Accurate Floating Point Sums and Products")
    (description
     "Most of the time floating point arithmetic does approximately the right thing.
When adding sums or having products of numbers that greatly differ in magnitude,
the floating point arithmetic may be incorrect.  This package implements the
Kahan (1965) sum <doi:10.1145/363707.363723>, Neumaier (1974) sum
<doi:10.1002/zamm.19740540106>, pairwise-sum (adapted from @code{NumPy}', See
Castaldo (2008) <doi:10.1137/070679946> for a discussion of accuracy), and
arbitrary precision sum (adapted from the fsum in Python ; Shewchuk (1997)
<https://people.eecs.berkeley.edu/~jrs/papers/robustr.pdf>).  In addition,
products are changed to long double precision for accuracy, or changed into a
log-sum for accuracy.")
    (license license:gpl2+)))

(define-public r-preciseplacement
  (package
    (name "r-preciseplacement")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "precisePlacement" version))
       (sha256
        (base32 "1w48k4kv3xm8br7dgbpx2rcdxjh0g0aw8xa1snqy0ig8a992804b"))))
    (properties `((upstream-name . "precisePlacement")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=precisePlacement")
    (synopsis
     "Suite of Functions to Help Get Plot Elements Exactly Where You Want Them")
    (description
     "This package provides a selection of tools that make it easier to place elements
onto a (base R) plot exactly where you want them.  It allows users to identify
points and distances on a plot in terms of inches, pixels, margin lines, data
units, and proportions of the plotting space, all in a manner more simple than
manipulating par().")
    (license license:expat)))

(define-public r-precisely
  (package
    (name "r-precisely")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "precisely" version))
       (sha256
        (base32 "1zzg2fgf58gjq081rklqjf8wxz8mf7rc3vyhbji3j3n6j0x125n4"))))
    (properties `((upstream-name . "precisely")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-shinythemes
                             r-shinycssloaders
                             r-shiny
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/malcolmbarrett/precisely")
    (synopsis "Estimate Sample Size Based on Precision Rather than Power")
    (description
     "Estimate sample size based on precision rather than power.  precisely is a study
planning tool to calculate sample size based on precision.  Power calculations
are focused on whether or not an estimate will be statistically significant;
calculations of precision are based on the same principles as power calculation
but turn the focus to the width of the confidence interval.  precisely is based
on the work of Rothman and Greenland (2018).")
    (license license:expat)))

(define-public r-precipe
  (package
    (name "r-precipe")
    (version "3.0.1-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pRecipe" version))
       (sha256
        (base32 "0mwv2xrxp4iws19dwph713xqnkgq3fnwfq1vmw68934qacyjmk79"))))
    (properties `((upstream-name . "pRecipe")))
    (build-system r-build-system)
    (inputs (list proj gdal))
    (propagated-inputs (list r-sp
                             r-sf
                             r-scales
                             r-raster
                             r-openair
                             r-ncdf4
                             r-magrittr
                             r-lubridate
                             r-ggpubr
                             r-ggplot2
                             r-foreach
                             r-doparallel
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MiRoVaGo/pRecipe")
    (synopsis "Precipitation R Recipes")
    (description
     "An open-access tool/framework to download, validate, visualize, and analyze
multi-source precipitation data.  More information and an example of
implementation can be found in Vargas Godoy and Markonis (2023,
<doi:10.1016/j.envsoft.2023.105711>).")
    (license license:gpl3)))

(define-public r-precintcon
  (package
    (name "r-precintcon")
    (version "2.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "precintcon" version))
       (sha256
        (base32 "1sf0mfqa77aqhbx3hg8pv582ibmfnv6vigqcd3xqsbq7nigy2ms9"))))
    (properties `((upstream-name . "precintcon")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-ggplot2))
    (home-page "https://github.com/lucasvenez/precintcon")
    (synopsis "Precipitation Intensity, Concentration and Anomaly Analysis")
    (description
     "It contains functions to analyze the precipitation intensity, concentration and
anomaly.")
    (license license:gpl2+)))

(define-public r-precast
  (package
    (name "r-precast")
    (version "1.6.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PRECAST" version))
       (sha256
        (base32 "17qk40j2by7bjc0r2wnyfca4drpzbrxp0m5r10a7494avqknjjk0"))))
    (properties `((upstream-name . "PRECAST")))
    (build-system r-build-system)
    (propagated-inputs (list r-seurat
                             r-scater
                             r-scales
                             r-rcpparmadillo
                             r-rcpp
                             r-purrr
                             r-pbapply
                             r-patchwork
                             r-mclust
                             r-matrix
                             r-mass
                             r-gtools
                             r-giraf
                             r-ggthemes
                             r-ggpubr
                             r-ggplot2
                             r-dr-sc
                             r-dplyr
                             r-cowplot
                             r-colorspace))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/feiyoung/PRECAST")
    (synopsis "Embedding and Clustering with Alignment for Spatial Datasets")
    (description
     "An efficient data integration method is provided for multiple spatial
transcriptomics data with non-cluster-relevant effects such as the complex batch
effects.  It unifies spatial factor analysis simultaneously with spatial
clustering and embedding alignment, requiring only partially shared cell/domain
clusters across datasets.  More details can be referred to Wei Liu, et al.
(2023) <doi:10.1038/s41467-023-35947-w>.")
    (license license:gpl3)))

(define-public r-pre
  (package
    (name "r-pre")
    (version "1.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pre" version))
       (sha256
        (base32 "170kfyhnb6hqyknkf4h6cxcaif6z2m8n6gslyj88sfch2xbfx0b1"))))
    (properties `((upstream-name . "pre")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-stringr
                             r-rpart
                             r-partykit
                             r-matrixmodels
                             r-matrix
                             r-glmnet
                             r-formula
                             r-earth))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/marjoleinF/pre")
    (synopsis "Prediction Rule Ensembles")
    (description
     "Derives prediction rule ensembles (PREs).  Largely follows the procedure for
deriving PREs as described in Friedman & Popescu (2008;
<DOI:10.1214/07-AOAS148>), with adjustments and improvements.  The main function
pre() derives prediction rule ensembles consisting of rules and/or linear terms
for continuous, binary, count, multinomial, and multivariate continuous
responses.  Function gpe() derives generalized prediction ensembles, consisting
of rules, hinge and linear functions of the predictor variables.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-prda
  (package
    (name "r-prda")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PRDA" version))
       (sha256
        (base32 "1lklwfakd54ylm5gizq36ng6498c2izapsz9yz2a95cjf860rr23"))))
    (properties `((upstream-name . "PRDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-pbapply r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://claudiozandonella.github.io/PRDA/")
    (synopsis "Conduct a Prospective or Retrospective Design Analysis")
    (description
     "An implementation of the \"Design Analysis\" proposed by Gelman and Carlin (2014)
<doi:10.1177/1745691614551642>.  It combines the evaluation of Power-Analysis
with other inferential-risks as Type-M error (i.e.  Magnitude) and Type-S error
(i.e.  Sign).  See also AltoÃ¨ et al. (2020) <doi:10.3389/fpsyg.2019.02893> and
Bertoldo et al. (2020) <doi:10.31234/osf.io/q9f86>.")
    (license license:gpl3)))

(define-public r-prcr
  (package
    (name "r-prcr")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prcr" version))
       (sha256
        (base32 "0k5vl14akqf8qvak6h7a19qxn4wzpyxq7lyvdhxfh8l4gs4ca2l6"))))
    (properties `((upstream-name . "prcr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-purrr
                             r-magrittr
                             r-lpsolve
                             r-irr
                             r-ggplot2
                             r-forcats
                             r-dplyr
                             r-class))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jrosen48/prcr")
    (synopsis "Person-Centered Analysis")
    (description
     "This package provides an easy-to-use yet adaptable set of tools to conduct
person-center analysis using a two-step clustering procedure.  As described in
Bergman and El-Khouri (1999)
<DOI:10.1002/(SICI)1521-4036(199910)41:6%3C753::AID-BIMJ753%3E3.0.CO;2-K>,
hierarchical clustering is performed to determine the initial partition for the
subsequent k-means clustering procedure.")
    (license license:expat)))

(define-public r-prclust
  (package
    (name "r-prclust")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prclust" version))
       (sha256
        (base32 "0p0sf8248aigs99py8mpzz743jnrf7n1nv5shag15arxz9yx7zn3"))))
    (properties `((upstream-name . "prclust")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=prclust")
    (synopsis "Penalized Regression-Based Clustering Method")
    (description
     "Clustering is unsupervised and exploratory in nature.  Yet, it can be performed
through penalized regression with grouping pursuit.  In this package, we provide
two algorithms for fitting the penalized regression-based clustering (PRclust)
with non-convex grouping penalties, such as group truncated lasso, MCP and SCAD.
One algorithm is based on quadratic penalty and difference convex method.
Another algorithm is based on difference convex and ADMM, called DC-ADD, which
is more efficient.  Generalized cross validation and stability based method were
provided to select the tuning parameters.  Rand index, adjusted Rand index and
Jaccard index were provided to estimate the agreement between estimated cluster
memberships and the truth.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-prcbench
  (package
    (name "r-prcbench")
    (version "1.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prcbench" version))
       (sha256
        (base32 "0h49cgzaqcy97gl57glh0z34g7dnx4wr4y8a1h7r3nxgkvdcvx74"))))
    (properties `((upstream-name . "prcbench")))
    (build-system r-build-system)
    (propagated-inputs (list r-rocr
                             r-rcpp
                             r-r6
                             r-prroc
                             r-precrec
                             r-memoise
                             r-gridextra
                             r-ggplot2
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://evalclass.github.io/prcbench/")
    (synopsis "Testing Workbench for Precision-Recall Curves")
    (description
     "This package provides a testing workbench to evaluate tools that calculate
precision-recall curves.  Saito and Rehmsmeier (2015)
<doi:10.1371/journal.pone.0118432>.")
    (license license:gpl3)))

(define-public r-praznik
  (package
    (name "r-praznik")
    (version "11.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "praznik" version))
       (sha256
        (base32 "18b5n5s5kra6fcgafxji8kznpp8gvlslrkrdshjvqzf3s25gzmn6"))))
    (properties `((upstream-name . "praznik")))
    (build-system r-build-system)
    (home-page "https://gitlab.com/mbq/praznik")
    (synopsis "Tools for Information-Based Feature Selection and Scoring")
    (description
     "This package provides a toolbox of fast, native and parallel implementations of
various information-based importance criteria estimators and feature selection
filters based on them, inspired by the overview by Brown, Pocock, Zhao and Lujan
(2012) <https://www.jmlr.org/papers/v13/brown12a.html>.  Contains, among other,
minimum redundancy maximal relevancy ('@code{mRMR}') method by Peng, Long and
Ding (2005) <doi:10.1109/TPAMI.2005.159>; joint mutual information ('JMI')
method by Yang and Moody (1999)
<https://papers.nips.cc/paper/1779-data-visualization-and-feature-selection-new-algorithms-for-nongaussian-data>;
double input symmetrical relevance ('DISR') method by Meyer and Bontempi (2006)
<doi:10.1007/11732242_9> as well as joint mutual information maximisation
('JMIM') method by Bennasar, Hicks and Setchi (2015)
<doi:10.1016/j.eswa.2015.07.007>.")
    (license license:gpl3)))

(define-public r-prana
  (package
    (name "r-prana")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PRANA" version))
       (sha256
        (base32 "1q902pnlan8f5ds9kwrl45im12xq44nr4x64x3xn3w2lhzq159dw"))))
    (properties `((upstream-name . "PRANA")))
    (build-system r-build-system)
    (propagated-inputs (list r-robustbase r-minet r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PRANA")
    (synopsis "Pseudo-Value Regression Approach for Network Analysis (PRANA)")
    (description
     "This package provides a novel pseudo-value regression approach for the
differential co-expression network analysis in expression data, which can
incorporate additional clinical variables in the model.  This is a direct
regression modeling for the differential network analysis, and it is therefore
computationally amenable for the most users.  The full methodological details
can be found in Ahn S et al (2023) <doi:10.1186/s12859-022-05123-w>.")
    (license license:gpl3)))

(define-public r-prais
  (package
    (name "r-prais")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "prais" version))
       (sha256
        (base32 "16h3771lr6vn0g92bfpdq7r3wpdkspbhmrl5948p4isc5qxy2cgx"))))
    (properties `((upstream-name . "prais")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich r-pcse r-lmtest))
    (home-page "https://github.com/franzmohr/prais")
    (synopsis "Prais-Winsten Estimator for AR(1) Serial Correlation")
    (description
     "The Prais-Winsten estimator (Prais & Winsten, 1954) takes into account AR(1)
serial correlation of the errors in a linear regression model.  The procedure
recursively estimates the coefficients and the error autocorrelation of the
specified model until sufficient convergence of the AR(1) coefficient is
attained.")
    (license license:gpl2)))

(define-public r-practools
  (package
    (name "r-practools")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PracTools" version))
       (sha256
        (base32 "1ridvpj9zgf987ysg86620r7daxh6r48gfjnyd6amsb8i6rdvp31"))))
    (properties `((upstream-name . "PracTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-usmap r-ggplot2 r-geosphere r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PracTools")
    (synopsis "Designing and Weighting Survey Samples")
    (description
     "This package provides functions and datasets to support Valliant, Dever, and
Kreuter (2018), <doi:10.1007/978-3-319-93632-1>, \"Practical Tools for Designing
and Weighting Survey Samples\".  Contains functions for sample size calculation
for survey samples using stratified or clustered one-, two-, and three-stage
sample designs, and single-stage audit sample designs.  Functions are included
that will group geographic units accounting for distances apart and measures of
size.  Other functions compute variance components for multistage designs and
sample sizes in two-phase designs.  A number of example data sets are included.")
    (license license:gpl3)))

(define-public r-practicalsigni
  (package
    (name "r-practicalsigni")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "practicalSigni" version))
       (sha256
        (base32 "1ymq5mkp0y0zjgjvfdq3wx5qig9fiibnff4irgwkrckvp58xc9fn"))))
    (properties `((upstream-name . "practicalSigni")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-shapleyvalue
                             r-randomforest
                             r-np
                             r-nns
                             r-hypergeo
                             r-generalcorr))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=practicalSigni")
    (synopsis
     "Practical Significance Ranking of Regressors and Exact t Density")
    (description
     "Consider a possibly nonlinear nonparametric regression with p regressors.  We
provide evaluations by 13 methods to rank regressors by their practical
significance or importance using various methods, including machine learning
tools.  Comprehensive methods are as follows.  m6=Generalized partial
correlation coefficient or GPCC by Vinod (2021)<doi:10.1007/s10614-021-10190-x>
and Vinod (2022)<https://www.mdpi.com/1911-8074/15/1/32>.  m7= a generalization
of psychologists effect size incorporating nonlinearity and many variables.  m8=
local linear partial (dy/dxi) using the np package for kernel regressions.  m9=
partial (dy/dxi) using the NNS package.  m10= importance measure using the NNS
boost function.  m11= Shapley Value measure of importance (cooperative game
theory).  m12 and m13= two versions of the random forest algorithm.  Taraldsen's
exact density for sampling distribution of correlations added.")
    (license license:gpl2+)))

(define-public r-practicalequidesign
  (package
    (name "r-practicalequidesign")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PracticalEquiDesign" version))
       (sha256
        (base32 "0jms5j9vg5g1llwkzjglj6k26wgj16jxdigllra4gjzk0azbgn9q"))))
    (properties `((upstream-name . "PracticalEquiDesign")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-temporal r-numderiv r-ggplot2 r-dplyr))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=PracticalEquiDesign")
    (synopsis "Design of Practical Equivalence Trials")
    (description
     "Sample size calculations for practical equivalence trial design with a time to
event endpoint.")
    (license license:gpl3)))

(define-public r-pracpac
  (package
    (name "r-pracpac")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pracpac" version))
       (sha256
        (base32 "0c9vr6lxw6yslfv8vap78mxdf10jz8rswas50ld0dnx1qrfpgliy"))))
    (properties `((upstream-name . "pracpac")))
    (build-system r-build-system)
    (propagated-inputs (list r-rprojroot
                             r-renv
                             r-pkgbuild
                             r-magrittr
                             r-glue
                             r-fs))
    (native-inputs (list r-knitr))
    (home-page "https://signaturescience.github.io/pracpac/")
    (synopsis "Practical 'R' Packaging in 'Docker'")
    (description
     "Streamline the creation of Docker images with R packages and dependencies
embedded.  The pracpac package provides a usethis'-like interface to creating
Dockerfiles with dependencies managed by renv'.  The pracpac functionality is
described in Nagraj and Turner (2023) <doi:10.48550/@code{arXiv.2303.07876>}.")
    (license license:expat)))

(define-public r-praatpicture
  (package
    (name "r-praatpicture")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "praatpicture" version))
       (sha256
        (base32 "1w7gzkkdjy2m5p82b9q7vksdakycay33kc7w3jvvp633b7pa0grp"))))
    (properties `((upstream-name . "praatpicture")))
    (build-system r-build-system)
    (propagated-inputs (list r-wrassp
                             r-tuner
                             r-soundgen
                             r-shinyjs
                             r-shiny
                             r-rstudioapi
                             r-rpraat
                             r-phontools
                             r-ipa
                             r-gsignal
                             r-gifski
                             r-emur
                             r-crayon
                             r-bslib
                             r-av))
    (home-page "https://github.com/rpuggaardrode/praatpicture")
    (synopsis "'Praat Picture' Style Plots of Acoustic Data")
    (description
     "Quickly and easily generate plots of acoustic data aligned with transcriptions
similar to those made in Praat using either derived signals generated directly
in R with wrassp or imported derived signals from Praat'.  Provides easy and
fast out-of-the-box solutions but also a high extent of flexibility.  Also
provides options for embedding audio in figures and animating figures.")
    (license license:expat)))

(define-public r-pra
  (package
    (name "r-pra")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PRA" version))
       (sha256
        (base32 "0j5lhaw4vhmcsxbi4fymky9zi8yk3zgkjlqjm7899w7y48qdcnfq"))))
    (properties `((upstream-name . "PRA")))
    (build-system r-build-system)
    (propagated-inputs (list r-minpack-lm r-mc2d))
    (native-inputs (list r-knitr))
    (home-page "https://paulgovan.github.io/PRA/")
    (synopsis "Project Risk Analysis")
    (description
     "Data analysis for Project Risk Management via the Second Moment Method, Monte
Carlo Simulation, Contingency Analysis, Sensitivity Analysis, Earned Value
Management, Learning Curves, Design Structure Matrices, and more.")
    (license license:expat)))

(define-public r-pqrfe
  (package
    (name "r-pqrfe")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pqrfe" version))
       (sha256
        (base32 "0apsrlgricjmv202yg1h3iz2715nmf05wcvyznnha0nil7cqg0qi"))))
    (properties `((upstream-name . "pqrfe")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mass))
    (home-page "https://cran.r-project.org/package=pqrfe")
    (synopsis "Penalized Quantile Regression with Fixed Effects")
    (description
     "Quantile regression with fixed effects is a general model for longitudinal data.
 Here we proposed to solve it by several methods.  The estimation methods
include three loss functions as check, asymmetric least square and asymmetric
Huber functions; and three structures as simple regression, fixed effects and
fixed effects with penalized intercepts by LASSO.")
    (license license:gpl2+)))

(define-public r-pqrbayes
  (package
    (name "r-pqrbayes")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pqrBayes" version))
       (sha256
        (base32 "0mbhkha99qyk5hkpk13ysb0686lcn6q9vq34838fz1l7xy47mzw3"))))
    (properties `((upstream-name . "pqrBayes")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-glmnet))
    (home-page "https://github.com/cenwu/pqrBayes")
    (synopsis "Bayesian Penalized Quantile Regression")
    (description
     "The quantile varying coefficient model is robust to data heterogeneity, outliers
and heavy-tailed distributions in the response variable due to the check loss
function in quantile regression.  In addition, it can flexibly model the dynamic
pattern of regression coefficients through nonparametric varying coefficient
functions.  Although high dimensional quantile varying coefficient model has
been examined extensively in the frequentist framework, the corresponding
Bayesian variable selection methods have rarely been developed.  In this
package, we have implemented the Gibbs samplers of the penalized Bayesian
quantile varying coefficient model with the spike-and-slab priors [Zhou et
al.(2023)]<doi:10.1016/j.csda.2023.107808>.  The Markov Chain Monte Carlo (MCMC)
algorithms of the proposed and alternative models can be efficiently performed
by using the package.")
    (license license:gpl2)))

(define-public r-pqlseq
  (package
    (name "r-pqlseq")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PQLseq" version))
       (sha256
        (base32 "0lyynzvv7n1igzfrw52021bnxprqcm2hjxmlrsxkx4898ca46g60"))))
    (properties `((upstream-name . "PQLseq")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-matrix r-foreach
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=PQLseq")
    (synopsis
     "Efficient Mixed Model Analysis of Count Data in Large-Scale Genomic Sequencing Studies")
    (description
     "An efficient tool designed for differential analysis of large-scale RNA
sequencing (RNAseq) data and Bisulfite sequencing (BSseq) data in the presence
of individual relatedness and population structure.  PQLseq first fits a
Generalized Linear Mixed Model (GLMM) with adjusted covariates, predictor of
interest and random effects to account for population structure and individual
relatedness, and then performs Wald tests for each gene in RNAseq or site in
BSseq.")
    (license license:gpl2+)))

(define-public r-pql
  (package
    (name "r-pql")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pql" version))
       (sha256
        (base32 "10gify86gywwahxj5hzwqnbx22a42jkmry2jkz7ywfsgznzldffw"))))
    (properties `((upstream-name . "pql")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma))
    (home-page "https://cran.r-project.org/package=pql")
    (synopsis
     "Partitioned Quasi-Likelihood for Distributed Statistical Inference")
    (description
     "In the big data setting, working data sets are often distributed on multiple
machines.  However, classical statistical methods are often developed to solve
the problems of single estimation or inference.  We employ a novel parallel
quasi-likelihood method in generalized linear models, to make the variances
between different sub-estimators relatively similar.  Estimates are obtained
from projection subsets of data and later combined by suitably-chosen unknown
weights.  The philosophy of the package is described in Guo G. (2020)
<doi:10.1007/s00180-020-00974-4>.")
    (license license:expat)))

(define-public r-pqantimalarials
  (package
    (name "r-pqantimalarials")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pqantimalarials" version))
       (sha256
        (base32 "0azxkf1rvk9cyzr4gbp4y2vcxrxw3d4f002d5gjkvv1f4kx8faw1"))))
    (properties `((upstream-name . "pqantimalarials")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-reshape2 r-rcolorbrewer r-plyr))
    (home-page "https://cran.r-project.org/package=pqantimalarials")
    (synopsis
     "web tool for estimating under-five deaths caused by poor-quality antimalarials in sub-Saharan Africa")
    (description
     "This package allows users to calculate the number of under-five child deaths
caused by consumption of poor quality antimalarials across 39 sub-Saharan
nations.  The package supports one function, that starts an interactive web tool
created using the shiny R package.  The web tool runs locally on the user's
machine.  The web tool allows users to set input parameters (prevalence of poor
quality antimalarials, case fatality rate of children who take poor quality
antimalarials, and sample size) which are then used to perform an uncertainty
analysis following the Latin hypercube sampling scheme.  Users can download the
output figures as PDFs, and the output data as CSVs.  Users can also download
their input parameters for reference.  This package was designed to accompany
the analysis presented in: J. Patrick Renschler, Kelsey Walters, Paul Newton,
Ramanan Laxminarayan \"Estimated under-five deaths associated with poor-quality
antimalarials in sub-Saharan Africa\", 2014.  Paper submitted.")
    (license license:gpl3)))

(define-public r-pptreeviz
  (package
    (name "r-pptreeviz")
    (version "2.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PPtreeViz" version))
       (sha256
        (base32 "008hlgi56p9wydpsb2f1dxi4slml7p417c34lw00hivbl53d6x97"))))
    (properties `((upstream-name . "PPtreeViz")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-partykit r-gridextra
                             r-ggplot2))
    (home-page "https://cran.r-project.org/package=PPtreeViz")
    (synopsis "Projection Pursuit Classification Tree Visualization")
    (description
     "This package provides tools for exploring projection pursuit classification tree
using various projection pursuit indexes.")
    (license license:gpl2+)))

(define-public r-pptreeregviz
  (package
    (name "r-pptreeregviz")
    (version "2.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PPtreeregViz" version))
       (sha256
        (base32 "0yg67fg4rjvpwp1xvznsbv635wmf6jvhp38irffccx915qvj7r51"))))
    (properties `((upstream-name . "PPtreeregViz")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-shapr
                             r-reshape2
                             r-rcpparmadillo
                             r-rcpp
                             r-pptreeviz
                             r-magrittr
                             r-ggplot2
                             r-dplyr
                             r-data-table
                             r-dalex))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sunsmiling/PPtreeregViz")
    (synopsis "Projection Pursuit Regression Tree Visualization")
    (description
     "It was developed as a tool for exploring PPTreereg (Projection Pursuit TREE of
REGression).  It uses various projection pursuit indexes and XAI
(@code{eXplainable} Artificial Intelligence) methods to help understand the
model by finding connections between the input variables and prediction values
of the model.  The @code{KernelSHAP} (Aas, Jullum and LÃ¸land (2019)
<@code{arXiv:1903.10464>}) algorithm was modified to fit âPPTreeregâ, and
some codes were modified from the shapr package (Sellereite, Nikolai, and Martin
Jullum (2020) <doi:10.21105/joss.02027>).  The implemented methods help to
explore the model at the single instance level as well as at the whole dataset
level.  Users can compare with other machine learning models by applying it to
the DALEX package of R'.")
    (license license:gpl3)))

(define-public r-pptcirc
  (package
    (name "r-pptcirc")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PPTcirc" version))
       (sha256
        (base32 "07py7zdl7nhc1a425mz7cmgl25k0k9fdid754pnkffn5n5xkbca1"))))
    (properties `((upstream-name . "PPTcirc")))
    (build-system r-build-system)
    (propagated-inputs (list r-progress r-circular))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Karlampm/PPTcirc")
    (synopsis "Projected Polya Tree for Circular Data")
    (description
     "This package provides functionality for the prior and posterior projected Polya
tree for the analysis of circular data (Nieto-Barajas and Nunez-Antonio (2019)
<@code{arXiv:1902.06020>}).")
    (license license:gpl3)))

(define-public r-ppsr
  (package
    (name "r-ppsr")
    (version "0.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ppsr" version))
       (sha256
        (base32 "0k8y8vk8y0nnp1w26h7q9xyfbdx2q61ijnwh202j76wq6znr8yva"))))
    (properties `((upstream-name . "ppsr")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr r-rpart r-parsnip r-gridextra r-ggplot2))
    (home-page "https://cran.r-project.org/package=ppsr")
    (synopsis "Predictive Power Score")
    (description
     "The Predictive Power Score (PPS) is an asymmetric, data-type-agnostic score that
can detect linear or non-linear relationships between two variables.  The score
ranges from 0 (no predictive power) to 1 (perfect predictive power).  PPS can be
useful for data exploration purposes, in the same way correlation analysis is.
For more information on PPS, see <https://github.com/paulvanderlaken/ppsr>.")
    (license license:gpl3+)))

(define-public r-ppsfs
  (package
    (name "r-ppsfs")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PPSFS" version))
       (sha256
        (base32 "180brwqcs2qjh01a1qwdy2a8g19scn4mxzc3s6pw8gyl2r3fzxi7"))))
    (properties `((upstream-name . "PPSFS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-brglm2))
    (home-page "https://github.com/paradoxical-rhapsody/PPSFS")
    (synopsis
     "Partial Profile Score Feature Selection in High-Dimensional Generalized Linear Interaction Models")
    (description
     "This is an implementation of the partial profile score feature selection (PPSFS)
approach to generalized linear (interaction) models.  The PPSFS is highly
scalable even for ultra-high-dimensional feature space.  See the paper by Xu,
Luo and Chen (2021, <doi:10.4310/21-SII706>).")
    (license license:gpl3)))

(define-public r-ppseq
  (package
    (name "r-ppseq")
    (version "0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ppseq" version))
       (sha256
        (base32 "1bprhx8cqn8bsr5ypg2i9mp9pwhk679axvw68f0398ql6yc6bk3h"))))
    (properties `((upstream-name . "ppseq")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-purrr
                             r-plotly
                             r-patchwork
                             r-ggplot2
                             r-furrr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/zabore/ppseq")
    (synopsis
     "Design Clinical Trials using Sequential Predictive Probability Monitoring")
    (description
     "This package provides functions are available to calibrate designs over a range
of posterior and predictive thresholds, to plot the various design options, and
to obtain the operating characteristics of optimal accuracy and optimal
efficiency designs.")
    (license license:expat)))

(define-public r-ppsbm
  (package
    (name "r-ppsbm")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ppsbm" version))
       (sha256
        (base32 "0y4whxv14jqx7mqxj7n427vpbxb5rkwj8xvx0mj4knnwy9v437z4"))))
    (properties `((upstream-name . "ppsbm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rfast r-gtools r-clue))
    (home-page "https://cran.r-project.org")
    (synopsis "Clustering in Longitudinal Networks")
    (description
     "Stochastic block model used for dynamic graphs represented by Poisson processes.
 To model recurrent interaction events in continuous time, an extension of the
stochastic block model is proposed where every individual belongs to a latent
group and interactions between two individuals follow a conditional
inhomogeneous Poisson process with intensity driven by the individualsâ latent
groups.  The model is shown to be identifiable and its estimation is based on a
semiparametric variational expectation-maximization algorithm.  Two versions of
the method are developed, using either a nonparametric histogram approach (with
an adaptive choice of the partition size) or kernel intensity estimators.  The
number of latent groups can be selected by an integrated classification
likelihood criterion.  Y. Baraud and L. BirgÃ© (2009).
<doi:10.1007/s00440-007-0126-6>.  C. Biernacki, G. Celeux and G. Govaert (2000).
<doi:10.1109/34.865189>.  M. Corneli, P. Latouche and F. Rossi (2016).
<doi:10.1016/j.neucom.2016.02.031>.  J.-J. Daudin, F. Picard and S. Robin
(2008). <doi:10.1007/s11222-007-9046-7>.  A. P. Dempster, N. M. Laird and D. B.
Rubin (1977). <http://www.jstor.org/stable/2984875>.  G. GrÃ©goire (1993).
<http://www.jstor.org/stable/4616289>.  L. Hubert and P. Arabie (1985).
<doi:10.1007/BF01908075>.  M. Jordan, Z. Ghahramani, T. Jaakkola and L. Saul
(1999). <doi:10.1023/A:1007665907178>.  C. Matias, T. Rebafka and F. Villers
(2018). <doi:10.1093/biomet/asy016>.  C. Matias and S. Robin (2014).
<doi:10.1051/proc/201447004>.  H. Ramlau-Hansen (1983).
<doi:10.1214/aos/1176346152>.  P. Reynaud-Bouret (2006).
<doi:10.3150/bj/1155735930>.")
    (license license:gpl2+)))

(define-public r-pps
  (package
    (name "r-pps")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pps" version))
       (sha256
        (base32 "0k06rkzdx07qcndqym44q6xyawdd253i3kbyqlmlavadsizp657g"))))
    (properties `((upstream-name . "pps")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pps")
    (synopsis "PPS Sampling")
    (description
     "This package provides functions to select samples using PPS (probability
proportional to size) sampling.  The package also includes a function for
stratified simple random sampling, a function to compute joint inclusion
probabilities for Sampford's method of PPS sampling, and a few utility
functions.  The user's guide pps-ug.pdf is included in the .../pps/doc
directory.  The methods are described in standard survey sampling theory books
such as Cochran's \"Sampling Techniques\"; see the user's guide for references.")
    (license license:gpl2+)))

(define-public r-pprl
  (package
    (name "r-pprl")
    (version "0.3.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PPRL" version))
       (sha256
        (base32 "1m49n7kl9675hrlh6vqq69sp7bdiv4fwlncqpp58kz67jk1ypnf0"))))
    (properties `((upstream-name . "PPRL")))
    (build-system r-build-system)
    (propagated-inputs (list r-settings r-rcpp))
    (home-page "https://cran.r-project.org/package=PPRL")
    (synopsis "Privacy Preserving Record Linkage")
    (description
     "This package provides a toolbox for deterministic, probabilistic and
privacy-preserving record linkage techniques.  Combines the functionality of the
Merge @code{ToolBox} (<https://www.record-linkage.de>) with current
privacy-preserving techniques.")
    (license license:gpl3)))

(define-public r-pprep
  (package
    (name "r-pprep")
    (version "0.42.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ppRep" version))
       (sha256
        (base32 "1s4izrqw2rmipdr385nivg8av711q3q1ri59qnqw774iwwni8cwb"))))
    (properties `((upstream-name . "ppRep")))
    (build-system r-build-system)
    (propagated-inputs (list r-hypergeo))
    (home-page "https://github.com/SamCH93/ppRep")
    (synopsis "Analysis of Replication Studies using Power Priors")
    (description
     "This package provides functionality for Bayesian analysis of replication studies
using power prior approaches (Pawel et al., 2023)
<doi:10.1007/s11749-023-00888-5>.")
    (license license:gpl3)))

(define-public r-pprank
  (package
    (name "r-pprank")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ppRank" version))
       (sha256
        (base32 "0qjdxzacg9c118w8fq8sjvkfxyahhbdqfd3ab7qdlfrxkilzc29b"))))
    (properties `((upstream-name . "ppRank")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=ppRank")
    (synopsis "Classification of Algorithms")
    (description
     "This package implements the Bi-objective Lexicographical Classification method
and Performance Assessment Ratio at 10% metric for algorithm classification.
Constructs matrices representing algorithm performance under multiple criteria,
facilitating decision-making in algorithm selection and evaluation.  Analyzes
and compares algorithm performance based on various metrics to identify the most
suitable algorithms for specific tasks.  This package includes methods for
algorithm classification and evaluation, with examples provided in the
documentation.  Carvalho (2019) presents a statistical evaluation of algorithmic
computational experimentation with infeasible solutions
<doi:10.48550/@code{arXiv.1902.00101>}.  Moreira and Carvalho (2023) analyze
power in preprocessing methodologies for datasets with missing values
<doi:10.1080/03610918.2023.2234683>.")
    (license license:gpl3)))

(define-public r-ppqplan
  (package
    (name "r-ppqplan")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PPQplan" version))
       (sha256
        (base32 "072r7myqsnpyla37q6bj9glwvbygb9g8xkl60q055yzxmfh4rsbr"))))
    (properties `((upstream-name . "PPQplan")))
    (build-system r-build-system)
    (propagated-inputs (list r-plotly r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://allenzhuaz.github.io/PPQplan/")
    (synopsis
     "Process Performance Qualification (PPQ) Plans in Chemistry, Manufacturing and Controls (CMC) Statistical Analysis")
    (description
     "Assessment for statistically-based PPQ sampling plan, including calculating the
passing probability, optimizing the baseline and high performance cutoff points,
visualizing the PPQ plan and power dynamically.  The analytical idea is based on
the simulation methods from the textbook Burdick, R. K., @code{LeBlond}, D. J.,
Pfahler, L. B., Quiroz, J., Sidor, L., Vukovinsky, K., & Zhang, L. (2017).
Statistical Methods for CMC Applications.  In Statistical Applications for
Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical Industry (pp.
227-250).  Springer, Cham.")
    (license license:gpl3)))

(define-public r-ppmsuite
  (package
    (name "r-ppmsuite")
    (version "0.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ppmSuite" version))
       (sha256
        (base32 "1n83v59v5b68d0xw3yh6xrpy480857rkkwfxclbnwnh7cj14cybm"))))
    (properties `((upstream-name . "ppmSuite")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=ppmSuite")
    (synopsis
     "Collection of Models that Employ Product Partition Distributions as a Prior on Partitions")
    (description
     "This package provides a suite of functions that fit models that use PPM type
priors for partitions.  Models include hierarchical Gaussian and probit ordinal
models with a (covariate dependent) PPM. If a covariate dependent product
partition model is selected, then all the options detailed in Page, G.L.;
Quintana, F.A. (2018) <doi:10.1007/s11222-017-9777-z> are available.  If
covariate values are missing, then the approach detailed in Page, G.L.;
Quintana, F.A.; Mueller, P (2020) <doi:10.1080/10618600.2021.1999824> is
employed.  Also included in the package is a function that fits a Gaussian
likelihood spatial product partition model that is detailed in Page, G.L.;
Quintana, F.A. (2016) <doi:10.1214/15-BA971>, and multivariate PPM change point
models that are detailed in Quinlan, J.J.; Page, G.L.; Castro, L.M. (2023)
<doi:10.1214/22-BA1344>.  In addition, a function that fits a univariate or
bivariate functional data model that employs a PPM or a PPMx to cluster curves
based on B-spline coefficients is provided.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-ppmr
  (package
    (name "r-ppmr")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PPMR" version))
       (sha256
        (base32 "07fky5xc1dmmn23wwdjm46c86i7q20dv5f2147828v52hhky873g"))))
    (properties `((upstream-name . "PPMR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=PPMR")
    (synopsis "Probabilistic Two Sample Mendelian Randomization")
    (description
     "Efficient statistical inference of two-sample MR (Mendelian Randomization)
analysis.  It can account for the correlated instruments and the horizontal
pleiotropy, and can provide the accurate estimates of both causal effect and
horizontal pleiotropy effect as well as the two corresponding p-values.  There
are two main functions in the PPMR package.  One is PMR_individual() for
individual level data, the other is PMR_summary() for summary data.")
    (license license:gpl3)))

(define-public r-ppmlasso
  (package
    (name "r-ppmlasso")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ppmlasso" version))
       (sha256
        (base32 "082f5ij43iyfw1i864va4cdklfnv5zs4i21f1kcai2dlcqs5kc0v"))))
    (properties `((upstream-name . "ppmlasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-spatstat-model
                             r-spatstat-geom
                             r-spatstat-explore
                             r-spatstat
                             r-plyr
                             r-lattice
                             r-data-table))
    (home-page "https://cran.r-project.org/package=ppmlasso")
    (synopsis "Point Process Models with LASSO-Type Penalties")
    (description
     "Toolkit for fitting point process models with sequences of LASSO penalties
(\"regularisation paths\"), as described in Renner, I.W. and Warton, D.I. (2013)
<doi:10.1111/j.1541-0420.2012.01824.x>.  Regularisation paths of Poisson point
process models or area-interaction models can be fitted with LASSO, adaptive
LASSO or elastic net penalties.  A number of criteria are available to judge the
bias-variance tradeoff.")
    (license license:gpl3)))

(define-public r-ppmiss
  (package
    (name "r-ppmiss")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PPMiss" version))
       (sha256
        (base32 "06vj4r6s2lnz74l49762chxkixxs63v88w5wknkmhdzv6795c4f4"))))
    (properties `((upstream-name . "PPMiss")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-pracma r-copula))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=PPMiss")
    (synopsis
     "Copula-Based Estimator for Long-Range Dependent Processes under Missing Data")
    (description
     "This package implements the copula-based estimator for univariate long-range
dependent processes, introduced in Pumi et al. (2023)
<doi:10.1007/s00362-023-01418-z>.  Notably, this estimator is capable of
handling missing data and has been shown to perform exceptionally well, even
when up to 70% of data is missing (as reported in <@code{arXiv:2303.04754>}) and
has been found to outperform several other commonly applied estimators.")
    (license license:gpl3+)))

(define-public r-ppmhr
  (package
    (name "r-ppmhr")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ppmHR" version))
       (sha256
        (base32 "1fc9rrk3ir3dq5d518ih67dvknhz1ln6668wy2mzsdndaysgm12g"))))
    (properties `((upstream-name . "ppmHR")))
    (build-system r-build-system)
    (propagated-inputs (list r-nleqslv))
    (home-page "https://cran.r-project.org/package=ppmHR")
    (synopsis
     "Privacy-Protecting Hazard Ratio Estimation in Distributed Data Networks")
    (description
     "An implementation of the one-step privacy-protecting method for estimating the
overall and site-specific hazard ratios using inverse probability weighted Cox
models in distributed data network studies, as proposed by Shu, Yoshida,
Fireman, and Toh (2019) <doi: 10.1177/0962280219869742>.  This method only
requires sharing of summary-level riskset tables instead of individual-level
data.  Both the conventional inverse probability weights and the stabilized
weights are implemented.")
    (license license:gpl2+)))

(define-public r-ppmf
  (package
    (name "r-ppmf")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ppmf" version))
       (sha256
        (base32 "0xphk27frlcr87jwjbgwf77c8bhv0nswmffj9ny2vd1b176bi9xn"))))
    (properties `((upstream-name . "ppmf")))
    (build-system r-build-system)
    (propagated-inputs (list r-zip
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-readr
                             r-magrittr
                             r-dplyr
                             r-censable))
    (home-page "https://github.com/christopherkenny/ppmf/")
    (synopsis "Read Census Privacy Protected Microdata Files")
    (description
     "This package implements data processing described in
<doi:10.1126/sciadv.abk3283> to align modern differentially private data with
formatting of older US Census data releases.  The primary goal is to read in
Census Privacy Protected Microdata Files data in a reproducible way.  This
includes tools for aggregating to relevant levels of geography by creating
geographic identifiers which match the US Census Bureau's numbering.
Additionally, there are tools for grouping race numeric identifiers into
categories, consistent with OMB (Office of Management and Budget)
classifications.  Functions exist for downloading and linking to existing
sources of privacy protected microdata.")
    (license license:expat)))

(define-public r-pplasso
  (package
    (name "r-pplasso")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PPLasso" version))
       (sha256
        (base32 "11xqpq7s8j1vrsf0x31pvk861wz4d3yg7a4gcwv43s3w5dxbljls"))))
    (properties `((upstream-name . "PPLasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-glmnet r-ggplot2 r-genlasso r-cvcovest))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PPLasso")
    (synopsis "Prognostic Predictive Lasso for Biomarker Selection")
    (description
     "We provide new tools for the identification of prognostic and predictive
biomarkers.  For further details we refer the reader to the paper: Zhu et al.
Identification of prognostic and predictive biomarkers in high-dimensional data
with PPLasso.  BMC Bioinformatics.  2023 Jan 23;24(1):25.")
    (license license:gpl2)))

(define-public r-ppitables
  (package
    (name "r-ppitables")
    (version "0.5.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ppitables" version))
       (sha256
        (base32 "1wrscfgdbhnb7z0n44yw3yhw5dmr5jd7g1m69nkx61n6dfzj7mi2"))))
    (properties `((upstream-name . "ppitables")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-tibble))
    (home-page "https://github.com/katilingban/ppitables")
    (synopsis
     "Lookup Tables to Generate Poverty Likelihoods and Rates using the Poverty Probability Index (PPI)")
    (description
     "The Poverty Probability Index (PPI) is a poverty measurement tool for
organizations and businesses with a mission to serve the poor.  The PPI is
statistically-sound, yet simple to use: the answers to 10 questions about a
householdâs characteristics and asset ownership are scored to compute the
likelihood that the household is living below the poverty line â or above by
only a narrow margin.  This package contains country-specific lookup data tables
used as reference to determine the poverty likelihood of a household based on
their score from the country-specific PPI questionnaire.  These lookup tables
have been extracted from documentation of the PPI found at
<https://www.povertyindex.org> and managed by Innovations for Poverty Action
<https://poverty-action.org/>.")
    (license license:expat)))

(define-public r-ppgmmga
  (package
    (name "r-ppgmmga")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ppgmmga" version))
       (sha256
        (base32 "0ms6pqhdvws6dp27mgvwdm039h6z3psgbdmy2k6wq1wqr5jbsiz1"))))
    (properties `((upstream-name . "ppgmmga")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-mclust
                             r-ggplot2
                             r-ga
                             r-crayon
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/luca-scr/ppgmmga")
    (synopsis
     "Projection Pursuit Based on Gaussian Mixtures and Evolutionary Algorithms")
    (description
     "Projection Pursuit (PP) algorithm for dimension reduction based on Gaussian
Mixture Models (GMMs) for density estimation using Genetic Algorithms (GAs) to
maximise an approximated negentropy index.  For more details see Scrucca and
Serafini (2019) <doi:10.1080/10618600.2019.1598871>.")
    (license license:gpl2+)))

(define-public r-ppgm
  (package
    (name "r-ppgm")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ppgm" version))
       (sha256
        (base32 "0fvir7fw1qzx1z0kjaaqsq13ljk5l0i4rdvpl75j6nnpd59yhyyh"))))
    (properties `((upstream-name . "ppgm")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringi
                             r-sp
                             r-sf
                             r-phytools
                             r-phangorn
                             r-geiger
                             r-fields
                             r-ape
                             r-animation))
    (home-page "https://cran.r-project.org/package=ppgm")
    (synopsis
     "PaleoPhyloGeographic Modeling of Climate Niches and Species Distributions")
    (description
     "Reconstruction of paleoclimate niches using phylogenetic comparative methods and
projection reconstructed niches onto paleoclimate maps.  The user can specify
various models of trait evolution or estimate the best fit model, include
fossils, use one or multiple phylogenies for inference, and make animations of
shifting suitable habitat through time.  This model was first used in Lawing and
Polly (2011), and further implemented in Lawing et al (2016) and Rivera et al
(2020).  Lawing and Polly (2011) <doi:10.1371/journal.pone.0028554> \"Pleistocene
climate, phylogeny and climate envelope models: An integrative approach to
better understand species response to climate change\" Lawing et al (2016)
<doi:10.1086/687202> \"Including fossils in phylogenetic climate reconstructions:
A deep time perspective on the climatic niche evolution and diversification of
spiny lizards (Sceloporus)\" Rivera et al (2020) <doi:10.1111/jbi.13915>
\"Reconstructing historical shifts in suitable habitat of Sceloporus lineages
using phylogenetic niche modelling.\".")
    (license license:gpl3+)))

(define-public r-ppgam
  (package
    (name "r-ppgam")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ppgam" version))
       (sha256
        (base32 "0k5xcsjk3kb6k702dd5aq1z6m8g6w0kly0z169ymviidicgqphw3"))))
    (properties `((upstream-name . "ppgam")))
    (build-system r-build-system)
    (propagated-inputs (list r-mgcv r-mass r-evgam))
    (home-page "https://cran.r-project.org/package=ppgam")
    (synopsis "Generalised Additive Point Process Models")
    (description
     "This package provides methods for fitting point processes with parameters of
generalised additive model (GAM) form are provided.  For an introduction to
point processes see Cox, D.R & Isham, V. (Point Processes, 1980, CRC Press),
GAMs see Wood, S.N. (2017) <doi:10.1201/9781315370279>, and the fitting approach
see Wood, S.N., Pya, N. & Safken, B. (2016) <doi:10.1080/01621459.2016.1180986>.")
    (license license:gpl3)))

(define-public r-ppforest
  (package
    (name "r-ppforest")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PPforest" version))
       (sha256
        (base32 "1byn2l91nws91xgjjal610vv4yns12z861rq3iks4kv4giwgdfq1"))))
    (properties `((upstream-name . "PPforest")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rcpparmadillo
                             r-rcpp
                             r-plyr
                             r-magrittr
                             r-dplyr
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/natydasilva/PPforest")
    (synopsis "Projection Pursuit Classification Forest")
    (description
     "This package implements projection pursuit forest algorithm for supervised
classification.")
    (license license:gpl2+)))

(define-public r-ppendemic
  (package
    (name "r-ppendemic")
    (version "0.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ppendemic" version))
       (sha256
        (base32 "1glk6l78pb4076a059api2a963yfly5d47dc4pkf6bj3df927l10"))))
    (properties `((upstream-name . "ppendemic")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-readr
                             r-purrr
                             r-progress
                             r-memoise
                             r-fuzzyjoin
                             r-dplyr
                             r-assertthat))
    (home-page "https://github.com/PaulESantos/ppendemic/")
    (synopsis "Glimpse at the Diversity of Peru's Endemic Plants")
    (description
     "Introducing a novel and updated database showcasing Peru's endemic plants.  This
meticulously compiled and revised botanical collection encompasses a remarkable
assemblage of over 7,249 distinct species.  The data for this resource was
sourced from the work of Govaerts, R., Nic Lughadha, E., Black, N. et al.,
titled The World Checklist of Vascular Plants: A continuously updated resource
for exploring global plant diversity', published in Sci Data 8, 215 (2021)
<doi:10.1038/s41597-021-00997-6>.")
    (license license:expat)))

(define-public r-ppdiag
  (package
    (name "r-ppdiag")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ppdiag" version))
       (sha256
        (base32 "1p2nbkv2jsqdwwipyp3mz9cpy9lkia2cmigbcpjsa2d2i655hvs5"))))
    (properties `((upstream-name . "ppdiag")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://owenward.github.io/ppdiag/")
    (synopsis
     "Diagnosis and Visualizations Tools for Temporal Point Processes")
    (description
     "This package provides a suite of diagnostic tools for univariate point
processes.  This includes tools for simulating and fitting both common and more
complex temporal point processes.  We also include functions to visualise these
point processes and collect existing diagnostic tools of Brown et al. (2002)
<doi:10.1162/08997660252741149> and Wu et al. (2021)
<doi:10.1002/9781119821588.ch7>, which can be used to assess the fit of a chosen
point process model.")
    (license license:expat)))

(define-public r-ppcspatial
  (package
    (name "r-ppcspatial")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ppcSpatial" version))
       (sha256
        (base32 "0dxl5vv41462vhhdvbjczcghp0rv2f2101qygn4cldfihja397y9"))))
    (properties `((upstream-name . "ppcSpatial")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmap
                             r-tidyr
                             r-shiny
                             r-scales
                             r-pakpc2017
                             r-magrittr
                             r-leaflet
                             r-htmlwidgets
                             r-htmltools
                             r-ggplot2
                             r-dplyr))
    (home-page "https://github.com/MYaseen208/ppcSpatial")
    (synopsis "Spatial Analysis of Pakistan Population Census")
    (description
     "Spatial Analysis for exploration of Pakistan Population Census 2017
(<https://www.pbs.gov.pk/content/population-census>).  It uses data from R
package @code{PakPC2017}'.")
    (license license:gpl3)))

(define-public r-ppclust
  (package
    (name "r-ppclust")
    (version "1.1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ppclust" version))
       (sha256
        (base32 "1lrha08i5cp8f6670d6207jp6ad4vnmwq0lxfrlr9vhaxbs5akgi"))))
    (properties `((upstream-name . "ppclust")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-inaparc))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://cran.r-project.org/package=ppclust")
    (synopsis "Probabilistic and Possibilistic Cluster Analysis")
    (description
     "Partitioning clustering divides the objects in a data set into non-overlapping
subsets or clusters by using the prototype-based probabilistic and possibilistic
clustering algorithms.  This package covers a set of the functions for Fuzzy
C-Means (Bezdek, 1974) <doi:10.1080/01969727308546047>, Possibilistic C-Means
(Krishnapuram & Keller, 1993) <doi:10.1109/91.227387>, Possibilistic Fuzzy
C-Means (Pal et al, 2005) <doi:10.1109/TFUZZ.2004.840099>, Possibilistic
Clustering Algorithm (Yang et al, 2006) <doi:10.1016/j.patcog.2005.07.005>,
Possibilistic C-Means with Repulsion (Wachs et al, 2006)
<doi:10.1007/3-540-31662-0_6> and the other variants of hard and soft clustering
algorithms.  The cluster prototypes and membership matrices required by these
partitioning algorithms are initialized with different initialization techniques
that are available in the package inaparc'.  As the distance metrics, not only
the Euclidean distance but also a set of the commonly used distance metrics are
available to use with some of the algorithms in the package.")
    (license license:gpl2+)))

(define-public r-ppci
  (package
    (name "r-ppci")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PPCI" version))
       (sha256
        (base32 "141811iq0g2rq7lxpz9a44kyjfylj85yqgcjq7b23xfg2wbcp8dd"))))
    (properties `((upstream-name . "PPCI")))
    (build-system r-build-system)
    (propagated-inputs (list r-rarpack))
    (home-page "https://cran.r-project.org/package=PPCI")
    (synopsis "Projection Pursuit for Cluster Identification")
    (description
     "This package implements recently developed projection pursuit algorithms for
finding optimal linear cluster separators.  The clustering algorithms use
optimal hyperplane separators based on minimum density, Pavlidis et.  al (2016)
<http://jmlr.org/papers/volume17/15-307/15-307.pdf>; minimum normalised cut,
Hofmeyr (2017) <doi:10.1109/TPAMI.2016.2609929>; and maximum variance ratio
clusterability, Hofmeyr and Pavlidis (2015) <doi:10.1109/SSCI.2015.116>.")
    (license license:gpl3)))

(define-public r-ppcdt
  (package
    (name "r-ppcdt")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PPCDT" version))
       (sha256
        (base32 "06984x73pb37vw8wjgpwd27j6h5j30p0idppmbbap0zz4vprgmpm"))))
    (properties `((upstream-name . "PPCDT")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=PPCDT")
    (synopsis "An Optimal Subset Selection for Distributed Hypothesis Testing")
    (description
     "In the era of big data, data redundancy and distributed characteristics present
novel challenges to data analysis.  This package introduces a method for
estimating optimal subsets of redundant distributed data, based on PPCDT
(Conjunction of Power and P-value in Distributed Settings).  Leveraging PPC
technology, this approach can efficiently extract valuable information from
redundant distributed data and determine the optimal subset.  Experimental
results demonstrate that this method not only enhances data quality and
utilization efficiency but also assesses its performance effectively.  The
philosophy of the package is described in Guo G. (2020)
<doi:10.1007/s00180-020-00974-4>.")
    (license license:asl2.0)))

(define-public r-ppcc
  (package
    (name "r-ppcc")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ppcc" version))
       (sha256
        (base32 "17kf97rh0b1zxh7r12rxc2qd1zlrcvlljk69k8w2jchdxiw398sb"))))
    (properties `((upstream-name . "ppcc")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=ppcc")
    (synopsis "Probability Plot Correlation Coefficient Test")
    (description
     "Calculates the Probability Plot Correlation Coefficient (PPCC) between a
continuous variable X and a specified distribution.  The corresponding composite
hypothesis test that was first introduced by Filliben (1975) <doi:
10.1080/00401706.1975.10489279> can be performed to test whether the sample X is
element of either the Normal, log-Normal, Exponential, Uniform, Cauchy,
Logistic, Generalized Logistic, Gumbel (GEVI), Weibull, Generalized Extreme
Value, Pearson III (Gamma 2), Mielke's Kappa, Rayleigh or Generalized Logistic
Distribution.  The PPCC test is performed with a fast Monte-Carlo simulation.")
    (license license:gpl3)))

(define-public r-ppca
  (package
    (name "r-ppca")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pPCA" version))
       (sha256
        (base32 "0xr8n5v50q9wsx3ki8xsyxjdmx6kgdzzpv3fbq53n4i6x0381b0f"))))
    (properties `((upstream-name . "pPCA")))
    (build-system r-build-system)
    (propagated-inputs (list r-rspectra r-rcpp r-matrix))
    (home-page "https://cran.r-project.org/package=pPCA")
    (synopsis
     "Partial Principal Component Analysis of Partitioned Large Sparse Matrices")
    (description
     "This package performs partial principal component analysis of a large sparse
matrix.  The matrix may be stored as a list of matrices to be concatenated
(implicitly) horizontally.  Useful application includes cases where the number
of total nonzero entries exceed the capacity of 32 bit integers (e.g., with
large Single Nucleotide Polymorphism data).")
    (license license:gpl3)))

(define-public r-ppbigdata
  (package
    (name "r-ppbigdata")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PPbigdata" version))
       (sha256
        (base32 "0v9p3mrxivq4ragi6039zgyxp9ksx3g3f6bksy9z08f8vyp1m1a8"))))
    (properties `((upstream-name . "PPbigdata")))
    (build-system r-build-system)
    (propagated-inputs (list r-weights
                             r-tourr
                             r-rstiefel
                             r-mclust
                             r-mass
                             r-magrittr
                             r-gtools
                             r-dplyr
                             r-datanugget))
    (home-page "https://cran.r-project.org/package=PPbigdata")
    (synopsis "Projection Pursuit for Big Data Based on Data Nuggets")
    (description
     "Perform 1-dim/2-dim projection pursuit, grand tour and guided tour for big data
based on data nuggets.  Reference papers: [1] Beavers et al., (2024)
<doi:10.1080/10618600.2024.2341896>. [2] Duan, Y., Cabrera, J., & Emir, B.
(2023). \"A New Projection Pursuit Index for Big Data.\"
<doi:10.48550/@code{arXiv.2312.06465>}.")
    (license license:gpl2)))

(define-public r-pp
  (package
    (name "r-pp")
    (version "0.6.3-11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PP" version))
       (sha256
        (base32 "1z7v5jiljm534lhr92vz9z3cafwcp6hdxyrr1nhli2arxc1nin76"))))
    (properties `((upstream-name . "PP")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jansteinfeld/PP")
    (synopsis "Person Parameter Estimation")
    (description
     "The PP package includes estimation of (MLE, WLE, MAP, EAP, ROBUST) person
parameters for the 1,2,3,4-PL model and the GPCM (generalized partial credit
model).  The parameters are estimated under the assumption that the item
parameters are known and fixed.  The package is useful e.g. in the case that
items from an item pool / item bank with known item parameters are administered
to a new population of test-takers and an ability estimation for every
test-taker is needed.")
    (license license:gpl3)))

(define-public r-powriclpm
  (package
    (name "r-powriclpm")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "powRICLPM" version))
       (sha256
        (base32 "1l6gdafy5mmfdixn7wsjz1lnaqibfjy0zdsr1cjmvik93x173l9w"))))
    (properties `((upstream-name . "powRICLPM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-progressr
                             r-lifecycle
                             r-lavaan
                             r-ggplot2
                             r-future-apply
                             r-future
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://jeroendmulder.github.io/powRICLPM/")
    (synopsis "Perform Power Analysis for the RI-CLPM and STARTS Model")
    (description
     "Perform user-friendly power analyses for the random intercept cross-lagged panel
model (RI-CLPM) and the bivariate stable trait autoregressive trait state
(STARTS) model.  The strategy as proposed by Mulder (2023)
<doi:10.1080/10705511.2022.2122467> is implemented.  Extensions include the use
of parameter constraints over time, bounded estimation, generation of data with
skewness and kurtosis, and the option to setup the power analysis for Mplus.")
    (license license:expat)))

(define-public r-powerupr
  (package
    (name "r-powerupr")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PowerUpR" version))
       (sha256
        (base32 "0bnkngdqhzhsbkzp4cxhgm8rzn83q4vm3fj2gkp7z4qznp37lh3j"))))
    (properties `((upstream-name . "PowerUpR")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PowerUpR")
    (synopsis "Power Analysis Tools for Multilevel Randomized Experiments")
    (description
     "Includes tools to calculate statistical power, minimum detectable effect size
(MDES), MDES difference (MDESD), and minimum required sample size for various
multilevel randomized experiments (MRE) with continuous outcomes.  Accomodates
14 types of MRE designs to detect main treatment effect, seven types of MRE
designs to detect moderated treatment effect (2-1-1, 2-1-2, 2-2-1, 2-2-2, 3-3-1,
3-3-2, and 3-3-3 designs; <total.lev> - <trt.lev> - <mod.lev>), five types of
MRE designs to detect mediated treatment effects (2-1-1, 2-2-1, 3-1-1, 3-2-1,
and 3-3-1 designs; <trt.lev> - <med.lev> - <out.lev>), four types of partially
nested (PN) design to detect main treatment effect, and three types of PN
designs to detect mediated treatment effects (2/1, 3/1, 3/2; <trt.arm.lev> /
<ctrl.arm.lev>).  See @code{PowerUp}! Excel series at
<https://www.causalevaluation.org/>.")
    (license license:gpl3+)))

(define-public r-powertost
  (package
    (name "r-powertost")
    (version "1.5-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PowerTOST" version))
       (sha256
        (base32 "1yp862b9ibgifdwh8f5ws3qbycdp24hvsigj5ira8isdci7vvyv3"))))
    (properties `((upstream-name . "PowerTOST")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-cubature))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Detlew/PowerTOST")
    (synopsis "Power and Sample Size for (Bio)Equivalence Studies")
    (description
     "This package contains functions to calculate power and sample size for various
study designs used in bioequivalence studies.  Use known.designs() to see the
designs supported.  Power and sample size can be obtained based on different
methods, amongst them prominently the TOST procedure (two one-sided t-tests).
See README and NEWS for further information.")
    (license license:gpl2+)))

(define-public r-powersurvepi
  (package
    (name "r-powersurvepi")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "powerSurvEpi" version))
       (sha256
        (base32 "1p1fw4jq4rxc273hmycxf4bkqm6zmfw0jdy2s65jy10q37r53ial"))))
    (properties `((upstream-name . "powerSurvEpi")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-pracma))
    (home-page "https://cran.r-project.org/package=powerSurvEpi")
    (synopsis
     "Power and Sample Size Calculation for Survival Analysis of Epidemiological Studies")
    (description
     "This package provides functions to calculate power and sample size for testing
main effect or interaction effect in the survival analysis of epidemiological
studies (non-randomized studies), taking into account the correlation between
the covariate of the interest and other covariates.  Some calculations also take
into account the competing risks and stratified analysis.  This package also
includes a set of functions to calculate power and sample size for testing main
effect in the survival analysis of randomized clinical trials and conditional
logistic regression for nested case-control study.")
    (license license:gpl2+)))

(define-public r-powersdi
  (package
    (name "r-powersdi")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PowerSDI" version))
       (sha256
        (base32 "01r91awxmjm69sr405s11gp16ajsmqc6jmd2ilhmdy3vcw1vc70p"))))
    (properties `((upstream-name . "PowerSDI")))
    (build-system r-build-system)
    (propagated-inputs (list r-nasapower r-lubridate r-lmom))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/gabrielblain/PowerSDI")
    (synopsis "Calculate Standardised Drought Indices Using NASA POWER Data")
    (description
     "This package provides a set of functions designed to calculate the standardised
precipitation and standardised precipitation evapotranspiration indices using
NASA POWER data as described in Blain et al. (2023) <doi:10.2139/ssrn.4442843>.
These indices are calculated using a reference data source.  The functions
verify if the indices estimates meet the assumption of normality and how well
NASA POWER estimates represent real-world data.  Indices are calculated in a
routine mode.  Potential evapotranspiration amounts and the difference between
rainfall and potential evapotranspiration are also calculated.  The functions
adopt a basic time scale that splits each month into four periods.  Days 1 to 7,
days 8 to 14, days 15 to 21, and days 22 to 28, 29, 30, or 31, where TS=4
corresponds to a 1-month length moving window (calculated 4 times per month) and
TS=48 corresponds to a 12-month length moving window (calculated 4 times per
month).")
    (license license:expat)))

(define-public r-powerpls
  (package
    (name "r-powerpls")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "powerPLS" version))
       (sha256
        (base32 "1g36snvgm38g0x22b1ldryx331zir2yw5p2pvmk8i38n0fy6hqlc"))))
    (properties `((upstream-name . "powerPLS")))
    (build-system r-build-system)
    (propagated-inputs (list r-simukde
                             r-nipals
                             r-mvtnorm
                             r-mass
                             r-ks
                             r-foreach
                             r-fksum
                             r-compositions))
    (home-page "https://github.com/angeella/powerPLS")
    (synopsis "Power Analysis for PLS Classification")
    (description
     "It estimates power and sample size for Partial Least Squares-based methods
described in Andreella, et al., (2024), <doi:10.48550/@code{arXiv.2403.10289>}.")
    (license license:gpl2+)))

(define-public r-powerpkg
  (package
    (name "r-powerpkg")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "powerpkg" version))
       (sha256
        (base32 "18cq3zsdvzmqjp0niq0libjk4riarmiar3h6lifq54spqgrvvxh2"))))
    (properties `((upstream-name . "powerpkg")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=powerpkg")
    (synopsis "Power Analyses for the Affected Sib Pair and the TDT Design")
    (description
     "There are two main functions: (1) To estimate the power of testing for linkage
using an affected sib pair design, as a function of the recurrence risk ratios.
We will use analytical power formulae as implemented in R. These are based on a
Mathematica notebook created by Martin Farrall. (2) To examine how the power of
the transmission disequilibrium test (TDT) depends on the disease allele
frequency, the marker allele frequency, the strength of the linkage
disequilibrium, and the magnitude of the genetic effect.  We will use an R
program that implements the power formulae of Abel and Muller-Myhsok (1998).
These formulae allow one to quickly compute power of the TDT approach under a
variety of different conditions.  This R program was modeled on Martin Farrall's
Mathematica notebook.")
    (license license:gpl2+)))

(define-public r-powernormal
  (package
    (name "r-powernormal")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PowerNormal" version))
       (sha256
        (base32 "1kryqcjvgwk0l4z3gqxdiz47mha3jw5583jlb9z7ml6v7cam06sm"))))
    (properties `((upstream-name . "PowerNormal")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PowerNormal")
    (synopsis "Power Normal Distribution")
    (description
     "Miscellaneous functions for a descriptive analysis and initial Bayesian and
classical inference for the power parameter of the the Power Normal (PN)
distribution.  This miscellaneous will be extend for more distributions into the
power family and the three-parameter model.")
    (license license:gpl2)))

(define-public r-powernlsem
  (package
    (name "r-powernlsem")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "powerNLSEM" version))
       (sha256
        (base32 "1iy5jkrn5lr8h64rv33jyv5qdn4jymwv80xp0ii3hirscv3pssbd"))))
    (properties `((upstream-name . "powerNLSEM")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rlang
                             r-pbapply
                             r-numderiv
                             r-mvtnorm
                             r-lavaan
                             r-ggplot2
                             r-crayon))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jpirmer/powerNLSEM")
    (synopsis "Simulation-Based Power Estimation (MSPE) for Nonlinear SEM")
    (description
     "Model-implied simulation-based power estimation (MSPE) for nonlinear (and
linear) SEM, path analysis and regression analysis.  A theoretical framework is
used to approximate the relation between power and sample size for given type I
error rates and effect sizes.  The package offers an adaptive search algorithm
to find the optimal N for given effect sizes and type I error rates.  Plots can
be used to visualize the power relation to N for different parameters of
interest (POI).  Theoretical justifications are given in Irmer et al. (2024a)
<doi:10.31219/osf.io/pe5bj> and detailed description are given in Irmer et al.
(2024b) <doi:10.3758/s13428-024-02476-3>.")
    (license license:gpl3)))

(define-public r-powermediation
  (package
    (name "r-powermediation")
    (version "0.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "powerMediation" version))
       (sha256
        (base32 "08zszskhd21pbjamch5q7v7vx2p78c1d53zrp31hmpxk1ymx2xf3"))))
    (properties `((upstream-name . "powerMediation")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=powerMediation")
    (synopsis "Power/Sample Size Calculation for Mediation Analysis")
    (description
     "This package provides functions to calculate power and sample size for testing
(1) mediation effects; (2) the slope in a simple linear regression; (3) odds
ratio in a simple logistic regression; (4) mean change for longitudinal study
with 2 time points; (5) interaction effect in 2-way ANOVA; and (6) the slope in
a simple Poisson regression.")
    (license license:gpl2+)))

(define-public r-powerly
  (package
    (name "r-powerly")
    (version "1.8.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "powerly" version))
       (sha256
        (base32 "0vc2wpx2b4i9vx9jvvlywq5w4dx22bn4r9fcwz123h1m09252ng8"))))
    (properties `((upstream-name . "powerly")))
    (build-system r-build-system)
    (propagated-inputs (list r-splines2
                             r-rlang
                             r-r6
                             r-quadprog
                             r-qgraph
                             r-progress
                             r-patchwork
                             r-osqp
                             r-mvtnorm
                             r-ggplot2
                             r-bootnet))
    (home-page "https://powerly.dev")
    (synopsis "Sample Size Analysis for Psychological Networks and More")
    (description
     "An implementation of the sample size computation method for network models
proposed by Constantin et al. (2021) <doi:10.31234/osf.io/j5v7u>.  The
implementation takes the form of a three-step recursive algorithm designed to
find an optimal sample size given a model specification and a performance
measure of interest.  It starts with a Monte Carlo simulation step for computing
the performance measure and a statistic at various sample sizes selected from an
initial sample size range.  It continues with a monotone curve-fitting step for
interpolating the statistic across the entire sample size range.  The final step
employs stratified bootstrapping to quantify the uncertainty around the fitted
curve.")
    (license license:expat)))

(define-public r-powerlate
  (package
    (name "r-powerlate")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "powerLATE" version))
       (sha256
        (base32 "0bxwzskqc4f4f5nvzxd5dm2pd5524x68zdzhiqdp1fsfwb555rkd"))))
    (properties `((upstream-name . "powerLATE")))
    (build-system r-build-system)
    (home-page "https://github.com/kbansak/powerLATE_tutorial")
    (synopsis "Generalized Power Analysis for LATE")
    (description
     "An implementation of the generalized power analysis for the local average
treatment effect (LATE), proposed by Bansak (2020) <doi:10.1214/19-STS732>.
Power analysis is in the context of estimating the LATE (also known as the
complier average causal effect, or CACE), with calculations based on a test of
the null hypothesis that the LATE equals 0 with a two-sided alternative.  The
method uses standardized effect sizes to place a conservative bound on the power
under minimal assumptions.  Package allows users to recover power, sample size
requirements, or minimum detectable effect sizes.  Package also allows users to
work with absolute effects rather than effect sizes, to specify an additional
assumption to narrow the bounds, and to incorporate covariate adjustment.")
    (license license:gpl2+)))

(define-public r-powerjoin
  (package
    (name "r-powerjoin")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "powerjoin" version))
       (sha256
        (base32 "19b7y7aqf5159x2fi5nrx179nc673qqzkv9c8kgw85hmmjzjgnb3"))))
    (properties `((upstream-name . "powerjoin")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidyselect
                             r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-glue
                             r-dplyr
                             r-cli))
    (home-page "https://github.com/moodymudskipper/powerjoin")
    (synopsis "Extensions of 'dplyr' and 'fuzzyjoin' Join Functions")
    (description
     "We extend dplyr and fuzzyjoin join functions with features to preprocess the
data, apply various data checks, and deal with conflicting columns.")
    (license license:expat)))

(define-public r-powerindexr
  (package
    (name "r-powerindexr")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "powerindexR" version))
       (sha256
        (base32 "1q8g60awv2sq4fm0kd2dzrgkwfk6mjvglgwihqspapb1zv9ild4z"))))
    (properties `((upstream-name . "powerindexR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=powerindexR")
    (synopsis "Measuring the Power in Voting Systems")
    (description
     "This R package allows the determination of some distributions of the voters
power when passing laws in weighted voting situations.")
    (license license:gpl2)))

(define-public r-powergwasinteraction
  (package
    (name "r-powergwasinteraction")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "powerGWASinteraction" version))
       (sha256
        (base32 "1i8gfsk9qzx54yn661i4x9k7n7b6r1jd808wv1hcq7870mzyb27k"))))
    (properties `((upstream-name . "powerGWASinteraction")))
    (build-system r-build-system)
    (propagated-inputs (list r-pwr r-mvtnorm))
    (home-page "https://cran.r-project.org/package=powerGWASinteraction")
    (synopsis "Power Calculations for GxE and GxG Interactions for GWAS")
    (description
     "Analytical power calculations for @code{GxE} and @code{GxG} interactions for
case-control studies of candidate genes and genome-wide association studies
(GWAS).  This includes power calculation for four two-step screening and testing
procedures.  It can also calculate power for @code{GxE} and @code{GxG} without
any screening.")
    (license license:gpl2+)))

(define-public r-powerest
  (package
    (name "r-powerest")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PoweREST" version))
       (sha256
        (base32 "0y101jbazh2hfly08p5yfipdqyahxx668dm135ik20arqpfl01cl"))))
    (properties `((upstream-name . "PoweREST")))
    (build-system r-build-system)
    (propagated-inputs (list r-xgboost
                             r-seurat
                             r-scam
                             r-resample
                             r-rayshader
                             r-plotly
                             r-magrittr
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PoweREST")
    (synopsis
     "Bootstrap-Based Power Estimation Tool for Spatial Transcriptomics")
    (description
     "Power estimation and sample size calculation for 10X Visium Spatial
Transcriptomics data to detect differential expressed genes between two
conditions based on bootstrap resampling.  See Shui et al. (2024)
<doi:10.1101/2024.08.30.610564> for method details.")
    (license license:expat)))

(define-public r-powereqtl
  (package
    (name "r-powereqtl")
    (version "0.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "powerEQTL" version))
       (sha256
        (base32 "124mwkjf5m19znn7zdnncdc3klgqyid08kvihk0gws2y9bw0m1zs"))))
    (properties `((upstream-name . "powerEQTL")))
    (build-system r-build-system)
    (propagated-inputs (list r-nlme r-glmmadaptive))
    (home-page "https://cran.r-project.org/package=powerEQTL")
    (synopsis
     "Power and Sample Size Calculation for Bulk Tissue and Single-Cell eQTL Analysis")
    (description
     "Power and sample size calculation for bulk tissue and single-cell @code{eQTL}
analysis based on ANOVA, simple linear regression, or linear mixed effects
model.  It can also calculate power/sample size for testing the association of a
SNP to a continuous type phenotype.  Please see the reference: Dong X, Li X,
Chang T-W, Scherzer CR, Weiss ST, Qiu W. (2021)
<doi:10.1093/bioinformatics/btab385>.")
    (license license:gpl2+)))

(define-public r-powercomprisk
  (package
    (name "r-powercomprisk")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "powerCompRisk" version))
       (sha256
        (base32 "11xprjn9hzi8gxrsi7wivwgchg9h2apgxa63fp466ngjfy5ybxf4"))))
    (properties `((upstream-name . "powerCompRisk")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://cran.r-project.org/package=powerCompRisk")
    (synopsis
     "Power Analysis Tool for Joint Testing Hazards with Competing Risks Data")
    (description
     "This package provides a power analysis tool for jointly testing the cause-1
cause-specific hazard and the any-cause hazard with competing risks data.")
    (license license:gpl2)))

(define-public r-powerbydesign
  (package
    (name "r-powerbydesign")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "powerbydesign" version))
       (sha256
        (base32 "006m127rc49bshgljd5d3ii6sfx24ndn2690ixj0vnqm2dp3zjnw"))))
    (properties `((upstream-name . "powerbydesign")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-reshape2
                             r-plyr
                             r-mass
                             r-lme4
                             r-ggplot2
                             r-gdata))
    (home-page "https://cran.r-project.org/package=powerbydesign")
    (synopsis "Power Estimates for ANOVA Designs")
    (description
     "This package provides functions for bootstrapping the power of ANOVA designs
based on estimated means and standard deviations of the conditions.  Please
refer to the documentation of the boot.power.anova() function for further
details.")
    (license license:gpl3+)))

(define-public r-powerbir
  (package
    (name "r-powerbir")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "powerbiR" version))
       (sha256
        (base32 "0b7409j55m5nnp5nm9sgij635xpm39qkm3hajg3x0x8zxvfczfz6"))))
    (properties `((upstream-name . "powerbiR")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-httr r-data-table r-azureauth))
    (home-page "https://cran.r-project.org/package=powerbiR")
    (synopsis "An Interface to the 'Power BI REST APIs'")
    (description
     "Makes it easy to push data to Power BI using R and the Power BI REST APIs (see
<https://docs.microsoft.com/en-us/rest/api/power-bi/>).  A set of functions for
turning data frames into Power BI datasets and refreshing these datasets are
provided.  Administrative tasks such as monitoring refresh statuses and pulling
metadata about workspaces and users are also supported.")
    (license license:expat)))

(define-public r-powerbal
  (package
    (name "r-powerbal")
    (version "0.0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "poweRbal" version))
       (sha256
        (base32 "16n331pyc41cy59cvp9fidvy3dzi68nw64lg6862amzna72nk85b"))))
    (properties `((upstream-name . "poweRbal")))
    (build-system r-build-system)
    (propagated-inputs (list r-treebalance
                             r-scales
                             r-r-utils
                             r-phytools
                             r-diversitree
                             r-ape))
    (home-page "https://cran.r-project.org/package=poweRbal")
    (synopsis
     "Phylogenetic Tree Models and the Power of Tree Shape Statistics")
    (description
     "The first goal of this package is to provide a multitude of tree models, i.e.,
functions that generate rooted binary trees with a given number of leaves.
Second, the package allows for an easy evaluation and comparison of tree shape
statistics by estimating their power to differentiate between different tree
models.  Please note that this R package was developed alongside the manuscript
\"Tree balance in phylogenetic models\" by S. J. Kersting, K. Wicke, and M.
Fischer (2024) <doi:10.48550/@code{arXiv.2406.05185>}, which provides further
background and the respective mathematical definitions.  This project was
supported by the project @code{ArtIGROW}, which is a part of the WIR!-Alliance
@code{ArtIFARM} â Artificial Intelligence in Farming funded by the German
Federal Ministry of Education and Research (No.  03WIR4805).")
    (license license:gpl3+)))

(define-public r-power2stage
  (package
    (name "r-power2stage")
    (version "0.5-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Power2Stage" version))
       (sha256
        (base32 "1g4zrprbd5q1dn4jfq5c9ayss4p0ljhbsfr3h8h9sjbmcg62rsal"))))
    (properties `((upstream-name . "Power2Stage")))
    (build-system r-build-system)
    (propagated-inputs (list r-powertost r-mvtnorm r-cubature))
    (home-page "https://github.com/Detlew/Power2Stage")
    (synopsis
     "Power and Sample-Size Distribution of 2-Stage Bioequivalence Studies")
    (description
     "This package contains functions to obtain the operational characteristics of
bioequivalence studies in Two-Stage Designs (TSD) via simulations.")
    (license license:gpl2+)))

(define-public r-power-transform
  (package
    (name "r-power-transform")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "power.transform" version))
       (sha256
        (base32 "0jfpza0bvjgn4zh5shzi6lhcqv0z9dz0qlsad9kdvankjn1l24kr"))))
    (properties `((upstream-name . "power.transform")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-nloptr r-data-table))
    (home-page "https://github.com/oncoray/power.transform")
    (synopsis "Location and Scale Invariant Power Transformations")
    (description
     "Location- and scale-invariant Box-Cox and Yeo-Johnson power transformations
allow for transforming variables with distributions distant from 0 to normality.
 Transformers are implemented as S4 objects.  These allow for transforming new
instances to normality after optimising fitting parameters on other data.  A
test for central normality allows for rejecting transformations that fail to
produce a suitably normal distribution, independent of sample number.")
    (license (license:fsdg-compatible "EUPL"))))

(define-public r-powdr
  (package
    (name "r-powdr")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "powdR" version))
       (sha256
        (base32 "1bkx43xd46gzgiphknqyz2nyvviia0xkn4h8z4k2dwcfq92iwsbw"))))
    (properties `((upstream-name . "powdR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-shinywidgets
                             r-shiny
                             r-rxylib
                             r-reshape
                             r-plyr
                             r-plotly
                             r-nnls
                             r-ggpubr
                             r-ggplot2
                             r-factominer
                             r-factoextra
                             r-dt
                             r-baseline))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/benmbutler/powdR")
    (synopsis "Full Pattern Summation of X-Ray Powder Diffraction Data")
    (description
     "Full pattern summation of X-ray powder diffraction data as described in Chipera
and Bish (2002) <doi:10.1107/S0021889802017405> and Butler and Hillier (2021)
<doi:10.1016/j.cageo.2020.104662>.  Derives quantitative estimates of
crystalline and amorphous phase concentrations in complex mixtures.")
    (license (list license:gpl2
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-powdist
  (package
    (name "r-powdist")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "powdist" version))
       (sha256
        (base32 "1my88ag5q9hwkn2wy79jl9008gpvg0bsrnyc81gkdfi7pjh2mp1z"))))
    (properties `((upstream-name . "powdist")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmutil r-normalp r-gamlss-dist))
    (home-page "https://cran.r-project.org/package=powdist")
    (synopsis "Power and Reversal Power Distributions")
    (description
     "Density, distribution function, quantile function and random generation for the
family of power and reversal power distributions.")
    (license license:gpl3)))

(define-public r-pow-int
  (package
    (name "r-pow-int")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pow.int" version))
       (sha256
        (base32 "0rpv14ni7ikhy7rd8z04xy5mvmnagj1ji5a31l56jyldwjrkqa9v"))))
    (properties `((upstream-name . "pow.int")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pow.int")
    (synopsis "Binary Exponentiation")
    (description "Fast exponentiation when the exponent is an integer.")
    (license license:gpl3+)))

(define-public r-povmap
  (package
    (name "r-povmap")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "povmap" version))
       (sha256
        (base32 "1qr4i7ph17mzhjamssc6h4q02mw6jykl2kqc5n3i7vfp88q4frcp"))))
    (properties `((upstream-name . "povmap")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey
                             r-stringr
                             r-spdep
                             r-sf
                             r-saerobust
                             r-reshape2
                             r-readods
                             r-parallelmap
                             r-openxlsx
                             r-nlme
                             r-mumin
                             r-moments
                             r-mass
                             r-hlmdiag
                             r-gridextra
                             r-ggplot2
                             r-formula-tools
                             r-boot
                             r-bestnormalize))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/SSA-Statistical-Team-Projects/povmap")
    (synopsis "Extension to the 'emdi' Package")
    (description
     "The R package povmap supports small area estimation of means and poverty
headcount rates.  It adds several new features to the emdi package (see \"The R
Package emdi for Estimating and Mapping Regionally Disaggregated Indicators\" by
Kreutzmann et al. (2019) <doi:10.18637/jss.v091.i07>).  These include new
options for incorporating survey weights, ex-post benchmarking of estimates, two
additional transformations, several new convenient functions to assist with
reporting results, and a wrapper function to facilitate access from Stata'.")
    (license license:gpl2)))

(define-public r-pov
  (package
    (name "r-pov")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "POV" version))
       (sha256
        (base32 "0mqhi410rwpgr6x5h57wqg0f5k01vva0c280zlfpmf8php121x7j"))))
    (properties `((upstream-name . "POV")))
    (build-system r-build-system)
    (propagated-inputs (list r-formula-tools r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/PaulAntonDeen/POV-R-Package")
    (synopsis "Partition of Variation Variance Component Analysis Method")
    (description
     "An implementation of the Partition Of variation (POV) method as developed by Dr.
Thomas A Little <https://thomasalittleconsulting.com> in 1993 for the analysis
of semiconductor data for hard drive manufacturing.  POV is based on sequential
sum of squares and is an exact method that explains all observed variation.  It
quantitates both the between and within factor variation effects and can
quantitate the influence of both continuous and categorical factors.")
    (license license:gpl3)))

(define-public r-poumm
  (package
    (name "r-poumm")
    (version "2.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "POUMM" version))
       (sha256
        (base32 "0pwa97sffb571nfqnbc35dj9lr8z07wjsjnwibj9dhj2dr5b2cfp"))))
    (properties `((upstream-name . "POUMM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp
                             r-lamw
                             r-ggplot2
                             r-foreach
                             r-data-table
                             r-coda
                             r-ape
                             r-adaptmcmc))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://venelin.github.io/POUMM/index.html")
    (synopsis "The Phylogenetic Ornstein-Uhlenbeck Mixed Model")
    (description
     "The Phylogenetic Ornstein-Uhlenbeck Mixed Model (POUMM) allows to estimate the
phylogenetic heritability of continuous traits, to test hypotheses of neutral
evolution versus stabilizing selection, to quantify the strength of stabilizing
selection, to estimate measurement error and to make predictions about the
evolution of a phenotype and phenotypic variation in a population.  The package
implements combined maximum likelihood and Bayesian inference of the univariate
Phylogenetic Ornstein-Uhlenbeck Mixed Model, fast parallel likelihood
calculation, maximum likelihood inference of the genotypic values at the tips,
functions for summarizing and plotting traces and posterior samples, functions
for simulation of a univariate continuous trait evolution model along a
phylogenetic tree.  So far, the package has been used for estimating the
heritability of quantitative traits in macroevolutionary and epidemiological
studies, see e.g. Bertels et al. (2017) <doi:10.1093/molbev/msx246> and Mitov
and Stadler (2018) <doi:10.1093/molbev/msx328>.  The algorithm for parallel
POUMM likelihood calculation has been published in Mitov and Stadler (2019)
<doi:10.1111/2041-210X.13136>.")
    (license license:gpl3+)))

(define-public r-pould
  (package
    (name "r-pould")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pould" version))
       (sha256
        (base32 "0hr6j08whbc1jydz20rg0cphfiwx4pyrav49w9qs9dkcaqrpid7l"))))
    (properties `((upstream-name . "pould")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-haplo-stats r-ggplot2 r-gap
                             r-bigdawg))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pould")
    (synopsis "Phased or Unphased Linkage Disequilibrium")
    (description
     "Computes the D', Wn, and conditional asymmetric linkage disequilibrium (ALD)
measures for pairs of genetic loci.  Performs these linkage disequilibrium (LD)
calculations on phased genotype data recorded using Genotype List (GL) String or
columnar formats.  Alternatively, generates expectation-maximization (EM)
estimated haplotypes from phased data, or performs LD calculations on EM
estimated haplotypes.  Performs sign tests comparing LD values for phased and
unphased datasets, and generates heat-maps for each LD measure.  Described by
Osoegawa et al. (2019a) <doi:10.1016/j.humimm.2019.01.010>, and Osoegawa et.
al. (2019b) <doi:10.1016/j.humimm.2019.05.018>.")
    (license license:gpl3+)))

(define-public r-pottsutils
  (package
    (name "r-pottsutils")
    (version "0.3-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PottsUtils" version))
       (sha256
        (base32 "165k4sjh3kqkb8bzi68wx00yl77yfbgs70fcbpzmsmcw4g4hdpzn"))))
    (properties `((upstream-name . "PottsUtils")))
    (build-system r-build-system)
    (propagated-inputs (list r-miscf))
    (home-page "https://cran.r-project.org/package=PottsUtils")
    (synopsis "Utility Functions of the Potts Models")
    (description
     "There are three sets of functions.  The first produces basic properties of a
graph and generates samples from multinomial distributions to facilitate the
simulation functions (they maybe used for other purposes as well).  The second
provides various simulation functions for a Potts model in Potts, R. B. (1952)
<doi:10.1017/S0305004100027419>.  The third currently includes only one function
which computes the normalizing constant of a Potts model based on simulation
results.")
    (license license:gpl2)))

(define-public r-potts
  (package
    (name "r-potts")
    (version "0.5-11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "potts" version))
       (sha256
        (base32 "1sm2y2nv3hpqmcshza74yy9awg7ygp6fna6pkzvqv4cakwvvxnck"))))
    (properties `((upstream-name . "potts")))
    (build-system r-build-system)
    (home-page "http://www.stat.umn.edu/geyer/mcmc/")
    (synopsis "Markov Chain Monte Carlo for Potts Models")
    (description
     "Do Markov chain Monte Carlo (MCMC) simulation of Potts models (Potts, 1952,
<doi:10.1017/S0305004100027419>), which are the multi-color generalization of
Ising models (so, as as special case, also simulates Ising models).  Use the
Swendsen-Wang algorithm (Swendsen and Wang, 1987,
<doi:10.1103/@code{PhysRevLett.58.86>}) so MCMC is fast.  Do maximum composite
likelihood estimation of parameters (Besag, 1975, <doi:10.2307/2987782>,
Lindsay, 1988, <doi:10.1090/conm/080>).")
    (license license:gpl2+)))

(define-public r-potools
  (package
    (name "r-potools")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "potools" version))
       (sha256
        (base32 "0bfjqkl387s262m1xxq3li2s894mz8nfa3m9v8dcrf20gq40as6q"))))
    (properties `((upstream-name . "potools")))
    (build-system r-build-system)
    (inputs (list gnu-gettext))
    (propagated-inputs (list r-glue r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MichaelChirico/potools")
    (synopsis "Tools for Internationalization and Portability in R Packages")
    (description
     "Translating messages in R packages is managed using the po top-level directory
and the gettext program.  This package provides some helper functions for
building this support in R packages, e.g. common validation & I/O tasks.")
    (license license:gpl3)))

(define-public r-potions
  (package
    (name "r-potions")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "potions" version))
       (sha256
        (base32 "01dzgcapd21x88kp16pj0sqkx9r6xyfa9c6kxlna4vfpkwk99sh7"))))
    (properties `((upstream-name . "potions")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-stringi
                             r-rrapply
                             r-rlang
                             r-purrr
                             r-lobstr
                             r-jsonlite))
    (native-inputs (list r-knitr))
    (home-page "https://potions.ala.org.au")
    (synopsis "Easy Options Management")
    (description
     "Store and retrieve data from options() using syntax derived from the here
package.  potions makes it straightforward to update and retrieve options,
either in the workspace or during package development, without overwriting
global options.")
    (license (license:fsdg-compatible "MPL-2.0"))))

(define-public r-potential
  (package
    (name "r-potential")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "potential" version))
       (sha256
        (base32 "1zq82jlqxmz1cjg0wi1r7cqxq32f3dk6gzx11fnf5mfy44613vl2"))))
    (properties `((upstream-name . "potential")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-mapiso r-foreach r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/riatelab/potential")
    (synopsis "Implementation of the Potential Model")
    (description
     "This package provides functions to compute the potential model as defined by
Stewart (1941) <doi:10.1126/science.93.2404.89>.  Several options are available
to customize the model, such as the possibility to fine-tune the distance
friction functions or to use custom distance matrices.  Some computations are
parallelized to improve their efficiency.")
    (license license:gpl3)))

(define-public r-pot
  (package
    (name "r-pot")
    (version "1.1-11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "POT" version))
       (sha256
        (base32 "0dkcikrz4xf8r49a0fap8nw8ydkz56kdmc7wjn9jannngl010z6v"))))
    (properties `((upstream-name . "POT")))
    (build-system r-build-system)
    (home-page "https://pot.r-forge.r-project.org/")
    (synopsis "Generalized Pareto Distribution and Peaks Over Threshold")
    (description
     "Some functions useful to perform a Peak Over Threshold analysis in univariate
and bivariate cases, see Beirlant et al. (2004) <doi:10.1002/0470012382>.  A
user guide is available in the vignette.")
    (license license:gpl2+)))

(define-public r-postpack
  (package
    (name "r-postpack")
    (version "0.5.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "postpack" version))
       (sha256
        (base32 "01h2la7ih7s9110162apbqjjn2i6il5m4300ga0r44r0fyw340a5"))))
    (properties `((upstream-name . "postpack")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-mcmcse r-coda r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://bstaton1.github.io/postpack/")
    (synopsis
     "Utilities for Processing Posterior Samples Stored in 'mcmc.lists'")
    (description
     "The aim of postpack is to provide the infrastructure for a standardized workflow
for mcmc.list objects.  These objects can be used to store output from models
fitted with Bayesian inference using JAGS', @code{WinBUGS}', @code{OpenBUGS}',
NIMBLE', Stan', or even custom MCMC algorithms.  Although the coda R package
provides some methods for these objects, it is somewhat limited in easily
performing post-processing tasks for specific nodes.  Models are ever increasing
in their complexity and the number of tracked nodes, and oftentimes a user may
wish to summarize/diagnose sampling behavior for only a small subset of nodes at
a time for a particular question or figure.  Thus, many postpack functions
support performing tasks on a subset of nodes, where the subset is specified
with regular expressions.  The functions in postpack streamline the extraction,
summarization, and diagnostics of specific monitored nodes after model fitting.
Further, because there is rarely only ever one model under consideration,
postpack scales efficiently to perform the same tasks on output from multiple
models simultaneously, facilitating rapid assessment of model sensitivity to
changes in assumptions.")
    (license license:expat)))

(define-public r-postm
  (package
    (name "r-postm")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "POSTm" version))
       (sha256
        (base32 "0gd6nqm66lms7yscf53qn6ny6rqhqa6c5l256867q52xlgcnjx35"))))
    (properties `((upstream-name . "POSTm")))
    (build-system r-build-system)
    (propagated-inputs (list r-compquadform r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=POSTm")
    (synopsis
     "Phylogeny-Guided OTU-Specific Association Test for Microbiome Data")
    (description
     "This package implements the Phylogeny-Guided Microbiome OTU-Specific Association
Test method, which boosts the testing power by adaptively borrowing information
from phylogenetically close OTUs (operational taxonomic units) of the target
OTU. This method is built on a kernel machine regression framework and allows
for flexible modeling of complex microbiome effects, adjustments for covariates,
and can accommodate both continuous and binary outcomes.")
    (license license:gpl2)))

(define-public r-postlogic
  (package
    (name "r-postlogic")
    (version "0.1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "postlogic" version))
       (sha256
        (base32 "11bpmfn375zpyg90dh44h86cvppzri1smmkn26jyydqjz74f4q9w"))))
    (properties `((upstream-name . "postlogic")))
    (build-system r-build-system)
    (home-page "https://github.com/RDocTaskForce/postlogic")
    (synopsis "Infix and Postfix Logic Operators")
    (description
     "This package provides adds postfix and infix logic operators for if, then,
unless, and otherwise.")
    (license license:gpl2)))

(define-public r-postlightmercury
  (package
    (name "r-postlightmercury")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "postlightmercury" version))
       (sha256
        (base32 "1345ckp50jplcdqp3a250c6mhapqh9322jzpxj5dyqmbg2nmgp9i"))))
    (properties `((upstream-name . "postlightmercury")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tibble
                             r-rvest
                             r-purrr
                             r-jsonlite
                             r-crul))
    (home-page "https://cran.r-project.org/package=postlightmercury")
    (synopsis "Parses Web Pages using Postlight Mercury")
    (description
     "This is a wrapper for the Mercury Parser API. The Mercury Parser is a single API
endpoint that takes a URL and gives you back the content reliably and easily.
With just one API request, Mercury takes any web article and returns only the
relevant content â headline, author, body text, relevant images and more â
free from any clutter.  Itâs reliable, easy-to-use and free.  See the webpage
here: <https://mercury.postlight.com/>.")
    (license license:expat)))

(define-public r-postinfectious
  (package
    (name "r-postinfectious")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "postinfectious" version))
       (sha256
        (base32 "1v327zxxiwc1cdksk6s39lkkgck5dxg169pp3jiqmm7xs35dcl6y"))))
    (properties `((upstream-name . "postinfectious")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=postinfectious")
    (synopsis
     "Estimating the Incubation Period Distribution of Post-Infectious Syndrome")
    (description
     "This package provides functions to estimate the incubation period distribution
of post-infectious syndrome which is defined as the time between the symptom
onset of the antecedent infection and that of the post-infectious syndrome.")
    (license license:gpl2)))

(define-public r-posthoc
  (package
    (name "r-posthoc")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "postHoc" version))
       (sha256
        (base32 "1l7r9md9iyav5vvjykpsnqv9whspzcmnppfpqm6qy5s5vk6b8zfd"))))
    (properties `((upstream-name . "postHoc")))
    (build-system r-build-system)
    (propagated-inputs (list r-multcomp r-igraph))
    (native-inputs (list r-knitr))
    (home-page "https://tildeweb.au.dk/au33031/astatlab/software/posthoc")
    (synopsis "Tools for Post-Hoc Analysis")
    (description
     "This package implements a range of facilities for post-hoc analysis and
summarizing linear models, generalized linear models and generalized linear
mixed models, including grouping and clustering via pairwise comparisons using
graph representations and efficient algorithms for finding maximal cliques of a
graph.  Includes also non-parametric toos for post-hoc analysis.  It has S3
methods for printing summarizing, and producing plots, line and barplots
suitable for post-hoc analyses.")
    (license license:gpl3+)))

(define-public r-postggir
  (package
    (name "r-postggir")
    (version "2.4.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "postGGIR" version))
       (sha256
        (base32 "1jxmvgzq4hv7sy2zcs8jicp0223mhv3mywhxbay2nv3bl0h4xfgs"))))
    (properties `((upstream-name . "postGGIR")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xlsx
                             r-tidyr
                             r-survival
                             r-refund
                             r-minpack-lm
                             r-kableextra
                             r-ineq
                             r-ggir
                             r-dplyr
                             r-denseflmm
                             r-cosinor2
                             r-cosinor
                             r-actfrag
                             r-actcr
                             r-accelerometry
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/dora201888/postGGIR")
    (synopsis "Data Processing after Running 'GGIR' for Accelerometer Data")
    (description
     "Generate all necessary R/Rmd/shell files for data processing after running GGIR
(v2.4.0) for accelerometer data.  In part 1, all csv files in the GGIR output
directory were read, transformed and then merged.  In part 2, the GGIR output
files were checked and summarized in one excel sheet.  In part 3, the merged
data was cleaned according to the number of valid hours on each night and the
number of valid days for each subject.  In part 4, the cleaned activity data was
imputed by the average Euclidean norm minus one (ENMO) over all the valid days
for each subject.  Finally, a comprehensive report of data processing was
created using Rmarkdown, and the report includes few exploratory plots and
multiple commonly used features extracted from minute level actigraphy data.")
    (license license:gpl3)))

(define-public r-posteriorbootstrap
  (package
    (name "r-posteriorbootstrap")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PosteriorBootstrap" version))
       (sha256
        (base32 "078b287brz4nlbdzbbvf5avy4qjv5hnz2wy61bwkk8l2kbg1j6as"))))
    (properties `((upstream-name . "PosteriorBootstrap")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-e1071))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/alan-turing-institute/PosteriorBootstrap/")
    (synopsis "Non-Parametric Sampling with Parallel Monte Carlo")
    (description
     "An implementation of a non-parametric statistical model using a parallelised
Monte Carlo sampling scheme.  The method implemented in this package allows
non-parametric inference to be regularized for small sample sizes, while also
being more accurate than approximations such as variational Bayes.  The
concentration parameter is an effective sample size parameter, determining the
faith we have in the model versus the data.  When the concentration is low, the
samples are close to the exact Bayesian logistic regression method; when the
concentration is high, the samples are close to the simplified variational Bayes
logistic regression.  The method is described in full in the paper Lyddon,
Walker, and Holmes (2018), \"Nonparametric learning from Bayesian models with
randomized objective functions\" <@code{arXiv:1806.11544>}.")
    (license license:expat)))

(define-public r-posterdown
  (package
    (name "r-posterdown")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "posterdown" version))
       (sha256
        (base32 "13plrrnylw3yldm0md1gr5drccfjk9f2wwlhz6lrmb3px7w5nqs0"))))
    (properties `((upstream-name . "posterdown")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml r-rmarkdown r-pagedown))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/brentthorne/posterdown")
    (synopsis "Generate PDF Conference Posters Using R Markdown")
    (description
     "Use rmarkdown and pagedown to generate HTML and PDF conference posters.")
    (license license:expat)))

(define-public r-postdoc
  (package
    (name "r-postdoc")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "postdoc" version))
       (sha256
        (base32 "0169wgld7snnfm447nw3hsjxy8sw9vkvlh6cmvdz30wpb869c8xm"))))
    (properties `((upstream-name . "postdoc")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-prismjs r-katex r-jsonlite r-curl))
    (home-page "https://cran.r-project.org/package=postdoc")
    (synopsis "Minimal and Uncluttered Package Documentation")
    (description
     "Generates simple and beautiful one-page HTML reference manuals with package
documentation.  Math rendering and syntax highlighting are done server-side in R
such that no @code{JavaScript} libraries are needed in the browser, which makes
the documentation portable and fast to load.")
    (license license:expat)))

(define-public r-postcodesior
  (package
    (name "r-postcodesior")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PostcodesioR" version))
       (sha256
        (base32 "0mxfcc913l6mhijzfn5rqmri7qbdg1g94g4nqadyxkzw067w7drh"))))
    (properties `((upstream-name . "PostcodesioR")))
    (build-system r-build-system)
    (propagated-inputs (list r-httr))
    (native-inputs (list r-knitr))
    (home-page "https://docs.ropensci.org/PostcodesioR/")
    (synopsis "API Wrapper Around 'Postcodes.io'")
    (description
     "Free UK geocoding using data from Office for National Statistics.  It is using
several functions to get information about post codes, outward codes, reverse
geocoding, nearest post codes/outward codes, validation, or randomly generate a
post code.  API wrapper around <https://postcodes.io>.")
    (license license:gpl3)))

(define-public r-postcards
  (package
    (name "r-postcards")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "postcards" version))
       (sha256
        (base32 "0kc8nbrqw5h14qw47rxl20yz9q0s8gk2g3lg7kcgsx7f1q514qsw"))))
    (properties `((upstream-name . "postcards")))
    (build-system r-build-system)
    (propagated-inputs (list r-rstudioapi r-rmarkdown))
    (home-page "https://github.com/seankross/postcards")
    (synopsis "Create Beautiful, Simple Personal Websites")
    (description
     "This package provides a collection of R Markdown templates for creating simple
and easy to personalize single page websites.")
    (license license:expat)))

(define-public r-possa
  (package
    (name "r-possa")
    (version "0.6.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "POSSA" version))
       (sha256
        (base32 "02lla3q3wkiw78b5v7pf7ali7s2l6aqc1bbaqf35zyihgiz3598g"))))
    (properties `((upstream-name . "POSSA")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/gasparl/possa")
    (synopsis
     "Power Simulation for Sequential Analyses and Multiple Hypotheses")
    (description
     "Calculates, via simulation, power and appropriate stopping alpha boundaries
(and/or futility bounds) for sequential analyses (i.e., group sequential design)
as well as for multiple hypotheses (multiple tests included in an analysis),
given any specified global error rate.  This enables the sequential use of
practically any significance test, as long as the underlying data can be
simulated in advance to a reasonable approximation.  LukÃ¡cs (2022)
<doi:10.21105/joss.04643>.")
    (license license:bsd-2)))

(define-public r-posratiodist
  (package
    (name "r-posratiodist")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PosRatioDist" version))
       (sha256
        (base32 "0hjjnc3k12ckydi0kk1l2z5dsni7zbjk346b50markzmd18qppcn"))))
    (properties `((upstream-name . "PosRatioDist")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://cran.r-project.org/package=PosRatioDist")
    (synopsis
     "Quotient of Random Variables Conditioned to the Positive Quadrant")
    (description
     "Computes the exact probability density function of X/Y conditioned on positive
quadrant for series of bivariate distributions,for more details see
Nadarajah,Song and Si (2019) <DOI:10.1080/03610926.2019.1576893>.")
    (license license:gpl2+)))

(define-public r-posologyr
  (package
    (name "r-posologyr")
    (version "1.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "posologyr" version))
       (sha256
        (base32 "0cwqcsbp5mafimrvwmha78nqggssm3dm9n50cqkc5rfjcyxi953g"))))
    (properties `((upstream-name . "posologyr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rxode2 r-mvtnorm r-data-table))
    (home-page "https://levenc.github.io/posologyr/")
    (synopsis "Individual Dose Optimization using Population Pharmacokinetics")
    (description
     "Optimize drug regimens through model-informed precision dosing, using individual
pharmacokinetic (PK) and pharmacokinetic-pharmacodynamic (PK-PD) profiles.  By
integrating therapeutic drug monitoring (TDM) data with population models,
posologyr provides accurate posterior estimates and enables the calculation of
personalized dosing regimens.  The empirical Bayes estimates are computed
following the method described by Kang et al. (2012)
<doi:10.4196/kjpp.2012.16.2.97>.")
    (license license:agpl3)))

(define-public r-posiadjrsquared
  (package
    (name "r-posiadjrsquared")
    (version "0.0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PoSIAdjRSquared" version))
       (sha256
        (base32 "1awdrw16pansw8m0lvm2ahbrmv2h3mv7yrcknj6dqypjqpiv5adg"))))
    (properties `((upstream-name . "PoSIAdjRSquared")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam r-lmf))
    (home-page "https://cran.r-project.org/package=PoSIAdjRSquared")
    (synopsis "Post-Selection Inference for Adjusted R Squared")
    (description
     "Conduct post-selection inference for regression coefficients in linear models
after they have been selected by adjusted R squared.  The p-values and
confidence intervals are valid after model selection with the same data.  This
allows the user to use all data for both model selection and inference without
losing control over the type I error rate.  The provided tests are more powerful
than data splitting, which bases inference on less data since it discards all
information used for selection.")
    (license license:expat)))

(define-public r-posi
  (package
    (name "r-posi")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PoSI" version))
       (sha256
        (base32 "1xwlj8dibaik37z6vj9q9vawavi7d49906wvxdd4drjzxzapq4ng"))))
    (properties `((upstream-name . "PoSI")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PoSI")
    (synopsis "Valid Post-Selection Inference for Linear LS Regression")
    (description
     "In linear LS regression, calculate for a given design matrix the multiplier K of
coefficient standard errors such that the confidence intervals [b - K*SE(b), b +
K*SE(b)] have a guaranteed coverage probability for all coefficient estimates b
in any submodels after performing arbitrary model selection.")
    (license license:gpl3)))

(define-public r-posetr
  (package
    (name "r-posetr")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "POSetR" version))
       (sha256
        (base32 "00qz9z9mwbgvxh7775il2avrxdn116l8v31z3bksifvy81krw95a"))))
    (properties `((upstream-name . "POSetR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-rcpp r-igraph))
    (home-page "https://cran.r-project.org/package=POSetR")
    (synopsis "Partially Ordered Sets in R")
    (description
     "This package provides a set of basic tools for generating, analyzing,
summarizing and visualizing finite partially ordered sets.  In particular, it
implements flexible and very efficient algorithms for the extraction of linear
extensions and for the computation of mutual ranking probabilities and other
user-defined functionals, over them.  The package is meant as a computationally
efficient \"engine\", for the implementation of data analysis procedures, on
systems of multidimensional ordinal indicators and partially ordered data, in
the spirit of Fattore, M. (2016) \"Partially ordered sets and the measurement of
multidimensional ordinal deprivation\", Social Indicators Research
<DOI:10.1007/s11205-015-1059-6>, and Fattore M. and Arcagni, A. (2018) \"A
reduced posetic approach to the measurement of multidimensional ordinal
deprivation\", Social Indicators Research <DOI:10.1007/s11205-016-1501-4>.")
    (license license:gpl2+)))

(define-public r-poset
  (package
    (name "r-poset")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "poset" version))
       (sha256
        (base32 "012abgxx4nazr7ls7iiwpvxcxwkcfc08iyggjbd0d2hdm29h4fq0"))))
    (properties `((upstream-name . "poset")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://sites.google.com/view/lmaowisc/")
    (synopsis "Analysis of Partially Ordered Data")
    (description
     "Win ratio approach to partially ordered data, such as multivariate ordinal
responses under product (consensus) or prioritized order.  Two-sample tests and
multiplicative regression models are implemented (Mao, 2024, under revision).")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-portvine
  (package
    (name "r-portvine")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "portvine" version))
       (sha256
        (base32 "0ikz7lcrj3sky9ybbnpnlzxqbv004d7rl5sqdx90gvx1lvbs8aaq"))))
    (properties `((upstream-name . "portvine")))
    (build-system r-build-system)
    (propagated-inputs (list r-wdm
                             r-tidyr
                             r-rvinecopulib
                             r-rugarch
                             r-rlang
                             r-rcppthread
                             r-rcppeigen
                             r-rcpp
                             r-ppcor
                             r-kde1d
                             r-future-apply
                             r-dtplyr
                             r-dplyr
                             r-data-table
                             r-checkmate
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/EmanuelSommer/portvine")
    (synopsis "Vine Based (Un)Conditional Portfolio Risk Measure Estimation")
    (description
     "Following Sommer (2022) <https://mediatum.ub.tum.de/1658240> portfolio level
risk estimates (e.g. Value at Risk, Expected Shortfall) are estimated by
modeling each asset univariately by an ARMA-GARCH model and then their cross
dependence via a Vine Copula model in a rolling window fashion.  One can even
condition on variables/time series at certain quantile levels to stress test the
risk measure estimates.")
    (license license:expat)))

(define-public r-portsort
  (package
    (name "r-portsort")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "portsort" version))
       (sha256
        (base32 "0swl39dn7lzwvps18bva4l64a441gkf6lbwcwrhlf93f1ardvxji"))))
    (properties `((upstream-name . "portsort")))
    (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=portsort")
    (synopsis "Factor-Based Portfolio Sorts")
    (description
     "Designed to aid both academic researchers and asset managers in conducting
factor based portfolio sorts.  Provides functionality to sort assets into
portfolios for up to three factors via a conditional or unconditional sorting
procedure.")
    (license license:gpl2+)))

(define-public r-portrisk
  (package
    (name "r-portrisk")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PortRisk" version))
       (sha256
        (base32 "05yxqcv0cijy3s9zx68f9xy59jv55kmj3v0pz5pgl17j23kb9rlc"))))
    (properties `((upstream-name . "PortRisk")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-tseries r-mcmcpack r-mass r-copula))
    (home-page "https://cran.r-project.org/package=PortRisk")
    (synopsis "Portfolio Risk Analysis")
    (description
     "Risk Attribution of a portfolio with Volatility Risk Analysis.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-portn
  (package
    (name "r-portn")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "portn" version))
       (sha256
        (base32 "1n759fwk2lwz8nqrc04r3720pffyhinmz2x83g5553ywxs0ds4da"))))
    (properties `((upstream-name . "portn")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsolnp))
    (home-page "https://github.com/ysd2004/portn")
    (synopsis "Portfolio Analysis for Nature")
    (description
     "The functions are designed to find the efficient mean-variance frontier or
portfolio weights for static portfolio (called Markowitz portfolio) analysis in
resource economics or nature conservation.  Using the nonlinear programming
solver ('Rsolnp'), this package deals with the quadratic minimization of the
variance-covariances without shorting (i.e., non-negative portfolio weights)
studied in Ando and Mallory (2012) <doi:10.1073/pnas.1114653109>.  See the
examples, testing versions, and more details from:
<https://github.com/ysd2004/portn>.")
    (license license:gpl2+)))

(define-public r-portion
  (package
    (name "r-portion")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "portion" version))
       (sha256
        (base32 "0sqmv5f0jsbm4p9l02cm2zf8jbjxxyb9hb0cmry65jlnhy4axbm3"))))
    (properties `((upstream-name . "portion")))
    (build-system r-build-system)
    (home-page "https://github.com/loelschlaeger/portion")
    (synopsis "Extracting a Data Portion")
    (description
     "This package provides a simple method to extract portions of a vector, matrix,
or data.frame.  The relative portion size and the way the portion is selected
can be chosen.")
    (license license:gpl3+)))

(define-public r-portfoliooptim
  (package
    (name "r-portfoliooptim")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PortfolioOptim" version))
       (sha256
        (base32 "01fw1zr1gr6nlcgw8dlc66b3ygkl6w571lw73p13h0q17y1i872d"))))
    (properties `((upstream-name . "PortfolioOptim")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsymphony))
    (home-page "https://cran.r-project.org/package=PortfolioOptim")
    (synopsis "Small/Large Sample Portfolio Optimization")
    (description
     "Two functions for financial portfolio optimization by linear programming are
provided.  One function implements Benders decomposition algorithm and can be
used for very large data sets.  The other, applicable for moderate sample sizes,
finds optimal portfolio which has the smallest distance to a given benchmark
portfolio.")
    (license license:gpl3)))

(define-public r-portfoliobacktest
  (package
    (name "r-portfoliobacktest")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "portfolioBacktest" version))
       (sha256
        (base32 "1qf7bbf3frivp0q55h3fj9ki2igaa6djpg3zv1x6hh53nl25dvbb"))))
    (properties `((upstream-name . "portfolioBacktest")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xts
                             r-rlang
                             r-r-utils
                             r-quantmod
                             r-quadprog
                             r-performanceanalytics
                             r-pbapply
                             r-ggplot2
                             r-evaluate
                             r-digest))
    (native-inputs (list r-rmarkdown r-r-rsp r-knitr))
    (home-page "https://CRAN.R-project.org/package=portfolioBacktest")
    (synopsis "Automated Backtesting of Portfolios over Multiple Datasets")
    (description
     "Automated backtesting of multiple portfolios over multiple datasets of stock
prices in a rolling-window fashion.  Intended for researchers and practitioners
to backtest a set of different portfolios, as well as by a course instructor to
assess the students in their portfolio design in a fully automated and
convenient manner, with results conveniently formatted in tables and plots.
Each portfolio design is easily defined as a function that takes as input a
window of the stock prices and outputs the portfolio weights.  Multiple
portfolios can be easily specified as a list of functions or as files in a
folder.  Multiple datasets can be conveniently extracted randomly from different
markets, different time periods, and different subsets of the stock universe.
The results can be later assessed and ranked with tables based on a number of
performance criteria (e.g., expected return, volatility, Sharpe ratio, drawdown,
turnover rate, return on investment, computational time, etc.), as well as
plotted in a number of ways with nice barplots and boxplots.")
    (license license:gpl3)))

(define-public r-portfolioanalytics
  (package
    (name "r-portfolioanalytics")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PortfolioAnalytics" version))
       (sha256
        (base32 "0rncchlc3n5kyfw7hfbxivi45gr64i89ylzr20mh9lqrqi0rif6a"))))
    (properties `((upstream-name . "PortfolioAnalytics")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xts
                             r-roi-plugin-symphony
                             r-pso
                             r-performanceanalytics
                             r-mco
                             r-gensa
                             r-foreach))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/braverock/PortfolioAnalytics")
    (synopsis
     "Portfolio Analysis, Including Numerical Methods for Optimization of Portfolios")
    (description "Portfolio optimization and analysis routines and graphics.")
    (license license:gpl3)))

(define-public r-portfolio-optimization
  (package
    (name "r-portfolio-optimization")
    (version "1.0-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "portfolio.optimization" version))
       (sha256
        (base32 "1rdhwffsjc1pa1qq7rqy6dwk8yrcblkmijz94p2w7sf2v4jmwxxr"))))
    (properties `((upstream-name . "portfolio.optimization")))
    (build-system r-build-system)
    (propagated-inputs (list r-xts r-modopt-matlab r-mass r-magrittr))
    (home-page "http://www.finance-r.com/")
    (synopsis "Contemporary Portfolio Optimization")
    (description
     "Simplify your portfolio optimization process by applying a contemporary modeling
way to model and solve your portfolio problems.  While most approaches and
packages are rather complicated this one tries to simplify things and is
agnostic regarding risk measures as well as optimization solvers.  Some of the
methods implemented are described by Konno and Yamazaki (1991)
<doi:10.1287/mnsc.37.5.519>, Rockafellar and Uryasev (2001)
<doi:10.21314/JOR.2000.038> and Markowitz (1952)
<doi:10.1111/j.1540-6261.1952.tb01525.x>.")
    (license license:expat)))

(define-public r-portfolio
  (package
    (name "r-portfolio")
    (version "0.5-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "portfolio" version))
       (sha256
        (base32 "12nbv64gwycshcrsiilfvsw9f6p24c8vdr2jbv9a5xx4c5r7xpaa"))))
    (properties `((upstream-name . "portfolio")))
    (build-system r-build-system)
    (propagated-inputs (list r-nlme r-lattice))
    (home-page "https://github.com/dgerlanc/portfolio")
    (synopsis "Analysing Equity Portfolios")
    (description
     "This package provides classes for analysing and implementing equity portfolios,
including routines for generating tradelists and calculating exposures to
user-specified risk factors.")
    (license license:gpl2+)))

(define-public r-portes
  (package
    (name "r-portes")
    (version "6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "portes" version))
       (sha256
        (base32 "1jv0jvwknlv9faind0gsyh6j5khh1ffd9wbnbk7ddlp1l1kzlsnr"))))
    (properties `((upstream-name . "portes")))
    (build-system r-build-system)
    (propagated-inputs (list r-vars r-forecast))
    (home-page "https://cran.r-project.org/package=portes")
    (synopsis "Portmanteau Tests for Time Series Models")
    (description
     "This package contains common univariate and multivariate portmanteau test
statistics for time series models.  These tests are based on using asymptotic
distributions such as chi-square distribution and based on using the Monte Carlo
significance tests.  Also, it can be used to simulate from univariate and
multivariate seasonal time series models.")
    (license license:gpl2+)))

(define-public r-portalr
  (package
    (name "r-portalr")
    (version "0.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "portalr" version))
       (sha256
        (base32 "1n6p1ighh5m209snfsy7fr71mz7bg9lyg4lnscaczaq9x91sjk9d"))))
    (properties `((upstream-name . "portalr")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-tidyselect
                             r-tidyr
                             r-rlang
                             r-magrittr
                             r-lunar
                             r-lubridate
                             r-httr
                             r-forecast
                             r-dplyr
                             r-clipr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://weecology.github.io/portalr/")
    (synopsis "Create Useful Summaries of the Portal Data")
    (description
     "Download and generate summaries for the rodent, plant, ant, and weather data
from the Portal Project.  Portal is a long-term (and ongoing) experimental
monitoring site in the Chihuahuan desert.  The raw data files can be found at
<https://github.com/weecology/portaldata>.")
    (license license:expat)))

(define-public r-portalhacienda
  (package
    (name "r-portalhacienda")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PortalHacienda" version))
       (sha256
        (base32 "0qxx4s5z0gsnymhbqxsw0hnp2jgqgq48iqdlanvzn5hgd92nhhiq"))))
    (properties `((upstream-name . "PortalHacienda")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xts
                             r-timetk
                             r-tibble
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-httr
                             r-forecast
                             r-dplyr
                             r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/fmgarciadiaz/PortalHacienda-CRAN")
    (synopsis "Acceder Con R a Los Datos Del Portal De Hacienda")
    (description
     "Obtener listado de datos, acceder y extender series del Portal de Datos de
Hacienda.Las proyecciones se realizan con forecast', Hyndman RJ, Khandakar Y
(2008) <doi:10.18637/jss.v027.i03>.  Search, download and forecast time-series
from the Ministry of Economy of Argentina.  Forecasts are built with the
forecast package, Hyndman RJ, Khandakar Y (2008) <doi:10.18637/jss.v027.i03>.")
    (license license:gpl3)))

(define-public r-port4me
  (package
    (name "r-port4me")
    (version "0.7.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "port4me" version))
       (sha256
        (base32 "1rvp9aj0m2dbkw03xl8sa4qvl20n5smik8dbq5cj0254rwcvmahd"))))
    (properties `((upstream-name . "port4me")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/HenrikBengtsson/port4me")
    (synopsis "Get the Same, Personal, Free 'TCP' Port over and over")
    (description
     "An R implementation of the cross-platform, language-independent \"port4me\"
algorithm (<https://github.com/@code{HenrikBengtsson/port4me>}), which (1) finds
a free Transmission Control Protocol ('TCP') port in [1024,65535] that the user
can open, (2) is designed to work in multi-user environments, (3), gives
different users, different ports, (4) gives the user the same port over time
with high probability, (5) gives different ports for different software tools,
and (6) requires no configuration.")
    (license license:expat)))

(define-public r-porridge
  (package
    (name "r-porridge")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "porridge" version))
       (sha256
        (base32 "0zya6zv8rsmbwv330jmx2pz1cn97m359nb0a7cjiyjgbggygxryf"))))
    (properties `((upstream-name . "porridge")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-pracma
                             r-mvtnorm
                             r-matrix
                             r-mass))
    (home-page "https://www.math.vu.nl/~wvanwie/")
    (synopsis "Ridge-Type Penalized Estimation of a Potpourri of Models")
    (description
     "The name of the package is derived from the French, pour ridge, and provides
functionality for ridge-type estimation of a potpourri of models.  Currently,
this estimation concerns that of various Gaussian graphical models from
different study designs.  Among others it considers the regular Gaussian
graphical model and a mixture of such models.  The porridge-package implements
the estimation of the former either from i) data with replicated observations by
penalized loglikelihood maximization using the regular ridge penalty on the
parameters (van Wieringen, Chen, 2021) or ii) from non-replicated data by means
of either a ridge estimator with multiple shrinkage targets (as presented in van
Wieringen et al.  2020, <doi:10.1016/j.jmva.2020.104621>) or the generalized
ridge estimator that allows for both the inclusion of quantitative and
qualitative prior information on the precision matrix via element-wise
penalization and shrinkage (van Wieringen, 2019,
<doi:10.1080/10618600.2019.1604374>).  Additionally, the porridge-package
facilitates the ridge penalized estimation of a mixture of Gaussian graphical
models (Aflakparast et al., 2018).  On another note, the package also includes
functionality for ridge-type estimation of the generalized linear model (as
presented in van Wieringen, Binder, 2022, <doi:10.1080/10618600.2022.2035231>).")
    (license license:gpl2+)))

(define-public r-poputils
  (package
    (name "r-poputils")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "poputils" version))
       (sha256
        (base32 "01g7zca29pfizi3gjk4jpbjrx36dbnp3qgcmfwrcp3mzz4bs0znn"))))
    (properties `((upstream-name . "poputils")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidyselect
                             r-tibble
                             r-rvec
                             r-rlang
                             r-cpp11
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://bayesiandemography.github.io/poputils/")
    (synopsis "Demographic Analysis and Data Manipulation")
    (description
     "Perform tasks commonly encountered when preparing and analysing demographic
data.  Some functions are intended for end users, and others for developers.
Includes functions for working with life tables.")
    (license license:expat)))

(define-public r-populr
  (package
    (name "r-populr")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "populR" version))
       (sha256
        (base32 "1j0ip93pd5sq2rcqd0pw5c0nmi1pjjaxi5k4xk97kgwnqcq5ib7n"))))
    (properties `((upstream-name . "populR")))
    (build-system r-build-system)
    (propagated-inputs (list r-usethis
                             r-units
                             r-sf
                             r-rlang
                             r-osmdata
                             r-metrics
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mbatsaris/populR/")
    (synopsis "Population Downscaling Using Areal Interpolation")
    (description
     "Given a set of source zone polygons such as census tracts or city blocks
alongside with population counts and a target zone of incogruent yet
superimposed polygon features (such as individual buildings) @code{populR}
transforms population counts from the former to the latter using Areal
Interpolation methods.")
    (license license:gpl3)))

(define-public r-populationpdxdesign
  (package
    (name "r-populationpdxdesign")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "populationPDXdesign" version))
       (sha256
        (base32 "0p73ddv3j1s1vs4j3axnsf39n626qjv0w1qlq9p7km4s6729bhgv"))))
    (properties `((upstream-name . "populationPDXdesign")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinycssloaders
                             r-shiny
                             r-roxygen2
                             r-plyr
                             r-ggplot2
                             r-devtools))
    (home-page "https://cran.r-project.org/package=populationPDXdesign")
    (synopsis "Designing Population PDX Studies")
    (description
     "Run simulations to assess the impact of various designs features and the
underlying biological behaviour on the outcome of a Patient Derived Xenograft
(PDX) population study.  This project can either be deployed to a server as a
shiny app or installed locally as a package and run the app using the command
@code{populationPDXdesignApp}()'.")
    (license license:gpl3+)))

(define-public r-populationgrowthr
  (package
    (name "r-populationgrowthr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PopulationGrowthR" version))
       (sha256
        (base32 "0cq7vnmslb4y9d9a4jk7z5xajw4vr2q1qqhkak9m9gidyr4dp5zl"))))
    (properties `((upstream-name . "PopulationGrowthR")))
    (build-system r-build-system)
    (propagated-inputs (list r-dplyr))
    (home-page "https://cran.r-project.org/package=PopulationGrowthR")
    (synopsis "Linear Population Growth Scenarios")
    (description
     "Fit linear splines to species time series to detect population growth scenarios
based on Hyndman, R J and Mesgaran, M B and Cousens, R D (2015)
<doi:10.1007/s10530-015-0962-8>.")
    (license license:gpl3)))

(define-public r-population
  (package
    (name "r-population")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "population" version))
       (sha256
        (base32 "00kagbz4pr4c5z6cb5w4y9nlbjzaa2awc45z2cvrivng76376br3"))))
    (properties `((upstream-name . "population")))
    (build-system r-build-system)
    (propagated-inputs (list r-abind))
    (home-page "https://cran.r-project.org/package=population")
    (synopsis "Models for Simulating Populations")
    (description
     "Run population simulations using an Individual-Based Model (IBM) compiled in C.")
    (license license:gpl3)))

(define-public r-poptrend
  (package
    (name "r-poptrend")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "poptrend" version))
       (sha256
        (base32 "1vwz6j318x6p92mksnn19jbx8667pqdc7nxgm6qa87kprsrymz6s"))))
    (properties `((upstream-name . "poptrend")))
    (build-system r-build-system)
    (propagated-inputs (list r-mgcv))
    (home-page "https://github.com/jknape/poptrend")
    (synopsis
     "Estimate Smooth and Linear Trends from Population Count Survey Data")
    (description
     "This package provides functions to estimate and plot smooth or linear population
trends, or population indices, from animal or plant count survey data.")
    (license license:gpl3)))

(define-public r-popstudy
  (package
    (name "r-popstudy")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "popstudy" version))
       (sha256
        (base32 "09lavka4hr4f8m2j8wqvd4pqxbxhclac1p2f2vq15xk7n4xfbhw3"))))
    (properties `((upstream-name . "popstudy")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-scales
                             r-rdpack
                             r-rcompanion
                             r-rainbow
                             r-moments
                             r-magrittr
                             r-lubridate
                             r-hmisc
                             r-here
                             r-ggpubr
                             r-ggplot2
                             r-forecast
                             r-dplyr
                             r-desctools
                             r-demography
                             r-corrr
                             r-corrplot
                             r-correlation))
    (home-page "https://www.cesargamboasanabria.com")
    (synopsis "Applied Techniques to Demographic and Time Series Analysis")
    (description
     "The use of overparameterization is proposed with combinatorial analysis to test
a broader spectrum of possible ARIMA models.  In the selection of ARIMA models,
the most traditional methods such as correlograms or others, do not usually
cover many alternatives to define the number of coefficients to be estimated in
the model, which represents an estimation method that is not the best.  The
popstudy package contains several tools for statistical analysis in demography
and time series based in Shryock research (Shryock et.  al. (1980)
<https://books.google.co.cr/books?id=8Oo6AQAAMAAJ>).")
    (license license:gpl3)))

(define-public r-popreconstruct
  (package
    (name "r-popreconstruct")
    (version "1.0-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "popReconstruct" version))
       (sha256
        (base32 "0nmf93dy2qf7z9mafpy99ry53aapbxlismn7drn732r8gvravapx"))))
    (properties `((upstream-name . "popReconstruct")))
    (build-system r-build-system)
    (propagated-inputs (list r-coda))
    (home-page "https://cran.r-project.org/package=popReconstruct")
    (synopsis "Reconstruct Human Populations of the Recent Past")
    (description
     "This package implements the Bayesian hierarchical model described by Wheldon,
Raftery, Clark and Gerland (see: <doi:10.1080/01621459.2012.737729>) for
simultaneously estimating age-specific population counts, fertility rates,
mortality rates and net international migration flows, at the national level.")
    (license license:gpl3)))

(define-public r-poppyramid
  (package
    (name "r-poppyramid")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "popPyramid" version))
       (sha256
        (base32 "1fvq63d0pjhpzyf6pb2v5w9wy0pskiv2n07jipg98bgyiih1w584"))))
    (properties `((upstream-name . "popPyramid")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-ggplot2 r-dplyr))
    (home-page "https://github.com/musajajorge/popPyramid")
    (synopsis "Population Pyramids")
    (description
     "This package provides functions that facilitate the elaboration of population
pyramids.")
    (license license:gpl3)))

(define-public r-poppr
  (package
    (name "r-poppr")
    (version "2.9.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "poppr" version))
       (sha256
        (base32 "0cn62r584dfvz57in0666m4fsx9yd7xv8rawwgrhrqxs911pgzfd"))))
    (properties `((upstream-name . "poppr")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-shiny
                             r-rlang
                             r-progressr
                             r-polysat
                             r-pegas
                             r-magrittr
                             r-igraph
                             r-ggplot2
                             r-dplyr
                             r-boot
                             r-ape
                             r-adegenet
                             r-ade4))
    (native-inputs (list r-knitr))
    (home-page "https://grunwaldlab.github.io/poppr/")
    (synopsis "Genetic Analysis of Populations with Mixed Reproduction")
    (description
     "Population genetic analyses for hierarchical analysis of partially clonal
populations built upon the architecture of the adegenet package.  Originally
described in Kamvar, Tabima, and GrÃ¼nwald (2014) <doi:10.7717/peerj.281> with
version 2.0 described in Kamvar, Brooks, and GrÃ¼nwald (2015)
<doi:10.3389/fgene.2015.00208>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-poppcr
  (package
    (name "r-poppcr")
    (version "0.1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "popPCR" version))
       (sha256
        (base32 "03ppgjmdp3rivlgqs7aq082j4rzndf9fw5aq8mzci7fvjkj7whc4"))))
    (properties `((upstream-name . "popPCR")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=popPCR")
    (synopsis
     "Classify Digital PCR Droplets by Fitting Fluorescence Populations")
    (description
     "Estimates DNA target concentration by classifying digital PCR (polymerase chain
reaction) droplets as positive, negative, or rain, using
Expectation-Maximization Clustering.  The fitting is accomplished using the
EMMIXskew R package (v.  1.0.3) by Kui Wang, Angus Ng, and Geoff
@code{McLachlan} (2018) as based on their paper \"Multivariate Skew t Mixture
Models: Applications to Fluorescence-Activated Cell Sorting Data\"
<doi:10.1109/DICTA.2009.88>.")
    (license license:gpl3+)))

(define-public r-popkin
  (package
    (name "r-popkin")
    (version "1.3.23")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "popkin" version))
       (sha256
        (base32 "07wlja7c96az0d82c3b0m1kmc04sbgp73asbaks26jwvjw4vmby5"))))
    (properties `((upstream-name . "popkin")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/StoreyLab/popkin/")
    (synopsis "Estimate Kinship and FST under Arbitrary Population Structure")
    (description
     "This package provides functions to estimate the kinship matrix of individuals
from a large set of biallelic SNPs, and extract inbreeding coefficients and the
generalized FST (Wright's fixation index).  Method described in Ochoa and Storey
(2021) <doi:10.1371/journal.pgen.1009241>.")
    (license license:gpl3)))

(define-public r-popinf
  (package
    (name "r-popinf")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "POPInf" version))
       (sha256
        (base32 "1h8fjl87srg2vs4p3pk0az4nr7zzcw08mvw99c6j9j344kzp2p49"))))
    (properties `((upstream-name . "POPInf")))
    (build-system r-build-system)
    (propagated-inputs (list r-randomforest r-mass))
    (home-page "https://arxiv.org/abs/2311.14220")
    (synopsis "Assumption-Lean and Data-Adaptive Post-Prediction Inference")
    (description
     "Implementation of assumption-lean and data-adaptive post-prediction inference
(POPInf), for valid and efficient statistical inference based on data predicted
by machine learning.  See Miao, Miao, Wu, Zhao, and Lu (2023)
<@code{arXiv:2311.14220>}.")
    (license license:gpl3)))

(define-public r-popgenreport
  (package
    (name "r-popgenreport")
    (version "3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PopGenReport" version))
       (sha256
        (base32 "19df1178kznrrzfkhaaf0lkmfk6kasl0x39k3zv3b5w6k7697igs"))))
    (properties `((upstream-name . "PopGenReport")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-vegan
                             r-sp
                             r-rgooglemaps
                             r-reshape2
                             r-raster
                             r-r-utils
                             r-plyr
                             r-pegas
                             r-mmod
                             r-lattice
                             r-knitr
                             r-ggplot2
                             r-ggally
                             r-genetics
                             r-gdistance
                             r-gap
                             r-dismo
                             r-calibrate
                             r-adegenet
                             r-ade4))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/green-striped-gecko/PopGenReport")
    (synopsis
     "Simple Framework to Analyse Population and Landscape Genetic Data")
    (description
     "This package provides beginner friendly framework to analyse population genetic
data.  Based on adegenet objects it uses knitr to create comprehensive reports
on spatial genetic data.  For detailed information how to use the package refer
to the comprehensive tutorials or visit <http://www.popgenreport.org/>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-popgenr
  (package
    (name "r-popgenr")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "popgenr" version))
       (sha256
        (base32 "164dhizka0yadzihbjqkfs5p0hknw966mq0grigs7r9ppbq9kajh"))))
    (properties `((upstream-name . "popgenr")))
    (build-system r-build-system)
    (home-page
     "https://global.oup.com/academic/product/population-genetics-with-r-9780198829546?cc=gb&lang=en#")
    (synopsis
     "Accompaniment to Population Genetics with R: An Introduction for Life Scientists")
    (description
     "This package provides several data sets and functions to accompany the book
\"Population Genetics with R: An Introduction for Life Scientists\" (2021,
ISBN:9780198829546).")
    (license license:cc0)))

(define-public r-popgenhelpr
  (package
    (name "r-popgenhelpr")
    (version "1.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PopGenHelpR" version))
       (sha256
        (base32 "0qkfrv5qshzb06q0y6vp9f01xcs9zqc7zb8nmpyl1sb18a6bvv2r"))))
    (properties `((upstream-name . "PopGenHelpR")))
    (build-system r-build-system)
    (propagated-inputs (list r-vcfr
                             r-spdep
                             r-spdata
                             r-scatterpie
                             r-rlang
                             r-reshape2
                             r-magrittr
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://kfarleigh.github.io/PopGenHelpR/")
    (synopsis "Streamline Population Genomic and Genetic Analyses")
    (description
     "Estimate commonly used population genomic statistics and generate publication
quality figures. @code{PopGenHelpR} uses vcf, geno (012), and csv files to
generate output.")
    (license license:gpl3+)))

(define-public r-popepi
  (package
    (name "r-popepi")
    (version "0.4.12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "popEpi" version))
       (sha256
        (base32 "13fasp84hxxpbn6gmfjfl1kzija7y11mvlhc77rpqnj1l5wcm1rn"))))
    (properties `((upstream-name . "popEpi")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-epi r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/FinnishCancerRegistry/popEpi")
    (synopsis "Functions for Epidemiological Analysis using Population Data")
    (description
     "Enables computation of epidemiological statistics, including those where counts
or mortality rates of the reference population are used.  Currently supported:
excess hazard models (Dickman, Sloggett, Hills, and Hakulinen (2012)
<doi:10.1002/sim.1597>), rates, mean survival times, relative/net survival (in
particular the Ederer II (Ederer and Heise (1959)) and Pohar Perme (Pohar Perme,
Stare, and Esteve (2012) <doi:10.1111/j.1541-0420.2011.01640.x>) estimators),
and standardized incidence and mortality ratios, all of which can be easily
adjusted for by covariates such as age.  Fast splitting and aggregation of Lexis
objects (from package Epi') and other computations achieved using data.table'.")
    (license license:expat)))

(define-public r-poped
  (package
    (name "r-poped")
    (version "0.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PopED" version))
       (sha256
        (base32 "10aja60d72ql1fz67xwbi4qf9z8jdz7smdm97avc1d5jqvw3fwhn"))))
    (properties `((upstream-name . "PopED")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-purrr
                             r-mvtnorm
                             r-mass
                             r-magrittr
                             r-gtools
                             r-ggplot2
                             r-dplyr
                             r-codetools
                             r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://andrewhooker.github.io/PopED/")
    (synopsis "Population (and Individual) Optimal Experimental Design")
    (description
     "Optimal experimental designs for both population and individual studies based on
nonlinear mixed-effect models.  Often this is based on a computation of the
Fisher Information Matrix.  This package was developed for pharmacometric
problems, and examples and predefined models are available for these types of
systems.  The methods are described in Nyberg et al. (2012)
<doi:10.1016/j.cmpb.2012.05.005>, and Foracchia et al. (2004)
<doi:10.1016/S0169-2607(03)00073-7>.")
    (license license:lgpl3+)))

(define-public r-popdesign
  (package
    (name "r-popdesign")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PoPdesign" version))
       (sha256
        (base32 "14vgxjzq9wlg3fgh1vy84v04ij51bgy97pl9yi61ysa84pgac6cb"))))
    (properties `((upstream-name . "PoPdesign")))
    (build-system r-build-system)
    (propagated-inputs (list r-magick r-knitr r-iso))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PoPdesign")
    (synopsis "Posterior Predictive (PoP) Design for Phase I Clinical Trials")
    (description
     "The primary goal of phase I clinical trials is to find the maximum tolerated
dose (MTD).  To reach this objective, we introduce a new design for phase I
clinical trials, the posterior predictive (@code{PoP}) design.  The @code{PoP}
design is an innovative model-assisted design that is as simply as the
conventional algorithmic designs as its decision rules can be pre-tabulated
prior to the onset of trial, but is of more flexibility of selecting diverse
target toxicity rates and cohort sizes.  The @code{PoP} design has desirable
properties, such as coherence and consistency.  Moreover, the @code{PoP} design
provides better empirical performance than the BOIN and Keyboard design with
respect to high average probabilities of choosing the MTD and slightly lower
risk of treating patients at subtherapeutic or overly toxic doses.")
    (license license:gpl2)))

(define-public r-popdemo
  (package
    (name "r-popdemo")
    (version "1.3-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "popdemo" version))
       (sha256
        (base32 "0xvqp71spqh5khi2gdzxmj8pp71i6l2qpcgcw8s9xbqwx9w905mq"))))
    (properties `((upstream-name . "popdemo")))
    (build-system r-build-system)
    (propagated-inputs (list r-mcmcpack r-expm))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=popdemo")
    (synopsis "Demographic Modelling Using Projection Matrices")
    (description
     "This package provides tools for modelling populations and demography using
matrix projection models, with deterministic and stochastic model
implementations.  Includes population projection, indices of short- and
long-term population size and growth, perturbation analysis, convergence to
stability or stationarity, and diagnostic and manipulation tools.")
    (license license:gpl2+)))

(define-public r-popbio
  (package
    (name "r-popbio")
    (version "2.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "popbio" version))
       (sha256
        (base32 "0fpf13d0h5mmi1xrxfqgz1map8v6g388kcm901ajdnsf57awbqpg"))))
    (properties `((upstream-name . "popbio")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=popbio")
    (synopsis "Construction and Analysis of Matrix Population Models")
    (description
     "Construct and analyze projection matrix models from a demography study of marked
individuals classified by age or stage.  The package covers methods described in
Matrix Population Models by Caswell (2001) and Quantitative Conservation Biology
by Morris and Doak (2002).")
    (license license:gpl3)))

(define-public r-popbayes
  (package
    (name "r-popbayes")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "popbayes" version))
       (sha256
        (base32 "1qrxjk1y52rsf203nnylpksjv2hv3x8pk2flvjlwxj5fv5335a9y"))))
    (properties `((upstream-name . "popbayes")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-usethis r-r2jags))
    (native-inputs (list r-knitr))
    (home-page "https://frbcesab.github.io/popbayes/")
    (synopsis
     "Bayesian Model to Estimate Population Trends from Counts Series")
    (description
     "This package infers the trends of one or several animal populations over time
from series of counts.  It does so by accounting for count precision (provided
or inferred based on expert knowledge, e.g. guesstimates), smoothing the
population rate of increase over time, and accounting for the maximum
demographic potential of species.  Inference is carried out in a Bayesian
framework.  This work is part of the FRB-CESAB working group
@code{AfroBioDrivers}
<https://www.fondationbiodiversite.fr/en/the-frb-in-action/programs-and-projects/le-cesab/afrobiodrivers/>.")
    (license license:gpl2+)))

(define-public r-pop-wolf
  (package
    (name "r-pop-wolf")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pop.wolf" version))
       (sha256
        (base32 "113l9skpggjxjpwxibfp611z2gnjikfqh6kmj6in52sn9q4yqv83"))))
    (properties `((upstream-name . "pop.wolf")))
    (build-system r-build-system)
    (propagated-inputs (list r-abind))
    (home-page "https://cran.r-project.org/package=pop.wolf")
    (synopsis "Models for Simulating Wolf Populations")
    (description
     "Simulate the dynamic of wolf populations using a specific Individual-Based Model
(IBM) compiled in C, see Chapron et al. (2016)
<doi:10.1016/j.ecolmodel.2016.08.012>.")
    (license license:gpl3)))

(define-public r-pop-lion
  (package
    (name "r-pop-lion")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pop.lion" version))
       (sha256
        (base32 "1rj95wwcxvsfd1smd21717y9c0672a8vnpnbdsa63pxbzdxc4kaj"))))
    (properties `((upstream-name . "pop.lion")))
    (build-system r-build-system)
    (propagated-inputs (list r-testthat r-abind))
    (home-page "https://cran.r-project.org/package=pop.lion")
    (synopsis "Models for Simulating Lion Populations")
    (description
     "Simulate the dynamic of lion populations using a specific Individual-Based Model
(IBM) compiled in C.")
    (license license:gpl3)))

(define-public r-pop
  (package
    (name "r-pop")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pop" version))
       (sha256
        (base32 "0hbxdrkasb69x7ipddvyrzl5vvc7dd6rzj1vy7v3chg3rzgq89cj"))))
    (properties `((upstream-name . "pop")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-igraph))
    (home-page "https://cran.r-project.org/package=pop")
    (synopsis "Flexible Syntax for Population Dynamic Modelling")
    (description
     "Population dynamic models underpin a range of analyses and applications in
ecology and epidemiology.  The various approaches for analysing population
dynamics models (MPMs, IPMs, ODEs, POMPs, PVA) each require the model to be
defined in a different way.  This makes it difficult to combine different
modelling approaches and data types to solve a given problem.  pop aims to
provide a flexible and easy to use common interface for constructing population
dynamic models and enabling to them to be fitted and analysed in lots of
different ways.")
    (license license:expat)))

(define-public r-poolvim
  (package
    (name "r-poolvim")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "poolVIM" version))
       (sha256
        (base32 "19yw6pp5l3jmla4wjbvpjq132f645yks49pzsdv123f3qfr8f0m6"))))
    (properties `((upstream-name . "poolVIM")))
    (build-system r-build-system)
    (propagated-inputs (list r-ranger r-hmisc))
    (home-page "https://cran.r-project.org/package=poolVIM")
    (synopsis
     "Gene-Based Association Tests using the Actual Impurity Reduction (AIR) Variable Importance")
    (description
     "Gene-based association tests using the actual impurity reduction (AIR) variable
importance.  The function aggregates AIR importance measures from a group of
SNPs or probes and outputs a p-value for each gene.  The procedures builds upon
the method described in <doi:10.1093/Bioinformatics/Bty373> and will be
published soon.")
    (license license:gpl2+)))

(define-public r-pooltestr
  (package
    (name "r-pooltestr")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PoolTestR" version))
       (sha256
        (base32 "0n9fihb2yc6jnxs1v0yq872pg7b1jd7lx7bwa9d1r8yv36haxzvr"))))
    (properties `((upstream-name . "PoolTestR")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppeigen
                             r-rcpp
                             r-progress
                             r-lme4
                             r-dplyr
                             r-brms
                             r-bh))
    (home-page "https://github.com/AngusMcLure/PoolTestR")
    (synopsis "Prevalence and Regression for Pool-Tested (Group-Tested) Data")
    (description
     "An easy-to-use tool for working with presence/absence tests on pooled or grouped
samples.  The primary application is for estimating prevalence of a marker in a
population based on the results of tests on pooled specimens.  This sampling
method is often employed in surveillance of rare conditions in humans or animals
(e.g. molecular xenomonitoring).  The package was initially conceived as an
R-based alternative to the molecular xenomonitoring software, @code{PoolScreen}
<https://sites.uab.edu/statgenetics/software/>.  However, it goes further,
allowing for estimates of prevalence to be adjusted for hierarchical sampling
frames, and perform flexible mixed-effect regression analyses (@code{McLure} et
al.  Environmental Modelling and Software. <DOI:10.1016/j.envsoft.2021.105158>).
 The package is currently in early stages, however more features are planned or
in the works: e.g. adjustments for imperfect test specificity/sensitivity,
functions for helping with optimal experimental design, and functions for
spatial modelling.")
    (license license:gpl3+)))

(define-public r-poolr
  (package
    (name "r-poolr")
    (version "1.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "poolr" version))
       (sha256
        (base32 "1y8bish2s96b42xsld0x1nrjz9dzqhdr2w95vdpfymq7mg05d1ry"))))
    (properties `((upstream-name . "poolr")))
    (build-system r-build-system)
    (propagated-inputs (list r-mathjaxr))
    (home-page "https://cran.r-project.org/package=poolr")
    (synopsis "Methods for Pooling P-Values from (Dependent) Tests")
    (description
     "This package provides functions for pooling/combining the results (i.e.,
p-values) from (dependent) hypothesis tests.  Included are Fisher's method,
Stouffer's method, the inverse chi-square method, the Bonferroni method,
Tippett's method, and the binomial test.  Each method can be adjusted based on
an estimate of the effective number of tests or using empirically derived null
distribution using pseudo replicates.  For Fisher's, Stouffer's, and the inverse
chi-square method, direct generalizations based on multivariate theory are also
available (leading to Brown's method, Strube's method, and the generalized
inverse chi-square method).  An introduction can be found in Cinar and
Viechtbauer (2022) <doi:10.18637/jss.v101.i01>.")
    (license license:gpl2+)))

(define-public r-pooling
  (package
    (name "r-pooling")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pooling" version))
       (sha256
        (base32 "19mrzax3k35fhgjpnlrigf0nscsdl3xb7k82s5q472k4bcz7ya5b"))))
    (properties `((upstream-name . "pooling")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv
                             r-mvtnorm
                             r-ggrepel
                             r-ggplot2
                             r-dvmisc
                             r-dplyr
                             r-data-table
                             r-cubature))
    (home-page "https://cran.r-project.org/package=pooling")
    (synopsis "Fit Poolwise Regression Models")
    (description
     "This package provides functions for calculating power and fitting regression
models in studies where a biomarker is measured in \"pooled\" samples rather than
for each individual.  Approaches for handling measurement error follow the
framework of Schisterman et al. (2010) <doi:10.1002/sim.3823>.")
    (license license:gpl3)))

(define-public r-poolhelper
  (package
    (name "r-poolhelper")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "poolHelper" version))
       (sha256
        (base32 "17parinc1shm5nmyxn0lvl2pndjz881lzk4iwqhbnlk1hvwrs7sg"))))
    (properties `((upstream-name . "poolHelper")))
    (build-system r-build-system)
    (propagated-inputs (list r-scrm r-metrics r-mcmcpack))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/joao-mcarvalho/poolHelper")
    (synopsis "Simulates Pooled Sequencing Genetic Data")
    (description
     "Simulates pooled sequencing data under a variety of conditions.  Also allows for
the evaluation of the average absolute difference between allele frequencies
computed from genotypes and those computed from pooled data.  Carvalho et al.,
(2022) <doi:10.1101/2023.01.20.524733>.")
    (license license:gpl3+)))

(define-public r-poolfstat
  (package
    (name "r-poolfstat")
    (version "2.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "poolfstat" version))
       (sha256
        (base32 "17wama4lxl7q2i9j2v15kn33i2lszrsyim9qj8dihhx9r6p9vdls"))))
    (properties `((upstream-name . "poolfstat")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-ryacas
                             r-rcppprogress
                             r-rcpp
                             r-progress
                             r-nnls
                             r-matrix
                             r-foreach
                             r-doparallel
                             r-diagrammer
                             r-data-table
                             r-ape))
    (home-page "https://cran.r-project.org/package=poolfstat")
    (synopsis
     "Computing f-Statistics and Building Admixture Graphs Based on Allele Count or Pool-Seq Read Count Data")
    (description
     "This package provides functions for the computation of f- and D-statistics
(estimation of Fst', Patterson's F2', F3', F3*', F4 and D parameters) in
population genomics studies from allele count or Pool-Seq read count data and
for the fitting, building and visualization of admixture graphs.  The package
also includes several utilities to manipulate Pool-Seq data stored in standard
format (e.g., such as vcf files or rsync files generated by the the
@code{PoPoolation} software) and perform conversion to alternative format (as
used in the @code{BayPass} and @code{SelEstim} software).  As of version 2.0,
the package also includes utilities to manipulate standard allele count data
(e.g., stored in @code{TreeMix}', @code{BayPass} or @code{SelEstim} format).")
    (license license:gpl2+)))

(define-public r-pooledpeaks
  (package
    (name "r-pooledpeaks")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pooledpeaks" version))
       (sha256
        (base32 "1q9ycn19rn8g1yfccrljmh7s0b192z40k02cy74l684sipafbra1"))))
    (properties `((upstream-name . "pooledpeaks")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rlang
                             r-qpdf
                             r-pdftools
                             r-magrittr
                             r-fragman
                             r-dplyr
                             r-ape))
    (home-page "https://github.com/kmkuesters/pooledpeaks")
    (synopsis "Genetic Analysis of Pooled Samples")
    (description
     "Analyzing genetic data obtained from pooled samples.  This package can read in
Fragment Analysis output files, process the data, and score peaks, as well as
facilitate various analyses, including cluster analysis, calculation of genetic
distances and diversity indices, as well as bootstrap resampling for statistical
inference.  Specifically tailored to handle genetic data efficiently,
researchers can explore population structure, genetic differentiation, and
genetic relatedness among samples.  We updated some functions from
Covarrubias-Pazaran et al. (2016) <doi:10.1186/s12863-016-0365-6> to allow for
the use of new file formats and referenced the following to write our genetic
analysis functions: Long et al. (2022) <doi:10.1038/s41598-022-04776-0>, Jost
(2008) <doi:10.1111/j.1365-294x.2008.03887.x>, Nei (1973)
<doi:10.1073/pnas.70.12.3321>, Foulley et al. (2006)
<doi:10.1016/j.livprodsci.2005.10.021>, Chao et al. (2008)
<doi:10.1111/j.1541-0420.2008.01010.x>.")
    (license license:gpl3+)))

(define-public r-pooledmeangroup
  (package
    (name "r-pooledmeangroup")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PooledMeanGroup" version))
       (sha256
        (base32 "0i9s7qskjnji3mf6clsi69rnni57v8cysgr9gh3hvxjzlf1sq8y7"))))
    (properties `((upstream-name . "PooledMeanGroup")))
    (build-system r-build-system)
    (home-page "https://www.r-project.org")
    (synopsis "Pooled Mean Group Estimation of Dynamic Heterogenous Panels")
    (description
     "Calculates the pooled mean group (PMG) estimator for dynamic panel data models,
as described by Pesaran, Shin and Smith (1999)
<doi:10.1080/01621459.1999.10474156>.")
    (license license:gpl2+)))

(define-public r-pooledcohort
  (package
    (name "r-pooledcohort")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PooledCohort" version))
       (sha256
        (base32 "16n65jm1w2hxd1lrn3mj1z31ydgjc5jjhc4ffjscajhc140vf7yb"))))
    (properties `((upstream-name . "PooledCohort")))
    (build-system r-build-system)
    (propagated-inputs (list r-glue))
    (home-page "https://github.com/bcjaeger/PooledCohort")
    (synopsis
     "Predicted Risk for CVD using Pooled Cohort Equations, PREVENT Equations, and Other Contemporary CVD Risk Calculators")
    (description
     "The 2017 American College of Cardiology and American Heart Association blood
pressure guideline recommends using 10-year predicted atherosclerotic
cardiovascular disease risk to guide the decision to initiate or intensify
antihypertensive medication.  The guideline recommends using the Pooled Cohort
risk prediction equations to predict 10-year atherosclerotic cardiovascular
disease risk.  This package implements the original Pooled Cohort risk
prediction equations and also incorporates updated versions based on more
contemporary data and statistical methods.")
    (license license:expat)))

(define-public r-pooldilutionr
  (package
    (name "r-pooldilutionr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PoolDilutionR" version))
       (sha256
        (base32 "0jmizwhc2f6igp6y06g08q4jbknbmcg51fq13y663jpf6fkdc8kz"))))
    (properties `((upstream-name . "PoolDilutionR")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PoolDilutionR")
    (synopsis
     "Calculate Gross Biogeochemical Flux Rates from Isotope Pool Dilution Data")
    (description
     "Pool dilution is a isotope tracer technique wherein a biogeochemical pool is
artifically enriched with its heavy isotopologue and the gross productive and
consumptive fluxes of that pool are quantified by the change in pool size and
isotopic composition over time.  This package calculates gross production and
consumption rates from closed-system isotopic pool dilution time series data.
Pool size concentrations and heavy isotope (e.g., 15N) content are measured over
time and the model optimizes production rate (P) and the first order rate
constant (k) by minimizing error in the model-predicted total pool size, as well
as the isotopic signature.  The model optimizes rates by weighting information
against the signal:noise ratio of concentration and heavy- isotope signatures
using measurement precision as well as the magnitude of change over time.  The
calculations used here are based on von Fischer and Hedin (2002)
<doi:10.1029/2001GB001448> with some modifications.")
    (license license:expat)))

(define-public r-poolbal
  (package
    (name "r-poolbal")
    (version "0.1-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PoolBal" version))
       (sha256
        (base32 "11v615bri2ikdzrriidfzfn732465rfzjdw83rfncpaxgalchy59"))))
    (properties `((upstream-name . "PoolBal")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PoolBal")
    (synopsis "Balancing Central and Marginal Rejection of Pooled p-Values")
    (description
     "When using pooled p-values to adjust for multiple testing, there is an inherent
balance that must be struck between rejection based on weak evidence spread
among many tests and strong evidence in a few, explored in Salahub and Olford
(2023) <@code{arXiv:2310.16600>}.  This package provides functionality to
compute marginal and central rejection levels and the centrality quotient for
p-value pooling functions and provides implementations of the chi-squared
quantile pooled p-value (described in Salahub and Oldford (2023)) and a proposal
from Heard and Rubin-Delanchy (2018) <doi:10.1093/biomet/asx076> to control the
quotient's value.")
    (license license:gpl3+)))

(define-public r-poolabc
  (package
    (name "r-poolabc")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "poolABC" version))
       (sha256
        (base32 "0l1sg9w1a385g6cz82b73a13661bx716ppvabvji7b6p99i9kv99"))))
    (properties `((upstream-name . "poolABC")))
    (build-system r-build-system)
    (propagated-inputs (list r-scrm
                             r-rlang
                             r-rcolorbrewer
                             r-poolhelper
                             r-nnet
                             r-metricsweighted
                             r-locfit
                             r-ggplot2
                             r-foreach
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/joao-mcarvalho/poolABC")
    (synopsis "Approximate Bayesian Computation with Pooled Sequencing Data")
    (description
     "This package provides functions to simulate Pool-seq data under models of
demographic formation and to import Pool-seq data from real populations.
Implements two ABC algorithms for performing parameter estimation and model
selection using Pool-seq data.  Cross-validation can also be performed to assess
the accuracy of ABC estimates and model choice.  Carvalho et al., (2022)
<doi:10.1111/1755-0998.13834>.")
    (license license:gpl3+)))

(define-public r-pool
  (package
    (name "r-pool")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pool" version))
       (sha256
        (base32 "0xynrvcybsfa0chfw2ljld5dmikas22azhgcrji44sm7k3x90hqc"))))
    (properties `((upstream-name . "pool")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-r6 r-later r-dbi))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rstudio/pool")
    (synopsis "Object Pooling")
    (description
     "Enables the creation of object pools, which make it less computationally
expensive to fetch a new object.  Currently the only supported pooled objects
are DBI connections.")
    (license license:expat)))

(define-public r-pooh
  (package
    (name "r-pooh")
    (version "0.3-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pooh" version))
       (sha256
        (base32 "0qwa5j91aypasvsf4xcfbl6lz7llawdr38jiflzmfak2ad72rv7j"))))
    (properties `((upstream-name . "pooh")))
    (build-system r-build-system)
    (home-page "http://www.stat.umn.edu/geyer/pooh/")
    (synopsis "Partial Orders and Relations")
    (description
     "Finds equivalence classes corresponding to a symmetric relation or undirected
graph.  Finds total order consistent with partial order or directed graph
(so-called topological sort).")
    (license license:expat)))

(define-public r-poms
  (package
    (name "r-poms")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "POMS" version))
       (sha256
        (base32 "1ndry1csq7zsccdwd8nq9x4b9zq5j2ffykmhm38ari8n0zxxyrwy"))))
    (properties `((upstream-name . "POMS")))
    (build-system r-build-system)
    (propagated-inputs (list r-xnomial
                             r-phylolm
                             r-phangorn
                             r-mass
                             r-data-table
                             r-ape))
    (home-page "https://cran.r-project.org/package=POMS")
    (synopsis "Phylogenetic Organization of Metagenomic Signals")
    (description
     "Code to identify functional enrichments across diverse taxa in phylogenetic
tree, particularly where these taxa differ in abundance across samples in a
non-random pattern.  The motivation for this approach is to identify microbial
functions encoded by diverse taxa that are at higher abundance in certain
samples compared to others, which could indicate that such functions are broadly
adaptive under certain conditions.  See @code{GitHub} repository for tutorial
and examples: <https://github.com/gavinmdouglas/POMS/wiki>.  Citation: Gavin M.
Douglas, Molly G. Hayes, Morgan G. I. Langille, Elhanan Borenstein (2022)
<doi:10.1093/bioinformatics/btac655>.")
    (license license:gpl3)))

(define-public r-pompp
  (package
    (name "r-pompp")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pompp" version))
       (sha256
        (base32 "0svlzhsm1kfwgjpx2cas63jm8v5b9cxlksipv758vxlnc3jiarvk"))))
    (properties `((upstream-name . "pompp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppprogress r-rcppeigen r-rcpp r-geor r-coda))
    (home-page "https://cran.r-project.org/package=pompp")
    (synopsis "Presence-Only for Marked Point Process")
    (description
     "Inspired by Moreira and Gamerman (2022) <doi:10.1214/21-AOAS1569>, this
methodology expands the idea by including Marks in the point process.  Using
efficient C++ code, the estimation is possible and made faster with
@code{OpenMP} <https://www.openmp.org/> enabled computers.  This package was
developed under the project PTDC/MAT-STA/28243/2017, supported by Portuguese
funds through the Portuguese Foundation for Science and Technology (FCT).")
    (license license:gpl3+)))

(define-public r-pompom
  (package
    (name "r-pompom")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pompom" version))
       (sha256
        (base32 "09jy4lrb2p71yk604ndfhqfz6znlc3bq72k42f0d1i1a8g3by8h6"))))
    (properties `((upstream-name . "pompom")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-qgraph r-lavaan r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pompom")
    (synopsis "Person-Oriented Method and Perturbation on the Model")
    (description
     "An implementation of a hybrid method of person-oriented method and perturbation
on the model.  Pompom is the initials of the two methods.  The hybrid method
will provide a multivariate intraindividual variability metric (@code{iRAM}).
The person-oriented method used in this package refers to @code{uSEM} (unified
structural equation modeling, see Kim et al., 2007, Gates et al., 2010 and Gates
et al., 2012 for details).  Perturbation on the model was conducted according to
impulse response analysis introduced in Lutkepohl (2007).  Kim, J., Zhu, W.,
Chang, L., Bentler, P. M., & Ernst, T. (2007) <doi:10.1002/hbm.20259>.  Gates,
K. M., Molenaar, P. C. M., Hillary, F. G., Ram, N., & Rovine, M. J. (2010)
<doi:10.1016/j.neuroimage.2009.12.117>.  Gates, K. M., & Molenaar, P. C. M.
(2012) <doi:10.1016/j.neuroimage.2012.06.026>.  Lutkepohl, H. (2007,
ISBN:3540262393).")
    (license license:gpl2)))

(define-public r-pomp
  (package
    (name "r-pomp")
    (version "5.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pomp" version))
       (sha256
        (base32 "059j6g2ckyr0818llabwr05p1hfjwr9pb7bwyd9qgc6mdkdc0p50"))))
    (properties `((upstream-name . "pomp")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-mvtnorm r-digest r-desolve r-data-table r-coda))
    (home-page "https://kingaa.github.io/pomp/")
    (synopsis "Statistical Inference for Partially Observed Markov Processes")
    (description
     "This package provides tools for data analysis with partially observed Markov
process (POMP) models (also known as stochastic dynamical systems, hidden Markov
models, and nonlinear, non-Gaussian, state-space models).  The package provides
facilities for implementing POMP models, simulating them, and fitting them to
time series data by a variety of frequentist and Bayesian methods.  It is also a
versatile platform for implementation of inference methods for general POMP
models.")
    (license license:gpl3)))

(define-public r-pomodoro
  (package
    (name "r-pomodoro")
    (version "3.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pomodoro" version))
       (sha256
        (base32 "1y7a6097jlrrgqkxhb43n5nahf4m814inhmkm1495w0k5q1zmraf"))))
    (properties `((upstream-name . "pomodoro")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-randomforest
                             r-proc
                             r-ipred
                             r-gbm
                             r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/seymakalay/pomodoro")
    (synopsis "Predictive Power of Linear and Tree Modeling")
    (description
     "Runs generalized and multinominal logistic (GLM and MLM) models, as well as
random forest (RF), Bagging (BAG), and Boosting (BOOST).  This package prints
out to predictive outcomes easy for the selected data and data splits.")
    (license license:gpl3)))

(define-public r-pomic
  (package
    (name "r-pomic")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Pomic" version))
       (sha256
        (base32 "0xy5fgimbgqqac3n7akhwyvnrvd9fpvx9h8nk4spxrxc8x0km1w7"))))
    (properties `((upstream-name . "Pomic")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=Pomic")
    (synopsis "Pattern Oriented Modelling Information Criterion")
    (description
     "Calculations of an information criterion are proposed to check the quality of
simulations results of Agent-based models (ABM/IBM) or other non-linear
rule-based models.  The POMDEV measure (Pattern Oriented Modelling DEViance) is
based on the Kullback-Leibler divergence and likelihood theory.  It basically
indicates the deviance of simulation results from field observations.  Once
POMDEV scores and metropolis-hasting sampling on different model versions are
effectuated, POMIC scores (Pattern Oriented Modelling Information Criterion) can
be calculated.  This method could be further developed to incorporate multiple
patterns assessment.  Piou C, U Berger and V Grimm (2009)
<doi:10.1016/j.ecolmodel.2009.05.003>.")
    (license license:gpl2)))

(define-public r-pomdpsolve
  (package
    (name "r-pomdpsolve")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pomdpSolve" version))
       (sha256
        (base32 "0ywiqry0df1cjy3k8r3rzpd42rmnn578ih03qvp8qydq66bdrh3b"))))
    (properties `((upstream-name . "pomdpSolve")))
    (build-system r-build-system)
    (home-page "https://github.com/mhahsler/pomdpSolve")
    (synopsis
     "Interface to 'pomdp-solve' for Partially Observable Markov Decision Processes")
    (description
     "Installs an updated version of pomdp-solve', a program to solve Partially
Observable Markov Decision Processes (POMDPs) using a variety of exact and
approximate value iteration algorithms.  A convenient R infrastructure is
provided in the separate package pomdp.  Kaelbling, Littman and Cassandra (1998)
<doi:10.1016/S0004-3702(98)00023-X>.")
    (license license:gpl3+)))

(define-public r-pomdp
  (package
    (name "r-pomdp")
    (version "1.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pomdp" version))
       (sha256
        (base32 "1l873a4p0fzfmgz52v57i2ccmpsrv10lvbrm7mr39qwqsaklh3bv"))))
    (properties `((upstream-name . "pomdp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp
                             r-processx
                             r-pomdpsolve
                             r-matrix
                             r-igraph
                             r-foreach))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mhahsler/pomdp")
    (synopsis
     "Infrastructure for Partially Observable Markov Decision Processes (POMDP)")
    (description
     "This package provides the infrastructure to define and analyze the solutions of
Partially Observable Markov Decision Process (POMDP) models.  Interfaces for
various exact and approximate solution algorithms are available including value
iteration, point-based value iteration and SARSOP. Smallwood and Sondik (1973)
<doi:10.1287/opre.21.5.1071>.")
    (license license:gpl3+)))

(define-public r-pomcheckr
  (package
    (name "r-pomcheckr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pomcheckr" version))
       (sha256
        (base32 "0fm2v388ya7v4vrb5rvilghlf49d15jlwz0isf95ldr81a55m9ij"))))
    (properties `((upstream-name . "pomcheckr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-rlang
                             r-magrittr
                             r-ggplot2
                             r-dplyr
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pomcheckr")
    (synopsis "Graphical Check for Proportional Odds Assumption")
    (description
     "This package implements the method described at the UCLA Statistical Consulting
site <https://stats.idre.ucla.edu/r/dae/ordinal-logistic-regression/> for
checking if the proportional odds assumption holds for a cumulative logit model.")
    (license license:expat)))

(define-public r-pomaspu
  (package
    (name "r-pomaspu")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "POMaSPU" version))
       (sha256
        (base32 "0jz3jgzdykv0xvfw3ix0hbs32as6dp7p5v8bj5nddknx10d0siya"))))
    (properties `((upstream-name . "POMaSPU")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-matrixstats r-mass))
    (home-page "https://cran.r-project.org/package=POMaSPU")
    (synopsis
     "Adaptive Association Tests for Multiple Phenotypes using Proportional Odds Model (POM-aSPU)")
    (description
     "POM-@code{aSPU} test evaluates an association between an ordinal response and
multiple phenotypes, for details see Kim and Pan (2017)
<DOI:10.1002/gepi.22033>.")
    (license license:gpl3+)))

(define-public r-pomade
  (package
    (name "r-pomade")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "POMADE" version))
       (sha256
        (base32 "0jiiqgifdqicxh4dgpqhmizkazjgpsqp46ic7b78i7pdhlan2zj3"))))
    (properties `((upstream-name . "POMADE")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-purrr
                             r-magrittr
                             r-ggplot2
                             r-future
                             r-furrr
                             r-dplyr))
    (home-page "https://mikkelvembye.github.io/POMADE/")
    (synopsis "Power for Meta-Analysis of Dependent Effects")
    (description
     "This package provides functions to compute and plot power levels, minimum
detectable effect sizes, and minimum required sample sizes for the test of the
overall average effect size in meta-analysis of dependent effect sizes.")
    (license license:expat)))

(define-public r-polywog
  (package
    (name "r-polywog")
    (version "0.4-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "polywog" version))
       (sha256
        (base32 "02qk1cyvkd77mwlvhj1zzzi0bmy7qxz29j2v730wp7rz4w7h5x5n"))))
    (properties `((upstream-name . "polywog")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rcpp
                             r-ncvreg
                             r-misctools
                             r-matrix
                             r-iterators
                             r-glmnet
                             r-formula
                             r-foreach))
    (home-page "https://github.com/brentonk/polywog-package")
    (synopsis "Bootstrapped Basis Regression with Oracle Model Selection")
    (description
     "Routines for flexible functional form estimation via basis regression, with
model selection via the adaptive LASSO or SCAD to prevent overfitting.")
    (license license:gpl2+)))

(define-public r-polytrend
  (package
    (name "r-polytrend")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PolyTrend" version))
       (sha256
        (base32 "17n6phkzgaqrlzs8x1l5smnij1gxfklr0zj9pqfy5n8xqnpwssm5"))))
    (properties `((upstream-name . "PolyTrend")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PolyTrend")
    (synopsis "Trend Classification Algorithm")
    (description
     "This algorithm classifies the trends into linear, quadratic, cubic, concealed
and no-trend types.  The \"concealed trends\" are those trends that possess
quadratic or cubic forms, but the net change from the start of the time period
to the end of the time period hasn't been significant.  The \"no-trend\" category
includes simple linear trends with statistically in-significant slope
coefficient.")
    (license license:gpl2)))

(define-public r-polytree
  (package
    (name "r-polytree")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PolyTree" version))
       (sha256
        (base32 "0np5v1255crfp1x7613rdzgkzkm64ikkjm67xrsncijr202yh1gq"))))
    (properties `((upstream-name . "PolyTree")))
    (build-system r-build-system)
    (propagated-inputs (list r-igraph r-foci))
    (home-page "https://cran.r-project.org/package=PolyTree")
    (synopsis "Estimate Causal Polytree from Data")
    (description
     "Given a data matrix with rows representing data vectors and columns representing
variables, produces a directed polytree for the underlying causal structure.
Based on the algorithm developed in Chatterjee and Vidyasagar (2022)
<arxiv:2209.07028>.  The method is fully nonparametric, making no use of
linearity assumptions, and especially useful when the number of variables is
large.")
    (license license:expat)))

(define-public r-polysegratiomm
  (package
    (name "r-polysegratiomm")
    (version "0.6-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "polySegratioMM" version))
       (sha256
        (base32 "162xj52566kaxgfk7c14170xx07isyjmqb9skvhrdg7sz20lc3l1"))))
    (properties `((upstream-name . "polySegratioMM")))
    (build-system r-build-system)
    (propagated-inputs (list r-polysegratio r-lattice r-gtools r-coda))
    (home-page "https://github.com/petebaker/polysegratiomm")
    (synopsis "Bayesian Mixture Models for Marker Dosage in Autopolyploids")
    (description
     "Fits Bayesian mixture models to estimate marker dosage for dominant markers in
autopolyploids using JAGS (1.0 or greater) as outlined in Baker et al \"Bayesian
estimation of marker dosage in sugarcane and other autopolyploids\" (2010,
<doi:10.1007/s00122-010-1283-z>).  May be used in conjunction with
@code{polySegratio} for simulation studies and comparison with standard methods.")
    (license license:gpl3)))

(define-public r-polysegratio
  (package
    (name "r-polysegratio")
    (version "0.2-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "polySegratio" version))
       (sha256
        (base32 "0djw49mbhd7x6jr0f5qkb9akw3fw3ax4w9xv8iwa5f65q5gbxpdf"))))
    (properties `((upstream-name . "polySegratio")))
    (build-system r-build-system)
    (propagated-inputs (list r-gdata))
    (home-page "https://cran.r-project.org/package=polySegratio")
    (synopsis
     "Simulate and Test Marker Dosage for Dominant Markers in Autopolyploids")
    (description
     "Perform classic chi-squared tests and Ripol et al(1999) binomial confidence
interval approach for autopolyploid dominant markers.  Also, dominant markers
may be generated for families of offspring where either one or both of the
parents possess the marker.  Missing values and misclassified markers may be
generated at random.")
    (license license:gpl3)))

(define-public r-polysat
  (package
    (name "r-polysat")
    (version "1.7-7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "polysat" version))
       (sha256
        (base32 "0fpdy2yya91hz1n24j4mqiggrjjihddaia5p24phgbkw8rxqqp40"))))
    (properties `((upstream-name . "polysat")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://github.com/lvclark/polysat/wiki")
    (synopsis "Tools for Polyploid Microsatellite Analysis")
    (description
     "This package provides a collection of tools to handle microsatellite data of any
ploidy (and samples of mixed ploidy) where allele copy number is not known in
partially heterozygous genotypes.  It can import and export data in ABI
@code{GeneMapper}', Structure', ATetra', Tetrasat'/'Tetra', @code{GenoDive}',
SPA@code{GeDi}', POPDIST', STRand', and binary presence/absence formats.  It can
calculate pairwise distances between individuals using a stepwise mutation model
or infinite alleles model, with or without taking ploidies and allele
frequencies into account.  These distances can be used for the calculation of
clonal diversity statistics or used for further analysis in R. Allelic diversity
statistics and Polymorphic Information Content are also available.  polysat can
assist the user in estimating the ploidy of samples, and it can estimate allele
frequencies in populations, calculate pairwise or global differentiation
statistics based on those frequencies, and export allele frequencies to
SPA@code{GeDi} and adegenet'.  Functions are also included for assigning alleles
to isoloci in cases where one pair of microsatellite primers amplifies alleles
from two or more independently segregating isoloci.  polysat is described by
Clark and Jasieniuk (2011) <doi:10.1111/j.1755-0998.2011.02985.x> and Clark and
Schreier (2017) <doi:10.1111/1755-0998.12639>.")
    (license license:gpl2)))

(define-public r-polyreg
  (package
    (name "r-polyreg")
    (version "0.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "polyreg" version))
       (sha256
        (base32 "0fh4yp9b35ag6snz6l85xmn0pc18qn0jggzsyk12g05dfb6r6qvw"))))
    (properties `((upstream-name . "polyreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-nnet))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/matloff/polyreg")
    (synopsis "Polynomial Regression")
    (description
     "Automate formation and evaluation of polynomial regression models.  The
motivation for this package is described in Polynomial Regression As an
Alternative to Neural Nets by Xi Cheng, Bohdan Khomtchouk, Norman Matloff, and
Pete Mohanty (<@code{arXiv:1806.06850>}).")
    (license license:gpl2+)))

(define-public r-polyrad
  (package
    (name "r-polyrad")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "polyRAD" version))
       (sha256
        (base32 "12qlws0d6s94rand1a7azd7yv22qzij1aqmhrvw4az0y7jy2wka4"))))
    (properties `((upstream-name . "polyRAD")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringi r-rcpp r-pcamethods r-fastmatch))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/lvclark/polyRAD")
    (synopsis
     "Genotype Calling with Uncertainty from Sequencing Data in Polyploids and Diploids")
    (description
     "Read depth data from genotyping-by-sequencing (GBS) or restriction
site-associated DNA sequencing (RAD-seq) are imported and used to make Bayesian
probability estimates of genotypes in polyploids or diploids.  The genotype
probabilities, posterior mean genotypes, or most probable genotypes can then be
exported for downstream analysis. @code{polyRAD} is described by Clark et al.
(2019) <doi:10.1534/g3.118.200913>, and the Hind/He statistic for marker
filtering is described by Clark et al. (2022) <doi:10.1186/s12859-022-04635-9>.
A variant calling pipeline for highly duplicated genomes is also included and is
described by Clark et al. (2020, Version 1) <doi:10.1101/2020.01.11.902890>.")
    (license license:gpl2+)))

(define-public r-polyqtlr
  (package
    (name "r-polyqtlr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "polyqtlR" version))
       (sha256
        (base32 "0yz27785cfw50sw6nniiqmdggji502wh84zcvqf31wzznjryy9v5"))))
    (properties `((upstream-name . "polyqtlR")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2
                             r-rcpparmadillo
                             r-rcpp
                             r-rcolorbrewer
                             r-nlme
                             r-knitr
                             r-hmisc
                             r-foreach
                             r-doparallel
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=polyqtlR")
    (synopsis "QTL Analysis in Autopolyploid Bi-Parental F1 Populations")
    (description
     "Quantitative trait loci (QTL) analysis and exploration of meiotic patterns in
autopolyploid bi-parental F1 populations.  For all ploidy levels,
identity-by-descent (IBD) probabilities can be estimated.  Significance
thresholds, exploring QTL allele effects and visualising results are provided.
For more background and to reference the package see
<doi:10.1093/bioinformatics/btab574>.")
    (license license:gpl3)))

(define-public r-polypoly
  (package
    (name "r-polypoly")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "polypoly" version))
       (sha256
        (base32 "1xd5s2gj3x47dvq02lkjih6rbvmjh3hwybzwsvnq3dxvcj9jjkyw"))))
    (properties `((upstream-name . "polypoly")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-rlang r-reshape2 r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/tjmahr/polypoly")
    (synopsis "Helper Functions for Orthogonal Polynomials")
    (description
     "This package provides tools for reshaping, plotting, and manipulating matrices
of orthogonal polynomials.")
    (license license:gpl3)))

(define-public r-polypharmacy
  (package
    (name "r-polypharmacy")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "polypharmacy" version))
       (sha256
        (base32 "1rimhy7z5xglkxfp3yh4vqm5g075n8g09nl4a8nxxf6cyl504gcv"))))
    (properties `((upstream-name . "polypharmacy")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-lubridate
                             r-itertools
                             r-foreach
                             r-doparallel
                             r-data-table))
    (home-page "https://cran.r-project.org/package=polypharmacy")
    (synopsis "Calculate Several Polypharmacy Indicators")
    (description
     "Analyse prescription drug deliveries to calculate several indicators of
polypharmacy corresponding to the various definitions found in the literature.
Bjerrum, L., Rosholm, J. U., Hallas, J., & Kragstrup, J. (1997)
<doi:10.1007/s002280050329>.  Chan, D.-C., Hao, Y.-T., & Wu, S.-C. (2009a)
<doi:10.1002/pds.1712>.  Fincke, B. G., Snyder, K., Cantillon, C., Gaehde, S.,
Standring, P., Fiore, L., ...  Gagnon, D.R. (2005) <doi:10.1002/pds.966>.
Hovstadius, B., Astrand, B., & Petersson, G. (2009)
<doi:10.1186/1472-6904-9-11>.  Hovstadius, B., Astrand, B., & Petersson, G.
(2010) <doi:10.1002/pds.1921>.  Kennerfalk, A., RuigÃ³mez, A., Wallander, M.-A.,
Wilhelmsen, L., & Johansson, S. (2002) <doi:10.1345/aph.1A226>.  Masnoon, N.,
Shakib, S., Kalisch-Ellett, L., & Caughey, G. E. (2017)
<doi:10.1186/s12877-017-0621-2>.  Narayan, S. W., & Nishtala, P. S. (2015)
<doi:10.1007/s40801-015-0020-y>.  Nishtala, P. S., & Salahudeen, M. S. (2015)
<doi:10.1159/000368191>.  Park, H. Y., Ryu, H. N., Shim, M. K., Sohn, H. S., &
Kwon, J. W. (2016) <doi:10.5414/cp202484>.  Veehof, L., Stewart, R.,
Haaijer-Ruskamp, F., & Jong, B. M. (2000) <doi:10.1093/fampra/17.3.261>.")
    (license license:expat)))

(define-public r-polypatex
  (package
    (name "r-polypatex")
    (version "0.9.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PolyPatEx" version))
       (sha256
        (base32 "1z1hwvsw7b6cz5xfy35a5ldrzb3fhp3swiyz56v1zqblpsaz8n9g"))))
    (properties `((upstream-name . "PolyPatEx")))
    (build-system r-build-system)
    (propagated-inputs (list r-gtools))
    (home-page "https://cran.r-project.org/package=PolyPatEx")
    (synopsis "Paternity Exclusion in Autopolyploid Species")
    (description
     "This package provides functions to perform paternity exclusion via allele
matching, in autopolyploid species having ploidy 4, 6, or 8.  The marker data
used can be genotype data (copy numbers known) or allelic phenotype data (copy
numbers not known).")
    (license (license:fsdg-compatible "GPL-3 + file LICENSE"))))

(define-public r-polynomf
  (package
    (name "r-polynomf")
    (version "2.0-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PolynomF" version))
       (sha256
        (base32 "1xr47i0vizz98xdkyb7pca34x6yd8xa9kwj9bcrylr5ixrs52rvj"))))
    (properties `((upstream-name . "PolynomF")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PolynomF")
    (synopsis "Polynomials in R")
    (description
     "This package implements univariate polynomial operations in R, including
polynomial arithmetic, finding zeros, plotting, and some operations on lists of
polynomials.")
    (license license:gpl2)))

(define-public r-polymatrix
  (package
    (name "r-polymatrix")
    (version "0.9.16")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "polyMatrix" version))
       (sha256
        (base32 "13amrjl0nsyz6d9c9xxgnlkhdiicy8rx27ds5k47r8x48386vz14"))))
    (properties `((upstream-name . "polyMatrix")))
    (build-system r-build-system)
    (propagated-inputs (list r-polynom r-matrix))
    (home-page "https://github.com/namezys/polymatrix")
    (synopsis "Infrastructure for Manipulation Polynomial Matrices")
    (description
     "Implementation of class \"@code{polyMatrix}\" for storing a matrix of polynomials
and implements basic matrix operations; including a determinant and
characteristic polynomial.  It is based on the package polynom and uses a lot of
its methods to implement matrix operations.  This package includes 3 methods of
triangularization of polynomial matrices: Extended Euclidean algorithm which is
most classical but numerically unstable; Sylvester algorithm based on LQ
decomposition; Interpolation algorithm is based on LQ decomposition and Newton
interpolation.  Both methods are described in D. Henrion & M. Sebek, Reliable
numerical methods for polynomial matrix triangularization, IEEE Transactions on
Automatic Control (Volume 44, Issue 3, Mar 1999, Pages 497-508)
<doi:10.1109/9.751344> and in Salah Labhalla, Henri Lombardi & Roger Marlin,
Algorithmes de calcule de la reduction de Hermite d'une matrice a coefficients
polynomeaux, Theoretical Computer Science (Volume 161, Issue 1-2, July 1996,
Pages 69-92) <doi:10.1016/0304-3975(95)00090-9>.")
    (license license:expat)))

(define-public r-polymapr
  (package
    (name "r-polymapr")
    (version "1.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "polymapR" version))
       (sha256
        (base32 "0aschby014m0j0nrr2q4pfghhakx89jq6wn8pv2dnbfpbg779l18"))))
    (properties `((upstream-name . "polymapR")))
    (build-system r-build-system)
    (propagated-inputs (list r-mdsmap r-knitr r-igraph r-foreach r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=polymapR")
    (synopsis "Linkage Analysis in Outcrossing Polyploids")
    (description
     "Creation of linkage maps in polyploid species from marker dosage scores of an F1
cross from two heterozygous parents.  Currently works for outcrossing diploid,
autotriploid, autotetraploid and autohexaploid species, as well as segmental
allotetraploids.  Methods are described in a manuscript of Bourke et al. (2018)
<doi:10.1093/bioinformatics/bty371>.  Since version 1.1.0, both discrete and
probabilistic genotypes are acceptable input; for more details on the latter see
Liao et al. (2021) <doi:10.1007/s00122-021-03834-x>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-polyhedralcubature
  (package
    (name "r-polyhedralcubature")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "polyhedralCubature" version))
       (sha256
        (base32 "1la8cj261c4vshr4yd3vgplzv1ykrkgvlwrjm49fgzh142rk59gc"))))
    (properties `((upstream-name . "polyhedralCubature")))
    (build-system r-build-system)
    (propagated-inputs (list r-tessellation
                             r-spray
                             r-simplicialcubature
                             r-rcdd
                             r-qspray
                             r-ompr
                             r-matrix
                             r-magrittr
                             r-gmp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/stla/polyhedralCubature")
    (synopsis "Multiple Integration over Convex Polyhedra")
    (description
     "Evaluation of multiple integrals over convex polyhedra.  This is useful when the
bounds of the integrals are some linear combinations of the variables.")
    (license license:gpl3)))

(define-public r-polyhaplotyper
  (package
    (name "r-polyhaplotyper")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PolyHaplotyper" version))
       (sha256
        (base32 "1sc1n42h78nyv004ddykvh1ljjczyiv5rl9sgxhi207lql1zd3wa"))))
    (properties `((upstream-name . "PolyHaplotyper")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PolyHaplotyper")
    (synopsis
     "Assignment of Haplotypes Based on SNP Dosages in Diploids and Polyploids")
    (description
     "Infer the genetic composition of individuals in terms of haplotype dosages for a
haploblock, based on bi-allelic marker dosages, for any ploidy level.
Reference: Voorrips and Tumino: @code{PolyHaplotyper}: haplotyping in polyploids
based on bi-allelic marker dosage data.  Submitted to BMC Bioinformatics (2021).")
    (license license:gpl2)))

(define-public r-polyglotr
  (package
    (name "r-polyglotr")
    (version "1.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "polyglotr" version))
       (sha256
        (base32 "1l6dpbsslc1lynm39nx5x9kxb6zh54klfnrqb0i8lybavd14i24k"))))
    (properties `((upstream-name . "polyglotr")))
    (build-system r-build-system)
    (propagated-inputs (list r-urltools
                             r-tibble
                             r-stringr
                             r-rvest
                             r-rlang
                             r-rcurl
                             r-purrr
                             r-magrittr
                             r-jsonlite
                             r-httr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Tomeriko96/polyglotr/")
    (synopsis "Translate Text")
    (description
     "Provide easy methods to translate pieces of text.  Functions send requests to
translation services online.")
    (license license:expat)))

(define-public r-polycub
  (package
    (name "r-polycub")
    (version "0.9.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "polyCub" version))
       (sha256
        (base32 "14hifd0ixppy3bg49y4kpjgvwhvz6nj43nh37hgwsy5qdifayzq7"))))
    (properties `((upstream-name . "polyCub")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bastistician/polyCub")
    (synopsis "Cubature over Polygonal Domains")
    (description
     "Numerical integration of continuously differentiable functions f(x,y) over
simple closed polygonal domains.  The following cubature methods are
implemented: product Gauss cubature (Sommariva and Vianello, 2007,
<doi:10.1007/s10543-007-0131-2>), the simple two-dimensional midpoint rule
(wrapping spatstat.geom functions), and adaptive cubature for radially symmetric
functions via line integrate() along the polygon boundary (Meyer and Held, 2014,
<doi:10.1214/14-AOAS743>, Supplement B).  For simple integration along the axes,
the cubature package is more appropriate.")
    (license license:gpl2)))

(define-public r-polycrossdesigns
  (package
    (name "r-polycrossdesigns")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PolycrossDesigns" version))
       (sha256
        (base32 "0qhjy4pkksg45c6i3axwcnqa5miz4ky52w9qngf1d1x62lckb29i"))))
    (properties `((upstream-name . "PolycrossDesigns")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PolycrossDesigns")
    (synopsis "Polycross Designs (\"PolycrossDesigns\")")
    (description
     "This package provides a polycross is the pollination by natural hybridization of
a group of genotypes, generally selected, grown in isolation from other
compatible genotypes in such a way to promote random open pollination.  A
particular practical application of the polycross method occurs in the
production of a synthetic variety resulting from cross-pollinated plants.
Laying out these experiments in appropriate designs, known as polycross designs,
would not only save experimental resources but also gather more information from
the experiment.  Different experimental situations may arise in polycross
nurseries which may be requiring different polycross designs (Varghese et.  al.
(2015) <doi:10.1080/02664763.2015.1043860>. \" Experimental designs for open
pollination in polycross trials\").  This package contains a function named PD()
which generates nine types of polycross designs suitable for various
experimental situations.")
    (license license:gpl2+)))

(define-public r-polyapost
  (package
    (name "r-polyapost")
    (version "1.7-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "polyapost" version))
       (sha256
        (base32 "1q1ack0wpn21d8p4wfm0xyinw0lc0506x3zxyiqdwppgq9491r8n"))))
    (properties `((upstream-name . "polyapost")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcdd r-boot))
    (home-page "https://cran.r-project.org/package=polyapost")
    (synopsis "Simulating from the Polya Posterior")
    (description
     "Simulate via Markov chain Monte Carlo (hit-and-run algorithm) a Dirichlet
distribution conditioned to satisfy a finite set of linear equality and
inequality constraints (hence to lie in a convex polytope that is a subset of
the unit simplex).")
    (license license:gpl2+)))

(define-public r-polyaaeppli
  (package
    (name "r-polyaaeppli")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "polyaAeppli" version))
       (sha256
        (base32 "1rn1bryxxzsgi85kw4glmk10z61pqyxj79ngdl81b67vrw23fpxw"))))
    (properties `((upstream-name . "polyaAeppli")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=polyaAeppli")
    (synopsis "Implementation of the Polya-Aeppli Distribution")
    (description
     "This package provides functions for evaluating the mass density, cumulative
distribution function, quantile function and random variate generation for the
Polya-Aeppli distribution, also known as the geometric compound Poisson
distribution.  More information on the implementation can be found at Conrad J.
Burden (2014) <@code{arXiv:1406.2780>}.")
    (license license:gpl2+)))

(define-public r-polminer
  (package
    (name "r-polminer")
    (version "0.8.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "polmineR" version))
       (sha256
        (base32 "0cxjy3w4k52kdjf66yysvd1yd96rv5xrzqjrkdvl4wrhxvla3202"))))
    (properties `((upstream-name . "polmineR")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tm
                             r-stringi
                             r-slam
                             r-rlang
                             r-rcppcwb
                             r-pbapply
                             r-matrix
                             r-magrittr
                             r-lifecycle
                             r-knitr
                             r-jsonlite
                             r-fs
                             r-dt
                             r-data-table
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/PolMine/polmineR")
    (synopsis "Verbs and Nouns for Corpus Analysis")
    (description
     "Package for corpus analysis using the Corpus Workbench ('CWB',
<https://cwb.sourceforge.io>) as an efficient back end for indexing and querying
large corpora.  The package offers functionality to flexibly create subcorpora
and to carry out basic statistical operations (count, co-occurrences etc.).  The
original full text of documents can be reconstructed and inspected at any time.
Beyond that, the package is intended to serve as an interface to packages
implementing advanced statistical procedures.  Respective data structures
(document-term matrices, term-co-occurrence matrices etc.) can be created based
on the indexed corpora.")
    (license license:gpl3)))

(define-public r-pollster
  (package
    (name "r-pollster")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pollster" version))
       (sha256
        (base32 "0zjyy36mapg1c3fj8cav43f1x1aqg3ysf74fgsiiwqwjhdm08hpx"))))
    (properties `((upstream-name . "pollster")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-rlang
                             r-labelled
                             r-forcats
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pollster")
    (synopsis "Calculate Crosstab and Topline Tables of Weighted Survey Data")
    (description
     "Calculate common types of tables for weighted survey data.  Options include
topline and (2-way and 3-way) crosstab tables of categorical or ordinal data as
well as summary tables of weighted numeric variables.  Optionally, include the
margin of error at selected confidence intervals including the design effect.
The design effect is calculated as described by Kish (1965)
<doi:10.1002/bimj.19680100122> beginning on page 257.  Output takes the form of
tibbles (simple data frames).  This package conveniently handles labelled data,
such as that commonly used by Stata and SPSS. Complex survey design is not
supported at this time.")
    (license license:cc0)))

(define-public r-pollimetry
  (package
    (name "r-pollimetry")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pollimetry" version))
       (sha256
        (base32 "09zmcwlgzl4fnkdg2m424ibv3izzrm595c7pi4mc3bd1g8sa2ypn"))))
    (properties `((upstream-name . "pollimetry")))
    (build-system r-build-system)
    (propagated-inputs (list r-repmis r-brms))
    (home-page "https://cran.r-project.org/package=pollimetry")
    (synopsis "Estimate Pollinator Body Size and Co-Varying Ecological Traits")
    (description
     "This package provides tools to estimate pollinator body size and co-varying
traits.  This package contains novel Bayesian predictive models of pollinator
body size (for bees and hoverflies) as well as preexisting predictive models for
pollinator body size (currently implemented for ants, bees, butterflies, flies,
moths and wasps) as well as bee tongue length and foraging distance, total field
nectar loads and wing loading.  An additional @code{GitHub} repository
<https://github.com/liamkendall/pollimetrydata> provides model objects to use
the bodysize function internally.  All models are described in Kendall et al
(2018) <doi:10.1101/397604>.")
    (license license:gpl2+)))

(define-public r-pollen
  (package
    (name "r-pollen")
    (version "0.82.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pollen" version))
       (sha256
        (base32 "0vmggbvacpbdz4zvn3cinip1b0ac44vgbydmsk6698hwgx9iwmfp"))))
    (properties `((upstream-name . "pollen")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr r-lubridate r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://nowosad.github.io/pollen/")
    (synopsis "Analysis of Aerobiological Data")
    (description
     "Supports analysis of aerobiological data.  Available features include
determination of pollen season limits, replacement of outliers (Kasprzyk and
Walanus (2014) <doi:10.1007/s10453-014-9332-8>), calculation of growing degree
days (Baskerville and Emin (1969) <doi:10.2307/1933912>), and determination of
the base temperature for growing degree days (Yang et al. (1995)
<doi:10.1016/0168-1923(94)02185-M).")
    (license license:expat)))

(define-public r-polle
  (package
    (name "r-polle")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "polle" version))
       (sha256
        (base32 "1adzq67nqgdfi0n1fjag19pl529qwj0w70wrs182m4zp5a5k2zvl"))))
    (properties `((upstream-name . "polle")))
    (build-system r-build-system)
    (propagated-inputs (list r-targeted
                             r-survival
                             r-superlearner
                             r-progressr
                             r-policytree
                             r-lava
                             r-future-apply
                             r-dyntxregime
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=polle")
    (synopsis "Policy Learning")
    (description
     "Package for evaluating user-specified finite stage policies and learning optimal
treatment policies via doubly robust loss functions.  Policy learning methods
include doubly robust learning of the blip/conditional average treatment effect
and sequential policy tree learning.  The package also include methods for
optimal subgroup analysis.  See Nordland and Holst (2022)
<doi:10.48550/@code{arXiv.2212.02335>} for documentation and references.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-politicsr
  (package
    (name "r-politicsr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "politicsR" version))
       (sha256
        (base32 "1msf394i70g9k2lcfmp0drq8qm138wdhbf9qaa5342z4wcasmvdv"))))
    (properties `((upstream-name . "politicsR")))
    (build-system r-build-system)
    (propagated-inputs (list r-ineq))
    (home-page "https://cran.r-project.org/package=politicsR")
    (synopsis "Calculating Political System Metrics")
    (description
     "This package provides a toolbox to facilitate the calculation of political
system indicators for researchers.  This package offers a variety of basic
indicators related to electoral systems, party systems, elections, and
parliamentary studies, as well as others.  Main references are: Loosemore and
Hanby (1971) <doi:10.1017/S000712340000925X>; Gallagher (1991)
<doi:10.1016/0261-3794(91)90004-C>; Laakso and Taagepera (1979)
<doi:10.1177/001041407901200101>; Rae (1968) <doi:10.1177/001041406800100305>;
HirschmaÅ (1945) <ISBN:0-520-04082-1>; Kesselman (1966) <doi:10.2307/1953769>;
Jones and Mainwaring (2003) <doi:10.1177/13540688030092002>; Rice (1925)
<doi:10.2307/2142407>; Pedersen (1979) <doi:10.1111/j.1475-6765.1979.tb01267.x>;
SANTOS (2002) <ISBN:85-225-0395-8>.")
    (license license:gpl3+)))

(define-public r-politeness
  (package
    (name "r-politeness")
    (version "0.9.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "politeness" version))
       (sha256
        (base32 "1p3d7xyyx79cq3w5qg49fc25i7v2qhfqsr938jmrnvrx5ds42i40"))))
    (properties `((upstream-name . "politeness")))
    (build-system r-build-system)
    (propagated-inputs (list r-tm
                             r-textir
                             r-stringr
                             r-stringi
                             r-spacyr
                             r-quanteda
                             r-glmnet
                             r-ggplot2
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=politeness")
    (synopsis "Detecting Politeness Features in Text")
    (description
     "Detecting markers of politeness in English natural language.  This package
allows researchers to easily visualize and quantify politeness between groups of
documents.  This package combines prior research on the linguistic markers of
politeness.  We thank the Spencer Foundation, the Hewlett Foundation, and
Harvard's Institute for Quantitative Social Science for support.")
    (license license:expat)))

(define-public r-polite
  (package
    (name "r-polite")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "polite" version))
       (sha256
        (base32 "0k0zqmm1zd0kgmqns41gxfa8pak1k11c3zbvsr0xbqc6x1bc5xyj"))))
    (properties `((upstream-name . "polite")))
    (build-system r-build-system)
    (propagated-inputs (list r-usethis
                             r-rvest
                             r-robotstxt
                             r-ratelimitr
                             r-memoise
                             r-magrittr
                             r-httr))
    (home-page "https://github.com/dmi3kno/polite")
    (synopsis "Be Nice on the Web")
    (description
     "Be responsible when scraping data from websites by following polite principles:
introduce yourself, ask for permission, take slowly and never ask twice.")
    (license license:expat)))

(define-public r-polished
  (package
    (name "r-polished")
    (version "0.8.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "polished" version))
       (sha256
        (base32 "0qn7zlxw7jr0apvn23a6x00xn5fbg2c2hg6713biwsi7iqhb0amc"))))
    (properties `((upstream-name . "polished")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-uuid
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-shinywidgets
                             r-shinyjs
                             r-shinyfeedback
                             r-shinydashboard
                             r-shinycssloaders
                             r-shiny
                             r-rmarkdown
                             r-rlang
                             r-purrr
                             r-otp
                             r-lubridate
                             r-jsonlite
                             r-jose
                             r-httr
                             r-htmltools
                             r-dt
                             r-dplyr
                             r-digest
                             r-desc
                             r-automagic))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/tychobra/polished")
    (synopsis "Authentication and Hosting for 'shiny' Apps")
    (description
     "Authentication, user administration, hosting, and additional infrastructure for
shiny apps.  See <https://polished.tech> for additional documentation and
examples.")
    (license license:expat)))

(define-public r-poliscidata
  (package
    (name "r-poliscidata")
    (version "2.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "poliscidata" version))
       (sha256
        (base32 "1ywfhksc6gcc2xk5lp6s8nn4wib92sv7p3wq92dzabf75q6mscix"))))
    (properties `((upstream-name . "poliscidata")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-weights
                             r-survey
                             r-plyr
                             r-plotrix
                             r-hmisc
                             r-gplots
                             r-descr
                             r-car
                             r-abind))
    (home-page "https://cran.r-project.org/package=poliscidata")
    (synopsis
     "Datasets and Functions Featured in Pollock and Edwards, an R Companion to Essentials of Political Analysis, Second Edition")
    (description
     "Bundles the datasets and functions used in the textbook by Philip Pollock and
Barry Edwards, an R Companion to Essentials of Political Analysis, Second
Edition.")
    (license license:cc0)))

(define-public r-polimetrics
  (package
    (name "r-polimetrics")
    (version "1.2.1.14")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "polimetrics" version))
       (sha256
        (base32 "0zzqzcfmvb6a9lav5sdgzhx4637r068q3kyjqk1ykdjg2d7s7lrv"))))
    (properties `((upstream-name . "polimetrics")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyverse
                             r-stringr
                             r-rstatix
                             r-rlang
                             r-purrr
                             r-mass
                             r-gplots
                             r-ggplot2
                             r-formula-tools
                             r-dplyr
                             r-car))
    (home-page "https://cran.r-project.org/package=polimetrics")
    (synopsis "R Tools for Political Measures")
    (description
     "This is a collection of data and functions for common metrics in political
science research.  Data measuring ideology, and functions calculating
geographical diffusion and ideological diffusion - geog.diffuse() and
ideo.dist(), respectively.  Functions derived from methods developed in: Soule
and King (2006) <doi:10.1086/499908>, Berry et al. (1998) <doi:10.2307/2991759>,
Cruz-Aceves and Mallinson (2019) <doi:10.1177/0160323X20902818>, and Grossback
et al. (2004) <doi:10.1177/1532673X04263801>.")
    (license license:gpl3)))

(define-public r-policytree
  (package
    (name "r-policytree")
    (version "1.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "policytree" version))
       (sha256
        (base32 "1a0yqg754gb793k9zrhp2zb1j5lw1kv1ziv9dr7rk2zmzgs1k5pb"))))
    (properties `((upstream-name . "policytree")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-grf r-bh))
    (home-page "https://github.com/grf-labs/policytree")
    (synopsis
     "Policy Learning via Doubly Robust Empirical Welfare Maximization over Trees")
    (description
     "Learn optimal policies via doubly robust empirical welfare maximization over
trees.  Given doubly robust reward estimates, this package finds a rule-based
treatment prescription policy, where the policy takes the form of a shallow
decision tree that is globally (or close to) optimal.")
    (license license:expat)))

(define-public r-policyportfolios
  (package
    (name "r-policyportfolios")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PolicyPortfolios" version))
       (sha256
        (base32 "1syycldfzwymymhai0ifli93iv3p8p6xbyk25mvln6zgb43iind1"))))
    (properties `((upstream-name . "PolicyPortfolios")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-reshape2
                             r-mgcv
                             r-ineq
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "http://xavier-fim.net/packages/PolicyPortfolios/")
    (synopsis
     "Tools for Managing, Measuring and Visualizing Policy Portfolios")
    (description
     "This package provides tools for simplifying the creation and management of data
structures suitable for dealing with policy portfolios, that is, two-dimensional
spaces of policy instruments and policy targets.  The package also allows to
generate measures of portfolio characteristics and facilitates their
visualization.")
    (license license:gpl3)))

(define-public r-poldis
  (package
    (name "r-poldis")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "poldis" version))
       (sha256
        (base32 "0si3lvxajp4jlgwh1paksd48yqk3qknh8k5p4zas61056ckdyj8q"))))
    (properties `((upstream-name . "poldis")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-textstem
                             r-stringr
                             r-stringi
                             r-stringdist
                             r-spacyr
                             r-quanteda
                             r-purrr
                             r-dplyr))
    (home-page "http://henriquesposito.com/poldis/")
    (synopsis "Analyse Political Texts")
    (description
     "Wrangle and annotate different types of political texts.  It also introduces
Urgency Analysis, a new method for the analysis of urgency in political texts.")
    (license license:expat)))

(define-public r-polca
  (package
    (name "r-polca")
    (version "1.6.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "poLCA" version))
       (sha256
        (base32 "05kbkyrds430v729hi56q48yfwfi5m1f371gpb4h417z5fj6137d"))))
    (properties `((upstream-name . "poLCA")))
    (build-system r-build-system)
    (propagated-inputs (list r-scatterplot3d r-mass))
    (home-page "https://github.com/dlinzer/poLCA")
    (synopsis "Polytomous Variable Latent Class Analysis")
    (description
     "Latent class analysis and latent class regression models for polytomous outcome
variables.  Also known as latent structure analysis.")
    (license license:gpl2+)))

(define-public r-polaroid
  (package
    (name "r-polaroid")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "polaroid" version))
       (sha256
        (base32 "11i1xxvj3ik8kjpm8q1ind3qkwrgyhv0sdcynwj4wld2n6i1v2v5"))))
    (properties `((upstream-name . "polaroid")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny))
    (home-page "https://github.com/jhk0530/polaroid")
    (synopsis "Create Hex Stickers with 'shiny'")
    (description
     "Create hexagonal shape sticker image.  polaroid can be used in user's web
browser.  polaroid can be used in shinyapps.io'.  In both way, user can download
created @code{hexSticker} as PNG image.  polaroid is built based on
@code{argonDash}', colourpicker and @code{hexSticker} R package.")
    (license license:expat)))

(define-public r-polarcap
  (package
    (name "r-polarcap")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PolarCAP" version))
       (sha256
        (base32 "1qqh3lnr8ny9rg17vv4qyx30fn1xrpqqbr4wqvqd64bqijlmn1s5"))))
    (properties `((upstream-name . "PolarCAP")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-countrycode))
    (home-page "https://github.com/imehlhaff/PolarCAP")
    (synopsis "Access the Polarization in Comparative Attitudes Project")
    (description
     "Distributes data from the Polarization in Comparative Attitudes Project.  Helper
functions enable data retrieval in wide and tidy formats for user-defined
countries and years.  Provides support for case-insensitive country names in
many languages.  Mehlhaff (2022)
<https://imehlhaff.net/files/Polarization%20and%20Democracy.pdf>.")
    (license license:cc0)))

(define-public r-pokemon
  (package
    (name "r-pokemon")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pokemon" version))
       (sha256
        (base32 "13j0a893w803brmwsa98x04mmz35l2vmlj1i86m0qxbjb4ysnkbb"))))
    (properties `((upstream-name . "pokemon")))
    (build-system r-build-system)
    (home-page "https://github.com/williamorim/pokemon")
    (synopsis "Pokemon Data in English and Brazilian Portuguese")
    (description
     "This package provides a dataset of Pokemon information in both English and
Brazilian Portuguese.  The dataset contains 949 rows and 22 columns, including
information such as the Pokemon's name, ID, height, weight, stats, type, and
more.")
    (license license:expat)))

(define-public r-poistweedie
  (package
    (name "r-poistweedie")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "poistweedie" version))
       (sha256
        (base32 "0afr2rzn1bdbfk2ayzrxp62zwghlpml4lvhv7kixkk8a4raxv4h3"))))
    (properties `((upstream-name . "poistweedie")))
    (build-system r-build-system)
    (home-page "https://CRAN.R-project.org/package=poistweedie")
    (synopsis "Poisson-Tweedie Exponential Family Models")
    (description "Simulation of models Poisson-Tweedie.")
    (license license:gpl2+)))

(define-public r-poissonreg
  (package
    (name "r-poissonreg")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "poissonreg" version))
       (sha256
        (base32 "1hlp6lvag9a3yv1iapcs3r14w6y5pqdb0zwlhjw88g72amwp7g43"))))
    (properties `((upstream-name . "poissonreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-parsnip
                             r-glue
                             r-generics
                             r-dplyr))
    (home-page "https://github.com/tidymodels/poissonreg")
    (synopsis "Model Wrappers for Poisson Regression")
    (description
     "Bindings for Poisson regression models for use with the parsnip package.  Models
include simple generalized linear models, Bayesian models, and zero-inflated
Poisson models (Zeileis, Kleiber, and Jackman (2008)
<doi:10.18637/jss.v027.i08>).")
    (license license:expat)))

(define-public r-poissonpca
  (package
    (name "r-poissonpca")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PoissonPCA" version))
       (sha256
        (base32 "0hdfnq427cywkpb34nj8kdjrc3yligzqag0729qnhh90si0ad036"))))
    (properties `((upstream-name . "PoissonPCA")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PoissonPCA")
    (synopsis "Poisson-Noise Corrected PCA")
    (description
     "For a multivariate dataset with independent Poisson measurement error,
calculates principal components of transformed latent Poisson means.  T. Kenney,
T. Huang, H. Gu (2019) <@code{arXiv:1904.11745>}.")
    (license license:gpl3)))

(define-public r-poissonmultinomial
  (package
    (name "r-poissonmultinomial")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PoissonMultinomial" version))
       (sha256
        (base32 "0xbbs37i23f2swd15rxrw780q7cck9qz5yc4218knxgbj9p5pn8h"))))
    (properties `((upstream-name . "PoissonMultinomial")))
    (build-system r-build-system)
    (inputs (list fftw))
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mvtnorm))
    (home-page "https://cran.r-project.org/package=PoissonMultinomial")
    (synopsis "The Poisson-Multinomial Distribution")
    (description
     "Implementation of the exact, normal approximation, and simulation-based methods
for computing the probability mass function (pmf) and cumulative distribution
function (cdf) of the Poisson-Multinomial distribution, together with a random
number generator for the distribution.  The exact method is based on
multi-dimensional fast Fourier transformation (FFT) of the characteristic
function of the Poisson-Multinomial distribution.  The normal approximation
method uses a multivariate normal distribution to approximate the pmf of the
distribution based on central limit theorem.  The simulation method is based on
the law of large numbers.  Details about the methods are available in Lin, Wang,
and Hong (2022) <DOI:10.1007/s00180-022-01299-0>.")
    (license license:gpl2+)))

(define-public r-poissonbinomial
  (package
    (name "r-poissonbinomial")
    (version "1.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PoissonBinomial" version))
       (sha256
        (base32 "0vv1hmskd1ri3p9w7hxvs62jghia76l5y0b1m7vh600qqyg45k30"))))
    (properties `((upstream-name . "PoissonBinomial")))
    (build-system r-build-system)
    (inputs (list fftw))
    (propagated-inputs (list r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/fj86/PoissonBinomial")
    (synopsis
     "Efficient Computation of Ordinary and Generalized Poisson Binomial Distributions")
    (description
     "Efficient implementations of multiple exact and approximate methods as described
in Hong (2013) <doi:10.1016/j.csda.2012.10.006>, Biscarri, Zhao & Brunner (2018)
<doi:10.1016/j.csda.2018.01.007> and Zhang, Hong & Balakrishnan (2018)
<doi:10.1080/00949655.2018.1440294> for computing the probability mass,
cumulative distribution and quantile functions, as well as generating random
numbers for both the ordinary and generalized Poisson binomial distribution.")
    (license license:gpl3)))

(define-public r-poisson-glm-mix
  (package
    (name "r-poisson-glm-mix")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "poisson.glm.mix" version))
       (sha256
        (base32 "02mnrrynlmcbcywmgm829kajlixwr91aj49d43pkfc3yxcnj815w"))))
    (properties `((upstream-name . "poisson.glm.mix")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=poisson.glm.mix")
    (synopsis "Fit High Dimensional Mixtures of Poisson GLMs")
    (description
     "Mixtures of Poisson Generalized Linear Models for high dimensional count data
clustering.  The (multivariate) responses can be partitioned into set of blocks.
 Three different parameterizations of the linear predictor are considered.  The
models are estimated according to the EM algorithm with an efficient
initialization scheme <doi:10.1016/j.csda.2014.07.005>.")
    (license license:gpl2)))

(define-public r-poisnor
  (package
    (name "r-poisnor")
    (version "1.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PoisNor" version))
       (sha256
        (base32 "1qi54fg2b1qc305kaihfwr2dkh3b3dd6l67yp9gi61n29kfxaqmf"))))
    (properties `((upstream-name . "PoisNor")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-matrix r-corpcor))
    (home-page "https://cran.r-project.org/package=PoisNor")
    (synopsis
     "Simultaneous Generation of Multivariate Data with Poisson and Normal Marginals")
    (description
     "Generates multivariate data with count and continuous variables with a
pre-specified correlation matrix.  The count and continuous variables are
assumed to have Poisson and normal marginals, respectively.  The data generation
mechanism is a combination of the normal to anything principle and a connection
between Poisson and normal correlations in the mixture.  The details of the
method are explained in Yahav et al. (2012) <DOI:10.1002/asmb.901>.")
    (license license:gpl2)))

(define-public r-poisnonnor
  (package
    (name "r-poisnonnor")
    (version "1.6.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PoisNonNor" version))
       (sha256
        (base32 "1572ma1vk17gp2j9fvmbiaxmnmb773mw15z6mlcf1slgpayp1ldb"))))
    (properties `((upstream-name . "PoisNonNor")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-mass r-corpcor r-bb))
    (home-page "https://cran.r-project.org/package=PoisNonNor")
    (synopsis "Simultaneous Generation of Count and Continuous Data")
    (description
     "Generation of count (assuming Poisson distribution) and continuous data (using
Fleishman polynomials) simultaneously.  The details of the method are explained
in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-poismf
  (package
    (name "r-poismf")
    (version "0.4.0-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "poismf" version))
       (sha256
        (base32 "1pl0vxrczknfrxx70wc6wy0hjxzdk7jip9was5saxby3b5as2bhp"))))
    (properties `((upstream-name . "poismf")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (home-page "https://github.com/david-cortes/poismf")
    (synopsis
     "Factorization of Sparse Counts Matrices Through Poisson Likelihood")
    (description
     "This package creates a non-negative low-rank approximate factorization of a
sparse counts matrix by maximizing Poisson likelihood with L1/L2 regularization
(e.g. for implicit-feedback recommender systems or bag-of-words-based topic
modeling) (Cortes, (2018) <@code{arXiv:1811.01908>}), which usually leads to
very sparse user and item factors (over 90% zero-valued).  Similar to
hierarchical Poisson factorization (HPF), but follows an optimization-based
approach with regularization instead of a hierarchical prior, and is fit through
gradient-based methods instead of variational inference.")
    (license license:bsd-2)))

(define-public r-poisferobust
  (package
    (name "r-poisferobust")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "poisFErobust" version))
       (sha256
        (base32 "1i6y3745i385y3r45i0xk7ciw1k6brmn709k5qhx8xphq0k4an5n"))))
    (properties `((upstream-name . "poisFErobust")))
    (build-system r-build-system)
    (propagated-inputs (list r-glmmml r-data-table))
    (home-page "https://bitbucket.org/ew-btb/poisson-fe-robust")
    (synopsis "Poisson Fixed Effects Robust")
    (description
     "Computation of robust standard errors of Poisson fixed effects models, following
Wooldridge (1999).")
    (license license:expat)))

(define-public r-poisdoublesamp
  (package
    (name "r-poisdoublesamp")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "poisDoubleSamp" version))
       (sha256
        (base32 "0s0x3x3f620752ggai0jlkgrg1mh53giyh92q2lpwyvawyg8nyav"))))
    (properties `((upstream-name . "poisDoubleSamp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://github.com/dkahle/poisDoubleSamp")
    (synopsis "Confidence Intervals with Poisson Double Sampling")
    (description
     "This package provides functions to create confidence intervals for ratios of
Poisson rates under misclassification using double sampling.  Implementations of
the methods described in Kahle, D., P. Young, B. Greer, and D. Young (2016).
\"Confidence Intervals for the Ratio of Two Poisson Rates Under One-Way
Differential Misclassification Using Double Sampling.\" Computational Statistics
& Data Analysis, 95:122â132.")
    (license license:expat)))

(define-public r-poisbinordnor
  (package
    (name "r-poisbinordnor")
    (version "1.6.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PoisBinOrdNor" version))
       (sha256
        (base32 "02v9p82z85vkbhap2xn1rz8ibvvavsq8mgxxzicl749j4j9hcrmf"))))
    (properties `((upstream-name . "PoisBinOrdNor")))
    (build-system r-build-system)
    (propagated-inputs (list r-psych r-mvtnorm r-matrix r-genord r-corpcor))
    (home-page "https://cran.r-project.org/package=PoisBinOrdNor")
    (synopsis
     "Data Generation with Poisson, Binary, Ordinal and Normal Components")
    (description
     "Generation of multiple count, binary, ordinal and normal variables
simultaneously given the marginal characteristics and association structure.
The details of the method are explained in Demirtas et al. (2012)
<DOI:10.1002/sim.5362>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-poisbinordnonnor
  (package
    (name "r-poisbinordnonnor")
    (version "1.5.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PoisBinOrdNonNor" version))
       (sha256
        (base32 "16c0z3974lvvr7xvrv8frzn9m5sg2jicjan52xgg7dxlm3rsij1s"))))
    (properties `((upstream-name . "PoisBinOrdNonNor")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-mass r-genord r-corpcor r-bb))
    (home-page "https://cran.r-project.org/package=PoisBinOrdNonNor")
    (synopsis "Generation of Up to Four Different Types of Variables")
    (description
     "Generation of a chosen number of count, binary, ordinal, and continuous random
variables, with specified correlations and marginal properties.  The details of
the method are explained in Demirtas (2012) <DOI:10.1002/sim.5362>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-poisbinord
  (package
    (name "r-poisbinord")
    (version "1.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PoisBinOrd" version))
       (sha256
        (base32 "0s6v0minnwqx025nfn3kkkhbh8pzx6wgxars92kh6x9gdzf9i6ka"))))
    (properties `((upstream-name . "PoisBinOrd")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-matrix r-genord r-corpcor))
    (home-page "https://cran.r-project.org/package=PoisBinOrd")
    (synopsis "Data Generation with Poisson, Binary and Ordinal Components")
    (description
     "Generation of multiple count, binary and ordinal variables simultaneously given
the marginal characteristics and association structure.  Throughout the package,
the word Poisson is used to imply count data under the assumption of Poisson
distribution.  The details of the method are explained in Amatya, A. and
Demirtas, H. (2015) <DOI:10.1080/00949655.2014.953534>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-poisbinom
  (package
    (name "r-poisbinom")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "poisbinom" version))
       (sha256
        (base32 "0mjxg0s3nck7qrmq4bbrhwxfwr6fqml54s9k8q1mkfzlifa7qyf7"))))
    (properties `((upstream-name . "poisbinom")))
    (build-system r-build-system)
    (inputs (list fftw))
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=poisbinom")
    (synopsis "Faster Implementation of the Poisson-Binomial Distribution")
    (description
     "This package provides the probability, distribution, and quantile functions and
random number generator for the Poisson-Binomial distribution.  This package
relies on FFTW to implement the discrete Fourier transform, so that it is much
faster than the existing implementation of the same algorithm in R.")
    (license license:gpl2+)))

(define-public r-poisbinnonnor
  (package
    (name "r-poisbinnonnor")
    (version "1.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PoisBinNonNor" version))
       (sha256
        (base32 "0m8ajizij9kvp057rqmd10zadmvr4a1ka664jc9rdjh2xqgd5s2i"))))
    (properties `((upstream-name . "PoisBinNonNor")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-matrix r-corpcor r-bb))
    (home-page "https://cran.r-project.org/package=PoisBinNonNor")
    (synopsis "Data Generation with Poisson, Binary and Continuous Components")
    (description
     "Generation of multiple count, binary and continuous variables simultaneously
given the marginal characteristics and association structure.  Throughout the
package, the word Poisson is used to imply count data under the assumption of
Poisson distribution.  The details of the method are explained in Amatya et al.
(2015) <DOI:10.1080/00949655.2014.953534>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-pointres
  (package
    (name "r-pointres")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pointRes" version))
       (sha256
        (base32 "1a3yj94rg16fbc47ahwdq17i8pganm6vg6xfl3b0glybw5f2hpkj"))))
    (properties `((upstream-name . "pointRes")))
    (build-system r-build-system)
    (propagated-inputs (list r-tripler r-plyr r-ggplot2 r-dplr r-desctools))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pointRes")
    (synopsis "Analyzing Pointer Years and Components of Resilience")
    (description
     "This package provides functions to calculate and plot event and pointer years as
well as resilience indices.  Designed for dendroecological applications, but
also suitable to analyze patterns in other ecological time series.")
    (license license:gpl2+)))

(define-public r-pointr
  (package
    (name "r-pointr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pointr" version))
       (sha256
        (base32 "1qfxcyzi2sa49x00hidz054njj9mm7gkhv99faafb9x4gp3pcq0v"))))
    (properties `((upstream-name . "pointr")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr))
    (home-page "https://github.com/jsugarelli/pointr/")
    (synopsis "Working Comfortably with Pointers and Shortcuts to R Objects")
    (description
     "R has no built-in pointer functionality.  The pointr package fills this gap and
lets you create pointers to R objects, including subsets of dataframes.  This
makes your R code more readable and maintainable.")
    (license license:gpl3)))

(define-public r-pointfore
  (package
    (name "r-pointfore")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PointFore" version))
       (sha256
        (base32 "0ml6xcdl4ygr01q0cjwd11ql7wal91jnf3hs1rfhfr4mh8jpgivx"))))
    (properties `((upstream-name . "PointFore")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich
                             r-mass
                             r-lubridate
                             r-gmm
                             r-ggplot2
                             r-car
                             r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PointFore")
    (synopsis
     "Interpretation of Point Forecasts as State-Dependent Quantiles and Expectiles")
    (description
     "Estimate specification models for the state-dependent level of an optimal
quantile/expectile forecast.  Wald Tests and the test of overidentifying
restrictions are implemented.  Plotting of the estimated specification model is
possible.  The package contains two data sets with forecasts and realizations:
the daily accumulated precipitation at London, UK from the high-resolution model
of the European Centre for Medium-Range Weather Forecasts (ECMWF,
<https://www.ecmwf.int/>) and GDP growth Greenbook data by the US Federal
Reserve.  See Schmidt, Katzfuss and Gneiting (2015) <@code{arXiv:1506.01917>}
for more details on the identification and estimation of a directive behind a
point forecast.")
    (license license:cc0)))

(define-public r-pointedsdms
  (package
    (name "r-pointedsdms")
    (version "2.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PointedSDMs" version))
       (sha256
        (base32 "0cy5a6v1qn5n6rzw21lwx0yz9x6d6jc85ypr8sijvirqkw4h6x46"))))
    (properties `((upstream-name . "PointedSDMs")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-sp
                             r-sf
                             r-raster
                             r-r6
                             r-r-devices
                             r-inlabru
                             r-ggplot2
                             r-blockcv))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/PhilipMostert/PointedSDMs")
    (synopsis
     "Fit Models Derived from Point Processes to Species Distributions using 'inlabru'")
    (description
     "Integrated species distribution modeling is a rising field in quantitative
ecology thanks to significant rises in the quantity of data available, increases
in computational speed and the proven benefits of using such models.  Despite
this, the general software to help ecologists construct such models in an
easy-to-use framework is lacking.  We therefore introduce the R package
@code{PointedSDMs}': which provides the tools to help ecologists set up
integrated models and perform inference on them.  There are also functions
within the package to help run spatial cross-validation for model selection, as
well as generic plotting and predicting functions.  An introduction to these
methods is discussed in Issac, Jarzyna, Keil, Dambly, Boersch-Supan, Browning,
Freeman, Golding, Guillera-Arroita, Henrys, Jarvis, Lahoz-Monfort, Pagel,
Pescott, Schmucki, Simmonds and OâHara (2020)
<doi:10.1016/j.tree.2019.08.006>.")
    (license license:gpl3+)))

(define-public r-pointdensityp
  (package
    (name "r-pointdensityp")
    (version "0.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pointdensityP" version))
       (sha256
        (base32 "0n7p3b77m47avbr7n85knf31m9rlp43mdl3748ysj6wvxvhnivx4"))))
    (properties `((upstream-name . "pointdensityP")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table))
    (home-page "https://cran.r-project.org/package=pointdensityP")
    (synopsis "Point Density for Geospatial Data")
    (description
     "The function pointdensity returns a density count and the temporal average for
every point in the original list.  The dataframe returned includes four columns:
lat, lon, count, and date_avg.  The \"lat\" column is the original latitude data;
the \"lon\" column is the original longitude data; the \"count\" is the density
count of the number of points within a radius of radius*grid_size (the
neighborhood); and the date_avg column includes the average date of each point
in the neighborhood.")
    (license license:expat)))

(define-public r-pointblank
  (package
    (name "r-pointblank")
    (version "0.12.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pointblank" version))
       (sha256
        (base32 "1wwb9ncji7450rbbmrh9mznl4pnpmxq8ibx785bz9dsswdglzfhq"))))
    (properties `((upstream-name . "pointblank")))
    (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-tidyselect
                             r-tidyr
                             r-tibble
                             r-testthat
                             r-scales
                             r-rlang
                             r-magrittr
                             r-knitr
                             r-htmltools
                             r-gt
                             r-glue
                             r-fs
                             r-dplyr
                             r-digest
                             r-dbplyr
                             r-dbi
                             r-cli
                             r-blastula
                             r-base64enc))
    (native-inputs (list esbuild))
    (home-page "https://rstudio.github.io/pointblank/")
    (synopsis
     "Data Validation and Organization of Metadata for Local and Remote Tables")
    (description
     "Validate data in data frames, tibble objects, Spark @code{DataFrames}', and
database tables.  Validation pipelines can be made using easily-readable,
consecutive validation steps.  Upon execution of the validation plan, several
reporting options are available.  User-defined thresholds for failure rates
allow for the determination of appropriate reporting actions.  Many other
workflows are available including an information management workflow, where the
aim is to record, collect, and generate useful information on data tables.")
    (license license:expat)))

(define-public r-point
  (package
    (name "r-point")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "POINT" version))
       (sha256
        (base32 "11xxmz3ncgnbs8yn7d53y1bc0g9rrlgz0d1ixjrxh4wh9qlyfzq6"))))
    (properties `((upstream-name . "POINT")))
    (build-system r-build-system)
    (propagated-inputs (list r-rarpack r-matrix r-compquadform))
    (home-page "https://cran.r-project.org/package=POINT")
    (synopsis "Protein Structure Guided Local Test")
    (description
     "This package provides an implementation of a rare variant association test that
utilizes protein tertiary structure to increase signal and to identify likely
causal variants.  Performs structure-guided collapsing, which leads to local
tests that borrow information from neighboring variants on a protein and that
provide association information on a variant-specific level.  For details of the
implemented method see West, R. M., Lu, W., Rotroff, D. M., Kuenemann, M.,
Chang, S-M., Wagner M. J., Buse, J. B., Motsinger-Reif, A., Fourches, D., and
Tzeng, J-Y. (2019) <doi:10.1371/journal.pcbi.1006722>.")
    (license license:gpl2)))

(define-public r-poilog
  (package
    (name "r-poilog")
    (version "0.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "poilog" version))
       (sha256
        (base32 "0dk2c65b7aac2ak3ajgh3wa2jhqyfv1qp90flw8xcx3f5w5gyv7s"))))
    (properties `((upstream-name . "poilog")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=poilog")
    (synopsis "Poisson Lognormal and Bivariate Poisson Lognormal Distribution")
    (description
     "This package provides functions for obtaining the density, random deviates and
maximum likelihood estimates of the Poisson lognormal distribution and the
bivariate Poisson lognormal distribution.")
    (license license:gpl3)))

(define-public r-poiclaclu
  (package
    (name "r-poiclaclu")
    (version "1.0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PoiClaClu" version))
       (sha256
        (base32 "1q89b0nypz2iivmgwg1nb1l7p285wy00s40j3qp8zc78ra1rjhyx"))))
    (properties `((upstream-name . "PoiClaClu")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PoiClaClu")
    (synopsis
     "Classification and Clustering of Sequencing Data Based on a Poisson Model")
    (description
     "This package implements the methods described in the paper, Witten (2011)
Classification and Clustering of Sequencing Data using a Poisson Model, Annals
of Applied Statistics 5(4) 2493-2518.")
    (license license:gpl2)))

(define-public r-pogromcydanych
  (package
    (name "r-pogromcydanych")
    (version "1.7.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PogromcyDanych" version))
       (sha256
        (base32 "0601n76nybvahd5xsghwbydc7gfcvj1qbpqhw06d4mzy97am7yl0"))))
    (properties `((upstream-name . "PogromcyDanych")))
    (build-system r-build-system)
    (propagated-inputs (list r-smarterpoland r-dplyr))
    (home-page "https://cran.r-project.org/package=PogromcyDanych")
    (synopsis
     "DataCrunchers (PogromcyDanych) is the Massive Online Open Course that Brings R and Statistics to the People")
    (description
     "The data sets used in the online course ,,@code{PogromcyDanych}''.  You can
process data in many ways.  The course Data Crunchers will introduce you to this
variety.  For this reason we will work on datasets of different size (from
several to several hundred thousand rows), with various level of complexity
(from two to two thousand columns) and prepared in different formats (text data,
quantitative data and qualitative data).  All of these data sets were gathered
in a single big package called @code{PogromcyDanych} to facilitate access to
them.  It contains all sorts of data sets such as data about offer prices of
cars, results of opinion polls, information about changes in stock market
indices, data about names given to newborn babies, ski jumping results or
information about outcomes of breast cancer patients treatment.")
    (license license:gpl3)))

(define-public r-pogit
  (package
    (name "r-pogit")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pogit" version))
       (sha256
        (base32 "02h8j7bfayigq9az46mvi7n918p99533mnijnz1nkzrdi2v0x824"))))
    (properties `((upstream-name . "pogit")))
    (build-system r-build-system)
    (propagated-inputs (list r-plyr r-logistf r-ggplot2))
    (home-page "https://cran.r-project.org/package=pogit")
    (synopsis "Bayesian Variable Selection for a Poisson-Logistic Model")
    (description
     "Bayesian variable selection for regression models of under-reported count data
as well as for (overdispersed) Poisson, negative binomal and binomial logit
regression models using spike and slab priors.")
    (license license:gpl2)))

(define-public r-poet
  (package
    (name "r-poet")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "POET" version))
       (sha256
        (base32 "0w3jhj45sxisyrpcsazbrbcsz7rmraw71jjm0zixbcgc4klb98ar"))))
    (properties `((upstream-name . "POET")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=POET")
    (synopsis "Principal Orthogonal ComplEment Thresholding (POET) Method")
    (description
     "Estimate large covariance matrices in approximate factor models by thresholding
principal orthogonal complements.")
    (license license:gpl2)))

(define-public r-poems
  (package
    (name "r-poems")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "poems" version))
       (sha256
        (base32 "0pfiv55ql9kjqgkc4dblc5l36626mxknga2gbh1xw5wwzpbiw7xm"))))
    (properties `((upstream-name . "poems")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm
                             r-trend
                             r-raster
                             r-r6
                             r-qs
                             r-metrology
                             r-lhs
                             r-gdistance
                             r-fossil
                             r-foreach
                             r-doparallel
                             r-abc))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/GlobalEcologyLab/poems")
    (synopsis "Pattern-Oriented Ensemble Modeling System")
    (description
     "This package provides a framework of interoperable R6 classes (Chang, 2020,
<https://CRAN.R-project.org/package=R6>) for building ensembles of viable models
via the pattern-oriented modeling (POM) approach (Grimm et al.,2005,
<doi:10.1126/science.1116681>).  The package includes classes for encapsulating
and generating model parameters, and managing the POM workflow.  The workflow
includes: model setup; generating model parameters via Latin hyper-cube sampling
(Iman & Conover, 1980, <doi:10.1080/03610928008827996>); running multiple
sampled model simulations; collating summary results; and validating and
selecting an ensemble of models that best match known patterns.  By default,
model validation and selection utilizes an approximate Bayesian computation
(ABC) approach (Beaumont et al., 2002, <doi:10.1093/genetics/162.4.2025>),
although alternative user-defined functionality could be employed.  The package
includes a spatially explicit demographic population model simulation engine,
which incorporates default functionality for density dependence, correlated
environmental stochasticity, stage-based transitions, and distance-based
dispersal.  The user may customize the simulator by defining functionality for
translocations, harvesting, mortality, and other processes, as well as defining
the sequence order for the simulator processes.  The framework could also be
adapted for use with other model simulators by utilizing its extendable
(inheritable) base classes.")
    (license license:gpl3)))

(define-public r-podcleaner
  (package
    (name "r-podcleaner")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "podcleaner" version))
       (sha256
        (base32 "1r8mvf8bw329n3ifi2dyy4220nbij2rgkvq1xlqzynxf8qadxbrl"))))
    (properties `((upstream-name . "podcleaner")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-stringi
                             r-rlang
                             r-readr
                             r-purrr
                             r-progress
                             r-magrittr
                             r-fuzzyjoin
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=podcleaner")
    (synopsis "Legacy Scottish Post Office Directories Cleaner")
    (description
     "Attempts to clean optical character recognition (OCR) errors in legacy Scottish
Post Office Directories.  Further attempts to match records from trades and
general directories.")
    (license license:gpl3+)))

(define-public r-podbay
  (package
    (name "r-podbay")
    (version "1.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PoDBAY" version))
       (sha256
        (base32 "1c1dmggcc8k4h56063r97gjavd44llc51j54qs1wck9zir414i8w"))))
    (properties `((upstream-name . "PoDBAY")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-ggplot2 r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PoDBAY")
    (synopsis "Vaccine Efficacy Estimation Package")
    (description
     "Set of functions that implement the @code{PoDBAY} method, described in the
publication A method to estimate probability of disease and vaccine efficacy
from clinical trial immunogenicity data by Julie Dudasova, Regina Laube, Chandni
Valiathan, Matthew C. Wiener, Ferdous Gheyas, Pavel Fiser, Justina Ivanauskaite,
Frank Liu and Jeffrey R. Sachs (NPJ Vaccines, 2021),
<doi:10.1038/s41541-021-00377-6>.")
    (license license:gpl3)))

(define-public r-pod
  (package
    (name "r-pod")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "POD" version))
       (sha256
        (base32 "1xg2xzfmzg2qrwd02xy4fwsfmccigw6lm5266z6w1kxzb7p7bbw0"))))
    (properties `((upstream-name . "POD")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=POD")
    (synopsis "Probability of Detection for Qualitative PCR Methods")
    (description
     "This tool computes the probability of detection (POD) curve and the limit of
detection (LOD), i.e.  the number of copies of the target DNA sequence required
to ensure a 95 % probability of detection (LOD95).  Other quantiles of the LOD
can be specified.  This is a reimplementation of the mathematical-statistical
modelling of the validation of qualitative polymerase chain reaction (PCR)
methods within a single laboratory as provided by the commercial tool PROLab
<http://quodata.de/>.  The modelling itself has been described by Uhlig et al.
(2015) <doi:10.1007/s00769-015-1112-9>.")
    (license license:gpl3)))

(define-public r-pocrm
  (package
    (name "r-pocrm")
    (version "0.13")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pocrm" version))
       (sha256
        (base32 "0d4iggxjbl95fz872q9v1wr30nga39sdd5dvwa2zrrm73ak81cz3"))))
    (properties `((upstream-name . "pocrm")))
    (build-system r-build-system)
    (propagated-inputs (list r-nnet r-dfcrm))
    (home-page "https://cran.r-project.org/package=pocrm")
    (synopsis "Dose Finding in Drug Combination Phase I Trials Using PO-CRM")
    (description
     "This package provides functions to implement and simulate the partial order
continual reassessment method (PO-CRM) of Wages, Conaway and O'Quigley (2011)
<doi:10.1177/1740774511408748> for use in Phase I trials of combinations of
agents.  Provides a function for generating a set of initial guesses (skeleton)
for the toxicity probabilities at each combination that correspond to the set of
possible orderings of the toxicity probabilities specified by the user.")
    (license license:gpl2)))

(define-public r-pocre
  (package
    (name "r-pocre")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "POCRE" version))
       (sha256
        (base32 "0kni15dr7qhldn68w51mzg6pyk95dx717qy0f9230hpbg97791hz"))))
    (properties `((upstream-name . "POCRE")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma r-ggplot2 r-ebayesthresh))
    (home-page "https://cran.r-project.org/package=POCRE")
    (synopsis "Penalized Orthogonal-Components Regression")
    (description
     "Penalized orthogonal-components regression (POCRE) is a supervised dimension
reduction method for high-dimensional data.  It sequentially constructs
orthogonal components (with selected features) which are maximally correlated to
the response residuals.  POCRE can also construct common components for multiple
responses and thus build up latent-variable models.")
    (license license:gpl2)))

(define-public r-pocketapi
  (package
    (name "r-pocketapi")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pocketapi" version))
       (sha256
        (base32 "0n4fyx4ldm14zy3pz7gygwhahi3p25misvxlmy8l9kqqr51fg26a"))))
    (properties `((upstream-name . "pocketapi")))
    (build-system r-build-system)
    (propagated-inputs (list r-usethis
                             r-tibble
                             r-purrr
                             r-magrittr
                             r-jsonlite
                             r-httr
                             r-glue
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/CorrelAid/pocketapi/")
    (synopsis "Wrapper Around the 'Pocket' API")
    (description
     "This package provides functions that interface with the Pocket API
(<https://getpocket.com/developer/>).  Allows the user to get, add, and modify
items in their own Pocket account.")
    (license license:expat)))

(define-public r-poa
  (package
    (name "r-poa")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PoA" version))
       (sha256
        (base32 "0y8aqz1ainxdglxlfp9iqhvkmirpmc7dvwrcxz2l8lln86yrfqx6"))))
    (properties `((upstream-name . "PoA")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-pracma r-nloptr r-dplyr))
    (home-page "https://cran.r-project.org/package=PoA")
    (synopsis "Finds the Price of Anarchy for Routing Games")
    (description
     "Computes the optimal flow, Nash flow and the Price of Anarchy for any routing
game defined within the game theoretical framework.  The input is a routing game
in the form of itâs cost and flow functions.  Then transforms this into an
optimisation problem, allowing both Nash and Optimal flows to be solved by
nonlinear optimisation.  See <https://en.wikipedia.org/wiki/Congestion_game> and
Knight and Harper (2013) <doi:10.1016/j.ejor.2013.04.003> for more information.")
    (license license:gpl2)))

(define-public r-pnsibge
  (package
    (name "r-pnsibge")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PNSIBGE" version))
       (sha256
        (base32 "19p8gky9ilcz8bw4hk16bprykghjvl0vhlhy37kq01lsz3hxvpaj"))))
    (properties `((upstream-name . "PNSIBGE")))
    (build-system r-build-system)
    (propagated-inputs (list r-timedate
                             r-tibble
                             r-survey
                             r-readxl
                             r-readr
                             r-rcurl
                             r-projmgr
                             r-magrittr
                             r-httr
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=PNSIBGE")
    (synopsis "Downloading, Reading and Analyzing PNS Microdata")
    (description
     "This package provides tools for downloading, reading and analyzing the National
Survey of Health - PNS, a household survey from Brazilian Institute of Geography
and Statistics - IBGE. The data must be downloaded from the official website
<https://www.ibge.gov.br/>.  Further analysis must be made using package
survey'.")
    (license license:gpl3)))

(define-public r-pndsibge
  (package
    (name "r-pndsibge")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PNDSIBGE" version))
       (sha256
        (base32 "16shzhxp9vmlimp3r94ff652ca0ap7skb7iga6saynhnlk2fbm6b"))))
    (properties `((upstream-name . "PNDSIBGE")))
    (build-system r-build-system)
    (propagated-inputs (list r-timedate
                             r-tibble
                             r-survey
                             r-readxl
                             r-readr
                             r-rcurl
                             r-projmgr
                             r-magrittr
                             r-httr
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=PNDSIBGE")
    (synopsis
     "Downloading, Reading and Analyzing PNDS Microdata - Package in Development")
    (description
     "This package provides tools for downloading, reading and analyzing the National
Survey of Demographic and Health - PNDS, a household survey from Brazilian
Institute of Geography and Statistics - IBGE. The data must be downloaded from
the official website <https://www.ibge.gov.br/>.  Further analysis must be made
using package survey'.")
    (license license:gpl3)))

(define-public r-pnar
  (package
    (name "r-pnar")
    (version "1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PNAR" version))
       (sha256
        (base32 "0x7vfj4r3z3rsh056j8sh7jzsmk6wpzgksvidybl8ra2z1sc69x9"))))
    (properties `((upstream-name . "PNAR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rfast2
                             r-rfast
                             r-nloptr
                             r-igraph
                             r-foreach
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=PNAR")
    (synopsis "Poisson Network Autoregressive Models")
    (description
     "Quasi likelihood-based methods for estimating linear and log-linear Poisson
Network Autoregression models with p lags and covariates.  Tools for testing the
linearity versus several non-linear alternatives.  Tools for simulation of
multivariate count distributions, from linear and non-linear PNAR models, by
using a specific copula construction.  References include: Armillotta, M. and K.
Fokianos (2023). \"Nonlinear network autoregression\".  Annals of Statistics,
51(6): 2526--2552. <doi:10.1214/23-AOS2345>.  Armillotta, M. and K. Fokianos
(2024). \"Count network autoregression\".  Journal of Time Series Analysis, 45(4):
584--612. <doi:10.1111/jtsa.12728>.  Armillotta, M., Tsagris, M. and Fokianos,
K. (2024). \"Inference for Network Count Time Series with the R Package PNAR\".
The R Journal, 15/4: 255--269. <doi:10.32614/RJ-2023-094>.")
    (license license:gpl2+)))

(define-public r-pnadcibge
  (package
    (name "r-pnadcibge")
    (version "0.7.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PNADcIBGE" version))
       (sha256
        (base32 "1pj482lanhkw389f9dwdh5h24icdcrqhnsadbbbidcaij02yraym"))))
    (properties `((upstream-name . "PNADcIBGE")))
    (build-system r-build-system)
    (propagated-inputs (list r-timedate
                             r-tibble
                             r-survey
                             r-readxl
                             r-readr
                             r-rcurl
                             r-projmgr
                             r-magrittr
                             r-httr
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=PNADcIBGE")
    (synopsis "Downloading, Reading and Analyzing PNADC Microdata")
    (description
     "This package provides tools for downloading, reading and analyzing the
Continuous National Household Sample Survey - PNADC, a household survey from
Brazilian Institute of Geography and Statistics - IBGE. The data must be
downloaded from the official website <https://www.ibge.gov.br/>.  Further
analysis must be made using package survey'.")
    (license license:gpl3)))

(define-public r-pmxtools
  (package
    (name "r-pmxtools")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pmxTools" version))
       (sha256
        (base32 "1is56is0xg9syfwma90wqp7rxbgcv36qcpmcxnz0dlf7ypnvqsak"))))
    (properties `((upstream-name . "pmxTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tibble
                             r-stringr
                             r-scales
                             r-pknca
                             r-patchwork
                             r-mass
                             r-magrittr
                             r-ggplot2
                             r-gghalves
                             r-ggdist
                             r-dplyr
                             r-data-tree
                             r-chron))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/kestrel99/pmxTools")
    (synopsis "Pharmacometric and Pharmacokinetic Toolkit")
    (description
     "Pharmacometric tools for common data analytical tasks; closed-form solutions for
calculating concentrations at given times after dosing based on compartmental PK
models (1-compartment, 2-compartment and 3-compartment, covering infusions,
zero- and first-order absorption, and lag times, after single doses and at
steady state, per Bertrand & Mentre (2008)
<http://lixoft.com/wp-content/uploads/2016/03/PKPDlibrary.pdf>); parametric
simulation from NONMEM-generated parameter estimates and other output; and
parsing, tabulating and plotting results generated by Perl-speaks-NONMEM
(@code{PsN}).")
    (license license:gpl2)))

(define-public r-pmxpartab
  (package
    (name "r-pmxpartab")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pmxpartab" version))
       (sha256
        (base32 "13r4xy0jvxdwjz3gic67fzgq87v18vs7df1ggd3hq89h6f28fxar"))))
    (properties `((upstream-name . "pmxpartab")))
    (build-system r-build-system)
    (propagated-inputs (list r-table1 r-knitr r-htmltools r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pmxpartab")
    (synopsis "Parameter Tables for PMx Analyses")
    (description
     "Generate nicely formatted HTML tables to display estimation results for
pharmacometric models.")
    (license license:gpl3)))

(define-public r-pmxcv
  (package
    (name "r-pmxcv")
    (version "0.0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pmxcv" version))
       (sha256
        (base32 "0x74pjmj9335bnh2zbhidxwzq28f4a9281dhgnkbyllfq2kw2dsc"))))
    (properties `((upstream-name . "pmxcv")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pmxcv")
    (synopsis "Integration-Based Coefficients of Variance")
    (description
     "Estimate coefficient of variance percent (CV%) for any arbitrary distribution,
including some built-in estimates for commonly-used transformations in
pharmacometrics.  Methods are described in various sources, but applied here as
summarized in: Prybylski, (2024) <doi:10.1007/s40262-023-01343-2>.")
    (license license:expat)))

(define-public r-pmxcode
  (package
    (name "r-pmxcode")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pmxcode" version))
       (sha256
        (base32 "1m183y1pfd7as9j3ph1in52fi1fi82f9y7fphbjh4r0ch3pl4dws"))))
    (properties `((upstream-name . "pmxcode")))
    (build-system r-build-system)
    (propagated-inputs (list r-xfun
                             r-tidyr
                             r-shinyfiles
                             r-shinybs
                             r-shinyace
                             r-shiny
                             r-rlang
                             r-rhandsontable
                             r-readr
                             r-rclipboard
                             r-pillar
                             r-markdown
                             r-magrittr
                             r-jsonlite
                             r-htmlwidgets
                             r-htmltools
                             r-golem
                             r-glue
                             r-dplyr
                             r-config
                             r-bslib
                             r-bsicons))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pmxcode")
    (synopsis "Create Pharmacometric Models")
    (description
     "This package provides a user interface to create or modify pharmacometric models
for various modeling and simulation software platforms.")
    (license license:expat)))

(define-public r-pmwr
  (package
    (name "r-pmwr")
    (version "1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PMwR" version))
       (sha256
        (base32 "0qcxsz2szgjh437ykr5hc3yml85mw3kp5nj4dg6n6cbvk3sk8dq0"))))
    (properties `((upstream-name . "PMwR")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-textutils
                             r-orgutils
                             r-nmof
                             r-fastmatch
                             r-datetimeutils))
    (home-page "https://enricoschumann.net/PMwR/")
    (synopsis "Portfolio Management with R")
    (description
     "This package provides tools for the practical management of financial
portfolios: backtesting investment and trading strategies, computing profit/loss
and returns, analysing trades, handling lists of transactions, reporting, and
more.  The package provides a small set of reliable, efficient and convenient
tools for processing and analysing trade/portfolio data.  The manual provides
all the details; it is available from
<https://enricoschumann.net/R/packages/P@code{MwR/manual/PMwR.html>}.  Examples
and descriptions of new features are provided at
<https://enricoschumann.net/notes/P@code{MwR/>}.")
    (license license:gpl3)))

(define-public r-pmwg
  (package
    (name "r-pmwg")
    (version "0.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pmwg" version))
       (sha256
        (base32 "17q2w48bgk7m4jp5567ig0gvvcz14rswf36q8561ykd0kyaxq2vj"))))
    (properties `((upstream-name . "pmwg")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-mass r-condmvnorm r-coda r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/university-of-newcastle-research/pmwg")
    (synopsis "Particle Metropolis Within Gibbs")
    (description
     "This package provides an R implementation of the Particle Metropolis within
Gibbs sampler for model parameter, covariance matrix and random effect
estimation.  A more general implementation of the sampler based on the paper by
Gunawan, D., Hawkins, G. E., Tran, M. N., Kohn, R., & Brown, S. D. (2020)
<doi:10.1016/j.jmp.2020.102368>.  An HTML tutorial document describing the
package is available at
<https://university-of-newcastle-research.github.io/@code{samplerDoc/>} and
includes several detailed examples, some background and troubleshooting steps.")
    (license license:gpl3)))

(define-public r-pmvalsampsize
  (package
    (name "r-pmvalsampsize")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pmvalsampsize" version))
       (sha256
        (base32 "1wrdid3rcl7h7sdgm2gxkmcb4vdzg0krkmb09bh50y7nnrq70i22"))))
    (properties `((upstream-name . "pmvalsampsize")))
    (build-system r-build-system)
    (propagated-inputs (list r-proc))
    (home-page "https://cran.r-project.org/package=pmvalsampsize")
    (synopsis "Sample Size for External Validation of a Prediction Model")
    (description
     "Computes the minimum sample size required for the external validation of an
existing multivariable prediction model using the criteria proposed by Archer
(2020) <doi:10.1002/sim.8766> and Riley (2021) <doi:10.1002/sim.9025>.")
    (license license:gpl3+)))

(define-public r-pmultinom
  (package
    (name "r-pmultinom")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pmultinom" version))
       (sha256
        (base32 "0p2amb3y4jl24r21fii1qpmdbn5pfgywrx8k7x1jxpg49rk7k0rp"))))
    (properties `((upstream-name . "pmultinom")))
    (build-system r-build-system)
    (propagated-inputs (list r-fftw))
    (home-page "https://cran.r-project.org/package=pmultinom")
    (synopsis "One-Sided Multinomial Probabilities")
    (description
     "This package implements multinomial CDF (P(N1<=n1, ..., Nk<=nk)) and tail
probabilities (P(N1>n1, ..., Nk>nk)), as well as probabilities with both
constraints (P(l1<N1<=u1, ..., lk<Nk<=uk)).  Uses a method suggested by Bruce
Levin (1981) <doi:10.1214/aos/1176345593>.")
    (license license:agpl3)))

(define-public r-pmsampsize
  (package
    (name "r-pmsampsize")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pmsampsize" version))
       (sha256
        (base32 "0sch7inlg9lvb9y8cf3gqwmgxwn2zi3xw3fp7rnz05978f8fa088"))))
    (properties `((upstream-name . "pmsampsize")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pmsampsize")
    (synopsis "Sample Size for Development of a Prediction Model")
    (description
     "Computes the minimum sample size required for the development of a new
multivariable prediction model using the criteria proposed by Riley et al.
(2018) <doi: 10.1002/sim.7992>.  pmsampsize can be used to calculate the minimum
sample size for the development of models with continuous, binary or survival
(time-to-event) outcomes.  Riley et al. (2018) <doi: 10.1002/sim.7992> lay out a
series of criteria the sample size should meet.  These aim to minimise the
overfitting and to ensure precise estimation of key parameters in the prediction
model.")
    (license license:gpl3+)))

(define-public r-pmr
  (package
    (name "r-pmr")
    (version "1.2.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pmr" version))
       (sha256
        (base32 "14m3y1sywnd77gyifz2nv5q7h85f81wl5c8glvc100zgljvbgwvc"))))
    (properties `((upstream-name . "pmr")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pmr")
    (synopsis "Probability Models for Ranking Data")
    (description
     "Descriptive statistics (mean rank, pairwise frequencies, and marginal matrix),
Analytic Hierarchy Process models (with Saaty's and Koczkodaj's
inconsistencies), probability models (Luce models, distance-based models, and
rank-ordered logit models) and visualization with multidimensional preference
analysis for ranking data are provided.  Current, only complete rankings are
supported by this package.")
    (license license:gpl2)))

(define-public r-pmparser
  (package
    (name "r-pmparser")
    (version "1.0.20")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pmparser" version))
       (sha256
        (base32 "0csjvy730c0pd7b2rq732whv7wb3pahnrc1mvzh33ymlbhd6jng8"))))
    (properties `((upstream-name . "pmparser")))
    (build-system r-build-system)
    (inputs (list unzip sqlite))
    (propagated-inputs (list r-xml2
                             r-withr
                             r-rcurl
                             r-r-utils
                             r-jsonlite
                             r-iterators
                             r-glue
                             r-foreach
                             r-dbi
                             r-data-table
                             r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://pmparser.hugheylab.org")
    (synopsis
     "Create and Maintain a Relational Database of Data from PubMed/MEDLINE")
    (description
     "This package provides a simple interface for extracting various elements from
the publicly available @code{PubMed} XML files, incorporating @code{PubMed's}
regular updates, and combining the data with the NIH Open Citation Collection.
See Schoenbachler and Hughey (2021) <doi:10.7717/peerj.11071>.")
    (license license:gpl2)))

(define-public r-pmmltransformations
  (package
    (name "r-pmmltransformations")
    (version "1.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pmmlTransformations" version))
       (sha256
        (base32 "0315kb5x04pidpzr04yaxyynp0mz4ilzp7vkspq4agjkds5hyc88"))))
    (properties `((upstream-name . "pmmlTransformations")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://www.softwareag.com/zementis")
    (synopsis "Transforms Input Data from a PMML Perspective")
    (description
     "Allows for data to be transformed before using it to construct models.  Builds
structures to allow functions in the PMML package to output transformation
details in addition to the model in the resulting PMML file.  The Predictive
Model Markup Language (PMML) is an XML-based language which provides a way for
applications to define machine learning, statistical and data mining models and
to share models between PMML compliant applications.  More information about the
PMML industry standard and the Data Mining Group can be found at
<http://www.dmg.org>.  The generated PMML can be imported into any PMML
consuming application, such as Zementis Predictive Analytics products, which
integrate with web services, relational database systems and deploy natively on
Hadoop in conjunction with Hive, Spark or Storm, as well as allow predictive
analytics to be executed for IBM z Systems mainframe applications and real-time,
streaming analytics platforms.")
    (license (license:fsdg-compatible "GPL (>= 2.1)"))))

(define-public r-pmml
  (package
    (name "r-pmml")
    (version "2.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pmml" version))
       (sha256
        (base32 "1faad5yqyh6dx40056w74vlyw4wxjgh41jv8b2vf2gh26laf22kk"))))
    (properties `((upstream-name . "pmml")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml r-stringr))
    (native-inputs (list r-knitr))
    (home-page "https://open-source.softwareag.com/r-pmml/")
    (synopsis "Generate PMML for Various Models")
    (description
     "The Predictive Model Markup Language (PMML) is an XML-based language which
provides a way for applications to define machine learning, statistical and data
mining models and to share models between PMML compliant applications.  More
information about the PMML industry standard and the Data Mining Group can be
found at <http://dmg.org/>.  The generated PMML can be imported into any PMML
consuming application, such as Zementis Predictive Analytics products.  The
package isofor (used for anomaly detection) can be installed with
devtools::install_github(\"gravesee/isofor\").")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-pmledecon
  (package
    (name "r-pmledecon")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pmledecon" version))
       (sha256
        (base32 "10yry425y2ghcnp3mrjharf4blcqvwnxsm7kz90mzfq91qg4q5lv"))))
    (properties `((upstream-name . "pmledecon")))
    (build-system r-build-system)
    (propagated-inputs (list r-splitstackshape r-rmutil))
    (home-page "https://cran.r-project.org/package=pmledecon")
    (synopsis "Deconvolution Density Estimation using Penalized MLE")
    (description
     "Given a sample with additive measurement error, the package estimates the
deconvolution density - that is, the density of the underlying distribution of
the sample without measurement error.  The method maximises the log-likelihood
of the estimated density, plus a quadratic smoothness penalty.  The distribution
of the measurement error can be either a known family, or can be estimated from
a \"pure error\" sample.  For known error distributions, the package supports
Normal, Laplace or Beta distributed error.  For unknown error distribution, a
pure error sample independent from the data is used.")
    (license license:gpl3+)))

(define-public r-pmlbr
  (package
    (name "r-pmlbr")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pmlbr" version))
       (sha256
        (base32 "06kac7h4skccs48ixyjnxxry38dci9cgkckfvkpzaqhzzlbvmn0g"))))
    (properties `((upstream-name . "pmlbr")))
    (build-system r-build-system)
    (propagated-inputs (list r-fnn))
    (home-page "https://github.com/EpistasisLab/pmlbr")
    (synopsis
     "Interface to the Penn Machine Learning Benchmarks Data Repository")
    (description
     "Check available classification and regression data sets from the PMLB repository
and download them.  The PMLB repository
(<https://github.com/@code{EpistasisLab/pmlbr>}) contains a curated collection
of data sets for evaluating and comparing machine learning algorithms.  These
data sets cover a range of applications, and include binary/multi-class
classification problems and regression problems, as well as combinations of
categorical, ordinal, and continuous features.  There are currently over 150
datasets included in the PMLB repository.")
    (license (list license:gpl2
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-pmhtutorial
  (package
    (name "r-pmhtutorial")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pmhtutorial" version))
       (sha256
        (base32 "1hcw8hwpsivbhz86lnzy08q9n8vahnq8f7rmi4xkmg6b44582cqx"))))
    (properties `((upstream-name . "pmhtutorial")))
    (build-system r-build-system)
    (propagated-inputs (list r-quandl r-mvtnorm))
    (home-page "https://github.com/compops/pmh-tutorial-rpkg")
    (synopsis "Minimal Working Examples for Particle Metropolis-Hastings")
    (description
     "Routines for state estimate in a linear Gaussian state space model and a simple
stochastic volatility model using particle filtering.  Parameter inference is
also carried out in these models using the particle Metropolis-Hastings
algorithm that includes the particle filter to provided an unbiased estimator of
the likelihood.  This package is a collection of minimal working examples of
these algorithms and is only meant for educational use and as a start for
learning to them on your own.")
    (license license:gpl2)))

(define-public r-pmevapotranspiration
  (package
    (name "r-pmevapotranspiration")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PMEvapotranspiration" version))
       (sha256
        (base32 "1q9nz6jvbi280zdxwhd0zw3wxbq3sq26xbiby4q4bs2pm24mb3j6"))))
    (properties `((upstream-name . "PMEvapotranspiration")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PMEvapotranspiration")
    (synopsis
     "Calculation of the Penman-Monteith Evapotranspiration using Weather Variables")
    (description
     "The Food and Agriculture Organization-56 Penman-Monteith is one of the important
method for estimating evapotranspiration from vegetated land areas.  This
package helps to calculate reference evapotranspiration using the weather
variables collected from weather station.  Evapotranspiration is the process of
water transfer from the land surface to the atmosphere through evaporation from
soil and other surfaces and transpiration from plants.  The package aims to
support agricultural, hydrological, and environmental research by offering
accurate and accessible reference evapotranspiration calculation.  This package
has been developed using concept of CÃ³rdova et al.
(2015)<doi:10.1016/j.apm.2022.09.004> and Debnath et al. (2015)
<doi:10.1007/s40710-015-0107-1>.")
    (license license:gpl3)))

(define-public r-pmev
  (package
    (name "r-pmev")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pmev" version))
       (sha256
        (base32 "0pyzxp1i62y04yzv729mmn5iaz02gi3gg73h5yd35ksihd46vqfy"))))
    (properties `((upstream-name . "pmev")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-vdiffr
                             r-scales
                             r-rlang
                             r-lubridate
                             r-ggplot2
                             r-dplyr))
    (home-page "https://github.com/david-hammond/pmev")
    (synopsis "Calculates Earned Value for a Project Schedule")
    (description
     "Given a project schedule and associated costs, this package calculates the
earned value to date.  It is an implementation of Project Management Body of
Knowledge (PMBOK) methodologies (reference Project Management Institute. (2021).
 A guide to the Project Management Body of Knowledge (PMBOK guide) (7th ed.).
Project Management Institute, Newtown Square, PA, ISBN 9781628256673 (pdf)).")
    (license license:expat)))

(define-public r-pmetar
  (package
    (name "r-pmetar")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pmetar" version))
       (sha256
        (base32 "14vz227mq1kjrmhb0gknx4alk0krl6aaj7rss3mm2hf832kr5724"))))
    (properties `((upstream-name . "pmetar")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-rcurl
                             r-magrittr
                             r-lubridate
                             r-httr
                             r-dplyr
                             r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/prcwiek/pmetar")
    (synopsis "Processing METAR Weather Reports")
    (description
     "Allows to download current and historical METAR weather reports extract and
parse basic parameters and present main weather information.  Current reports
are downloaded from Aviation Weather Center
<https://aviationweather.gov/data/metar/> and historical reports from Iowa
Environmental Mesonet web page of Iowa State University ASOS-AWOS-METAR
<http://mesonet.agron.iastate.edu/AWOS/>.")
    (license license:gpl3+)))

(define-public r-pmem
  (package
    (name "r-pmem")
    (version "0.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pMEM" version))
       (sha256
        (base32 "0y18s0y3mh1jx43gpx32awysgxbh9gyyb5mp7kpj5asi98bwmzjj"))))
    (properties `((upstream-name . "pMEM")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pMEM")
    (synopsis "Predictive Moran's Eigenvector Maps")
    (description
     "Calculation of Predictive Moran's eigenvector maps (@code{pMEM}), as defined by
GuÃ©nard and Legendre (In Press) \"Spatially-explicit predictions using spatial
eigenvector maps\" <doi:10.5281/zenodo.13356457>.  Methods in Ecology and
Evolution.  This method enables scientists to predict the values of
spatially-structured environmental variables.  Multiple types of @code{pMEM} are
defined, each one implemented on the basis of spatial weighting function taking
a range parameter, and sometimes also a shape parameter.  The code's modular
nature enables programers to implement new @code{pMEM} by defining new spatial
weighting functions.")
    (license license:gpl3)))

(define-public r-pmd
  (package
    (name "r-pmd")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pmd" version))
       (sha256
        (base32 "14xra1d6qx6gxnpiwfsxz200qhmm7s4zy2j8h5kwbvbpnykdyr5c"))))
    (properties `((upstream-name . "pmd")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer r-igraph r-envigcms r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://yufree.github.io/pmd/")
    (synopsis
     "Paired Mass Distance Analysis for GC/LC-MS Based Non-Targeted Analysis and Reactomics Analysis")
    (description
     "Paired mass distance (PMD) analysis proposed in Yu, Olkowicz and Pawliszyn
(2018) <doi:10.1016/j.aca.2018.10.062> for gas/liquid chromatographyâmass
spectrometry (GC/LC-MS) based non-targeted analysis.  PMD analysis including
@code{GlobalStd} algorithm and structure/reaction directed analysis.
@code{GlobalStd} algorithm could found independent peaks in m/z-retention time
profiles based on retention time hierarchical cluster analysis and frequency
analysis of paired mass distances within retention time groups.  Structure
directed analysis could be used to find potential relationship among those
independent peaks in different retention time groups based on frequency of
paired mass distances.  Reactomics analysis could also be performed to build PMD
network, assign sources and make biomarker reaction discovery.  GUIs for PMD
analysis is also included as shiny applications.")
    (license license:gpl2)))

(define-public r-pmclust
  (package
    (name "r-pmclust")
    (version "0.2-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pmclust" version))
       (sha256
        (base32 "0wldf0bj406qxmlc7hsbamiak7qcwf8ixng9dfkswh511m3p05jd"))))
    (properties `((upstream-name . "pmclust")))
    (build-system r-build-system)
    (propagated-inputs (list r-pbdmpi r-mass))
    (home-page "https://pbdr.org/")
    (synopsis
     "Parallel Model-Based Clustering using Expectation-Gathering-Maximization Algorithm for Finite Mixture Gaussian Model")
    (description
     "Aims to utilize model-based clustering (unsupervised) for high dimensional and
ultra large data, especially in a distributed manner.  The code employs
@code{pbdMPI} to perform a expectation-gathering-maximization algorithm for
finite mixture Gaussian models.  The unstructured dispersion matrices are
assumed in the Gaussian models.  The implementation is default in the single
program multiple data programming model.  The code can be executed through
@code{pbdMPI} and MPI implementations such as @code{OpenMPI} and MPICH'.  See
the High Performance Statistical Computing website
<https://snoweye.github.io/hpsc/> for more information, documents and examples.")
    (license license:gpl2+)))

(define-public r-pmcalibration
  (package
    (name "r-pmcalibration")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pmcalibration" version))
       (sha256
        (base32 "1zhg55jdf8jd92216waa2g18mvni8qh6j2idgf77k6mcwb63m9zw"))))
    (properties `((upstream-name . "pmcalibration")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-pbapply
                             r-mgcv
                             r-mass
                             r-hmisc
                             r-chk
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/stephenrho/pmcalibration")
    (synopsis "Calibration Curves for Clinical Prediction Models")
    (description
     "Fit calibrations curves for clinical prediction models and calculate several
associated metrics (Eavg, E50, E90, Emax).  Ideally predicted probabilities from
a prediction model should align with observed probabilities.  Calibration curves
relate predicted probabilities (or a transformation thereof) to observed
outcomes via a flexible non-linear smoothing function.  pmcalibration allows
users to choose between several smoothers (regression splines, generalized
additive models/GAMs, lowess, loess).  Both binary and time-to-event outcomes
are supported.  See Van Calster et al. (2016)
<doi:10.1016/j.jclinepi.2015.12.005>; Austin and Steyerberg (2019)
<doi:10.1002/sim.8281>; Austin et al. (2020) <doi:10.1002/sim.8570>.")
    (license license:gpl3)))

(define-public r-pmc
  (package
    (name "r-pmc")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pmc" version))
       (sha256
        (base32 "1j2qiabq2qd4i8y28s87k0jxbbvv2yhjxmxsvvh9i8gzar17p5g7"))))
    (properties `((upstream-name . "pmc")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-phytools
                             r-ouch
                             r-ggplot2
                             r-geiger
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/cboettig/pmc")
    (synopsis "Phylogenetic Monte Carlo")
    (description
     "Monte Carlo based model choice for applied phylogenetics of continuous traits.
Method described in Carl Boettiger, Graham Coop, Peter Ralph (2012) Is your
phylogeny informative? Measuring the power of comparative methods, Evolution 66
(7) 2240-51. <doi:10.1111/j.1558-5646.2011.01574.x>.")
    (license license:cc0)))

(define-public r-pmartr
  (package
    (name "r-pmartr")
    (version "2.4.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pmartR" version))
       (sha256
        (base32 "0ay61ypvhc04r6j91h4jc5iqjxshk0dzcbqhxamrjdwmrs6yyzng"))))
    (properties `((upstream-name . "pmartR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-rrcov
                             r-rcpparmadillo
                             r-rcpp
                             r-rcolorbrewer
                             r-purrr
                             r-pcamethods
                             r-patchwork
                             r-parallelly
                             r-mvtnorm
                             r-magrittr
                             r-glmpca
                             r-ggplot2
                             r-foreach
                             r-e1071
                             r-dplyr
                             r-doparallel
                             r-data-table
                             r-bh))
    (home-page "https://pmartr.github.io/pmartR/")
    (synopsis
     "Panomics Marketplace - Quality Control and Statistical Analysis for Panomics Data")
    (description
     "This package provides functionality for quality control processing and
statistical analysis of mass spectrometry (MS) omics data, in particular
proteomic (either at the peptide or the protein level), lipidomic, and
metabolomic data, as well as RNA-seq based count data and nuclear magnetic
resonance (NMR) data.  This includes data transformation, specification of
groups that are to be compared against each other, filtering of features and/or
samples, data normalization, data summarization (correlation, PCA), and
statistical comparisons between defined groups.  Implements methods described
in: Webb-Robertson et al. (2014) <doi:10.1074/mcp.M113.030932>.  Webb-Robertson
et al. (2011) <doi:10.1002/pmic.201100078>.  Matzke et al. (2011)
<doi:10.1093/bioinformatics/btr479>.  Matzke et al. (2013)
<doi:10.1002/pmic.201200269>.  Polpitiya et al. (2008)
<doi:10.1093/bioinformatics/btn217>.  Webb-Robertson et al. (2010)
<doi:10.1021/pr1005247>.")
    (license license:bsd-2)))

(define-public r-pmapscore
  (package
    (name "r-pmapscore")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PMAPscore" version))
       (sha256
        (base32 "174ll5gr2csy4r5iz92zq6jqq7yfcpiyyigzklgmzpah5v0k5854"))))
    (properties `((upstream-name . "PMAPscore")))
    (build-system r-build-system)
    (propagated-inputs (list r-survminer
                             r-survival
                             r-proc
                             r-org-hs-eg-db
                             r-maftools
                             r-glmnet
                             r-clusterprofiler))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PMAPscore")
    (synopsis
     "Identify Prognosis-Related Pathways Altered by Somatic Mutation")
    (description
     "We innovatively defined a pathway mutation accumulate perturbation score
(PMAPscore) to reflect the position and the cumulative effect of the genetic
mutations at the pathway level.  Based on the PMAPscore of pathways, identified
prognosis-related pathways altered by somatic mutation and predict immunotherapy
efficacy by constructing a multiple-pathway-based risk model (Tarca, Adi
Laurentiu et al (2008) <doi:10.1093/bioinformatics/btn577>).")
    (license license:gpl2+)))

(define-public r-pm3
  (package
    (name "r-pm3")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pm3" version))
       (sha256
        (base32 "0mnhjvgra6a270ji1916siyn63qz9w1kmpqabsg3r68cjqbp0a0w"))))
    (properties `((upstream-name . "pm3")))
    (build-system r-build-system)
    (propagated-inputs (list r-tableone))
    (home-page "https://cran.r-project.org/package=pm3")
    (synopsis "Propensity Score Matching for Unordered 3-Group Data")
    (description
     "You can use this program for 3 sets of categorical data for propensity score
matching.  Assume that the data has 3 different categorical variables.  You can
use it to perform propensity matching of baseline indicator groupings.  The
matching will make the differences in the baseline data smaller.  This method
was described by Alvaro Fuentes (2022) <doi:10.1080/00273171.2021.1925521>.")
    (license license:gpl3)))

(define-public r-plutor
  (package
    (name "r-plutor")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plutor" version))
       (sha256
        (base32 "09wwv47rigjl61q2c7bhq5qa2gghklzia5pqm4vwp66v3j7nb1pn"))))
    (properties `((upstream-name . "plutor")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-scales
                             r-rlang
                             r-repr
                             r-rcolorbrewer
                             r-purrr
                             r-magrittr
                             r-ggsci
                             r-ggplot2
                             r-ggh4x
                             r-dplyr
                             r-cli
                             r-baizer))
    (home-page "https://github.com/william-swl/plutor")
    (synopsis "Useful Functions for Visualization")
    (description
     "In ancient Roman mythology, Pluto was the ruler of the underworld and presides
over the afterlife.  Pluto was frequently conflated with Plutus', the god of
wealth, because mineral wealth was found underground.  When plotting with R, you
try once, twice, practice again and again, and finally you get a pretty figure
you want.  It's a plot tour', a tour about repetition and reward.  Hope plutor
helps you on the tour!")
    (license license:expat)))

(define-public r-pluscode2
  (package
    (name "r-pluscode2")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plusCode2" version))
       (sha256
        (base32 "0kslqzngb5y8d5x7gc1kja345f69c7azilwzcspv127k00mg4n3k"))))
    (properties `((upstream-name . "plusCode2")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf))
    (home-page "https://github.com/Armando-d/plusCode2")
    (synopsis "Coordinates to 'Plus Code' Conversion Tool")
    (description
     "Generates Plus Code of geometric objects or data frames that contain them,
giving the possibility to specify the precision of the area.  The main feature
of the package comes from the open-source code developed by Google Inc.  present
in the repository
<https://github.com/google/open-location-code/blob/main/java/src/main/java/com/google/openlocationcode/@code{OpenLocationCode.java>}.
 For details about Plus Code', visit <https://maps.google.com/pluscodes/> or
<https://github.com/google/open-location-code>.")
    (license license:expat)))

(define-public r-pluscode
  (package
    (name "r-pluscode")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pluscode" version))
       (sha256
        (base32 "1j9yr5j6mb346a5dn7v9bsppgnxdj1ryimlla4a08rv7bjyq5i6q"))))
    (properties `((upstream-name . "pluscode")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-httr))
    (home-page "https://cran.r-project.org/package=pluscode")
    (synopsis "Encoder for Google 'Pluscodes'")
    (description
     "Retrieves a pluscode by inputting latitude and longitude.  Includes additional
functions to retrieve neighbouring pluscodes'.")
    (license license:gpl2)))

(define-public r-plume
  (package
    (name "r-plume")
    (version "0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plume" version))
       (sha256
        (base32 "1zl3mbi6igv3wirg6zjil1l36vlja1h964v6kwh7ma97mfrdh1km"))))
    (properties `((upstream-name . "plume")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-vctrs
                             r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-readr
                             r-r6
                             r-purrr
                             r-lifecycle
                             r-knitr
                             r-jsonlite
                             r-glue
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://arnaudgallou.github.io/plume/")
    (synopsis "Simple Author Handler for Scientific Writing")
    (description
     "Handles and formats author information in scientific writing in R Markdown and
Quarto'.  plume provides easy-to-use and flexible tools for injecting author
metadata in YAML headers as well as generating author and contribution lists
(among others) as strings from tabular data.")
    (license license:gpl3+)))

(define-public r-plumbr
  (package
    (name "r-plumbr")
    (version "0.6.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plumbr" version))
       (sha256
        (base32 "1ncrqg8j4baqrjkd95fkdanaylwl1ygdr7xzc891gig3rknx0n1i"))))
    (properties `((upstream-name . "plumbr")))
    (build-system r-build-system)
    (propagated-inputs (list r-objectsignals))
    (home-page "https://github.com/ggobi/plumbr/wiki")
    (synopsis "Mutable and Dynamic Data Models")
    (description
     "The base R data.frame, like any vector, is copied upon modification.  This
behavior is at odds with that of GUIs and interactive graphics.  To rectify
this, plumbr provides a mutable, dynamic tabular data model.  Models may be
chained together to form the complex plumbing necessary for sophisticated
graphical interfaces.  Also included is a general framework for linking
datasets; an typical use case would be a linked brush.")
    (license license:gpl2+)))

(define-public r-plumbertableau
  (package
    (name "r-plumbertableau")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plumbertableau" version))
       (sha256
        (base32 "1r4a7c5zagqm2qiyj2k8ywal0dg5kfx17ckkv1ks8wqzkpgr3c07"))))
    (properties `((upstream-name . "plumbertableau")))
    (build-system r-build-system)
    (propagated-inputs (list r-urltools
                             r-stringi
                             r-rlang
                             r-promises
                             r-plumber
                             r-markdown
                             r-magrittr
                             r-later
                             r-knitr
                             r-jsonlite
                             r-httr
                             r-httpuv
                             r-htmltools
                             r-debugme
                             r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://rstudio.github.io/plumbertableau/")
    (synopsis "Turn 'Plumber' APIs into 'Tableau' Extensions")
    (description
     "Build Plumber APIs that can be used in Tableau workbooks.  Annotations in R
comments allow APIs to conform to the Tableau Analytics Extension specification,
so that R code can be used to power Tableau workbooks.")
    (license license:expat)))

(define-public r-plumberdeploy
  (package
    (name "r-plumberdeploy")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plumberDeploy" version))
       (sha256
        (base32 "0icl35wck94scjyqw6di2sl6fq7zlvd8hm13fw79iqr0wyrich0r"))))
    (properties `((upstream-name . "plumberDeploy")))
    (build-system r-build-system)
    (propagated-inputs (list r-ssh r-lifecycle r-jsonlite r-analogsea))
    (home-page "https://github.com/meztez/plumberDeploy")
    (synopsis "Plumber Deployment")
    (description
     "Gives the ability to automatically deploy a plumber API from R functions on
@code{DigitalOcean} and other cloud-based servers.")
    (license license:expat)))

(define-public r-plugdensity
  (package
    (name "r-plugdensity")
    (version "0.8-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plugdensity" version))
       (sha256
        (base32 "1gxjr6984p4awps5syzknzjyhpnjzqshv9c3cdx4xbkp1ax3xrw9"))))
    (properties `((upstream-name . "plugdensity")))
    (build-system r-build-system)
    (home-page "http://curves-etc.r-forge.r-project.org/")
    (synopsis "Plug-in Kernel Density Estimation")
    (description
     "Kernel density estimation with global bandwidth selection via \"plug-in\".")
    (license license:gpl2+)))

(define-public r-plu
  (package
    (name "r-plu")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plu" version))
       (sha256
        (base32 "0s4k5vqgwbw7ayjhs2j8w9az7pn8km5hykkic1ii4jpdiy03c23g"))))
    (properties `((upstream-name . "plu")))
    (build-system r-build-system)
    (propagated-inputs (list r-lifecycle))
    (home-page "https://pkg.rossellhayes.com/plu/")
    (synopsis "Dynamically Pluralize Phrases")
    (description
     "Converts English phrases to singular or plural form based on the length of an
associated vector.  Contains helper functions to create natural language lists
from vectors and to include the length of a vector in natural language.")
    (license license:expat)))

(define-public r-pltesim
  (package
    (name "r-pltesim")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pltesim" version))
       (sha256
        (base32 "0mvqp8di2gwhd9wc9kvnnpxd3hkr9b9lj6g5qlkqcbrgkq6fjahs"))))
    (properties `((upstream-name . "pltesim")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-coresim))
    (home-page "https://CRAN.R-project.org/package=pltesim")
    (synopsis
     "Simulate Probabilistic Long-Term Effects in Models with Temporal Dependence")
    (description
     "Calculates and depicts probabilistic long-term effects in binary models with
temporal dependence variables.  The package performs two tasks.  First, it
calculates the change in the probability of the event occurring given a change
in a theoretical variable.  Second, it calculates the rolling difference in the
future probability of the event for two scenarios: one where the event occurred
at a given time and one where the event does not occur.  The package is
consistent with the recent movement to depict meaningful and easy-to-interpret
quantities of interest with the requisite measures of uncertainty.  It is the
first to make it easy for researchers to interpret short- and long-term effects
of explanatory variables in binary autoregressive models, which can have
important implications for the correct interpretation of these models.")
    (license license:gpl3+)))

(define-public r-plsvarsel
  (package
    (name "r-plsvarsel")
    (version "0.9.12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plsVarSel" version))
       (sha256
        (base32 "0130hqnjwgm2l7sacxh5m98gm35qxgqwylbk42jnlp03azwqdbax"))))
    (properties `((upstream-name . "plsVarSel")))
    (build-system r-build-system)
    (propagated-inputs (list r-progress
                             r-praznik
                             r-pls
                             r-mvtnorm
                             r-mass
                             r-genalg
                             r-bdsmatrix))
    (home-page "https://github.com/khliland/plsVarSel/")
    (synopsis "Variable Selection in Partial Least Squares")
    (description
     "Interfaces and methods for variable selection in Partial Least Squares.  The
methods include filter methods, wrapper methods and embedded methods.  Both
regression and classification is supported.")
    (license license:gpl2+)))

(define-public r-plsrglm
  (package
    (name "r-plsrglm")
    (version "1.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plsRglm" version))
       (sha256
        (base32 "1xpf4rz4midrl62yn39bq4ax5yl8h18kcyv2ckxis888yv5b1bqr"))))
    (properties `((upstream-name . "plsRglm")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-mass r-car r-boot r-bipartite))
    (native-inputs (list r-r-rsp))
    (home-page "https://fbertran.github.io/plsRglm/")
    (synopsis "Partial Least Squares Regression for Generalized Linear Models")
    (description
     "This package provides (weighted) Partial least squares Regression for
generalized linear models and repeated k-fold cross-validation of such models
using various criteria <@code{arXiv:1810.01005>}.  It allows for missing data in
the explanatory variables.  Bootstrap confidence intervals constructions are
also available.")
    (license license:gpl3)))

(define-public r-plsrcox
  (package
    (name "r-plsrcox")
    (version "1.7.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plsRcox" version))
       (sha256
        (base32 "1fkbynjlwi070yshnqcmw2f331yzr61f1mvm5dbw3iwg9ldhcbwr"))))
    (properties `((upstream-name . "plsRcox")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-survcomp
                             r-survauc
                             r-rms
                             r-risksetroc
                             r-plsrglm
                             r-pls
                             r-mixomics
                             r-lars
                             r-kernlab))
    (home-page "http://fbertran.github.io/plsRcox/")
    (synopsis
     "Partial Least Squares Regression for Cox Models and Related Techniques")
    (description
     "This package provides Partial least squares Regression and various regular,
sparse or kernel, techniques for fitting Cox models in high dimensional settings
<doi:10.1093/bioinformatics/btu660>, Bastien, P., Bertrand, F., Meyer N.,
Maumy-Bertrand, M. (2015), Deviance residuals-based sparse PLS and sparse kernel
PLS regression for censored data, Bioinformatics, 31(3):397-404.  Cross
validation criteria were studied in <@code{arXiv:1810.02962>}, Bertrand, F.,
Bastien, Ph.  and Maumy-Bertrand, M. (2018), Cross validating extensions of
kernel, sparse or regular partial least squares regression models to censored
data.")
    (license license:gpl3)))

(define-public r-plsrbeta
  (package
    (name "r-plsrbeta")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plsRbeta" version))
       (sha256
        (base32 "0xikaxg1n298j8mm4vzzn7bfma1lmhak39aawfhp3ny3j1d8llr4"))))
    (properties `((upstream-name . "plsRbeta")))
    (build-system r-build-system)
    (propagated-inputs (list r-plsrglm
                             r-mvtnorm
                             r-mass
                             r-formula
                             r-boot
                             r-betareg))
    (native-inputs (list r-knitr))
    (home-page "https://fbertran.github.io/plsRbeta/")
    (synopsis "Partial Least Squares Regression for Beta Regression Models")
    (description
     "This package provides Partial least squares Regression for (weighted) beta
regression models (Bertrand 2013, <http://journal-sfds.fr/article/view/215>) and
k-fold cross-validation of such models using various criteria.  It allows for
missing data in the explanatory variables.  Bootstrap confidence intervals
constructions are also available.")
    (license license:gpl3)))

(define-public r-plspm
  (package
    (name "r-plspm")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plspm" version))
       (sha256
        (base32 "00kbyi5qqm51wsli3h8kfshjs99wa602rbmi1jss65vw3p1jhsqs"))))
    (properties `((upstream-name . "plspm")))
    (build-system r-build-system)
    (propagated-inputs (list r-turner r-tester r-shape r-diagram r-amap))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/gastonstat/plspm")
    (synopsis "Partial Least Squares Path Modeling (PLS-PM)")
    (description
     "Partial Least Squares Path Modeling (PLS-PM), Tenenhaus, Esposito Vinzi,
Chatelin, Lauro (2005) <doi:10.1016/j.csda.2004.03.005>, analysis for both
metric and non-metric data, as well as REBUS analysis, Esposito Vinzi,
Trinchera, Squillacciotti, and Tenenhaus (2008) <doi:10.1002/asmb.728>.")
    (license license:gpl3)))

(define-public r-plsmselect
  (package
    (name "r-plsmselect")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plsmselect" version))
       (sha256
        (base32 "044mwdqv5vw0apmx3yhn6xkdmw6baalvb8c6hhmawcs279kwi1m9"))))
    (properties `((upstream-name . "plsmselect")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mgcv r-glmnet r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=plsmselect")
    (synopsis
     "Linear and Smooth Predictor Modelling with Penalisation and Variable Selection")
    (description
     "Fit a model with potentially many linear and smooth predictors.  Interaction
effects can also be quantified.  Variable selection is done using penalisation.
For l1-type penalties we use iterative steps alternating between using linear
predictors (lasso) and smooth predictors (generalised additive model).")
    (license license:gpl2)))

(define-public r-plsmod
  (package
    (name "r-plsmod")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plsmod" version))
       (sha256
        (base32 "1fyv2v3jmf1hdii9z477ijy5d9gvpc3jg9cyl9lbplgc18l2pd2b"))))
    (properties `((upstream-name . "plsmod")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-parsnip
                             r-mixomics
                             r-magrittr
                             r-generics
                             r-dplyr))
    (home-page "https://plsmod.tidymodels.org")
    (synopsis "Model Wrappers for Projection Methods")
    (description
     "Bindings for additional regression models for use with the parsnip package,
including ordinary and spare partial least squares models for regression and
classification (Rohart et al (2017) <doi:10.1371/journal.pcbi.1005752>).")
    (license license:expat)))

(define-public r-plsmmlasso
  (package
    (name "r-plsmmlasso")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plsmmLasso" version))
       (sha256
        (base32 "0xdpscm6villr4fby19jvg60y3mc81bcj6hwavrbmnbp02b4lhcj"))))
    (properties `((upstream-name . "plsmmLasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-scalreg
                             r-rlang
                             r-mvtnorm
                             r-mass
                             r-hdi
                             r-glmnet
                             r-ggplot2
                             r-dplyr))
    (home-page "https://github.com/Sami-Leon/plsmmLasso")
    (synopsis
     "Variable Selection and Inference for Partial Semiparametric Linear Mixed-Effects Model")
    (description
     "This package implements a partial linear semiparametric mixed-effects model
(PLSMM) featuring a random intercept and applies a lasso penalty to both the
fixed effects and the coefficients associated with the nonlinear function.  The
model also accommodates interactions between the nonlinear function and a
grouping variable, allowing for the capture of group-specific nonlinearities.
Nonlinear functions are modeled using a set of bases functions.  Estimation is
conducted using a penalized Expectation-Maximization algorithm, and the package
offers flexibility in choosing between various information criteria for model
selection.  Post-selection inference is carried out using a debiasing method,
while inference on the nonlinear functions employs a bootstrap approach.")
    (license license:gpl3+)))

(define-public r-plsimcpp
  (package
    (name "r-plsimcpp")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PLSiMCpp" version))
       (sha256
        (base32 "05pldxlszz4kcdqmzli31jgjd9v6sb3r89yaijc1i83c36ximarx"))))
    (properties `((upstream-name . "PLSiMCpp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-purrr r-crayon))
    (home-page "https://cran.r-project.org/package=PLSiMCpp")
    (synopsis "Methods for Partial Linear Single Index Model")
    (description
     "Estimation, hypothesis tests, and variable selection in partially linear
single-index models.  Please see H. (2010) at <doi:10.1214/10-AOS835> for more
details.")
    (license license:gpl2+)))

(define-public r-plsgenomics
  (package
    (name "r-plsgenomics")
    (version "1.5-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plsgenomics" version))
       (sha256
        (base32 "0a1vzpbjd0lyj9vzx6fav4swiv89f3aj8i82g4yj0vd83vwakrnr"))))
    (properties `((upstream-name . "plsgenomics")))
    (build-system r-build-system)
    (propagated-inputs (list r-rhpcblasctl
                             r-reshape2
                             r-plyr
                             r-mass
                             r-fields
                             r-boot))
    (home-page "https://github.com/gdurif/plsgenomics")
    (synopsis "PLS Analyses for Genomics")
    (description
     "Routines for PLS-based genomic analyses, implementing PLS methods for
classification with microarray data and prediction of transcription factor
activities from combined @code{ChIP-chip} analysis.  The >=1.2-1 versions
include two new classification methods for microarray data: GSIM and Ridge PLS.
The >=1.3 versions includes a new classification method combining variable
selection and compression in logistic regression context: logit-SPLS; and an
adaptive version of the sparse PLS.")
    (license license:gpl2+)))

(define-public r-plsdof
  (package
    (name "r-plsdof")
    (version "0.3-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plsdof" version))
       (sha256
        (base32 "1bqk98f9zyi4hvna1sfx4fwiivndlwanjaphlqa03bg6bghcj60p"))))
    (properties `((upstream-name . "plsdof")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://github.com/fbertran/plsdof/")
    (synopsis
     "Degrees of Freedom and Statistical Inference for Partial Least Squares Regression")
    (description
     "The plsdof package provides Degrees of Freedom estimates for Partial Least
Squares (PLS) Regression.  Model selection for PLS is based on various
information criteria (aic, bic, gmdl) or on cross-validation.  Estimates for the
mean and covariance of the PLS regression coefficients are available.  They
allow the construction of approximate confidence intervals and the application
of test procedures (Kramer and Sugiyama 2012 <doi:10.1198/jasa.2011.tm10107>).
Further, cross-validation procedures for Ridge Regression and Principal
Components Regression are available.")
    (license license:gpl2+)))

(define-public r-plsdepot
  (package
    (name "r-plsdepot")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plsdepot" version))
       (sha256
        (base32 "0n2idfkgisbr6b04dcv47yy5f7j1ndxs53p5hinpghg8ikncbvwf"))))
    (properties `((upstream-name . "plsdepot")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=plsdepot")
    (synopsis "Partial Least Squares (PLS) Data Analysis Methods")
    (description
     "Different methods for PLS analysis of one or two data tables such as Tucker's
Inter-Battery, NIPALS, SIMPLS, SIMPLS-CA, PLS Regression, and PLS Canonical
Analysis.  The main reference for this software is the awesome book (in French)
La Regression PLS: Theorie et Pratique by Michel Tenenhaus.")
    (license license:gpl3)))

(define-public r-plrmodels
  (package
    (name "r-plrmodels")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PLRModels" version))
       (sha256
        (base32 "1jpnxsjj3gj72my1q4mswqxpil7vbqfv5gwd51fvx1z8vbdvkwa8"))))
    (properties `((upstream-name . "PLRModels")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PLRModels")
    (synopsis "Statistical Inference in Partial Linear Regression Models")
    (description
     "This package contains statistical inference tools applied to Partial Linear
Regression (PLR) models.  Specifically, point estimation, confidence intervals
estimation, bandwidth selection, goodness-of-fit tests and analysis of
covariance are considered.  Kernel-based methods, combined with ordinary least
squares estimation, are used and time series errors are allowed.  In addition,
these techniques are also implemented for both parametric (linear) and
nonparametric regression models.")
    (license license:gpl3)))

(define-public r-plreg
  (package
    (name "r-plreg")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PLreg" version))
       (sha256
        (base32 "1fa8h0igxqgdfapiz0z80ll2m3nqvxw4qjcrw4knrg7ladjdicwl"))))
    (properties `((upstream-name . "PLreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-zipfr
                             r-vgam
                             r-nleqslv
                             r-generalizedhyperbolic
                             r-gamlss-dist
                             r-formula
                             r-envstats
                             r-bbmisc))
    (home-page "https://github.com/ffqueiroz/PLreg")
    (synopsis "Power Logit Regression for Modeling Bounded Data")
    (description
     "Power logit regression models for bounded continuous data, in which the density
generator may be normal, Student-t, power exponential, slash, hyperbolic,
sinh-normal, or type II logistic.  Diagnostic tools associated with the fitted
model, such as the residuals, local influence measures, leverage measures, and
goodness-of-fit statistics, are implemented.  The estimation process follows the
maximum likelihood approach and, currently, the package supports two types of
estimators: the usual maximum likelihood estimator and the penalized maximum
likelihood estimator.  More details about power logit regression models are
described in Queiroz and Ferrari (2022) <@code{arXiv:2202.01697>}.")
    (license license:gpl3+)))

(define-public r-plpoisson
  (package
    (name "r-plpoisson")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plpoisson" version))
       (sha256
        (base32 "0cyp8j6diafkadjav2bj64f01gr3c74p4psygna7kfzwx92mygv9"))))
    (properties `((upstream-name . "plpoisson")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=plpoisson")
    (synopsis "Prediction Limits for Poisson Distribution")
    (description
     "Prediction limits for the Poisson distribution are produced from both
frequentist and Bayesian viewpoints.  Limiting results are provided in a
Bayesian setting with uniform, Jeffreys and gamma as prior distributions.  More
details on the methodology are discussed in Bejleri and Nandram (2018)
<doi:10.1080/03610926.2017.1373814> and Bejleri, Sartore and Nandram (2021)
<doi:10.1007/s42952-021-00157-x>.")
    (license license:gpl3)))

(define-public r-plotwidgets
  (package
    (name "r-plotwidgets")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plotwidgets" version))
       (sha256
        (base32 "1n2hls9qzr3z1xamggj4l48ksz5m51qn5x63flyk34bh0cyhadi1"))))
    (properties `((upstream-name . "plotwidgets")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=plotwidgets")
    (synopsis
     "Spider Plots, ROC Curves, Pie Charts and More for Use in Other Plots")
    (description
     "Small self-contained plots for use in larger plots or to delegate plotting in
other functions.  Also contains a number of alternative color palettes and HSL
color space based tools to modify colors or palettes.")
    (license license:gpl2+)))

(define-public r-plottools
  (package
    (name "r-plottools")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PlotTools" version))
       (sha256
        (base32 "1pf04nci0kz2f2d8xxkw72zmfypz80zq86fii3pnqhsv6d1pk800"))))
    (properties `((upstream-name . "PlotTools")))
    (build-system r-build-system)
    (home-page "https://ms609.github.io/PlotTools/")
    (synopsis "Add Continuous Legends to Plots")
    (description
     "Annotate plots with legends for continuous variables and colour spectra using
the base graphics plotting tools; and manipulate irregular polygons.")
    (license license:gpl2+)))

(define-public r-plotsemm
  (package
    (name "r-plotsemm")
    (version "2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plotSEMM" version))
       (sha256
        (base32 "0fircrayhz92zvkigkyb1zpn07bvssqs1ip15p926zj3rl77fqgy"))))
    (properties `((upstream-name . "plotSEMM")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-rcpp r-plyr r-plotrix r-mplusautomation))
    (home-page "https://github.com/philchalmers/plotSEMM")
    (synopsis
     "Graphing Nonlinear Relations Among Latent Variables from Structural Equation Mixture Models")
    (description
     "This package contains a graphical user interface to generate the diagnostic
plots proposed by Bauer (2005; <doi:10.1207/s15328007sem1204_1>), Pek & Chalmers
(2015; <doi:10.1080/10705511.2014.937790>), and Pek, Chalmers, R. Kok, & Losardo
(2015; <doi:10.3102/1076998615589129>) to investigate nonlinear bivariate
relationships in latent regression models using structural equation mixture
models (SEMMs).")
    (license license:gpl2+)))

(define-public r-plotscaper
  (package
    (name "r-plotscaper")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plotscaper" version))
       (sha256
        (base32 "1r5fpdfldm5mc1qkma6d713fr2p00k3zb586slimk7s4p91rm4d0"))))
    (properties `((upstream-name . "plotscaper")))
    (build-system r-build-system)
    (propagated-inputs (list r-uuid r-knitr r-jsonlite r-httpuv r-htmlwidgets))
    (native-inputs (list r-knitr))
    (home-page "https://bartonicek.github.io/plotscaper/")
    (synopsis "Explore Your Data with Interactive Figures")
    (description
     "This package provides a framework for creating interactive figures for data
exploration.  All plots are automatically linked and support several kinds of
interactive features, including selection, zooming, panning, and parameter
manipulation.  The figures can be interacted with either manually, using a mouse
and a keyboard, or by running code from inside an active R session.")
    (license license:expat)))

(define-public r-plotscale
  (package
    (name "r-plotscale")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plotscale" version))
       (sha256
        (base32 "0h2g0rv1lh70nqmqydgg5swwnwmy08wbjajxyb1c2a716rad765j"))))
    (properties `((upstream-name . "plotscale")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=plotscale")
    (synopsis "Scale Graphics Devices Using Plot Dimensions")
    (description
     "Figures rendered on graphics devices are usually rescaled to fit pre-determined
device dimensions.  plotscale implements the reverse: desired plot dimensions
are specified and device dimensions are calculated to accommodate marginal
material, giving consistent proportions for plot elements.  Default methods
support grid graphics such as lattice and ggplot.  See \"example('devsize')\" and
\"vignette('plotscale')\".")
    (license license:gpl3)))

(define-public r-plotprotein
  (package
    (name "r-plotprotein")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plotprotein" version))
       (sha256
        (base32 "14kfb4xxpfp8klz31kb7cpc39636ax2cx2483vqqkciccb28f0ks"))))
    (properties `((upstream-name . "plotprotein")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml r-seqinr r-plyr r-plotrix r-ade4))
    (home-page "https://cran.r-project.org/package=plotprotein")
    (synopsis "Development of Visualization Tools for Protein Sequence")
    (description
     "The image of the amino acid transform on the protein level is drawn, and the
automatic routing of the functional elements such as the domain and the mutation
site is completed.")
    (license license:gpl3)))

(define-public r-plotprjnetworks
  (package
    (name "r-plotprjnetworks")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PlotPrjNetworks" version))
       (sha256
        (base32 "13kbyx2phxb3kss6l32f7krf4k5i350indlsmbhav686v0h3nsgp"))))
    (properties `((upstream-name . "PlotPrjNetworks")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-ggplot2))
    (home-page "https://cran.r-project.org/package=PlotPrjNetworks")
    (synopsis "Useful Networking Tools for Project Management")
    (description
     "Useful set of tools for plotting network diagrams in any kind of project.")
    (license license:gpl3)))

(define-public r-plotpc
  (package
    (name "r-plotpc")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plotpc" version))
       (sha256
        (base32 "1sf7n7mfyaijldm24bc8r8pfm8pp9cyaja7am14z2wpj2j9f9vyq"))))
    (properties `((upstream-name . "plotpc")))
    (build-system r-build-system)
    (home-page "http://www.milbo.users.sonic.net")
    (synopsis "Plot Principal Component Histograms Around a Scatter Plot")
    (description
     "Plot principal component histograms around a bivariate scatter plot.")
    (license license:gpl3)))

(define-public r-plotor
  (package
    (name "r-plotor")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plotor" version))
       (sha256
        (base32 "1wfhdmlc6042yvqnj3lv34swfnkr75ygsd48yg2mldxjm2b8ma09"))))
    (properties `((upstream-name . "plotor")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-scales
                             r-purrr
                             r-glue
                             r-ggplot2
                             r-dplyr
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/craig-parylo/plotor")
    (synopsis "Produces an Odds Ratio Plot from a Logistic Regression Model")
    (description
     "This package produces an Odds Ratio (OR) Plot to visualise the result of a
logistic regression analysis.  Provide it with a binomial regression model
produced by glm() and it will convert the estimates to odds ratios with a 95%
confidence interval and plot the results using ggplot2'.")
    (license license:expat)))

(define-public r-plotmo
  (package
    (name "r-plotmo")
    (version "3.6.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plotmo" version))
       (sha256
        (base32 "0wl2xlidib4r54nm1q5zndr9iilagpyc3xlxs0cpd487hdh3zcn8"))))
    (properties `((upstream-name . "plotmo")))
    (build-system r-build-system)
    (propagated-inputs (list r-plotrix r-formula))
    (home-page "http://www.milbo.users.sonic.net")
    (synopsis
     "Plot a Model's Residuals, Response, and Partial Dependence Plots")
    (description
     "Plot model surfaces for a wide variety of models using partial dependence plots
and other techniques.  Also plot model residuals and other information on the
model.")
    (license license:gpl3)))

(define-public r-plotmm
  (package
    (name "r-plotmm")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plotmm" version))
       (sha256
        (base32 "0ad8bkfbwrcv447wgygsh1akzc7v8mxgfw2ywb5024jy39zbj56b"))))
    (properties `((upstream-name . "plotmm")))
    (build-system r-build-system)
    (propagated-inputs (list r-wesanderson r-ggplot2 r-amerika))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=plotmm")
    (synopsis "Tidy Tools for Visualizing Mixture Models")
    (description
     "The main function, plot_mm(), is used for (gg)plotting output from mixture
models, including both densities and overlaying mixture weight component curves
from the fit models in line with the tidy principles.  The package includes
several additional functions for added plot customization.  Supported model
objects include: mixtools', EMCluster', and flexmix', with more from each in
active dev.  Supported mixture model specifications include mixtures of
univariate Gaussians, multivariate Gaussians, Gammas, logistic regressions,
linear regressions, and Poisson regressions.")
    (license license:expat)))

(define-public r-plotmelm
  (package
    (name "r-plotmelm")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plotMElm" version))
       (sha256
        (base32 "0wwqzrpkmq9gzazdzlk62qig6vz43niada6fxh1wcsjqjwkcccgp"))))
    (properties `((upstream-name . "plotMElm")))
    (build-system r-build-system)
    (propagated-inputs (list r-interactiontest r-ggplot2))
    (home-page "https://cran.r-project.org/package=plotMElm")
    (synopsis "Plot Marginal Effects from Linear Models")
    (description
     "Plot marginal effects for interactions estimated from linear models.")
    (license license:gpl3+)))

(define-public r-plotmcmc
  (package
    (name "r-plotmcmc")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plotMCMC" version))
       (sha256
        (base32 "0vxw6kbs1xvzi3sklxac9q1j4cbr8alr9c8fwifwyvpzpdk48ihf"))))
    (properties `((upstream-name . "plotMCMC")))
    (build-system r-build-system)
    (propagated-inputs (list r-lattice r-gplots r-coda))
    (home-page "https://cran.r-project.org/package=plotMCMC")
    (synopsis "MCMC Diagnostic Plots")
    (description
     "Markov chain Monte Carlo diagnostic plots.  The purpose of the package is to
combine existing tools from the coda and lattice packages, and make it easy to
adjust graphical details.")
    (license license:gpl3)))

(define-public r-plotlygeoassets
  (package
    (name "r-plotlygeoassets")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plotlyGeoAssets" version))
       (sha256
        (base32 "1c6i4dz5qmym1pcddgffcqgb76jz84252xldprg2caylrqvzv6b7"))))
    (properties `((upstream-name . "plotlyGeoAssets")))
    (build-system r-build-system)
    (home-page "https://github.com/cpsievert/plotlyGeoAssets")
    (synopsis "Render 'Plotly' Maps without an Internet Connection")
    (description
     "Includes @code{JavaScript} files that allow plotly maps to render without an
internet connection.")
    (license license:expat)))

(define-public r-plotluck
  (package
    (name "r-plotluck")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plotluck" version))
       (sha256
        (base32 "16wqj4xfa8b7kal6v8mp2zlhwf9l3y6y5n44qbpvy652gzb2rw2z"))))
    (properties `((upstream-name . "plotluck")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-rcolorbrewer
                             r-quantreg
                             r-plyr
                             r-hmisc
                             r-hexbin
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/stefan-schroedl/plotluck")
    (synopsis "'ggplot2' Version of \"I'm Feeling Lucky!\"")
    (description
     "Examines the characteristics of a data frame and a formula to automatically
choose the most suitable type of plot out of the following supported options:
scatter, violin, box, bar, density, hexagon bin, spine plot, and heat map.  The
aim of the package is to let the user focus on what to plot, rather than on the
\"how\" during exploratory data analysis.  It also automates handling of
observation weights, logarithmic axis scaling, reordering of factor levels, and
overlaying smoothing curves and median lines.  Plots are drawn using ggplot2'.")
    (license license:expat)))

(define-public r-plothmm
  (package
    (name "r-plothmm")
    (version "2023.8.28")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plotHMM" version))
       (sha256
        (base32 "12mxkd1m3ivk25sr5sg97q2l2v5m95pmfhbrhrp9jmmpqfr1l983"))))
    (properties `((upstream-name . "plotHMM")))
    (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=plotHMM")
    (synopsis "Plot Hidden Markov Models")
    (description
     "Hidden Markov Models are useful for modeling sequential data.  This package
provides several functions implemented in C++ for explaining the algorithms used
for Hidden Markov Models (forward, backward, decoding, learning).")
    (license license:gpl2+)))

(define-public r-plothelper
  (package
    (name "r-plothelper")
    (version "0.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plothelper" version))
       (sha256
        (base32 "1ibznjqs4gpbq1vx79gg0fkm842xfqgpmmgqji2m6s712j3bdn5k"))))
    (properties `((upstream-name . "plothelper")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-plyr
                             r-magick
                             r-gridextra
                             r-ggplot2
                             r-ggfittext
                             r-farver))
    (home-page
     "https://github.com/githubwwwjjj/plothelper/blob/master/README.md")
    (synopsis
     "New Plots Based on 'ggplot2' and Functions to Create Regular Shapes")
    (description
     "An extension to ggplot2 and magick'.  It contains three groups of functions:
Functions in the first group draw ggplot2 - based plots: geom_shading_bar()
draws barplot with shading colors in each bar.  geom_rect_cm(), geom_circle_cm()
and geom_ellipse_cm() draw rectangles, circles and ellipses with centimeter as
their unit.  Thus their sizes do not change when the coordinate system or the
aspect ratio changes.  annotation_transparent_text() draws labels with
transparent texts.  annotation_shading_polygon() draws irregular polygons with
shading colors.  Functions in the second group generate coordinates for regular
shapes and make linear transformations.  Functions in the third group are magick
- based functions facilitating image processing.")
    (license license:gpl3)))

(define-public r-plotgmm
  (package
    (name "r-plotgmm")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plotGMM" version))
       (sha256
        (base32 "1cgjajw3dprdlq9ri0gllbxcy33x311mbp9xdds8fgq6cs42ggyb"))))
    (properties `((upstream-name . "plotGMM")))
    (build-system r-build-system)
    (propagated-inputs (list r-wesanderson r-ggplot2 r-amerika))
    (home-page "https://cran.r-project.org/package=plotGMM")
    (synopsis "Tools for Visualizing Gaussian Mixture Models")
    (description
     "The main function, plot_GMM, is used for plotting output from Gaussian mixture
models (GMMs), including both densities and overlaying mixture weight component
curves from the fit GMM. The package also include the function, plot_cut_point,
which plots the cutpoint (mu) from the GMM over a histogram of the distribution
with several color options.  Finally, the package includes the function,
plot_mix_comps, which is used in the plot_GMM function, and can be used to
create a custom plot for overlaying mixture component curves from GMMs. For the
plot_mix_comps function, usage most often will be specifying the \"fun\" argument
within \"stat_function\" in a ggplot2 object.")
    (license license:expat)))

(define-public r-plotdk
  (package
    (name "r-plotdk")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plotDK" version))
       (sha256
        (base32 "0lyhhc8yjxj3jhw72pn7i565z64mifi5iyc375yavs7axlcsl2nr"))))
    (properties `((upstream-name . "plotDK")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-purrr
                             r-plotly
                             r-mapproj
                             r-ggplot2
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=plotDK")
    (synopsis
     "Plot Summary Statistics as Choropleth Maps of Danish Administrative Areas")
    (description
     "This package provides a ggplot2 front end to plot summary statistics on danish
provinces, regions, municipalities, and zipcodes.  The needed geoms of each of
the four levels are inherent in the package, thus making these types of plots
easy for the user.  This is essentially an updated port of the previously
available @code{mapDK} package by Sebastian Barfort.")
    (license license:expat)))

(define-public r-plotdap
  (package
    (name "r-plotdap")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plotdap" version))
       (sha256
        (base32 "151vaz61ycm95sazbn7d03wr7hcmvcmbz12rjls334lp38735954"))))
    (properties `((upstream-name . "plotdap")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-tidyr
                             r-sf
                             r-scales
                             r-rerddap
                             r-raster
                             r-maps
                             r-mapdata
                             r-magrittr
                             r-lubridate
                             r-lazyeval
                             r-ggplot2
                             r-ggnewscale
                             r-gganimate
                             r-dplyr
                             r-cmocean))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rmendels/plotdap")
    (synopsis
     "Easily Visualize Data from 'ERDDAP' Servers via the 'rerddap' Package")
    (description
     "Easily visualize and animate tabledap and griddap objects obtained via the
rerddap package in a simple one-line command, using either base graphics or
ggplot2 graphics.  plotdap handles extracting and reshaping the data, map
projections and continental outlines.  Optionally the data can be animated
through time using the gganmiate package.")
    (license license:expat)))

(define-public r-plotcontour
  (package
    (name "r-plotcontour")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PlotContour" version))
       (sha256
        (base32 "0jjzrxmx1k1s8fyfs9m5a864pmvjh2bdklbhrc3nbhb0c7wd3h9z"))))
    (properties `((upstream-name . "PlotContour")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-kernsmooth))
    (home-page "https://cran.r-project.org/package=PlotContour")
    (synopsis "Plot Contour Line")
    (description
     "This function plots a contour line with a user-defined probability and tightness
of fit.")
    (license license:gpl2)))

(define-public r-plotcli
  (package
    (name "r-plotcli")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plotcli" version))
       (sha256
        (base32 "0qk78gagn5i6zs4srr3q2kzhi5vlyxhmxgwg2ah9c24asnfa3hyg"))))
    (properties `((upstream-name . "plotcli")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rmarkdown
                             r-rlang
                             r-r6
                             r-knitr
                             r-ggplot2
                             r-crayon))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/cheuerde/plotcli")
    (synopsis "Command Line Interface Plotting")
    (description
     "The plotcli package provides terminal-based plotting in R. It supports colored
scatter plots, line plots, bar plots, and box plots.  The package allows users
to customize plot appearance, add titles, labels, ticks, and legends, and output
the plot as a text-based visualization.")
    (license license:lgpl3)))

(define-public r-plotbivinvgaus
  (package
    (name "r-plotbivinvgaus")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PlotBivInvGaus" version))
       (sha256
        (base32 "1qgrg43ry9carhn49zbm24gy77k1z7xnvgmr78xwg6b6j1v6xd8d"))))
    (properties `((upstream-name . "PlotBivInvGaus")))
    (build-system r-build-system)
    (propagated-inputs (list r-plotly))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PlotBivInvGaus")
    (synopsis
     "Density Contour Plot for Bivariate Inverse Gaussian Distribution")
    (description
     "Create the density contour plot for bivariate inverse Gaussian distribution for
given non negative random variables.")
    (license license:gpl2)))

(define-public r-plotbb
  (package
    (name "r-plotbb")
    (version "0.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plotbb" version))
       (sha256
        (base32 "0vyp558i0gvr9r3xldkd4ldkd6wx95w7xkkas8cyw0s7i13qqq60"))))
    (properties `((upstream-name . "plotbb")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-rlang r-magrittr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=plotbb")
    (synopsis "Grammar of Graphics for 'base' Plot")
    (description
     "Proof of concept for implementing grammar of graphics using base plot.  The
bbplot() function initializes a bbplot object to store input data, aesthetic
mapping, a list of layers and theme elements.  The object will be rendered as a
graphic using base plot command if it is printed.")
    (license license:artistic2.0)))

(define-public r-plotbart
  (package
    (name "r-plotbart")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plotBart" version))
       (sha256
        (base32 "10lkdrmc1f22m544w36ihpivicj1mw0x8cx1750ldyhphrd99za5"))))
    (properties `((upstream-name . "plotBart")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rpart
                             r-ggplot2
                             r-ggdendro
                             r-dplyr
                             r-bartcause))
    (native-inputs (list r-knitr))
    (home-page "https://priism-center.github.io/plotBart/")
    (synopsis "Diagnostic and Plotting Functions to Supplement 'bartCause'")
    (description
     "This package provides functions to assist in diagnostics and plotting during the
causal inference modeling process.  Supplements the @code{bartCause} package.")
    (license license:expat)))

(define-public r-plot4fun
  (package
    (name "r-plot4fun")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plot4fun" version))
       (sha256
        (base32 "07367xsdbkiiw2ll344bd5qkjdcs7g98iygr1a884g1nbq2rv649"))))
    (properties `((upstream-name . "plot4fun")))
    (build-system r-build-system)
    (propagated-inputs (list r-sysfonts
                             r-showtext
                             r-reshape2
                             r-plot3d
                             r-pcutils
                             r-magrittr
                             r-magick
                             r-gifski
                             r-ggplot2
                             r-ggforce))
    (home-page "https://cran.r-project.org/package=plot4fun")
    (synopsis "Just Plot for Fun")
    (description
     "Explore the world of R graphics with fun and interesting plot functions! Use
make_LED() to create dynamic LED screens, draw interconnected rings with
Olympic_rings(), and make festive Chinese couplets with chunlian().  Unleash
your creativity and turn data into exciting visuals!")
    (license license:gpl3)))

(define-public r-plot3drgl
  (package
    (name "r-plot3drgl")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plot3Drgl" version))
       (sha256
        (base32 "1p8vypid2v1n255hlpxxlbnf1lyv2jywls0jfm7scfms5aisk1vd"))))
    (properties `((upstream-name . "plot3Drgl")))
    (build-system r-build-system)
    (propagated-inputs (list r-rgl r-plot3d))
    (home-page "https://cran.r-project.org/package=plot3Drgl")
    (synopsis "Plotting Multi-Dimensional Data - Using 'rgl'")
    (description "The rgl implementation of plot3D functions.")
    (license license:gpl3+)))

(define-public r-plot-matrix
  (package
    (name "r-plot-matrix")
    (version "1.6.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plot.matrix" version))
       (sha256
        (base32 "1fsgzjcllrdlf55ww7dhyx1iaa59y2a6f1b1591j8m8in1fh7n6n"))))
    (properties `((upstream-name . "plot.matrix")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sigbertklinke/plot.matrix")
    (synopsis "Visualizes a Matrix as Heatmap")
    (description
     "Visualizes a matrix object plainly as heatmap.  It provides S3 functions to plot
simple matrices and loading matrices.")
    (license license:gpl3)))

(define-public r-plorn
  (package
    (name "r-plorn")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PLORN" version))
       (sha256
        (base32 "0x6rvg63mqc451rw0hpz1pjafdyxwraqhnj8cgbwjknhm5k4m1bl"))))
    (properties `((upstream-name . "PLORN")))
    (build-system r-build-system)
    (propagated-inputs (list r-kernlab r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/takakoizumi/PLORN")
    (synopsis "Prediction with Less Overfitting and Robust to Noise")
    (description
     "This package provides a method for the quantitative prediction with much
predictors.  This package provides functions to construct the quantitative
prediction model with less overfitting and robust to noise.")
    (license license:expat)))

(define-public r-plordprob
  (package
    (name "r-plordprob")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PLordprob" version))
       (sha256
        (base32 "1g23h3121g9csr85falm6vgzbva42wz3skhfr2rxmvlc3ca4afyp"))))
    (properties `((upstream-name . "PLordprob")))
    (build-system r-build-system)
    (propagated-inputs (list r-mnormt))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=PLordprob")
    (synopsis "Multivariate Ordered Probit Model via Pairwise Likelihood")
    (description
     "Multivariate ordered probit model, i.e.  the extension of the scalar ordered
probit model where the observed variables have dimension greater than one.
Estimation of the parameters is done via maximization of the pairwise
likelihood, a special case of the composite likelihood obtained as product of
bivariate marginal distributions.  The package uses the Fortran 77 subroutine
SADMVN by Alan Genz, with minor adaptations made by Adelchi Azzalini in his
\"mvnormt\" package for evaluating the two-dimensional Gaussian integrals involved
in the pairwise log-likelihood.  Optimization of the latter objective function
is performed via quasi-Newton box-constrained optimization algorithm, as
implemented in nlminb.")
    (license license:gpl2)))

(define-public r-plnr
  (package
    (name "r-plnr")
    (version "2022.11.23")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plnr" version))
       (sha256
        (base32 "1qi24zpvclamxxfppgll7rcmq7fysxf8chk6wlkwkzmjl5c3cs4n"))))
    (properties `((upstream-name . "plnr")))
    (build-system r-build-system)
    (propagated-inputs (list r-uuid
                             r-usethis
                             r-r6
                             r-purrr
                             r-glue
                             r-fs
                             r-foreach
                             r-digest
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://www.csids.no/plnr/")
    (synopsis "System for Planing Analyses")
    (description
     "This package provides a system to plan analyses within the mental model where
you have one (or more) datasets and want to run either A) the same function
multiple times with different arguments, or B) multiple functions.  This is
appropriate when you have multiple strata (e.g. locations, age groups) that you
want to apply the same function to, or you have multiple variables (e.g.
exposures) that you want to apply the same statistical method to, or when you
are creating the output for a report and you need multiple different tables or
graphs.")
    (license license:expat)))

(define-public r-plnmodels
  (package
    (name "r-plnmodels")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PLNmodels" version))
       (sha256
        (base32 "1fqfy3pipj8w3mma8ca33ppxpvam4hnzcklpma188s06vy75h1i9"))))
    (properties `((upstream-name . "PLNmodels")))
    (build-system r-build-system)
    (propagated-inputs (list r-torch
                             r-tidyr
                             r-rlang
                             r-rcpparmadillo
                             r-rcpp
                             r-r6
                             r-purrr
                             r-pscl
                             r-nloptr
                             r-matrix
                             r-mass
                             r-magrittr
                             r-igraph
                             r-gridextra
                             r-glassofast
                             r-ggplot2
                             r-future-apply
                             r-future
                             r-dplyr
                             r-corrplot))
    (native-inputs (list r-knitr))
    (home-page "https://pln-team.github.io/PLNmodels/")
    (synopsis "Poisson Lognormal Models")
    (description
     "The Poisson-lognormal model and variants (Chiquet, Mariadassou and Robin, 2021
<doi:10.3389/fevo.2021.588292>) can be used for a variety of multivariate
problems when count data are at play, including principal component analysis for
count data, discriminant analysis, model-based clustering and network inference.
 Implements variational algorithms to fit such models accompanied with a set of
functions for visualization and diagnostic.")
    (license license:gpl3+)))

(define-public r-pln
  (package
    (name "r-pln")
    (version "0.2-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pln" version))
       (sha256
        (base32 "1fasqp3dgspj0my1b1p4w8csq4jdb7089690c4gf5clg20qq0mmg"))))
    (properties `((upstream-name . "pln")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pln")
    (synopsis "Polytomous Logit-Normit (Graded Logistic) Model Estimation")
    (description
     "This package performs bivariate composite likelihood and full information
maximum likelihood estimation for polytomous logit-normit (graded logistic) item
response theory (IRT) models.")
    (license license:gpl3)))

(define-public r-plmmr
  (package
    (name "r-plmmr")
    (version "4.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plmmr" version))
       (sha256
        (base32 "12b31wx4n4sl8gx59qfkfki4j2sli7gx5qh9z22mpak8brycy8ld"))))
    (properties `((upstream-name . "plmmr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-ncvreg
                             r-matrix
                             r-glmnet
                             r-data-table
                             r-bigmemory
                             r-biglasso
                             r-bigalgebra
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://pbreheny.github.io/plmmr/")
    (synopsis "Penalized Linear Mixed Models for Correlated Data")
    (description
     "Fits penalized linear mixed models that correct for unobserved confounding
factors.  plmmr infers and corrects for the presence of unobserved confounding
effects such as population stratification and environmental heterogeneity.  It
then fits a linear model via penalized maximum likelihood.  Originally designed
for the multivariate analysis of single nucleotide polymorphisms (SNPs) measured
in a genome-wide association study (GWAS), plmmr eliminates the need for
subpopulation-specific analyses and post-analysis p-value adjustments.
Functions for the appropriate processing of PLINK files are also supplied.  For
examples, see the package homepage. <https://pbreheny.github.io/plmmr/>.")
    (license license:gpl3)))

(define-public r-plmixed
  (package
    (name "r-plmixed")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PLmixed" version))
       (sha256
        (base32 "1kn9w8xr9y1lnxa5hv95q3hipr7shs8pnhr6nr88v4rskgvvhffm"))))
    (properties `((upstream-name . "PLmixed")))
    (build-system r-build-system)
    (propagated-inputs (list r-optimx r-numderiv r-matrix r-lme4))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PLmixed")
    (synopsis
     "Estimate (Generalized) Linear Mixed Models with Factor Structures")
    (description
     "Utilizes the lme4 and optimx packages (previously the optim() function from
stats') to estimate (generalized) linear mixed models (GLMM) with factor
structures using a profile likelihood approach, as outlined in Jeon and
Rabe-Hesketh (2012) <doi:10.3102/1076998611417628> and Rockwood and Jeon (2019)
<doi:10.1080/00273171.2018.1516541>.  Factor analysis and item response models
can be extended to allow for an arbitrary number of nested and crossed random
effects, making it useful for multilevel and cross-classified models.")
    (license license:gpl2+)))

(define-public r-plmix
  (package
    (name "r-plmix")
    (version "2.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PLMIX" version))
       (sha256
        (base32 "05mnzsi7y71cvg50qx8hp4m31gqslldl34k41r1f8npyb6ldpdca"))))
    (properties `((upstream-name . "PLMIX")))
    (build-system r-build-system)
    (propagated-inputs (list r-statrank
                             r-reshape2
                             r-rcpp
                             r-rcdd
                             r-rankdist
                             r-radarchart
                             r-prefmod
                             r-pmr
                             r-plackettluce
                             r-mcmcpack
                             r-label-switching
                             r-gtools
                             r-gridextra
                             r-ggplot2
                             r-ggmcmc
                             r-foreach
                             r-coda
                             r-abind))
    (home-page "https://cran.r-project.org/package=PLMIX")
    (synopsis
     "Bayesian Analysis of Finite Mixtures of Plackett-Luce Models for Partial Rankings/Orderings")
    (description
     "Fit finite mixtures of Plackett-Luce models for partial top rankings/orderings
within the Bayesian framework.  It provides MAP point estimates via EM algorithm
and posterior MCMC simulations via Gibbs Sampling.  It also fits MLE as a
special case of the noninformative Bayesian analysis with vague priors.  In
addition to inferential techniques, the package assists other fundamental phases
of a model-based analysis for partial rankings/orderings, by including functions
for data manipulation, simulation, descriptive summary, model selection and
goodness-of-fit evaluation.  Main references on the methods are Mollica and
Tardella (2017) <doi.org/10.1007/s11336-016-9530-0> and Mollica and Tardella
(2014) <doi/10.1002/sim.6224>.")
    (license license:gpl2+)))

(define-public r-plm
  (package
    (name "r-plm")
    (version "2.6-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plm" version))
       (sha256
        (base32 "0xpd6r75g2mc9dsf8ckafbd3pjyrvlyrh12izj9yaqfw4v0vr9fx"))))
    (properties `((upstream-name . "plm")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-sandwich
                             r-rdpack
                             r-nlme
                             r-maxlik
                             r-mass
                             r-lmtest
                             r-lattice
                             r-formula
                             r-collapse
                             r-bdsmatrix))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=plm")
    (synopsis "Linear Models for Panel Data")
    (description
     "This package provides a set of estimators for models and (robust) covariance
matrices, and tests for panel data econometrics, including within/fixed effects,
random effects, between, first-difference, nested random effects as well as
instrumental-variable (IV) and Hausman-Taylor-style models, panel generalized
method of moments (GMM) and general FGLS models, mean groups (MG), demeaned MG,
and common correlated effects (CCEMG) and pooled (CCEP) estimators with common
factors, variable coefficients and limited dependent variables models.  Test
functions include model specification, serial correlation, cross-sectional
dependence, panel unit root and panel Granger (non-)causality.  Typical
references are general econometrics text books such as Baltagi (2021),
Econometric Analysis of Panel Data (<doi:10.1007/978-3-030-53953-5>), Hsiao
(2014), Analysis of Panel Data (<doi:10.1017/CBO9781139839327>), and Croissant
and Millo (2018), Panel Data Econometrics with R (<doi:10.1002/9781119504641>).")
    (license license:gpl2+)))

(define-public r-plis
  (package
    (name "r-plis")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PLIS" version))
       (sha256
        (base32 "08l16s3vncgk2vvcy3zswprcmx46qljbw9wlw2dhr3rinl62ci8x"))))
    (properties `((upstream-name . "PLIS")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PLIS")
    (synopsis "Multiplicity Control using Pooled LIS Statistic")
    (description
     "This package provides a multiple testing procedure for testing several groups of
hypotheses is implemented.  Linear dependency among the hypotheses within the
same group is modeled by using hidden Markov Models.  It is noted that a smaller
p value does not necessarily imply more significance due to the dependency.  A
typical application is to analyze genome wide association studies datasets,
where SNPs from the same chromosome are treated as a group and exhibit strong
linear genomic dependency.  See Wei Z, Sun W, Wang K, Hakonarson H (2009)
<doi:10.1093/bioinformatics/btp476> for more details.")
    (license license:gpl3)))

(define-public r-plinkqc
  (package
    (name "r-plinkqc")
    (version "0.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plinkQC" version))
       (sha256
        (base32 "0l5gf7wf33fhzby0n2qzph02v7an2gj2dji1i129hxsxqnynx7dy"))))
    (properties `((upstream-name . "plinkQC")))
    (build-system r-build-system)
    (inputs (list plink))
    (propagated-inputs (list r-upsetr
                             r-sys
                             r-r-utils
                             r-optparse
                             r-igraph
                             r-ggrepel
                             r-ggplot2
                             r-ggforce
                             r-dplyr
                             r-data-table
                             r-cowplot))
    (native-inputs (list r-knitr))
    (home-page "https://meyer-lab-cshl.github.io/plinkQC/")
    (synopsis "Genotype Quality Control with 'PLINK'")
    (description
     "Genotyping arrays enable the direct measurement of an individuals genotype at
thousands of markers. @code{plinkQC} facilitates genotype quality control for
genetic association studies as described by Anderson and colleagues (2010)
<doi:10.1038/nprot.2010.116>.  It makes PLINK basic statistics (e.g. missing
genotyping rates per individual, allele frequencies per genetic marker) and
relationship functions accessible from R and generates a per-individual and
per-marker quality control report.  Individuals and markers that fail the
quality control can subsequently be removed to generate a new, clean dataset.
Removal of individuals based on relationship status is optimised to retain as
many individuals as possible in the study.")
    (license license:expat)))

(define-public r-plinkfile
  (package
    (name "r-plinkfile")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plinkFile" version))
       (sha256
        (base32 "1vbhgwb08bbq4pmagrrn6zl1aw5cqcnfvicajj5c4y5qjm6y7s0m"))))
    (properties `((upstream-name . "plinkFile")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=plinkFile")
    (synopsis "'PLINK' (and 'GCTA') File Helpers")
    (description
     "Reads/write binary genotype file compatible with PLINK
<https://www.cog-genomics.org/plink/1.9/input#bed> into/from a R matrix;
traverse genotype data one windows of variants at a time, like apply() or a for
loop; reads/writes genotype relatedness/kinship matrices created by PLINK
<https://www.cog-genomics.org/plink/1.9/distance#make_rel> or GCTA
<https://cnsgenomics.com/software/gcta/#@code{MakingaGRM>} into/from a R square
matrix.  It is best used for bringing data produced by PLINK and GCTA into R
workflow.")
    (license license:gpl2+)))

(define-public r-plink
  (package
    (name "r-plink")
    (version "1.5-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plink" version))
       (sha256
        (base32 "0rn2i9i8af9aq0xgxhpcdchs2952lq2d2sg8x3js0zi3vcml76k0"))))
    (properties `((upstream-name . "plink")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod r-mass r-lattice))
    (home-page "https://cran.r-project.org/package=plink")
    (synopsis "IRT Separate Calibration Linking Methods")
    (description
     "Item response theory based methods are used to compute linking constants and
conduct chain linking of unidimensional or multidimensional tests for multiple
groups under a common item design.  The unidimensional methods include the
Mean/Mean, Mean/Sigma, Haebara, and Stocking-Lord methods for dichotomous (1PL,
2PL and 3PL) and/or polytomous (graded response, partial credit/generalized
partial credit, nominal, and multiple-choice model) items.  The multidimensional
methods include the least squares method and extensions of the Haebara and
Stocking-Lord method using single or multiple dilation parameters for
multidimensional extensions of all the unidimensional dichotomous and polytomous
item response models.  The package also includes functions for importing item
and/or ability parameters from common IRT software, conducting IRT true score
and observed score equating, and plotting item response curves/surfaces, vector
plots, information plots, and comparison plots for examining parameter drift.")
    (license license:gpl2+)))

(define-public r-plindleyroc
  (package
    (name "r-plindleyroc")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PLindleyROC" version))
       (sha256
        (base32 "09skjwl71p8374h48mqdgwlbqivgjalgbf69nfqaz7p0cifal6lc"))))
    (properties `((upstream-name . "PLindleyROC")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ErtanSU/PLindleyROC")
    (synopsis
     "Receiver Operating Characteristic Based on Power Lindley Distribution")
    (description
     "Receiver Operating Characteristic (ROC) analysis is performed assuming samples
are from the Power Lindley distribution.  Specificity, sensitivity, area under
the curve and ROC curve are provided.")
    (license license:gpl3)))

(define-public r-pliman
  (package
    (name "r-pliman")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pliman" version))
       (sha256
        (base32 "0pgv7ayb78k481yd071hjvympikhdw6q8wqb9d5fazp9avmjlfz7"))))
    (properties `((upstream-name . "pliman")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-stars
                             r-sf
                             r-rcpparmadillo
                             r-rcpp
                             r-foreach
                             r-doparallel))
    (home-page "https://github.com/TiagoOlivoto/pliman")
    (synopsis "Tools for Plant Image Analysis")
    (description
     "This package provides tools for single or batch image manipulation and analysis
as described by Olivoto (2022) <doi:10.1111/2041-210X.13803> that can be used to
quantify plant leaf area, assess disease severity, count objects, obtain shape
measures, object landmarks, and compute Elliptical Fourier Analysis of the
object outline, as described by Claude (2008) <doi:10.1007/978-0-387-77789-4>.
Additionally, the package includes tools for analyzing grids, which enables high
throughput field phenotyping using RGB imagery captured by unmanned aerial
vehicles.")
    (license license:gpl3+)))

(define-public r-plgraphics
  (package
    (name "r-plgraphics")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plgraphics" version))
       (sha256
        (base32 "04i79hrsqqygiq64n7bqzfvrhl1c3k2j3y58zzbqq79v9vshjy7p"))))
    (properties `((upstream-name . "plgraphics")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mass r-lme4 r-chron))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=plgraphics")
    (synopsis "User Oriented Plotting Functions")
    (description
     "Plots with high flexibility and easy handling, including informative regression
diagnostics for many models.")
    (license license:gpl2)))

(define-public r-plgp
  (package
    (name "r-plgp")
    (version "1.1-12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plgp" version))
       (sha256
        (base32 "06sk9i6fn66w0lrzkj5b95m3xb66d9b602kwh6nkxyh9bjryd70l"))))
    (properties `((upstream-name . "plgp")))
    (build-system r-build-system)
    (propagated-inputs (list r-tgp r-mvtnorm))
    (home-page "https://bobby.gramacy.com/r_packages/plgp/")
    (synopsis "Particle Learning of Gaussian Processes")
    (description
     "Sequential Monte Carlo (SMC) inference for fully Bayesian Gaussian process (GP)
regression and classification models by particle learning (PL) following Gramacy
& Polson (2011) <@code{arXiv:0909.5262>}.  The sequential nature of inference
and the active learning (AL) hooks provided facilitate thrifty sequential design
(by entropy) and optimization (by improvement) for classification and regression
models, respectively.  This package essentially provides a generic PL interface,
and functions (arguments to the interface) which implement the GP models and AL
heuristics.  Functions for a special, linked, regression/classification GP model
and an integrated expected conditional improvement (IECI) statistic provide for
optimization in the presence of unknown constraints.  Separable and isotropic
Gaussian, and single-index correlation functions are supported.  See the
examples section of ?plgp and demo(package=\"plgp\") for an index of demos.")
    (license license:lgpl2.0+)))

(define-public r-plfma
  (package
    (name "r-plfma")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plfMA" version))
       (sha256
        (base32 "14v3q4sv3inax3b1sp0i4scrz70ca9yphzikhx0z9dh2w5mggq0k"))))
    (properties `((upstream-name . "plfMA")))
    (build-system r-build-system)
    (propagated-inputs (list r-tkrplot r-rcolorbrewer r-limma r-gwidgets2tcltk
                             r-gwidgets2))
    (home-page "https://cran.r-project.org/package=plfMA")
    (synopsis "GUI to View, Design and Export Various Graphs of Data")
    (description
     "This package provides a graphical user interface for viewing and designing
various types of graphs of the data.  The graphs can be saved in different
formats of an image.")
    (license license:gpl2)))

(define-public r-plfm
  (package
    (name "r-plfm")
    (version "2.2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plfm" version))
       (sha256
        (base32 "1h4076hyxrbczhwm507cqbdphxis6hqp3srkgrxwqjsph6l7c02k"))))
    (properties `((upstream-name . "plfm")))
    (build-system r-build-system)
    (propagated-inputs (list r-sfsmisc r-abind))
    (home-page "https://cran.r-project.org/package=plfm")
    (synopsis "Probabilistic Latent Feature Analysis")
    (description
     "This package provides functions for estimating probabilistic latent feature
models with a disjunctive, conjunctive or additive mapping rule on (aggregated)
binary three-way data.")
    (license license:gpl2+)))

(define-public r-plfd
  (package
    (name "r-plfd")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PLFD" version))
       (sha256
        (base32 "0w6d3sy4cnn7sj5d2fbn510gc3y6hipd006jnyvkariaqwlh3fa0"))))
    (properties `((upstream-name . "PLFD")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mathjaxr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/paradoxical-rhapsody/PLFD")
    (synopsis
     "Portmanteau Local Feature Discrimination for Matrix-Variate Data")
    (description
     "The portmanteau local feature discriminant approach first identifies the local
discriminant features and their differential structures, then constructs the
discriminant rule by pooling the identified local features together.  This
method is applicable to high-dimensional matrix-variate data.  See the paper by
Xu, Luo and Chen (2021, <doi:10.1007/s13171-021-00255-2>).")
    (license license:gpl3)))

(define-public r-plexi
  (package
    (name "r-plexi")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PLEXI" version))
       (sha256
        (base32 "1y5pzigdgrapnxh7pxkbnskp0dm2w0mr3sk2rp7p8ny6a7rm5dj8"))))
    (properties `((upstream-name . "PLEXI")))
    (build-system r-build-system)
    (propagated-inputs (list r-keras
                             r-igraph
                             r-ggraph
                             r-ggplot2
                             r-assertthat
                             r-aggregation))
    (home-page "https://cran.r-project.org/package=PLEXI")
    (synopsis "Multiplex Network Analysis")
    (description
     "Interactions between different biological entities are crucial for the function
of biological systems.  In such networks, nodes represent biological elements,
such as genes, proteins and microbes, and their interactions can be defined by
edges, which can be either binary or weighted.  The dysregulation of these
networks can be associated with different clinical conditions such as diseases
and response to treatments.  However, such variations often occur locally and do
not concern the whole network.  To capture local variations of such networks, we
propose multiplex network differential analysis (MNDA).  MNDA allows to quantify
the variations in the local neighborhood of each node (e.g. gene) between the
two given clinical states, and to test for statistical significance of such
variation.  Yousefi et al. (2023) <doi:10.1101/2023.01.22.525058>.")
    (license license:gpl3+)))

(define-public r-plelma
  (package
    (name "r-plelma")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pleLMA" version))
       (sha256
        (base32 "1n6q4x8qv00j0hikzzr600xgikhr462zm73zllb9n0valhmzy18z"))))
    (properties `((upstream-name . "pleLMA")))
    (build-system r-build-system)
    (propagated-inputs (list r-mlogit r-dfidx))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pleLMA")
    (synopsis
     "Pseudo-Likelihood Estimation of Log-Multiplicative Association Models")
    (description
     "Log-multiplicative association models (LMA) are models for cross-classifications
of categorical variables where interactions are represented by products of
category scale values and an association parameter.  Maximum likelihood
estimation (MLE) fails for moderate to large numbers of categorical variables.
The @code{pleLMA} package overcomes this limitation of MLE by using
pseudo-likelihood estimation to fit the models to small or large
cross-classifications dichotomous or multi-category variables.  Originally
proposed by Besag (1974, <doi:10.1111/j.2517-6161.1974.tb00999.x>),
pseudo-likelihood estimation takes large complex models and breaks it down into
smaller ones.  Rather than maximizing the likelihood of the joint distribution
of all the variables, a pseudo-likelihood function, which is the product
likelihoods from conditional distributions, is maximized.  LMA models can be
derived from a number of different frameworks including (but not limited to)
graphical models and uni-dimensional and multi-dimensional item response theory
models.  More details about the models and estimation can be found in the
vignette.")
    (license license:gpl3+)))

(define-public r-pleiotest
  (package
    (name "r-pleiotest")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pleiotest" version))
       (sha256
        (base32 "05mgz42wn1y7q8ffgvak22r3k40q9rv1qvpal8yycp83p7hdzg7v"))))
    (properties `((upstream-name . "pleiotest")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-rcolorbrewer))
    (home-page "https://github.com/FerAguate/pleiotest")
    (synopsis "Fast Sequential Pleiotropy Test")
    (description
     "It performs a fast multi-trait genome-wide association analysis based on
seemingly unrelated regressions.  It tests for pleiotropic effects based on a
series of Intersection-Union Wald tests.  The package can handle large and
unbalanced data and plot results.")
    (license license:expat)))

(define-public r-pleio
  (package
    (name "r-pleio")
    (version "1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pleio" version))
       (sha256
        (base32 "1p2fksf026wv8cr6scdg9pclzrbamjf9nl1s0bpr7wrggjw67n0q"))))
    (properties `((upstream-name . "pleio")))
    (build-system r-build-system)
    (propagated-inputs (list r-rms r-matrix))
    (native-inputs (list r-r-rsp))
    (home-page "https://bioinformaticstools.mayo.edu/research/pleio/")
    (synopsis "Pleiotropy Test for Multiple Traits on a Genetic Marker")
    (description
     "Perform tests for pleiotropy of multiple traits of various variable types on
genotypes for a genetic marker.")
    (license license:gpl2+)))

(define-public r-plde
  (package
    (name "r-plde")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plde" version))
       (sha256
        (base32 "1g1b1sgyfxgfkjcr3sdkan8jd6r7yyi0izc83vyj11bdk08jx22h"))))
    (properties `((upstream-name . "plde")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=plde")
    (synopsis "Penalized Log-Density Estimation Using Legendre Polynomials")
    (description
     "We present a penalized log-density estimation method using Legendre polynomials
with lasso penalty to adjust estimate's smoothness.  Re-expressing the logarithm
of the density estimator via a linear combination of Legendre polynomials, we
can estimate parameters by maximizing the penalized log-likelihood function.
Besides, we proposed an implementation strategy that builds on the coordinate
decent algorithm, together with the Bayesian information criterion (BIC).")
    (license license:gpl2+)))

(define-public r-pldamixture
  (package
    (name "r-pldamixture")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pldamixture" version))
       (sha256
        (base32 "0gqk17n8vrbfxzyqfmxyw53dh7dakcgyhgg27k5mcby6mkc42yhb"))))
    (properties `((upstream-name . "pldamixture")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "https://github.com/bpriy/pldamixture")
    (synopsis "Post-Linkage Data Analysis Based on Mixture Modelling")
    (description
     "Perform inference in the secondary analysis setting with linked data potentially
containing mismatch errors.  Only the linked data file may be accessible and
information about the record linkage process may be limited or unavailable.
Implements the General Framework for Regression with Mismatched Data developed
by Slawski et al. (2023) <doi:10.48550/@code{arXiv.2306.00909>}.  The framework
uses a mixture model for pairs of linked records whose two components reflect
distributions conditional on match status, i.e., correct match or mismatch.
Inference is based on composite likelihood and the Expectation-Maximization (EM)
algorithm.  The package currently supports Cox Proportional Hazards Regression
(right-censored data only) and Generalized Linear Regression Models (Gaussian,
Gamma, Poisson, and Logistic (binary models only)).  Information about the
underlying record linkage process can be incorporated into the method if
available (e.g., assumed overall mismatch rate, safe matches, predictors of
match status, or predicted probabilities of correct matches).")
    (license license:gpl2)))

(define-public r-playerratings
  (package
    (name "r-playerratings")
    (version "1.1-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PlayerRatings" version))
       (sha256
        (base32 "04hdjv1gb5b3zym2ryjpqrqhs823nlkmbimfnxw1cfn921ljjgpj"))))
    (properties `((upstream-name . "PlayerRatings")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PlayerRatings")
    (synopsis "Dynamic Updating Methods for Player Ratings Estimation")
    (description
     "This package implements schemes for estimating player or team skill based on
dynamic updating.  Implemented methods include Elo, Glicko, Glicko-2 and
Stephenson.  Contains pdf documentation of a reproducible analysis using
approximately two million chess matches.  Also contains an Elo based method for
multi-player games where the result is a placing or a score.  This includes
zero-sum games such as poker and mahjong.")
    (license license:gpl3)))

(define-public r-playerchart
  (package
    (name "r-playerchart")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PlayerChart" version))
       (sha256
        (base32 "1rvy8jq422c6yrx0wyvsrqkdvcffwn7lpca2k17yzwiidd5w8l1a"))))
    (properties `((upstream-name . "PlayerChart")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-magrittr r-ggtext r-ggplot2 r-dplyr))
    (home-page "https://cran.r-project.org/package=PlayerChart")
    (synopsis "Generate Pizza Chart: Player Stats 0-100")
    (description
     "Create an interactive pizza chart visualizing a specific player's statistics
across various attributes in a sports dataset.  The chart is constructed based
on input parameters: data', a dataframe containing player data for any sports;
player_stats_col', a vector specifying the names of the columns from the
dataframe that will be used to create slices in the pizza chart, with statistics
ranging between 0 and 100; name_col', specifying the name of the column in the
dataframe that contains the player names; and player_name', representing the
specific player whose statistics will be visualized in the chart, serving as the
chart title.")
    (license license:expat)))

(define-public r-play
  (package
    (name "r-play")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "play" version))
       (sha256
        (base32 "0ys8z84ap4v37ifz84q18dms5b0rqin6dvb6av0vl5rjpbgpvbhd"))))
    (properties `((upstream-name . "play")))
    (build-system r-build-system)
    (propagated-inputs (list r-worldfootballr
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-purrr
                             r-ggplot2
                             r-forcats
                             r-dplyr))
    (home-page "https://joe-chelladurai.github.io/play/")
    (synopsis "Visualize Sports Data")
    (description
     "This package provides functions to visualise sports data.  Converts data into a
format suitable for plotting charts.  Helps to ease the process of working with
messy sports data to a more user friendly format.  Football data is accessed
through @code{worldfootballR} <https://github.com/@code{JaseZiv/worldfootballR>}
which gets data from FBref <https://fbref.com/en>, Transfermarkt
<https://www.transfermarkt.com/>, Understat <https://understat.com/>, and fotmob
<https://www.fotmob.com/>.")
    (license license:expat)))

(define-public r-platypus
  (package
    (name "r-platypus")
    (version "3.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Platypus" version))
       (sha256
        (base32 "03sws767aih5yihqr9vfnixp0njgzfgflvajf3sbhrzpj29dpkgg"))))
    (properties `((upstream-name . "Platypus")))
    (build-system r-build-system)
    (propagated-inputs (list r-useful
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-stringdist
                             r-seuratobject
                             r-seurat
                             r-seqinr
                             r-reshape2
                             r-plyr
                             r-matrix
                             r-magrittr
                             r-knitr
                             r-jsonlite
                             r-ggtree
                             r-ggseqlogo
                             r-ggplot2
                             r-ggalluvial
                             r-dplyr
                             r-cowplot
                             r-biostrings))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=Platypus")
    (synopsis "Single-Cell Immune Repertoire and Gene Expression Analysis")
    (description
     "We present Platypus', an open-source software platform providing a user-friendly
interface to investigate B-cell receptor and T-cell receptor repertoires from
@code{scSeq} experiments.  Platypus provides a framework to automate and ease
the analysis of single-cell immune repertoires while also incorporating
transcriptional information involving unsupervised clustering, gene expression
and gene ontology.  This R version of Platypus is part of the @code{ePlatypus}
ecosystem for computational analysis of immunogenomics data: Yermanos et al.
(2021) <doi:10.1093/nargab/lqab023>, Cotet et al. (2023)
<doi:10.1093/bioinformatics/btad553>.")
    (license license:gpl2)))

(define-public r-plattice
  (package
    (name "r-plattice")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plattice" version))
       (sha256
        (base32 "00kc0ymrdr0glxzix1hrwn2vxnkzvq6ji4hkd1fjp5p2fws9ymfq"))))
    (properties `((upstream-name . "plattice")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://cran.r-project.org/package=plattice")
    (synopsis "Lattice Plot for Panel Data")
    (description
     "It creates a lattice plot to visualize panel or longitudinal data.  The observed
values are plotted as dots and the fitted values as lines, both against time.
The plot is customizable and easy to edit, even if you do not know how to
construct a lattice plot from scratch.")
    (license license:gpl2+)))

(define-public r-platowork
  (package
    (name "r-platowork")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "platowork" version))
       (sha256
        (base32 "09ps20lqli50nhhd3368fwf8cjsbshn428fbbbckbxnynxk5cwsy"))))
    (properties `((upstream-name . "platowork")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/lassehjorthmadsen/platowork")
    (synopsis "Data from a Test of the PlatoWork tDCS Headset")
    (description
     "Data and analysis from an experiment with improving touch typing speed, using
the @code{tDCS} @code{PlatoWork} headset produced by @code{PlatoScience}.")
    (license license:expat)))

(define-public r-platformdesign
  (package
    (name "r-platformdesign")
    (version "2.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PlatformDesign" version))
       (sha256
        (base32 "0nnvi40lq6rxjxvf3p5mdn9gp4kp8nr73yzxgrpbg125cqf23vhm"))))
    (properties `((upstream-name . "PlatformDesign")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PlatformDesign")
    (synopsis
     "Optimal Two-Period Multiarm Platform Design with New Experimental Arms Added During the Trial")
    (description
     "Design parameters of the optimal two-period multiarm platform design
(controlling for either family-wise error rate or pair-wise error rate) can be
calculated using this package, allowing pre-planned deferred arms to be added
during the trial.  More details about the design method can be found in the
paper: Pan, H., Yuan, X. and Ye, J. (2022) \"An optimal two-period multiarm
platform design with new experimental arms added during the trial\".  Manuscript
submitted for publication.  For additional references: Dunnett, C. W. (1955)
<doi:10.2307/2281208>.")
    (license license:gpl3+)))

(define-public r-platetools
  (package
    (name "r-platetools")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "platetools" version))
       (sha256
        (base32 "1y4ih36y8z32pqd3cjz2qwysg4c8d6lqdgdb0pixi4i3nxsxq85b"))))
    (properties `((upstream-name . "platetools")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer r-ggplot2))
    (home-page "https://github.com/swarchal/platetools")
    (synopsis "Tools and Plots for Multi-Well Plates")
    (description
     "Collection of functions for working with multi-well microtitre plates, mainly
96, 384 and 1536 well plates.")
    (license license:expat)))

(define-public r-plater
  (package
    (name "r-plater")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plater" version))
       (sha256
        (base32 "0k2ha44ydcf180y44cgxd1haq55k96df39lahs6fji04imh75fii"))))
    (properties `((upstream-name . "plater")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://docs.ropensci.org/plater/")
    (synopsis "Read, Tidy, and Display Data from Microtiter Plates")
    (description
     "This package provides tools for interacting with data from experiments done in
microtiter plates.  Easily read in plate-shaped data and convert it to tidy
format, combine plate-shaped data with tidy data, and view tidy data in plate
shape.")
    (license license:gpl3)))

(define-public r-plasso
  (package
    (name "r-plasso")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plasso" version))
       (sha256
        (base32 "09pc210wb98xnhm9wxsqvf217gysj9c2piwwhzxdp3lxb3my533x"))))
    (properties `((upstream-name . "plasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-iterators r-glmnet r-foreach
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/stefan-1997/plasso")
    (synopsis "Cross-Validated (Post-) Lasso")
    (description
     "Built on top of the glmnet library by Friedman, Hastie and Tibshirani (2010)
<doi:10.18637/jss.v033.i01>, the plasso package follows Knaus (2022)
<doi:10.1093/ectj/utac015> and comes up with two functions that estimate least
squares Lasso and Post-Lasso models.  The plasso() function adds coefficient
paths for a Post-Lasso model to the standard glmnet output.  On top of that
cv.plasso() cross-validates the coefficient paths for both the Lasso and
Post-Lasso model and provides optimal hyperparameter values for the penalty term
lambda.")
    (license license:gpl3)))

(define-public r-plasmidprofiler
  (package
    (name "r-plasmidprofiler")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Plasmidprofiler" version))
       (sha256
        (base32 "10m3knmz0596rm30ncyfprar6l42bjm7r34n4pjz1biqmiz6yz6k"))))
    (properties `((upstream-name . "Plasmidprofiler")))
    (build-system r-build-system)
    (inputs (list pandoc))
    (propagated-inputs (list r-stringr
                             r-reshape2
                             r-rcolorbrewer
                             r-plyr
                             r-plotly
                             r-magrittr
                             r-htmlwidgets
                             r-gtable
                             r-gridextra
                             r-ggplot2
                             r-ggdendro
                             r-gdata
                             r-dplyr
                             r-ape))
    (home-page "https://cran.r-project.org/package=Plasmidprofiler")
    (synopsis "Visualization of Plasmid Profile Results")
    (description
     "This package contains functions developed to combine the results of querying a
plasmid database using short-read sequence typing with the results of a blast
analysis against the query results.")
    (license license:asl2.0)))

(define-public r-plasmamutationdetector2
  (package
    (name "r-plasmamutationdetector2")
    (version "1.1.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PlasmaMutationDetector2" version))
       (sha256
        (base32 "1dcmw6i488gxqqn52r0n9aclnz41ba2gc99vf6ac1wx5flcfm3l9"))))
    (properties `((upstream-name . "PlasmaMutationDetector2")))
    (build-system r-build-system)
    (propagated-inputs (list r-variantannotation
                             r-summarizedexperiment
                             r-s4vectors
                             r-rtracklayer
                             r-rsamtools
                             r-robustbase
                             r-ggplot2
                             r-genomicranges))
    (home-page "https://cran.r-project.org/package=PlasmaMutationDetector2")
    (synopsis "Tumor Mutation Detection in Plasma using Barcoding")
    (description
     "Aims at detecting single nucleotide variation (SNV) and insertion/deletion
(INDEL) in circulating tumor DNA (@code{ctDNA}), used as a surrogate marker for
tumor, at each base position of an Next Generation Sequencing (NGS) analysis
using barcoding.  Mutations are assessed by comparing the minor-allele frequency
at each position to the measured PER in control samples.  This package has been
used for Kjersti Tjensvoll, Morten Lapin, BjÃ¸rnar Gilje, Herish Garresori, Satu
Oltedal, Rakel Brendsdal Forthun, Anders Molven, Yves Rozenholc and Oddmund
N\\o{o}rdgaard (2022) <https://www.nature.com/articles/s41598-022-09698-5>.")
    (license license:expat)))

(define-public r-plasmamutationdetector
  (package
    (name "r-plasmamutationdetector")
    (version "1.7.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PlasmaMutationDetector" version))
       (sha256
        (base32 "0qra7q2pzw212qylyrfhv6zjcsldfw84b93cari0qjqnw4gyfph2"))))
    (properties `((upstream-name . "PlasmaMutationDetector")))
    (build-system r-build-system)
    (propagated-inputs (list r-variantannotation
                             r-summarizedexperiment
                             r-s4vectors
                             r-rtracklayer
                             r-rsamtools
                             r-robustbase
                             r-ggplot2
                             r-genomicranges))
    (home-page "https://cran.r-project.org/package=PlasmaMutationDetector")
    (synopsis "Tumor Mutation Detection in Plasma")
    (description
     "Aims at detecting single nucleotide variation (SNV) and insertion/deletion
(INDEL) in circulating tumor DNA (@code{ctDNA}), used as a surrogate marker for
tumor, at each base position of an Next Generation Sequencing (NGS) analysis.
Mutations are assessed by comparing the minor-allele frequency at each position
to the measured PER in control samples.")
    (license license:expat)))

(define-public r-plasma
  (package
    (name "r-plasma")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plasma" version))
       (sha256
        (base32 "02ygj3sjjds6iyh1d2jz8ifqc4m31zslplsmf88c8q8l67ilvrhl"))))
    (properties `((upstream-name . "plasma")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridislite
                             r-survival
                             r-polychrome
                             r-plsrcox
                             r-pls
                             r-oompabase
                             r-beanplot))
    (native-inputs (list r-r-rsp))
    (home-page "http://oompa.r-forge.r-project.org/")
    (synopsis "Partial LeAst Squares for Multiomic Analysis")
    (description
     "This package contains tools for supervised analyses of incomplete, overlapping
multiomics datasets.  Applies partial least squares in multiple steps to find
models that predict survival outcomes.  See Yamaguchi et al. (2023)
<doi:10.1101/2023.03.10.532096>.")
    (license license:asl2.0)))

(define-public r-plaqr
  (package
    (name "r-plaqr")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plaqr" version))
       (sha256
        (base32 "15pdb57123m3cahvcsyrrvxjwxvs2f389yxip91gjxn68jf6cx1l"))))
    (properties `((upstream-name . "plaqr")))
    (build-system r-build-system)
    (propagated-inputs (list r-quantreg))
    (home-page "https://cran.r-project.org/package=plaqr")
    (synopsis "Partially Linear Additive Quantile Regression")
    (description
     "Estimation, prediction, thresholding, transformation, and plotting for partially
linear additive quantile regression.  Intuitive functions for fitting and
plotting partially linear additive quantile regression models.  Uses and works
with functions from the quantreg package.")
    (license license:gpl2+)))

(define-public r-planttracker
  (package
    (name "r-planttracker")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plantTracker" version))
       (sha256
        (base32 "0qn07pb84j110zrml24gwb27za4wdxn0a4yfhc0c1174fipl6az1"))))
    (properties `((upstream-name . "plantTracker")))
    (build-system r-build-system)
    (propagated-inputs (list r-units r-sf r-matrix r-igraph))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/aestears/plantTracker")
    (synopsis "Extract Demographic and Competition Data from Fine-Scale Maps")
    (description
     "Extracts growth, survival, and local neighborhood density information from
repeated, fine-scale maps of organism occurrence.  Further information about
this package can be found in our journal article, \"@code{plantTracker}: An R
package to translate maps of plant occurrence into demographic data\" published
in 2022 in Methods in Ecology and Evolution (Stears, et al., 2022)
<doi:10.1111/2041-210X.13950>.")
    (license license:expat)))

(define-public r-plantphysior
  (package
    (name "r-plantphysior")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plantphysioR" version))
       (sha256
        (base32 "15hp22b1h37dp0llbifz369rdaqzn0jl9dw07z4c4j0jnmr50h8h"))))
    (properties `((upstream-name . "plantphysioR")))
    (build-system r-build-system)
    (home-page "https://github.com/rameshram96/plantphysioR")
    (synopsis "Fundamental Formulas for Plant Physiology")
    (description
     "This package provides functions tailored for scientific and student communities
involved in plant science research.  Functionalities encompass estimation
chlorophyll content according to Arnon (1949) <doi:10.1104/pp.24.1.1>,
determination water potential of Polyethylene glycol(PEG)6000 as in Michel and
Kaufmann (1973) <doi:10.1104/pp.51.5.914> and functions related to estimation of
yield related indices like Abiotic tolerance index as given by Moosavi et
al.(2008)<doi:10.22059/JDESERT.2008.27115>, Geometric mean productivity (GMP) by
Fernandez (1992) <ISBN:92-9058-081-X>, Golden Mean by Moradi et
al.(2012)<doi:10.14207/ejsd.2012.v1n3p543>, HAM by Schneider et
al.(1997)<doi:10.2135/cropsci1997.0011183X003700010007x>,MPI and TOL by Hossain
etal., (1990)<doi:10.2135/cropsci1990.0011183X003000030030x>, RDI by Fischer et
al. (1979)<doi:10.1071/AR9791001>,SSI by Fisher et
al.(1978)<doi:10.1071/AR9780897>, STI by Fernandez
(1993)<doi:10.22001/wvc.72511>,YSI by Bouslama & Schapaugh
(1984)<doi:10.2135/cropsci1984.0011183X002400050026x>, Yield index by Gavuzzi et
al.(1997)<doi:10.4141/P96-130>.")
    (license license:gpl3+)))

(define-public r-plantecowrap
  (package
    (name "r-plantecowrap")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plantecowrap" version))
       (sha256
        (base32 "0syxf63xy5whzw03ys5cxyw7689mr7ihgmqddv53jb8rlbwm84p8"))))
    (properties `((upstream-name . "plantecowrap")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-plantecophys r-minpack-lm r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jstinzi/plantecowrap")
    (synopsis "Enhancing Capabilities of 'plantecophys'")
    (description
     "This package provides wrapping functions to add to capabilities to plantecophys
(Duursma, 2015, <doi:10.1371/journal.pone.0143346>).  Key added capabilities
include temperature responses of mesophyll conductance (gm, gmeso), apparent
Michaelis-Menten constant for rubisco carboxylation in air (Km, Kcair),and
photorespiratory CO2 compensation point (@code{GammaStar}) for fitting A-Ci or
A-Cc curves for C3 plants (for temperature responses of gm, Km, &
@code{GammaStar}, see Bernacchi et al., 2002, <doi:10.1104/pp.008250>; for
theory on fitting A-Ci or A-Cc curves, see Farquhar et al., 1980;
<doi:10.1007/BF00386231>, von Caemmerer, 2000, ISBN:064306379X; Ethier &
Livingston, 2004 <doi:10.1111/j.1365-3040.2004.01140.x>; and Gu et al., 2010,
<doi:10.1111/j.1365-3040.2010.02192.x>).  Includes the ability to fit the
Arrhenius and modified Arrhenius temperature response functions (see Medlyn et
al., 2002, <doi:10.1046/j.1365-3040.2002.00891.x>) for maximum rubisco
carboxylation rates (Vcmax) and maximum electron transport rates (Jmax) (see
Farquhar et al., 1980; <doi:10.1007/BF00386231>).")
    (license license:expat)))

(define-public r-plantecophys
  (package
    (name "r-plantecophys")
    (version "1.4-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plantecophys" version))
       (sha256
        (base32 "1qf9a38n8nvvz8dvqra9mdsmkbkhxymjrin0wfis8g1ndf1f406c"))))
    (properties `((upstream-name . "plantecophys")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://bitbucket.org/remkoduursma/plantecophys")
    (synopsis "Modelling and Analysis of Leaf Gas Exchange Data")
    (description
     "Coupled leaf gas exchange model, A-Ci curve simulation and fitting, Ball-Berry
stomatal conductance models, leaf energy balance using Penman-Monteith,
Cowan-Farquhar optimization, humidity unit conversions.  See Duursma (2015)
<doi:10.1371/journal.pone.0143346>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-planscorer
  (package
    (name "r-planscorer")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "planscorer" version))
       (sha256
        (base32 "1dhhdzbkjchfkdg7ylda6hi7zsc0g9i5fc66mccg6a8vcf4nk3p6"))))
    (properties `((upstream-name . "planscorer")))
    (build-system r-build-system)
    (propagated-inputs (list r-webshot2
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-sf
                             r-rlang
                             r-readr
                             r-purrr
                             r-jsonlite
                             r-httr2
                             r-fs
                             r-dplyr
                             r-curl
                             r-cli))
    (home-page "http://christophertkenny.com/planscorer/")
    (synopsis "Score Redistricting Plans with 'PlanScore'")
    (description
     "This package provides access to the @code{PlanScore} Application Programming
Interface (<https://github.com/@code{PlanScore/PlanScore/blob/main/API.md>}) for
scoring redistricting plans.  Allows for upload of plans from block assignment
files and shape files.  For shapes in memory, such as from sf or redist', it
processes them to save and upload.  Includes tools for tidying responses and
saving output from the website.")
    (license license:expat)))

(define-public r-planr
  (package
    (name "r-planr")
    (version "0.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "planr" version))
       (sha256
        (base32 "1dw4n4jmpqmrklpcxi2yfcbqbf81kbrqr6sw3fwrg2ppn3hiz9af"))))
    (properties `((upstream-name . "planr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-rcpproll r-magrittr r-lubridate r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/nguyennico/planr")
    (synopsis "Tools for Supply Chain Management, Demand and Supply Planning")
    (description
     "Perform flexible and quick calculations for Demand and Supply Planning, such as
projected inventories and coverages, as well as replenishment plan.  For any
time bucket, daily, weekly or monthly, and any granularity level, product or
group of products.")
    (license license:expat)))

(define-public r-planningml
  (package
    (name "r-planningml")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "planningML" version))
       (sha256
        (base32 "1s9lf43kkiciqkrcyq9icxdqfs8a1za60pwpbxnrdsa7gzf76i29"))))
    (properties `((upstream-name . "planningML")))
    (build-system r-build-system)
    (propagated-inputs (list r-proc
                             r-mess
                             r-matrix
                             r-lubridate
                             r-glmnet
                             r-dplyr
                             r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=planningML")
    (synopsis
     "Sample Size Calculator for Machine Learning Applications in Healthcare")
    (description
     "Advances in automated document classification has led to identifying massive
numbers of clinical concepts from handwritten clinical notes.  These high
dimensional clinical concepts can serve as highly informative predictors in
building classification algorithms for identifying patients with different
clinical conditions, commonly referred to as patient phenotyping.  However, from
a planning perspective, it is critical to ensure that enough data is available
for the phenotyping algorithm to obtain a desired classification performance.
This challenge in sample size planning is further exacerbated by the high
dimension of the feature space and the inherent imbalance of the response class.
 Currently available sample size planning methods can be categorized into: (i)
model-based approaches that predict the sample size required for achieving a
desired accuracy using a linear machine learning classifier and (ii) learning
curve-based approaches (Figueroa et al. (2012) <doi:10.1186/1472-6947-12-8>)
that fit an inverse power law curve to pilot data to extrapolate performance.
We develop model-based approaches for imbalanced data with correlated features,
deriving sample size formulas for performance metrics that are sensitive to
class imbalance such as Area Under the receiver operating characteristic Curve
(AUC) and Matthews Correlation Coefficient (MCC).  This is done using a two-step
approach where we first perform feature selection using the innovated High
Criticism thresholding method (Hall and Jin (2010) <doi:10.1214/09-AOS764>),
then determine the sample size by optimizing the two performance metrics.
Further, we develop software in the form of an R package named @code{planningML}
and an R Shiny app to facilitate the convenient implementation of the developed
model-based approaches and learning curve approaches for imbalanced data.  We
apply our methods to the problem of phenotyping rare outcomes using the
MIMIC-III electronic health record database.  We show that our developed methods
which relate training data size and performance on AUC and MCC, can predict the
true or observed performance from linear ML classifiers such as LASSO and SVM at
different training data sizes.  Therefore, in high-dimensional classification
analysis with imbalanced data and correlated features, our approach can
efficiently and accurately determine the sample size needed for machine-learning
based classification.")
    (license license:gpl2)))

(define-public r-planets
  (package
    (name "r-planets")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "planets" version))
       (sha256
        (base32 "01zh7lgaykr9p186yy0vksn2pmc69b3xxh78w7z6wclvqjb7zh1p"))))
    (properties `((upstream-name . "planets")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=planets")
    (synopsis "Simple and Accessible Data from all Known Planets")
    (description
     "The goal of planets is to provide of very simple and accessible data containing
basic information from all known planets.")
    (license license:expat)))

(define-public r-planetnicfi
  (package
    (name "r-planetnicfi")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PlanetNICFI" version))
       (sha256
        (base32 "02xras3bhgrnhijda3nbmcpwzi90db85amqxi2v0p00z9i4p99i6"))))
    (properties `((upstream-name . "PlanetNICFI")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-sf r-httr r-glue r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mlampros/PlanetNICFI")
    (synopsis "Processing of the 'Planet NICFI' Satellite Imagery")
    (description
     "It includes functions to download and process the Planet NICFI (Norway's
International Climate and Forest Initiative) Satellite Imagery utilizing the
Planet Mosaics API
<https://developers.planet.com/docs/basemaps/reference/#tag/Basemaps-and-Mosaics>.
 GDAL (library for raster and vector geospatial data formats) and aria2c
(paralleled download utility) must be installed and configured in the user's
Operating System.")
    (license license:gpl3)))

(define-public r-planesmuestra
  (package
    (name "r-planesmuestra")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Planesmuestra" version))
       (sha256
        (base32 "0v7l4hrfckcf7zmk0ihq2ij0qli7x12j17vd6752d1yjk27fgk57"))))
    (properties `((upstream-name . "Planesmuestra")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=Planesmuestra")
    (synopsis
     "Functions for Calculating Dodge Romig, MIL STD 105E and MIL STD 414 Acceptance Sampling Plan")
    (description
     "Calculates an acceptance sampling plan, (sample size and acceptance number)
based in MIL STD 105E, Dodge Romig and MIL STD 414 tables and procedures.  The
arguments for each function are related to lot size, inspection level and
quality level.  The specific plan operating curve (OC), is calculated by the
binomial distribution.")
    (license license:gpl2+)))

(define-public r-planegeometry
  (package
    (name "r-planegeometry")
    (version "1.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PlaneGeometry" version))
       (sha256
        (base32 "0v4484kh8aqni0n6nfd9c1wiw62fj439647xb583pbmxi5ja58hq"))))
    (properties `((upstream-name . "PlaneGeometry")))
    (build-system r-build-system)
    (propagated-inputs (list r-uniformly
                             r-stringr
                             r-sdpt3r
                             r-rcdd
                             r-r6
                             r-fitconic
                             r-cvxr
                             r-carlson))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/stla/PlaneGeometry")
    (synopsis "Plane Geometry")
    (description
     "An extensive set of plane geometry routines.  Provides R6 classes representing
triangles, circles, circular arcs, ellipses, elliptical arcs, lines, hyperbolae,
and their plot methods.  Also provides R6 classes representing transformations:
rotations, reflections, homotheties, scalings, general affine transformations,
inversions, MÃ¶bius transformations.")
    (license license:gpl3)))

(define-public r-plan
  (package
    (name "r-plan")
    (version "0.4-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plan" version))
       (sha256
        (base32 "0p81pzysbl58580393hw90w5svqiva9yvcwlmx247fld83lbh9qq"))))
    (properties `((upstream-name . "plan")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/dankelley/plan")
    (synopsis "Tools for Project Planning")
    (description
     "Supports the creation of burndown charts and gantt diagrams.")
    (license license:gpl2+)))

(define-public r-plainview
  (package
    (name "r-plainview")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plainview" version))
       (sha256
        (base32 "1ckcwbkygflb25pz5k4d76dsc608zyd1q35hbfhym7h44krx5cz9"))))
    (properties `((upstream-name . "plainview")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridislite
                             r-raster
                             r-png
                             r-lattice
                             r-htmlwidgets
                             r-htmltools))
    (home-page "https://r-spatial.github.io/plainview/")
    (synopsis "Plot Raster Images Interactively on a Plain HTML Canvas")
    (description
     "This package provides methods for plotting potentially large (raster) images
interactively on a plain HTML canvas.  In contrast to package mapview data are
plotted without background map, but data can be projected to any spatial
coordinate reference system.  Supports plotting of classes @code{RasterLayer}',
@code{RasterStack}', @code{RasterBrick} (from package raster') as well as png
files located on disk.  Interactivity includes zooming, panning, and mouse
location information.  In case of multi-layer @code{RasterStacks} or
@code{RasterBricks}', RGB image plots are created (similar to
raster::@code{plotRGB} - but interactive).")
    (license license:expat)))

(define-public r-plackettluce
  (package
    (name "r-plackettluce")
    (version "0.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PlackettLuce" version))
       (sha256
        (base32 "04pid91wsf3m3mg45zis17xa251491011ihbmfsdy7m914l9wcyd"))))
    (properties `((upstream-name . "PlackettLuce")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich
                             r-rspectra
                             r-r6
                             r-qvcalc
                             r-psychotree
                             r-psychotools
                             r-partykit
                             r-matrixstats
                             r-matrix
                             r-igraph
                             r-cvxr))
    (native-inputs (list r-knitr))
    (home-page "https://hturner.github.io/PlackettLuce/")
    (synopsis "Plackett-Luce Models for Rankings")
    (description
     "This package provides functions to prepare rankings data and fit the
Plackett-Luce model jointly attributed to Plackett (1975) <doi:10.2307/2346567>
and Luce (1959, ISBN:0486441369).  The standard Plackett-Luce model is
generalized to accommodate ties of any order in the ranking.  Partial rankings,
in which only a subset of items are ranked in each ranking, are also
accommodated in the implementation.  Disconnected/weakly connected networks
implied by the rankings may be handled by adding pseudo-rankings with a
hypothetical item.  Optionally, a multivariate normal prior may be set on the
log-worth parameters and ranker reliabilities may be incorporated as proposed by
Raman and Joachims (2014) <doi:10.1145/2623330.2623654>.  Maximum a posteriori
estimation is used when priors are set.  Methods are provided to estimate
standard errors or quasi-standard errors for inference as well as to fit
Plackett-Luce trees.  See the package website or vignette for further details.")
    (license license:gpl3)))

(define-public r-placer
  (package
    (name "r-placer")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "placer" version))
       (sha256
        (base32 "0d7b6mkyyrcjn1pqxlj9mjis6vzks986i7vhc8ffkp8hz8sc7271"))))
    (properties `((upstream-name . "placer")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=placer")
    (synopsis "PLastic ACcumulation Estimate using R (PLACER)")
    (description
     "Assessment of the prevalence of plastic debris in bird nests based on bootstrap
replicates.  The package allows for calculating bootstrapped 95% confidence
intervals for the estimated prevalence of debris.  Combined with a Bayesian
approach, the resampling simulations can be also used to define appropriate
sample sizes to detect prevalence of plastics.  The method has wide application,
and can also be applied to estimate confidence intervals and define sample sizes
for the prevalence of plastics ingested by any other organisms.  The method is
described in Tavares et al. (Submitted).")
    (license license:expat)))

(define-public r-plac
  (package
    (name "r-plac")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "plac" version))
       (sha256
        (base32 "0n7d8pzz2fk4npx71c2h7yqp5r3pvfgp7jhh6q82vfc0a3kny6cs"))))
    (properties `((upstream-name . "plac")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rcppeigen r-rcpp))
    (home-page "https://github.com/942kid/plac")
    (synopsis
     "Pairwise Likelihood Augmented Cox Estimator for Left-Truncated Data")
    (description
     "This package provides a semi-parametric estimation method for the Cox model with
left-truncated data using augmented information from the marginal of truncation
times.")
    (license license:gpl3+)))

(define-public r-pl94171
  (package
    (name "r-pl94171")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PL94171" version))
       (sha256
        (base32 "0www52svnfrhf1szgr820zj6fzqxzrlqiyd0vlshajmh3s091jjk"))))
    (properties `((upstream-name . "PL94171")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-tinytiger
                             r-stringr
                             r-sf
                             r-readr
                             r-httr
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://corymccartan.com/PL94171/")
    (synopsis "Tabulate P.L. 94-171 Redistricting Data Summary Files")
    (description
     "This package provides tools to process legacy format summary redistricting data
files produced by the United States Census Bureau pursuant to P.L. 94-171.
These files are generally available earlier but are difficult to work with
as-is.")
    (license license:expat)))

(define-public r-pksensi
  (package
    (name "r-pksensi")
    (version "1.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pksensi" version))
       (sha256
        (base32 "0dds9xz484505zclf43gj1w33rm8iy9y0dprj1z0qjihkd9351k6"))))
    (properties `((upstream-name . "pksensi")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-foreach r-doparallel r-desolve
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/nanhung/pksensi")
    (synopsis
     "Global Sensitivity Analysis in Physiologically Based Kinetic Modeling")
    (description
     "Applying the global sensitivity analysis workflow to investigate the parameter
uncertainty and sensitivity in physiologically based kinetic (PK) models,
especially the physiologically based pharmacokinetic/toxicokinetic model with
multivariate outputs.  The package also provides some functions to check the
convergence and sensitivity of model parameters.  The workflow was first
mentioned in Hsieh et al., (2018) <doi:10.3389/fphar.2018.00588>, then further
refined (Hsieh et al., 2020 <doi:10.1016/j.softx.2020.100609>).")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-pksea
  (package
    (name "r-pksea")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pKSEA" version))
       (sha256
        (base32 "1k9javxbhx28hf5k3i66ggqwlws2w9qwp01g8f7jmyp92pxr3qqd"))))
    (properties `((upstream-name . "pKSEA")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pKSEA")
    (synopsis "Prediction-Based Kinase-Substrate Enrichment Analysis")
    (description
     "This package provides a tool for inferring kinase activity changes from
phosphoproteomics data. @code{pKSEA} uses kinase-substrate prediction scores to
weight observed changes in phosphopeptide abundance to calculate a
phosphopeptide-level contribution score, then sums up these contribution scores
by kinase to obtain a phosphoproteome-level kinase activity change score (KAC
score). @code{pKSEA} then assesses the significance of changes in predicted
substrate abundances for each kinase using permutation testing.  This results in
a permutation score (@code{pKSEA} significance score) reflecting the likelihood
of a similarly high or low KAC from random chance, which can then be interpreted
in an analogous manner to an empirically calculated p-value. @code{pKSEA}
contains default databases of kinase-substrate predictions from @code{NetworKIN}
(@code{NetworKINPred_db}) <http://networkin.info> Horn, et.  al (2014)
<doi:10.1038/nmeth.2968> and of known kinase-substrate links from
@code{PhosphoSitePlus} (KSEAdb) <https://www.phosphosite.org/> Hornbeck PV, et.
al (2015) <doi:10.1093/nar/gku1267>.")
    (license license:expat)))

(define-public r-pks
  (package
    (name "r-pks")
    (version "0.6-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pks" version))
       (sha256
        (base32 "1c0g160c8d2bqlhg5qwbdzfx5whnwin8hwywzyfzv14qk7dalb2p"))))
    (properties `((upstream-name . "pks")))
    (build-system r-build-system)
    (propagated-inputs (list r-sets))
    (home-page "https://www.mathpsy.uni-tuebingen.de/wickelmaier/")
    (synopsis "Probabilistic Knowledge Structures")
    (description
     "Fitting and testing probabilistic knowledge structures, especially the basic
local independence model (BLIM, Doignon & Flamagne, 1999) and the simple
learning model (SLM), using the minimum discrepancy maximum likelihood (MDML)
method (Heller & Wickelmaier, 2013 <doi:10.1016/j.endm.2013.05.145>).")
    (license license:gpl2+)))

(define-public r-pkr
  (package
    (name "r-pkr")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pkr" version))
       (sha256
        (base32 "1wi29nnljc096bazicrzvmdqachrwhgwcyq930jrbs6kk7hihrqz"))))
    (properties `((upstream-name . "pkr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rtf r-forestplot r-foreign r-binr))
    (home-page "https://cran.r-project.org/package=pkr")
    (synopsis "Pharmacokinetics in R")
    (description
     "Conduct a noncompartmental analysis as closely as possible to the most widely
used commercial software.  Some features are 1) CDISC SDTM terms 2) Automatic
slope selection with the same criterion of @code{WinNonlin(R}) 3) Supporting
both linear-up linear-down and linear-up log-down method 4) Interval(partial)
AUCs with linear or log interpolation method * Reference: Gabrielsson J, Weiner
D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and
Applications.  5th ed.  2016. (ISBN:9198299107).")
    (license license:gpl3)))

(define-public r-pkpdsim
  (package
    (name "r-pkpdsim")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PKPDsim" version))
       (sha256
        (base32 "0pzpr2lqgcssz5mgsb27z48x8sf9plbsd2m8rmkha1926fxks6na"))))
    (properties `((upstream-name . "PKPDsim")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rcpp
                             r-randtoolbox
                             r-mass
                             r-magrittr
                             r-jsonlite
                             r-data-table
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/InsightRX/PKPDsim")
    (synopsis
     "Tools for Performing Pharmacokinetic-Pharmacodynamic Simulations")
    (description
     "Simulate dose regimens for pharmacokinetic-pharmacodynamic (PK-PD) models
described by differential equation (DE) systems.  Simulation using ADVAN-style
analytical equations is also supported (Abuhelwa et al. (2015)
<doi:10.1016/j.vascn.2015.03.004>).")
    (license license:expat)))

(define-public r-pknca
  (package
    (name "r-pknca")
    (version "0.11.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PKNCA" version))
       (sha256
        (base32 "1az62z5w9fydzbhzdlr0h1wzwby144vc7z0w907q3i43v3ziilb0"))))
    (properties `((upstream-name . "PKNCA")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-nlme
                             r-lifecycle
                             r-dplyr
                             r-digest
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://billdenney.github.io/pknca/")
    (synopsis "Perform Pharmacokinetic Non-Compartmental Analysis")
    (description
     "Compute standard Non-Compartmental Analysis (NCA) parameters for typical
pharmacokinetic analyses and summarize them.")
    (license license:agpl3)))

(define-public r-pkmon
  (package
    (name "r-pkmon")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pkmon" version))
       (sha256
        (base32 "0na3z7v67gjpz15fkbf4yfwk4kiz7y79z2f7ccay8x855jzlbklx"))))
    (properties `((upstream-name . "pkmon")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pkmon")
    (synopsis
     "Least-Squares Estimator under k-Monotony Constraint for Discrete Functions")
    (description
     "We implement two least-squares estimators under k-monotony constraint using a
method based on the Support Reduction Algorithm from Groeneboom et al (2008)
<DOI:10.1111/j.1467-9469.2007.00588.x>.  The first one is a projection estimator
on the set of k-monotone discrete functions.  The second one is a projection on
the set of k-monotone discrete probabilities.  This package provides functions
to generate samples from the spline basis from Lefevre and Loisel (2013)
<DOI:10.1239/jap/1378401239>, and from mixtures of splines.")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-pklmtest
  (package
    (name "r-pklmtest")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PKLMtest" version))
       (sha256
        (base32 "0i1kxczlhn95jw1w7vw87svqhpq9j6xi4fhy974yjx9xhax171hq"))))
    (properties `((upstream-name . "PKLMtest")))
    (build-system r-build-system)
    (propagated-inputs (list r-ranger))
    (home-page "https://cran.r-project.org/package=PKLMtest")
    (synopsis "Classification Based MCAR Test")
    (description
     "Implementation of a KL-based (Kullback-Leibler) test for MCAR (Missing
Completely At Random) in the context of missing data as introduced in Michel et
al. (2021) <@code{arXiv:2109.10150>}.")
    (license license:gpl3)))

(define-public r-pkgverse
  (package
    (name "r-pkgverse")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pkgverse" version))
       (sha256
        (base32 "0lsv7s9vsjvfpy19nxbdlbm363j01wwqqnrp0nlrik590hfq4w83"))))
    (properties `((upstream-name . "pkgverse")))
    (build-system r-build-system)
    (propagated-inputs (list r-usethis r-devtools))
    (home-page "https://pkgverse.mikewk.com")
    (synopsis "Build a Meta-Package Universe")
    (description
     "Build your own universe of packages similar to the tidyverse package
<https://tidyverse.org/> with this meta-package creator.  Create a
package-verse, or meta package, by supplying a custom name for the collection of
packages and the vector of desired package names to includeâ and optionally
supply a destination directory, an indicator of whether to keep the created
package directory, and/or a vector of verbs implement via the usethis
<http://usethis.r-lib.org/> package.")
    (license license:cc0)))

(define-public r-pkgsearch
  (package
    (name "r-pkgsearch")
    (version "3.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pkgsearch" version))
       (sha256
        (base32 "0b4w6sk65r77sfkvnyirxk0mpikqladi14pmc9sqhfcjbwvqgjx7"))))
    (properties `((upstream-name . "pkgsearch")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-curl))
    (home-page "https://github.com/r-hub/pkgsearch")
    (synopsis "Search and Query CRAN R Packages")
    (description
     "Search CRAN metadata about packages by keyword, popularity, recent activity,
package name and more.  Uses the R-hub search server, see <https://r-pkg.org>
and the CRAN metadata database, that contains information about CRAN packages.
Note that this is _not_ a CRAN project.")
    (license license:expat)))

(define-public r-pkgnews
  (package
    (name "r-pkgnews")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pkgnews" version))
       (sha256
        (base32 "0kqyy1fchpcyclxh6gly23pkfcf5b3hpsq1bhj4ykzbl4bc002h2"))))
    (properties `((upstream-name . "pkgnews")))
    (build-system r-build-system)
    (inputs (list pandoc))
    (home-page "https://github.com/owenjonesuob/pkgnews")
    (synopsis "Retrieve R Package News Files")
    (description
     "Read R package news files, regardless of whether or not the package is
installed.")
    (license license:expat)))

(define-public r-pkgnet
  (package
    (name "r-pkgnet")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pkgnet" version))
       (sha256
        (base32 "10sjvd3mwwlbj71zmdmczyrx5s95zax0w7m07nr6413c96ahny14"))))
    (properties `((upstream-name . "pkgnet")))
    (build-system r-build-system)
    (propagated-inputs (list r-visnetwork
                             r-rmarkdown
                             r-rlang
                             r-r6
                             r-magrittr
                             r-knitr
                             r-igraph
                             r-glue
                             r-futile-logger
                             r-dt
                             r-data-table
                             r-covr
                             r-assertthat))
    (home-page "https://github.com/uptake/pkgnet")
    (synopsis "Get Network Representation of an R Package")
    (description
     "This package provides tools from the domain of graph theory can be used to
quantify the complexity and vulnerability to failure of a software package.
That is the guiding philosophy of this package.  pkgnet provides tools to
analyze the dependencies between functions in an R package and between its
imported packages.  See the pkgnet website for vignettes and other supplementary
information.")
    (license license:bsd-3)))

(define-public r-pkgndep
  (package
    (name "r-pkgndep")
    (version "1.99.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pkgndep" version))
       (sha256
        (base32 "1llpizr2wkhfgygr9fd79qxym0xcrxfvz8w5n0kwvkvlq3iqid44"))))
    (properties `((upstream-name . "pkgndep")))
    (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-hash
                             r-globaloptions
                             r-getoptlong
                             r-complexheatmap
                             r-brew
                             r-biocversion
                             r-biocmanager))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://github.com/jokergoo/pkgndep")
    (synopsis "Analyze Dependency Heaviness of R Packages")
    (description
     "This package provides a new metric named dependency heaviness is proposed that
measures the number of additional dependency packages that a parent package
brings to its child package and are unique to the dependency packages imported
by all other parents.  The dependency heaviness analysis is visualized by a
customized heatmap.  The package is described in
<doi:10.1093/bioinformatics/btac449>.  We have also performed the dependency
heaviness analysis on the CRAN/Bioconductor package ecosystem and the results
are implemented as a web-based database which provides comprehensive tools for
querying dependencies of individual R packages.  The systematic analysis on the
CRAN/Bioconductor ecosystem is described in <doi:10.1016/j.jss.2023.111610>.
From pkgndep version 2.0.0, the heaviness database includes snapshots of the
CRAN/Bioconductor ecosystems for many old R versions.")
    (license license:expat)))

(define-public r-pkglite
  (package
    (name "r-pkglite")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pkglite" version))
       (sha256
        (base32 "1ldz68c09192qy8k52fcx1k714dzwcjck9gla16z2g3zcyijizyi"))))
    (properties `((upstream-name . "pkglite")))
    (build-system r-build-system)
    (propagated-inputs (list r-remotes r-magrittr r-crayon))
    (native-inputs (list r-knitr))
    (home-page "https://merck.github.io/pkglite/")
    (synopsis "Compact Package Representations")
    (description
     "This package provides a tool, grammar, and standard to represent and exchange R
package source code as text files.  Converts one or more source packages to a
text file and restores the package structures from the file.")
    (license license:gpl3)))

(define-public r-pkgkitten
  (package
    (name "r-pkgkitten")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pkgKitten" version))
       (sha256
        (base32 "1ff2qhrgmlbayz7k12p7qs42w38g7ipwnq97wiw830dyf7k7c3f7"))))
    (properties `((upstream-name . "pkgKitten")))
    (build-system r-build-system)
    (home-page "https://github.com/eddelbuettel/pkgkitten")
    (synopsis "Create Simple Packages Which Do not Upset R Package Checks")
    (description
     "This package provides a function kitten() which creates cute little packages
which pass R package checks.  This sets it apart from package.skeleton() which
it calls, and which leaves imperfect files behind.  As this is not exactly
helpful for beginners, kitten() offers an alternative.  Unit test support can be
added via the tinytest package (if present), and documentation-creation support
can be added via roxygen2 (if present).")
    (license license:gpl2+)))

(define-public r-pkggraphr
  (package
    (name "r-pkggraphr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pkgGraphR" version))
       (sha256
        (base32 "0qmrbwgm6aclzdgpcwr2c2bvakh3rcrf9z8m5mqd9vxgjykjfgks"))))
    (properties `((upstream-name . "pkgGraphR")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr r-dplyr r-diagrammer))
    (native-inputs (list r-knitr))
    (home-page "https://gitlab.com/doliv071/pkggraphr")
    (synopsis "Graph the Relationship Between Functions in an R Package")
    (description
     "It is often useful when developing an R package to track the relationship
between functions in order to appropriately test and track changes.  This
package generates a graph of the relationship between all R functions in a
package.  It can also be used on any directory containing .R files which can be
very useful for shiny apps or other non-package workflows.")
    (license license:gpl3+)))

(define-public r-pkggraph
  (package
    (name "r-pkggraph")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pkggraph" version))
       (sha256
        (base32 "1isiywgm20rypc5qr03p0k7lis76zzd96x5ncbvia644d2n13brm"))))
    (properties `((upstream-name . "pkggraph")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-rcolorbrewer
                             r-plyr
                             r-networkd3
                             r-network
                             r-matrix
                             r-intergraph
                             r-igraph
                             r-htmltools
                             r-ggplot2
                             r-ggnetwork
                             r-dplyr
                             r-data-table
                             r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/talegari/pkggraph")
    (synopsis
     "Consistent and Intuitive Platform to Explore the Dependencies of Packages on the Comprehensive R Archive Network Like Repositories")
    (description
     "Interactively explore various dependencies of a package(s) (on the Comprehensive
R Archive Network Like repositories) and perform analysis using tidy philosophy.
 Most of the functions return a tibble object (enhancement of dataframe') which
can be used for further analysis.  The package offers functions to produce
network and igraph dependency graphs.  The plot method produces a static plot
based on ggnetwork and plotd3 function produces an interactive D3 plot based on
@code{networkD3}'.")
    (license license:gpl3)))

(define-public r-pkgfilecache
  (package
    (name "r-pkgfilecache")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pkgfilecache" version))
       (sha256
        (base32 "0nzikz51444s6fpr3yvzshs2wv4nja6v0a44i791i4djm1qkyibb"))))
    (properties `((upstream-name . "pkgfilecache")))
    (build-system r-build-system)
    (propagated-inputs (list r-rappdirs r-downloader r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/dfsp-spirit/pkgfilecache")
    (synopsis "Download and Manage Optional Package Data")
    (description
     "Manage optional data for your package.  The data can be hosted anywhere, and you
have to give a Uniform Resource Locator (URL) for each file.  File integrity
checks are supported.  This is useful for package authors who need to ship more
than the 5 Megabyte of data currently allowed by the the Comprehensive R Archive
Network (CRAN).")
    (license license:expat)))

(define-public r-pkgdepr
  (package
    (name "r-pkgdepr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pkgdepR" version))
       (sha256
        (base32 "098fcj1s5rcjxmn57449f4zk9z2w4mqwxkvxxjazd1nj7wa45z8p"))))
    (properties `((upstream-name . "pkgdepR")))
    (build-system r-build-system)
    (propagated-inputs (list r-visnetwork r-viridislite r-scales r-magrittr
                             r-dplyr))
    (home-page "https://pkgdepR.org/")
    (synopsis "Statically Determine Function Dependencies Between Packages")
    (description
     "Statically determine and visualize the function dependencies within and across
packages.  This may be useful for managing function dependencies across a code
base of multiple R packages.")
    (license license:expat)))

(define-public r-pkgdepends
  (package
    (name "r-pkgdepends")
    (version "0.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pkgdepends" version))
       (sha256
        (base32 "025ni959zvqfaxs6af9pka7gn23yzgbabpy28bsa45v75hi4i79m"))))
    (properties `((upstream-name . "pkgdepends")))
    (build-system r-build-system)
    (propagated-inputs (list r-zip
                             r-r6
                             r-ps
                             r-processx
                             r-pkgcache
                             r-pkgbuild
                             r-lpsolve
                             r-jsonlite
                             r-filelock
                             r-desc
                             r-curl
                             r-cli
                             r-callr))
    (home-page "https://r-lib.github.io/pkgdepends/")
    (synopsis "Package Dependency Resolution and Downloads")
    (description
     "Find recursive dependencies of R packages from various sources.  Solve the
dependencies to obtain a consistent set of packages to install.  Download
packages, and install them.  It supports packages on CRAN', Bioconductor and
other CRAN-like repositories, @code{GitHub}', package URLs', and local package
trees and files.  It caches metadata and package files via the pkgcache package,
and performs all HTTP requests, downloads, builds and installations in parallel.
 pkgdepends is the workhorse of the pak package.")
    (license license:expat)))

(define-public r-pkgcond
  (package
    (name "r-pkgcond")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pkgcond" version))
       (sha256
        (base32 "03a4blq3niw1mljzylamvmr3l43czqlww4i6n7zb0dlym9x8gs1w"))))
    (properties `((upstream-name . "pkgcond")))
    (build-system r-build-system)
    (propagated-inputs (list r-assertthat))
    (home-page "https://github.com/RDocTaskForce/pkgcond")
    (synopsis "Classed Error and Warning Conditions")
    (description
     "This provides utilities for creating classed error and warning conditions based
on where the error originated.")
    (license license:gpl2)))

(define-public r-pkgcache
  (package
    (name "r-pkgcache")
    (version "2.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pkgcache" version))
       (sha256
        (base32 "1isvbb2l6l2wk2grh7cz85q80nbghp5287jr137sal4gviprgk7v"))))
    (properties `((upstream-name . "pkgcache")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6
                             r-processx
                             r-jsonlite
                             r-filelock
                             r-curl
                             r-cli
                             r-callr))
    (home-page "https://r-lib.github.io/pkgcache/")
    (synopsis "Cache 'CRAN'-Like Metadata and R Packages")
    (description
     "Metadata and package cache for CRAN-like repositories.  This is a utility
package to be used by package management tools that want to take advantage of
caching.")
    (license license:expat)))

(define-public r-pkdata
  (package
    (name "r-pkdata")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pkdata" version))
       (sha256
        (base32 "1kmcalidw2zidd6i8ds0k057b3187by2k8z6764va43d5garbbcv"))))
    (properties `((upstream-name . "pkdata")))
    (build-system r-build-system)
    (propagated-inputs (list r-lubridate))
    (home-page "https://cran.r-project.org/package=pkdata")
    (synopsis "Creates Pharmacokinetic/Pharmacodynamic (PK/PD) Data")
    (description
     "Prepare pharmacokinetic/pharmacodynamic (PK/PD) data for PK/PD analyses.  This
package provides functions to standardize infusion and bolus dose data while
linking it to drug level or concentration data.")
    (license license:gpl3+)))

(define-public r-pkconverter
  (package
    (name "r-pkconverter")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PKconverter" version))
       (sha256
        (base32 "1zk2sjrjhq7n2k2p29r2qc6qdghr3riyjvvawz0g74lidg7hm1ly"))))
    (properties `((upstream-name . "PKconverter")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinythemes r-shinydashboard r-shiny))
    (home-page "https://cran.r-project.org/package=PKconverter")
    (synopsis "The Parameter Converter of the Pharmacokinetic Models")
    (description
     "Pharmacokinetics is the study of drug absorption, distribution, metabolism, and
excretion.  The pharmacokinetics model explains that how the drug concentration
change as the drug moves through the different compartments of the body.  For
pharmacokinetic modeling and analysis, it is essential to understand the basic
pharmacokinetic parameters.  All parameters are considered, but only some of
parameters are used in the model.  Therefore, we need to convert the estimated
parameters to the other parameters after fitting the specific pharmacokinetic
model.  This package is developed to help this converting work.  For more
detailed explanation of pharmacokinetic parameters, see \"Gabrielsson and Weiner\"
(2007), \"ISBN-10: 9197651001\"; \"Benet and Zia-Amirhosseini\" (1995) <DOI:
10.1177/019262339502300203>; \"Mould and Upton\" (2012) <DOI: 10.1038/psp.2012.4>;
\"Mould and Upton\" (2013) <DOI: 10.1038/psp.2013.14>.")
    (license license:gpl2+)))

(define-public r-pkbioanalysis
  (package
    (name "r-pkbioanalysis")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PKbioanalysis" version))
       (sha256
        (base32 "104sm6wpplc0cljm6xdaif6ck4macix880nzx0hp9kb0w9f91066"))))
    (properties `((upstream-name . "PKbioanalysis")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-units
                             r-tidyselect
                             r-tidyr
                             r-stringr
                             r-shinywidgets
                             r-shinyjs
                             r-shinyalert
                             r-shiny
                             r-rlang
                             r-htmltools
                             r-glue
                             r-ggplot2
                             r-ggforce
                             r-duckdb
                             r-dt
                             r-dplyr
                             r-diagrammer
                             r-dbi
                             r-checkmate
                             r-bslib
                             r-bsicons))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/OmarAshkar/PKbioanalysis")
    (synopsis "Pharmacokinetic Bioanalysis Experiments Design and Exploration")
    (description
     "Automate pharmacokinetic/pharmacodynamic bioanalytical procedures based on best
practices and regulatory recommendations.  The package impose regulatory
constrains and sanity checking for common bioanalytical procedures.
Additionally, PKbioanalysis provides a relational infrastructure for plate
management and injection sequence.")
    (license license:agpl3+)))

(define-public r-pk4adi
  (package
    (name "r-pk4adi")
    (version "0.1.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pk4adi" version))
       (sha256
        (base32 "0iq34byjg5hvmg282z6ppjag5df0y0adij1d1zn32966c1mq9ad2"))))
    (properties `((upstream-name . "pk4adi")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table))
    (home-page "https://www.r-project.org")
    (synopsis "PK for Anesthetic Depth Indicators")
    (description
     "Calculate and compare the prediction probability (PK) values for Anesthetic
Depth Indicators.  The PK values are widely used for measuring the performance
of anesthetic depth and were first proposed by the group of Dr. Warren D. Smith
in the paper Warren D. Smith; Robert C. Dutton; Ty N. Smith (1996)
<doi:10.1097/00000542-199601000-00005> and Warren D. Smith; Robert C. Dutton; Ty
N. Smith (1996)
<doi:10.1002/(SICI)1097-0258(19960615)15:11%3C1199::AID-SIM218%3E3.0.CO;2-Y>.
The authors provided two Microsoft Excel files in xls format for calculating and
comparing PK values.  This package provides an easy-to-use API for calculating
and comparing PK values in R.")
    (license license:expat)))

(define-public r-pk
  (package
    (name "r-pk")
    (version "1.3-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PK" version))
       (sha256
        (base32 "0mynn21bmqn2jnaprlfvz0bf3mfjirdrc88n8d28hv74bk1a59vp"))))
    (properties `((upstream-name . "PK")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PK")
    (synopsis "Basic Non-Compartmental Pharmacokinetics")
    (description
     "Estimation of pharmacokinetic parameters using non-compartmental theory.")
    (license license:gpl2)))

(define-public r-pixiedust
  (package
    (name "r-pixiedust")
    (version "0.9.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pixiedust" version))
       (sha256
        (base32 "17gaahjzmjgqdkjqjpa7rh3wj1wx55v032ylijqaqklpfnbn7jkq"))))
    (properties `((upstream-name . "pixiedust")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-reshape2
                             r-magrittr
                             r-labelvector
                             r-knitr
                             r-htmltools
                             r-checkmate
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/nutterb/pixiedust")
    (synopsis "Tables so Beautifully Fine-Tuned You Will Believe It's Magic")
    (description
     "The introduction of the broom package has made converting model objects into
data frames as simple as a single function.  While the broom package focuses on
providing tidy data frames that can be used in advanced analysis, it
deliberately stops short of providing functionality for reporting models in
publication-ready tables.  pixiedust provides this functionality with a
programming interface intended to be similar to ggplot2's system of layers with
fine tuned control over each cell of the table.  Options for output include
printing to the console and to the common markdown formats (markdown, HTML, and
@code{LaTeX}).  With a little pixiedust (and happy thoughts) tables can really
fly.")
    (license license:gpl2+)))

(define-public r-pixels
  (package
    (name "r-pixels")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pixels" version))
       (sha256
        (base32 "1vvahrajm8ys5vgfc0iyzj2a6vzvgaakkj6i0m2sy40l2w44dn0q"))))
    (properties `((upstream-name . "pixels")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-miniui r-htmlwidgets))
    (home-page "https://github.com/javierluraschi/pixels")
    (synopsis "Tools for Working with Image Pixels")
    (description
     "This package provides tools to show and draw image pixels using HTML widgets and
Shiny applications.  It can be used to visualize the MNIST dataset for
handwritten digit recognition or to create new image recognition datasets.")
    (license license:expat)))

(define-public r-pixelpuzzle
  (package
    (name "r-pixelpuzzle")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pixelpuzzle" version))
       (sha256
        (base32 "0z0afcjv9wpz5fw21vhv11jx2wrl4fy5nf9q5x5adwncsdmrqjhz"))))
    (properties `((upstream-name . "pixelpuzzle")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-cli r-beepr))
    (home-page "https://github.com/rolkra/pixelpuzzle")
    (synopsis "Puzzle Game for the R Console")
    (description
     "Puzzle game that can be played in the R console.  Restore the pixel art by
shifting rows.")
    (license license:gpl3)))

(define-public r-pixelclasser
  (package
    (name "r-pixelclasser")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pixelclasser" version))
       (sha256
        (base32 "0nkym141dmyy8783nv78pps3lyfpk31fdziq990j45napv8kiamy"))))
    (properties `((upstream-name . "pixelclasser")))
    (build-system r-build-system)
    (propagated-inputs (list r-tiff r-jpeg))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://cran.r-project.org/package=pixelclasser")
    (synopsis "Classifies Image Pixels by Colour")
    (description
     "This package contains functions to classify the pixels of an image file by its
colour.  It implements a simple form of the techniques known as Support Vector
Machine adapted to this particular problem.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-pixarfilms
  (package
    (name "r-pixarfilms")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pixarfilms" version))
       (sha256
        (base32 "1qycg7ar9l3myj14xvidxrm9svdf5xmw7ss0m33q266nzwssiqpk"))))
    (properties `((upstream-name . "pixarfilms")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/erictleung/pixarfilms")
    (synopsis "Pixar Films and Achievements")
    (description
     "Data about Disney Pixar films provided by Wikipedia.  This package contains data
about the films, the people involved, and their awards.")
    (license license:cc0)))

(define-public r-piwikpror
  (package
    (name "r-piwikpror")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "piwikproR" version))
       (sha256
        (base32 "0xyhqpg34052m1liyzm77fkg7my42zvd7s8l232s575dfkvafqs8"))))
    (properties `((upstream-name . "piwikproR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-rjson
                             r-readr
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-httr
                             r-glue
                             r-fs
                             r-dplyr
                             r-digest))
    (native-inputs (list r-knitr))
    (home-page "https://piwikpror.rstats-tips.net")
    (synopsis "Access 'Piwik Pro' Website Statistics")
    (description
     "Run Queries against the API of Piwik Pro
<https://developers.piwik.pro/en/latest/custom_reports/http_api/http_api.html>.
The result is a tibble.")
    (license license:gpl3+)))

(define-public r-pivottabler
  (package
    (name "r-pivottabler")
    (version "1.5.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pivottabler" version))
       (sha256
        (base32 "1kf9z2ashfvgjl0sviryx2r9b9k1k9j9b9nzivr1nxm0d9x9n3yn"))))
    (properties `((upstream-name . "pivottabler")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6 r-htmlwidgets r-htmltools r-dplyr
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "http://www.pivottabler.org.uk/")
    (synopsis "Create Pivot Tables")
    (description
     "Create regular pivot tables with just a few lines of R. More complex pivot
tables can also be created, e.g. pivot tables with irregular layouts, multiple
calculations and/or derived calculations based on multiple data frames.  Pivot
tables are constructed using R only and can be written to a range of output
formats (plain text, HTML', Latex and Excel'), including with
styling/formatting.")
    (license license:gpl3)))

(define-public r-pivotea
  (package
    (name "r-pivotea")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pivotea" version))
       (sha256
        (base32 "104xpmanp0l3fw2hnay6xpbzgcjfpqzbjp5mz6imyzwv5199fw4m"))))
    (properties `((upstream-name . "pivotea")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-rlang r-purrr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/matutosi/pivotea")
    (synopsis "Create Pivot Table Easily")
    (description "Pivot easily by specifying rows, columns, values and split.")
    (license license:expat)))

(define-public r-pivotaltrackr
  (package
    (name "r-pivotaltrackr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pivotaltrackR" version))
       (sha256
        (base32 "141aaanb00qc3z4mdws42zhg7j6bcd2i1gp6nf6h6h7ikkmr0v0y"))))
    (properties `((upstream-name . "pivotaltrackR")))
    (build-system r-build-system)
    (propagated-inputs (list r-httr r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://enpiar.com/r/pivotaltrackR/")
    (synopsis "Client for the 'Pivotal Tracker' API")
    (description
     "Pivotal Tracker <https://www.pivotaltracker.com> is a project management
software-as-a-service that provides a REST API. This package provides an R
interface to that API, allowing you to query it and work with its responses.")
    (license license:expat)))

(define-public r-pivmet
  (package
    (name "r-pivmet")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pivmet" version))
       (sha256
        (base32 "0cz8grr03w29dnlhmf5r8yjcklhw6qz8rdf8ng1hdqcbp3ysplhk"))))
    (properties `((upstream-name . "pivmet")))
    (build-system r-build-system)
    (inputs (list pandoc pandoc))
    (propagated-inputs (list r-scales
                             r-runjags
                             r-rstan
                             r-rjags
                             r-mvtnorm
                             r-mclust
                             r-mass
                             r-corpcor
                             r-cluster
                             r-bayesplot
                             r-bayesmix))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/leoegidi/pivmet")
    (synopsis
     "Pivotal Methods for Bayesian Relabelling and k-Means Clustering")
    (description
     "Collection of pivotal algorithms for: relabelling the MCMC chains in order to
undo the label switching problem in Bayesian mixture models; fitting sparse
finite mixtures; initializing the centers of the classical k-means algorithm in
order to obtain a better clustering solution.  For further details see Egidi,
PappadÃ , Pauli and Torelli (2018b)<ISBN:9788891910233>.")
    (license license:gpl2)))

(define-public r-pisart
  (package
    (name "r-pisart")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pisaRT" version))
       (sha256
        (base32 "1dpskdi4kd9zajj2pl7n686sd2w8kai7zilrwsrcfgl1dkfr1m82"))))
    (properties `((upstream-name . "pisaRT")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pisaRT")
    (synopsis "Small Example Response and Response Time Data from PISA 2018")
    (description
     "Scored responses and responses times from the Canadian subsample of the PISA
2018 assessment, accessible as the \"Cognitive items total time/visits data file\"
by OECD (2020) <https://www.oecd.org/pisa/data/2018database/>.")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-piratings
  (package
    (name "r-piratings")
    (version "0.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "piratings" version))
       (sha256
        (base32 "0krrcf2bwicdf9fn657vkcjg5zliwi82hw3xmaqi46mdix8896gh"))))
    (properties `((upstream-name . "piratings")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=piratings")
    (synopsis "Calculate Pi Ratings for Teams Competing in Sport Matches")
    (description
     "Calculate and optimize dynamic performance ratings of association football teams
competing in matches, in accordance with the method used in the research paper
\"Determining the level of ability of football teams by dynamic ratings based on
the relative discrepancies in scores between adversaries\", by Constantinou and
Fenton (2013) <doi:10.1515/jqas-2012-0036> This dynamic rating system has proven
to provide superior results for predicting association football outcomes.")
    (license license:gpl2)))

(define-public r-piqp
  (package
    (name "r-piqp")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "piqp" version))
       (sha256
        (base32 "0cipmcbxfyply01kccv0bycip4lcg46s15g97fms8qijhpwi2n3c"))))
    (properties `((upstream-name . "piqp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp r-r6 r-matrix))
    (native-inputs (list r-knitr))
    (home-page "https://predict-epfl.github.io/piqp-r/")
    (synopsis
     "R Interface to Proximal Interior Point Quadratic Programming Solver")
    (description
     "An embedded proximal interior point quadratic programming solver, which can
solve dense and sparse quadratic programs, described in Schwan, Jiang, Kuhn, and
Jones (2023) <doi:10.48550/@code{arXiv.2304.00290>}.  Combining an infeasible
interior point method with the proximal method of multipliers, the algorithm can
handle ill-conditioned convex quadratic programming problems without the need
for linear independence of the constraints.  The solver is written in header
only C++ 14 leveraging the Eigen library for vectorized linear algebra.  For
small dense problems, vectorized instructions and cache locality can be
exploited more efficiently.  Allocation free problem updates and re-solves are
also provided.")
    (license license:bsd-2)))

(define-public r-piper
  (package
    (name "r-piper")
    (version "0.6.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pipeR" version))
       (sha256
        (base32 "16lj227x24pviqb4dcrj7a55l31p5mm0cbwsb6m3y3n6k5wcd2nh"))))
    (properties `((upstream-name . "pipeR")))
    (build-system r-build-system)
    (home-page "https://renkun.me/pipeR")
    (synopsis "Multi-Paradigm Pipeline Implementation")
    (description
     "This package provides various styles of function chaining methods: Pipe
operator, Pipe object, and pipeline function, each representing a distinct
pipeline model yet sharing almost a common set of features: A value can be piped
to the first unnamed argument of a function and to dot symbol in an enclosed
expression.  The syntax is designed to make the pipeline more readable and
friendly to a wide range of operations.")
    (license license:expat)))

(define-public r-pipenostics
  (package
    (name "r-pipenostics")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pipenostics" version))
       (sha256
        (base32 "1pqv5jz3dm5k7ldvja7h5s8zd0mbgm1dbkfmfppnxhv2nmnzm3p1"))))
    (properties `((upstream-name . "pipenostics")))
    (build-system r-build-system)
    (propagated-inputs (list r-iapws r-checkmate))
    (home-page "https://omega1x.github.io/pipenostics/")
    (synopsis
     "Diagnostics, Reliability and Predictive Maintenance of Pipeline Systems")
    (description
     "This package provides functions representing some useful empirical and
data-driven models of heat loss, corrosion diagnostics, reliability and
predictive maintenance of pipeline systems.  The package is an option for
technical engineering departments of heat generating and heat transfer companies
that use or plan to use regulatory calculations in their activities.  Methods
are described in Timashev et al. (2016) <doi:10.1007/978-3-319-25307-7>,
A.C.Reddy (2017) <doi:10.1016/j.matpr.2017.07.081>, Minenergo (2008)
<https://docs.cntd.ru/document/902148459>, Minenergo (2005)
<https://docs.cntd.ru/document/1200035568>, Xing LU. (2014)
<doi:10.1080/23744731.2016.1258371>.")
    (license license:gpl3)))

(define-public r-pipeliner
  (package
    (name "r-pipeliner")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pipeliner" version))
       (sha256
        (base32 "1cyyd1bin89dh0zrhic3i541bj8nnaphg6jq2jqkhnxn2nvax11g"))))
    (properties `((upstream-name . "pipeliner")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/alexioannides/pipeliner")
    (synopsis "Machine Learning Pipelines for R")
    (description
     "This package provides a framework for defining pipelines of functions for
applying data transformations, model estimation and inverse-transformations,
resulting in predicted value generation (or model-scoring) functions that
automatically apply the entire pipeline of functions required to go from input
to predicted output.")
    (license license:asl2.0)))

(define-public r-pipefittr
  (package
    (name "r-pipefittr")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pipefittr" version))
       (sha256
        (base32 "0pzd7f0rhn0rydncki666zlajdwyiay3apa17cb0xy5454d99mvh"))))
    (properties `((upstream-name . "pipefittr")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-shiny r-rstudioapi r-miniui
                             r-magrittr))
    (home-page "https://cran.r-project.org/package=pipefittr")
    (synopsis "Convert Nested Functions to Pipes")
    (description
     "To take nested function calls and convert them to a more readable form using
pipes from package magrittr'.")
    (license license:expat)))

(define-public r-pipebind
  (package
    (name "r-pipebind")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pipebind" version))
       (sha256
        (base32 "0z9xs4q06bis9xmzx260c0jn7v5vwvsrjlq32xd42vp56m0f63vw"))))
    (properties `((upstream-name . "pipebind")))
    (build-system r-build-system)
    (home-page "https://github.com/bwiernik/pipebind/")
    (synopsis
     "Flexible Binding for Complex Function Evaluation with the Base R |> Pipe")
    (description
     "This package provides a simple function to bind a piped object to a placeholder
symbol to enable complex function evaluation with the base R |> pipe.")
    (license license:gpl3)))

(define-public r-pipe-design
  (package
    (name "r-pipe-design")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pipe.design" version))
       (sha256
        (base32 "0r0szkdzifxmnnbr6675w8ij4zwqgxj3gwahc7a74mxr51f7qh8j"))))
    (properties `((upstream-name . "pipe.design")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable r-gtools r-ggplot2))
    (home-page "https://cran.r-project.org/package=pipe.design")
    (synopsis
     "Dual-Agent Dose Escalation for Phase I Trials using the PIPE Design")
    (description
     "This package implements the Product of Independent beta Probabilities dose
Escalation (PIPE) design for dual-agent Phase I trials as described in Mander
AP, Sweeting MJ (2015) <DOI:10.1002/sim.6434>.")
    (license license:gpl2+)))

(define-public r-pinyin
  (package
    (name "r-pinyin")
    (version "1.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pinyin" version))
       (sha256
        (base32 "0rw9qd5hbcmpi30vdqs31ijy37d8jlpqv0xif2nndb8cwj8x5bw2"))))
    (properties `((upstream-name . "pinyin")))
    (build-system r-build-system)
    (propagated-inputs (list r-splitstackshape r-data-table))
    (home-page "https://github.com/pzhaonet/pinyin")
    (synopsis
     "Convert Chinese Characters into Pinyin, Sijiao, Wubi or Other Codes")
    (description
     "Convert Chinese characters into Pinyin (the official romanization system for
Standard Chinese in mainland China, Malaysia, Singapore, and Taiwan.  See
<https://en.wikipedia.org/wiki/Pinyin> for details), Sijiao (four or five
numerical digits per character.  See
<https://en.wikipedia.org/wiki/Four-Corner_Method>.), Wubi (an input method with
five strokes.  See <https://en.wikipedia.org/wiki/Wubi_method>) or user-defined
codes.")
    (license license:expat)))

(define-public r-pinterestadsr
  (package
    (name "r-pinterestadsr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pinterestadsR" version))
       (sha256
        (base32 "0fjnb9kp3s3v7xp8cwrdljms6vwldq3n37lz5ly0j6wsw4p4ssx5"))))
    (properties `((upstream-name . "pinterestadsR")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite))
    (native-inputs (list r-knitr))
    (home-page "https://windsor.ai/")
    (synopsis "Access to Pinterest Ads via the 'Windsor.ai' API")
    (description
     "Collect marketing data from Pinterest Ads using the Windsor.ai API
<https://windsor.ai/api-fields/>.  Use four spaces when indenting paragraphs
within the Description.")
    (license license:gpl3)))

(define-public r-pinstimation
  (package
    (name "r-pinstimation")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PINstimation" version))
       (sha256
        (base32 "0vys27ws0nk0bqf6pxgr689q3rnyx14ar560004g6pi0ii59mbyk"))))
    (properties `((upstream-name . "PINstimation")))
    (build-system r-build-system)
    (propagated-inputs (list r-skellam
                             r-rmarkdown
                             r-rdpack
                             r-nloptr
                             r-knitr
                             r-future
                             r-furrr
                             r-dplyr
                             r-coda))
    (native-inputs (list r-knitr))
    (home-page "https://www.pinstimation.com")
    (synopsis "Estimation of the Probability of Informed Trading")
    (description
     "This package provides a comprehensive bundle of utilities for the estimation of
probability of informed trading models: original PIN in Easley and O'Hara (1992)
and Easley et al. (1996); Multilayer PIN (MPIN) in Ersan (2016); Adjusted PIN
(@code{AdjPIN}) in Duarte and Young (2009); and volume-synchronized PIN (VPIN)
in Easley et al. (2011, 2012).  Implementations of various estimation methods
suggested in the literature are included.  Additional compelling features
comprise posterior probabilities, an implementation of an
expectation-maximization (EM) algorithm, and PIN decomposition into layers, and
into bad/good components.  Versatile data simulation tools, and trade
classification algorithms are among the supplementary utilities.  The package
provides fast, compact, and precise utilities to tackle the sophisticated,
error-prone, and time-consuming estimation procedure of informed trading, and
this solely using the raw trade-level data.")
    (license license:gpl3+)))

(define-public r-pinsplus
  (package
    (name "r-pinsplus")
    (version "2.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PINSPlus" version))
       (sha256
        (base32 "1r4fw3hm0bp3spxkcrwwcmirkp4a8myqwpw8d6cxmz7ns6202860"))))
    (properties `((upstream-name . "PINSPlus")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppparallel
                             r-rcpparmadillo
                             r-rcpp
                             r-mclust
                             r-matrixstats
                             r-irlba
                             r-impute
                             r-foreach
                             r-fnn
                             r-entropy
                             r-doparallel
                             r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PINSPlus")
    (synopsis
     "Clustering Algorithm for Data Integration and Disease Subtyping")
    (description
     "This package provides a robust approach for omics data integration and disease
subtyping.  PINSPlus is fast and supports the analysis of large datasets with
hundreds of thousands of samples and features.  The software automatically
determines the optimal number of clusters and then partitions the samples in a
way such that the results are robust against noise and data perturbation (Nguyen
et al. (2019) <DOI: 10.1093/bioinformatics/bty1049>, Nguyen et al. (2017)<DOI:
10.1101/gr.215129.116>, Nguyen et al. (2021)<DOI: 10.3389/fonc.2021.725133>).")
    (license license:lgpl2.0+)))

(define-public r-pins
  (package
    (name "r-pins")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pins" version))
       (sha256
        (base32 "07hggz1yjhcgvknhsp183gwd4lzqwwsvw7vdv2rn8rx8yh23ylcp"))))
    (properties `((upstream-name . "pins")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-withr
                             r-whisker
                             r-tibble
                             r-rlang
                             r-rappdirs
                             r-purrr
                             r-magrittr
                             r-lifecycle
                             r-jsonlite
                             r-httr
                             r-glue
                             r-generics
                             r-fs
                             r-digest
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://pins.rstudio.com/")
    (synopsis "Pin, Discover, and Share Resources")
    (description
     "Publish data sets, models, and other R objects, making it easy to share them
across projects and with your colleagues.  You can pin objects to a variety of
\"boards\", including local folders (to share on a networked drive or with
@code{DropBox}'), Posit Connect', AWS S3', and more.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-pinp
  (package
    (name "r-pinp")
    (version "0.0.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pinp" version))
       (sha256
        (base32 "1qah1b9chn237pfxisjsn6rr4h9h9zdqrk323c0k1qds7vm0xpra"))))
    (properties `((upstream-name . "pinp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmarkdown r-knitr))
    (native-inputs (list r-knitr))
    (home-page "http://dirk.eddelbuettel.com/code/pinp.html")
    (synopsis "'pinp' is not 'PNAS'")
    (description
     "This package provides a PNAS'-alike style for rmarkdown', derived from the
Proceedings of the National Academy of Sciences of the United States of America
('PNAS', see <https://www.pnas.org>) @code{LaTeX} style, and adapted for use
with markdown and pandoc'.")
    (license license:gpl3)))

(define-public r-pinochet
  (package
    (name "r-pinochet")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pinochet" version))
       (sha256
        (base32 "0qy18mlw34cv9p7b3qb5bidxyq44k0hzj91hm5vp6wkjhik4knvi"))))
    (properties `((upstream-name . "pinochet")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "http://github.com/danilofreire/pinochet")
    (synopsis "Data About the Victims of the Pinochet Regime, 1973-1990")
    (description
     "Packages data about the victims of the Pinochet regime as compiled by the
Chilean National Commission for Truth and Reconciliation Report (1991,
ISBN:9780268016463).")
    (license license:expat)))

(define-public r-pinnacle-data
  (package
    (name "r-pinnacle-data")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pinnacle.data" version))
       (sha256
        (base32 "1sl36i2857b3xwx7iwgy8mkgjvz7nfa74ch1n8kcxbj59ib7mp8p"))))
    (properties `((upstream-name . "pinnacle.data")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble))
    (home-page "https://github.com/marcoblume/pinnacle.data")
    (synopsis "Market Odds Data from Pinnacle")
    (description
     "Market odds from from Pinnacle, an online sports betting bookmaker (see
<https://www.pinnacle.com> for more information).  Included are datasets for the
Major League Baseball (MLB) 2016 season and the USA election 2016.  These
datasets can be used to build models and compare statistical information with
the information from prediction markets.The Major League Baseball (MLB) 2016
dataset can be used for sabermetrics analysis and also can be used in
conjunction with other popular Major League Baseball (MLB) datasets such as
Retrosheets or the Lahman package by merging by @code{GameID}.")
    (license license:gpl3)))

(define-public r-pinma
  (package
    (name "r-pinma")
    (version "1.1-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PINMA" version))
       (sha256
        (base32 "18sm4j2srwkf1yp487d0hbmmy8max5h9966mq43pf9f2fwa0j4mc"))))
    (properties `((upstream-name . "PINMA")))
    (build-system r-build-system)
    (propagated-inputs (list r-metafor r-mass))
    (home-page "https://cran.r-project.org/package=PINMA")
    (synopsis
     "Improved Methods for Constructing Prediction Intervals for Network Meta-Analysis")
    (description
     "Improved methods to construct prediction intervals for network meta-analysis.
The parametric bootstrap and Kenward-Roger-type adjustment by Noma et al. (2022)
<forthcoming> are implementable.")
    (license license:gpl3)))

(define-public r-pingr
  (package
    (name "r-pingr")
    (version "2.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pingr" version))
       (sha256
        (base32 "0qyqmv3anrdh9416rnq1l0mnhdwfl3x603rcg8kfwgwcb2zc0a9w"))))
    (properties `((upstream-name . "pingr")))
    (build-system r-build-system)
    (propagated-inputs (list r-processx))
    (home-page "https://r-lib.github.io/pingr/")
    (synopsis "Check if a Remote Computer is Up")
    (description
     "Check if a remote computer is up.  It can either just call the system ping
command, or check a specified TCP port.")
    (license license:expat)))

(define-public r-pingers
  (package
    (name "r-pingers")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pingers" version))
       (sha256
        (base32 "09np6aph6rhvdh6mwky8a57i38bi2amwi7x91p9kfb2mzkql8k8w"))))
    (properties `((upstream-name . "pingers")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tictoc
                             r-tibble
                             r-stringr
                             r-reshape2
                             r-plotly
                             r-lubridate
                             r-dplyr
                             r-data-table))
    (home-page "https://github.com/JesseVent/pingers")
    (synopsis "Identify, Ping, and Log Internet Provider Connection Data")
    (description
     "To assist you with troubleshooting internet connection issues and assist in
isolating packet loss on your network.  It does this by allowing you to retrieve
the top trace route destinations your internet provider uses, and recursively
ping each server in series while capturing the results and writing them to a log
file.  Each iteration it queries the destinations again, before shuffling the
sequence of destinations to ensure the analysis is unbiased and consistent
across each trace route.")
    (license license:expat)))

(define-public r-pimeta
  (package
    (name "r-pimeta")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pimeta" version))
       (sha256
        (base32 "1mx0m438db4n041bns6sm8sr8vrj6w8b2kvpmrkkp0bly2vpv40q"))))
    (properties `((upstream-name . "pimeta")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-rcppeigen r-rcpp r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pimeta")
    (synopsis "Prediction Intervals for Random-Effects Meta-Analysis")
    (description
     "An implementation of prediction intervals for random-effects meta-analysis:
Higgins et al. (2009) <doi:10.1111/j.1467-985X.2008.00552.x>, Partlett and Riley
(2017) <doi:10.1002/sim.7140>, and Nagashima et al. (2019)
<doi:10.1177/0962280218773520>, <@code{arXiv:1804.01054>}.")
    (license license:gpl3)))

(define-public r-pim
  (package
    (name "r-pim")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pim" version))
       (sha256
        (base32 "0kwlm6n23ccs0jz4plsqprg6inj3qv74zhvg9gpxyj3ksvdxp58i"))))
    (properties `((upstream-name . "pim")))
    (build-system r-build-system)
    (propagated-inputs (list r-nleqslv r-bb))
    (home-page "https://github.com/CenterForStatistics-UGent/pim")
    (synopsis "Fit Probabilistic Index Models")
    (description
     "Fit a probabilistic index model as described in Thas et al, 2012:
<doi:10.1111/j.1467-9868.2011.01020.x>.  The interface to the modeling function
has changed in this new version.  The old version is still available at R-Forge.")
    (license license:gpl2+)))

(define-public r-pikchr
  (package
    (name "r-pikchr")
    (version "0.97")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pikchr" version))
       (sha256
        (base32 "072wnyv28wldncsqyhl7wgqsc80jy9gc0k04mf47x6mf96yikl15"))))
    (properties `((upstream-name . "pikchr")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rsvg
                             r-rlang
                             r-purrr
                             r-knitr
                             r-htmltools
                             r-brio))
    (native-inputs (list r-knitr))
    (home-page "<https://github.com/StrategicProjects/pikchr>")
    (synopsis "R Wrapper for 'pikchr' (PIC) Diagram Language")
    (description
     "An R interface to pikchr (<https://pikchr.org>, pronounced âpictureâ), a
PIC'-like markup language for creating diagrams within technical documentation.
Originally developed by Brian Kernighan, PIC has been adapted into pikchr by D.
Richard Hipp, the creator of SQLite'.  pikchr is designed to be embedded in
fenced code blocks of Markdown or other documentation markup languages, making
it ideal for generating diagrams in text-based formats.  This package allows R
users to seamlessly integrate the descriptive syntax of pikchr for diagram
creation directly within the R environment.")
    (license license:gpl2+)))

(define-public r-pijavski
  (package
    (name "r-pijavski")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Pijavski" version))
       (sha256
        (base32 "0jspc19fn1mqv3zjr89sy7rqbbyjk3ij3rhrg2dsw167w63lpykx"))))
    (properties `((upstream-name . "Pijavski")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=Pijavski")
    (synopsis "Global Univariate Minimization")
    (description
     "Global univariate minimization of Lipschitz functions is performed by using
Pijavski method, which was published in Pijavski (1972)
<DOI:10.1016/0041-5553(72)90115-2>.")
    (license license:lgpl3)))

(define-public r-piggyback
  (package
    (name "r-piggyback")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "piggyback" version))
       (sha256
        (base32 "1vkawf3jk8ddzapgvbixzq2kswnprfpjkvinby4z5x9gg535lfwq"))))
    (properties `((upstream-name . "piggyback")))
    (build-system r-build-system)
    (propagated-inputs (list r-memoise
                             r-lubridate
                             r-jsonlite
                             r-httr
                             r-glue
                             r-gh
                             r-fs
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ropensci/piggyback")
    (synopsis "Managing Larger Data on a GitHub Repository")
    (description
     "Because larger (> 50 MB) data files cannot easily be committed to git, a
different approach is required to manage data associated with an analysis in a
@code{GitHub} repository.  This package provides a simple work-around by
allowing larger (up to 2 GB) data files to piggyback on a repository as assets
attached to individual @code{GitHub} releases.  These files are not handled by
git in any way, but instead are uploaded, downloaded, or edited directly by
calls through the @code{GitHub} API. These data files can be versioned manually
by creating different releases.  This approach works equally well with public or
private repositories.  Data can be uploaded and downloaded programmatically from
scripts.  No authentication is required to download data from public
repositories.")
    (license license:gpl3)))

(define-public r-pieglyph
  (package
    (name "r-pieglyph")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PieGlyph" version))
       (sha256
        (base32 "1fjcxap6af2h9j2v26r3f4f1gyhgkar06p2y8p7j862i1fpgrrcd"))))
    (properties `((upstream-name . "PieGlyph")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-scales
                             r-rlang
                             r-purrr
                             r-plyr
                             r-ggplot2
                             r-ggiraph
                             r-ggforce
                             r-forcats
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://rishvish.github.io/PieGlyph/")
    (synopsis "Axis Invariant Scatter Pie Plots")
    (description
     "Extends ggplot2 to help replace points in a scatter plot with pie-chart glyphs
showing the relative proportions of different categories.  The pie glyphs are
independent of the axes and plot dimensions, to prevent distortions when the
plot dimensions are changed.")
    (license license:gpl3+)))

(define-public r-piecewisesem
  (package
    (name "r-piecewisesem")
    (version "2.3.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "piecewiseSEM" version))
       (sha256
        (base32 "0cgzvfcc0pwz4qvdmx7xj5wl8cki2ss0kjmlqz2kmxklfiz3y5c0"))))
    (properties `((upstream-name . "piecewiseSEM")))
    (build-system r-build-system)
    (propagated-inputs (list r-performance
                             r-nlme
                             r-mumin
                             r-multcomp
                             r-mass
                             r-lme4
                             r-igraph
                             r-emmeans
                             r-diagrammer
                             r-car))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jslefche/")
    (synopsis "Piecewise Structural Equation Modeling")
    (description
     "This package implements piecewise structural equation modeling from a single
list of structural equations, with new methods for non-linear, latent, and
composite variables, standardized coefficients, query-based prediction and
indirect effects.  See <http://jslefche.github.io/@code{piecewiseSEM/>} for
more.")
    (license license:gpl3)))

(define-public r-piecepackr
  (package
    (name "r-piecepackr")
    (version "1.13.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "piecepackr" version))
       (sha256
        (base32 "12xcl0p85dxa1zr5ps4aa3jqqlf2hnwyghj8g00f5km7534irxcs"))))
    (properties `((upstream-name . "piecepackr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-rlang
                             r-r6
                             r-purrr
                             r-png
                             r-jpeg
                             r-grimport2
                             r-gridgeometry))
    (home-page "https://trevorldavis.com/piecepackr/")
    (synopsis "Board Game Graphics")
    (description
     "This package provides functions to make board game graphics with the ggplot2',
grid', rayrender', rayvertex', and rgl packages.  Specializes in game diagrams,
animations, and \"Print & Play\" layouts for the piecepack
<https://www.ludism.org/ppwiki> but can make graphics for other board game
systems.  Includes configurations for several public domain game systems such as
checkers, (double-18) dominoes, go, piecepack', playing cards, etc.")
    (license license:expat)))

(define-public r-piecenorms
  (package
    (name "r-piecenorms")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "piecenorms" version))
       (sha256
        (base32 "0cxr26j7gda9c8zqw4rcnhbm8740gfrq9fi7p24bg4z4cp78fl5n"))))
    (properties `((upstream-name . "piecenorms")))
    (build-system r-build-system)
    (propagated-inputs (list r-vdiffr
                             r-univariateml
                             r-scales
                             r-rlang
                             r-r6
                             r-dplyr
                             r-coinr
                             r-classint))
    (home-page "https://github.com/david-hammond/piecenorms")
    (synopsis "Calculate a Piecewise Normalised Score Using Class Intervals")
    (description
     "This package provides an implementation of piecewise normalisation techniques
useful when dealing with the communication of skewed and highly skewed data.  It
also provides utilities that recommends a normalisation technique based on the
distribution of the data.")
    (license license:expat)))

(define-public r-piecemaker
  (package
    (name "r-piecemaker")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "piecemaker" version))
       (sha256
        (base32 "15zk36cpip49286djl45qqdfbj4w3yc6if1vk4bqphrascrg2val"))))
    (properties `((upstream-name . "piecemaker")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-stringi r-rlang r-glue r-cli))
    (home-page "https://github.com/macmillancontentscience/piecemaker")
    (synopsis "Tools for Preparing Text for Tokenizers")
    (description
     "Tokenizers break text into pieces that are more usable by machine learning
models.  Many tokenizers share some preparation steps.  This package provides
those shared steps, along with a simple tokenizer.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-pieceexpintensity
  (package
    (name "r-pieceexpintensity")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PieceExpIntensity" version))
       (sha256
        (base32 "023hq0gg1vi0j3yf8p5lisgs8wfp5qwyd4akxxzx7wad2985gxb3"))))
    (properties `((upstream-name . "PieceExpIntensity")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=PieceExpIntensity")
    (synopsis
     "Bayesian Model to Find Changepoints Based on Rates and Count Data")
    (description
     "This function fits a reversible jump Bayesian piecewise exponential model that
also includes the intensity of each event considered along with the rate of
events.")
    (license license:gpl2)))

(define-public r-pid
  (package
    (name "r-pid")
    (version "0.50")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pid" version))
       (sha256
        (base32 "05s3xqf95d4avh7gkr49jsm8jzacbv694c3wgppkkc40zip6vkc7"))))
    (properties `((upstream-name . "pid")))
    (build-system r-build-system)
    (propagated-inputs (list r-png r-ggplot2 r-frf2-catlg128 r-frf2 r-doe-base))
    (home-page "https://learnche.org/pid")
    (synopsis "Process Improvement using Data")
    (description
     "This package provides a collection of scripts and data files for the statistics
text: \"Process Improvement using Data\" <https://learnche.org/pid> and the online
course \"Experimentation for Improvement\" found on Coursera.  The package
contains code for designed experiments, data sets and other convenience
functions used in the book.")
    (license license:bsd-2)))

(define-public r-picr
  (package
    (name "r-picr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "picR" version))
       (sha256
        (base32 "0x5a9qgn66w06sl2c448ricby1bqiw3cpli1mnpslanym7m60nsb"))))
    (properties `((upstream-name . "picR")))
    (build-system r-build-system)
    (home-page "https://github.com/javenrflo/picR")
    (synopsis "Predictive Information Criteria for Model Selection")
    (description
     "Computation of predictive information criteria (PIC) from select model object
classes for model selection in predictive contexts.  In contrast to the more
widely used Akaike Information Criterion (AIC), which are derived under the
assumption that target(s) of prediction (i.e.  validation data) are
independently and identically distributed to the fitting data, the PIC are
derived under less restrictive assumptions and thus generalize AIC to the more
practically relevant case of training/validation data heterogeneity.  The
methodology featured in this package is based on Flores (2021)
<https://iro.uiowa.edu/esploro/outputs/doctoral/A-new-class-of-information-criteria/9984097169902771?institution=01IOWA_INST>
\"A new class of information criteria for improved prediction in the presence of
training/validation data heterogeneity\".")
    (license license:gpl3+)))

(define-public r-picker
  (package
    (name "r-picker")
    (version "0.2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "picker" version))
       (sha256
        (base32 "1n7h1lvrivy7czzhisd9p3g187ivcyhzyjj7ahkimyb296gy8z1b"))))
    (properties `((upstream-name . "picker")))
    (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/hms-dbmi/picker")
    (synopsis "Pick Data Points from a Deck.gl Scatterplot")
    (description
     "Performant interactive scatterplot for ~ 1 million points.  Zoom, pan, and pick
points.  Includes tooltips, labels, a grid overlay, legend, and coupled
interactions across multiple plots.")
    (license license:expat)))

(define-public r-picclip
  (package
    (name "r-picclip")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "picClip" version))
       (sha256
        (base32 "1gxl9v39zm2hx04rdfnnbrg6q5rnpzj7aybva6zxh3skfi0dbidr"))))
    (properties `((upstream-name . "picClip")))
    (build-system r-build-system)
    (propagated-inputs (list r-testthat r-stringr r-shiny r-htmltools
                             r-base64enc))
    (home-page "https://github.com/deppemj/picClip")
    (synopsis "Paste Box Input for 'Shiny'")
    (description
     "This package provides a Shiny input widget, @code{pasteBoxInput}, that allows
users to paste images directly into a Shiny application.  The pasted images are
captured as Base64 encoded strings and can be used within the application for
various purposes, such as display or further processing.  This package is
particularly useful for applications that require easy and quick image uploads
without the need for traditional file selection dialog boxes.")
    (license license:gpl3)))

(define-public r-picbayes
  (package
    (name "r-picbayes")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PICBayes" version))
       (sha256
        (base32 "1js6zrnyc8v345cds3pypb2g9dd3vvj3bjq159qc113m9ll2xk0b"))))
    (properties `((upstream-name . "PICBayes")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mcmcpack r-coda))
    (home-page "https://cran.r-project.org/package=PICBayes")
    (synopsis "Bayesian Models for Partly Interval-Censored Data")
    (description
     "This package contains functions to fit proportional hazards (PH) model to partly
interval-censored (PIC) data (Pan et al. (2020) <doi:10.1177/0962280220921552>),
PH model with spatial frailty to spatially dependent PIC data (Pan and Cai
(2021) <doi:10.1080/03610918.2020.1839497>), and mixed effects PH model to
clustered PIC data.  Each random intercept/random effect can follow both a
normal prior and a Dirichlet process mixture prior.  It also includes the
corresponding functions for general interval-censored data.")
    (license license:gpl2+)))

(define-public r-picasso
  (package
    (name "r-picasso")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "picasso" version))
       (sha256
        (base32 "1z7zm88wjp7fia7054l7i55f152iddrjj9wbcpcskhc8s6kfg53c"))))
    (properties `((upstream-name . "picasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-mass))
    (home-page "https://cran.r-project.org/package=picasso")
    (synopsis "Pathwise Calibrated Sparse Shooting Algorithm")
    (description
     "Computationally efficient tools for fitting generalized linear model with convex
or non-convex penalty.  Users can enjoy the superior statistical property of
non-convex penalty such as SCAD and MCP which has significantly less estimation
error and overfitting compared to convex penalty such as lasso and ridge.
Computation is handled by multi-stage convex relaxation and the @code{PathwIse}
CAlibrated Sparse Shooting @code{algOrithm} (PICASSO) which exploits warm start
initialization, active set updating, and strong rule for coordinate preselection
to boost computation, and attains a linear convergence to a unique sparse local
optimum with optimal statistical properties.  The computation is
memory-optimized using the sparse matrix output.")
    (license license:gpl3)))

(define-public r-piar
  (package
    (name "r-piar")
    (version "0.8.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "piar" version))
       (sha256
        (base32 "0153cyfnw7cprvm2gkk0ajishs2rhbwdlx6xd07ls8jpzm3jl5k6"))))
    (properties `((upstream-name . "piar")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-gpindex))
    (native-inputs (list r-knitr))
    (home-page "https://marberts.github.io/piar/")
    (synopsis "Price Index Aggregation")
    (description
     "Most price indexes are made with a two-step procedure, where period-over-period
elemental indexes are first calculated for a collection of elemental aggregates
at each point in time, and then aggregated according to a price index
aggregation structure.  These indexes can then be chained together to form a
time series that gives the evolution of prices with respect to a fixed base
period.  This package contains a collection of functions that revolve around
this work flow, making it easy to build standard price indexes, and implement
the methods described by Balk (2008, <doi:10.1017/CBO9780511720758>), von der
Lippe (2007, <doi:10.3726/978-3-653-01120-3>), and the CPI manual (2020,
<doi:10.5089/9781484354841.069>) for bilateral price indexes.")
    (license license:expat)))

(define-public r-phytosanitarycalculator
  (package
    (name "r-phytosanitarycalculator")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PhytosanitaryCalculator" version))
       (sha256
        (base32 "1545xy338wanrkpy55ji4ap9wf7pldrb07pmhxcmhp73w3l82r2c"))))
    (properties `((upstream-name . "PhytosanitaryCalculator")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-acceptancesampling))
    (home-page "https://cran.r-project.org/package=PhytosanitaryCalculator")
    (synopsis "Phytosanitary Calculator for Inspection Plans Based on Risks")
    (description
     "This package provides a Shiny application for calculating phytosanitary
inspection plans based on risks.")
    (license license:gpl3)))

(define-public r-phytools
  (package
    (name "r-phytools")
    (version "2.3-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phytools" version))
       (sha256
        (base32 "10fh8svaxdk7s9abbzih9pj6carmv4r6fzbxlfafygxyjnk20c4p"))))
    (properties `((upstream-name . "phytools")))
    (build-system r-build-system)
    (propagated-inputs (list r-scatterplot3d
                             r-phangorn
                             r-optimparallel
                             r-numderiv
                             r-nlme
                             r-mnormt
                             r-mass
                             r-maps
                             r-foreach
                             r-expm
                             r-doparallel
                             r-deoptim
                             r-combinat
                             r-coda
                             r-clustergeneration
                             r-ape))
    (home-page "https://github.com/liamrevell/phytools")
    (synopsis "Phylogenetic Tools for Comparative Biology (and Other Things)")
    (description
     "This package provides a wide range of methods for phylogenetic analysis -
concentrated in phylogenetic comparative biology, but also including numerous
techniques for visualizing, analyzing, manipulating, reading or writing, and
even inferring phylogenetic trees.  Included among the functions in phylogenetic
comparative biology are various for ancestral state reconstruction,
model-fitting, and simulation of phylogenies and trait data.  A broad range of
plotting methods for phylogenies and comparative data include (but are not
restricted to) methods for mapping trait evolution on trees, for projecting
trees into phenotype space or a onto a geographic map, and for visualizing
correlated speciation between trees.  Lastly, numerous functions are designed
for reading, writing, analyzing, inferring, simulating, and manipulating
phylogenetic trees and comparative data.  For instance, there are functions for
computing consensus phylogenies from a set, for simulating phylogenetic trees
and data under a range of models, for randomly or non-randomly attaching species
or clades to a tree, as well as for a wide range of other manipulations and
analyses that phylogenetic biologists might find useful in their research.")
    (license license:gpl2+)))

(define-public r-phytoclass
  (package
    (name "r-phytoclass")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phytoclass" version))
       (sha256
        (base32 "12b1xmzwcp90km4irjsrnks44b1rd82p8yn9cn4m6hsaiq6mb45c"))))
    (properties `((upstream-name . "phytoclass")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rcppml
                             r-metrics
                             r-ggplot2
                             r-dynamictreecut
                             r-dplyr
                             r-bestnormalize))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/AndyMcKenzieFromNZ/phytoclass")
    (synopsis "Estimate Chla Concentrations of Phytoplankton Groups")
    (description
     "Determine the chlorophyll a (Chl a) concentrations of different phytoplankton
groups based on their pigment biomarkers.  The method uses non-negative matrix
factorisation and simulated annealing to minimise error between the observed and
estimated values of pigment concentrations (Hayward et al. (2023)
<doi:10.1002/lom3.10541>).  The approach is similar to the widely used CHEMTAX
program (Mackey et al.  1996) <doi:10.3354/meps144265>, but is more
straightforward, accurate, and not reliant on initial guesses for the pigment to
Chl a ratios for phytoplankton groups.")
    (license license:expat)))

(define-public r-physortr
  (package
    (name "r-physortr")
    (version "1.0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PhySortR" version))
       (sha256
        (base32 "19426xjdii74qds0wmq5sa88zys81nyqb9lvf9pfihbjgrjr770a"))))
    (properties `((upstream-name . "PhySortR")))
    (build-system r-build-system)
    (propagated-inputs (list r-phytools r-ape))
    (home-page "https://cran.r-project.org/package=PhySortR")
    (synopsis "Fast, Flexible Tool for Sorting Phylogenetic Trees")
    (description
     "Screens and sorts phylogenetic trees in both traditional and extended Newick
format.  Allows for the fast and flexible screening (within a tree) of Exclusive
clades that comprise only the target taxa and/or Non- Exclusive clades that
includes a defined portion of non-target taxa.")
    (license license:gpl3+)))

(define-public r-physiology
  (package
    (name "r-physiology")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "physiology" version))
       (sha256
        (base32 "1z7xymwgj7bqn0yvcz3q8pvwhpr5vx5qd0x8sayal1vgpqc0nccb"))))
    (properties `((upstream-name . "physiology")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://jackwasey.github.io/physiology/")
    (synopsis
     "Calculate physiologic characteristics of awake and anesthetized adults, children and infants")
    (description
     "This package provides a variety of formulae are provided for estimation of
physiologic characteristics of infants, children, and adults.  Calculations
include: body surface area, ideal weight, airway dead-space, the alveolar gas
equation, and GFR. Each formula is referenced to the original publication.
Future functions will cover more material with a focus on anaesthesia, critical
care and peri-operative medicine.")
    (license license:gpl3)))

(define-public r-physactbedrest
  (package
    (name "r-physactbedrest")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PhysActBedRest" version))
       (sha256
        (base32 "0ww9gcni9f0mwya1d3v45wazrss7lq8b75xcp4fb4kxwbh9s35kw"))))
    (properties `((upstream-name . "PhysActBedRest")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-lubridate r-chron))
    (home-page "https://cran.r-project.org/package=PhysActBedRest")
    (synopsis "Marks Periods of 'Bedrest' in Actigraph Accelerometer Data")
    (description
     "This package contains a function to categorize accelerometer readings collected
in free-living (e.g., for 24 hours/day for 7 days), preprocessed and compressed
as counts (unit-less value) in a specified time period termed epoch (e.g., 1
minute) as either bedrest (sleep) or active.  The input is a matrix with a
timestamp column and a column with number of counts per epoch.  The output is
the same dataframe with an additional column termed bedrest.  In the bedrest
column each line (epoch) contains a function-generated classification br or a
denoting bedrest/sleep and activity, respectively.  The package is designed to
be used after wear/nonwear marking function in the @code{PhysicalActivity}
package.  Version 1.1 adds preschool thresholds and corrects for possible errors
in algorithm implementation.")
    (license license:gpl3+)))

(define-public r-phyreg
  (package
    (name "r-phyreg")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phyreg" version))
       (sha256
        (base32 "0m1psq79zl7h8791pl8g6i0ybda7vgddza8r9mwqhg4sw1q4bzcp"))))
    (properties `((upstream-name . "phyreg")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=phyreg")
    (synopsis "The Phylogenetic Regression of Grafen (1989)")
    (description
     "This package provides general linear model facilities (single y-variable,
multiple x-variables with arbitrary mixture of continuous and categorical and
arbitrary interactions) for cross-species data.  The method is, however, based
on the nowadays rather uncommon situation in which uncertainty about a phylogeny
is well represented by adopting a single polytomous tree.  The theory is in A.
Grafen (1989, Proc.  R. Soc.  B 326, 119-157) and aims to cope with both
recognised phylogeny (closely related species tend to be similar) and
unrecognised phylogeny (a polytomy usually indicates ignorance about the true
sequence of binary splits).")
    (license (list license:gpl2 license:gpl3))))

(define-public r-phyr
  (package
    (name "r-phyr")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phyr" version))
       (sha256
        (base32 "0jvjxvxx2nz9ra9vp00bmm3dxr2am4zs8cvxij8w4jjyskznxc96"))))
    (properties `((upstream-name . "phyr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rcpparmadillo
                             r-rcpp
                             r-nloptr
                             r-mvtnorm
                             r-matrix
                             r-lme4
                             r-latticeextra
                             r-gridextra
                             r-dplyr
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://daijiang.github.io/phyr/")
    (synopsis "Model Based Phylogenetic Analysis")
    (description
     "This package provides a collection of functions to do model-based phylogenetic
analysis.  It includes functions to calculate community phylogenetic diversity,
to estimate correlations among functional traits while accounting for
phylogenetic relationships, and to fit phylogenetic generalized linear mixed
models.  The Bayesian phylogenetic generalized linear mixed models are fitted
with the INLA package (<https://www.r-inla.org>).")
    (license license:gpl3)))

(define-public r-phylter
  (package
    (name "r-phylter")
    (version "0.9.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phylter" version))
       (sha256
        (base32 "1dkm6wfrmbbx7ygfbvk68mv4ay3986wzy0wadxskbbvbvqcshjqa"))))
    (properties `((upstream-name . "phylter")))
    (build-system r-build-system)
    (propagated-inputs (list r-rspectra
                             r-rfast
                             r-reshape2
                             r-rcppeigen
                             r-rcpparmadillo
                             r-rcpp
                             r-ggplot2
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=phylter")
    (synopsis "Detect and Remove Outliers in Phylogenomics Datasets")
    (description
     "Analyzis and filtering of phylogenomics datasets.  It takes an input either a
collection of gene trees (then transformed to matrices) or directly a collection
of gene matrices and performs an iterative process to identify what species in
what genes are outliers, and whose elimination significantly improves the
concordance between the input matrices.  The methods builds upon the Distatis
approach (Abdi et al. (2005) <doi:10.1101/2021.09.08.459421>), a generalization
of classical multidimensional scaling to multiple distance matrices.")
    (license license:gpl2+)))

(define-public r-phylotypr
  (package
    (name "r-phylotypr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phylotypr" version))
       (sha256
        (base32 "1p7k7qw89ihwzpkdvr5zcrrbj6j3n7vclch1miggs7mwb92apabq"))))
    (properties `((upstream-name . "phylotypr")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringi r-rfast r-readr r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mothur/phylotypr")
    (synopsis "Classifying DNA Sequences to Taxonomic Groupings")
    (description
     "Classification based analysis of DNA sequences to taxonomic groupings.  This
package primarily implements Naive Bayesian Classifier from the Ribosomal
Database Project.  This approach has traditionally been used to classify 16S
@code{rRNA} gene sequences to bacterial taxonomic outlines; however, it can be
used for any type of gene sequence.  The method was originally described by
Wang, Garrity, Tiedje, and Cole in Applied and Environmental Microbiology
73(16):5261-7 <doi:10.1128/AEM.00062-07>.  The package also provides functions
to read in FASTA'-formatted sequence data.")
    (license license:expat)))

(define-public r-phylotop
  (package
    (name "r-phylotop")
    (version "2.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phyloTop" version))
       (sha256
        (base32 "1f7sr0qp5i0kj1j2xr7fwpkjnpfnpdjy51dx33yz2q8jmn2axxdf"))))
    (properties `((upstream-name . "phyloTop")))
    (build-system r-build-system)
    (propagated-inputs (list r-phylobase r-nhpoisson r-igraph r-ape))
    (home-page "https://michellekendall.github.io/phyloTop/")
    (synopsis "Calculating Topological Properties of Phylogenies")
    (description
     "This package provides tools for calculating and viewing topological properties
of phylogenetic trees.")
    (license license:gpl2)))

(define-public r-phylotate
  (package
    (name "r-phylotate")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phylotate" version))
       (sha256
        (base32 "092kvn32j06vm8ilwjkgidcz11r3qhsmi0cb0g0nnykw0a0vii4p"))))
    (properties `((upstream-name . "phylotate")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=phylotate")
    (synopsis "Phylogenies with Annotations")
    (description
     "This package provides functions to read and write APE-compatible phylogenetic
trees in NEXUS and Newick formats, while preserving annotations.")
    (license license:expat)))

(define-public r-phylosignal
  (package
    (name "r-phylosignal")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phylosignal" version))
       (sha256
        (base32 "13gahqnxvqzvyr4x1aq3cq08832ljizsa5c71hf44175dfmdz5cd"))))
    (properties `((upstream-name . "phylosignal")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-phylobase
                             r-igraph
                             r-dbi
                             r-boot
                             r-ape
                             r-adephylo))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=phylosignal")
    (synopsis "Exploring the Phylogenetic Signal in Continuous Traits")
    (description
     "This package provides a collection of tools to explore the phylogenetic signal
in univariate and multivariate data.  The package provides functions to plot
traits data against a phylogenetic tree, different measures and tests for the
phylogenetic signal, methods to describe where the signal is located and a
phylogenetic clustering method.")
    (license license:gpl3)))

(define-public r-phyloseqgraphtest
  (package
    (name "r-phyloseqgraphtest")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phyloseqGraphTest" version))
       (sha256
        (base32 "0kp7ll92zcgzjlipp75xz0x1jiyp6d96qg8nzpj8f3y4dvhkppf5"))))
    (properties `((upstream-name . "phyloseqGraphTest")))
    (build-system r-build-system)
    (propagated-inputs (list r-phyloseq r-igraph r-ggplot2 r-ggnetwork))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jfukuyama/phyloseqGraphTest")
    (synopsis "Graph-Based Permutation Tests for Microbiome Data")
    (description
     "This package provides functions for graph-based multiple-sample testing and
visualization of microbiome data, in particular data stored in phyloseq objects.
 The tests are based on those described in Friedman and Rafsky (1979)
<http://www.jstor.org/stable/2958919>, and the tests are described in more
detail in Callahan et al. (2016) <doi:10.12688/f1000research.8986.1>.")
    (license license:cc0)))

(define-public r-phylosem
  (package
    (name "r-phylosem")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phylosem" version))
       (sha256
        (base32 "0x93v0316xd79ab0pdc0w4pb4ljds89ihpp7a61kfdla7v2548bj"))))
    (properties `((upstream-name . "phylosem")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmb
                             r-sem
                             r-rcppeigen
                             r-phylopath
                             r-phylobase
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://james-thorson-noaa.github.io/phylosem/")
    (synopsis "Phylogenetic Structural Equation Model")
    (description
     "Applies phylogenetic comparative methods (PCM) and phylogenetic trait imputation
using structural equation models (SEM), extending methods from Thorson et al.
(2023) <doi:10.1111/2041-210X.14076>.  This implementation includes a minimal
set of features, to allow users to easily read all of the documentation and
source code.  PCM using SEM includes phylogenetic linear models and structural
equation models as nested submodels, but also allows imputation of missing
values.  Features and comparison with other packages are described in Thorson
and van der Bijl (2023) <doi:10.1111/jeb.14234>.")
    (license license:gpl3)))

(define-public r-phylosamp
  (package
    (name "r-phylosamp")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phylosamp" version))
       (sha256
        (base32 "1y48c4qjcxcxia2ggp2dfi051z8zpp0a0ks3a7h69gsw34vh9kd7"))))
    (properties `((upstream-name . "phylosamp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-lifecycle r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/HopkinsIDD/phylosamp")
    (synopsis
     "Sample Size Calculations for Molecular and Phylogenetic Studies")
    (description
     "This package implements novel tools for estimating sample sizes needed for
phylogenetic studies, including studies focused on estimating the probability of
true pathogen transmission between two cases given phylogenetic linkage and
studies focused on tracking pathogen variants at a population level.  Methods
described in Wohl, Giles, and Lessler (2021) and in Wohl, Lee, @code{DiPrete},
and Lessler (2023).")
    (license license:gpl2)))

(define-public r-phyloregion
  (package
    (name "r-phyloregion")
    (version "1.0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phyloregion" version))
       (sha256
        (base32 "10i5s3dv71nr0m3wmph2rynzg9zj8xqzb6a25r5kj66r8v91jz3d"))))
    (properties `((upstream-name . "phyloregion")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-terra
                             r-smoothr
                             r-predicts
                             r-phangorn
                             r-matrix
                             r-maptpx
                             r-igraph
                             r-colorspace
                             r-clustmixtype
                             r-betapart
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/darunabas/phyloregion")
    (synopsis "Biogeographic Regionalization and Macroecology")
    (description
     "Computational infrastructure for biogeography, community ecology, and
biodiversity conservation (Daru et al.  2020) <doi:10.1111/2041-210X.13478>.  It
is based on the methods described in Daru et al. (2020)
<doi:10.1038/s41467-020-15921-6>.  The original conceptual work is described in
Daru et al. (2017) <doi:10.1016/j.tree.2017.08.013> on patterns and processes of
biogeographical regionalization.  Additionally, the package contains fast and
efficient functions to compute more standard conservation measures such as
phylogenetic diversity, phylogenetic endemism, evolutionary distinctiveness and
global endangerment, as well as compositional turnover (e.g., beta diversity).")
    (license license:agpl3)))

(define-public r-phyloraster
  (package
    (name "r-phyloraster")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phyloraster" version))
       (sha256
        (base32 "0xildxiv8k2hrxdp0zkf939zbag62griq1ksalq0vfb32sb5l106"))))
    (properties `((upstream-name . "phyloraster")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-sesraster r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://CRAN.R-project.org/package=phyloraster")
    (synopsis "Evolutionary Diversity Metrics for Raster Data")
    (description
     "Phylogenetic Diversity (PD, Faith 1992), Evolutionary Distinctiveness (ED, Isaac
et al.  2007), Phylogenetic Endemism (PE, Rosauer et al.  2009; Laffan et al.
2016), and Weighted Endemism (WE, Laffan et al.  2016) for presence-absence
raster.  Faith, D. P. (1992) <doi:10.1016/0006-3207(92)91201-3> Isaac, N. J. et
al. (2007) <doi:10.1371/journal.pone.0000296> Laffan, S. W. et al. (2016)
<doi:10.1111/2041-210X.12513> Rosauer, D. et al. (2009)
<doi:10.1111/j.1365-294X.2009.04311.x>.")
    (license license:gpl3+)))

(define-public r-phylopath
  (package
    (name "r-phylopath")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phylopath" version))
       (sha256
        (base32 "1a99b55xi28d0nygnb6y4a1n9y28zjz2sw6k0hfv09kb3xzr03wy"))))
    (properties `((upstream-name . "phylopath")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-purrr
                             r-phylolm
                             r-igraph
                             r-ggraph
                             r-ggplot2
                             r-ggm
                             r-future-apply
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://Ax3man.github.io/phylopath/")
    (synopsis "Perform Phylogenetic Path Analysis")
    (description
     "This package provides a comprehensive and easy to use R implementation of
confirmatory phylogenetic path analysis as described by Von Hardenberg and
Gonzalez-Voyer (2012) <doi:10.1111/j.1558-5646.2012.01790.x>.")
    (license license:gpl3)))

(define-public r-phylopairs
  (package
    (name "r-phylopairs")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phylopairs" version))
       (sha256
        (base32 "0rspl6py8zjx5cs4q3cba1f7lvfvry4c06m5ng2ckwggjwpjcnam"))))
    (properties `((upstream-name . "phylopairs")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-phytools
                             r-loo
                             r-bh
                             r-ape))
    (home-page "https://cran.r-project.org/package=phylopairs")
    (synopsis "Comparative Analyses of Lineage-Pair Traits")
    (description
     "Facilitates the testing of causal relationships among lineage-pair traits in a
phylogenetically informed context.  Lineage-pair traits are characters that are
defined for pairs of lineages instead of individual taxa.  Examples include the
strength of reproductive isolation, range overlap, competition coefficient, diet
niche similarity, and relative hybrid fitness.  Users supply a lineage-pair
dataset and a phylogeny.  phylopairs calculates a covariance matrix for the
pairwise-defined data and provides built-in models to test for relationships
among variables while taking this covariance into account.  Bayesian sampling is
run through built-in Stan programs via the rstan package.  The various models
and methods that this package makes available are described in Anderson et al.
(In Review), Coyne and Orr (1989) <doi:10.1111/j.1558-5646.1989.tb04233.x>,
Fitzpatrick (2002) <doi:10.1111/j.0014-3820.2002.tb00860.x>, and Castillo (2007)
<doi:10.1002/ece3.3093>.")
    (license license:gpl3+)))

(define-public r-phylometrics
  (package
    (name "r-phylometrics")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phylometrics" version))
       (sha256
        (base32 "1pmr6l3wmaf91wdlsc5m63l07fibngnly2qzkma0rdi463ii03il"))))
    (properties `((upstream-name . "phylometrics")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://cran.r-project.org/package=phylometrics")
    (synopsis "Estimating Statistical Errors of Phylogenetic Metrics")
    (description
     "This package provides functions to estimate statistical errors of phylogenetic
metrics particularly to detect binary trait influence on diversification, as
well as a function to simulate trees with fixed number of sampled taxa and trait
prevalence.")
    (license license:gpl2+)))

(define-public r-phylolm
  (package
    (name "r-phylolm")
    (version "2.6.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phylolm" version))
       (sha256
        (base32 "0sg2a7isy5f5phk2db4v2b78apdmd3k9c8jdwpkrm3lw7vxj52v5"))))
    (properties `((upstream-name . "phylolm")))
    (build-system r-build-system)
    (propagated-inputs (list r-future-apply r-ape))
    (home-page "https://github.com/lamho86/phylolm")
    (synopsis "Phylogenetic Linear Regression")
    (description
     "This package provides functions for fitting phylogenetic linear models and
phylogenetic generalized linear models.  The computation uses an algorithm that
is linear in the number of tips in the tree.  The package also provides
functions for simulating continuous or binary traits along the tree.  Other
tools include functions to test the adequacy of a population tree.")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-phylogr
  (package
    (name "r-phylogr")
    (version "1.0.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PHYLOGR" version))
       (sha256
        (base32 "1fq2ny093b4881nwgj6wr8qd5g4dcxb51sdx40ndyxxpq6jj16n9"))))
    (properties `((upstream-name . "PHYLOGR")))
    (build-system r-build-system)
    (home-page "http://ligarto.org/rdiaz")
    (synopsis "Functions for Phylogenetically Based Statistical Analyses")
    (description
     "Manipulation and analysis of phylogenetically simulated data sets and
phylogenetically based analyses using GLS.")
    (license license:gpl2+)))

(define-public r-phylogeneticem
  (package
    (name "r-phylogeneticem")
    (version "1.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PhylogeneticEM" version))
       (sha256
        (base32 "1yxg13jm6jhciip76wh9936d10hxhjqdvlj15f249jzn7vfs6pyx"))))
    (properties `((upstream-name . "PhylogeneticEM")))
    (build-system r-build-system)
    (propagated-inputs (list r-robustbase
                             r-rcpparmadillo
                             r-rcpp
                             r-plyr
                             r-matrix
                             r-mass
                             r-linselect
                             r-glmnet
                             r-gglasso
                             r-foreach
                             r-capushe
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/pbastide/PhylogeneticEM")
    (synopsis "Automatic Shift Detection using a Phylogenetic EM")
    (description
     "Implementation of the automatic shift detection method for Brownian Motion (BM)
or OrnsteinâUhlenbeck (OU) models of trait evolution on phylogenies.  Some
tools to handle equivalent shifts configurations are also available.  See
Bastide et al. (2017) <doi:10.1111/rssb.12206> and Bastide et al. (2018)
<doi:10.1093/sysbio/syy005>.")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-phylocomr
  (package
    (name "r-phylocomr")
    (version "0.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phylocomr" version))
       (sha256
        (base32 "1ig9l4y9ma8fnzz7za1qmx8ibb24qsi3gcgn8v6fcksscj2agaad"))))
    (properties `((upstream-name . "phylocomr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-sys))
    (native-inputs (list r-knitr))
    (home-page "https://docs.ropensci.org/phylocomr/")
    (synopsis "Interface to 'Phylocom'")
    (description
     "Interface to Phylocom (<https://phylodiversity.net/phylocom/>), a library for
analysis of phylogenetic community structure and character evolution.  Includes
low level methods for interacting with the three executables, as well as higher
level interfaces for methods like aot', ecovolve', bladj', phylomatic', and
more.")
    (license license:bsd-2)))

(define-public r-phyloclim
  (package
    (name "r-phyloclim")
    (version "0.9.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phyloclim" version))
       (sha256
        (base32 "107kilh0gwr84ig54g92zyk7zv553pky2bzpjqgf9fjwbm6fvmrs"))))
    (properties `((upstream-name . "phyloclim")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp r-raster r-ape))
    (home-page "https://cran.r-project.org/package=phyloclim")
    (synopsis "Integrating Phylogenetics and Climatic Niche Modeling")
    (description
     "This package implements some methods in phyloclimatic modeling: estimation of
ancestral climatic niches, age-range-correlation, niche equivalency test and
background-similarity test.")
    (license license:gpl2+)))

(define-public r-phylocanvas
  (package
    (name "r-phylocanvas")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phylocanvas" version))
       (sha256
        (base32 "1l5br500lwwf7vp8wph5ykpkpqd15lypvhzy0c1cc8g8c6f7pckn"))))
    (properties `((upstream-name . "phylocanvas")))
    (build-system r-build-system)
    (propagated-inputs (list r-phylobase r-htmlwidgets r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/zachcp/phylocanvas")
    (synopsis
     "Interactive Phylogenetic Trees Using the 'Phylocanvas' JavaScript Library")
    (description
     "Create and customize interactive phylogenetic trees using the phylocanvas
@code{JavaScript} library and the htmlwidgets package.  These trees can be used
directly from the R console, from RStudio', in Shiny apps, and in R Markdown
documents.  See <http://phylocanvas.org/> for more information on the
phylocanvas library.")
    (license license:expat)))

(define-public r-phylin
  (package
    (name "r-phylin")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phylin" version))
       (sha256
        (base32 "0v6fyk2prja448vdi7ipwzws28fjfi188pfncjf9mhn8jrgpfxl6"))))
    (properties `((upstream-name . "phylin")))
    (build-system r-build-system)
    (home-page "https://www.r-project.org")
    (synopsis "Spatial Interpolation of Genetic Data")
    (description
     "The spatial interpolation of genetic distances between samples is based on a
modified kriging method that accepts a genetic distance matrix and generates a
map of probability of lineage presence.  This package also offers tools to
generate a map of potential contact zones between groups with user-defined
thresholds in the tree to account for old and recent divergence.  Additionally,
it has functions for IDW interpolation using genetic data and midpoints.")
    (license license:gpl2+)))

(define-public r-phylepic
  (package
    (name "r-phylepic")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phylepic" version))
       (sha256
        (base32 "16ll8w5zsdiw48rhz3gl3svp7xiwjap0bwy6hpjpyvsk6dzjx3mi"))))
    (properties `((upstream-name . "phylepic")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidygraph
                             r-scales
                             r-rlang
                             r-igraph
                             r-ggraph
                             r-ggplot2
                             r-ggnewscale
                             r-forcats
                             r-dplyr
                             r-cowplot
                             r-cli
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/cidm-ph/phylepic")
    (synopsis
     "Combined Visualisation of Phylogenetic and Epidemiological Data")
    (description
     "This package provides a collection of utilities and ggplot2 extensions to assist
with visualisations in genomic epidemiology.  This includes the phylepic chart,
a visual combination of a phylogenetic tree and a matched epidemic curve.  The
included ggplot2 extensions such as date axes binned by week are relevant for
other applications in epidemiology and beyond.  The approach is described in
Suster et al. (2024) <doi:10.1101/2024.04.02.24305229>.")
    (license license:expat)))

(define-public r-phyext2
  (package
    (name "r-phyext2")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phyext2" version))
       (sha256
        (base32 "0j871kgqm9fll0vdgh071z77ib51y8pxxm0ssjszljvvpx1mb8rb"))))
    (properties `((upstream-name . "phyext2")))
    (build-system r-build-system)
    (propagated-inputs (list r-phylobase r-ape))
    (home-page "https://cran.r-project.org/package=phyext2")
    (synopsis
     "An Extension (for Package 'SigTree') of Some of the Classes in Package 'phylobase'")
    (description
     "Based on (but not identical to) the no-longer-maintained package phyext',
provides enhancements to phylobase classes, specifically for use by package
@code{SigTree}'; provides classes and methods which help users manipulate
branch-annotated trees (as in @code{SigTree}'); also provides support for a few
other extra features.")
    (license license:gpl3)))

(define-public r-phuassess
  (package
    (name "r-phuassess")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phuassess" version))
       (sha256
        (base32 "0jplj9gih32dllx2hw5aqvc9b94sbrbv66s3a5r8mdbpjh93rhng"))))
    (properties `((upstream-name . "phuassess")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=phuassess")
    (synopsis "Proportional Habitat Use Assessment")
    (description
     "Assessment of habitat selection by means of the permutation-based combination of
sign tests (Fattorini et al., 2014 <DOI:10.1007/s10651-013-0250-7>).  To
exemplify the application of this procedure, habitat selection is assessed for a
population of European Brown Hares settled in central Italy.")
    (license license:gpl2+)))

(define-public r-phsmm
  (package
    (name "r-phsmm")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PHSMM" version))
       (sha256
        (base32 "1phfdzhfdhyry11plfd8a2lcn3rg02m84brw7fp2lg3hsxjg5ch7"))))
    (properties `((upstream-name . "PHSMM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=PHSMM")
    (synopsis
     "Penalised Maximum Likelihood Estimation for Hidden Semi-Markov Models")
    (description
     "This package provides tools for penalised maximum likelihood estimation of
hidden semi-Markov models (HSMMs) with flexible state dwell-time distributions.
These include functions for model fitting, model checking and state-decoding.
The package considers HSMMs for univariate time series with state-dependent
gamma, normal, Poisson or Bernoulli distributions.  For details, see Pohle, J.,
Adam, T. and Beumer, L.T. (2021): Flexible estimation of the state dwell-time
distribution in hidden semi-Markov models. <@code{arXiv:2101.09197>}.")
    (license license:gpl3)))

(define-public r-phsmethods
  (package
    (name "r-phsmethods")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phsmethods" version))
       (sha256
        (base32 "05z1l7jppg5mfrwbgkx59ybjc4321cihrh5j6bsvx72waapswic9"))))
    (properties `((upstream-name . "phsmethods")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-scales
                             r-rlang
                             r-readr
                             r-magrittr
                             r-lubridate
                             r-lifecycle
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Public-Health-Scotland/phsmethods")
    (synopsis "Standard Methods for Use in Public Health Scotland")
    (description
     "This package provides a collection of methods for commonly undertaken analytical
tasks, primarily developed for Public Health Scotland (PHS) analysts, but the
package is also generally useful to others working in the healthcare space,
particularly since it has functions for working with Community Health Index
(CHI) numbers.  The package can help to make data manipulation and analysis more
efficient and reproducible.")
    (license license:gpl2+)))

(define-public r-phreeqc
  (package
    (name "r-phreeqc")
    (version "3.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phreeqc" version))
       (sha256
        (base32 "0ywhxqc33ybwlch2ym6wgm5s4dillhwg3ccdiz9iklv0i85grmd8"))))
    (properties `((upstream-name . "phreeqc")))
    (build-system r-build-system)
    (home-page "https://www.usgs.gov/software/phreeqc-version-3")
    (synopsis "R Interface to Geochemical Modeling Software")
    (description
     "This package provides a geochemical modeling program developed by the US
Geological Survey that is designed to perform a wide variety of aqueous
geochemical calculations, including speciation, batch-reaction, one-dimensional
reactive-transport, and inverse geochemical calculations.")
    (license license:gpl3)))

(define-public r-phrases
  (package
    (name "r-phrases")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phrases" version))
       (sha256
        (base32 "1am90fgi8bqf4mmw797knczj7pv7py2in4rm0m6l1xz4zfjihqw3"))))
    (properties `((upstream-name . "phrases")))
    (build-system r-build-system)
    (propagated-inputs (list r-usethis r-tidyverse))
    (home-page "https://github.com/sumanstats/phrases")
    (synopsis "Phrasal Verbs in English Club Website")
    (description
     "This package contains all phrasal verbs listed in
<https://www.englishclub.com/ref/Phrasal_Verbs/> as data frame.  Useful for
educational purpose as well as for text mining.")
    (license license:gpl3)))

(define-public r-photosynthesis
  (package
    (name "r-photosynthesis")
    (version "2.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "photosynthesis" version))
       (sha256
        (base32 "1bilhqzhwxanlxvn98piysv45bpw7zh7f5w56rvllxgrpmra68pq"))))
    (properties `((upstream-name . "photosynthesis")))
    (build-system r-build-system)
    (propagated-inputs (list r-units
                             r-tealeaves
                             r-stringr
                             r-rlang
                             r-readr
                             r-purrr
                             r-progress
                             r-nlme
                             r-minpack-lm
                             r-magrittr
                             r-lifecycle
                             r-gunit
                             r-glue
                             r-ggplot2
                             r-furrr
                             r-dplyr
                             r-crayon
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/cdmuir/photosynthesis")
    (synopsis "Tools for Plant Ecophysiology & Modeling")
    (description
     "This package contains modeling and analytical tools for plant ecophysiology.
MODELING: Simulate C3 photosynthesis using the Farquhar, von Caemmerer, Berry
(1980) <doi:10.1007/BF00386231> model as described in Buckley and Diaz-Espejo
(2015) <doi:10.1111/pce.12459>.  It uses units to ensure that parameters are
properly specified and transformed before calculations.  Temperature response
functions get automatically \"baked\" into all parameters based on leaf
temperature following Bernacchi et al. (2002) <doi:10.1104/pp.008250>.  The
package includes boundary layer, cuticular, stomatal, and mesophyll conductances
to CO2, which each can vary on the upper and lower portions of the leaf.  Use
straightforward functions to simulate photosynthesis over environmental
gradients such as Photosynthetic Photon Flux Density (PPFD) and leaf
temperature, or over trait gradients such as CO2 conductance or photochemistry.
ANALYTICAL TOOLS: Fit ACi (Farquhar et al. (1980) <doi:10.1007/BF00386231>) and
AQ curves (Marshall & Biscoe (1980) <doi:10.1093/jxb/31.1.29>), temperature
responses (Heskel et al. (2016) <doi:10.1073/pnas.1520282113>; Kruse et al.
(2008) <doi:10.1111/j.1365-3040.2008.01809.x>, Medlyn et al. (2002)
<doi:10.1046/j.1365-3040.2002.00891.x>, Hobbs et al. (2013)
<doi:10.1021/cb4005029>), respiration in the light (Kok (1956)
<doi:10.1016/0006-3002(56)90003-8>, Walker & Ort (2015) <doi:10.1111/pce.12562>,
Yin et al. (2009) <doi:10.1111/j.1365-3040.2009.01934.x>, Yin et al. (2011)
<doi:10.1093/jxb/err038>), mesophyll conductance (Harley et al. (1992)
<doi:10.1104/pp.98.4.1429>), pressure-volume curves (Koide et al. (2000)
<doi:10.1007/978-94-009-2221-1_9>, Sack et al. (2003)
<doi:10.1046/j.0016-8025.2003.01058.x>, Tyree et al. (1972)
<doi:10.1093/jxb/23.1.267>), hydraulic vulnerability curves (Ogle et al. (2009)
<doi:10.1111/j.1469-8137.2008.02760.x>, Pammenter et al. (1998)
<doi:10.1093/treephys/18.8-9.589>), and tools for running sensitivity analyses
particularly for variables with uncertainty (e.g. g_mc(), gamma_star(), R_d()).")
    (license license:expat)))

(define-public r-photosynq
  (package
    (name "r-photosynq")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PhotosynQ" version))
       (sha256
        (base32 "10n06sfbwv5w96m3yv5ws7yddfv2n8pp1rc730870wiavl6cbn63"))))
    (properties `((upstream-name . "PhotosynQ")))
    (build-system r-build-system)
    (propagated-inputs (list r-httr r-getpass))
    (home-page "https://github.com/Photosynq/PhotosynQ-R")
    (synopsis "Connect to PhotosynQ")
    (description
     "Connect R to the @code{PhotosynQ} platform (<https://photosynq.org>).  It allows
to login and logout, as well as receive project information and project data.
Further it transforms the received JSON objects into a data frame, which can be
used for the final data analysis.")
    (license (license:fsdg-compatible "MIT + file LICENCE"))))

(define-public r-photobiologywavebands
  (package
    (name "r-photobiologywavebands")
    (version "0.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "photobiologyWavebands" version))
       (sha256
        (base32 "0p4as2kpcjy0mc38pmjjlpngbf4568zn5rpz9rhrlc0ynarrgnzj"))))
    (properties `((upstream-name . "photobiologyWavebands")))
    (build-system r-build-system)
    (propagated-inputs (list r-photobiology))
    (native-inputs (list r-knitr))
    (home-page "https://docs.r4photobiology.info/photobiologyWavebands/")
    (synopsis "Waveband Definitions for UV, VIS, and IR Radiation")
    (description
     "Constructors of waveband objects for commonly used biological spectral weighting
functions (BSWFs) and for different wavebands describing named ranges of
wavelengths in the ultraviolet (UV), visible (VIS) and infrared (IR) regions of
the electromagnetic spectrum.  Part of the r4photobiology suite, Aphalo P. J.
(2015) <doi:10.19232/uv4pb.2015.1.14>.")
    (license license:gpl2+)))

(define-public r-photobiologysun
  (package
    (name "r-photobiologysun")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "photobiologySun" version))
       (sha256
        (base32 "00mb44wk5q8slvibr2iafwljr9w7m5qf0zk6bn5zji5i4l01gpi5"))))
    (properties `((upstream-name . "photobiologySun")))
    (build-system r-build-system)
    (propagated-inputs (list r-photobiology))
    (native-inputs (list r-knitr))
    (home-page "http://www.r4photobiology.info")
    (synopsis "Data for Sunlight Spectra")
    (description
     "Data for the extraterrestrial solar spectral irradiance and ground level solar
spectral irradiance and irradiance.  In addition data for shade light under
vegetation and irradiance time series from different broadband sensors.  Part of
the r4photobiology suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.")
    (license license:gpl2+)))

(define-public r-photobiologysensors
  (package
    (name "r-photobiologysensors")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "photobiologySensors" version))
       (sha256
        (base32 "0jc801c8fm5fdbs67nc8d5cga9c2cfdfrjh6i6qf60g7kxddsnsf"))))
    (properties `((upstream-name . "photobiologySensors")))
    (build-system r-build-system)
    (propagated-inputs (list r-photobiology))
    (native-inputs (list r-knitr))
    (home-page "https://www.r4photobiology.info")
    (synopsis "Response Data for Light Sensors")
    (description
     "Spectral response data for broadband ultraviolet and visible radiation sensors.
Angular response data for broadband ultraviolet and visible radiation sensors
and diffusers used as entrance optics.  Data obtained from multiple sources were
used: author-supplied data from scientific research papers, sensor-manufacturer
supplied data, and published sensor specifications.  Part of the r4photobiology
suite Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.")
    (license license:gpl2+)))

(define-public r-photobiologyplants
  (package
    (name "r-photobiologyplants")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "photobiologyPlants" version))
       (sha256
        (base32 "0va72n6fa5w0xffyyhqsmpgy4ac6mva5gxdxl907ch1d02r8zgya"))))
    (properties `((upstream-name . "photobiologyPlants")))
    (build-system r-build-system)
    (propagated-inputs (list r-photobiologywavebands r-photobiology))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=photobiologyPlants")
    (synopsis "Plant Photobiology Related Functions and Data")
    (description
     "This package provides functions for quantifying visible (VIS) and ultraviolet
(UV) radiation in relation to the photoreceptors Phytochromes, Cryptochromes,
and UVR8 which are present in plants.  It also includes data sets on the optical
properties of plants.  Part of the r4photobiology suite, Aphalo P. J. (2015)
<doi:10.19232/uv4pb.2015.1.14>.")
    (license license:gpl2+)))

(define-public r-photobiologyleds
  (package
    (name "r-photobiologyleds")
    (version "0.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "photobiologyLEDs" version))
       (sha256
        (base32 "0868ja97wy0cj3nia31xp646yni0q56na87rz63pb33spkw36r6p"))))
    (properties `((upstream-name . "photobiologyLEDs")))
    (build-system r-build-system)
    (propagated-inputs (list r-photobiology))
    (native-inputs (list r-knitr))
    (home-page "https://docs.r4photobiology.info/photobiologyLEDs/")
    (synopsis "Spectral Data for Light-Emitting-Diodes")
    (description
     "Spectral emission data for some frequently used light emitting diodes available
as electronic components.  Part of the r4photobiology suite, Aphalo P. J. (2015)
<doi:10.19232/uv4pb.2015.1.14>.")
    (license license:gpl2+)))

(define-public r-photobiologylamps
  (package
    (name "r-photobiologylamps")
    (version "0.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "photobiologyLamps" version))
       (sha256
        (base32 "1sl6340zblq6j3djdygqydf9x0bh5jm0mir8j1jjw2snbppy6dfl"))))
    (properties `((upstream-name . "photobiologyLamps")))
    (build-system r-build-system)
    (propagated-inputs (list r-photobiology))
    (native-inputs (list r-knitr))
    (home-page "https://www.r4photobiology.info/")
    (synopsis "Spectral Irradiance Data for Lamps")
    (description
     "Spectral emission data for some frequently used lamps including bulbs and
flashlights based on led emitting diodes (LEDs) but excluding LEDs available as
electronic components.  Original spectral irradiance data for incandescent-,
LED- and discharge lamps are included.  They are complemented by data on the
effect of temperature on the emission by fluorescent tubes.  Part of the
r4photobiology suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.")
    (license license:gpl2+)))

(define-public r-photobiologyinout
  (package
    (name "r-photobiologyinout")
    (version "0.4.28-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "photobiologyInOut" version))
       (sha256
        (base32 "0c5smimj3jinw0h2fv0dqwq7rpklwm1r0mxzrs5wh0hfcinzc7wm"))))
    (properties `((upstream-name . "photobiologyInOut")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-readxl
                             r-readr
                             r-photobiology
                             r-lubridate
                             r-jsonlite
                             r-dplyr
                             r-colorspec
                             r-anytime))
    (native-inputs (list r-knitr))
    (home-page "https://docs.r4photobiology.info/photobiologyInOut/")
    (synopsis "Read Spectral and Logged Data from Foreign Files")
    (description
     "This package provides functions for reading, and in some cases writing, foreign
files containing spectral data from spectrometers and their associated software,
output from daylight simulation models in common use, and some spectral data
repositories.  As well as functions for exchange of spectral data with other R
packages.  Part of the r4photobiology suite, Aphalo P. J. (2015)
<doi:10.19232/uv4pb.2015.1.14>.")
    (license license:gpl2+)))

(define-public r-photobiologyfilters
  (package
    (name "r-photobiologyfilters")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "photobiologyFilters" version))
       (sha256
        (base32 "03shhykqwmmcpkzpy42kb3rn5n5kwahhjmbmspqnli5vfdvq4gim"))))
    (properties `((upstream-name . "photobiologyFilters")))
    (build-system r-build-system)
    (propagated-inputs (list r-photobiology))
    (native-inputs (list r-knitr))
    (home-page "https://docs.r4photobiology.info/photobiologyFilters/")
    (synopsis "Spectral Transmittance and Spectral Reflectance Data")
    (description
     "Spectral transmittance data for frequently used filters and similar materials.
Plastic sheets and films; photography filters; theatrical gels; machine-vision
filters; various types of window glass; optical glass and some laboratory
plastics and glassware.  Spectral reflectance data for frequently encountered
materials.  Part of the r4photobiology suite, Aphalo P. J. (2015)
<doi:10.19232/uv4pb.2015.1.14>.")
    (license license:gpl2+)))

(define-public r-photobiology
  (package
    (name "r-photobiology")
    (version "0.11.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "photobiology" version))
       (sha256
        (base32 "1n5hilm5i2hyb9ksfhz88fv65n70gbc2j2xqjxlqhhzmvyz26ikc"))))
    (properties `((upstream-name . "photobiology")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-splus2r
                             r-rlang
                             r-polynom
                             r-plyr
                             r-lubridate
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://docs.r4photobiology.info/photobiology/")
    (synopsis "Photobiological Calculations")
    (description
     "Definitions of classes, methods, operators and functions for use in photobiology
and radiation meteorology and climatology.  Calculation of effective (weighted)
and not-weighted irradiances/doses, fluence rates, transmittance, reflectance,
absorptance, absorbance and diverse ratios and other derived quantities from
spectral data.  Local maxima and minima: peaks, valleys and spikes.  Conversion
between energy-and photon-based units.  Wavelength interpolation.  Astronomical
calculations related solar angles and day length.  Colours and vision.  This
package is part of the r4photobiology suite, Aphalo, P. J. (2015)
<doi:10.19232/uv4pb.2015.1.14>.")
    (license license:gpl2+)))

(define-public r-phosphoricons
  (package
    (name "r-phosphoricons")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phosphoricons" version))
       (sha256
        (base32 "0p14gw09im5zzpsfl0dr9xpcs0c0585flrk3hv2k62ww5n9wcymn"))))
    (properties `((upstream-name . "phosphoricons")))
    (build-system r-build-system)
    (propagated-inputs (list r-htmltools))
    (home-page "https://dreamrs.github.io/phosphoricons/")
    (synopsis "'Phosphor' Icons for R")
    (description
     "Use Phosphor icons in shiny applications or rmarkdown documents.  Icons are
available in 5 different weights and can be customized by setting color, size,
orientation and more.")
    (license license:expat)))

(define-public r-phonr
  (package
    (name "r-phonr")
    (version "1.0-7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phonR" version))
       (sha256
        (base32 "0al2cbynnbvmd90lk1w1g1ppslqq0ng8vbb6bl7m4kqwd2lgv056"))))
    (properties `((upstream-name . "phonR")))
    (build-system r-build-system)
    (propagated-inputs (list r-splancs r-plotrix r-deldir))
    (home-page "http://drammock.github.io/phonR/")
    (synopsis "Tools for Phoneticians and Phonologists")
    (description
     "This package provides tools for phoneticians and phonologists, including
functions for normalization and plotting of vowels.")
    (license license:gpl3)))

(define-public r-phonics
  (package
    (name "r-phonics")
    (version "1.3.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phonics" version))
       (sha256
        (base32 "1g4gmfz190xaj800gawl79fzhmgimssa31kqzw8a70scp5psz3fw"))))
    (properties `((upstream-name . "phonics")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-data-table r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://jameshoward.us/phonics-in-r/")
    (synopsis "Phonetic Spelling Algorithms")
    (description
     "This package provides a collection of phonetic algorithms including Soundex,
Metaphone, NYSIIS, Caverphone, and others.  The package is documented in
<doi:10.18637/jss.v095.i08>.")
    (license license:bsd-2)))

(define-public r-phonfieldwork
  (package
    (name "r-phonfieldwork")
    (version "0.0.17")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phonfieldwork" version))
       (sha256
        (base32 "0ysxa6zxd8mb798bm6vsdhnnnl2y2vyhfi4kbv64qsgjbyjqjkvh"))))
    (properties `((upstream-name . "phonfieldwork")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tuner
                             r-rmarkdown
                             r-readr
                             r-phontools
                             r-mime))
    (native-inputs (list r-knitr))
    (home-page "https://CRAN.R-project.org/package=phonfieldwork")
    (synopsis "Linguistic Phonetic Fieldwork Tools")
    (description
     "There are a lot of different typical tasks that have to be solved during
phonetic research and experiments.  This includes creating a presentation that
will contain all stimuli, renaming and concatenating multiple sound files
recorded during a session, automatic annotation in Praat @code{TextGrids} (this
is one of the sound annotation standards provided by Praat software, see Boersma
& Weenink 2020 <https://www.fon.hum.uva.nl/praat/>), creating an html table with
annotations and spectrograms, and converting multiple formats ('Praat
@code{TextGrid}, ELAN', EXMA@code{RaLDA}', Audacity', subtitles .srt', and FLEx
flextext).  All of these tasks can be solved by a mixture of different tools
(any programming language has programs for automatic renaming, and Praat
contains scripts for concatenating and renaming files, etc.).  phonfieldwork
provides a functionality that will make it easier to solve those tasks
independently of any additional tools.  You can also compare the functionality
with other packages: @code{rPraat}
<https://CRAN.R-project.org/package=@code{rPraat>}, @code{textgRid}
<https://CRAN.R-project.org/package=@code{textgRid>}.")
    (license license:gpl2+)))

(define-public r-phonenumber
  (package
    (name "r-phonenumber")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phonenumber" version))
       (sha256
        (base32 "1gwh7cg1zyy197sj16wcxqyz2rx5z83cd4rwmf22ycpgvz2dwzw9"))))
    (properties `((upstream-name . "phonenumber")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://stevemyles.site/phonenumber/")
    (synopsis "Convert Letters to Numbers and Back as on a Telephone Keypad")
    (description
     "Convert English letters to numbers or numbers to English letters as on a
telephone keypad.  When converting letters to numbers, a character vector is
returned with \"A,\" \"B,\" or \"C\" becoming 2, \"D,\" \"E\", or \"F\" becoming 3, etc.
When converting numbers to letters, a character vector is returned with multiple
elements (i.e., \"2\" becomes a vector of \"A,\" \"B,\" and \"C\").")
    (license license:expat)))

(define-public r-phoenix
  (package
    (name "r-phoenix")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phoenix" version))
       (sha256
        (base32 "0vb1y6ga3gjgm7xhaa2pv61cc4a8i45bdf1pvk2ym4l6c717s3yp"))))
    (properties `((upstream-name . "phoenix")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/CU-DBMI-Peds/phoenix/")
    (synopsis "The Phoenix Pediatric Sepsis and Septic Shock Criteria")
    (description
     "Implementation of the Phoenix and Phoenix-8 Sepsis Criteria as described in
\"Development and Validation of the Phoenix Criteria for Pediatric Sepsis and
Septic Shock\" by Sanchez-Pinto, Bennett, @code{DeWitt}, Russell et al. (2024)
<doi:10.1001/jama.2024.0196> (Drs.  Sanchez-Pinto and Bennett contributed
equally to this manuscript; Dr. @code{DeWitt} and Mr. Russell contributed
equally to the manuscript), \"International Consensus Criteria for Pediatric
Sepsis and Septic Shock\" by Schlapbach, Watson, Sorce, Argent, et al. (2024)
<doi:10.1001/jama.2024.0179> (Drs Schlapbach, Watson, Sorce, and Argent
contributed equally) and the application note \"phoenix: an R package and Python
module for calculating the Phoenix pediatric sepsis score and criteria\" by
@code{DeWitt}, Russell, Rebull, Sanchez-Pinto, and Bennett (2024)
<doi:10.1093/jamiaopen/ooae066>.")
    (license license:expat)))

(define-public r-phoenics
  (package
    (name "r-phoenics")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phoenics" version))
       (sha256
        (base32 "1a7dws11pf2bpgkvchddr14v6zvbn16xwqrdmmz9plw9lindc00j"))))
    (properties `((upstream-name . "phoenics")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-lme4
                             r-factominer
                             r-factoextra
                             r-blme))
    (home-page "https://forgemia.inra.fr/panoramics/phoenics")
    (synopsis
     "Pathways Longitudinal and Differential Analysis in Metabolomics")
    (description
     "Perform a differential analysis at pathway level based on metabolite
quantifications and information on pathway metabolite composition.  The method
is based on a Principal Component Analysis step and on a linear mixed model.
Automatic query of metabolic pathways is also implemented.")
    (license license:gpl3)))

(define-public r-phm
  (package
    (name "r-phm")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phm" version))
       (sha256
        (base32 "05ymrynv5zzrbj3vsdrijay841hv5jbkn8c0v4l6dqndysl9xis4"))))
    (properties `((upstream-name . "phm")))
    (build-system r-build-system)
    (propagated-inputs (list r-tm
                             r-smallstuff
                             r-shiny
                             r-nlp
                             r-matrix
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=phm")
    (synopsis "Phrase Mining")
    (description
     "This package provides functions to extract and handle commonly occurring
principal phrases obtained from collections of texts.")
    (license license:gpl3)))

(define-public r-phitestr
  (package
    (name "r-phitestr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PhitestR" version))
       (sha256
        (base32 "113f176q9kaghmqivqcg9l6zczx7dmawhybb8hwb2xpb0mhcgh4l"))))
    (properties `((upstream-name . "PhitestR")))
    (build-system r-build-system)
    (propagated-inputs (list r-fitdistrplus))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PhitestR")
    (synopsis "Analyzing the Heterogeneity of Single-Cell Populations")
    (description
     "This package provides a bioinformatics method developed for analyzing the
heterogeneity of single-cell populations.  Phitest provides an objective and
automatic method to evaluate the performance of clustering and quality of cell
clusters.")
    (license license:gpl3)))

(define-public r-phinfiniteestimates
  (package
    (name "r-phinfiniteestimates")
    (version "2.9.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PHInfiniteEstimates" version))
       (sha256
        (base32 "1j00bb0jk912bkv6yhwnx1f2nr2bw72z9c5y4xby1mphljz059b7"))))
    (properties `((upstream-name . "PHInfiniteEstimates")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rdpack r-nph r-lpsolve r-coxphf))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=PHInfiniteEstimates")
    (synopsis "Tools for Inference in the Presence of a Monotone Likelihood")
    (description
     "Proportional hazards estimation in the presence of a partially monotone
likelihood has difficulties, in that finite estimators do not exist.  These
difficulties are related to those arising from logistic and multinomial
regression.  References for methods are given in the separate function
documents.  Supported by grant NSF DMS 1712839.")
    (license license:gpl3)))

(define-public r-philipshue
  (package
    (name "r-philipshue")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PhilipsHue" version))
       (sha256
        (base32 "03ghwi14jsz1ff70dl30p6w0d9v29kc70kip2bcinxm6v4l2q383"))))
    (properties `((upstream-name . "PhilipsHue")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml r-purrr r-pkgload r-httr))
    (native-inputs (list r-knitr))
    (home-page "https://fascinatingfingers.gitlab.io/philipshue")
    (synopsis "R Interface to the Philips Hue API")
    (description
     "Control Philips Hue smart lighting.  Use this package to connect to a Hue bridge
on your local network (remote authentication not yet supported) and control your
smart lights through the Philips Hue API. All API V1 endpoints are supported.
See API documentation at <https://developers.meethue.com/>.")
    (license license:gpl3+)))

(define-public r-phidelta
  (package
    (name "r-phidelta")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phiDelta" version))
       (sha256
        (base32 "0g1g5516p8i1gfpvsy75rdirib8sx1ki2mbf1cvgsnzh70h72gbv"))))
    (properties `((upstream-name . "phiDelta")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=phiDelta")
    (synopsis "Tool for Phi Delta Analysis of Features")
    (description
     "Analysis of features by phi delta diagrams.  In particular, functions for
reading data and calculating phi and delta as well as the functionality to plot
it.  Moreover it is possible to do further analysis on the data by generating
rankings.  For more information on phi delta diagrams, see also Giuliano Armano
(2015) <doi:10.1016/j.ins.2015.07.028>.")
    (license license:gpl2+)))

(define-public r-phia
  (package
    (name "r-phia")
    (version "0.3-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phia" version))
       (sha256
        (base32 "1lsprm49njwp0gfn4knvwdq7d5lpq4irhysl8f26glcz9x929sff"))))
    (properties `((upstream-name . "phia")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-car))
    (home-page "https://github.com/heliosdrm/phia")
    (synopsis "Post-Hoc Interaction Analysis")
    (description
     "Analysis of terms in linear, generalized and mixed linear models, on the basis
of multiple comparisons of factor contrasts.  Specially suited for the analysis
of interaction terms.")
    (license license:gpl3+)))

(define-public r-phi2rho
  (package
    (name "r-phi2rho")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Phi2rho" version))
       (sha256
        (base32 "14bbnpr4jpbmj914hg8782nkqr3mbp3xmywaiicb2w4wn6m6ljm0"))))
    (properties `((upstream-name . "Phi2rho")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmpfr))
    (home-page "https://cran.r-project.org/package=Phi2rho")
    (synopsis "Owen's T Function and Bivariate Normal Integral")
    (description
     "Computes the Owen's T function or the bivariate normal integral using one of the
following: modified Euler's arctangent series, tetrachoric series, or Vasicek's
series.  For the methods, see Komelj, J. (2023) <doi:10.4236/ajcm.2023.134026>
(or reprint <@code{arXiv:2312.00011>} with better typography) and Vasicek, O. A.
(1998) <doi:10.21314/JCF.1998.015>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-phevis
  (package
    (name "r-phevis")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PheVis" version))
       (sha256
        (base32 "1anj079vdpfbkilbvk0b2b78qk87gc7lmivzl8syq6lb3dxkja2l"))))
    (properties `((upstream-name . "PheVis")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-viridis
                             r-tidyr
                             r-rcpp
                             r-randomforest
                             r-purrr
                             r-lme4
                             r-knitr
                             r-glmnet
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PheVis")
    (synopsis
     "Automatic Phenotyping of Electronic Health Record at Visit Resolution")
    (description
     "Using Electronic Health Record (EHR) is difficult because most of the time the
true characteristic of the patient is not available.  Instead we can retrieve
the International Classification of Disease code related to the disease of
interest or we can count the occurrence of the Unified Medical Language System.
None of them is the true phenotype which needs chart review to identify.
However chart review is time consuming and costly. @code{PheVis} is an algorithm
which is phenotyping (i.e identify a characteristic) at the visit level in an
unsupervised fashion.  It can be used for chronic or acute diseases.  An example
of how to use @code{PheVis} is available in the vignette.  Basically there are
two functions that are to be used: `train_phevis()` which trains the algorithm
and `test_phevis()` which get the predicted probabilities.  The detailed method
is described in preprint by FertÃ© et al. (2020)
<doi:10.1101/2020.06.15.20131458>.")
    (license license:gpl2+)))

(define-public r-pheval
  (package
    (name "r-pheval")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PHeval" version))
       (sha256
        (base32 "1xr525f9v1py61kmv9n50dy89f7nc6mvdb0gqab23frn21f4bawv"))))
    (properties `((upstream-name . "PHeval")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mvtnorm))
    (home-page "https://cran.r-project.org/package=PHeval")
    (synopsis
     "Evaluation of the Proportional Hazards Assumption and Test for Comparing Survival Curves with a Standardized Score Process")
    (description
     "This package provides tools for the test for the comparison of survival curves,
the evaluation of the goodness-of-fit and the predictive capacity of the
proportional hazards model.")
    (license license:gpl2+)))

(define-public r-phers
  (package
    (name "r-phers")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phers" version))
       (sha256
        (base32 "00w84l5p9rwq3hgjx792fk2yhrnal143ph29ybcqrha40hglbbas"))))
    (properties `((upstream-name . "phers")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-iterators
                             r-foreach
                             r-data-table
                             r-checkmate
                             r-bedmatrix))
    (home-page "https://phers.hugheylab.org")
    (synopsis "Calculate Phenotype Risk Scores")
    (description
     "Use phenotype risk scores based on linked clinical and genetic data to study
Mendelian disease and rare genetic variants.  See Bastarache et al.  2018
<doi:10.1126/science.aal4043>.")
    (license license:gpl2)))

(define-public r-phenthauproc
  (package
    (name "r-phenthauproc")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PHENTHAUproc" version))
       (sha256
        (base32 "1bhd147mhv1xj4q6yqz92n8vi6nji093y78pi1jkav1plwx2qf3c"))))
    (properties `((upstream-name . "PHENTHAUproc")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-rlang r-lubridate))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PHENTHAUproc")
    (synopsis "Phenology Modelling of Thaumetopoea Processionea")
    (description
     "This package provides methods to calculate and present PHENTHAUproc', an early
warning and decision support system for hazard assessment and control of oak
processionary moth (OPM) using local and spatial temperature data.  It was
created by Halbig et al.  2024 (<doi:10.1016/j.foreco.2023.121525>) at FVA
(<https://www.fva-bw.de/en/homepage/>) Forest Research Institute
Baden-Wuerttemberg, Germany and at BOKU - University of Natural Ressources and
Life Sciences, Vienna, Austria.")
    (license license:expat)))

(define-public r-phenotypesimulator
  (package
    (name "r-phenotypesimulator")
    (version "0.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PhenotypeSimulator" version))
       (sha256
        (base32 "19c79kls6i1qpsk41lp8whva5in47m2wlr0v3ggw5bblp4fkd43m"))))
    (properties `((upstream-name . "PhenotypeSimulator")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-snpstats
                             r-reshape2
                             r-rcpp
                             r-r-utils
                             r-optparse
                             r-mvtnorm
                             r-hmisc
                             r-ggplot2
                             r-dplyr
                             r-data-table
                             r-cowplot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/HannahVMeyer/PhenotypeSimulator")
    (synopsis
     "Flexible Phenotype Simulation from Different Genetic and Noise Models")
    (description
     "Simulation is a critical part of method development and assessment in
quantitative genetics. @code{PhenotypeSimulator} allows for the flexible
simulation of phenotypes under different models, including genetic variant and
infinitesimal genetic effects (reflecting population structure) as well as
non-genetic covariate effects, observational noise and additional correlation
effects.  The different phenotype components are combined into a final phenotype
while controlling for the proportion of variance explained by each of the
components.  For each effect component, the number of variables, their
distribution and the design of their effect across traits can be customised.
For the simulation of the genetic effects, external genotype data from a number
of standard software ('plink', hapgen2'/ impute2', genome', bimbam', simple text
files) can be imported.  The final simulated phenotypes and its components can
be automatically saved into .rds or .csv files.  In addition, they can be saved
in formats compatible with commonly used genetic association software ('gemma',
bimbam', plink', snptest', @code{LiMMBo}').")
    (license license:expat)))

(define-public r-phenotype
  (package
    (name "r-phenotype")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Phenotype" version))
       (sha256
        (base32 "0bfg2vk35hrfzmpjpnv3yhnsfr17505c49msbppqrrbpw5wgawy5"))))
    (properties `((upstream-name . "Phenotype")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-lme4))
    (home-page "https://github.com/biozhp/Phenotype")
    (synopsis "Tool for Phenotypic Data Processing")
    (description
     "Large-scale phenotypic data processing is essential in research.  Researchers
need to eliminate outliers from the data in order to obtain true and reliable
results.  Best linear unbiased prediction (BLUP) is a standard method for
estimating random effects of a mixed model.  This method can be used to process
phenotypic data under different conditions and is widely used in animal and
plant breeding.  The Phenotype can remove outliers from phenotypic data and
performs the best linear unbiased prediction (BLUP), help researchers quickly
complete phenotypic data analysis.  H.P.Piepho. (2008)
<doi:10.1007/s10681-007-9449-8>.")
    (license license:artistic2.0)))

(define-public r-phenorm
  (package
    (name "r-phenorm")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PheNorm" version))
       (sha256
        (base32 "10223yfaajzq1ip5qj3kk6iyjzrbb619jkrsxawb0dpkrj56d8v7"))))
    (properties `((upstream-name . "PheNorm")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/celehs/PheNorm")
    (synopsis
     "Unsupervised Gold-Standard Label Free Phenotyping Algorithm for EHR Data")
    (description
     "The algorithm combines the most predictive variable, such as count of the main
International Classification of Diseases (ICD) codes, and other Electronic
Health Record (EHR) features (e.g. health utilization and processed clinical
note data), to obtain a score for accurate risk prediction and disease
classification.  In particular, it normalizes the surrogate to resemble gaussian
mixture and leverages the remaining features through random corruption
denoising.  Background and details about the method can be found at Yu et al.
(2018) <doi:10.1093/jamia/ocx111>.")
    (license license:gpl3)))

(define-public r-phenopix
  (package
    (name "r-phenopix")
    (version "2.4.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phenopix" version))
       (sha256
        (base32 "12nd5jfg70ysv51shkn00bdjaa1xm9891qyn9vvgq1k3fqnrr2n9"))))
    (properties `((upstream-name . "phenopix")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-strucchange
                             r-stringr
                             r-sp
                             r-raster
                             r-plyr
                             r-jpeg
                             r-iterators
                             r-gtools
                             r-foreach
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=phenopix")
    (synopsis "Process Digital Images of a Vegetation Cover")
    (description
     "This package provides a collection of functions to process digital images,
depict greenness index trajectories and extract relevant phenological stages.")
    (license license:gpl2)))

(define-public r-phenomap
  (package
    (name "r-phenomap")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phenomap" version))
       (sha256
        (base32 "12yzp1801csbhq7b0341rg641wf040mj2p2mgabs2b400pm7h4sr"))))
    (properties `((upstream-name . "phenomap")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-stringr r-plyr r-phenex r-doparallel))
    (home-page "https://github.com/JepsonNomad/phenomap")
    (synopsis "Projecting Satellite-Derived Phenology in Space")
    (description
     "This takes in a series of multi-layer raster files and returns a phenology
projection raster, following methodologies described in John (2016)
<https://etda.libraries.psu.edu/catalog/13521clj5135>.")
    (license license:gpl3)))

(define-public r-phenology
  (package
    (name "r-phenology")
    (version "10.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phenology" version))
       (sha256
        (base32 "088288idp4jgj1jcrqxij9p5phlh425r41v7wnvxghp066cg1snr"))))
    (properties `((upstream-name . "phenology")))
    (build-system r-build-system)
    (propagated-inputs (list r-optimx r-numderiv r-helpersmg))
    (home-page "https://cran.r-project.org/package=phenology")
    (synopsis
     "Tools to Manage a Parametric Function that Describes Phenology and More")
    (description
     "This package provides functions used to fit and test the phenology of species
based on counts.  Based on Girondot, M. (2010) <doi:10.3354/esr00292> for the
phenology function, Girondot, M. (2017) <doi:10.1016/j.ecolind.2017.05.063> for
the convolution of negative binomial, Girondot, M. and Rizzo, A. (2015)
<doi:10.2993/etbi-35-02-337-353.1> for Bayesian estimate, Pfaller JB, ...,
Girondot M (2019) <doi:10.1007/s00227-019-3545-x> for tag-loss estimate, Hancock
J, ..., Girondot M (2019) <doi:10.1016/j.ecolmodel.2019.04.013> for nesting
history, Laloe J-O, ..., Girondot M, Hays GC (2020)
<doi:10.1007/s00227-020-03686-x> for aggregating several seasons.")
    (license license:gpl2)))

(define-public r-phenolocrop
  (package
    (name "r-phenolocrop")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phenolocrop" version))
       (sha256
        (base32 "07wihmqcj32c40a19ivmva7kmm6j79bkbng08j05cr1m21whz2y9"))))
    (properties `((upstream-name . "phenolocrop")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=phenolocrop")
    (synopsis "Time-Series Models to the Crop Phenology")
    (description
     "Fit a time-series model to a crop phenology data, such as time-series rice
canopy height.  This package returns the model parameters as the summary
statistics of crop phenology, and these parameters will be useful to
characterize the growth pattern of each cultivar and predict manually-measured
traits, such as days to heading and biomass.  Please see Taniguchi et al. (2022)
<doi:10.3389/fpls.2022.998803> for detail.  This package has been designed for
scientific use.  Use for commercial purposes shall not be allowed.")
    (license (license:fsdg-compatible "CC BY-NC 4.0"))))

(define-public r-phenofit
  (package
    (name "r-phenofit")
    (version "0.3.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phenofit" version))
       (sha256
        (base32 "1b6laz7idd4cbliidv0xqz0k1h5f2f0v9lz82qhsy7p7pm9zagi6"))))
    (properties `((upstream-name . "phenofit")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-zeallot
                             r-ucminf
                             r-stringr
                             r-rcpparmadillo
                             r-rcpp
                             r-purrr
                             r-optimx
                             r-numderiv
                             r-magrittr
                             r-lubridate
                             r-gridextra
                             r-ggplot2
                             r-dplyr
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/eco-hydro/phenofit")
    (synopsis "Extract Remote Sensing Vegetation Phenology")
    (description
     "The merits of TIMESAT and phenopix are adopted.  Besides, a simple and growing
season dividing method and a practical snow elimination method based on
Whittaker were proposed.  7 curve fitting methods and 4 phenology extraction
methods were provided.  Parameters boundary are considered for every curve
fitting methods according to their ecological meaning.  And optimx is used to
select best optimization method for different curve fitting methods.  Reference:
Kong, D., (2020).  R package: A state-of-the-art Vegetation Phenology extraction
package, phenofit version 0.3.1, <doi:10.5281/zenodo.5150204>; Kong, D., Zhang,
Y., Wang, D., Chen, J., & Gu, X. (2020).  Photoperiod Explains the
Asynchronization Between Vegetation Carbon Phenology and Vegetation Greenness
Phenology.  Journal of Geophysical Research: Biogeosciences, 125(8),
e2020JG005636. <doi:10.1029/2020JG005636>; Kong, D., Zhang, Y., Gu, X., & Wang,
D. (2019).  A robust method for reconstructing global MODIS EVI time series on
the Google Earth Engine.  ISPRS Journal of Photogrammetry and Remote Sensing,
155, 13â24; Zhang, Q., Kong, D., Shi, P., Singh, V.P., Sun, P., 2018.
Vegetation phenology on the Qinghai-Tibetan Plateau and its response to climate
change (1982â2013).  Agric.  For.  Meteorol.  248, 408â417.
<doi:10.1016/j.agrformet.2017.10.026>.")
    (license (list license:gpl2
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-phenocdm
  (package
    (name "r-phenocdm")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phenoCDM" version))
       (sha256
        (base32 "1cyqwc36zwa2a4ljy0xnqz0hhw0r98qk2an5y002lrchkqydplwp"))))
    (properties `((upstream-name . "phenoCDM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjags))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=phenoCDM")
    (synopsis
     "Continuous Development Models for Incremental Time-Series Analysis")
    (description
     "Using the Bayesian state-space approach, we developed a continuous development
model to quantify dynamic incremental changes in the response variable.  While
the model was originally developed for daily changes in forest green-up, the
model can be used to predict any similar process.  The CDM can capture both
timing and rate of nonlinear processes.  Unlike statics methods, which aggregate
variations into a single metric, our dynamic model tracks the changing impacts
over time.  The CDM accommodates nonlinear responses to variation in predictors,
which changes throughout development.")
    (license license:expat)))

(define-public r-phenocamr
  (package
    (name "r-phenocamr")
    (version "1.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phenocamr" version))
       (sha256
        (base32 "1w9lzlmp4q2x12qhp4xmschcd79razy5fybq94381l7dshrwvzln"))))
    (properties `((upstream-name . "phenocamr")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-modistools
                             r-memoise
                             r-jsonlite
                             r-httr
                             r-daymetr
                             r-changepoint))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bluegreen-labs/phenocamr")
    (synopsis
     "Facilitates 'PhenoCam' Data Access and Time Series Post-Processing")
    (description
     "Programmatic interface to the @code{PhenoCam} web services
(<https://phenocam.nau.edu/webcam>).  Allows for easy downloading of
@code{PhenoCam} data directly to your R workspace or your computer and provides
post-processing routines for consistent and easy timeseries outlier detection,
smoothing and estimation of phenological transition dates.  Methods for this
package are described in detail in Hufkens et.  al (2018)
<doi:10.1111/2041-210X.12970>.")
    (license license:agpl3)))

(define-public r-pheno
  (package
    (name "r-pheno")
    (version "1.7-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pheno" version))
       (sha256
        (base32 "1hg2gqz3ibawn5ah1hffgfp15cis75nwd04jha09mjpkn1w8h9lg"))))
    (properties `((upstream-name . "pheno")))
    (build-system r-build-system)
    (propagated-inputs (list r-sparsem r-quantreg r-nlme))
    (home-page "https://cran.r-project.org/package=pheno")
    (synopsis "Auxiliary Functions for Phenological Data Analysis")
    (description
     "This package provides some easy-to-use functions for time series analyses of
(plant-) phenological data sets.  These functions mainly deal with the
estimation of combined phenological time series and are usually wrappers for
functions that are already implemented in other R packages adapted to the
special structure of phenological data and the needs of phenologists.  Some date
conversion functions to handle Julian dates are also provided.")
    (license license:gpl2+)))

(define-public r-phenmodel
  (package
    (name "r-phenmodel")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phenModel" version))
       (sha256
        (base32 "07jsc8jiwgxvhv4m6nr7llm22zzh5hd3pakw5c93jzim2835zvkp"))))
    (properties `((upstream-name . "phenModel")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape r-ggplot2 r-dplyr))
    (home-page "https://cran.r-project.org/package=phenModel")
    (synopsis "Insect Phenology Model Evaluation Based on Daily Temperatures")
    (description
     "Generates predicted stage change days for an insect, based on daily temperatures
and development rate parameters, as developed by Pollard (2014)
<http://mural.maynoothuniversity.ie/view/ethesisauthor/Pollard=3ACiaran_P=2E=3A=3A.html>.
 A few example datasets are included and implemented for P. vulgatissima, the
blue willow beetle, but the approach can be readily applied to other species
that display similar behaviour.")
    (license license:gpl2+)))

(define-public r-phenmod
  (package
    (name "r-phenmod")
    (version "1.2-7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phenmod" version))
       (sha256
        (base32 "1gxag58gbm3xmy9w0qx7j70bgdy8bwwjh2xz1iffbghvshximi1s"))))
    (properties `((upstream-name . "phenmod")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer r-pheno r-lattice r-gstat))
    (home-page "https://cran.r-project.org/package=phenmod")
    (synopsis
     "Auxiliary Functions for Phenological Data Processing, Modelling and Result Handling")
    (description
     "This package provides functions for phenological data preprocessing, modelling
and result handling.  For more information, please refer to Lange et al. (2016)
<doi:10.1007/s00484-016-1161-8>.")
    (license license:gpl2+)))

(define-public r-phenix
  (package
    (name "r-phenix")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PHENIX" version))
       (sha256
        (base32 "1gd9ycrwawi81al4f5v559km1112giwqnwvvk8ynj4kdb5bqpyq4"))))
    (properties `((upstream-name . "PHENIX")))
    (build-system r-build-system)
    (propagated-inputs (list r-suppdists r-ppcor))
    (home-page "https://cran.r-project.org/package=PHENIX")
    (synopsis "Phenotypic Integration Index")
    (description
     "This package provides functions to estimate the size-controlled phenotypic
integration index, a novel method by Torices & MÃ©ndez (2014) to solve problems
due to individual size when estimating integration (namely, larger individuals
have larger components, which will drive a correlation between components only
due to resource availability that might obscure the observed measures of
integration).  In addition, the package also provides the classical estimation
by Wagner (1984), bootstrapping and jackknife methods to calculate confidence
intervals and a significance test for both integration indices.")
    (license license:gpl2+)))

(define-public r-phenex
  (package
    (name "r-phenex")
    (version "1.4-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phenex" version))
       (sha256
        (base32 "00lsymflbmlxzxz8rxcpc94pc9zmhybial9f2xkz98h6h45zgrjl"))))
    (properties `((upstream-name . "phenex")))
    (build-system r-build-system)
    (propagated-inputs (list r-foreach r-deoptim))
    (home-page "https://cran.r-project.org/package=phenex")
    (synopsis "Auxiliary Functions for Phenological Data Analysis")
    (description
     "This package provides some easy-to-use functions for spatial analyses of
(plant-) phenological data sets and satellite observations of vegetation.")
    (license license:gpl2+)))

(define-public r-phenesse
  (package
    (name "r-phenesse")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phenesse" version))
       (sha256
        (base32 "1f5m8pq1n6x9ic4b0cb7j3dwfn1xfkbbpn0zr32frkiai3jmzv3j"))))
    (properties `((upstream-name . "phenesse")))
    (build-system r-build-system)
    (propagated-inputs (list r-fitdistrplus r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mbelitz/phenesse")
    (synopsis "Estimate Phenological Metrics using Presence-Only Data")
    (description
     "Generates Weibull-parameterized estimates of phenology for any percentile of a
distribution using the framework established in Cooke (1979)
<doi:10.1093/biomet/66.2.367>.  Extensive testing against other estimators
suggest the weib_percentile() function is especially useful in generating more
accurate and less biased estimates of onset and offset (Belitz et al.  2020
<doi.org:10.1111/2041-210X.13448>.  Non-parametric bootstrapping can be used to
generate confidence intervals around those estimates, although this is
computationally expensive.  Additionally, this package offers an easy way to
perform non-parametric bootstrapping to generate confidence intervals for
quantile estimates, mean estimates, or any statistical function of interest.")
    (license license:cc0)))

(define-public r-pheindicatormethods
  (package
    (name "r-pheindicatormethods")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PHEindicatormethods" version))
       (sha256
        (base32 "0gjlq8vnfgc5hlscy64ypx08vafwvc2xbk971nznby4v2fkkm1h3"))))
    (properties `((upstream-name . "PHEindicatormethods")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-dplyr
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PHEindicatormethods")
    (synopsis "Common Public Health Statistics and their Confidence Intervals")
    (description
     "This package provides functions to calculate commonly used public health
statistics and their confidence intervals using methods approved for use in the
production of Public Health England indicators such as those presented via
Fingertips (<http://fingertips.phe.org.uk/>).  It provides functions for the
generation of proportions, crude rates, means, directly standardised rates,
indirectly standardised rates, standardised mortality ratios, slope and relative
index of inequality and life expectancy.  Statistical methods are referenced in
the following publications.  Breslow NE, Day NE (1987)
<doi:10.1002/sim.4780080614>.  Dobson et al (1991) <doi:10.1002/sim.4780100317>.
 Armitage P, Berry G (2002) <doi:10.1002/9780470773666>.  Wilson EB. (1927)
<doi:10.1080/01621459.1927.10502953>.  Altman DG et al (2000, ISBN:
978-0-727-91375-3).  Chiang CL. (1968, ISBN: 978-0-882-75200-6).  Newell C.
(1994, ISBN: 978-0-898-62451-9).  Eayres DP, Williams ES (2004)
<doi:10.1136/jech.2003.009654>.  Silcocks PBS et al (2001)
<doi:10.1136/jech.55.1.38>.  Low and Low (2004) <doi:10.1093/pubmed/fdh175>.")
    (license license:gpl3)))

(define-public r-phecodemap
  (package
    (name "r-phecodemap")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phecodemap" version))
       (sha256
        (base32 "12dhyzazdsf938zp7i4ckbh2h5fvxg209mb4mq319akp34746h50"))))
    (properties `((upstream-name . "phecodemap")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinydashboardplus
                             r-shinydashboard
                             r-shinycssloaders
                             r-shinybs
                             r-shiny
                             r-rintrojs
                             r-readr
                             r-purrr
                             r-plotly
                             r-golem
                             r-dt
                             r-dplyr
                             r-config
                             r-collapsibletree))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/celehs/phecodemap")
    (synopsis
     "Visualization for PheCode Mapping with ICD-9 and ICD-10-CM Codes")
    (description
     "To build a shiny app for visualization of the hierarchy of @code{PheCode}
Mapping with International Classification of Diseases (ICD).  The same
@code{PheCode} hierarchy is displayed in two ways: as a sunburst plot and as a
tree.")
    (license license:gpl3+)))

(define-public r-phecap
  (package
    (name "r-phecap")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PheCAP" version))
       (sha256
        (base32 "00dfy0iby21kqfxz2npz4dn1klxcdlll8xrk2hsmc56fjfidbdhz"))))
    (properties `((upstream-name . "PheCAP")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmysql r-glmnet))
    (native-inputs (list r-knitr))
    (home-page "https://celehs.github.io/PheCAP/")
    (synopsis
     "High-Throughput Phenotyping with EHR using a Common Automated Pipeline")
    (description
     "Implement surrogate-assisted feature extraction (SAFE) and common machine
learning approaches to train and validate phenotyping models.  Background and
details about the methods can be found at Zhang et al. (2019)
<doi:10.1038/s41596-019-0227-6>, Yu et al. (2017) <doi:10.1093/jamia/ocw135>,
and Liao et al. (2015) <doi:10.1136/bmj.h1885>.")
    (license license:gpl3)))

(define-public r-pheble
  (package
    (name "r-pheble")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pheble" version))
       (sha256
        (base32 "14q66ldpksxsh5q7mpgp4d9p43kbx1v4yqd35p3gqi6qxrlb5q1z"))))
    (properties `((upstream-name . "pheble")))
    (build-system r-build-system)
    (propagated-inputs (list r-themis
                             r-sparselda
                             r-rpartscore
                             r-randomforest
                             r-pls
                             r-party
                             r-nnet
                             r-mlmetrics
                             r-mda
                             r-matrix
                             r-mass
                             r-magrittr
                             r-klar
                             r-kknn
                             r-kernlab
                             r-ipred
                             r-hdclassif
                             r-hda
                             r-gmodels
                             r-glmnet
                             r-frbs
                             r-evtree
                             r-earth
                             r-e1071
                             r-dplyr
                             r-doparallel
                             r-data-table
                             r-catools
                             r-caret
                             r-c50
                             r-adabag))
    (home-page "https://cran.r-project.org/package=pheble")
    (synopsis "Classifying High-Dimensional Phenotypes with Ensemble Learning")
    (description
     "This package provides a system for binary and multi-class classification of
high-dimensional phenotypic data using ensemble learning.  By combining
predictions from different classification models, this package attempts to
improve performance over individual learners.  The pre-processing, training,
validation, and testing are performed end-to-end to minimize user input and
simplify the process of classification.")
    (license license:gpl3+)))

(define-public r-phdcocktail
  (package
    (name "r-phdcocktail")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phdcocktail" version))
       (sha256
        (base32 "02gsyjqmafl5aw18lzd54ldfy521r9r9iwqlc44yqn70pi0i8rrs"))))
    (properties `((upstream-name . "phdcocktail")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-rstudioapi r-rcolorbrewer r-here
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://dahhamalsoud.github.io/phdcocktail/")
    (synopsis "Enhance the Ease of R Experience as an Emerging Researcher")
    (description
     "This package provides a toolkit of functions to help: i) effortlessly transform
collected data into a publication ready format, ii) generate insightful
visualizations from clinical data, iii) report summary statistics in a
publication-ready format, iv) efficiently export, save and reload R objects
within the framework of R projects.")
    (license license:expat)))

(define-public r-phd
  (package
    (name "r-phd")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phd" version))
       (sha256
        (base32 "14ck98h8ivaw88ybl5pwv70s5jwhkhdgmxg7q0b5bphj8hyf616r"))))
    (properties `((upstream-name . "phd")))
    (build-system r-build-system)
    (propagated-inputs (list r-glmnet))
    (home-page "https://cran.r-project.org/package=phd")
    (synopsis "Permutation Testing in High-Dimensional Linear Models")
    (description
     "This package provides permutation methods for testing in high-dimensional linear
models.  The tests are often robust against heteroscedasticity and non-normality
and usually perform well under anti-sparsity.  See Hemerik, Thoresen and Finos
(2021) <doi:10.1080/00949655.2020.1836183>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-phclust
  (package
    (name "r-phclust")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PHclust" version))
       (sha256
        (base32 "18jby44hnk132l0wviqb0wi6nxkg1xisqjh8cxpss2rhwah3l4h1"))))
    (properties `((upstream-name . "PHclust")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PHclust")
    (synopsis "Poisson Hurdle Clustering for Sparse Microbiome Data")
    (description
     "Clustering analysis for sparse microbiome data, based on a Poisson hurdle model.")
    (license license:gpl3)))

(define-public r-phater
  (package
    (name "r-phater")
    (version "1.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phateR" version))
       (sha256
        (base32 "16xb6fy0aa024054jwfbavjbzhwj6g4p6yrzy1lnzvpn8kb23hn5"))))
    (properties `((upstream-name . "phateR")))
    (build-system r-build-system)
    (propagated-inputs (list r-reticulate r-memoise r-matrix r-ggplot2))
    (home-page "https://cran.r-project.org/package=phateR")
    (synopsis
     "PHATE - Potential of Heat-Diffusion for Affinity-Based Transition Embedding")
    (description
     "PHATE is a tool for visualizing high dimensional single-cell data with natural
progressions or trajectories.  PHATE uses a novel conceptual framework for
learning and visualizing the manifold inherent to biological systems in which
smooth transitions mark the progressions of cells from one state to another.  To
see how PHATE can be applied to single-cell RNA-seq datasets from hematopoietic
stem cells, human embryonic stem cells, and bone marrow samples, check out our
publication in Nature Biotechnology at <doi:10.1038/s41587-019-0336-3>.")
    (license (list license:gpl2
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-phasetyper
  (package
    (name "r-phasetyper")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PhaseTypeR" version))
       (sha256
        (base32 "063hl41jnzya189g8pm0kjvh7w4h31hfb4zhvrnh6nk4kr5xa70m"))))
    (properties `((upstream-name . "PhaseTypeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-igraph r-expm))
    (native-inputs (list r-knitr))
    (home-page "https://rivasiker.github.io/PhaseTypeR/")
    (synopsis "General-Purpose Phase-Type Functions")
    (description
     "General implementation of core function from phase-type theory.
@code{PhaseTypeR} can be used to model continuous and discrete phase-type
distributions, both univariate and multivariate.  The package includes functions
for outputting the mean and (co)variance of phase-type distributions; their
density, probability and quantile functions; functions for random draws;
functions for reward-transformation; and functions for plotting the
distributions as networks.  For more information on these functions please refer
to Bladt and Nielsen (2017, ISBN: 978-1-4939-8377-3) and Campillo Navarro (2019)
<https://orbit.dtu.dk/en/publications/order-statistics-and-multivariate-discrete-phase-type-distributio>.")
    (license license:gpl3)))

(define-public r-phasetype
  (package
    (name "r-phasetype")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PhaseType" version))
       (sha256
        (base32 "1ls652wiy8wfzj08pyi76ghfiv55i5x40drb547hnnh1f0583i0x"))))
    (properties `((upstream-name . "PhaseType")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape r-ggplot2 r-coda))
    (home-page "https://www.louisaslett.com/PhaseType/")
    (synopsis "Inference for Phase-Type Distributions")
    (description
     "This package provides functions to perform Bayesian inference on absorption time
data for Phase-type distributions.  The methods of Bladt et al (2003)
<doi:10.1080/03461230110106435> and Aslett (2012)
<https://www.louisaslett.com/@code{PhD_Thesis.pdf>} are provided.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-phaser
  (package
    (name "r-phaser")
    (version "2.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phaseR" version))
       (sha256
        (base32 "1gq882r4jkq8f0xm3qmjh4zx540sgpdhlj8894dhf5g6vhgaa1kd"))))
    (properties `((upstream-name . "phaseR")))
    (build-system r-build-system)
    (propagated-inputs (list r-desolve))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mjg211/phaseR")
    (synopsis
     "Phase Plane Analysis of One- And Two-Dimensional Autonomous ODE Systems")
    (description
     "This package performs a qualitative analysis of one- and two-dimensional
autonomous ordinary differential equation systems, using phase plane methods.
Programs are available to identify and classify equilibrium points, plot the
direction field, and plot trajectories for multiple initial conditions.  In the
one-dimensional case, a program is also available to plot the phase portrait.
Whilst in the two-dimensional case, programs are additionally available to plot
nullclines and stable/unstable manifolds of saddle points.  Many example systems
are provided for the user.  For further details can be found in Grayling (2014)
<doi:10.32614/RJ-2014-023>.")
    (license license:expat)))

(define-public r-phase1rmd
  (package
    (name "r-phase1rmd")
    (version "1.0.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phase1RMD" version))
       (sha256
        (base32 "0dnyqa6jias2jqjqjpiq32jnd21ghb2shw45vdq8b5xyb8rxclwj"))))
    (properties `((upstream-name . "phase1RMD")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjags
                             r-mvtnorm
                             r-ggplot2
                             r-coda
                             r-boot
                             r-arrayhelpers))
    (home-page "https://cran.r-project.org/package=phase1RMD")
    (synopsis "Repeated Measurement Design for Phase I Clinical Trial")
    (description
     "This package implements our Bayesian phase I repeated measurement design that
accounts for multidimensional toxicity endpoints from multiple treatment cycles.
 The package also provides a novel design to account for both multidimensional
toxicity endpoints and early-stage efficacy endpoints in the phase I design.
For both designs, functions are provided to recommend the next dosage selection
based on the data collected in the available patient cohorts and to simulate
trial characteristics given design parameters.  Yin, Jun, et al. (2017)
<doi:10.1002/sim.7134>.")
    (license license:gpl2+)))

(define-public r-phase1prmd
  (package
    (name "r-phase1prmd")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phase1PRMD" version))
       (sha256
        (base32 "0gr73nsp0an0ci6l86h9fw6l791jdw0b1497qi58w25zypggdyac"))))
    (properties `((upstream-name . "phase1PRMD")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-rjags
                             r-reshape2
                             r-rcolorbrewer
                             r-plyr
                             r-mass
                             r-knitr
                             r-kableextra
                             r-gridextra
                             r-ggplot2
                             r-dplyr
                             r-coda
                             r-arrayhelpers))
    (home-page "https://cran.r-project.org/package=phase1PRMD")
    (synopsis
     "Personalized Repeated Measurement Design for Phase I Clinical Trials")
    (description
     "This package implements Bayesian phase I repeated measurement design that
accounts for multidimensional toxicity endpoints and longitudinal efficacy
measure from multiple treatment cycles.  The package provides flags to fit a
variety of model-based phase I design, including 1 stage models with or without
individualized dose modification, 3-stage models with or without individualized
dose modification, etc.  Functions are provided to recommend dosage selection
based on the data collected in the available patient cohorts and to simulate
trial characteristics given design parameters.  Yin, Jun, et al. (2017)
<doi:10.1002/sim.7134>.")
    (license license:gpl2+)))

(define-public r-phase12compare
  (package
    (name "r-phase12compare")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Phase12Compare" version))
       (sha256
        (base32 "0n9rjgdllpnj451a8d4p8x3mjf23czcmk7cn1c9v3l7mg4jn18h2"))))
    (properties `((upstream-name . "Phase12Compare")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mvtnorm))
    (home-page "https://cran.r-project.org/package=Phase12Compare")
    (synopsis
     "Simulates SPSO and Efftox Phase 12 Trials with Correlated Outcomes")
    (description
     "Simulating and conducting four phase 12 clinical trials with correlated binary
bivariate outcomes described.  Uses the Efftox (efficacy and toxicity tradeoff,
<https://biostatistics.mdanderson.org/@code{SoftwareDownload/SingleSoftware/Index/2>})
and SPSO (Semi-Parametric Stochastic Ordering) models with Utility and
Desirability based objective functions for dose finding.")
    (license license:gpl2)))

(define-public r-phase123
  (package
    (name "r-phase123")
    (version "2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Phase123" version))
       (sha256
        (base32 "0bz867wsnrflzlzrql1vgacymx70rb9wik9jw3g34i2vigpl8x8i"))))
    (properties `((upstream-name . "Phase123")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=Phase123")
    (synopsis "Simulating and Conducting Phase 123 Trials")
    (description
     "This package contains three simulation functions for implementing the entire
Phase 123 trial and the separate Eff-Tox and Phase 3 portions of the trial,
which may be beneficial for use on clusters.  The functions
@code{AssignEffTox}() and @code{RandomizeEffTox}() assign doses to patient
cohorts during phase 12 and Reoptimize() determines the optimal dose to continue
with during Phase 3.  The functions @code{ReturnMeansAgent}() and
@code{ReturnMeanControl}() gives the true mean survival for the agent doses and
control and @code{ReturnOCS}() gives the operating characteristics of the
design.")
    (license license:gpl2)))

(define-public r-phase
  (package
    (name "r-phase")
    (version "1.2.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phase" version))
       (sha256
        (base32 "12v453jn99jzvicr5xipzkgb1yqmrkv9gbpw7ykl19kakw7qnqs3"))))
    (properties `((upstream-name . "phase")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-zeitgebr
                             r-wesanderson
                             r-signal
                             r-shinythemes
                             r-shinyfiles
                             r-shinydashboard
                             r-shinycssloaders
                             r-shiny
                             r-pracma
                             r-plotly
                             r-lubridate
                             r-circular
                             r-behavr))
    (home-page "https://cran.r-project.org/package=phase")
    (synopsis "Analyse Biological Time-Series Data")
    (description
     "Compiles functions to trim, bin, visualise, and analyse activity/sleep
time-series data collected from the Drosophila Activity Monitor (DAM) system
(Trikinetics, USA).  The following methods were used to compute periodograms -
Chi-square periodogram: Sokolove and Bushell (1978)
<doi:10.1016/0022-5193(78)90022-X>, Lomb-Scargle periodogram: Lomb (1976)
<doi:10.1007/BF00648343>, Scargle (1982) <doi:10.1086/160554> and Ruf (1999)
<doi:10.1076/brhm.30.2.178.1422>, and Autocorrelation: Eijzenbach et al. (1986)
<doi:10.1111/j.1440-1681.1986.tb00943.x>.  Identification of activity peaks is
done after using a Savitzky-Golay filter (Savitzky and Golay (1964)
<doi:10.1021/ac60214a047>) to smooth raw activity data.  Three methods to
estimate anticipation of activity are used based on the following papers - Slope
method: Fernandez et al. (2020) <doi:10.1016/j.cub.2020.04.025>, Harrisingh
method: Harrisingh et al. (2007) <doi:10.1523/JNEUROSCI.3680-07.2007>, and
Stoleru method: Stoleru et al. (2004) <doi:10.1038/nature02926>.  Rose plots and
circular analysis are based on methods from - Batschelet (1981)
<ISBN:0120810506> and Zar (2010) <ISBN:0321656865>.")
    (license license:expat)))

(define-public r-pharmr
  (package
    (name "r-pharmr")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pharmr" version))
       (sha256
        (base32 "0vxaas0rrzfcia47bhqd54d2sjnwgx3djbfy9ggf2hr3cw3dz7fg"))))
    (properties `((upstream-name . "pharmr")))
    (build-system r-build-system)
    (inputs (list python))
    (propagated-inputs (list r-reticulate r-altair))
    (home-page "https://github.com/pharmpy/pharmr")
    (synopsis "Interface to the 'Pharmpy' 'Pharmacometrics' Library")
    (description
     "Interface to the Pharmpy pharmacometrics library.  The Reticulate package is
used to interface Python from R.")
    (license license:lgpl3+)))

(define-public r-pharmaversesdtm
  (package
    (name "r-pharmaversesdtm")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pharmaversesdtm" version))
       (sha256
        (base32 "0nd6v6lg8a05z83f1h2wqan5vq7p26hvkrjh36f6521aakw4if74"))))
    (properties `((upstream-name . "pharmaversesdtm")))
    (build-system r-build-system)
    (home-page "https://pharmaverse.github.io/pharmaversesdtm/")
    (synopsis "SDTM Test Data for the 'Pharmaverse' Family of Packages")
    (description
     "This package provides a set of Study Data Tabulation Model (SDTM) datasets from
the Clinical Data Interchange Standards Consortium (CDISC) pilot project used
for testing and developing Analysis Data Model (A@code{DaM}) datasets inside the
pharmaverse family of packages.  SDTM dataset specifications are described in:
CDISC Submission Data Standards Team (2021)
<https://www.cdisc.org/system/files/members/standard/foundational/SDTMIG%20v3.4-FINAL_2022-07-21.pdf>.")
    (license license:asl2.0)))

(define-public r-pharmaverseadam
  (package
    (name "r-pharmaverseadam")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pharmaverseadam" version))
       (sha256
        (base32 "1nwm0mbaaca5ry449h1llvv44wcy2lgd4bs3wfacb9ag2pfgglc8"))))
    (properties `((upstream-name . "pharmaverseadam")))
    (build-system r-build-system)
    (home-page "https://pharmaverse.github.io/pharmaverseadam/")
    (synopsis "ADaM Test Data for the 'Pharmaverse' Family of Packages")
    (description
     "This package provides a set of Analysis Data Model (A@code{DaM}) datasets
constructed using the Study Data Tabulation Model (SDTM) datasets contained in
the pharmaversesdtm package and the template scripts from the admiral family of
packages.  A@code{DaM} dataset specifications are described in: CDISC Analysis
Data Model Team (2021)
<https://www.cdisc.org/system/files/members/standard/foundational/A@code{DaMIG_v1.3.pdf>}.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-pharmaverse
  (package
    (name "r-pharmaverse")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pharmaverse" version))
       (sha256
        (base32 "0wvil5fswdzfvmqii5j7y81ik2bwjznywb8c9nj3yd99az96qxg4"))))
    (properties `((upstream-name . "pharmaverse")))
    (build-system r-build-system)
    (propagated-inputs (list r-httr))
    (home-page "https://github.com/pharmaverse/pharmaverse-pkg")
    (synopsis "Navigate 'Pharmaverse'")
    (description
     "The pharmaverse is a set of packages that compose multiple pathways through
clinical data generation and reporting in the pharmaceutical industry.  This
package is designed to guide users to our work-spaces on @code{GitHub}', Slack
and @code{LinkedIn} as well as our website and examples.  Learn more about the
pharmaverse at <https://pharmaverse.org>.")
    (license license:expat)))

(define-public r-pharmartf
  (package
    (name "r-pharmartf")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pharmaRTF" version))
       (sha256
        (base32 "1h16r4yhlm6mbwq3k24ssm70bsrg2hvykqh8dayigz5wi2nq5c1f"))))
    (properties `((upstream-name . "pharmaRTF")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-purrr r-huxtable r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pharmaRTF")
    (synopsis "Enhanced RTF Wrapper for Use with Existing Table Packages")
    (description
     "Enhanced RTF wrapper written in R for use with existing R tables packages such
as Huxtable or GT'.  This package fills a gap where tables in certain packages
can be written out to RTF, but cannot add certain metadata or features to the
document that are required/expected in a report for a regulatory submission,
such as multiple levels of titles and footnotes, making the document landscape,
and controlling properties such as margins.")
    (license license:expat)))

(define-public r-phantsem
  (package
    (name "r-phantsem")
    (version "1.0.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phantSEM" version))
       (sha256
        (base32 "099kflbj66wfkl7b6f1l68763b7qkpjihp4r1xih5lvjwc8ap0b9"))))
    (properties `((upstream-name . "phantSEM")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-lavaan r-dplyr r-corpcor))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=phantSEM")
    (synopsis
     "Create Phantom Variables in Structural Equation Models for Sensitivity Analyses")
    (description
     "Create phantom variables, which are variables that were not observed, for the
purpose of sensitivity analyses for structural equation models.  The package
makes it easier for a user to test different combinations of covariances between
the phantom variable(s) and observed variables.  The package may be used to
assess a model's or effect's sensitivity to temporal bias (e.g., if
cross-sectional data were collected) or confounding bias.")
    (license license:expat)))

(define-public r-phagecocktail
  (package
    (name "r-phagecocktail")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PhageCocktail" version))
       (sha256
        (base32 "0lr1gnwpaqrf8ny5ssx2cjv8hi6q2ilmr7qif3jl9agh4w0nkp89"))))
    (properties `((upstream-name . "PhageCocktail")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-smerc
                             r-rjsonio
                             r-readxl
                             r-factoextra
                             r-bipartite))
    (home-page "https://cran.r-project.org/package=PhageCocktail")
    (synopsis "Design of the Best Phage Cocktail")
    (description
     "There are 4 possible methods: \"@code{ExhaustiveSearch}\"; \"@code{ExhaustivePhi}\";
\"@code{ClusteringSearch}\"; and \"@code{ClusteringPhi}\".
\"@code{ExhaustiveSearch\"-->} gives you the best phage cocktail from a
phage-bacteria infection network.  It checks different phage cocktail sizes from
1 to 7 and only stops before if it lyses all bacteria.  Other option is when
users have decided not to obtain a phage cocktail size higher than a limit
value. \"@code{ExhaustivePhi\"-->} firstly, it finds Phi out.  Phi is a formula
indicating the necessary phage cocktail size.  Phi needs nestedness temperature
and fill, which are internally calculated.  This function will only look for the
best combination (phage cocktail) with a Phi size. \"@code{ClusteringSearch\"-->}
firstly, an agglomerative hierarchical clustering using Ward's algorithm is
calculated for phages.  They will be clustered according to bacteria lysed by
them. @code{PhageCocktail}() chooses how many clusters are needed in order to
select 1 phage per cluster.  Using the phages selected during the clustering, it
checks different phage cocktail sizes from 1 to 7 and only stops before if it
lyses all bacteria.  Other option is when users have decided not to obtain a
phage cocktail size higher than a limit value. \"@code{ClusteringPhi\"-->}
firstly, an agglomerative hierarchical clustering using Ward's algorithm is
calculated for phages.  They will be clustered according to bacteria lysed by
them. @code{PhageCocktail}() chooses how many clusters are needed in order to
select 1 phage per cluster.  Once the function has one phage per cluster, it
calculates Phi.  If the number of clusters is less than Phi number, it will be
changed to obtain, as minimum, this quantity of candidates (phages).  Then, it
calculates the best combination of Phi phages using those selected during the
clustering with Ward algorithm.  If you use @code{PhageCocktail}, please cite it
as: \"@code{PhageCocktail}: An R Package to Design Phage Cocktails from
Experimental Phage-Bacteria Infection Networks\".  MarÃ­a Victoria DÃ­az-GaliÃ¡n,
Miguel A. Vega-RodrÃ­guez, Felipe Molina.  Computer Methods and Programs in
Biomedicine, 221, 106865, Elsevier Ireland, Clare, Ireland, 2022, pp.  1-9,
ISSN: 0169-2607. <doi:10.1016/j.cmpb.2022.106865>.")
    (license license:gpl3)))

(define-public r-phacking
  (package
    (name "r-phacking")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "phacking" version))
       (sha256
        (base32 "0spx3aq35mpd6z87zfn4019jnazang45w3ly2p8kgxcp0wqj67h8"))))
    (properties `((upstream-name . "phacking")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rlang
                             r-rdpack
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-purrr
                             r-metafor
                             r-metabias
                             r-ggplot2
                             r-dplyr
                             r-bh))
    (home-page "https://github.com/mathurlabstanford/phacking")
    (synopsis "Sensitivity Analysis for p-Hacking in Meta-Analyses")
    (description
     "Fits right-truncated meta-analysis (RTMA), a bias correction for the joint
effects of p-hacking (i.e., manipulation of results within studies to obtain
significant, positive estimates) and traditional publication bias (i.e., the
selective publication of studies with significant, positive results) in
meta-analyses [see Mathur MB (2022). \"Sensitivity analysis for p-hacking in
meta-analyses.\" <doi:10.31219/osf.io/ezjsx>.].  Unlike publication bias alone,
p-hacking that favors significant, positive results (termed \"affirmative\") can
distort the distribution of affirmative results.  To bias-correct results from
affirmative studies would require strong assumptions on the exact nature of
p-hacking.  In contrast, joint p-hacking and publication bias do not distort the
distribution of published nonaffirmative results when there is stringent
p-hacking (e.g., investigators who hack always eventually obtain an affirmative
result) or when there is stringent publication bias (e.g., nonaffirmative
results from hacked studies are never published).  This means that any published
nonaffirmative results are from unhacked studies.  Under these assumptions, RTMA
involves analyzing only the published nonaffirmative results to essentially
impute the full underlying distribution of all results prior to selection due to
p-hacking and/or publication bias.  The package also provides diagnostic plots
described in Mathur (2022).")
    (license license:expat)))

(define-public r-ph2mult
  (package
    (name "r-ph2mult")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ph2mult" version))
       (sha256
        (base32 "0w3w18fkr9xzv6mpb00cz5id33zgsnl6s8zgjpchzvrhmpjpp437"))))
    (properties `((upstream-name . "ph2mult")))
    (build-system r-build-system)
    (propagated-inputs (list r-clinfun))
    (home-page "https://cran.r-project.org/package=ph2mult")
    (synopsis "Phase II Clinical Trial Design for Multinomial Endpoints")
    (description
     "Provide multinomial design methods under intersection-union test (IUT) and
union-intersection test (UIT) scheme for Phase II trial.  The design types
include : Minimax (minimize the maximum sample size), Optimal (minimize the
expected sample size), Admissible (minimize the Bayesian risk) and Maxpower
(maximize the exact power level).")
    (license license:gpl2+)))

(define-public r-ph2bye
  (package
    (name "r-ph2bye")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ph2bye" version))
       (sha256
        (base32 "0h5d8d4bagkp6g0nb67cb225ki5p1amnclnfj6afgdcsli5clr67"))))
    (properties `((upstream-name . "ph2bye")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam r-rcpp r-nleqslv r-animation))
    (home-page "https://allen.shinyapps.io/BayesDesign/")
    (synopsis "Phase II Clinical Trial Design Using Bayesian Methods")
    (description
     "Calculate the Bayesian posterior/predictive probability and determine the sample
size and stopping boundaries for single-arm Phase II design.")
    (license license:gpl2+)))

(define-public r-ph2bayes
  (package
    (name "r-ph2bayes")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ph2bayes" version))
       (sha256
        (base32 "1xm7930gycxrz308dnfnp023yidqik52sbrli35f3773mnfcid2g"))))
    (properties `((upstream-name . "ph2bayes")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=ph2bayes")
    (synopsis "Bayesian Single-Arm Phase II Designs")
    (description
     "An implementation of Bayesian single-arm phase II design methods for binary
outcome based on posterior probability (Thall and Simon (1994)
<doi:10.2307/2533377>) and predictive probability (Lee and Liu (2008)
<doi:10.1177/1740774508089279>).")
    (license license:gpl3)))

(define-public r-ph1xbar
  (package
    (name "r-ph1xbar")
    (version "0.11.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PH1XBAR" version))
       (sha256
        (base32 "0ikh8jrf2yq9f444wa5fkiwri5xfc9r19f7862p3s11xmax8dycn"))))
    (properties `((upstream-name . "PH1XBAR")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma r-mvtnorm r-forecast))
    (home-page "https://github.com/bolus123/PH1XBAR")
    (synopsis "Phase I Shewhart X-Bar Chart")
    (description
     "The purpose of PH1XBAR is to build a Phase I Shewhart control chart for the
basic Shewhart, the variance components and the ARMA models in R for subgrouped
and individual data.  More details can be found: Yao and Chakraborti (2020)
<doi: 10.1002/qre.2793>, Yao and Chakraborti (2021) <doi:
10.1080/08982112.2021.1878220>, and Yao et al. (2023) <doi:
10.1080/00224065.2022.2139783>.")
    (license license:gpl3)))

(define-public r-pgtools
  (package
    (name "r-pgtools")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pgTools" version))
       (sha256
        (base32 "1262q30ci6h10sk54s9wbadz6v22y4k6m32sclxw93kkvgvlkj6b"))))
    (properties `((upstream-name . "pgTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-toolbox r-stringi r-odbc r-dbi r-data-table))
    (home-page "https://github.com/tconwell/pgTools")
    (synopsis "Functions for Generating PostgreSQL Statements/Scripts")
    (description
     "Create @code{PostgreSQL} statements/scripts from R, optionally executing the SQL
statements.  Common SQL operations are included, although not every configurable
option is available at this time.  SQL output is intended to be compliant with
@code{PostgreSQL} syntax specifications. @code{PostgreSQL} documentation is
available here <https://www.postgresql.org/docs/current/index.html>.")
    (license license:gpl3+)))

(define-public r-pgsc
  (package
    (name "r-pgsc")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pgsc" version))
       (sha256
        (base32 "1y4szqn1jhp3yn8q738dpbhnqcqk45vgbmczamai154w3wb0qai7"))))
    (properties `((upstream-name . "pgsc")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-rcpparmadillo r-rcpp r-nloptr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/philipbarrett/pgsc")
    (synopsis "Computes Powell's Generalized Synthetic Control Estimator")
    (description
     "Computes the generalized synthetic control estimator described in Powell (2017)
<doi:10.7249/WR1142>.  Provides both point estimates, and hypothesis testing.")
    (license license:gpl2)))

(define-public r-pgrn
  (package
    (name "r-pgrn")
    (version "0.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pGRN" version))
       (sha256
        (base32 "1sy5nhwcn516q4pjlg96qqaqvs1ig7ixz4r54n9njkf5z92wnhfw"))))
    (properties `((upstream-name . "pGRN")))
    (build-system r-build-system)
    (propagated-inputs (list r-visnetwork
                             r-tidygraph
                             r-proxy
                             r-lmtest
                             r-igraph
                             r-ggraph
                             r-ggplot2
                             r-future
                             r-foreach
                             r-dtw
                             r-doparallel
                             r-bigmemory))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://cran.r-project.org/package=pGRN")
    (synopsis
     "Single-Cell RNA Sequencing Pseudo-Time Based Gene Regulatory Network Inference")
    (description
     "Inference and visualize gene regulatory network based on single-cell RNA
sequencing pseudo-time information.")
    (license license:expat)))

(define-public r-pgrdup
  (package
    (name "r-pgrdup")
    (version "0.2.3.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PGRdup" version))
       (sha256
        (base32 "0z2nnjlaazrmfnjmwhsmhp8h3ifgykmsy6znqvcl77v9565libz9"))))
    (properties `((upstream-name . "PGRdup")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringi
                             r-stringdist
                             r-igraph
                             r-gridextra
                             r-ggplot2
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PGRdup")
    (synopsis
     "Discover Probable Duplicates in Plant Genetic Resources Collections")
    (description
     "This package provides functions to aid the identification of probable/possible
duplicates in Plant Genetic Resources (PGR) collections using passport databases
comprising of information records of each constituent sample.  These include
methods for cleaning the data, creation of a searchable Key Word in Context
(KWIC) index of keywords associated with sample records and the identification
of nearly identical records with similar information by fuzzy, phonetic and
semantic matching of keywords.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-pgraph
  (package
    (name "r-pgraph")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pgraph" version))
       (sha256
        (base32 "1mfpyj4ahs5szkzzv5fs9g26651vcnign3mkfgp23g8nlrs50cqa"))))
    (properties `((upstream-name . "pgraph")))
    (build-system r-build-system)
    (propagated-inputs (list r-sam r-glmnet r-glasso r-energy))
    (home-page "https://cran.r-project.org/package=pgraph")
    (synopsis "Build Dependency Graphs using Projection")
    (description
     "This package implements a general framework for creating dependency graphs using
projection as introduced in Fan, Feng and Xia (2019)<@code{arXiv:1501.01617>}.
Both lasso and sparse additive model projections are implemented.  Both Pearson
correlation and distance covariance options are available to generate the graph.")
    (license license:gpl2)))

(define-public r-pgpx
  (package
    (name "r-pgpx")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pGPx" version))
       (sha256
        (base32 "037grwk41z5zdcw6nc004x5hj5s6q8y7zck417ylgyygsvgazhsf"))))
    (properties `((upstream-name . "pGPx")))
    (build-system r-build-system)
    (propagated-inputs (list r-rgenoud
                             r-rcpparmadillo
                             r-rcpp
                             r-randtoolbox
                             r-pracma
                             r-pbivnorm
                             r-kriginv
                             r-dicekriging))
    (home-page "https://doi.org/10.1137/141000749")
    (synopsis "Pseudo-Realizations for Gaussian Process Excursions")
    (description
     "Computes pseudo-realizations from the posterior distribution of a Gaussian
Process (GP) with the method described in Azzimonti et al. (2016)
<doi:10.1137/141000749>.  The realizations are obtained from simulations of the
field at few well chosen points that minimize the expected distance in measure
between the true excursion set of the field and the approximate one.  Also
implements a R interface for (the main function of) Distance Transform of
sampled Functions (<https://cs.brown.edu/people/pfelzens/dt/index.html>).")
    (license license:gpl3)))

(define-public r-pgnorm
  (package
    (name "r-pgnorm")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pgnorm" version))
       (sha256
        (base32 "1k9z7pvmranr8m62v7amc0pj6lwzh3wqi79gg3mflifn1mr6c057"))))
    (properties `((upstream-name . "pgnorm")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pgnorm")
    (synopsis "The p-Generalized Normal Distribution")
    (description
     "Evaluation of the pdf and the cdf of the univariate, noncentral, p-generalized
normal distribution.  Sampling from the univariate, noncentral, p-generalized
normal distribution using either the p-generalized polar method, the
p-generalized rejecting polar method, the Monty Python method, the Ziggurat
method or the method of Nardon and Pianca.  The package also includes routines
for the simulation of the bivariate, p-generalized uniform distribution and the
simulation of the corresponding angular distribution.")
    (license license:gpl2+)))

(define-public r-pgmm
  (package
    (name "r-pgmm")
    (version "1.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pgmm" version))
       (sha256
        (base32 "08fz5la19fnzsk80d1nsqg0fd27z3bq3s8kzizj11r5k1fn95sy8"))))
    (properties `((upstream-name . "pgmm")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pgmm")
    (synopsis "Parsimonious Gaussian Mixture Models")
    (description
     "Carries out model-based clustering or classification using parsimonious Gaussian
mixture models. @code{McNicholas} and Murphy (2008)
<doi:10.1007/s11222-008-9056-0>, @code{McNicholas} (2010)
<doi:10.1016/j.jspi.2009.11.006>, @code{McNicholas} and Murphy (2010)
<doi:10.1093/bioinformatics/btq498>, @code{McNicholas} et al. (2010)
<doi:10.1016/j.csda.2009.02.011>.")
    (license license:gpl2+)))

(define-public r-pgm2
  (package
    (name "r-pgm2")
    (version "1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PGM2" version))
       (sha256
        (base32 "03282pcq7gw47awc2mxjsbz6w3zpqjhqd7fzg8la4p00cd0vvmr4"))))
    (properties `((upstream-name . "PGM2")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PGM2")
    (synopsis "Nested Resolvable Designs and their Associated Uniform Designs")
    (description
     "Construction method of nested resolvable designs from a projective geometry
defined on Galois field of order 2.  The obtained Resolvable designs are used to
build uniform design.  The presented results are based on
<https://eudml.org/doc/219563> and A. Boudraa et al. (See references).")
    (license license:gpl3)))

(define-public r-pglm
  (package
    (name "r-pglm")
    (version "0.2-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pglm" version))
       (sha256
        (base32 "16ghdgnpwyqgfa6z6gc93bw933dvazb3alv1867cbn7qnp2gaj3j"))))
    (properties `((upstream-name . "pglm")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod r-plm r-maxlik r-formula))
    (home-page "https://cran.r-project.org/package=pglm")
    (synopsis "Panel Generalized Linear Models")
    (description
     "Estimation of panel models for glm-like models: this includes binomial models
(logit and probit), count models (poisson and negbin) and ordered models (logit
and probit), as described in: Baltagi (2013) Econometric Analysis of Panel Data,
ISBN-13:978-1-118-67232-7, Hsiao (2014) Analysis of Panel Data
<doi:10.1017/CBO9781139839327> and Croissant and Millo (2018), Panel Data
Econometrics with R, ISBN:978-1-118-94918-4.")
    (license license:gpl2+)))

(define-public r-pgkdesphere
  (package
    (name "r-pgkdesphere")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pgKDEsphere" version))
       (sha256
        (base32 "01ck72mjzpkihkifsg2ca3xxivq7xq47483pl7dwy8xfjm2s0csd"))))
    (properties `((upstream-name . "pgKDEsphere")))
    (build-system r-build-system)
    (propagated-inputs (list r-rotasym
                             r-rgl
                             r-rcpparmadillo
                             r-rcpp
                             r-movmf
                             r-matrixstats
                             r-dirstats
                             r-directional
                             r-circular))
    (home-page "https://cran.r-project.org/package=pgKDEsphere")
    (synopsis
     "Parametrically Guided Kernel Density Estimator for Spherical Data")
    (description
     "Nonparametric density estimation for (hyper)spherical data by means of a
parametrically guided kernel estimator (adaptation of the method of Hjort and
Glad (1995) <doi:10.1214/aos/1176324627> to the spherical setting).  The package
also allows the data-driven selection of the smoothing parameter and the
representation of the estimated density for circular and spherical data.
Estimators of the density without guide can also be obtained.")
    (license license:gpl2+)))

(define-public r-pgirmess
  (package
    (name "r-pgirmess")
    (version "2.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pgirmess" version))
       (sha256
        (base32 "1c1sccpc31xxrdmvybl1xviwfwq05mdhfk2brfaf0mq8kb7p2lkn"))))
    (properties `((upstream-name . "pgirmess")))
    (build-system r-build-system)
    (propagated-inputs (list r-spdep r-sp r-sf r-boot))
    (home-page "https://github.com/pgiraudoux/pgirmess")
    (synopsis "Spatial Analysis and Data Mining for Field Ecologists")
    (description
     "Set of tools for reading, writing and transforming spatial and seasonal data,
model selection and specific statistical tests for ecologists.  It includes
functions to interpolate regular positions of points between landmarks, to
discretize polylines into regular point positions, link distant observations to
points and convert a bounding box in a spatial object.  It also provides
miscellaneous functions for field ecologists such as spatial statistics and
inference on diversity indexes, writing data.frame with Chinese characters.")
    (license license:gpl2+)))

(define-public r-pgenlibr
  (package
    (name "r-pgenlibr")
    (version "0.3.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pgenlibr" version))
       (sha256
        (base32 "1wbbm4n9ilwwa7mgb3z81nj0kh11nrgbfbc936cb95pjgs18v4h3"))))
    (properties `((upstream-name . "pgenlibr")))
    (build-system r-build-system)
    (inputs (list zlib))
    (propagated-inputs (list r-rcpp))
    (native-inputs (list pkg-config))
    (home-page "https://cran.r-project.org/package=pgenlibr")
    (synopsis "PLINK 2 Binary (.pgen) Reader")
    (description
     "This package provides a thin wrapper over PLINK 2's core libraries which
provides an R interface for reading .pgen files.  A minimal .pvar loader is also
included.  Chang et al. (2015) \\doi{10.1186/s13742-015-0047-8}.")
    (license license:lgpl3+)))

(define-public r-pgee-mixed
  (package
    (name "r-pgee-mixed")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pgee.mixed" version))
       (sha256
        (base32 "115vvpv54q80486i3xs2yhmfyx476cf4g1v43q6l7wj3jwzcrvbg"))))
    (properties `((upstream-name . "pgee.mixed")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mvtnorm r-copula))
    (home-page "http://github.com/kaos42/pgee.mixed")
    (synopsis
     "Penalized Generalized Estimating Equations for Bivariate Mixed Outcomes")
    (description
     "Perform simultaneous estimation and variable selection for correlated bivariate
mixed outcomes (one continuous outcome and one binary outcome per cluster) using
penalized generalized estimating equations.  In addition, clustered Gaussian and
binary outcomes can also be modeled.  The SCAD, MCP, and LASSO penalties are
supported.  Cross-validation can be performed to find the optimal regularization
parameter(s).")
    (license license:gpl2)))

(define-public r-pgee
  (package
    (name "r-pgee")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PGEE" version))
       (sha256
        (base32 "1qcxbqnbpplnfjy83nm9fj9y4abzgbxnsggzx7ix3vb9qs18cdwh"))))
    (properties `((upstream-name . "PGEE")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-mass))
    (home-page "https://cran.r-project.org/package=PGEE")
    (synopsis "Penalized Generalized Estimating Equations in High-Dimension")
    (description
     "Fits penalized generalized estimating equations to longitudinal data with
high-dimensional covariates.")
    (license license:gpl2+)))

(define-public r-pgdraw
  (package
    (name "r-pgdraw")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pgdraw" version))
       (sha256
        (base32 "1s1b1izpgryrqsjkdh3yvr137054sipj3kwdkzlx2af8xjr53f85"))))
    (properties `((upstream-name . "pgdraw")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=pgdraw")
    (synopsis "Generate Random Samples from the Polya-Gamma Distribution")
    (description
     "Generates random samples from the Polya-Gamma distribution using an
implementation of the algorithm described in J. Windle's @code{PhD} thesis
(2013)
<https://repositories.lib.utexas.edu/bitstream/handle/2152/21842/WINDLE-DISSERTATION-2013.pdf>.
 The underlying implementation is in C.")
    (license license:gpl3+)))

(define-public r-pgam
  (package
    (name "r-pgam")
    (version "0.4.17")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pgam" version))
       (sha256
        (base32 "03sln908xwg909m3pxi6ihpq3vzb0v0mj6yac7bx29x5rxcd6psy"))))
    (properties `((upstream-name . "pgam")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pgam")
    (synopsis "Poisson-Gamma Additive Models")
    (description
     "This work is an extension of the state space model for Poisson count data,
Poisson-Gamma model, towards a semiparametric specification.  Just like the
generalized additive models (GAM), cubic splines are used for covariate
smoothing.  The semiparametric models are fitted by an iterative process that
combines maximization of likelihood and backfitting algorithm.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-pg
  (package
    (name "r-pg")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pg" version))
       (sha256
        (base32 "0dbviqi7v1jyxj8nav0w25mghgbv077mgcnznr0pfh85lwzpkawr"))))
    (properties `((upstream-name . "pg")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://tmsalab.github.io/pg/")
    (synopsis "Polya Gamma Distribution Sampler")
    (description
     "This package provides access to a high performant random distribution sampler
for the Polya Gamma Distribution using either C++ headers for Rcpp or
@code{RcppArmadillo} and R'.")
    (license license:gpl3+)))

(define-public r-pfr
  (package
    (name "r-pfr")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pfr" version))
       (sha256
        (base32 "09x8b3qa3bfg4sh0qavzsw72xh64a0qhic7pm3gn03znavg7pzlz"))))
    (properties `((upstream-name . "pfr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rstudioapi r-inline))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://cran.r-project.org/package=pfr")
    (synopsis "Interface to the 'C++' Library 'Pf'")
    (description
     "Builds and runs c++ code for classes that encapsulate state space model,
particle filtering algorithm pairs.  Algorithms include the Bootstrap Filter
from Gordon et al. (1993) <doi:10.1049/ip-f-2.1993.0015>, the generic SISR
filter, the Auxiliary Particle Filter from Pitt et al (1999)
<doi:10.2307/2670179>, and a variety of Rao-Blackwellized particle filters
inspired by Andrieu et al. (2002) <doi:10.1111/1467-9868.00363>.  For more
details on the c++ library pf', see Brown (2020) <doi:10.21105/joss.02599>.")
    (license license:gpl3+)))

(define-public r-pflr
  (package
    (name "r-pflr")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PFLR" version))
       (sha256
        (base32 "1i4fsab96iiidnjk3lzd1zlz5yc665lsv4lk7d18aw4npr6gzpl8"))))
    (properties `((upstream-name . "PFLR")))
    (build-system r-build-system)
    (propagated-inputs (list r-psych r-mass r-glmnet r-flare r-fda))
    (home-page "https://cran.r-project.org/package=PFLR")
    (synopsis "Estimating Penalized Functional Linear Regression")
    (description
     "Implementation of commonly used penalized functional linear regression models,
including the Smooth and Locally Sparse (S@code{LoS}) method by Lin et al.
(2016) <doi:10.1080/10618600.2016.1195273>, Nested Group bridge Regression (NGR)
method by Guan et al. (2020) <doi:10.1080/10618600.2020.1713797>, Functional
Linear Regression That's interpretable (FLIRTI) by James et al. (2009)
<doi:10.1214/08-AOS641>, and the Penalized B-spline regression method.")
    (license license:gpl2)))

(define-public r-pfim
  (package
    (name "r-pfim")
    (version "6.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PFIM" version))
       (sha256
        (base32 "0kp213ig73vj34qgwpc1zsnvz2hf8j0mjcqnsbb541s92zv2x4y7"))))
    (properties `((upstream-name . "PFIM")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-scales
                             r-rmarkdown
                             r-rcpp
                             r-pracma
                             r-matrix
                             r-knitr
                             r-kableextra
                             r-inline
                             r-ggplot2
                             r-devtools
                             r-desolve
                             r-deriv))
    (native-inputs (list r-knitr))
    (home-page "http://www.pfim.biostat.fr/")
    (synopsis "Population Fisher Information Matrix")
    (description
     "Evaluate or optimize designs for nonlinear mixed effects models using the Fisher
Information matrix.  Methods used in the package refer to MentrÃ© F, Mallet A,
Baccar D (1997) <doi:10.1093/biomet/84.2.429>, Retout S, Comets E, Samson A,
MentrÃ© F (2007) <doi:10.1002/sim.2910>, Bazzoli C, Retout S, MentrÃ© F (2009)
<doi:10.1002/sim.3573>, Le Nagard H, Chao L, Tenaillon O (2011)
<doi:10.1186/1471-2148-11-326>, Combes FP, Retout S, Frey N, MentrÃ© F (2013)
<doi:10.1007/s11095-013-1079-3> and Seurat J, Tang Y, MentrÃ© F, Nguyen TT
(2021) <doi:10.1016/j.cmpb.2021.106126>.")
    (license license:gpl2+)))

(define-public r-pfica
  (package
    (name "r-pfica")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pfica" version))
       (sha256
        (base32 "10njqflljcp8rxm5l7aqg6x93wbivdzvkjh7q6z01syblgb9755l"))))
    (properties `((upstream-name . "pfica")))
    (build-system r-build-system)
    (propagated-inputs (list r-whitening r-fda r-expm))
    (home-page "https://github.com/m-vidal/pfica")
    (synopsis "Independent Components Analysis Techniques for Functional Data")
    (description
     "This package performs smoothed (and non-smoothed) principal/independent
components analysis of functional data.  Various functional pre-whitening
approaches are implemented as discussed in Vidal and Aguilera (2022) âNovel
whitening approaches in functional settings\", <doi:10.1002/sta4.516>.  Further
whitening representations of functional data can be derived in terms of a few
principal components, providing an avenue to explore hidden structures in low
dimensional settings: see Vidal, Rosso and Aguilera (2021) âBi-smoothed
functional independent component analysis for EEG artifact removalâ,
<doi:10.3390/math9111243>.")
    (license license:gpl2+)))

(define-public r-pfa
  (package
    (name "r-pfa")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pfa" version))
       (sha256
        (base32 "0ikdd7ps8wnjp9nm66w447m06hqxnnk553jglxikl2w5d9vk6b31"))))
    (properties `((upstream-name . "pfa")))
    (build-system r-build-system)
    (propagated-inputs (list r-quantreg r-poet r-lars))
    (home-page "https://cran.r-project.org/package=pfa")
    (synopsis
     "Estimates False Discovery Proportion Under Arbitrary Covariance Dependence")
    (description
     "Estimate the false discovery proportion (FDP) by Principal Factor Approximation
method with general known and unknown covariance dependence.")
    (license license:gpl2)))

(define-public r-pez
  (package
    (name "r-pez")
    (version "1.2-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pez" version))
       (sha256
        (base32 "1z3lpbijivr32r9y0lh2yxsww7sz9kqmsvnril1gzn09d498xz3b"))))
    (properties `((upstream-name . "pez")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-quantreg
                             r-picante
                             r-phytools
                             r-mvtnorm
                             r-matrix
                             r-fd
                             r-caper
                             r-ape
                             r-animation
                             r-ade4))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pez")
    (synopsis "Phylogenetics for the Environmental Sciences")
    (description
     "Eco-phylogenetic and community phylogenetic analyses.  Keeps community
ecological and phylogenetic data matched up and comparable using
comparative.comm objects.  Wrappers for common community phylogenetic indices
('pez.shape', pez.evenness', pez.dispersion', and pez.dissimilarity metrics).
Implementation of Cavender-Bares (2004) correlation of phylogenetic and
ecological matrices ('fingerprint.regression').  Phylogenetic Generalised Linear
Mixed Models (PGLMMs; pglmm') following Ives & Helmus (2011) and Rafferty & Ives
(2013).  Simulation of null assemblages, traits, and phylogenies ('scape',
sim.meta.comm').")
    (license license:gpl3)))

(define-public r-pexm
  (package
    (name "r-pexm")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pexm" version))
       (sha256
        (base32 "17wc0qxcs1p282lrv2ji8lj3c8d656b4dl2mjbsywgyq29lzl1lz"))))
    (properties `((upstream-name . "pexm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjags r-msm r-coda))
    (home-page "https://github.com/vdinizm/pexm")
    (synopsis
     "Loading a JAGS Module for the Piecewise Exponential Distribution")
    (description
     "Load the Just Another Gibbs Sampling (JAGS) module pexm'.  The module provides
the tools to work with the Piecewise Exponential (PE) distribution in a Bayesian
model with the corresponding Markov Chain Monte Carlo algorithm (Gibbs Sampling)
implemented via JAGS. Details about the module implementation can be found in
Mayrink et al. (2021) <doi:10.18637/jss.v100.i08>.")
    (license license:gpl3)))

(define-public r-pewdata
  (package
    (name "r-pewdata")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pewdata" version))
       (sha256
        (base32 "1a4ci8hzpmwxs48lsppswcwkxv72yn44w486m9izwvx8wr1nlh2d"))))
    (properties `((upstream-name . "pewdata")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rselenium
                             r-rio
                             r-purrr
                             r-magrittr
                             r-foreign))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/fsolt/pewdata")
    (synopsis "Reproducible Retrieval of Pew Research Center Datasets")
    (description
     "Reproducible, programmatic retrieval of survey datasets from the Pew Research
Center.")
    (license license:expat)))

(define-public r-petrinetr
  (package
    (name "r-petrinetr")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "petrinetR" version))
       (sha256
        (base32 "0s67clfpphwxx8lyb2559sk6i6a5zmxdds0pzfj23blbacnlk16c"))))
    (properties `((upstream-name . "petrinetR")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-visnetwork
                             r-purrr
                             r-lifecycle
                             r-dplyr
                             r-diagrammer))
    (home-page "https://bupar.net")
    (synopsis "Building, Visualizing, Exporting and Replaying Petri Nets")
    (description
     "This package provides functions for the construction of Petri Nets.  Petri Nets
can be replayed by firing enabled transitions.  Silent transitions will be
hidden by the execution handler.  Also includes functionalities for the
visualization of Petri Nets and export of Petri Nets to PNML (Petri Net Markup
Language) files.")
    (license license:gpl3)))

(define-public r-petitr
  (package
    (name "r-petitr")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "petitr" version))
       (sha256
        (base32 "0i13zhdrdka0ij0khdzj6ylvvcn4wxvplb8a368i1s64cpgak30c"))))
    (properties `((upstream-name . "petitr")))
    (build-system r-build-system)
    (home-page "none")
    (synopsis "Relative Growth Rate")
    (description
     "Calculates the relative growth rate (RGR) of a series of individuals by building
a life table and solving the Lotka-Birch equation. (See Birch, L. C. 1948.  The
intrinsic rate of natural increase of an insect population. - Journal of Animal
Ecology 17: 15-26) <doi:10.2307/1605>.")
    (license license:gpl2+)))

(define-public r-petfinder
  (package
    (name "r-petfinder")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PetfindeR" version))
       (sha256
        (base32 "1pb3mqv4pxl971j210m7vwhz9qqbhb7l5fpmb1vjwkc5hx11zh9l"))))
    (properties `((upstream-name . "PetfindeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/aschleg/PetfindeR")
    (synopsis "'Petfinder' API Wrapper")
    (description
     "Wrapper of the Petfinder API <https://www.petfinder.com/developers/v2/docs/>
that implements methods for interacting with and extracting data from the
Petfinder database.  The Petfinder REST API allows access to the Petfinder
database, one of the largest online databases of adoptable animals and animal
welfare organizations across North America.")
    (license license:expat)))

(define-public r-petests
  (package
    (name "r-petests")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PEtests" version))
       (sha256
        (base32 "053vnwbjyh197m3vvzgwl8xikl6dglln00r1jlpsj2w68h1kz4pi"))))
    (properties `((upstream-name . "PEtests")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PEtests")
    (synopsis "Power-Enhanced (PE) Tests for High-Dimensional Data")
    (description
     "Two-sample power-enhanced mean tests, covariance tests, and simultaneous tests
on mean vectors and covariance matrices for high-dimensional data.  Methods of
these PE tests are presented in Yu, Li, and Xue (2022)
<doi:10.1080/01621459.2022.2126781>; Yu, Li, Xue, and Li (2022)
<doi:10.1080/01621459.2022.2061354>.")
    (license license:gpl3+)))

(define-public r-petersenlab
  (package
    (name "r-petersenlab")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "petersenlab" version))
       (sha256
        (base32 "1333k1kcy1ih9z09bl37zalsqrgf2xkqkqnsm28clc0r5np8df1d"))))
    (properties `((upstream-name . "petersenlab")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-viridislite
                             r-tidyselect
                             r-stringr
                             r-scales
                             r-reshape2
                             r-rcolorbrewer
                             r-psych
                             r-plyr
                             r-nlme
                             r-mvtnorm
                             r-mix
                             r-mitools
                             r-lavaan
                             r-hmisc
                             r-ggplot2
                             r-dplyr
                             r-digest))
    (home-page "https://github.com/DevPsyLab/petersenlab")
    (synopsis "Collection of R Functions by the Petersen Lab")
    (description
     "This package provides a collection of R functions that are widely used by the
Petersen Lab.  Included are functions for various purposes, including evaluating
the accuracy of judgments and predictions, performing scoring of assessments,
generating correlation matrices, conversion of data between various types, data
management, psychometric evaluation, extensions related to latent variable
modeling, various plotting capabilities, and other miscellaneous useful
functions.  By making the package available, we hope to make our methods
reproducible and replicable by others and to help others perform their data
processing and analysis methods more easily and efficiently.  The codebase is in
<doi:10.5281/zenodo.7602890>.  The package is described in Petersen (2024)
<doi:10.1201/9781003357421>, <doi:10.5281/zenodo.6466590>.")
    (license license:expat)))

(define-public r-petersen
  (package
    (name "r-petersen")
    (version "2024.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Petersen" version))
       (sha256
        (base32 "03xhsmlgqgp037ccy2q2a65cspcy1qxm1vzb6yljhv61r60zfqjz"))))
    (properties `((upstream-name . "Petersen")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-spas
                             r-rlang
                             r-reshape2
                             r-plyr
                             r-numderiv
                             r-msm
                             r-matrix
                             r-mass
                             r-ggplot2
                             r-formula-tools
                             r-btspas
                             r-bbmle
                             r-aiccmodavg))
    (native-inputs (list r-rmarkdown r-r-rsp r-knitr))
    (home-page "https://github.com/cschwarz-stat-sfu-ca/Petersen")
    (synopsis "Estimators for Two-Sample Capture-Recapture Studies")
    (description
     "This package provides a comprehensive implementation of Petersen-type estimators
and its many variants for two-sample capture-recapture studies.  A conditional
likelihood approach is used that allows for tag loss; non reporting of tags;
reward tags; categorical, geographical and temporal stratification; partial
stratification; reverse capture-recapture; and continuous variables in modeling
the probability of capture.  Many examples from fisheries management are
presented.")
    (license license:gpl2)))

(define-public r-pestr
  (package
    (name "r-pestr")
    (version "0.8.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pestr" version))
       (sha256
        (base32 "15w89cipky9di4csbp0cbw7s3qslr5mk94wivps77i3k0j9ys74g"))))
    (properties `((upstream-name . "pestr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rsqlite
                             r-rlang
                             r-readr
                             r-magrittr
                             r-jsonlite
                             r-httr
                             r-dplyr
                             r-dbi
                             r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mczyzj/pestr")
    (synopsis "Interface to Download Data on Pests and Hosts from 'EPPO'")
    (description
     "Set of tools to automatize extraction of data on pests from EPPO Data Services
and EPPO Global Database and to put them into tables with human readable format.
 Those function use EPPO database API', thus you first need to register on
<https://data.eppo.int> (free of charge).  Additional helpers allow to download,
check and connect to SQLite EPPO database'.")
    (license license:expat)))

(define-public r-pesticideloadindicator
  (package
    (name "r-pesticideloadindicator")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PesticideLoadIndicator" version))
       (sha256
        (base32 "109pasc3q0zwwp2pamx1pqz1rvhw269bnbqdh79xy77w82jpma15"))))
    (properties `((upstream-name . "PesticideLoadIndicator")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rlang r-readxl r-magrittr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PesticideLoadIndicator")
    (synopsis "Computes Danish Pesticide Load Indicator")
    (description
     "Computes the Danish Pesticide Load Indicator as described in Kudsk et al. (2018)
<doi:10.1016/j.landusepol.2017.11.010> and Moehring et al. (2019)
<doi:10.1016/j.scitotenv.2018.07.287> for pesticide use data.  Additionally
offers the possibility to directly link pesticide use data to pesticide
properties given access to the Pesticide properties database (Lewis et al.,
2016) <doi:10.1080/10807039.2015.1133242>.")
    (license license:expat)))

(define-public r-pesel
  (package
    (name "r-pesel")
    (version "0.7.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pesel" version))
       (sha256
        (base32 "06d10yzarnfifvdx6lch4fpag5qignjpij513kip1d9afrs3gm73"))))
    (properties `((upstream-name . "pesel")))
    (build-system r-build-system)
    (home-page "https://github.com/psobczyk/pesel")
    (synopsis "Automatic Estimation of Number of Principal Components in PCA")
    (description
     "Automatic estimation of number of principal components in PCA with PEnalized
SEmi-integrated Likelihood (PESEL).  See Piotr Sobczyk, Malgorzata Bogdan, Julie
Josse \"Bayesian dimensionality reduction with PCA using penalized
semi-integrated likelihood\" (2017) <doi:10.1080/10618600.2017.1340302>.")
    (license license:gpl3)))

(define-public r-perutimber
  (package
    (name "r-perutimber")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "perutimber" version))
       (sha256
        (base32 "119zilijflhj8a9hn4swdpgy99ffa9n7b53wphgi4641hlnlpif5"))))
    (properties `((upstream-name . "perutimber")))
    (build-system r-build-system)
    (propagated-inputs (list r-lifecycle))
    (home-page "https://github.com/PaulESantos/perutimber")
    (synopsis "Catalogue of the Timber Forest Species of the Peruvian Amazon")
    (description
     "Access the data of the Catalogue of the Timber Forest Species of the Peruvian
Amazon VÃ¡squez MartÃ­nez, R., & Rojas GonzÃ¡les,
R.D.P.(2022)<doi:10.21704/rfp.v37i3.1956>.")
    (license license:expat)))

(define-public r-peruse
  (package
    (name "r-peruse")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "peruse" version))
       (sha256
        (base32 "10j942fr5jpygl4z8vn2ww2kjiplga5s45c448bclnq1qx3as36k"))))
    (properties `((upstream-name . "peruse")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-magrittr))
    (home-page "https://github.com/jacgoldsm/peruse")
    (synopsis "Tidy API for Sequence Iteration and Set Comprehension")
    (description
     "This package provides a friendly API for sequence iteration and set
comprehension.")
    (license license:gpl2)))

(define-public r-peruflorads43
  (package
    (name "r-peruflorads43")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "peruflorads43" version))
       (sha256
        (base32 "12wsnnsjf6ih8lkpq4k20awicxwcdv7m466vvw2afmky844kq0ss"))))
    (properties `((upstream-name . "peruflorads43")))
    (build-system r-build-system)
    (home-page "https://github.com/PaulESantos/peruflorads43")
    (synopsis
     "Reviewed Official Classification of Endangered Wild Flora Species in Peru")
    (description
     "Provide users with a convenient way to access and analyze information on
endangered plant species in Peru based on `Decreto Supremo N 043-2006-AG -
Aprueban categorizacion de especies amenazadas de flora
silvestre`<https://sinia.minam.gob.pe/normas/aprueban-categorizacion-especies-amenazadas-flora-silvestre>.")
    (license license:expat)))

(define-public r-perturbr
  (package
    (name "r-perturbr")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "perturbR" version))
       (sha256
        (base32 "1sdk5ls8x66lds4cy40xbl383px4xmrh21cw4bgy83rzbjjzwp2h"))))
    (properties `((upstream-name . "perturbR")))
    (build-system r-build-system)
    (propagated-inputs (list r-igraph r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=perturbR")
    (synopsis "Random Perturbation of Count Matrices")
    (description
     "The @code{perturbR}() function incrementally perturbs network edges (using the
@code{rewireR} function)and compares the resulting community detection solutions
from the rewired networks with the solution found for the original network.
These comparisons aid in understanding the stability of the original solution.
The package requires symmetric, weighted (specifically, count)
matrices/networks.")
    (license license:gpl2)))

(define-public r-persval
  (package
    (name "r-persval")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "persval" version))
       (sha256
        (base32 "12gij3lvd8lvdpcmnyzxjwy4060xv3q5smvaz3ni82165f90dw47"))))
    (properties `((upstream-name . "persval")))
    (build-system r-build-system)
    (propagated-inputs (list r-fmsb))
    (home-page "https://github.com/g-corbelli/persval")
    (synopsis "Computing Personal Values Scores")
    (description
     "Compute personal values scores from various questionnaires based on the
theoretical constructs proposed by professor Shalom H. Schwartz.  Designed for
researchers and practitioners in psychology, sociology, and related fields, the
package facilitates the quantification and visualization of different dimensions
related to personal values from survey data.  It incorporates the recommended
statistical adjustment to enhance the accuracy and interpretation of the
results.  Note: The package persval is independently developed based on the
personal values theoretical framework, and is not directly endorsed by professor
Schwartz.")
    (license license:gpl3)))

(define-public r-perspectev
  (package
    (name "r-perspectev")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "perspectev" version))
       (sha256
        (base32 "175s1nq5z4gfs5qb39lq230g6n0v8fxzs5hr9j2rgx0knpbjfq03"))))
    (properties `((upstream-name . "perspectev")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-mapproj
                             r-ggplot2
                             r-foreach
                             r-doparallel
                             r-boot
                             r-ape))
    (home-page "https://cran.r-project.org/package=perspectev")
    (synopsis "Permutation of Species During Turnover Events")
    (description
     "This package provides a robust framework for analyzing the extent to which
differential survival with respect to higher level trait variation is reducible
to lower level variation.  In addition to its primary test, it also provides
functions for simulation-based power analysis, reading in common data set
formats, and visualizing results.  Temporarily contains an edited version of
function hr.mcp() from package wild1', written by Glen Sargeant.  For tutorial
see: http://evolve.zoo.ox.ac.uk/Evolve/Perspectev.html.")
    (license license:gpl3)))

(define-public r-personr
  (package
    (name "r-personr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "personr" version))
       (sha256
        (base32 "12iyzdc2kksy74npzdgy19lnhm9l9c7f4lkwshmsp6by09kb2dm3"))))
    (properties `((upstream-name . "personr")))
    (build-system r-build-system)
    (propagated-inputs (list r-whisker r-shiny r-rmarkdown r-rlang r-ggplot2))
    (home-page "https://github.com/flujoo/personr")
    (synopsis "Test Your Personality")
    (description
     "An R-package-version of an open online science-based personality test from
<https://openpsychometrics.org/tests/IPIP-BFFM/>, providing a better-designed
interface and a more detailed report.  The core command launch_test() opens a
personality test in your browser, and generates a report after you click
\"Submit\".  In this report, your results are compared with other people's, to
show what these results mean.  Other people's data is from
<https://openpsychometrics.org/_rawdata/BIG5.zip>.")
    (license license:gpl2)))

(define-public r-personalr
  (package
    (name "r-personalr")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "personalr" version))
       (sha256
        (base32 "0lla3hy624bqmhwmrsr6my3lw00xm52d935zxjnh7sx05pikkcsv"))))
    (properties `((upstream-name . "personalr")))
    (build-system r-build-system)
    (propagated-inputs (list r-xfun
                             r-withr
                             r-usethis
                             r-rstudioapi
                             r-rprojroot
                             r-purrr
                             r-magrittr
                             r-glue
                             r-fs
                             r-devtools
                             r-desc))
    (home-page "https://mrcaseb.github.io/personalr/index.html")
    (synopsis "Automated Personal Package Setup")
    (description
     "This package provides functions to setup a personal R package that attaches
given libraries and exports personal helper functions.")
    (license license:expat)))

(define-public r-personalized2part
  (package
    (name "r-personalized2part")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "personalized2part" version))
       (sha256
        (base32 "104flqjyf0x0619msyc0vlkg61znvhf0v86fsdpksdxkgwb7wv7j"))))
    (properties `((upstream-name . "personalized2part")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp r-personalized r-hdtweedie
                             r-foreach))
    (home-page "https://github.com/jaredhuling/personalized2part")
    (synopsis
     "Two-Part Estimation of Treatment Rules for Semi-Continuous Data")
    (description
     "This package implements the methodology of Huling, Smith, and Chen (2020)
<doi:10.1080/01621459.2020.1801449>, which allows for subgroup identification
for semi-continuous outcomes by estimating individualized treatment rules.  It
uses a two-part modeling framework to handle semi-continuous data by separately
modeling the positive part of the outcome and an indicator of whether each
outcome is positive, but still results in a single treatment rule.  High
dimensional data is handled with a cooperative lasso penalty, which encourages
the coefficients in the two models to have the same sign.")
    (license license:gpl2+)))

(define-public r-personalized
  (package
    (name "r-personalized")
    (version "0.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "personalized" version))
       (sha256
        (base32 "0dx8qy89s2za4v17k02gi4ybkpahbgkyizsb42s4ckx5gjvw1ck7"))))
    (properties `((upstream-name . "personalized")))
    (build-system r-build-system)
    (propagated-inputs (list r-xgboost
                             r-survival
                             r-plotly
                             r-mgcv
                             r-kernlab
                             r-glmnet
                             r-ggplot2
                             r-foreach))
    (native-inputs (list r-knitr))
    (home-page "https://jaredhuling.org/personalized/")
    (synopsis
     "Estimation and Validation Methods for Subgroup Identification and Personalized Medicine")
    (description
     "This package provides functions for fitting and validation of models for
subgroup identification and personalized medicine / precision medicine under the
general subgroup identification framework of Chen et al. (2017)
<doi:10.1111/biom.12676>.  This package is intended for use for both randomized
controlled trials and observational studies and is described in detail in Huling
and Yu (2021) <doi:10.18637/jss.v098.i05>.")
    (license license:gpl2)))

(define-public r-persomicsarray
  (package
    (name "r-persomicsarray")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PersomicsArray" version))
       (sha256
        (base32 "1d5gxd65b01m13rgbdhk6w3l43vqcbdk0s1pbgc8h6cnipj55z0i"))))
    (properties `((upstream-name . "PersomicsArray")))
    (build-system r-build-system)
    (propagated-inputs (list r-tiff r-stringr r-raster r-jpeg))
    (home-page "https://cran.r-project.org/package=PersomicsArray")
    (synopsis "Automated Persomics Array Image Extraction")
    (description
     "Automated identification of printed array positions from high content microscopy
images and the export of those positions as individual images written to output
as multi-layered tiff files.")
    (license license:gpl3)))

(define-public r-persianstemmer
  (package
    (name "r-persianstemmer")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PersianStemmer" version))
       (sha256
        (base32 "1dpfkafijg7g7hzka93l1ymq5rcnjsfgvkvbpdx3vjnid05kh4v1"))))
    (properties `((upstream-name . "PersianStemmer")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PersianStemmer")
    (synopsis "Persian Stemmer for Text Analysis")
    (description "Allows users to stem Persian texts for text analysis.")
    (license license:gpl2+)))

(define-public r-perseusr
  (package
    (name "r-perseusr")
    (version "0.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PerseusR" version))
       (sha256
        (base32 "1k03flbnjndx5mm26hysk64z89858m50kjs8gyldm4s5f09iny6p"))))
    (properties `((upstream-name . "PerseusR")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml r-stringr r-plyr r-biobase))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PerseusR")
    (synopsis "Perseus R Interop")
    (description
     "Enables the interoperability between the Perseus platform for omics data
analysis (Tyanova et al.  2016) <doi:10.1038/nmeth.3901> and R. It provides the
foundation for developing and running Perseus plugins implemented in R by
providing all required input and output handling, including data and parameter
parsing as described in Rudolph and Cox 2018 <doi:10.1101/447268>.")
    (license license:expat)))

(define-public r-persdx
  (package
    (name "r-persdx")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "persDx" version))
       (sha256
        (base32 "14lafg5xhpp7m2shhf7ymfikl83ldqzssji61vwgqhvk78kaw3dc"))))
    (properties `((upstream-name . "persDx")))
    (build-system r-build-system)
    (propagated-inputs (list r-survivalroc r-proc))
    (home-page "https://cran.r-project.org/package=persDx")
    (synopsis
     "Personalized Diagnostics Rules for Subgroup Identification and Personalized Biomarker Discovery")
    (description
     "Tailoring the optimal biomarker(s) for disease screening or diagnosis based on
subjects individual characteristics.")
    (license license:gpl2+)))

(define-public r-perryexamples
  (package
    (name "r-perryexamples")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "perryExamples" version))
       (sha256
        (base32 "1abfrri0lq7iwdlk1qfzlq8378xpyvzfwcg1i18rmndfwsyyihjp"))))
    (properties `((upstream-name . "perryExamples")))
    (build-system r-build-system)
    (propagated-inputs (list r-robustbase r-quantreg r-perry r-lars))
    (home-page "https://cran.r-project.org/package=perryExamples")
    (synopsis
     "Examples for Integrating Prediction Error Estimation into Regression Models")
    (description
     "Examples for integrating package perry for prediction error estimation into
regression models.")
    (license license:gpl2+)))

(define-public r-perry
  (package
    (name "r-perry")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "perry" version))
       (sha256
        (base32 "0c3i9dc6hfdjfkjq2p630w6l1pz21mmqb8p0ikv6m159cfl3i3nq"))))
    (properties `((upstream-name . "perry")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://cran.r-project.org/package=perry")
    (synopsis
     "Resampling-Based Prediction Error Estimation for Regression Models")
    (description
     "This package provides tools that allow developers to write functions for
prediction error estimation with minimal programming effort and assist users
with model selection in regression problems.")
    (license license:gpl2+)))

(define-public r-perplexr
  (package
    (name "r-perplexr")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "perplexR" version))
       (sha256
        (base32 "19rxk8xf7pb8mbd4j6zdxwj62hfzczx8nvbh7qqlnib2w6pgdpw2"))))
    (properties `((upstream-name . "perplexR")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny
                             r-rstudioapi
                             r-miniui
                             r-jsonlite
                             r-httr
                             r-clipr))
    (home-page "https://github.com/GabrielKaiserQFin/perplexR")
    (synopsis "Coding Assistant using Perplexity's Large Language Models")
    (description
     "This package provides a coding assistant using Perplexity's Large Language
Models <https://www.perplexity.ai/> API. A set of functions and RStudio add-ins
that aim to help R developers.")
    (license license:gpl3+)))

(define-public r-permutest
  (package
    (name "r-permutest")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "permutest" version))
       (sha256
        (base32 "1y34l09bh4l7mjzdbm0dycsx7021icqj9wla6v1lpfid4lbr6y1p"))))
    (properties `((upstream-name . "permutest")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=permutest")
    (synopsis
     "Run Permutation Tests and Construct Associated Confidence Intervals")
    (description
     "This package implements permutation tests for any test statistic and
randomization scheme and constructs associated confidence intervals as described
in Glazer and Stark (2024) <doi:10.48550/@code{arXiv.2405.05238>}.")
    (license license:gpl3+)))

(define-public r-permutes
  (package
    (name "r-permutes")
    (version "2.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "permutes" version))
       (sha256
        (base32 "1ibpjg72vfvw3yvldqka0c70ydxvl2vbpqv1cn07yiwf7l0d5f51"))))
    (properties `((upstream-name . "permutes")))
    (build-system r-build-system)
    (propagated-inputs (list r-plyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=permutes")
    (synopsis "Permutation Tests for Time Series Data")
    (description
     "Helps you determine the analysis window to use when analyzing densely-sampled
time-series data, such as EEG data, using permutation testing (Maris &
Oostenveld, 2007) <doi:10.1016/j.jneumeth.2007.03.024>.  These permutation tests
can help identify the timepoints where significance of an effect begins and
ends, and the results can be plotted in various types of heatmap for reporting.
Mixed-effects models are supported using an implementation of the approach by
Lee & Braun (2012) <doi:10.1111/j.1541-0420.2011.01675.x>.")
    (license (license:fsdg-compatible "FreeBSD"))))

(define-public r-permutations
  (package
    (name "r-permutations")
    (version "1.1-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "permutations" version))
       (sha256
        (base32 "1cw6z9cw1pvkyf6c9v0rkfx3mr0bpncji4hsj1smxpq3y0pdwajx"))))
    (properties `((upstream-name . "permutations")))
    (build-system r-build-system)
    (propagated-inputs (list r-partitions r-numbers r-magic r-freealg))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/RobinHankin/permutations")
    (synopsis "The Symmetric Group: Permutations of a Finite Set")
    (description
     "Manipulates invertible functions from a finite set to itself.  Can transform
from word form to cycle form and back.  To cite the package in publications
please use Hankin (2020) \"Introducing the permutations R package\",
@code{SoftwareX}, volume 11 <doi:10.1016/j.softx.2020.100453>.")
    (license license:gpl2)))

(define-public r-permutationr
  (package
    (name "r-permutationr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PermutationR" version))
       (sha256
        (base32 "1rxz5zkl99bkx6zw8mkr4zr02g7w8wkc9sz3z2wwvgb8rmb7vyir"))))
    (properties `((upstream-name . "PermutationR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PermutationR")
    (synopsis "Conduct Permutation Analysis of Variance in R")
    (description
     "Conduct permutation One-Way or Two-Way Analysis of Variance in R. Use different
permutation types for two-way designs.")
    (license license:expat)))

(define-public r-permuco
  (package
    (name "r-permuco")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "permuco" version))
       (sha256
        (base32 "1zih4hb91507cqd6r6vsxqsv8q299i37p764mf33yyplr9w22vrb"))))
    (properties `((upstream-name . "permuco")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-permute r-matrix))
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "https://github.com/jaromilfrossard/permuco")
    (synopsis
     "Permutation Tests for Regression, (Repeated Measures) ANOVA/ANCOVA and Comparison of Signals")
    (description
     "This package provides functions to compute p-values based on permutation tests.
Regression, ANOVA and ANCOVA, omnibus F-tests, marginal unilateral and bilateral
t-tests are available.  Several methods to handle nuisance variables are
implemented (Kherad-Pajouh, S., & Renaud, O. (2010)
<doi:10.1016/j.csda.2010.02.015> ; Kherad-Pajouh, S., & Renaud, O. (2014)
<doi:10.1007/s00362-014-0617-3> ; Winkler, A. M., Ridgway, G. R., Webster, M.
A., Smith, S. M., & Nichols, T. E. (2014)
<doi:10.1016/j.neuroimage.2014.01.060>).  An extension for the comparison of
signals issued from experimental conditions (e.g. EEG/ERP signals) is provided.
Several corrections for multiple testing are possible, including the
cluster-mass statistic (Maris, E., & Oostenveld, R. (2007)
<doi:10.1016/j.jneumeth.2007.03.024>) and the threshold-free cluster enhancement
(Smith, S. M., & Nichols, T. E. (2009) <doi:10.1016/j.neuroimage.2008.03.061>).")
    (license license:gpl2+)))

(define-public r-permubiome
  (package
    (name "r-permubiome")
    (version "1.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "permubiome" version))
       (sha256
        (base32 "102xmz27c97nc6wlllqq1k3nkvv28a0j7vx1msl0c7m6w2z6395b"))))
    (properties `((upstream-name . "permubiome")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-matrix
                             r-gridextra
                             r-ggplot2
                             r-dplyr
                             r-dabestr))
    (home-page "https://cran.r-project.org/package=permubiome")
    (synopsis
     "Permutation Based Test for Biomarker Discovery in Microbiome Data")
    (description
     "The permubiome R package was created to perform a permutation-based
non-parametric analysis on microbiome data for biomarker discovery aims.  This
test executes thousands of comparisons in a pairwise manner, after a random
shuffling of data into the different groups of study with a prior selection of
the microbiome features with the largest variation among groups.  Previous to
the permutation test itself, data can be normalized according to different
methods proposed to handle microbiome data ('proportions or Anders').  The
median-based differences between groups resulting from the multiple simulations
are fitted to a normal distribution with the aim to calculate their
significance.  A multiple testing correction based on Benjamini-Hochberg method
(fdr) is finally applied to extract the differentially presented features
between groups of your dataset.  LATEST UPDATES: v1.1 and olders incorporates
function to parse COLUMN format; v1.2 and olders incorporates -optimize-
function to maximize evaluation of features with largest inter-class variation;
v1.3 and olders includes the -size.effect- function to perform estimation
statistics using the bootstrap-coupled approach implemented in the dabestr
(>=0.3.0) R package.  Current v1.3.2 fixed bug with \"Class\" recognition and
updated dabestr functions.")
    (license license:gpl3)))

(define-public r-perms
  (package
    (name "r-perms")
    (version "1.14")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "perms" version))
       (sha256
        (base32 "1qw6pqiw4jn5qgdr0i8h0m1jlaa218khbn8z3xdp82csx4y8ia1l"))))
    (properties `((upstream-name . "perms")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-foreach r-doparallel))
    (home-page "https://cran.r-project.org/package=perms")
    (synopsis "Fast Permutation Computation")
    (description
     "This package implements the algorithm of Christensen (2024)
<doi:10.1214/22-BA1353> for estimating marginal likelihoods via permutation
counting.")
    (license license:bsd-2)))

(define-public r-permpath
  (package
    (name "r-permpath")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "permPATH" version))
       (sha256
        (base32 "1xjhpndlqdafv7g5yrps2rby8by76sh6y7kg2jhwbh6bv7x0d975"))))
    (properties `((upstream-name . "permPATH")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable r-r2html))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=permPATH")
    (synopsis "Permutation Based Gene Expression Pathway Analysis")
    (description
     "Can be used to carry out permutation based gene expression pathway analysis.
This work was supported by a National Institute of Allergy and Infectious
Disease/National Institutes of Health contract (No.  HHSN272200900059C).")
    (license license:gpl3)))

(define-public r-permimp
  (package
    (name "r-permimp")
    (version "1.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "permimp" version))
       (sha256
        (base32 "1zq513bkallkkqghjilvgc5iw0pcglrdyc0r2sns4ck07a3q378j"))))
    (properties `((upstream-name . "permimp")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-randomforest r-party r-ipred))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=permimp")
    (synopsis "Conditional Permutation Importance")
    (description
     "An add-on to the party package, with a faster implementation of the
partial-conditional permutation importance for random forests.  The standard
permutation importance is implemented exactly the same as in the party package.
The conditional permutation importance can be computed faster, with an option to
be backward compatible to the party implementation.  The package is compatible
with random forests fit using the party and the @code{randomForest} package.
The methods are described in Strobl et al. (2007) <doi:10.1186/1471-2105-8-25>
and Debeer and Strobl (2020) <doi:10.1186/s12859-020-03622-2>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-permgs
  (package
    (name "r-permgs")
    (version "0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "permGS" version))
       (sha256
        (base32 "0d2kp3c1fmnjjmsvc2qwh6m66yqvy2vrrxgv1fj2i4clsbavfa0y"))))
    (properties `((upstream-name . "permGS")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-coin))
    (home-page "https://cran.r-project.org/package=permGS")
    (synopsis "Permutational Group Sequential Test for Time-to-Event Data")
    (description
     "Permutational group-sequential tests for time-to-event data based on the
log-rank test statistic.  Supports exact permutation test when the censoring
distributions are equal in the treatment and the control group and approximate
imputation-permutation methods when the censoring distributions are different.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-permcor
  (package
    (name "r-permcor")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PermCor" version))
       (sha256
        (base32 "1ib8126hi9chch4jgyd0h26qjc791197yx7rzq88dkj0aaqlrrr1"))))
    (properties `((upstream-name . "PermCor")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PermCor")
    (synopsis "Robust Permutation Tests of Correlation Coefficients")
    (description
     "This package provides tools for statistical testing of correlation coefficients
through robust permutation method and large sample approximation method.
Tailored to different types of correlation coefficients including Pearson
correlation coefficient, weighted Pearson correlation coefficient, Spearman
correlation coefficient, and Lin's concordance correlation coefficient.The
robust permutation test controls type I error under general scenarios when
sample size is small and two variables are dependent but uncorrelated.  The
large sample approximation test generally controls type I error when the sample
size is large (>200).")
    (license license:gpl3)))

(define-public r-permchacko
  (package
    (name "r-permchacko")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "permChacko" version))
       (sha256
        (base32 "0djmhbmjxq1id1qdnalndmvb9zqy5vhbsapk9zf2mv5z4rnkycyk"))))
    (properties `((upstream-name . "permChacko")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://ocbe-uio.github.io/permChacko/")
    (synopsis "Chacko Test for Order-Restriction with Permutation")
    (description
     "This package implements an extension of the Chacko chi-square test for ordered
vectors (Chacko, 1966, <https://www.jstor.org/stable/25051572>).  Our extension
brings the Chacko test to the computer age by implementing a permutation test to
offer a numeric estimate of the p-value, which is particularly useful when the
analytic solution is not available.")
    (license license:gpl3+)))

(define-public r-permat
  (package
    (name "r-permat")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PerMat" version))
       (sha256
        (base32 "0ad9xdi1w7zis2ks1jvl4l54rfcdgayipydjcqz5hfq6qkz5k31y"))))
    (properties `((upstream-name . "PerMat")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PerMat")
    (synopsis "Performance Metrics in Predictive Modeling")
    (description
     "Performance metric provides different performance measures like mean squared
error, root mean square error, mean absolute deviation, mean absolute percentage
error etc.  of a fitted model.  These can provide a way for forecasters to
quantitatively compare the performance of competing models.  For method details
see (i) Pankaj Das (2020)
<http://krishi.icar.gov.in/jspui/handle/123456789/44138>.")
    (license license:expat)))

(define-public r-permanova
  (package
    (name "r-permanova")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PERMANOVA" version))
       (sha256
        (base32 "1lmnp1g1i1kqa30cvakkl8g2m2h5jl73qx8dy37kv62mv0knlg5m"))))
    (properties `((upstream-name . "PERMANOVA")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable r-scales r-matrix r-mass r-deldir))
    (home-page "https://cran.r-project.org/package=PERMANOVA")
    (synopsis
     "Multivariate Analysis of Variance Based on Distances and Permutations")
    (description
     "Calculates multivariate analysis of variance based on permutations and some
associated pictorial representations.  The pictorial representation is based on
the principal coordinates of the group means.  There are some original results
that will be published soon.")
    (license license:gpl2+)))

(define-public r-permalgo
  (package
    (name "r-permalgo")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PermAlgo" version))
       (sha256
        (base32 "0hwla559vfv0r8bhrc8g0cvbsjgg4f3nx4y1hb4fxkvddi6pfb5a"))))
    (properties `((upstream-name . "PermAlgo")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PermAlgo")
    (synopsis "Permutational Algorithm to Simulate Survival Data")
    (description
     "This version of the permutational algorithm generates a dataset in which event
and censoring times are conditional on an user-specified list of covariates,
some or all of which are time-dependent.")
    (license license:gpl2)))

(define-public r-perk
  (package
    (name "r-perk")
    (version "0.0.9.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PERK" version))
       (sha256
        (base32 "0x62cmfl71s0mlhd22wifhfvhdvnz4ab6hzwinj549rvvrl8sw3j"))))
    (properties `((upstream-name . "PERK")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-viridis
                             r-tidyr
                             r-tibble
                             r-shinywidgets
                             r-shinyjs
                             r-shiny
                             r-readr
                             r-plotly
                             r-magrittr
                             r-golem
                             r-ggplot2
                             r-forcats
                             r-dt
                             r-dplyr
                             r-config
                             r-colourpicker
                             r-bs4dash))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jkkishore85/PERK/")
    (synopsis "Predicting Environmental Concentration and Risk")
    (description
     "This package provides a Shiny Web Application to predict and visualize
concentrations of pharmaceuticals in the aqueous environment.  Jagadeesan K.,
Barden R. and Kasprzyk-Hordern B. (2022)
<https://www.ssrn.com/abstract=4306129>.")
    (license license:gpl3+)))

(define-public r-periscope2
  (package
    (name "r-periscope2")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "periscope2" version))
       (sha256
        (base32 "00zjap5l8aqyn6ykf3vmhsgiahkkgzkakjzc7z0wlm5ksv0cskdm"))))
    (properties `((upstream-name . "periscope2")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-writexl
                             r-shinywidgets
                             r-shinyfeedback
                             r-shiny
                             r-lubridate
                             r-lifecycle
                             r-fresh
                             r-dt
                             r-bs4dash))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Aggregate-Genius/periscope2")
    (synopsis
     "Enterprise Streamlined 'shiny' Application Framework Using 'bs4Dash'")
    (description
     "This package provides a framework for building enterprise, scalable and
UI-standardized shiny applications.  It brings enhanced features such as
bootstrap v4 <https://getbootstrap.com/docs/4.0/getting-started/introduction/>,
additional and enhanced shiny modules, customizable UI features, as well as an
enhanced application file organization paradigm.  This update allows developers
to harness the ability to build powerful applications and enriches the shiny
developers experience when building and maintaining applications.")
    (license license:gpl3)))

(define-public r-periscope
  (package
    (name "r-periscope")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "periscope" version))
       (sha256
        (base32 "0w7p8clpaa605jcyvlkj86w8l92pgvilifwnagqj3x1nqaxgyhw3"))))
    (properties `((upstream-name . "periscope")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-writexl
                             r-shinydashboard
                             r-shinybs
                             r-shiny
                             r-lubridate
                             r-ggplot2
                             r-fresh
                             r-dt))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/cb4ds/periscope")
    (synopsis "Enterprise Streamlined 'Shiny' Application Framework")
    (description
     "An enterprise-targeted scalable and UI-standardized shiny framework including a
variety of developer convenience functions with the goal of both streamlining
robust application development while assisting with creating a consistent user
experience regardless of application or developer.")
    (license license:gpl3)))

(define-public r-periodictable
  (package
    (name "r-periodictable")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PeriodicTable" version))
       (sha256
        (base32 "133rwbvapk99dmh3vi8p7sa81d7hjmy0g4a5hcmwr3mdq8jzgwrj"))))
    (properties `((upstream-name . "PeriodicTable")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PeriodicTable")
    (synopsis "Periodic Table of the Elements")
    (description
     "This package provides a dataset containing properties for chemical elements.
Helper functions are also provided to access some atomic properties.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-periodics
  (package
    (name "r-periodics")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "peRiodiCS" version))
       (sha256
        (base32 "1366gmn6qfciwcjsfnbl3hfa16ffn11g00fm0kv74ilvm3nnawk3"))))
    (properties `((upstream-name . "peRiodiCS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rms r-hmisc))
    (home-page "https://cran.r-project.org/package=peRiodiCS")
    (synopsis "Functions for Generating Periodic Curves")
    (description
     "This package provides functions for generating variants of curves: restricted
cubic spline, periodic restricted cubic spline, periodic cubic spline.  Periodic
splines can be used to model data that has periodic nature / seasonality.")
    (license license:gpl3)))

(define-public r-performanceestimation
  (package
    (name "r-performanceestimation")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "performanceEstimation" version))
       (sha256
        (base32 "08jx2zl6xh0rp54xa70gb717wbfdzfrx9b47i3b3ly41qaf85vrc"))))
    (properties `((upstream-name . "performanceEstimation")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-parallelmap r-ggplot2 r-dplyr))
    (home-page "https://github.com/ltorgo/performanceEstimation")
    (synopsis
     "An Infra-Structure for Performance Estimation of Predictive Models")
    (description
     "An infra-structure for estimating the predictive performance of predictive
models.  In this context, it can also be used to compare and/or select among
different alternative ways of solving one or more predictive tasks.  The main
goal of the package is to provide a generic infra-structure to estimate the
values of different metrics of predictive performance using different estimation
procedures.  These estimation tasks can be applied to any solutions (workflows)
to the predictive tasks.  The package provides easy to use standard workflows
that allow the usage of any available R modeling algorithm together with some
pre-defined data pre-processing steps and also prediction post- processing
methods.  It also provides means for addressing issues related with the
statistical significance of the observed differences.")
    (license license:gpl2+)))

(define-public r-perfit
  (package
    (name "r-perfit")
    (version "1.4.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PerFit" version))
       (sha256
        (base32 "13917nvxii8pg69h1wcpmlv45sdrdldd3zxdrypr4mn456ff74k4"))))
    (properties `((upstream-name . "PerFit")))
    (build-system r-build-system)
    (propagated-inputs (list r-mirt
                             r-matrix
                             r-mass
                             r-ltm
                             r-irtoys
                             r-hmisc
                             r-fda))
    (home-page "https://cran.r-project.org/package=PerFit")
    (synopsis "Person Fit")
    (description
     "Several person-fit statistics (PFSs; Meijer and Sijtsma, 2001,
<doi:10.1177/01466210122031957>) are offered.  These statistics allow assessing
whether individual response patterns to tests or questionnaires are
(im)plausible given the other respondents in the sample or given a specified
item response theory model.  Some PFSs apply to dichotomous data, such as the
likelihood-based PFSs (lz, lz*) and the group-based PFSs (personal biserial
correlation, caution index, (normed) number of Guttman errors,
agreement/disagreement/dependability statistics, U3, ZU3, NCI, Ht).  PFSs
suitable to polytomous data include extensions of lz, U3, and (normed) number of
Guttman errors.")
    (license license:gpl2+)))

(define-public r-perccalc
  (package
    (name "r-perccalc")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "perccalc" version))
       (sha256
        (base32 "14557k7khc3l5m0vlam0fvbsb6p78v9gfcnx5ksxcvrab28xj7p3"))))
    (properties `((upstream-name . "perccalc")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-multcomp))
    (native-inputs (list r-knitr))
    (home-page "https://cimentadaj.github.io/perccalc/")
    (synopsis "Estimate Percentiles from an Ordered Categorical Variable")
    (description
     "An implementation of two functions that estimate values for percentiles from an
ordered categorical variable as described by Reardon (2011,
isbn:978-0-87154-372-1).  One function estimates percentile differences from two
percentiles while the other returns the values for every percentile from 1 to
100.")
    (license license:expat)))

(define-public r-perc
  (package
    (name "r-perc")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Perc" version))
       (sha256
        (base32 "1nwbj56m7pxrg8dyv36501b0lvpq8gbhcr07vs4nps8n3rqwwg81"))))
    (properties `((upstream-name . "Perc")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=Perc")
    (synopsis
     "Using Percolation and Conductance to Find Information Flow Certainty in a Direct Network")
    (description
     "To find the certainty of dominance interactions with indirect interactions being
considered.")
    (license license:gpl2+)))

(define-public r-perarma
  (package
    (name "r-perarma")
    (version "1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "perARMA" version))
       (sha256
        (base32 "1d3gg5yb2gj3mfmk72lps40hkiaf65wv7zhhgxf918kqyqi5zhs2"))))
    (properties `((upstream-name . "perARMA")))
    (build-system r-build-system)
    (propagated-inputs (list r-signal r-matrix r-matlab r-gnm r-corpcor))
    (home-page "https://cran.r-project.org/package=perARMA")
    (synopsis "Periodic Time Series Analysis")
    (description
     "Identification, model fitting and estimation for time series with periodic
structure.  Additionally, procedures for simulation of periodic processes and
real data sets are included.  Hurd, H. L., Miamee, A. G. (2007)
<doi:10.1002/9780470182833> Box, G. E. P., Jenkins, G. M., Reinsel, G. (1994)
<doi:10.1111/jtsa.12194> Brockwell, P. J., Davis, R. A. (1991,
ISBN:978-1-4419-0319-8) Bretz, F., Hothorn, T., Westfall, P. (2010, ISBN:
9780429139543) Westfall, P. H., Young, S. S. (1993, ISBN:978-0-471-55761-6)
Bloomfield, P., Hurd, H. L.,Lund, R. (1994)
<doi:10.1111/j.1467-9892.1994.tb00181.x> Dehay, D., Hurd, H. L. (1994,
ISBN:0-7803-1023-3) Vecchia, A. (1985) <doi:10.1080/00401706.1985.10488076>
Vecchia, A. (1985) <doi:10.1111/j.1752-1688.1985.tb00167.x> Jones, R.,
Brelsford, W. (1967) <doi:10.1093/biomet/54.3-4.403> Makagon, A. (1999)
<https://www.math.uni.wroc.pl/~pms/files/19.2/Article/19.2.5.pdf> Sakai, H.
(1989) <doi:10.1111/j.1467-9892.1991.tb00069.x> Gladyshev, E. G. (1961)
<https://www.mathnet.ru/php/archive.phtml?wshow=paper&jrnid=dan&paperid=24851>
Ansley (1979) <doi:10.1093/biomet/66.1.59> Hurd, H. L., Gerr, N. L. (1991)
<doi:10.1111/j.1467-9892.1991.tb00088.x>.")
    (license license:gpl2+)))

(define-public r-peramo
  (package
    (name "r-peramo")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "peramo" version))
       (sha256
        (base32 "0ngzf6piglr00r4i1m3r9mvlw67hs96lbfxv832vwisf631fdvlf"))))
    (properties `((upstream-name . "peramo")))
    (build-system r-build-system)
    (propagated-inputs (list r-parameters r-magrittr r-lme4 r-emmeans r-dplyr))
    (home-page "https://cran.r-project.org/package=peramo")
    (synopsis "Permutation Tests for Randomization Model")
    (description
     "Perform permutation-based hypothesis testing for randomized experiments as
suggested in Ludbrook & Dudley (1998) <doi:10.2307/2685470> and Ernst (2004)
<doi:10.1214/088342304000000396>, introduced in Pham et al. (2022)
<doi:10.1016/j.chemosphere.2022.136736>.")
    (license license:gpl3)))

(define-public r-peptoolkit
  (package
    (name "r-peptoolkit")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "peptoolkit" version))
       (sha256
        (base32 "067x3v8m3ph2mqcglbd2h4y7xsslcvdr5iailiqxzfqg77mcqxx6"))))
    (properties `((upstream-name . "peptoolkit")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-peptides r-dplyr r-caret))
    (home-page "https://github.com/jrcodina/peptoolkit")
    (synopsis "Toolkit for Using Peptide Sequences in Machine Learning")
    (description
     "This toolkit is designed for manipulation and analysis of peptides.  It provides
functionalities to assist researchers in peptide engineering and proteomics.
Users can manipulate peptides by adding amino acids at every position, count
occurrences of each amino acid at each position, and transform amino acid counts
based on probabilities.  The package offers functionalities to select the best
versus the worst peptides and analyze these peptides, which includes counting
specific residues, reducing peptide sequences, extracting features through One
Hot Encoding (OHE), and utilizing Quantitative Structure-Activity Relationship
(QSAR) properties (based in the package Peptides by Osorio et al. (2015)
<doi:10.32614/RJ-2015-001>).  This package is intended for both researchers and
bioinformatics enthusiasts working on peptide-based projects, especially for
their use with machine learning.")
    (license license:gpl3+)))

(define-public r-peptides
  (package
    (name "r-peptides")
    (version "2.4.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Peptides" version))
       (sha256
        (base32 "199bbfcdmy8mipjchsyxwxa3jssy60n9n9j7hcsab30kwjyq22zm"))))
    (properties `((upstream-name . "Peptides")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://github.com/dosorio/Peptides/")
    (synopsis
     "Calculate Indices and Theoretical Physicochemical Properties of Protein Sequences")
    (description
     "Includes functions to calculate several physicochemical properties and indices
for amino-acid sequences as well as to read and plot XVG output files from the
GROMACS molecular dynamics package.")
    (license license:gpl2)))

(define-public r-pepsavims
  (package
    (name "r-pepsavims")
    (version "0.9.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PepSAVIms" version))
       (sha256
        (base32 "0m4jrq6wcjy0k80gvrnm9f7kvi1ifx9182xfq0psyaxpj0ggvzij"))))
    (properties `((upstream-name . "PepSAVIms")))
    (build-system r-build-system)
    (propagated-inputs (list r-elasticnet))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/dpritchLibre/PepSAVIms")
    (synopsis "PepSAVI-MS Data Analysis")
    (description
     "An implementation of the data processing and data analysis portion of a pipeline
named the @code{PepSAVI-MS} which is currently under development by the Hicks
laboratory at the University of North Carolina.  The statistical analysis
package presented herein provides a collection of software tools used to
facilitate the prioritization of putative bioactive peptides from a complex
biological matrix.  Tools are provided to deconvolute mass spectrometry features
into a single representation for each peptide charge state, filter compounds to
include only those possibly contributing to the observed bioactivity, and
prioritize these remaining compounds for those most likely contributing to each
bioactivity data set.")
    (license (license:fsdg-compatible "CC BY-NC-SA 4.0"))))

(define-public r-pepr
  (package
    (name "r-pepr")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pepr" version))
       (sha256
        (base32 "0q2pfsch6g3zhaj6cffg6rzmlrmzwpvny84nls6x5jjljhan4nzz"))))
    (properties `((upstream-name . "pepr")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml r-stringr r-rcurl r-pryr r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pepr")
    (synopsis "Reading Portable Encapsulated Projects")
    (description
     "This package provides a PEP, or Portable Encapsulated Project, is a dataset that
subscribes to the PEP structure for organizing metadata.  It is written using a
simple YAML + CSV format, it is your one-stop solution to metadata management
across data analysis environments.  This package reads this standardized project
configuration structure into R. Described in Sheffield et al. (2021)
<doi:10.1093/gigascience/giab077>.")
    (license license:bsd-2)))

(define-public r-peppm
  (package
    (name "r-peppm")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "peppm" version))
       (sha256
        (base32 "1lvk1rjsx39mb93s9pclriw4fmf1sx05s59qqx5m3aqb4q8ffxkg"))))
    (properties `((upstream-name . "peppm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://github.com/fndemarqui/peppm")
    (synopsis "Piecewise Exponential Distribution with Random Time Grids")
    (description
     "Fits the Piecewise Exponential distribution with random time grids using the
clustering structure of the Product Partition Models.  Details of the
implemented model can be found in Demarqui et al. (2008)
<doi:10.1007/s10985-008-9086-0>.")
    (license license:gpl2+)))

(define-public r-peperr
  (package
    (name "r-peperr")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "peperr" version))
       (sha256
        (base32 "0045bc206y13467kw5xh3awkwcz24sb5xmdcgh5x1w6q1cab7ygp"))))
    (properties `((upstream-name . "peperr")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-snowfall))
    (home-page "https://github.com/fbertran/peperr/")
    (synopsis "Parallelised Estimation of Prediction Error")
    (description
     "Designed for prediction error estimation through resampling techniques, possibly
accelerated by parallel execution on a compute cluster.  Newly developed model
fitting routines can be easily incorporated.  Methods used in the package are
detailed in Porzelius Ch., Binder H. and Schumacher M. (2009)
<doi:10.1093/bioinformatics/btp062> and were used, for instance, in Porzelius
Ch., Schumacher M.and Binder H. (2011) <doi:10.1007/s00180-011-0236-6>.")
    (license license:gpl2+)))

(define-public r-pepe
  (package
    (name "r-pepe")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pepe" version))
       (sha256
        (base32 "1ihms63crxq0k7hd2yjyxmfw0yfy668srw012vi8mf8p31jdcr3j"))))
    (properties `((upstream-name . "pepe")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-psych r-ggplot2 r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/seymakalay/pepe")
    (synopsis "Data Manipulation")
    (description
     "Is designed to make easier printing summary statistics (for continues and factor
level) tables in Latex, and plotting by factor.")
    (license license:gpl3)))

(define-public r-pepbvs
  (package
    (name "r-pepbvs")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PEPBVS" version))
       (sha256
        (base32 "1v0pl5jj2bh5iq45xgs35wzfz5hdklsnb72j4rnipy3qbm7zzliy"))))
    (properties `((upstream-name . "PEPBVS")))
    (build-system r-build-system)
    (inputs (list gsl))
    (propagated-inputs (list r-rcppgsl r-rcpparmadillo r-rcpp r-matrix))
    (home-page "https://cran.r-project.org/package=PEPBVS")
    (synopsis
     "Bayesian Variable Selection using Power-Expected-Posterior Prior")
    (description
     "This package performs Bayesian variable selection under normal linear models for
the data with the model parameters following as prior either the
power-expected-posterior (PEP) or the intrinsic (a special case of the former)
(Fouskakis and Ntzoufras (2022) <doi: 10.1214/21-BA1288>, Fouskakis and
Ntzoufras (2020) <doi: 10.3390/econometrics8020017>).  The prior distribution on
model space is the uniform on model space or the uniform on model dimension (a
special case of the beta-binomial prior).  The selection can be done either with
full enumeration of all possible models or using the Markov Chain Monte Carlo
Model Composition (MC3) algorithm (Madigan and York (1995) <doi:
10.2307/1403615>).  Complementary functions for making predictions, as well as
plotting and printing the results are also provided.")
    (license license:gpl2+)))

(define-public r-pepa
  (package
    (name "r-pepa")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pEPA" version))
       (sha256
        (base32 "1a2jij7rwkcf77krwgazldbgh0hjg7dg0911x7np42sjgiqn2li5"))))
    (properties `((upstream-name . "pEPA")))
    (build-system r-build-system)
    (home-page "https://CRAN.R-project.org/package=pEPA")
    (synopsis "Tests of Equal Predictive Accuracy for Panels of Forecasts")
    (description
     "Allows to perform the tests of equal predictive accuracy for panels of
forecasts.  Main references: Qu et al. (2024)
<doi:10.1016/j.ijforecast.2023.08.001> and Akgun et al. (2024)
<doi:10.1016/j.ijforecast.2023.02.001>.")
    (license license:gpl3)))

(define-public r-peopleanalyticsdata
  (package
    (name "r-peopleanalyticsdata")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "peopleanalyticsdata" version))
       (sha256
        (base32 "0ig65y4sl6wagzcvj8wwjw2dblb61hgbsqvp76wvxbhga1q0wnpf"))))
    (properties `((upstream-name . "peopleanalyticsdata")))
    (build-system r-build-system)
    (home-page "http://peopleanalytics-regression-book.org")
    (synopsis
     "Data Sets for Keith McNulty's Handbook of Regression Modeling in People Analytics")
    (description
     "Data sets for statistical inference modeling related to People Analytics.
Contains various data sets from the book Handbook of Regression Modeling in
People Analytics by Keith @code{McNulty} (2020).")
    (license license:expat)))

(define-public r-peopleanalytics
  (package
    (name "r-peopleanalytics")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "peopleanalytics" version))
       (sha256
        (base32 "0ay85yxx30p4i8icn8l8ppjddiwkbw1g6ymnmvw5gnd9hgmwbrx0"))))
    (properties `((upstream-name . "peopleanalytics")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=peopleanalytics")
    (synopsis
     "Data Sets for Craig Starbuck's Book, \"The Fundamentals of People Analytics: With Applications in R\"")
    (description
     "Data sets associated with modeling examples in Craig Starbuck's book, \"The
Fundamentals of People Analytics: With Applications in R\".")
    (license license:expat)))

(define-public r-pensynth
  (package
    (name "r-pensynth")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pensynth" version))
       (sha256
        (base32 "1ph3552y3jsm9kmv68bx15xn383djq2yhc34rpvazc8dyd3impkv"))))
    (properties `((upstream-name . "pensynth")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-clarabel))
    (home-page "https://github.com/vankesteren/pensynth")
    (synopsis "Penalized Synthetic Control Estimation")
    (description
     "Estimate penalized synthetic control models and perform hold-out validation to
determine their penalty parameter.  This method is based on the work by Abadie &
L'Hour (2021) <doi:10.1080/01621459.2021.1971535>.  Penalized synthetic controls
smoothly interpolate between one-to-one matching and the synthetic control
method.")
    (license license:expat)))

(define-public r-pensim
  (package
    (name "r-pensim")
    (version "1.3.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pensim" version))
       (sha256
        (base32 "18z43p1iswmnvwaf3k5vhv6rzzmfn618har0a0bl9j5nfzx20rpx"))))
    (properties `((upstream-name . "pensim")))
    (build-system r-build-system)
    (propagated-inputs (list r-penalized r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://waldronlab.io/pensim/")
    (synopsis
     "Simulation of High-Dimensional Data and Parallelized Repeated Penalized Regression")
    (description
     "Simulation of continuous, correlated high-dimensional data with time to event or
binary response, and parallelized functions for Lasso, Ridge, and Elastic Net
penalized regression with repeated starts and two-dimensional tuning of the
Elastic Net.")
    (license license:gpl2+)))

(define-public r-pense
  (package
    (name "r-pense")
    (version "2.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pense" version))
       (sha256
        (base32 "00z4safn8p0k7ib3jks60lz5cdbm5384cpf95p6js8a8an8p8lb6"))))
    (properties `((upstream-name . "pense")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-rcpparmadillo r-rcpp r-matrix
                             r-lifecycle))
    (native-inputs (list r-knitr))
    (home-page "https://dakep.github.io/pense-rpkg/")
    (synopsis "Penalized Elastic Net S/MM-Estimator of Regression")
    (description
     "Robust penalized (adaptive) elastic net S and M estimators for linear
regression.  The methods are proposed in Cohen Freue, G. V., Kepplinger, D.,
SalibiÃ¡n-Barrera, M., and Smucler, E. (2019)
<https://projecteuclid.org/euclid.aoas/1574910036>.  The package implements the
extensions and algorithms described in Kepplinger, D. (2020)
<doi:10.14288/1.0392915>.")
    (license license:expat)))

(define-public r-penppml
  (package
    (name "r-penppml")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "penppml" version))
       (sha256
        (base32 "1pxfnq5z7aygd2i3qcn2nvzvbw83gp4zhy3bavir3vpjd655llc3"))))
    (properties `((upstream-name . "penppml")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-rcppeigen
                             r-rcpp
                             r-matrixstats
                             r-magrittr
                             r-glmnet
                             r-fixest
                             r-dplyr
                             r-devtools
                             r-collapse))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/tomzylkin/penppml")
    (synopsis "Penalized Poisson Pseudo Maximum Likelihood Regression")
    (description
     "This package provides a set of tools that enables efficient estimation of
penalized Poisson Pseudo Maximum Likelihood regressions, using lasso or ridge
penalties, for models that feature one or more sets of high-dimensional fixed
effects.  The methodology is based on Breinlich, Corradi, Rocha, Ruta, Santos
Silva, and Zylkin (2021) <http://hdl.handle.net/10986/35451> and takes advantage
of the method of alternating projections of Gaure (2013)
<doi:10.1016/j.csda.2013.03.024> for dealing with HDFE, as well as the
coordinate descent algorithm of Friedman, Hastie and Tibshirani (2010)
<doi:10.18637/jss.v033.i01> for fitting lasso regressions.  The package is also
able to carry out cross-validation and to implement the plugin lasso of Belloni,
Chernozhukov, Hansen and Kozbur (2016) <doi:10.1080/07350015.2015.1102733>.")
    (license license:expat)))

(define-public r-penphcure
  (package
    (name "r-penphcure")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "penPHcure" version))
       (sha256
        (base32 "1c90d6f78npvzsvcy9vh8s6izfgbcxzly37lpwhqgdl5daww03l1"))))
    (properties `((upstream-name . "penPHcure")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rdpack r-rcpparmadillo r-rcpp r-mass))
    (home-page "https://github.com/a-beretta/penPHcure")
    (synopsis
     "Variable Selection in PH Cure Model with Time-Varying Covariates")
    (description
     "Implementation of the semi-parametric proportional-hazards (PH) of Sy and Taylor
(2000) <doi:10.1111/j.0006-341X.2000.00227.x> extended to time-varying
covariates.  Estimation and variable selection are based on the methodology
described in Beretta and Heuchenne (2019) <doi:10.1080/02664763.2018.1554627>;
confidence intervals of the parameter estimates may be computed using a
bootstrap approach.  Moreover, data following the PH cure model may be simulated
using a method similar to Hendry (2014) <doi:10.1002/sim.5945>, where the
event-times are generated on a continuous scale from a piecewise exponential
distribution conditional on time-varying covariates.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-penmsm
  (package
    (name "r-penmsm")
    (version "0.99")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "penMSM" version))
       (sha256
        (base32 "1xdcxnagvjdpgnfa5914gb41v5y4lsvh63lbz1d2l8bl9mpff3lm"))))
    (properties `((upstream-name . "penMSM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=penMSM")
    (synopsis "Estimating Regularized Multi-state Models Using L1 Penalties")
    (description
     "Structured fusion Lasso penalized estimation of multi-state models with the
penalty applied to absolute effects and absolute effect differences (i.e.,
effects on transition-type specific hazard rates).")
    (license license:gpl2+)))

(define-public r-penic
  (package
    (name "r-penic")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PenIC" version))
       (sha256
        (base32 "0vcqawmbm8z6lkcxfq1b2png9viz5q669npbv0s7dn6smwhanlzz"))))
    (properties `((upstream-name . "PenIC")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv r-matrix r-mass))
    (home-page "https://cran.r-project.org/package=PenIC")
    (synopsis
     "Semiparametric Regression Analysis of Interval-Censored Data using Penalized Splines")
    (description
     "Currently incorporate the generalized odds-rate model (a type of linear
transformation model) for interval-censored data based on penalized monotonic
B-Spline.  More methods under other semiparametric models such as cure model or
additive model will be included in future versions.  For more details see Lu,
M., Liu, Y., Li, C. and Sun, J. (2019) <@code{arXiv:1912.11703>}.")
    (license license:gpl2+)))

(define-public r-penfa
  (package
    (name "r-penfa")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "penfa" version))
       (sha256
        (base32 "1r8lq4kvlwlk7q0mdzkrdm9sx7lqq4l27jfxa1zcfh50sk1qlam2"))))
    (properties `((upstream-name . "penfa")))
    (build-system r-build-system)
    (propagated-inputs (list r-trust r-mgcv r-mass r-gjrm))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/egeminiani/penfa")
    (synopsis "Single- And Multiple-Group Penalized Factor Analysis")
    (description
     "Fits single- and multiple-group penalized factor analysis models via a
trust-region algorithm with integrated automatic multiple tuning parameter
selection (Geminiani et al., 2021 <doi:10.1007/s11336-021-09751-8>).  Available
penalties include lasso, adaptive lasso, scad, mcp, and ridge.")
    (license license:gpl3)))

(define-public r-pendensity
  (package
    (name "r-pendensity")
    (version "0.2.13")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pendensity" version))
       (sha256
        (base32 "1wcsib7m2161nqqrmgdvrj7lr8zq6njc9xr935hwfh3qbbgkmfjb"))))
    (properties `((upstream-name . "pendensity")))
    (build-system r-build-system)
    (propagated-inputs (list r-lattice r-fda))
    (home-page "https://cran.r-project.org/package=pendensity")
    (synopsis "Density Estimation with a Penalized Mixture Approach")
    (description
     "Estimation of univariate (conditional) densities using penalized B-splines with
automatic selection of optimal smoothing parameter.")
    (license license:gpl2+)))

(define-public r-pencoxfrail
  (package
    (name "r-pencoxfrail")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PenCoxFrail" version))
       (sha256
        (base32 "1g5qggazvfj38436kj144q46aa59s4idnqnr5yv9c3svjikmw1r7"))))
    (properties `((upstream-name . "PenCoxFrail")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rcpparmadillo r-rcpp r-matrix
                             r-coxme))
    (home-page "https://cran.r-project.org/package=PenCoxFrail")
    (synopsis "Regularization in Cox Frailty Models")
    (description
     "Different regularization approaches for Cox Frailty Models by penalization
methods are provided.")
    (license license:gpl2+)))

(define-public r-pencopulacond
  (package
    (name "r-pencopulacond")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pencopulaCond" version))
       (sha256
        (base32 "18hjjxnd0l2ms20ddqkghfbd4as5kq2rgwqzpz4y38k6nw76x511"))))
    (properties `((upstream-name . "pencopulaCond")))
    (build-system r-build-system)
    (propagated-inputs (list r-tsp
                             r-quadprog
                             r-pacotest
                             r-latticeextra
                             r-lattice
                             r-igraph
                             r-foreach
                             r-fda
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=pencopulaCond")
    (synopsis "Estimating Non-Simplified Vine Copulas Using Penalized Splines")
    (description
     "Estimating Non-Simplified Vine Copulas Using Penalized Splines.")
    (license license:gpl2+)))

(define-public r-pencal
  (package
    (name "r-pencal")
    (version "2.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pencal" version))
       (sha256
        (base32 "1wafrq4qgljycdmxhgc2jgbd2ayr9h4nbvss6l5dk2fxsq7yyv6x"))))
    (properties `((upstream-name . "pencal")))
    (build-system r-build-system)
    (propagated-inputs (list r-survivalroc
                             r-survival
                             r-survcomp
                             r-riskregression
                             r-purrr
                             r-nlme
                             r-matrix
                             r-mass
                             r-magic
                             r-lcmm
                             r-glmnet
                             r-foreach
                             r-dplyr
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://mirkosignorelli.github.io/r")
    (synopsis
     "Penalized Regression Calibration (PRC) for the Dynamic Prediction of Survival")
    (description
     "Computes penalized regression calibration (PRC), a statistical method for the
dynamic prediction of survival when many longitudinal predictors are available.
PRC is described in Signorelli (2024) <doi:10.48550/@code{arXiv.2309.15600>} and
in Signorelli et al. (2021) <doi:10.1002/sim.9178>.")
    (license license:gpl3+)))

(define-public r-penaltylearning
  (package
    (name "r-penaltylearning")
    (version "2024.9.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "penaltyLearning" version))
       (sha256
        (base32 "0fip36rx11w9n3q17139gxdm12bknclx287r1qlxandr7hwpac9g"))))
    (properties `((upstream-name . "penaltyLearning")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-data-table))
    (home-page "https://github.com/tdhock/penaltyLearning")
    (synopsis "Penalty Learning")
    (description
     "Implementations of algorithms from Learning Sparse Penalties for Change-point
Detection using Max Margin Interval Regression, by Hocking, Rigaill, Vert, Bach
<http://proceedings.mlr.press/v28/hocking13.html> published in proceedings of
ICML2013.")
    (license license:gpl3)))

(define-public r-penalizedsvm
  (package
    (name "r-penalizedsvm")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "penalizedSVM" version))
       (sha256
        (base32 "1vafdczbqrwj9cagjd7la81jsrqkn917sxppxvz3i608yfl32igl"))))
    (properties `((upstream-name . "penalizedSVM")))
    (build-system r-build-system)
    (propagated-inputs (list r-tgp
                             r-statmod
                             r-mlegp
                             r-mass
                             r-e1071
                             r-corpcor))
    (home-page "https://cran.r-project.org/package=penalizedSVM")
    (synopsis "Feature Selection SVM using Penalty Functions")
    (description
     "Support Vector Machine (SVM) classification with simultaneous feature selection
using penalty functions is implemented.  The smoothly clipped absolute deviation
(SCAD), L1-norm', Elastic Net ('L1-norm and L2-norm') and Elastic SCAD (SCAD and
L2-norm') penalties are available.  The tuning parameters can be found using
either a fixed grid or a interval search.")
    (license license:gpl2+)))

(define-public r-penalizedclr
  (package
    (name "r-penalizedclr")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "penalizedclr" version))
       (sha256
        (base32 "1dg54h6mgy8s3bq3s4xmj4lj0z6wvf0nj5np81mfzzp6fhjqyxhm"))))
    (properties `((upstream-name . "penalizedclr")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-penalized r-clogitl1))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=penalizedclr")
    (synopsis "Integrative Penalized Conditional Logistic Regression")
    (description
     "This package implements L1 and L2 penalized conditional logistic regression with
penalty factors allowing for integration of multiple data sources.  Implements
stability selection for variable selection.")
    (license license:expat)))

(define-public r-penalizedcdf
  (package
    (name "r-penalizedcdf")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "penalizedcdf" version))
       (sha256
        (base32 "17r9f7n6icn2yhfy5ibr8zccr10yq5k4vgyh59fgdzzf5qk407n3"))))
    (properties `((upstream-name . "penalizedcdf")))
    (build-system r-build-system)
    (propagated-inputs (list r-plot-matrix))
    (home-page "https://cran.r-project.org/package=penalizedcdf")
    (synopsis
     "Estimate a Penalized Linear Model using the CDF Penalty Function")
    (description
     "Utilize the CDF penalty function to estimate a penalized linear model.  It
enables you to display some graphical representations and determine whether the
Karush-Kuhn-Tucker conditions are met.  For more details about the theory,
please refer to Cuntrera, D., Augugliaro, L., & Muggeo, V. M. (2022)
<@code{arXiv:2212.08582>}.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-penaft
  (package
    (name "r-penaft")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "penAFT" version))
       (sha256
        (base32 "1yhg9fr3fwpxxcp0ri4iqfwa9f2y134y5b13qfils4y15cp0rnzj"))))
    (properties `((upstream-name . "penAFT")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-matrix r-irlba r-ggplot2))
    (home-page "ajmolstad.github.io/research")
    (synopsis
     "Fit the Regularized Gehan Estimator with Elastic Net and Sparse Group Lasso Penalties")
    (description
     "The semiparametric accelerated failure time (AFT) model is an attractive
alternative to the Cox proportional hazards model.  This package provides a
suite of functions for fitting one popular estimator of the semiparametric AFT
model, the regularized Gehan estimator.  Specifically, we provide functions for
cross-validation, prediction, coefficient extraction, and visualizing both trace
plots and cross-validation curves.  For further details, please see Suder, P. M.
and Molstad, A. J., (2022+) Scalable algorithms for semiparametric accelerated
failure time models in high dimensions, to appear in Statistics in Medicine
<doi:10.1002/sim.9264>.")
    (license license:gpl2+)))

(define-public r-pemultinom
  (package
    (name "r-pemultinom")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pemultinom" version))
       (sha256
        (base32 "0xxcar0vmp6cc58471n5lnp5z5avrn3cv60mjdc96ifms0ghi3vl"))))
    (properties `((upstream-name . "pemultinom")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-nnet r-magrittr r-foreach r-doparallel))
    (home-page "https://cran.r-project.org/package=pemultinom")
    (synopsis "L1-Penalized Multinomial Regression with Statistical Inference")
    (description
     "We aim for fitting a multinomial regression model with Lasso penalty and doing
statistical inference (calculating confidence intervals of coefficients and
p-values for individual variables).  It implements 1) the coordinate descent
algorithm to fit an l1-penalized multinomial regression model (parameterized
with a reference level); 2) the debiasing approach to obtain the inference
results, which is described in Tian et al. (2023) <@code{arXiv:2302.02310>}.")
    (license license:gpl2)))

(define-public r-pems-utils
  (package
    (name "r-pems-utils")
    (version "0.2.29.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pems.utils" version))
       (sha256
        (base32 "0nmawl1l2wf77r02qpazqwm5708fk98k7l07wyybviwb1j3x1pqj"))))
    (properties `((upstream-name . "pems.utils")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-rcpp
                             r-rcolorbrewer
                             r-loa
                             r-latticeextra
                             r-lattice
                             r-ggplot2
                             r-dplyr
                             r-baseline))
    (home-page "http://pems.r-forge.r-project.org/")
    (synopsis
     "Portable Emissions (and Other Mobile) Measurement System Utilities")
    (description
     "Utility functions for the handling, analysis and visualisation of data from
portable emissions measurement systems ('PEMS') and other similar mobile
activity monitoring devices.  The package includes a dedicated pems data class
that manages many of the quality control, unit handling and data archiving
issues that can hinder efforts to standardise PEMS research.")
    (license license:gpl2+)))

(define-public r-pempi
  (package
    (name "r-pempi")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pempi" version))
       (sha256
        (base32 "1hvqszfnqwbya73fs36d8mqz277nfbgrwga5z9jfxdnkalxw5bxq"))))
    (properties `((upstream-name . "pempi")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/stephaneguerrier/pempi")
    (synopsis "Proportion Estimation with Marginal Proxy Information")
    (description
     "This package provides a system contains easy-to-use tools for the conditional
estimation of the prevalence of an emerging or rare infectious diseases using
the methods proposed in Guerrier et al. (2023) <@code{arXiv:2012.10745>}.")
    (license license:agpl3)))

(define-public r-pema
  (package
    (name "r-pema")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pema" version))
       (sha256
        (base32 "0ymzq5s86z97sac1gq8qdh37p2aa39j4pz1n29ga3l0kmv5cxjky"))))
    (properties `((upstream-name . "pema")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-sn
                             r-shiny
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-ggplot2
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/cjvanlissa/pema")
    (synopsis "Penalized Meta-Analysis")
    (description
     "Conduct penalized meta-analysis, see Van Lissa, Van Erp, & Clapper (2023)
<doi:10.31234/osf.io/6phs5>.  In meta-analysis, there are often between-study
differences.  These can be coded as moderator variables, and controlled for
using meta-regression.  However, if the number of moderators is large relative
to the number of studies, such an analysis may be overfit.  Penalized
meta-regression is useful in these cases, because it shrinks the regression
slopes of irrelevant moderators towards zero.")
    (license license:gpl3+)))

(define-public r-pelvis
  (package
    (name "r-pelvis")
    (version "2.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PELVIS" version))
       (sha256
        (base32 "0bn8zxw0lrc5bxm7iq216jnw35vr7zj2ajw0wn2rv7nx3zyd0r6z"))))
    (properties `((upstream-name . "PELVIS")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-mass r-htmlwidgets r-dt))
    (home-page "https://gitlab.com/f-santos/pelvis/")
    (synopsis
     "Probabilistic Sex Estimate using Logistic Regression, Based on VISual Traits of the Human Os Coxae")
    (description
     "An R-Shiny application implementing a method of sexing the human os coxae based
on logistic regressions and Bruzek's nonmetric traits <doi:10.1002/ajpa.23855>.")
    (license (list (license:fsdg-compatible "CeCILL-2")
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-pell
  (package
    (name "r-pell")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pell" version))
       (sha256
        (base32 "1621lnv6sii072did3kj6wapgh9iiqajayvkrmz7q86bp89405sm"))))
    (properties `((upstream-name . "pell")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Curious-Joe/pell")
    (synopsis "Data About Historic Pell Grant Distribution in the US")
    (description
     "Historic Pell grant data as provided by the US Department of Education.  This
package contains data about how much pell grant was awarded by which institution
in which year.  This data comes from the US Department of Education.  Raw data
can be downloaded from here:
<https://www2.ed.gov/finaid/prof/resources/data/pell-institution.html>.")
    (license license:cc0)))

(define-public r-pekit
  (package
    (name "r-pekit")
    (version "1.0.0.1000")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PEkit" version))
       (sha256
        (base32 "023k4swypw8l4hglqxysd53c5f4fq9v3ibha9jm5kvyg3piw3a4p"))))
    (properties `((upstream-name . "PEkit")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PEkit")
    (synopsis "Partition Exchangeability Toolkit")
    (description
     "Bayesian supervised predictive classifiers, hypothesis testing, and parametric
estimation under Partition Exchangeability are implemented.  The two classifiers
presented are the marginal classifier (that assumes test data is i.i.d.) next to
a more computationally costly but accurate simultaneous classifier (that finds a
labelling for the entire test dataset at once based on simultanous use of all
the test data to predict each label).  We also provide the Maximum Likelihood
Estimation (MLE) of the only underlying parameter of the partition
exchangeability generative model as well as hypothesis testing statistics for
equality of this parameter with a single value, alternative, or multiple
samples.  We present functions to simulate the sequences from Ewens Sampling
Formula as the realisation of the Poisson-Dirichlet distribution and their
respective probabilities.")
    (license license:expat)))

(define-public r-peip
  (package
    (name "r-peip")
    (version "2.2-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PEIP" version))
       (sha256
        (base32 "1xvwpsnyxnf3qi1hq5r7dx4bi4s03v53r0m979hyyzcc7w9fr5r5"))))
    (properties `((upstream-name . "PEIP")))
    (build-system r-build-system)
    (propagated-inputs (list r-rseis
                             r-pracma
                             r-matrix
                             r-geigen
                             r-fields
                             r-bvls))
    (home-page "https://cran.r-project.org/package=PEIP")
    (synopsis "Geophysical Inverse Theory and Optimization")
    (description
     "Several functions introduced in Aster et al.'s book on inverse theory.  The
functions are often translations of MATLAB code developed by the authors to
illustrate concepts of inverse theory as applied to geophysics.  Generalized
inversion, tomographic inversion algorithms (conjugate gradients, ART and
SIRT'), non-linear least squares, first and second order Tikhonov
regularization, roughness constraints, and procedures for estimating smoothing
parameters are included.")
    (license license:gpl2+)))

(define-public r-peiman2
  (package
    (name "r-peiman2")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PEIMAN2" version))
       (sha256
        (base32 "01jsyp9yk576v3bc0a33y0f8w9v4b47g5hc0zahf3zl4bkp8i4wv"))))
    (properties `((upstream-name . "PEIMAN2")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyverse
                             r-stringr
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-lifecycle
                             r-glue
                             r-ggplot2
                             r-forcats
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PEIMAN2")
    (synopsis
     "Post-Translational Modification Enrichment, Integration, and Matching Analysis")
    (description
     "This package provides functions and mined database from @code{UniProt} focusing
on post-translational modifications to do single enrichment analysis (SEA) and
protein set enrichment analysis (PSEA).  Payman Nickchi, Mehdi Mirzaie, Marc
Baumann, Amir Ata Saei, Mohieddin Jafari (2022)
<@code{bioRxiv:10.1101/2022.11.09.515610>}.")
    (license license:gpl3+)))

(define-public r-pegrouptesting
  (package
    (name "r-pegrouptesting")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PEGroupTesting" version))
       (sha256
        (base32 "1z863ard0cc2g8ibxnmxhiqhvs53r6b8gpnavnfchm9432ifcbwi"))))
    (properties `((upstream-name . "PEGroupTesting")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PEGroupTesting")
    (synopsis "Population Proportion Estimation using Group Testing")
    (description
     "The population proportion using group testing can be estimated by different
methods.  Four functions including p.mle(), p.gart(), p.burrow() and p.order()
are provided to implement four estimating methods including the maximum
likelihood estimate, Gart's estimate, Burrow's estimate, and order statistic
estimate.")
    (license license:gpl2)))

(define-public r-peerperformance
  (package
    (name "r-peerperformance")
    (version "2.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PeerPerformance" version))
       (sha256
        (base32 "0cdv7ndbjxzaayxkrxh99jsyivlipbnipv21d4rmf8j8ssfq74r5"))))
    (properties `((upstream-name . "PeerPerformance")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich r-lmtest))
    (home-page "https://github.com/ArdiaD/PeerPerformance")
    (synopsis "Luck-Corrected Peer Performance Analysis in R")
    (description
     "This package provides functions to perform the peer performance analysis of
funds returns as described in Ardia and Boudt (2018)
<doi:10.1016/j.jbankfin.2017.10.014>.")
    (license license:gpl2+)))

(define-public r-peermodels
  (package
    (name "r-peermodels")
    (version "0.10.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "peermodels" version))
       (sha256
        (base32 "116xn5w7rwm9q0m7bj4z29l4hspj2nqdcxzir9dqpdqmf7z58v67"))))
    (properties `((upstream-name . "peermodels")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-jsonlite r-httr2 r-httr r-curl))
    (home-page "https://www.peermodelsnetwork.com")
    (synopsis
     "Client-Side R API Wrapper for Peer Models Network Model Repository")
    (description
     "Enables direct cloud access to health care decision models hosted on the PRISM
server of the Peer Models Network.")
    (license license:gpl3)))

(define-public r-pedtricks
  (package
    (name "r-pedtricks")
    (version "0.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pedtricks" version))
       (sha256
        (base32 "1l1ysyipwlbfvhm6d8dqyvn2hchy7dnmlgnx5l32znv0dv4s0zcl"))))
    (properties `((upstream-name . "pedtricks")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-nadiv
                             r-mvtnorm
                             r-mcmcglmm
                             r-matrix
                             r-kinship2
                             r-igraph
                             r-ggplot2
                             r-genetics
                             r-dplyr))
    (home-page "https://juliengamartin.github.io/pedtricks/")
    (synopsis "Visualize, Summarize and Simulate Data from Pedigrees")
    (description
     "Sensitivity and power analysis, for calculating statistics describing pedigrees
from wild populations, and for visualizing pedigrees.")
    (license license:expat)))

(define-public r-pedtools
  (package
    (name "r-pedtools")
    (version "2.7.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pedtools" version))
       (sha256
        (base32 "0ip8r88xky8z6dphif2p86l7mfasgaxac0bgi1rrfj385lr5swbb"))))
    (properties `((upstream-name . "pedtools")))
    (build-system r-build-system)
    (propagated-inputs (list r-pedmut r-kinship2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/magnusdv/pedtools")
    (synopsis "Creating and Working with Pedigrees and Marker Data")
    (description
     "This package provides a comprehensive collection of tools for creating,
manipulating and visualising pedigrees and genetic marker data.  Pedigrees can
be read from text files or created on the fly with built-in functions.  A range
of utilities enable modifications like adding or removing individuals, breaking
loops, and merging pedigrees.  An online tool for creating pedigrees
interactively, based on pedtools', is available at
<https://magnusdv.shinyapps.io/quickped>.  pedtools is the hub of the pedsuite',
a collection of packages for pedigree analysis.  A detailed presentation of the
pedsuite is given in the book Pedigree Analysis in R (Vigeland, 2021,
ISBN:9780128244302).")
    (license license:gpl3)))

(define-public r-pedsuite
  (package
    (name "r-pedsuite")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pedsuite" version))
       (sha256
        (base32 "0arydd5lmwsqp0g7akd7w0w5mc7bs4dap38bnap9qh0znf94kka2"))))
    (properties `((upstream-name . "pedsuite")))
    (build-system r-build-system)
    (propagated-inputs (list r-verbalisr
                             r-segregatr
                             r-ribd
                             r-pedtools
                             r-pedprobr
                             r-pedmut
                             r-pedfamilias
                             r-pedbuildr
                             r-paramlink2
                             r-ibdsim2
                             r-forrel
                             r-dvir))
    (native-inputs (list r-knitr))
    (home-page "https://magnusdv.github.io/pedsuite/")
    (synopsis
     "Easy Installation of the 'pedsuite' Packages for Pedigree Analysis")
    (description
     "The pedsuite is a collection of packages for pedigree analysis, covering
applications in forensic genetics, medical genetics and more.  A detailed
presentation of the pedsuite is given in the book Pedigree Analysis in R
(Vigeland, 2021, ISBN: 9780128244302).")
    (license license:gpl3+)))

(define-public r-pedsimulate
  (package
    (name "r-pedsimulate")
    (version "1.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pedSimulate" version))
       (sha256
        (base32 "11p7gkxzd9ajyivxnpw4mzdn4c812gihl8q0x5w6h3s9ixsk8qrl"))))
    (properties `((upstream-name . "pedSimulate")))
    (build-system r-build-system)
    (home-page "https://github.com/nilforooshan/pedSimulate")
    (synopsis "Pedigree, Genetic Merit, Phenotype, and Genotype Simulation")
    (description
     "Simulate pedigree, genetic merits and phenotypes with random/non-random matings
followed by random/non-random selection with different intensities and patterns
in males and females.  Genotypes can be simulated for a given pedigree, or an
appended pedigree to an existing pedigree with genotypes.  Mrode, R. A. (2005)
<ISBN:9780851989969, 0851989969>; Nilforooshan, M.A. (2022)
<doi:10.37496/rbz5120210131>.")
    (license license:gpl3)))

(define-public r-pedquant
  (package
    (name "r-pedquant")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pedquant" version))
       (sha256
        (base32 "07b2jd3fryv3jdhp5wrsycnzfwhbx55dw34lxll60zn73m4ii24w"))))
    (properties `((upstream-name . "pedquant")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xefun
                             r-ttr
                             r-stringi
                             r-rvest
                             r-readxl
                             r-readr
                             r-performanceanalytics
                             r-lubridate
                             r-jsonlite
                             r-httr
                             r-echarts4r
                             r-data-table
                             r-curl))
    (home-page "https://github.com/ShichenXie/pedquant")
    (synopsis "Public Economic Data and Quantitative Analysis")
    (description
     "This package provides an interface to access public economic and financial data
for economic research and quantitative analysis.  The data sources including
NBS, FRED, Sina, Eastmoney and etc.  It also provides quantitative functions for
trading strategies based on the data.table', TTR', @code{PerformanceAnalytics}
and etc packages.")
    (license license:gpl3)))

(define-public r-pedprobr
  (package
    (name "r-pedprobr")
    (version "0.9.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pedprobr" version))
       (sha256
        (base32 "17gzrv43gz8r1y31vzmc1arkr4wvzirk64vps6d98gk0r4i5vv71"))))
    (properties `((upstream-name . "pedprobr")))
    (build-system r-build-system)
    (propagated-inputs (list r-pedtools r-pedmut))
    (home-page "https://github.com/magnusdv/pedprobr")
    (synopsis "Probability Computations on Pedigrees")
    (description
     "An implementation of the Elston-Stewart algorithm for calculating pedigree
likelihoods given genetic marker data (Elston and Stewart (1971)
<doi:10.1159/000152448>).  The standard algorithm is extended to allow inbred
founders.  pedprobr is part of the ped suite', a collection of packages for
pedigree analysis in R. In particular, pedprobr depends on pedtools for pedigree
manipulations and pedmut for mutation modelling.  For more information, see
Pedigree Analysis in R (Vigeland, 2021, ISBN:9780128244302).")
    (license license:gpl2+)))

(define-public r-pedometrics
  (package
    (name "r-pedometrics")
    (version "0.12.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pedometrics" version))
       (sha256
        (base32 "06s1iyv2nldqqkh1n3cgl3a7ribk17kyadgfx76lpgy016fi7vz5"))))
    (properties `((upstream-name . "pedometrics")))
    (build-system r-build-system)
    (inputs (list pandoc))
    (propagated-inputs (list r-rcpp r-latticeextra r-lattice))
    (home-page
     "https://github.com/Laboratorio-de-Pedometria/pedometrics-package")
    (synopsis "Miscellaneous Pedometric Tools")
    (description
     "An R implementation of methods employed in the field of pedometrics, soil
science discipline dedicated to studying the spatial, temporal, and
spatio-temporal variation of soil using statistical and computational methods.
The methods found here include the calibration of linear regression models using
covariate selection strategies, computation of summary validation statistics for
predictions, generation of summary plots, evaluation of the local quality of a
geostatistical model of uncertainty, and so on.  Other functions simply extend
the functionalities of or facilitate the usage of functions from other packages
that are commonly used for the analysis of soil data.  Formerly available
versions of suggested packages no longer available from CRAN can be obtained
from the CRAN archive <https://cran.r-project.org/src/contrib/Archive/>.")
    (license license:gpl2+)))

(define-public r-pedmut
  (package
    (name "r-pedmut")
    (version "0.7.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pedmut" version))
       (sha256
        (base32 "1wjz0mqwwvxn44lyrdnns0zsadglfs50np8sy6b0w07bpr6izlii"))))
    (properties `((upstream-name . "pedmut")))
    (build-system r-build-system)
    (home-page "https://github.com/magnusdv/pedmut")
    (synopsis "Mutation Models for Pedigree Likelihood Computations")
    (description
     "This package provides a collection of functions for modelling mutations in
pedigrees with marker data, as used e.g. in likelihood computations with
microsatellite data.  Implemented models include equal, proportional and
stepwise models, as well as random models for experimental work, and custom
models allowing the user to apply any valid mutation matrix.  Allele lumping is
done following the lumpability criteria of Kemeny and Snell (1976),
ISBN:0387901922.")
    (license license:gpl3)))

(define-public r-pedmermaid
  (package
    (name "r-pedmermaid")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pedMermaid" version))
       (sha256
        (base32 "135pmjhhg2f8fgp3x5dddfssbfkdwxq86mlpqzhfiwn9pa07xbfc"))))
    (properties `((upstream-name . "pedMermaid")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/nilforooshan/pedMermaid")
    (synopsis "Pedigree Mermaid Syntax")
    (description
     "Generate Mermaid syntax for a pedigree flowchart from a pedigree data frame.
Mermaid syntax is commonly used to generate plots, charts, diagrams, and
flowcharts.  It is a textual syntax for creating reproducible illustrations.
This package generates Mermaid syntax from a pedigree data frame to visualize a
pedigree flowchart.  The Mermaid syntax can be embedded in a Markdown or R
Markdown file, or viewed on Mermaid editors and renderers.  Links shape, style,
and orientation can be customized via function arguments, and nodes shapes and
styles can be customized via optional columns in the pedigree data frame.")
    (license license:gpl3+)))

(define-public r-pedigreetools
  (package
    (name "r-pedigreetools")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pedigreeTools" version))
       (sha256
        (base32 "03kmpizrjsr1x4av7zis2swrz0wr6b708bpss545mvjwm7zq3wc4"))))
    (properties `((upstream-name . "pedigreeTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (home-page "https://github.com/Rpedigree/pedigreeTools/")
    (synopsis "Versatile Functions for Working with Pedigrees")
    (description
     "This package provides tools to sort, edit and prune pedigrees and to extract the
inbreeding coefficients and the relationship matrix (includes code for pedigrees
from self-pollinated species).  The use of pedigree data is central to genetics
research within the animal and plant breeding communities to predict breeding
values.  The relationship matrix between the individuals can be derived from
pedigree structure ('Vazquez et al., 2010') <doi:10.2527/jas.2009-1952>.")
    (license license:gpl3)))

(define-public r-pedigreemm
  (package
    (name "r-pedigreemm")
    (version "0.3-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pedigreemm" version))
       (sha256
        (base32 "1bnvd25z8ff0d00wijp2vw9xw11fczqrlblw3ag39pjzn98a1zf7"))))
    (properties `((upstream-name . "pedigreemm")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-lme4))
    (home-page "https://github.com/anainesvs/pedigreemm/")
    (synopsis "Pedigree-Based Mixed-Effects Models")
    (description "Fit pedigree-based mixed-effects models.")
    (license license:gpl2+)))

(define-public r-pedigree
  (package
    (name "r-pedigree")
    (version "1.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pedigree" version))
       (sha256
        (base32 "1b3zhjn6sl1pnkfqim6xzk6qdak6acdpim585xhjl1rbc41540qx"))))
    (properties `((upstream-name . "pedigree")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape r-matrix r-haplosim))
    (home-page "https://cran.r-project.org/package=pedigree")
    (synopsis "Pedigree Functions")
    (description "Pedigree related functions.")
    (license license:gpl2+)))

(define-public r-pedgene
  (package
    (name "r-pedgene")
    (version "3.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pedgene" version))
       (sha256
        (base32 "18b0galqql6pci0bfy8k93p29mqsk707wz4lr72plnciqg04l5sf"))))
    (properties `((upstream-name . "pedgene")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey r-matrix r-kinship2 r-compquadform))
    (home-page "https://analytictools.mayo.edu/research/pedgene/")
    (synopsis "Gene-Level Variant Association Tests for Pedigree Data")
    (description
     "Gene-level variant association tests with disease status for pedigree data:
kernel and burden association statistics.")
    (license license:gpl2+)))

(define-public r-pedfamilias
  (package
    (name "r-pedfamilias")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pedFamilias" version))
       (sha256
        (base32 "015cpgzk7ampam0q85hmwnscwxv72kiyvs4p9rsp00r468zwyfca"))))
    (properties `((upstream-name . "pedFamilias")))
    (build-system r-build-system)
    (propagated-inputs (list r-pedtools r-pedmut))
    (home-page "https://github.com/magnusdv/pedFamilias")
    (synopsis "Import and Export 'Familias' Files")
    (description
     "This package provides tools for exchanging pedigree data between the pedsuite
packages and the Familias software for forensic kinship computations (Egeland et
al. (2000) <doi:10.1016/s0379-0738(00)00147-x>).  These functions were split out
from the forrel package to streamline maintenance and provide a lightweight
alternative for packages otherwise independent of forrel'.")
    (license license:gpl3+)))

(define-public r-pedbuildr
  (package
    (name "r-pedbuildr")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pedbuildr" version))
       (sha256
        (base32 "1yq5ag62yf6qjkxvr4g0xggag0ic5gyby4y5vnhlakwn0h78yirv"))))
    (properties `((upstream-name . "pedbuildr")))
    (build-system r-build-system)
    (propagated-inputs (list r-ribd
                             r-pedtools
                             r-pedprobr
                             r-pedmut
                             r-glue
                             r-forrel))
    (home-page "https://github.com/magnusdv/pedbuildr")
    (synopsis "Pedigree Reconstruction")
    (description
     "Reconstruct pedigrees from genotype data, by optimising the likelihood over all
possible pedigrees subject to given restrictions.  Tailor-made plots facilitate
evaluation of the output.  This package is part of the pedsuite ecosystem for
pedigree analysis.  In particular, it imports pedprobr for calculating pedigree
likelihoods and forrel for estimating pairwise relatedness.")
    (license license:gpl3)))

(define-public r-pedbp
  (package
    (name "r-pedbp")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pedbp" version))
       (sha256
        (base32 "0yrlmwiizdwpyvdd4dlhq6s9bjs3xjcrpzp6wx3vr4sbzfjjqmwm"))))
    (properties `((upstream-name . "pedbp")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-rcpparmadillo r-rcpp r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/dewittpe/pedbp/")
    (synopsis "Pediatric Blood Pressure")
    (description
     "Data and utilities for estimating pediatric blood pressure percentiles by sex,
age, and optionally height (stature) as described in Martin et.al. (2022)
<doi:10.1001/jamanetworkopen.2022.36918>.  Blood pressure percentiles for
children under one year of age come from Gemelli et.al. (1990)
<doi:10.1007/BF02171556>.  Estimates of blood pressure percentiles for children
at least one year of age are informed by data from the National Heart, Lung, and
Blood Institute (NHLBI) and the Centers for Disease Control and Prevention (CDC)
<doi:10.1542/peds.2009-2107C> or from Lo et.al. (2013)
<doi:10.1542/peds.2012-1292>.  The flowchart for selecting the informing data
source comes from Martin et.al. (2022) <doi:10.1542/hpeds.2021-005998>.")
    (license license:gpl2)))

(define-public r-pedalfast-data
  (package
    (name "r-pedalfast-data")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pedalfast.data" version))
       (sha256
        (base32 "0dr1zfg3sxmah2zghwpmv3a6znap7hbkkz88z0668ki34lf22pvm"))))
    (properties `((upstream-name . "pedalfast.data")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pedalfast.data")
    (synopsis "PEDALFAST Data")
    (description
     "Data files and documentation for PEDiatric @code{vALidation} @code{oF}
@code{vAriableS} in TBI (PEDALFAST).  The data was used in \"Functional Status
Scale in Children With Traumatic Brain Injury: A Prospective Cohort Study\" by
Bennett, Dixon, et al (2016) <doi:10.1097/PCC.0000000000000934>.")
    (license license:gpl2)))

(define-public r-pecora
  (package
    (name "r-pecora")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pecora" version))
       (sha256
        (base32 "0l66brd5d24hn279ck785jy4qnxiv9lc3d1czh4f3l25d0hj99xi"))))
    (properties `((upstream-name . "pecora")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-matrixstats))
    (home-page "https://cran.r-project.org/package=pecora")
    (synopsis "Permutation Conditional Random Tests")
    (description
     "It provides functions to perform permutation conditional random one-sample and
two-samples t-tests in a multivariate framework.")
    (license license:gpl2+)))

(define-public r-pec
  (package
    (name "r-pec")
    (version "2023.04.12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pec" version))
       (sha256
        (base32 "15ggf3fa1p5r0dl7k2j99cc920jvmk0p1ny0jzyrpddh8fcgwlk5"))))
    (properties `((upstream-name . "pec")))
    (build-system r-build-system)
    (propagated-inputs (list r-timereg
                             r-survival
                             r-rms
                             r-riskregression
                             r-prodlim
                             r-lava
                             r-foreach))
    (home-page "https://cran.r-project.org/package=pec")
    (synopsis
     "Prediction Error Curves for Risk Prediction Models in Survival Analysis")
    (description
     "Validation of risk predictions obtained from survival models and competing risk
models based on censored data using inverse weighting and cross-validation.
Most of the pec functionality has been moved to @code{riskRegression}'.")
    (license license:gpl2+)))

(define-public r-pearsonica
  (package
    (name "r-pearsonica")
    (version "1.2-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PearsonICA" version))
       (sha256
        (base32 "04xi911wzkgsh1ajqamgwgv7wnpgyc20brjcr4jh6gfkfhacjprv"))))
    (properties `((upstream-name . "PearsonICA")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PearsonICA")
    (synopsis
     "Independent Component Analysis using Score Functions from the Pearson System")
    (description
     "The Pearson-ICA algorithm is a mutual information-based method for blind
separation of statistically independent source signals.  It has been shown that
the minimization of mutual information leads to iterative use of score
functions, i.e.  derivatives of log densities.  The Pearson system allows
adaptive modeling of score functions.  The flexibility of the Pearson system
makes it possible to model a wide range of source distributions including
asymmetric distributions.  The algorithm is designed especially for problems
with asymmetric sources but it works for symmetric sources as well.")
    (license license:agpl3)))

(define-public r-pearsonds
  (package
    (name "r-pearsonds")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PearsonDS" version))
       (sha256
        (base32 "1s07zdqa769rx0b0nhr3jrx5siy5ywc43kgm1574gzqck3dkqpa1"))))
    (properties `((upstream-name . "PearsonDS")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PearsonDS")
    (synopsis "Pearson Distribution System")
    (description
     "Implementation of the Pearson distribution system, including full support for
the (d,p,q,r)-family of functions for probability distributions and fitting via
method of moments and maximum likelihood method.")
    (license license:gpl2+)))

(define-public r-pearson7
  (package
    (name "r-pearson7")
    (version "1.0-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pearson7" version))
       (sha256
        (base32 "0vdhhds1lngaff7dkx4b1az3syqw8b22w2jskcrw0m49wp1dbbha"))))
    (properties `((upstream-name . "pearson7")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pearson7")
    (synopsis
     "Maximum Likelihood Inference for the Pearson VII Distribution with Shape Parameter 3/2")
    (description
     "Supports maximum likelihood inference for the Pearson VII distribution with
shape parameter 3/2 and free location and scale parameters.  This distribution
is relevant when estimating the velocity of processive motor proteins with
random detachment.")
    (license license:gpl2+)))

(define-public r-peaksegoptimal
  (package
    (name "r-peaksegoptimal")
    (version "2024.10.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PeakSegOptimal" version))
       (sha256
        (base32 "1vrfj0kv8lhhw72zd9c4r27bhm0l1db1b1c5a9xxjsqx2bsjs68v"))))
    (properties `((upstream-name . "PeakSegOptimal")))
    (build-system r-build-system)
    (propagated-inputs (list r-penaltylearning))
    (home-page "https://github.com/tdhock/PeakSegOptimal")
    (synopsis "Optimal Segmentation Subject to Up-Down Constraints")
    (description
     "Computes optimal changepoint models using the Poisson likelihood for
non-negative count data, subject to the @code{PeakSeg} constraint: the first
change must be up, second change down, third change up, etc.  For more info
about the models and algorithms, read \"Constrained Dynamic Programming and
Supervised Penalty Learning Algorithms for Peak Detection\"
<https://jmlr.org/papers/v21/18-843.html> by TD Hocking et al.")
    (license license:gpl3)))

(define-public r-peaksegjoint
  (package
    (name "r-peaksegjoint")
    (version "2024.10.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PeakSegJoint" version))
       (sha256
        (base32 "176pzc2qhnv6xq00chjnavlbav42nmr64hiib73h3cia4x7v9693"))))
    (properties `((upstream-name . "PeakSegJoint")))
    (build-system r-build-system)
    (propagated-inputs (list r-penaltylearning r-peakerror))
    (home-page "https://github.com/tdhock/PeakSegJoint")
    (synopsis "Joint Peak Detection in Several ChIP-Seq Samples")
    (description
     "Jointly segment several @code{ChIP-seq} samples to find the peaks which are the
same and different across samples.  The fast approximate maximum Poisson
likelihood algorithm is described in \"@code{PeakSegJoint}: fast supervised peak
detection via joint segmentation of multiple count data samples\"
<doi:10.48550/@code{arXiv.1506.01286>} by TD Hocking and G Bourque.")
    (license license:gpl3)))

(define-public r-peaksegdp
  (package
    (name "r-peaksegdp")
    (version "2024.1.24")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PeakSegDP" version))
       (sha256
        (base32 "0kqbjxprvxswkmvqzzzb4yxihz2cn6z40y021cfh6sr0k8hydd2a"))))
    (properties `((upstream-name . "PeakSegDP")))
    (build-system r-build-system)
    (home-page "https://github.com/tdhock/PeakSegDP")
    (synopsis
     "Dynamic Programming Algorithm for Peak Detection in ChIP-Seq Data")
    (description
     "This package provides a quadratic time dynamic programming algorithm can be used
to compute an approximate solution to the problem of finding the most likely
changepoints with respect to the Poisson likelihood, subject to a constraint on
the number of segments, and the changes which must alternate: up, down, up,
down, etc.  For more info read <http://proceedings.mlr.press/v37/hocking15.html>
\"@code{PeakSeg}: constrained optimal segmentation and supervised penalty
learning for peak detection in count data\" by TD Hocking et al, proceedings of
ICML2015.")
    (license license:gpl3)))

(define-public r-peaksegdisk
  (package
    (name "r-peaksegdisk")
    (version "2024.10.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PeakSegDisk" version))
       (sha256
        (base32 "05lvfcrm51cnz1phxmmz7kgk18951jm4pkmsjzypph1q7sfr1pvk"))))
    (properties `((upstream-name . "PeakSegDisk")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/tdhock/PeakSegDisk")
    (synopsis "Disk-Based Constrained Change-Point Detection")
    (description
     "Disk-based implementation of Functional Pruning Optimal Partitioning with
up-down constraints <doi:10.18637/jss.v101.i10> for single-sample peak calling
(independently for each sample and genomic problem), can handle huge data sets
(10^7 or more).")
    (license license:gpl3)))

(define-public r-peakram
  (package
    (name "r-peakram")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "peakRAM" version))
       (sha256
        (base32 "0mj7f8jqkfky61xag2km0svbgvfmif3c1d8ccav6pv1963cg3cvv"))))
    (properties `((upstream-name . "peakRAM")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "http://github.com/tpq/peakRAM")
    (synopsis
     "Monitor the Total and Peak RAM Used by an Expression or Function")
    (description
     "When working with big data sets, RAM conservation is critically important.
However, it is not always enough to just monitor the size of the objects
created.  So-called \"copy-on-modify\" behavior, characteristic of R, means that
some expressions or functions may require an unexpectedly large amount of RAM
overhead.  For example, replacing a single value in a matrix duplicates that
matrix in the back-end, making this task require twice as much RAM as that used
by the matrix itself.  This package makes it easy to monitor the total and peak
RAM used so that developers can quickly identify and eliminate RAM hungry code.")
    (license license:gpl2)))

(define-public r-peakerror
  (package
    (name "r-peakerror")
    (version "2023.9.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PeakError" version))
       (sha256
        (base32 "07hidwjf0pgahdw12jlvv2cygfvizv1zjir49nmra01zm1yr5k62"))))
    (properties `((upstream-name . "PeakError")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PeakError")
    (synopsis "Compute the Label Error of Peak Calls")
    (description
     "Chromatin immunoprecipitation DNA sequencing results in genomic tracks that show
enriched regions or peaks where proteins are bound.  This package implements
fast C code that computes the true and false positives with respect to a
database of annotated region labels.")
    (license license:gpl3)))

(define-public r-peacots
  (package
    (name "r-peacots")
    (version "1.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "peacots" version))
       (sha256
        (base32 "1mhbch6y580pc07asm77zcwfmc99gvf7ibd0mrsgrg1mk6ybx4iy"))))
    (properties `((upstream-name . "peacots")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=peacots")
    (synopsis "Periodogram Peaks in Correlated Time Series")
    (description
     "Calculates the periodogram of a time series, maximum-likelihood fits an
Ornstein-Uhlenbeck state space (OUSS) null model and evaluates the statistical
significance of periodogram peaks against the OUSS null hypothesis.  The OUSS is
a parsimonious model for stochastically fluctuating variables with linear
stabilizing forces, subject to uncorrelated measurement errors.  Contrary to the
classical white noise null model for detecting cyclicity, the OUSS model can
account for temporal correlations typically occurring in ecological and
geological time series.  Citation: Louca, Stilianos and Doebeli, Michael (2015)
<doi:10.1890/14-0126.1>.")
    (license license:gpl3)))

(define-public r-peacock-test
  (package
    (name "r-peacock-test")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Peacock.test" version))
       (sha256
        (base32 "0ypvwywipyh8lm8xkazsfjgjpvvyi2g8kid63qff11372p33m6xs"))))
    (properties `((upstream-name . "Peacock.test")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=Peacock.test")
    (synopsis "Two and Three Dimensional Kolmogorov-Smirnov Two-Sample Tests")
    (description
     "The original definition of the two and three dimensional Kolmogorov-Smirnov
two-sample test statistics given by Peacock (1983) is implemented.  Two
R-functions: peacock2 and peacock3, are provided to compute the test statistics
in two and three dimensional spaces, respectively.  Note the Peacock test is
different from the Fasano and Franceschini test (1987).  The latter is a variant
of the Peacock test.")
    (license license:gpl2)))

(define-public r-peach
  (package
    (name "r-peach")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PEACH" version))
       (sha256
        (base32 "0ciq2cci35rv39pgpp7i30jxlmr85726kh07ww22ryq3bm04ylg8"))))
    (properties `((upstream-name . "PEACH")))
    (build-system r-build-system)
    (propagated-inputs (list r-mnormt r-metap r-mass))
    (home-page "https://cran.r-project.org/package=PEACH")
    (synopsis
     "Pareto Enrichment Analysis for Combining Heterogeneous Datasets")
    (description
     "This package provides a meta gene set analysis tool developed based on
principles of Pareto dominance (William B T Mock (2011)
<doi:10.1007/978-1-4020-9160-5_341>).  It is designed to combine gene set
analysis p-values from multiple transcriptome datasets (e.g., microarray and
RNA-Seq).  The novel Pareto method for p-value combination allows PEACH to
properly model heterogeneity and correlation in Omics datasets.")
    (license license:gpl2)))

(define-public r-peacesciencer
  (package
    (name "r-peacesciencer")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "peacesciencer" version))
       (sha256
        (base32 "06mlz2yjihm4xbdipv167ldf8z7r0ldwfn9day5c3lphg73fym5b"))))
    (properties `((upstream-name . "peacesciencer")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-stevemisc
                             r-rlang
                             r-magrittr
                             r-lifecycle
                             r-geosphere
                             r-dplyr))
    (home-page "https://github.com/svmiller/peacesciencer/")
    (synopsis "Tools and Data for Quantitative Peace Science Research")
    (description
     "These are useful tools and data sets for the study of quantitative peace
science.  The goal for this package is to include tools and data sets for doing
original research that mimics well what a user would have to previously get from
a software package that may not be well-sourced or well-supported.  Those
software bundles were useful the extent to which they encourage replications of
long-standing analyses by starting the data-generating process from scratch.
However, a lot of the functionality can be done relatively quickly and more
transparently in the R programming language.")
    (license license:gpl2)))

(define-public r-pdynmc
  (package
    (name "r-pdynmc")
    (version "0.9.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pdynmc" version))
       (sha256
        (base32 "0lw0hnqp3czwrjwm7jbbqmkdp1rwwkfffrahs28ywbxpka0mzv1v"))))
    (properties `((upstream-name . "pdynmc")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-optimx r-matrix r-mass r-data-table))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/markusfritsch/pdynmc")
    (synopsis
     "Moment Condition Based Estimation of Linear Dynamic Panel Data Models")
    (description
     "Linear dynamic panel data modeling based on linear and nonlinear moment
conditions as proposed by Holtz-Eakin, Newey, and Rosen (1988)
<doi:10.2307/1913103>, Ahn and Schmidt (1995)
<doi:10.1016/0304-4076(94)01641-C>, and Arellano and Bover (1995)
<doi:10.1016/0304-4076(94)01642-D>.  Estimation of the model parameters relies
on the Generalized Method of Moments (GMM) and instrumental variables (IV)
estimation, numerical optimization (when nonlinear moment conditions are
employed) and the computation of closed form solutions (when estimation is based
on linear moment conditions).  One-step, two-step and iterated estimation is
available.  For inference and specification testing, Windmeijer (2005)
<doi:10.1016/j.jeconom.2004.02.005> and doubly corrected standard errors (Hwang,
Kang, Lee, 2021 <doi:10.1016/j.jeconom.2020.09.010>) are available.
Additionally, serial correlation tests, tests for overidentification, and Wald
tests are provided.  Functions for visualizing panel data structures and
modeling results obtained from GMM estimation are also available.  The plot
methods include functions to plot unbalanced panel structure, coefficient ranges
and coefficient paths across GMM iterations (the latter is implemented according
to the plot shown in Hansen and Lee, 2021 <doi:10.3982/ECTA16274>).  For a more
detailed description of the GMM-based functionality, please see Fritsch, Pua,
Schnurbus (2021) <doi:10.32614/RJ-2021-035>.  For more detail on the IV-based
estimation routines, see Fritsch, Pua, and Schnurbus (WP, 2024) and Han and
Phillips (2010) <doi:10.1017/S026646660909063X>.")
    (license license:gpl2+)))

(define-public r-pdxtrees
  (package
    (name "r-pdxtrees")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pdxTrees" version))
       (sha256
        (base32 "0cw1zrkawc2wnbd2pif8732kl92qvklxvx6hnikx3siz22psvbl8"))))
    (properties `((upstream-name . "pdxTrees")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-magrittr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mcconvil/pdxTrees")
    (synopsis "Data Package of Portland, Oregon Trees")
    (description
     "An engaging collection of datasets from Portland Parks and Recreation.  The city
of Portland inventoried every tree in over 170 parks and along the streets in 96
neighborhoods.")
    (license license:gpl3)))

(define-public r-pdxpower
  (package
    (name "r-pdxpower")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PDXpower" version))
       (sha256
        (base32 "0dpwc3wpsqm844lzlymanfi4pj7ajhqsqf025vx64j815j3q1122"))))
    (properties `((upstream-name . "PDXpower")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-nlme r-ggpubr r-ggplot2
                             r-frailtypack))
    (home-page "https://cran.r-project.org/package=PDXpower")
    (synopsis
     "Time to Event Outcome in Experimental Designs of Pre-Clinical Studies")
    (description
     "Conduct simulation-based customized power calculation for clustered time to
event data in a mixed crossed/nested design, where a number of cell lines and a
number of mice within each cell line are considered to achieve a desired
statistical power, motivated by Eckel-Passow and colleagues (2021)
<doi:10.1093/neuonc/noab137> and Li and colleagues (2024)
<doi:10.48550/@code{arXiv.2404.08927>}.  This package provides two commonly used
models for powering a design, linear mixed effects and Cox frailty model.  Both
models account for within-subject (cell line) correlation while holding
different distributional assumptions about the outcome.  Alternatively, the
counterparts of fixed effects model are also available, which produces similar
estimates of statistical power.")
    (license license:gpl2+)))

(define-public r-pdtoolkit
  (package
    (name "r-pdtoolkit")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PDtoolkit" version))
       (sha256
        (base32 "0g4s5fw0m128kq9dhaa4j76b2islhbbj9a6dqbmcmvlqla0bqm3k"))))
    (properties `((upstream-name . "PDtoolkit")))
    (build-system r-build-system)
    (propagated-inputs (list r-rpart r-monobin r-dplyr))
    (home-page "https://github.com/andrija-djurovic/PDtoolkit")
    (synopsis
     "Collection of Tools for PD Rating Model Development and Validation")
    (description
     "The goal of this package is to cover the most common steps in probability of
default (PD) rating model development and validation.  The main procedures
available are those that refer to univariate, bivariate, multivariate analysis,
calibration and validation.  Along with accompanied monobin and
@code{monobinShiny} packages, PDtoolkit provides functions which are suitable
for different data transformation and modeling tasks such as: imputations,
monotonic binning of numeric risk factors, binning of categorical risk factors,
weights of evidence (@code{WoE}) and information value (IV) calculations,
@code{WoE} coding (replacement of risk factors modalities with @code{WoE}
values), risk factor clustering, area under curve (AUC) calculation and others.
Additionally, package provides set of validation functions for testing
homogeneity, heterogeneity, discriminatory and predictive power of the model.")
    (license license:gpl3+)))

(define-public r-pdt
  (package
    (name "r-pdt")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pdt" version))
       (sha256
        (base32 "1a6h1877vpl0giwjkz7q7p1mzjp9ngkhijhn6l57a1q4sbl84way"))))
    (properties `((upstream-name . "pdt")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pdt")
    (synopsis "Permutation Distancing Test")
    (description
     "Permutation (randomisation) test for single-case phase design data with two
phases (e.g., pre- and post-treatment).  Correction for dependency of
observations is done through stepwise resampling the time series while varying
the distance between observations.  The required distance 0,1,2,3..  is
determined based on repeated dependency testing while stepwise increasing the
distance.  In preparation: Vroegindeweij et al. \"A Permutation distancing test
for single-case observational AB phase design data: A Monte Carlo simulation
study\".")
    (license license:gpl3)))

(define-public r-pdspecest
  (package
    (name "r-pdspecest")
    (version "1.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pdSpecEst" version))
       (sha256
        (base32 "1sf2d7vh7a1qc0cq230an8pzg9qi9g640z0ql8x2pp9wfawfzn1f"))))
    (properties `((upstream-name . "pdSpecEst")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-rcpparmadillo r-rcpp r-multitaper
                             r-ddalpha))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/JorisChau/pdSpecEst")
    (synopsis "An Analysis Toolbox for Hermitian Positive Definite Matrices")
    (description
     "An implementation of data analysis tools for samples of symmetric or Hermitian
positive definite matrices, such as collections of covariance matrices or
spectral density matrices.  The tools in this package can be used to perform:
(i) intrinsic wavelet transforms for curves (1D) or surfaces (2D) of Hermitian
positive definite matrices with applications to dimension reduction, denoising
and clustering in the space of Hermitian positive definite matrices; and (ii)
exploratory data analysis and inference for samples of positive definite
matrices by means of intrinsic data depth functions and rank-based hypothesis
tests in the space of Hermitian positive definite matrices.")
    (license license:gpl2)))

(define-public r-pdshiny
  (package
    (name "r-pdshiny")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PDShiny" version))
       (sha256
        (base32 "0s09sywvy83dj06rkxzyi06wfymwnfpgf3lpgjbb8ps8q3byd4vs"))))
    (properties `((upstream-name . "PDShiny")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny))
    (home-page "https://cran.r-project.org/package=PDShiny")
    (synopsis "'Probability Distribution Shiny'")
    (description
     "Interactive shiny application for working with Probability Distributions.
Calculations and Graphs are provided.")
    (license license:gpl2)))

(define-public r-pdsce
  (package
    (name "r-pdsce")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PDSCE" version))
       (sha256
        (base32 "1amy4rnvlcfvpma8pqd6wli1j0pjjk49i1qywa43lzgdwgd5dl9v"))))
    (properties `((upstream-name . "PDSCE")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PDSCE")
    (synopsis "Positive Definite Sparse Covariance Estimators")
    (description
     "Compute and tune some positive definite and sparse covariance estimators.")
    (license license:gpl2)))

(define-public r-pdr
  (package
    (name "r-pdr")
    (version "1.9.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pdR" version))
       (sha256
        (base32 "1rf7wpdswhn0m6nikzsj0xr0v1rdl4g96gkp59ikbvwjvzf58l7s"))))
    (properties `((upstream-name . "pdR")))
    (build-system r-build-system)
    (propagated-inputs (list r-plm r-pglm r-lme4 r-glmmtmb))
    (home-page "https://cran.r-project.org/package=pdR")
    (synopsis
     "Threshold Model and Unit Root Tests in Cross-Section and Time Series Data")
    (description
     "Threshold model, panel version of Hylleberg et al. (1990)
<DOI:10.1016/0304-4076(90)90080-D> seasonal unit root tests, and panel unit root
test of Chang (2002) <DOI:10.1016/S0304-4076(02)00095-7>.")
    (license license:gpl2+)))

(define-public r-pdqutils
  (package
    (name "r-pdqutils")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PDQutils" version))
       (sha256
        (base32 "0mmfi0ifwbkjm0274sddpwkj27cvdkwngnka8vdmfm4b2nn7vgsp"))))
    (properties `((upstream-name . "PDQutils")))
    (build-system r-build-system)
    (propagated-inputs (list r-orthopolynom r-moments))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/shabbychef/PDQutils")
    (synopsis
     "PDQ Functions via Gram Charlier, Edgeworth, and Cornish Fisher Approximations")
    (description
     "This package provides a collection of tools for approximating the PDQ functions
(respectively, the cumulative distribution, density, and quantile) of
probability distributions via classical expansions involving moments and
cumulants.")
    (license license:lgpl3)))

(define-public r-pdqr
  (package
    (name "r-pdqr")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pdqr" version))
       (sha256
        (base32 "0yvdvibr5a8bbz6zyvfjv4lzl0fjg7kz8jh67pw4nc1zk1kd8imh"))))
    (properties `((upstream-name . "pdqr")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/echasnovski/pdqr")
    (synopsis "Work with Custom Distribution Functions")
    (description
     "Create, transform, and summarize custom random variables with distribution
functions (analogues of p*()', d*()', q*()', and r*() functions from base R).
Two types of distributions are supported: \"discrete\" (random variable has finite
number of output values) and \"continuous\" (infinite number of values in the form
of continuous random variable).  Functions for distribution transformations and
summaries are available.  Implemented approaches often emphasize approximate and
numerical solutions: all distributions assume finite support and finite values
of density function; some methods implemented with simulation techniques.")
    (license license:expat)))

(define-public r-pdpdb
  (package
    (name "r-pdpdb")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PdPDB" version))
       (sha256
        (base32 "05aqg0rqrnlmvjxa32ms1vywgwhw5zlfkyf2ij0fn06rp8l2z3lv"))))
    (properties `((upstream-name . "PdPDB")))
    (build-system r-build-system)
    (propagated-inputs (list r-tseries r-plyr r-dendextend))
    (home-page "https://cran.r-project.org/package=PdPDB")
    (synopsis "Pattern Discovery in PDB Structures of Metalloproteins")
    (description
     "Looks for amino acid and/or nucleotide patterns and/or small ligands coordinated
to a given prosthetic centre.  Files have to be in the local file system and
contain proper extension.")
    (license license:expat)))

(define-public r-pdp
  (package
    (name "r-pdp")
    (version "0.8.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pdp" version))
       (sha256
        (base32 "0v1pr8fvx1z11kci6rna5bd8bk280p6lqrpxr3akflspbmpbcgg2"))))
    (properties `((upstream-name . "pdp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-lattice r-ggplot2 r-foreach))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bgreenwell/pdp")
    (synopsis "Partial Dependence Plots")
    (description
     "This package provides a general framework for constructing partial dependence
(i.e., marginal effect) plots from various types machine learning models in R.")
    (license license:gpl2+)))

(define-public r-pdn
  (package
    (name "r-pdn")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PDN" version))
       (sha256
        (base32 "082ydvwcjc2rs7hlakrkgbbm9c419m408a14a9dgfdwm16yif3jd"))))
    (properties `((upstream-name . "PDN")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-network r-glmnet r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PDN")
    (synopsis "Personalized Disease Network")
    (description
     "Building patient level networks for prediction of medical outcomes and draw the
cluster of network.  This package is based on paper Personalized disease
networks for understanding and predicting cardiovascular diseases and other
complex processes (See Cabrera et al.
<http://circ.ahajournals.org/content/134/Suppl_1/A14957>).")
    (license license:gpl2)))

(define-public r-pdmod
  (package
    (name "r-pdmod")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pdmod" version))
       (sha256
        (base32 "04bk9gjg2c55hk6k1hy0m29927s8a5ig6mr4xb89npam68g0pcms"))))
    (properties `((upstream-name . "pdmod")))
    (build-system r-build-system)
    (propagated-inputs (list r-mco))
    (home-page "https://cran.r-project.org/package=pdmod")
    (synopsis
     "Proximal/Distal Modeling Framework for Pavlovian Conditioning Phenomena")
    (description
     "Fits a model of Pavlovian conditioning phenomena, such as response extinction
and spontaneous recovery, and partial reinforcement extinction effects.
Competing proximal and distal reward predictions, computed using fast and slow
learning rates, combine according to their uncertainties and the recency of
information.  The resulting mean prediction drives the response rate.")
    (license license:gpl2+)))

(define-public r-pdmif
  (package
    (name "r-pdmif")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PDMIF" version))
       (sha256
        (base32 "0mlc31f4z0k5xk1fil0hx5dcbm7afia74kxb7xmnx4jcblidxnd2"))))
    (properties `((upstream-name . "PDMIF")))
    (build-system r-build-system)
    (propagated-inputs (list r-quantreg r-ncvreg r-diagonals))
    (home-page "https://cran.r-project.org/package=PDMIF")
    (synopsis "Fits Heterogeneous Panel Data Models")
    (description
     "Fits heterogeneous panel data models with interactive effects for linear
regression, logistic, count, probit, quantile, and clustering.  Based on Ando,
T. and Bai, J. (2015) \"A simple new test for slope homogeneity in panel data
models with interactive effects\" <doi: 10.1016/j.econlet.2015.09.019>, Ando, T.
and Bai, J. (2015) \"Asset Pricing with a General Multifactor Structure\" <doi:
10.1093/jjfinex/nbu026> , Ando, T. and Bai, J. (2016) \"Panel data models with
grouped factor structure under unknown group membership\" <doi:
10.1002/jae.2467>, Ando, T. and Bai, J. (2017) \"Clustering huge number of
financial time series: A panel data approach with high-dimensional predictors
and factor structures\" <doi: 10.1080/01621459.2016.1195743>, Ando, T. and Bai,
J. (2020) \"Quantile co-movement in financial markets\" <doi:
10.1080/01621459.2018.1543598>, Ando, T., Bai, J. and Li, K. (2021) \"Bayesian
and maximum likelihood analysis of large-scale panel choice models with
unobserved heterogeneity\" <doi: 10.1016/j.jeconom.2020.11.013.>.")
    (license license:expat)))

(define-public r-pdm
  (package
    (name "r-pdm")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PDM" version))
       (sha256
        (base32 "1pbdy13ji9c6krgh2r1qgrzmf4hdmrdzylsr71n3hh5jq4n9xg8x"))))
    (properties `((upstream-name . "PDM")))
    (build-system r-build-system)
    (propagated-inputs (list r-jpeg))
    (home-page "https://cran.r-project.org/package=PDM")
    (synopsis "Photogrammetric Distances Measurer")
    (description
     "Measures real distances in pictures.  With PDM() function, you can choose one
*.jpg file, select the measure in mm of scale, starting and and finishing point
in the graphical scale, the name of the measure, and starting and and finishing
point of the measures.  After, ask the user for a new measure.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-pdi
  (package
    (name "r-pdi")
    (version "0.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pdi" version))
       (sha256
        (base32 "04lz16al9c3ycdswfpf1fanghglpzbl8pfggr9bjmcn7k2v91ysv"))))
    (properties `((upstream-name . "pdi")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-readxl
                             r-randomforest
                             r-purrr
                             r-magrittr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://jasenfinch.github.io/pdi")
    (synopsis "Phenotypic Index Measures for Oak Decline Severity")
    (description
     "Oak declines are complex disease syndromes and consist of many visual indicators
that include aspects of tree size, crown condition and trunk condition.  This
can cause difficulty in the manual classification of symptomatic and
non-symptomatic trees from what is in reality a broad spectrum of oak tree
health condition.  Two phenotypic oak decline indexes have been developed to
quantitatively describe and differentiate oak decline syndromes in Quercus
robur.  This package provides a toolkit to generate these decline indexes from
phenotypic descriptors using the machine learning algorithm random forest.  The
methodology for generating these indexes is outlined in Finch et al. (2121)
<doi:10.1016/j.foreco.2021.118948>.")
    (license license:gpl3)))

(define-public r-pdftables
  (package
    (name "r-pdftables")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pdftables" version))
       (sha256
        (base32 "1gnwjijr89cczchc7yi4w5xiw0dalbymvj23rymm8cfra34iwn5p"))))
    (properties `((upstream-name . "pdftables")))
    (build-system r-build-system)
    (propagated-inputs (list r-httr))
    (native-inputs (list r-knitr))
    (home-page "https://www.github.com/expersso/pdftables")
    (synopsis "Programmatic Conversion of PDF Tables")
    (description
     "Allows the user to convert PDF tables to formats more amenable to analysis
('.csv', .xml', or .xlsx') by wrapping the PDFTables API. In order to use the
package, the user needs to sign up for an API account on the PDFTables website
(<https://pdftables.com/pdf-to-excel-api>).  The package works by taking a PDF
file as input, uploading it to PDFTables, and returning a file with the
extracted data.")
    (license license:cc0)))

(define-public r-pdfsearch
  (package
    (name "r-pdfsearch")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pdfsearch" version))
       (sha256
        (base32 "0mk1s8b7cdz025xn9kg5xnw388sndhjm80ckf71daxviknrzcscf"))))
    (properties `((upstream-name . "pdfsearch")))
    (build-system r-build-system)
    (propagated-inputs (list r-tokenizers r-tibble r-stringi r-pdftools))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/lebebr01/pdfsearch")
    (synopsis "Search Tools for PDF Files")
    (description
     "Includes functions for keyword search of pdf files.  There is also a wrapper
that includes searching of all files within a single directory.")
    (license license:expat)))

(define-public r-pdfminer
  (package
    (name "r-pdfminer")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pdfminer" version))
       (sha256
        (base32 "0hm9nalazqcg4n1sqcjan8wm1rvv4lnflmlkrqkj5yhhmza7xkfh"))))
    (properties `((upstream-name . "pdfminer")))
    (build-system r-build-system)
    (inputs (list python-pandas))
    (propagated-inputs (list r-jsonlite r-checkmate))
    (home-page "https://cran.r-project.org/package=pdfminer")
    (synopsis "Read Portable Document Format (PDF) Files")
    (description
     "This package provides an interface to PDFMiner
<https://github.com/pdfminer/pdfminer.six> a Python package for extracting
information from PDF'-files.  PDFMiner has the goal to get all information
available in a PDF'-file, position of the characters, font type, font size and
informations about lines.  Which makes it the perfect starting point for
extracting tables from PDF'-files.  More information can be found in the package
README'-file.")
    (license license:expat)))

(define-public r-pdfetch
  (package
    (name "r-pdfetch")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pdfetch" version))
       (sha256
        (base32 "19c4xylyvx9fxk9bxvbsvzkbl2hwgfk9l5xnykdx951kh8ssd274"))))
    (properties `((upstream-name . "pdfetch")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xts
                             r-xml2
                             r-xml
                             r-tidyr
                             r-stringr
                             r-readr
                             r-quantmod
                             r-magrittr
                             r-lubridate
                             r-jsonlite
                             r-httr
                             r-dplyr))
    (home-page "https://github.com/abielr/pdfetch")
    (synopsis
     "Fetch Economic and Financial Time Series Data from Public Sources")
    (description
     "Download economic and financial time series from public sources, including the
St Louis Fed's FRED system, Yahoo Finance, the US Bureau of Labor Statistics,
the US Energy Information Administration, the World Bank, Eurostat, the European
Central Bank, the Bank of England, the UK's Office of National Statistics,
Deutsche Bundesbank, and INSEE.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-pdfestimator
  (package
    (name "r-pdfestimator")
    (version "4.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PDFEstimator" version))
       (sha256
        (base32 "1h64scfy7db8p8gyczrzhscfssk1cbzhr41z8jvs0n1d1xa2f982"))))
    (properties `((upstream-name . "PDFEstimator")))
    (build-system r-build-system)
    (propagated-inputs (list r-plot3d r-multirng))
    (home-page "https://cran.r-project.org/package=PDFEstimator")
    (synopsis "Multivariate Nonparametric Probability Density Estimator")
    (description
     "Farmer, J., D. Jacobs (2108) <DOI:10.1371/journal.pone.0196937>.  A multivariate
nonparametric density estimator based on the maximum-entropy method.  Accurately
predicts a probability density function (PDF) for random data using a novel
iterative scoring function to determine the best fit without overfitting to the
sample.")
    (license license:gpl2+)))

(define-public r-pdfcluster
  (package
    (name "r-pdfcluster")
    (version "1.0-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pdfCluster" version))
       (sha256
        (base32 "0d52xmrzsjc51j0gzd3729rjqh391643yq840b6hlpc2w4ycc46m"))))
    (properties `((upstream-name . "pdfCluster")))
    (build-system r-build-system)
    (propagated-inputs (list r-geometry))
    (home-page "https://cran.r-project.org/package=pdfCluster")
    (synopsis "Cluster Analysis via Nonparametric Density Estimation")
    (description
     "Cluster analysis via nonparametric density estimation is performed.
Operationally, the kernel method is used throughout to estimate the density.
Diagnostics methods for evaluating the quality of the clustering are available.
The package includes also a routine to estimate the probability density function
obtained by the kernel method, given a set of data with arbitrary dimensions.")
    (license license:gpl2)))

(define-public r-pder
  (package
    (name "r-pder")
    (version "1.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pder" version))
       (sha256
        (base32 "0qrf0igxzalvsanj1mqryi0xl9lq0wlpg8bbgasbx3ibq4yqwn0s"))))
    (properties `((upstream-name . "pder")))
    (build-system r-build-system)
    (propagated-inputs (list r-plm))
    (home-page "https://cran.r-project.org/package=pder")
    (synopsis "Panel Data Econometrics with R")
    (description
     "Data sets for the Panel Data Econometrics with R <doi:10.1002/9781119504641>
book.")
    (license license:gpl2+)))

(define-public r-pde
  (package
    (name "r-pde")
    (version "1.4.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PDE" version))
       (sha256
        (base32 "0qzy70m8i64616lakhjil0jid3lfliqsyfg6w3hsg9662pl0wbf3"))))
    (properties `((upstream-name . "PDE")))
    (build-system r-build-system)
    (inputs (list xpdf))
    (propagated-inputs (list r-tcltk2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PDE")
    (synopsis "Extract Tables and Sentences from PDFs with User Interface")
    (description
     "The PDE (Pdf Data Extractor) allows the extraction of information and tables
optionally based on search words from PDF (Portable Document Format) files and
enables the visualization of the results, both by providing a convenient
user-interface.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-pdc
  (package
    (name "r-pdc")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pdc" version))
       (sha256
        (base32 "0503n7aiy0qrl790yfjvpm7bbyz1i4818rlg96q0fvzb58zqmyvc"))))
    (properties `((upstream-name . "pdc")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pdc")
    (synopsis "Permutation Distribution Clustering")
    (description
     "Permutation Distribution Clustering is a clustering method for time series.
Dissimilarity of time series is formalized as the divergence between their
permutation distributions.  The permutation distribution was proposed as measure
of the complexity of a time series.")
    (license license:gpl3+)))

(define-public r-pda
  (package
    (name "r-pda")
    (version "1.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pda" version))
       (sha256
        (base32 "0cdbjp6yrbqhkznsr282iyy76nzw9dz8z0gz48y12czjk9k6nvfd"))))
    (properties `((upstream-name . "pda")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rvest
                             r-rcpparmadillo
                             r-rcpp
                             r-plyr
                             r-ordinal
                             r-numderiv
                             r-minqa
                             r-metafor
                             r-mass
                             r-jsonlite
                             r-httr
                             r-glmnet
                             r-data-table))
    (home-page "https://cran.r-project.org/package=pda")
    (synopsis "Privacy-Preserving Distributed Algorithms")
    (description
     "This package provides a collection of privacy-preserving distributed algorithms
for conducting multi-site data analyses.  The regression analyses can be linear
regression for continuous outcome, logistic regression for binary outcome, Cox
proportional hazard regression for time-to event outcome, Poisson regression for
count outcome, or multi-categorical regression for nominal or ordinal outcome.
The PDA algorithm runs on a lead site and only requires summary statistics from
collaborating sites, with one or few iterations.  The package can be used
together with the online system (<https://pda-ota.pdamethods.org/>) for safe and
convenient collaboration.  For more information, please visit our software
websites: <https://github.com/Penncil/pda>, and <https://pdamethods.org/>.")
    (license license:asl2.0)))

(define-public r-pcvr
  (package
    (name "r-pcvr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcvr" version))
       (sha256
        (base32 "12zkw3xlzclms5bj477aarxxnl28p77hakvf38y2r09vzbjhs0bv"))))
    (properties `((upstream-name . "pcvr")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-survival
                             r-scales
                             r-rlang
                             r-quantreg
                             r-patchwork
                             r-nlme
                             r-mgcv
                             r-lmesplines
                             r-lme4
                             r-jsonlite
                             r-igraph
                             r-ggridges
                             r-ggplot2
                             r-factominer
                             r-extradistr
                             r-data-table
                             r-car
                             r-bayestestr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/danforthcenter/pcvr")
    (synopsis "Plant Phenotyping and Bayesian Statistics")
    (description
     "Analyse common types of plant phenotyping data, provide a simplified interface
to longitudinal growth modeling and select Bayesian statistics, and streamline
use of @code{PlantCV} output.  Several Bayesian methods and reporting guidelines
for Bayesian methods are described in Kruschke (2018)
<doi:10.1177/2515245918771304>, Kruschke (2013) <doi:10.1037/a0029146>, and
Kruschke (2021) <doi:10.1038/s41562-021-01177-7>.")
    (license license:gpl2)))

(define-public r-pcv
  (package
    (name "r-pcv")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcv" version))
       (sha256
        (base32 "03rpfxdpinr1a6964gch0j852h96jfk4sccs4bv1pzkvv2qsnzlr"))))
    (properties `((upstream-name . "pcv")))
    (build-system r-build-system)
    (home-page "https://github.com/svkucheryavski/pcv")
    (synopsis "Procrustes Cross-Validation")
    (description
     "This package implements Procrustes cross-validation method for Principal
Component Analysis, Principal Component Regression and Partial Least Squares
regression models.  S. Kucheryavskiy (2023) <doi:10.1016/j.aca.2023.341096>.")
    (license license:expat)))

(define-public r-pcutils
  (package
    (name "r-pcutils")
    (version "0.2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcutils" version))
       (sha256
        (base32 "1732qrr9f419a2g4wi7dnf97209k57qk9myb839sybdb3irvra9j"))))
    (properties `((upstream-name . "pcutils")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-scales
                             r-reshape2
                             r-rcolorbrewer
                             r-magrittr
                             r-ggplot2
                             r-dplyr))
    (home-page "https://github.com/Asa12138/pcutils")
    (synopsis "Some Useful Functions for Statistics and Visualization")
    (description
     "Offers a range of utilities and functions for everyday programming tasks.
1.Data Manipulation.  Such as grouping and merging, column splitting, and
character expansion.  2.File Handling.  Read and convert files in popular
formats.  3.Plotting Assistance.  Helpful utilities for generating color
palettes, validating color formats, and adding transparency.  4.Statistical
Analysis.  Includes functions for pairwise comparisons and multiple testing
corrections, enabling perform statistical analyses with ease.  5.Graph Plotting,
Provides efficient tools for creating doughnut plot and multi-layered doughnut
plot; Venn diagrams, including traditional Venn diagrams, upset plots, and
flower plots; Simplified functions for creating stacked bar plots, or a box plot
with alphabets group for multiple comparison group.")
    (license license:gpl3)))

(define-public r-pcts
  (package
    (name "r-pcts")
    (version "0.15.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcts" version))
       (sha256
        (base32 "1cr44w561sh05qpi0jyf2ypj4dadwyqkbaqn347v533bywasip2h"))))
    (properties `((upstream-name . "pcts")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xts
                             r-sarima
                             r-rdpack
                             r-polynomf
                             r-mcompanion
                             r-matrix
                             r-lubridate
                             r-lagged
                             r-gbutils
                             r-bb))
    (native-inputs (list r-knitr))
    (home-page
     "https://geobosh.github.io/pcts/https://github.com/GeoBosh/pcts/")
    (synopsis
     "Periodically Correlated and Periodically Integrated Time Series")
    (description
     "This package provides classes and methods for modelling and simulation of
periodically correlated (PC) and periodically integrated time series.  Compute
theoretical periodic autocovariances and related properties of PC autoregressive
moving average models.  Some original methods including Boshnakov & Iqelan
(2009) <doi:10.1111/j.1467-9892.2009.00617.x>, Boshnakov (1996)
<doi:10.1111/j.1467-9892.1996.tb00281.x>.")
    (license license:gpl2+)))

(define-public r-pctax
  (package
    (name "r-pctax")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pctax" version))
       (sha256
        (base32 "153vbgs3fw3x5n20wn58r3958kqrzcx0y15z5xjyc0lzhc66bzfs"))))
    (properties `((upstream-name . "pctax")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-tibble
                             r-scales
                             r-reshape2
                             r-rcolorbrewer
                             r-pcutils
                             r-magrittr
                             r-ggrepel
                             r-ggpubr
                             r-ggplot2
                             r-ggnewscale
                             r-dplyr
                             r-ade4))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Asa12138/pctax")
    (synopsis "Professional Comprehensive Omics Data Analysis")
    (description
     "This package provides a comprehensive suite of tools for analyzing omics data.
It includes functionalities for alpha diversity analysis, beta diversity
analysis, differential abundance analysis, community assembly analysis,
visualization of phylogenetic tree, and functional enrichment analysis.  With a
progressive approach, the package offers a range of analysis methods to explore
and understand the complex communities.  It is designed to support researchers
and practitioners in conducting in-depth and professional omics data analysis.")
    (license license:gpl3)))

(define-public r-pct
  (package
    (name "r-pct")
    (version "0.9.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pct" version))
       (sha256
        (base32 "15rshwdrfkr0ahnywss0qqbcyfxakknv9ffvb58nzc960c97zmy8"))))
    (properties `((upstream-name . "pct")))
    (build-system r-build-system)
    (propagated-inputs (list r-stplanr r-sf r-readr r-crul r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://itsleeds.github.io/pct/")
    (synopsis "Propensity to Cycle Tool")
    (description
     "This package provides functions and example data to teach and increase the
reproducibility of the methods and code underlying the Propensity to Cycle Tool
(PCT), a research project and web application hosted at <https://www.pct.bike/>.
 For an academic paper on the methods, see Lovelace et al (2017)
<doi:10.5198/jtlu.2016.862>.")
    (license license:gpl3)))

(define-public r-pcsteiner
  (package
    (name "r-pcsteiner")
    (version "1.0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcSteiner" version))
       (sha256
        (base32 "021r5c0rw962ywraq75scb2gqv3dxxq4vnaqpia83ljj5ab11qa0"))))
    (properties `((upstream-name . "pcSteiner")))
    (build-system r-build-system)
    (propagated-inputs (list r-igraph))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/krashkov/pcSteiner")
    (synopsis
     "Convenient Tool for Solving the Prize-Collecting Steiner Tree Problem")
    (description
     "The Prize-Collecting Steiner Tree problem asks to find a subgraph connecting a
given set of vertices with the most expensive nodes and least expensive edges.
Since it is proven to be NP-hard, exact and efficient algorithm does not exist.
This package provides convenient functionality for obtaining an approximate
solution to this problem using loopy belief propagation algorithm.")
    (license license:gpl3)))

(define-public r-pcsstools
  (package
    (name "r-pcsstools")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcsstools" version))
       (sha256
        (base32 "0pqc6qpl95dxphi77wcgi0v22a1q455iqaplsrvg32cf0mn3gikg"))))
    (properties `((upstream-name . "pcsstools")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-gtools))
    (home-page "https://github.com/jackmwolf/pcsstools/")
    (synopsis "Tools for Regression Using Pre-Computed Summary Statistics")
    (description
     "Defines functions to describe regression models using only pre-computed summary
statistics (i.e.  means, variances, and covariances) in place of individual
participant data.  Possible models include linear models for linear
combinations, products, and logical combinations of phenotypes.  Implements
methods presented in Wolf et al. (2021) <doi:10.3389/fgene.2021.745901> Wolf et
al. (2020) <doi:10.1142/9789811215636_0063> and Gasdaska et al. (2019)
<doi:10.1142/9789813279827_0036>.")
    (license license:gpl3+)))

(define-public r-pcsinr
  (package
    (name "r-pcsinr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PCSinR" version))
       (sha256
        (base32 "1z8yjryw3c43vc6fxcwiindmyq6r63pc1zr3aybwmys6mv697bb4"))))
    (properties `((upstream-name . "PCSinR")))
    (build-system r-build-system)
    (home-page "https://github.com/felixhenninger/PCSinR")
    (synopsis "Parallel Constraint Satisfaction Networks in R")
    (description
     "Parallel Constraint Satisfaction (PCS) models are an increasingly common class
of models in Psychology, with applications to reading and word recognition
(@code{McClelland} & Rumelhart, 1981), judgment and decision making (GlÃ¶ckner &
Betsch, 2008; GlÃ¶ckner, Hilbig, & Jekel, 2014), and several other fields (e.g.
Read, Vanman, & Miller, 1997).  In each of these fields, they provide a
quantitative model of psychological phenomena, with precise predictions
regarding choice probabilities, decision times, and often the degree of
confidence.  This package provides the necessary functions to create and
simulate basic Parallel Constraint Satisfaction networks within R.")
    (license license:gpl3+)))

(define-public r-pcse
  (package
    (name "r-pcse")
    (version "1.9.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcse" version))
       (sha256
        (base32 "1rb9146vjkf36vc5v3frykmsff7kp6qp1hhmj8pak583in4rz8f8"))))
    (properties `((upstream-name . "pcse")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pcse")
    (synopsis "Panel-Corrected Standard Error Estimation in R")
    (description
     "This package provides a function to estimate panel-corrected standard errors.
Data may contain balanced or unbalanced panels.")
    (license license:gpl3+)))

(define-public r-pcs
  (package
    (name "r-pcs")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PCS" version))
       (sha256
        (base32 "0v9vczc9hml6mzxglh106a9f5gy3x5m471lkzaw3ps8z76sgrpyr"))))
    (properties `((upstream-name . "PCS")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod r-multtest))
    (home-page "https://cran.r-project.org/package=PCS")
    (synopsis "Calculate the Probability of Correct Selection (PCS)")
    (description
     "Given k populations (can be in thousands), what is the probability that a given
subset of size t contains the true top t populations? This package finds this
probability and offers three tuning parameters (G, d, L) to relax the
definition.")
    (license license:gpl3)))

(define-public r-pcra
  (package
    (name "r-pcra")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PCRA" version))
       (sha256
        (base32 "0nc7pfvy43drjhfgql7bxf93xlkaqg083hc5cvirya1blrx2m48j"))))
    (properties `((upstream-name . "PCRA")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xts
                             r-robustbase
                             r-robstattm
                             r-r-cache
                             r-quadprog
                             r-portfolioanalytics
                             r-performanceanalytics
                             r-lattice
                             r-data-table
                             r-corpcor
                             r-boot))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=PCRA")
    (synopsis "Companion to Portfolio Construction and Risk Analysis")
    (description
     "This package provides a collection of functions and data sets that support
teaching a quantitative finance MS level course on Portfolio Construction and
Risk Analysis, and the writing of a textbook for such a course.  The package is
unique in providing several real-world data sets that may be used for problem
assignments and student projects.  The data sets include cross-sections of stock
data from the Center for Research on Security Prices, LLC (CRSP), corresponding
factor exposures data from S&P Global, and several SP500 data sets.")
    (license license:gpl2)))

(define-public r-pcr
  (package
    (name "r-pcr")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcr" version))
       (sha256
        (base32 "1lmgm8k3s7780ivrdj0zk1ninx7b5jffir6q5p2p47h62qqjmp5d"))))
    (properties `((upstream-name . "pcr")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MahShaaban/pcr")
    (synopsis "Analyzing Real-Time Quantitative PCR Data")
    (description
     "Calculates the amplification efficiency and curves from real-time quantitative
PCR (Polymerase Chain Reaction) data.  Estimates the relative expression from
PCR data using the double delta CT and the standard curve methods Livak &
Schmittgen (2001) <doi:10.1006/meth.2001.1262>.  Tests for statistical
significance using two-group tests and linear regression Yuan et al. (2006)
<doi: 10.1186/1471-2105-7-85>.")
    (license license:gpl3)))

(define-public r-pcps
  (package
    (name "r-pcps")
    (version "1.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PCPS" version))
       (sha256
        (base32 "0gxymz0yws1q1bxkpxi0lmbv57xx62h3dl7z3i2cnbagswkax3zh"))))
    (properties `((upstream-name . "PCPS")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-syncsa
                             r-rcpparmadillo
                             r-picante
                             r-phylobase
                             r-nlme
                             r-ape))
    (home-page "https://cran.r-project.org/package=PCPS")
    (synopsis "Principal Coordinates of Phylogenetic Structure")
    (description
     "Set of functions for analysis of Principal Coordinates of Phylogenetic Structure
(PCPS).")
    (license license:gpl2)))

(define-public r-pcovr
  (package
    (name "r-pcovr")
    (version "2.7.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PCovR" version))
       (sha256
        (base32 "0fzs071wnrf84fah0nzzavjiqvch031xjsmlj6nj7cjrmy7p7mv1"))))
    (properties `((upstream-name . "PCovR")))
    (build-system r-build-system)
    (propagated-inputs (list r-threeway r-matrix r-mass r-gparotation))
    (home-page "https://cran.r-project.org/package=PCovR")
    (synopsis "Principal Covariates Regression")
    (description
     "Analyzing regression data with many and/or highly collinear predictor variables,
by simultaneously reducing the predictor variables to a limited number of
components and regressing the criterion variables on these components (de Jong
S. & Kiers H. A. L. (1992) <doi:10.1016/0169-7439(92)80100-I>).  Several
rotation and model selection options are provided.")
    (license license:gpl2+)))

(define-public r-pcode
  (package
    (name "r-pcode")
    (version "0.9.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pCODE" version))
       (sha256
        (base32 "17igf35324r0dkjff7m4v9zlhsbpw4k1idvgswq3mvv449jvybsa"))))
    (properties `((upstream-name . "pCODE")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma r-mass r-fda r-desolve))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/alex-haixuw/PCODE")
    (synopsis
     "Estimation of an Ordinary Differential Equation Model by Parameter Cascade Method")
    (description
     "An implementation of the parameter cascade method in Ramsay, J. O., Hooker,G.,
Campbell, D., and Cao, J. (2007) for estimating ordinary differential equation
models with missing or complete observations.  It combines smoothing method and
profile estimation to estimate any non-linear dynamic system.  The package also
offers variance estimates for parameters of interest based on either bootstrap
or Delta method.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-pcobw
  (package
    (name "r-pcobw")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PCObw" version))
       (sha256
        (base32 "07nfy733vfg19xdkqz1fjxhr6xn4cawbch7bqn93habb477mha1k"))))
    (properties `((upstream-name . "PCObw")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PCObw")
    (synopsis
     "Bandwidth Selector with Penalized Comparison to Overfitting Criterion")
    (description
     "Bandwidth selector according to the Penalised Comparison to Overfitting (P.C.O.)
criterion as described in Varet, S., Lacour, C., Massart, P., Rivoirard, V.,
(2019) <https://hal.archives-ouvertes.fr/hal-02002275>.  It can be used with
univariate and multivariate data.")
    (license license:gpl2+)))

(define-public r-pco
  (package
    (name "r-pco")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pco" version))
       (sha256
        (base32 "0k1m450wfmlym976g7p9g8arqrvnsxgdpcazk5kh3m3jsrvrcchf"))))
    (properties `((upstream-name . "pco")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pco")
    (synopsis "Panel Cointegration Tests")
    (description
     "Computation of the Pedroni (1999) panel cointegration test statistics.  Reported
are the empirical and the standardized values.")
    (license license:gpl2)))

(define-public r-pcnetmeta
  (package
    (name "r-pcnetmeta")
    (version "2.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcnetmeta" version))
       (sha256
        (base32 "18q3fprxzbmm2nsqazppjkl5rvbmn1s9985f2wmzm2dslwpm5a23"))))
    (properties `((upstream-name . "pcnetmeta")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjags r-coda))
    (home-page "https://cran.r-project.org/package=pcnetmeta")
    (synopsis "Patient-Centered Network Meta-Analysis")
    (description
     "This package performs Bayesian arm-based network meta-analysis for datasets with
binary, continuous, and count outcomes (Zhang et al., 2014
<doi:10.1177/1740774513498322>; Lin et al., 2017 <doi:10.18637/jss.v080.i05>).")
    (license license:gpl2+)))

(define-public r-pcmrs
  (package
    (name "r-pcmrs")
    (version "0.1-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PCMRS" version))
       (sha256
        (base32 "0bf85zv3nl13gsdi96cd2qg7mfppsccs0ci7l204hws8nmbvri83"))))
    (properties `((upstream-name . "PCMRS")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod
                             r-rcpparmadillo
                             r-rcpp
                             r-mvtnorm
                             r-ltm
                             r-cubature))
    (home-page "https://cran.r-project.org/package=PCMRS")
    (synopsis "Model Response Styles in Partial Credit Models")
    (description
     "Implementation of PCMRS (Partial Credit Model with Response Styles) as proposed
in by Tutz, Schauberger and Berger (2018) <doi:10.1177/0146621617748322> .
PCMRS is an extension of the regular partial credit model.  PCMRS allows for an
additional person parameter that characterizes the response style of the person.
 By taking the response style into account, the estimates of the item parameters
are less biased than in partial credit models.")
    (license license:gpl2+)))

(define-public r-pcmbasecpp
  (package
    (name "r-pcmbasecpp")
    (version "0.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PCMBaseCpp" version))
       (sha256
        (base32 "0k40mxs8w1vk7qwpkv8gnq6cq3kpj7wvf0i6f1qn1wmm2vvz1kfy"))))
    (properties `((upstream-name . "PCMBaseCpp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-pcmbase r-data-table
                             r-abind))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/venelin/PCMBaseCpp")
    (synopsis
     "Fast Likelihood Calculation for Phylogenetic Comparative Models")
    (description
     "This package provides a C++ backend for multivariate phylogenetic comparative
models implemented in the R-package PCMBase'.  Can be used in combination with
PCMBase to enable fast and parallel likelihood calculation.  Implements the
pruning likelihood calculation algorithm described in Mitov et al. (2018)
<@code{arXiv:1809.09014>}.  Uses the SPLITT C++ library for parallel tree
traversal described in Mitov and Stadler (2018) <doi:10.1111/2041-210X.13136>.")
    (license license:gpl3+)))

(define-public r-pcmbase
  (package
    (name "r-pcmbase")
    (version "1.2.14")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PCMBase" version))
       (sha256
        (base32 "0vnkznr7rgvja8l7dikc56xf9gvk7g9lx7qp882rbyj4wbk50dmf"))))
    (properties `((upstream-name . "PCMBase")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-mvtnorm
                             r-ggplot2
                             r-expm
                             r-data-table
                             r-ape
                             r-abind))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://venelin.github.io/PCMBase/")
    (synopsis
     "Simulation and Likelihood Calculation of Phylogenetic Comparative Models")
    (description
     "Phylogenetic comparative methods represent models of continuous trait data
associated with the tips of a phylogenetic tree.  Examples of such models are
Gaussian continuous time branching stochastic processes such as Brownian motion
(BM) and Ornstein-Uhlenbeck (OU) processes, which regard the data at the tips of
the tree as an observed (final) state of a Markov process starting from an
initial state at the root and evolving along the branches of the tree.  The
PCMBase R package provides a general framework for manipulating such models.
This framework consists of an application programming interface for specifying
data and model parameters, and efficient algorithms for simulating trait
evolution under a model and calculating the likelihood of model parameters for
an assumed model and trait data.  The package implements a growing collection of
models, which currently includes BM, OU, BM/OU with jumps, two-speed OU as well
as mixed Gaussian models, in which different types of the above models can be
associated with different branches of the tree.  The PCMBase package is limited
to trait-simulation and likelihood calculation of (mixed) Gaussian phylogenetic
models.  The PCMFit package provides functionality for inference of these models
to tree and trait data.  The package web-site
<https://venelin.github.io/PCMBase/> provides access to the documentation and
other resources.")
    (license license:gpl3+)))

(define-public r-pcmabc
  (package
    (name "r-pcmabc")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcmabc" version))
       (sha256
        (base32 "1wrdbinc3y71q4ix8cb1vn4nhaa0bp9yxw1n8d5bzdyhg29df2hj"))))
    (properties `((upstream-name . "pcmabc")))
    (build-system r-build-system)
    (propagated-inputs (list r-yuima r-phangorn r-mvslouch r-ape))
    (home-page "https://cran.r-project.org/package=pcmabc")
    (synopsis
     "Approximate Bayesian Computations for Phylogenetic Comparative Methods")
    (description
     "Fits by ABC, the parameters of a stochastic process modelling the phylogeny and
evolution of a suite of traits following the tree.  The user may define an
arbitrary Markov process for the trait and phylogeny.  Importantly,
trait-dependent speciation models are handled and fitted to data.  See K.
Bartoszek, P. Lio (2019) <doi:10.5506/A@code{PhysPolBSupp.12.25>}.  The
suggested geiger package can be obtained from CRAN's archive
<https://cran.r-project.org/src/contrib/Archive/geiger/>, suggested to take
latest version.  Otherwise its required code is present in the pcmabc package.
The suggested distory package can be obtained from CRAN's archive
<https://cran.r-project.org/src/contrib/Archive/distory/>, suggested to take
latest version.")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file LICENCE")))))

(define-public r-pclassoreg
  (package
    (name "r-pclassoreg")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PCLassoReg" version))
       (sha256
        (base32 "1m79aiij4dglfz5qhm5h76hxpgszrpzfw37nvx2y6gcq6dya8krl"))))
    (properties `((upstream-name . "PCLassoReg")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-grpreg))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/weiliu123/PCLassoReg")
    (synopsis
     "Group Regression Models for Risk Protein Complex Identification")
    (description
     "Two protein complex-based group regression models (PCLasso and PCLasso2) for
risk protein complex identification.  PCLasso is a prognostic model that
identifies risk protein complexes associated with survival.  PCLasso2 is a
classification model that identifies risk protein complexes associated with
classes.  For more information, see Wang and Liu (2021)
<doi:10.1093/bib/bbab212>.")
    (license license:gpl3)))

(define-public r-pclasso
  (package
    (name "r-pclasso")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcLasso" version))
       (sha256
        (base32 "1gkr1kvm4lrd63jcarv9b7gjjcyyny6z49gvy2rcqg17zfqdxysv"))))
    (properties `((upstream-name . "pcLasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-svd))
    (native-inputs (list r-knitr gfortran))
    (home-page "https://arxiv.org/abs/1810.04651")
    (synopsis "Principal Components Lasso")
    (description
     "This package provides a method for fitting the entire regularization path of the
principal components lasso for linear and logistic regression models.  The
algorithm uses cyclic coordinate descent in a path-wise fashion.  See URL below
for more information on the algorithm.  See Tay, K., Friedman, J. ,Tibshirani,
R., (2014) Principal component-guided sparse regression
<@code{arXiv:1810.04651>}.")
    (license license:gpl3)))

(define-public r-pcl
  (package
    (name "r-pcl")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PCL" version))
       (sha256
        (base32 "0sqlwg0aiw9f1z4x5nixv4x3rq5rnm5i1hijdqygnh9dvckfrgs0"))))
    (properties `((upstream-name . "PCL")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PCL")
    (synopsis "Proximal Causal Learning")
    (description
     "We fit causal models using proxies.  We implement two stage proximal least
squares estimator.  E.J. Tchetgen Tchetgen, A. Ying, Y. Cui, X. Shi, and W.
Miao. (2020).  An Introduction to Proximal Causal Learning. @code{arXiv}
e-prints, @code{arXiv-2009} <@code{arXiv:2009.10982>}.")
    (license license:gpl2+)))

(define-public r-pcirt
  (package
    (name "r-pcirt")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcIRT" version))
       (sha256
        (base32 "0crll51s14nisnaxjln7bc3b1a181v358nfkh1p5iiyn98clacl6"))))
    (properties `((upstream-name . "pcIRT")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-combinat))
    (home-page "https://github.com/christinehohensinn/pcIRT")
    (synopsis "IRT Models for Polytomous and Continuous Item Responses")
    (description
     "Estimates the multidimensional polytomous Rasch model (Rasch, 1961) with
conditional maximum likelihood estimation.")
    (license license:gpl3)))

(define-public r-pcict
  (package
    (name "r-pcict")
    (version "0.5-4.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PCICt" version))
       (sha256
        (base32 "1akfvlxmklwzlzdg1q1828dkf8rqy3pyypywhd06g1f0g87xk5x7"))))
    (properties `((upstream-name . "PCICt")))
    (build-system r-build-system)
    (home-page "https://www.r-project.org")
    (synopsis
     "Implementation of POSIXct Work-Alike for 365 and 360 Day Calendars")
    (description
     "This package provides a work-alike to R's POSIXct class which implements 360-
and 365-day calendars in addition to the gregorian calendar.")
    (license license:gpl2)))

(define-public r-pci
  (package
    (name "r-pci")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pci" version))
       (sha256
        (base32 "08wfwy2ncm3cxbg31wimw0bd64lwwp19wsjd970zvwxs7wgpsdx2"))))
    (properties `((upstream-name . "pci")))
    (build-system r-build-system)
    (propagated-inputs (list r-vek))
    (home-page "https://github.com/samsemegne/pci")
    (synopsis "Collection of Process Capability Index Functions")
    (description
     "This package provides a collection of process capability index functions, such
as C_p(), C_pk(), C_pm(), and others, along with metadata about each, like
@code{LaTeX} equations and R expressions.  Its primary purpose is to form a
foundation for other quality control packages to build on top of, by providing
basic resources and functions.  The indices belong to the field of statistical
quality control, and quantify the degree to which a manufacturing process is
able to create items that adhere to a certain standard of quality.  For details
see Montgomery, D. C. (2019, ISBN:978-1-119-39930-8).")
    (license license:gpl3)))

(define-public r-pchc
  (package
    (name "r-pchc")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pchc" version))
       (sha256
        (base32 "0h1b1n9mw22wsh51nciphgyqk2cp98qkm4wskxw4ryj6lyqjk4k2"))))
    (properties `((upstream-name . "pchc")))
    (build-system r-build-system)
    (propagated-inputs (list r-robustbase
                             r-rfast2
                             r-rfast
                             r-foreach
                             r-doparallel
                             r-dcov
                             r-bnlearn
                             r-bigstatsr))
    (home-page "https://cran.r-project.org/package=pchc")
    (synopsis "Bayesian Network Learning with the PCHC and Related Algorithms")
    (description
     "Bayesian network learning using the PCHC algorithm.  PCHC stands for PC
Hill-Climbing, a new hybrid algorithm that uses PC to construct the skeleton of
the BN and then applies the Hill-Climbing greedy search.  More algorithms and
variants have been added, such as MMHC, FEDHC, and the Tabu search variants,
PCTABU, MMTABU and FEDTABU. The relevant papers are: a) Tsagris M. (2021).  A
new scalable Bayesian network learning algorithm with applications to economics.
 Computational Economics, 57(1): 341-367. <doi:10.1007/s10614-020-10065-7>.  b)
Tsagris M. (2022).  The FEDHC Bayesian Network Learning Algorithm.  Mathematics
2022, 10(15): 2604. <doi:10.3390/math10152604>.")
    (license license:gpl2+)))

(define-public r-pch
  (package
    (name "r-pch")
    (version "2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pch" version))
       (sha256
        (base32 "15dvrcrbcql54wf011r1a3pl3v2b551mq0pf4wcr7wjrf330h1w3"))))
    (properties `((upstream-name . "pch")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-hmisc))
    (home-page "https://cran.r-project.org/package=pch")
    (synopsis
     "Piecewise Constant Hazard Models for Censored and Truncated Data")
    (description
     "Piecewise constant hazard models for survival data.  The package allows for
right-censored, left-truncated, and interval-censored data.")
    (license license:gpl2)))

(define-public r-pcgse
  (package
    (name "r-pcgse")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PCGSE" version))
       (sha256
        (base32 "1j990gjd34dqfaqmnsmmpyxn6xbzrq0ncci85s87s7k15r8b22b4"))))
    (properties `((upstream-name . "PCGSE")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmtstat r-mass))
    (home-page "https://cran.r-project.org/package=PCGSE")
    (synopsis "Principal Component Gene Set Enrichment")
    (description
     "This package contains logic for computing the statistical association of
variable groups, i.e., gene sets, with respect to the principal components of
genomic data.")
    (license license:gpl2+)))

(define-public r-pcgii
  (package
    (name "r-pcgii")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PCGII" version))
       (sha256
        (base32 "0bh37354qk2hj90f9l2908466bvwzjmxr70nyivhqkx2ffp5j42n"))))
    (properties `((upstream-name . "PCGII")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-igraph r-glmnet r-dplyr r-corpcor))
    (native-inputs (list r-knitr))
    (home-page "https://haowang47.github.io/PCGII/")
    (synopsis "Partial Correlation Graph with Information Incorporation")
    (description
     "Large-scale gene expression studies allow gene network construction to uncover
associations among genes.  This package is developed for estimating and testing
partial correlation graphs with prior information incorporated.")
    (license license:expat)))

(define-public r-pcgen
  (package
    (name "r-pcgen")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcgen" version))
       (sha256
        (base32 "1v2fmxbj9gz5sg0hizw03ad8cfm9v25708h7fvnf6gb42b2mmyb3"))))
    (properties `((upstream-name . "pcgen")))
    (build-system r-build-system)
    (propagated-inputs (list r-sommer
                             r-pcalg
                             r-matrix
                             r-mass
                             r-lme4
                             r-hmisc
                             r-graph
                             r-ggm))
    (home-page "https://cran.r-project.org/package=pcgen")
    (synopsis
     "Reconstruction of Causal Networks for Data with Random Genetic Effects")
    (description
     "This package implements the pcgen algorithm, which is a modified version of the
standard pc-algorithm, with specific conditional independence tests and modified
orientation rules.  pcgen extends the approach of Valente et al. (2010)
<doi:10.1534/genetics.109.112979> with reconstruction of direct genetic effects.")
    (license license:gpl3)))

(define-public r-pcg
  (package
    (name "r-pcg")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcg" version))
       (sha256
        (base32 "194j72hcp7ywq1q3dd493pwkn1fmdg647gmhxcd1jm6xgijhvv87"))))
    (properties `((upstream-name . "pcg")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pcg")
    (synopsis "Preconditioned Conjugate Gradient Algorithm for solving Ax=b")
    (description
     "The package solves linear system of equations Ax=b by using Preconditioned
Conjugate Gradient Algorithm where A is real symmetric positive definite matrix.
 A suitable preconditioner matrix may be provided by user.  This can also be
used to minimize quadratic function (x'Ax)/2-bx for unknown x.")
    (license license:gpl2+)))

(define-public r-pcfam
  (package
    (name "r-pcfam")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PCFAM" version))
       (sha256
        (base32 "196jzv0f6j60zkyagm9n98339ipn8mrhmhl3q4g053z8f6mm9zmv"))))
    (properties `((upstream-name . "PCFAM")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PCFAM")
    (synopsis
     "Computation of Ancestry Scores with Mixed Families and Unrelated Individuals")
    (description
     "We provide several algorithms to compute the genotype ancestry scores (such as
eigenvector projections) in the case where highly correlated individuals are
involved.")
    (license license:gpl2)))

(define-public r-pcfactorstan
  (package
    (name "r-pcfactorstan")
    (version "1.5.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcFactorStan" version))
       (sha256
        (base32 "09mjvgvdlc85246jgi4x99jr2x2j6c5vn3y3gwjxw61cmmyybqzf"))))
    (properties `((upstream-name . "pcFactorStan")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-rstantools
                             r-rstan
                             r-reshape2
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-mvtnorm
                             r-loo
                             r-lifecycle
                             r-igraph
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jpritikin/pcFactorStan")
    (synopsis "Stan Models for the Paired Comparison Factor Model")
    (description
     "This package provides convenience functions and pre-programmed Stan models
related to the paired comparison factor model.  Its purpose is to make fitting
paired comparison data using Stan easy.  This package is described in Pritikin
(2020) <doi:10.1016/j.heliyon.2020.e04821>.")
    (license license:gpl3+)))

(define-public r-pcev
  (package
    (name "r-pcev")
    (version "2.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcev" version))
       (sha256
        (base32 "1l0afcdzkxzkhkc8ln2fzi50hc7nfq46lg6hhx6rrzkb41w82iqw"))))
    (properties `((upstream-name . "pcev")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmtstat r-corpcor))
    (native-inputs (list r-knitr))
    (home-page "http://github.com/GreenwoodLab/pcev")
    (synopsis "Principal Component of Explained Variance")
    (description
     "Principal component of explained variance (PCEV) is a statistical tool for the
analysis of a multivariate response vector.  It is a dimension- reduction
technique, similar to Principal component analysis (PCA), that seeks to maximize
the proportion of variance (in the response vector) being explained by a set of
covariates.")
    (license license:gpl2+)))

(define-public r-pcensmix
  (package
    (name "r-pcensmix")
    (version "1.2-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcensmix" version))
       (sha256
        (base32 "1yrz4hdj52in8z65qyl18kh7frv75rb8ss5ljv3vbm6yhxi32jnd"))))
    (properties `((upstream-name . "pcensmix")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pcensmix")
    (synopsis "Model Fitting to Progressively Censored Mixture Data")
    (description
     "This package provides functions for generating progressively Type-II censored
data in a mixture structure and fitting models using a constrained EM algorithm.
 It can also create a progressive Type-II censored version of a given real
dataset to be considered for model fitting.")
    (license license:gpl2+)))

(define-public r-pcds-ugraph
  (package
    (name "r-pcds-ugraph")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcds.ugraph" version))
       (sha256
        (base32 "1mfqrc25ppqa88yqymrzlncpw8rviqgsrq50isa2v79hncijlj5v"))))
    (properties `((upstream-name . "pcds.ugraph")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-pcds r-interp))
    (home-page "https://cran.r-project.org/package=pcds.ugraph")
    (synopsis
     "Underlying Graphs of Proximity Catch Digraphs and Their Applications")
    (description
     "This package contains the functions for construction and visualization of
underlying and reflexivity graphs of the three families of the proximity catch
digraphs (PCDs), see (Ceyhan (2005) ISBN:978-3-639-19063-2), and for computing
the edge density of these PCD-based graphs which are then used for testing the
patterns of segregation and association against complete spatial randomness
(CSR)) or uniformity in one and two dimensional cases.  The PCD families
considered are Arc-Slice PCDs, Proportional-Edge (PE) PCDs (Ceyhan et al. (2006)
<doi:10.1016/j.csda.2005.03.002>) and Central Similarity PCDs (Ceyhan et al.
(2007) <doi:10.1002/cjs.5550350106>).  See also (Ceyhan (2016)
<doi:10.1016/j.stamet.2016.07.003>) for edge density of the underlying and
reflexivity graphs of PE-PCDs.  The package also has tools for visualization of
PCD-based graphs for one, two, and three dimensional data.")
    (license license:gpl2)))

(define-public r-pcds
  (package
    (name "r-pcds")
    (version "0.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcds" version))
       (sha256
        (base32 "0cgi54lhgx0lc84nsj91qh8dy8a3f6r7zpkiqwwsx2vn4lm3lsp1"))))
    (properties `((upstream-name . "pcds")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack
                             r-plotrix
                             r-plot3d
                             r-interp
                             r-gmoip
                             r-combinat))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pcds")
    (synopsis "Proximity Catch Digraphs and Their Applications")
    (description
     "This package contains the functions for construction and visualization of
various families of the proximity catch digraphs (PCDs), see (Ceyhan (2005)
ISBN:978-3-639-19063-2), for computing the graph invariants for testing the
patterns of segregation and association against complete spatial randomness
(CSR) or uniformity in one, two and three dimensional cases.  The package also
has tools for generating points from these spatial patterns.  The graph
invariants used in testing spatial point data are the domination number (Ceyhan
(2011) <doi:10.1080/03610921003597211>) and arc density (Ceyhan et al. (2006)
<doi:10.1016/j.csda.2005.03.002>; Ceyhan et al. (2007)
<doi:10.1002/cjs.5550350106>).  The PCD families considered are Arc-Slice PCDs,
Proportional-Edge PCDs, and Central Similarity PCDs.")
    (license license:gpl2)))

(define-public r-pcdpca
  (package
    (name "r-pcdpca")
    (version "0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcdpca" version))
       (sha256
        (base32 "0ys4hrin0w3f87d1d986wvrfkfwapzj9hz7d7v92r60ns74g7r5m"))))
    (properties `((upstream-name . "pcdpca")))
    (build-system r-build-system)
    (propagated-inputs (list r-freqdom r-fda))
    (home-page "https://cran.r-project.org/package=pcdpca")
    (synopsis
     "Dynamic Principal Components for Periodically Correlated Functional Time Series")
    (description
     "Method extends multivariate and functional dynamic principal components to
periodically correlated multivariate time series.  This package allows you to
compute true dynamic principal components in the presence of periodicity.  We
follow implementation guidelines as described in Kidzinski, Kokoszka and
Jouzdani (2017), in Principal component analysis of periodically correlated
functional time series <@code{arXiv:1612.00040>}.")
    (license license:gpl3)))

(define-public r-pcdimension
  (package
    (name "r-pcdimension")
    (version "1.1.13")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PCDimension" version))
       (sha256
        (base32 "0mpf7m4daqaip18yfsyanrmbjq4ib9fw1b65ajdazicm400bw6gb"))))
    (properties `((upstream-name . "PCDimension")))
    (build-system r-build-system)
    (propagated-inputs (list r-oompabase r-kernlab r-cpm r-classdiscovery
                             r-changepoint))
    (home-page "http://oompa.r-forge.r-project.org/")
    (synopsis "Finding the Number of Significant Principal Components")
    (description
     "This package implements methods to automate the Auer-Gervini graphical Bayesian
approach for determining the number of significant principal components.
Automation uses clustering, change points, or simple statistical models to
distinguish \"long\" from \"short\" steps in a graph showing the posterior number of
components as a function of a prior parameter.  See <doi:10.1101/237883>.")
    (license license:asl2.0)))

(define-public r-pccc
  (package
    (name "r-pccc")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pccc" version))
       (sha256
        (base32 "0jh8yadks76ip9jmgvld9nycdm74f5gllkyhfmnrir8vi6xwvbad"))))
    (properties `((upstream-name . "pccc")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/CUD2V/pccc")
    (synopsis "Pediatric Complex Chronic Conditions")
    (description
     "An implementation of the pediatric complex chronic conditions (CCC)
classification system using R and C++.")
    (license license:gpl2)))

(define-public r-pcbs
  (package
    (name "r-pcbs")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PCBS" version))
       (sha256
        (base32 "0hvqcmrx2aszmgw24bhvnj8am3bk05hnzk63pvmnc7p7f21hmdf6"))))
    (properties `((upstream-name . "PCBS")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-ggrepel r-ggplot2 r-dplyr r-data-table))
    (home-page "https://github.com/katlande/PCBS")
    (synopsis "Principal Component BiSulfite")
    (description
     "This package provides a system for fast, accurate, and flexible whole genome
bisulfite sequencing (WGBS) data analysis of two-condition comparisons.
Principal Component @code{BiSulfite}, PCBS', assigns methylated loci eigenvector
values from the treatment-delineating principal component in lieu of running
millions of pairwise statistical tests, which dramatically increases analysis
flexibility and reduces computational requirements.  Methods:
<https://katlande.github.io/PCBS/articles/Differential_Methylation.html>.")
    (license license:gpl3)))

(define-public r-pcatsapiclientr
  (package
    (name "r-pcatsapiclientr")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcatsAPIclientR" version))
       (sha256
        (base32 "13d7cvpjjhxzln4lg7s17wl1apz83b4y4l361j2m4f8wyqwc6b6a"))))
    (properties `((upstream-name . "pcatsAPIclientR")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-httr))
    (home-page "https://cran.r-project.org/package=pcatsAPIclientR")
    (synopsis "'PCATS' API Client")
    (description
     "This package provides an R interface to the PCATS API
<https://pcats.research.cchmc.org/api/__docs__/>, allowing R users to submit
tasks and retrieve results.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-pcapam50
  (package
    (name "r-pcapam50")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PCAPAM50" version))
       (sha256
        (base32 "09jmpw38q3j0yk27r54d3y6fbgh69a6712zklv8qrfyq59kkshnc"))))
    (properties `((upstream-name . "PCAPAM50")))
    (build-system r-build-system)
    (propagated-inputs (list r-lattice r-impute r-complexheatmap r-biobase))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PCAPAM50")
    (synopsis "Enhanced 'PAM50' Subtyping of Breast Cancer")
    (description
     "Accurate classification of breast cancer tumors based on gene expression data is
not a trivial task, and it lacks standard practices.The PAM50 classifier, which
uses 50 gene centroid correlation distances to classify tumors, faces challenges
with balancing estrogen receptor (ER) status and gene centering.  The PCAPAM50
package leverages principal component analysis and iterative PAM50 calls to
create a gene expression-based ER-balanced subset for gene centering, avoiding
the use of protein expression-based ER data resulting into an enhanced Breast
Cancer subtyping.")
    (license license:gpl3+)))

(define-public r-pcaone
  (package
    (name "r-pcaone")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcaone" version))
       (sha256
        (base32 "0ds64x9g2jbxhk2qdgpshr8lkksklaswjxphyw43jimz5lslds11"))))
    (properties `((upstream-name . "pcaone")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp))
    (home-page "https://github.com/Zilong-Li/PCAoneR")
    (synopsis
     "Fast and Accurate Randomized Singular Value Decomposition Algorithms with 'PCAone'")
    (description
     "Fast and Accurate Randomized Singular Value Decomposition (RSVD) methods
proposed in the PCAone paper by Li (2023)
<https://genome.cshlp.org/content/33/9/1599>.")
    (license license:gpl3+)))

(define-public r-pcamixdata
  (package
    (name "r-pcamixdata")
    (version "3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PCAmixdata" version))
       (sha256
        (base32 "0flrsnbchwk06dmkg3vqykp9n4pqs265szn1r10navp8ki3rrmvh"))))
    (properties `((upstream-name . "PCAmixdata")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PCAmixdata")
    (synopsis "Multivariate Analysis of Mixed Data")
    (description
     "This package implements principal component analysis, orthogonal rotation and
multiple factor analysis for a mixture of quantitative and qualitative
variables.")
    (license license:gpl2+)))

(define-public r-pcamatchr
  (package
    (name "r-pcamatchr")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PCAmatchR" version))
       (sha256
        (base32 "0ycplivjiz2w3wiqnqga0ywwdbd9mxqzm92k3bjva8009jxxijax"))))
    (properties `((upstream-name . "PCAmatchR")))
    (build-system r-build-system)
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/machiela-lab/PCAmatchR")
    (synopsis "Match Cases to Controls Based on Genotype Principal Components")
    (description
     "Matches cases to controls based on genotype principal components (PC).  In order
to produce better results, matches are based on the weighted distance of PCs
where the weights are equal to the % variance explained by that PC. A weighted
Mahalanobis distance metric (Kidd et al. (1987)
<DOI:10.1016/0031-3203(87)90066-5>) is used to determine matches.")
    (license license:expat)))

(define-public r-pcalls
  (package
    (name "r-pcalls")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcalls" version))
       (sha256
        (base32 "1vdgvrzfv69p3p5zc9i6gxq53f36s0mia4idzrbsda661f1bhmxk"))))
    (properties `((upstream-name . "pcalls")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pcalls")
    (synopsis "Pricing of Different Types of Call")
    (description
     "Compute the price of different types of call using different methods.  The types
available are Vanilla European Calls, Vanilla American Calls and American
Digital Calls.  Available methods are Montecarlo Simulation, Montecarlo
Simulation with Antithetic Variates, Black-Scholes and the Binary Tree.")
    (license license:gpl3)))

(define-public r-pcalibrate
  (package
    (name "r-pcalibrate")
    (version "0.2-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pCalibrate" version))
       (sha256
        (base32 "0gi4hjnykn4gbj9krmw4z1qy4lbxkvacczkks3zkwalw9ylwkpxl"))))
    (properties `((upstream-name . "pCalibrate")))
    (build-system r-build-system)
    (propagated-inputs (list r-mcmcpack r-exact2x2))
    (home-page "https://cran.r-project.org/package=pCalibrate")
    (synopsis "Bayesian Calibrations of p-Values")
    (description
     "This package implements transformations of p-values to the smallest possible
Bayes factor within the specified class of alternative hypotheses, as described
in Held & Ott (2018, <doi:10.1146/annurev-statistics-031017-100307>).  Covers
several common testing scenarios such as z-tests, t-tests, likelihood ratio
tests and the F-test.")
    (license license:gpl2+)))

(define-public r-pcalg
  (package
    (name "r-pcalg")
    (version "2.7-12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcalg" version))
       (sha256
        (base32 "0wz94x8jg9810pr3sx8zccsidqz99xbffq4smwqn0hnz3r00g3w9"))))
    (properties `((upstream-name . "pcalg")))
    (build-system r-build-system)
    (propagated-inputs (list r-vcd
                             r-sfsmisc
                             r-robustbase
                             r-rcpparmadillo
                             r-rcpp
                             r-rbgl
                             r-igraph
                             r-graph
                             r-ggm
                             r-fastica
                             r-corpcor
                             r-clue
                             r-bh
                             r-bdsmatrix
                             r-abind))
    (home-page "https://pcalg.r-forge.r-project.org/")
    (synopsis "Methods for Graphical Models and Causal Inference")
    (description
     "This package provides functions for causal structure learning and causal
inference using graphical models.  The main algorithms for causal structure
learning are PC (for observational data without hidden variables), FCI and RFCI
(for observational data with hidden variables), and GIES (for a mix of data from
observational studies (i.e.  observational data) and data from experiments
involving interventions (i.e.  interventional data) without hidden variables).
For causal inference the IDA algorithm, the Generalized Backdoor Criterion
(GBC), the Generalized Adjustment Criterion (GAC) and some related functions are
implemented.  Functions for incorporating background knowledge are provided.")
    (license license:gpl2+)))

(define-public r-pcal1
  (package
    (name "r-pcal1")
    (version "1.5.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcaL1" version))
       (sha256
        (base32 "0k4kq4j45wsplag3nklyhyycpw65jin4i1l6ayci971y1pr3n113"))))
    (properties `((upstream-name . "pcaL1")))
    (build-system r-build-system)
    (inputs (list zlib))
    (native-inputs (list pkg-config))
    (home-page "https://cran.r-project.org/package=pcaL1")
    (synopsis "L1-Norm PCA Methods")
    (description
     "Implementations of several methods for principal component analysis using the L1
norm.  The package depends on COIN-OR Clp version >= 1.17.4.  The methods
implemented are PCA-L1 (Kwak 2008) <DOI:10.1109/TPAMI.2008.114>, L1-PCA (Ke and
Kanade 2003, 2005) <DOI:10.1109/CVPR.2005.309>, L1-PCA* (Brooks, Dula, and Boone
2013) <DOI:10.1016/j.csda.2012.11.007>, L1-PCAhp (Visentin, Prestwich and
Armagan 2016) <DOI:10.1007/978-3-319-46227-1_37>, @code{wPCA} (Park and Klabjan
2016) <DOI: 10.1109/ICDM.2016.0054>, @code{awPCA} (Park and Klabjan 2016) <DOI:
10.1109/ICDM.2016.0054>, PCA-Lp (Kwak 2014) <DOI:10.1109/TCYB.2013.2262936>, and
@code{SharpEl1-PCA} (Brooks and Dula, submitted).")
    (license license:gpl3+)))

(define-public r-pcal
  (package
    (name "r-pcal")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcal" version))
       (sha256
        (base32 "07119k5f4h9jc48bicl4q5i7bdrkha3rz22r6g44c8ilnkvjhw6j"))))
    (properties `((upstream-name . "pcal")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack))
    (home-page "https://pedro-teles-fonseca.github.io/pcal/")
    (synopsis "Calibration of P-Values for Point Null Hypothesis Testing")
    (description
     "Calibrate p-values under a robust perspective using the methods developed by
Sellke, Bayarri, and Berger (2001) <doi:10.1198/000313001300339950> and obtain
measures of the evidence provided by the data in favor of point null hypotheses
which are safer and more straightforward to interpret.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-pcadsc
  (package
    (name "r-pcadsc")
    (version "0.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PCADSC" version))
       (sha256
        (base32 "0pwkmmnsw3c6ch2k5wdc8ig71994pdp79v3a5aa9n9aq55a8c2ir"))))
    (properties `((upstream-name . "PCADSC")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-pander r-matrix r-ggplot2))
    (home-page "https://github.com/annepetersen1/PCADSC")
    (synopsis
     "Tools for Principal Component Analysis-Based Data Structure Comparisons")
    (description
     "This package provides a suite of non-parametric, visual tools for assessing
differences in data structures for two datasets that contain different
observations of the same variables.  These tools are all based on Principal
Component Analysis (PCA) and thus effectively address differences in the
structures of the covariance matrices of the two datasets.  The PCASDC tools
consist of easy-to-use, intuitive plots that each focus on different aspects of
the PCA decompositions.  The cumulative eigenvalue (CE) plot describes
differences in the variance components (eigenvalues) of the deconstructed
covariance matrices.  The angle plot presents the information loss when moving
from the PCA decomposition of one dataset to the PCA decomposition of the other.
 The chroma plot describes the loading patterns of the two datasets, thereby
presenting the relative weighting and importance of the variables from the
original dataset.")
    (license license:gpl2)))

(define-public r-pcadapt
  (package
    (name "r-pcadapt")
    (version "4.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pcadapt" version))
       (sha256
        (base32 "1b5sk6dy51465n96qyycy1i1lkiky7lwxycdmynv66xrkyyn3gas"))))
    (properties `((upstream-name . "pcadapt")))
    (build-system r-build-system)
    (propagated-inputs (list r-rspectra
                             r-rmio
                             r-rcpp
                             r-mmapcharr
                             r-magrittr
                             r-ggplot2
                             r-data-table
                             r-bigutilsr))
    (home-page "https://github.com/bcm-uga/pcadapt")
    (synopsis "Fast Principal Component Analysis for Outlier Detection")
    (description
     "This package provides methods to detect genetic markers involved in biological
adaptation.  pcadapt provides statistical tools for outlier detection based on
Principal Component Analysis.  Implements the method described in (Luu, 2016)
<DOI:10.1111/1755-0998.12592> and later revised in (PrivÃ©, 2020)
<DOI:10.1093/molbev/msaa053>.")
    (license license:gpl2+)))

(define-public r-pbv
  (package
    (name "r-pbv")
    (version "0.5-47")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pbv" version))
       (sha256
        (base32 "14n7szbhasw0qly3cbzh4jw8plinbxarzdbj46vilq3am7ph8yp1"))))
    (properties `((upstream-name . "pbv")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://github.com/alexanderrobitzsch/pbv")
    (synopsis "Probabilities for Bivariate Normal Distribution")
    (description
     "Computes probabilities of the bivariate normal distribution in a vectorized R
function (Drezner & Wesolowsky, 1990, <doi:10.1080/00949659008811236>).")
    (license license:gpl2+)))

(define-public r-pbtdesigns
  (package
    (name "r-pbtdesigns")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PBtDesigns" version))
       (sha256
        (base32 "0kcxqca3vgn3xz451prl42d7fwh4i4bd7divmj4p5rvrmyj023ni"))))
    (properties `((upstream-name . "PBtDesigns")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=PBtDesigns")
    (synopsis "Partially Balanced t-Designs (PBtDesigns)")
    (description
     "The t-designs represent a generalized class of balanced incomplete block designs
in which the number of blocks in which any t-tuple of treatments (t >= 2) occur
together is a constant.  When the focus of an experiment lies in grading and
selecting treatment subgroups, t-designs would be preferred over the
conventional ones, as they have the additional advantage of t-tuple balance.
t-designs can be advantageously used in identifying the best crop-livestock
combination for a particular location in Integrated Farming Systems that will
help in generating maximum profit.  But as the number of components increases,
the number of possible t-component combinations will also increase.  Most often,
combinations derived from specific components are only practically feasible, for
example, in a specific locality, farmers may not be interested in keeping a pig
or goat and hence combinations involving these may not be of any use in that
locality.  In such situations partially balanced t-designs with few selected
combinations appearing in a constant number of blocks (while others not at all
appearing) may be useful (Sayantani Karmakar, Cini Varghese, Seema Jaggi & Mohd
Harun (2021)<doi:10.1080/03610918.2021.2008436>).  Further, every location may
not have the resources to form equally sized homogeneous blocks.  Partially
balanced t-designs with unequal block sizes (Damaraju Raghavarao & Bei Zhou
(1998)<doi:10.1080/03610929808832657>.  Sayantani Karmakar, Cini Varghese, Seema
Jaggi & Mohd Harun (2022).\" Partially Balanced t-designs with unequal block
sizes\") prove to be more suitable for such situations.This package generates
three series of partially balanced t-designs namely Series 1, Series 2 and
Series 3.  Series 1 and Series 2 are designs having equal block sizes and with
treatment structures 4(t + 1) and a prime number, respectively.  Series 3
consists of designs with unequal block sizes and with treatment structure
n(n-1)/2.  This package is based on the function named P@code{BtD}() for
generating partially balanced t-designs along with their parameters, information
matrices, average variance factors and canonical efficiency factors.")
    (license license:gpl2+)))

(define-public r-pbsmodelling
  (package
    (name "r-pbsmodelling")
    (version "2.69.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PBSmodelling" version))
       (sha256
        (base32 "117xsm1p6cynbax9p6mcz5y5jzzcxfrfbvkzcb7i5gdlqnw5mpp2"))))
    (properties `((upstream-name . "PBSmodelling")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-xml))
    (home-page "https://github.com/pbs-software/pbs-modelling")
    (synopsis "GUI Tools Made Easy: Interact with Models and Explore Data")
    (description
     "This package provides software to facilitate the design, testing, and operation
of computer models.  It focuses particularly on tools that make it easy to
construct and edit a customized graphical user interface ('GUI').  Although our
simplified GUI language depends heavily on the R interface to the Tcl/Tk
package, a user does not need to know Tcl/Tk'.  Examples illustrate models built
with other R packages, including PBSmapping', PBSddesolve', and BRugs'.  A
complete user's guide PBSmodelling-UG.pdf shows how to use this package
effectively.")
    (license license:gpl2+)))

(define-public r-pbsmapping
  (package
    (name "r-pbsmapping")
    (version "2.74.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PBSmapping" version))
       (sha256
        (base32 "0flyqb7p6y9k0zbdnnw0ym9qm2mamw8ajd42b3s03vpmsnc5ja8c"))))
    (properties `((upstream-name . "PBSmapping")))
    (build-system r-build-system)
    (home-page "https://github.com/pbs-software/pbs-mapping")
    (synopsis "Mapping Fisheries Data and Spatial Analysis Tools")
    (description
     "This software has evolved from fisheries research conducted at the Pacific
Biological Station (PBS) in Nanaimo', British Columbia, Canada.  It extends the
R language to include two-dimensional plotting features similar to those
commonly available in a Geographic Information System (GIS).  Embedded C code
speeds algorithms from computational geometry, such as finding polygons that
contain specified point events or converting between longitude-latitude and
Universal Transverse Mercator (UTM) coordinates.  Additionally, we include C++
code developed by Angus Johnson for the Clipper library, data for a global
shoreline, and other data sets in the public domain.  Under the user's R library
directory .@code{libPaths}()', specifically in ./PBSmapping/doc', a complete
user's guide is offered and should be consulted to use package functions
effectively.")
    (license license:gpl2+)))

(define-public r-pbsddesolve
  (package
    (name "r-pbsddesolve")
    (version "1.13.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PBSddesolve" version))
       (sha256
        (base32 "0wa7h93449lr8shkrjy49h53vwzk2zy1h0c9ddkhh1axd978ms39"))))
    (properties `((upstream-name . "PBSddesolve")))
    (build-system r-build-system)
    (home-page "https://github.com/pbs-software/pbs-ddesolve")
    (synopsis "Solver for Delay Differential Equations")
    (description
     "This package provides functions for solving systems of delay differential
equations by interfacing with numerical routines written by Simon N. Wood,
including contributions from Benjamin J. Cairns.  These numerical routines first
appeared in Simon Wood's solv95 program.  This package includes a vignette and a
complete user's guide.  PBSddesolve originally appeared on CRAN under the name
ddesolve'.  That version is no longer supported.  The current name emphasizes a
close association with other PBS packages, particularly PBSmodelling'.")
    (license license:gpl2+)))

(define-public r-pbsadmb
  (package
    (name "r-pbsadmb")
    (version "1.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PBSadmb" version))
       (sha256
        (base32 "1745j4rli5j5sml5rfb0wr93xm2hflg2bwlzch5yx2smrh0wysbb"))))
    (properties `((upstream-name . "PBSadmb")))
    (build-system r-build-system)
    (propagated-inputs (list r-pbsmodelling))
    (home-page "https://github.com/pbs-software/pbs-admb")
    (synopsis "ADMB for R Using Scripts or GUI")
    (description
     "This package provides a collection of software provides R support for ADMB
(Automatic Differentiation Model Builder) and a GUI interface facilitates the
conversion of ADMB template code to C code followed by compilation to a binary
executable.  Stand-alone functions can also be run by users not interested in
clicking a GUI'.")
    (license license:gpl2+)))

(define-public r-pbs
  (package
    (name "r-pbs")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pbs" version))
       (sha256
        (base32 "0cpgs6k5h8y2cia01zs1p4ri8r7ljg2z4x8xcbx73s680dvnxa2w"))))
    (properties `((upstream-name . "pbs")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pbs")
    (synopsis "Periodic B Splines")
    (description "Periodic B Splines Basis.")
    (license license:gpl2)))

(define-public r-pbrackets
  (package
    (name "r-pbrackets")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pBrackets" version))
       (sha256
        (base32 "1rhsl59wqplpx4iavhm133fywkjdsbxw1x60fz22hby0cc9ckg6i"))))
    (properties `((upstream-name . "pBrackets")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pBrackets")
    (synopsis "Plot Brackets")
    (description
     "Adds different kinds of brackets to a plot, including braces, chevrons,
parentheses or square brackets.")
    (license license:gpl3+)))

(define-public r-pbr
  (package
    (name "r-pbr")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pbr" version))
       (sha256
        (base32 "1y984v487hqn22cnhn3cghbvn5bxpvpqsrsizjd06vjwnflpazg2"))))
    (properties `((upstream-name . "pbr")))
    (build-system r-build-system)
    (propagated-inputs (list r-leaflet r-jsonlite r-httr r-htmltools))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pbr")
    (synopsis "Find a Cold One Near You")
    (description
     "In short, this package is a locator for cool, refreshing beverages.  It will
find and return the nearest location where you can get a cold one.")
    (license license:expat)))

(define-public r-pbox
  (package
    (name "r-pbox")
    (version "0.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pbox" version))
       (sha256
        (base32 "037wry356n3z5z4ggd6k5yna778134vjagaii1r4yahg15ksp7gk"))))
    (properties `((upstream-name . "pbox")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-purrr
                             r-gamlss-dist
                             r-gamlss
                             r-data-table
                             r-copula))
    (native-inputs (list r-knitr))
    (home-page "https://www.r-project.org")
    (synopsis "Exploring Multivariate Spaces with Probability Boxes")
    (description
     "Advanced statistical library offering a method to encapsulate and query the
probability space of a dataset effortlessly using Probability Boxes (p-boxes).
Its distinctive feature lies in the ease with which users can navigate and
analyze marginal, joint, and conditional probabilities while taking into account
the underlying correlation structure inherent in the data using copula theory
and models.  A comprehensive explanation is available in the paper \"pbox:
Exploring Multivariate Spaces with Probability Boxes\" to be published in the
Journal of Statistical Software.")
    (license license:gpl3)))

(define-public r-pbo
  (package
    (name "r-pbo")
    (version "1.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pbo" version))
       (sha256
        (base32 "0grj5h06kz8w2p7rzdibg95zpy30r056w14hmy7ij3bpi98sm79z"))))
    (properties `((upstream-name . "pbo")))
    (build-system r-build-system)
    (propagated-inputs (list r-latticeextra r-lattice r-foreach))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mrbcuda/pbo")
    (synopsis "Probability of Backtest Overfitting")
    (description
     "Following the method of Bailey et al., computes for a collection of candidate
models the probability of backtest overfitting, the performance degradation and
probability of loss, and the stochastic dominance.")
    (license license:expat)))

(define-public r-pbnpa
  (package
    (name "r-pbnpa")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PBNPA" version))
       (sha256
        (base32 "1wmgcmspilfv6pjy7696j2xmq8m41p27plnr1qr0fnzyz66pf37m"))))
    (properties `((upstream-name . "PBNPA")))
    (build-system r-build-system)
    (propagated-inputs (list r-metarnaseq))
    (home-page "https://cran.r-project.org/package=PBNPA")
    (synopsis
     "Permutation Based Non-Parametric Analysis of CRISPR Screen Data")
    (description
     "Permutation based non-parametric analysis of CRISPR screen data.  Details about
this algorithm are published in the following paper published on BMC genomics,
Jia et al. (2017) <doi:10.1186/s12864-017-3938-5>: A permutation-based
non-parametric analysis of CRISPR screen data.  Please cite this paper if you
use this algorithm for your paper.")
    (license license:gpl2+)))

(define-public r-pbm
  (package
    (name "r-pbm")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pbm" version))
       (sha256
        (base32 "0nkqhrvlzf2imkq5pbni3c1k4vc75y26rra3czbchs111v7jrcqv"))))
    (properties `((upstream-name . "pbm")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jonathanrd/pbm")
    (synopsis "Protein Binding Models")
    (description
     "Binding models which are useful when analysing protein-ligand interactions by
techniques such as Biolayer Interferometry (BLI) or Surface Plasmon Resonance
(SPR).  Naman B. Shah, Thomas M. Duncan (2014) <doi:10.3791/51383>.  Hoang H.
Nguyen et al. (2015) <doi:10.3390/s150510481>.  After initial binding parameters
are known, binding curves can be simulated and parameters can be varied.  The
models within this package may also be used to fit a curve to measured binding
data using non-linear regression.")
    (license license:expat)))

(define-public r-pbir
  (package
    (name "r-pbir")
    (version "0.1-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PBIR" version))
       (sha256
        (base32 "1br53bxqp2bhljph2a0nvdlxf695iknymgwy2drfc7pm383w1ma1"))))
    (properties `((upstream-name . "PBIR")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-cmprsk))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PBIR")
    (synopsis
     "Estimating the Probability of Being in Response and Related Outcomes")
    (description
     "Make statistical inference on the probability of being in response, the duration
of response, and the cumulative response rate up to a given time point.  The
method can be applied to analyze phase II randomized clinical trials with the
endpoints being time to treatment response and time to progression or death.")
    (license license:gpl2+)))

(define-public r-pbimisc
  (package
    (name "r-pbimisc")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PBImisc" version))
       (sha256
        (base32 "18gjp66q6l0w6vsgm6d5sjgpa906z1gyyp6yf58lq5vyg1bnfmcl"))))
    (properties `((upstream-name . "PBImisc")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-lme4))
    (home-page "http://www.biecek.pl/R/")
    (synopsis
     "Set of Datasets Used in My Classes or in the Book 'Modele Liniowe i Mieszane w R, Wraz z Przykladami w Analizie Danych'")
    (description
     "This package provides a set of datasets and functions used in the book Modele
liniowe i mieszane w R, wraz z przykladami w analizie danych'.  Datasets either
come from real studies or are created to be as similar as possible to real
studies.")
    (license license:gpl2+)))

(define-public r-pbibd
  (package
    (name "r-pbibd")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PBIBD" version))
       (sha256
        (base32 "0zh73bsiwmsqw1vhs2dazndsf0894kfv3vfv88wr4fnh7njysz6r"))))
    (properties `((upstream-name . "PBIBD")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=PBIBD")
    (synopsis "Partially Balanced Incomplete Block Designs")
    (description
     "The PBIB designs are important type of incomplete block designs having wide area
of their applications for example in agricultural experiments, in plant
breeding, in sample surveys etc.  This package constructs various series of PBIB
designs and assists in checking all the necessary conditions of PBIB designs and
the association scheme on which these designs are based on.  It also assists in
calculating the efficiencies of PBIB designs with any number of associate
classes.  The package also constructs Youden-m square designs which are
Row-Column designs for the two-way elimination of heterogeneity.  The incomplete
columns of these Youden-m square designs constitute PBIB designs.  With the
present functionality, the package will be of immense importance for the
researchers as it will help them to construct PBIB designs, to check if their
PBIB designs and association scheme satisfy various necessary conditions for the
existence, to calculate the efficiencies of PBIB designs based on any
association scheme and to construct Youden-m square designs for the two-way
elimination of heterogeneity.  R. C. Bose and K. R. Nair (1939)
<http://www.jstor.org/stable/40383923>.")
    (license license:gpl2+)))

(define-public r-pbdslap
  (package
    (name "r-pbdslap")
    (version "0.3-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pbdSLAP" version))
       (sha256
        (base32 "0l6w1kxn0clqi36lqg494d71dd3rv77v94nzqn0rp7zv33n63cnp"))))
    (properties `((upstream-name . "pbdSLAP")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-pbdmpi))
    (native-inputs (list gfortran))
    (home-page "https://pbdr.org/")
    (synopsis "Programming with Big Data -- Scalable Linear Algebra Packages")
    (description
     "Utilizing scalable linear algebra packages mainly including BLACS', PBLAS', and
@code{ScaLAPACK} in double precision via @code{pbdMPI} based on @code{ScaLAPACK}
version 2.0.2.")
    (license (license:fsdg-compatible "Mozilla Public License 2.0"))))

(define-public r-pbd
  (package
    (name "r-pbd")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PBD" version))
       (sha256
        (base32 "1n8gq8ms6gszr469hblipbgalc5m7gfiknpsbqywxml62sr4wbsb"))))
    (properties `((upstream-name . "PBD")))
    (build-system r-build-system)
    (propagated-inputs (list r-phytools r-desolve r-ddd r-ape r-ade4))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PBD")
    (synopsis "Protracted Birth-Death Model of Diversification")
    (description
     "Conducts maximum likelihood analysis and simulation of the protracted
birth-death model of diversification.  See Etienne, R.S. & J. Rosindell 2012
<doi:10.1093/sysbio/syr091>; Lambert, A., H. Morlon & R.S. Etienne 2014,
<doi:10.1007/s00285-014-0767-x>; Etienne, R.S., H. Morlon & A. Lambert 2014,
<doi:10.1111/evo.12433>.")
    (license license:gpl2)))

(define-public r-pbcc
  (package
    (name "r-pbcc")
    (version "0.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pbcc" version))
       (sha256
        (base32 "09yx6p0v8pa049l85kwm0rw15amyna9r35kbbahby3lfb212syb2"))))
    (properties `((upstream-name . "pbcc")))
    (build-system r-build-system)
    (propagated-inputs (list r-rgenoud r-qcc r-ggpubr r-ggplot2))
    (home-page "https://github.com/kzst/pbcc")
    (synopsis "Percentile-Based Control Chart")
    (description
     "Design and implementation of Percentile-based Shewhart Control Charts for
continuous data.  Faraz (2019) <doi:10.1002/qre.2384>.")
    (license license:gpl2+)))

(define-public r-pbbd
  (package
    (name "r-pbbd")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pbbd" version))
       (sha256
        (base32 "07g029qw5949500c879iv86zwg2fldqrz8wzfz335jmcinqimgj6"))))
    (properties `((upstream-name . "pbbd")))
    (build-system r-build-system)
    (propagated-inputs (list r-ibd))
    (home-page "https://cran.r-project.org/package=pbbd")
    (synopsis "Position Balanced and Nearly Position Balanced Block Designs")
    (description
     "Generates a position balanced or nearly position balanced block design with
given parameters.  This package can also convert a given proper and
equireplicate block design into a position balanced or nearly position balanced
block design.")
    (license license:gpl2+)))

(define-public r-pbatr
  (package
    (name "r-pbatr")
    (version "2.2-17")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pbatR" version))
       (sha256
        (base32 "1i4rjq8p6j5d7nh9lyrz5zh2s7jf81ljl0jgam2nkrzx3vagy91r"))))
    (properties `((upstream-name . "pbatR")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rootsolve))
    (home-page
     "https://academic.oup.com/bioinformatics/article-abstract/22/24/3103/208723")
    (synopsis
     "Pedigree/Family-Based Genetic Association Tests Analysis and Power")
    (description
     "This R package provides power calculations via internal simulation methods.  The
package also provides a frontend to the now abandoned PBAT program (developed by
Christoph Lange), and reads in the corresponding output and displays results and
figures when appropriate.  The license of this R package itself is GPL. However,
to have the program interact with the PBAT program for some functionality of the
R package, users must additionally obtain the PBAT program from Christoph Lange,
and accept his license.  Both the data analysis and power calculations have
command line and graphical interfaces using tcltk.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-pbanova
  (package
    (name "r-pbanova")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pbANOVA" version))
       (sha256
        (base32 "0m7946jv4zbs0k74klxkl8nbwirc48g6paqrfl5fadkd17xxqq2j"))))
    (properties `((upstream-name . "pbANOVA")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmisc
                             r-plyr
                             r-mass
                             r-lmtest
                             r-dplyr
                             r-desctools))
    (home-page "https://cran.r-project.org/package=pbANOVA")
    (synopsis "Parametric Bootstrap for ANOVA Models")
    (description
     "Parametric bootstrap (PB) has been used for three-way ANOVA model with unequal
group variances.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-pawscore
  (package
    (name "r-pawscore")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pawscore" version))
       (sha256
        (base32 "0pf8lczi0g3cwi4hxrz4lggphpkbabswj0sppm7h688ijb1ph9qg"))))
    (properties `((upstream-name . "pawscore")))
    (build-system r-build-system)
    (propagated-inputs (list r-signal r-brglm2))
    (home-page "https://cran.r-project.org/package=pawscore")
    (synopsis "Pain Assessment at Withdrawal Speeds (PAWS)")
    (description
     "Automated pain scoring from paw withdrawal tracking data.  Based on Jones et al.
(2020) \"A machine-vision approach for automated pain measurement at millisecond
timescales\" <doi:10.7554/@code{eLife.57258>}.")
    (license license:gpl3)))

(define-public r-pawacc
  (package
    (name "r-pawacc")
    (version "1.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pawacc" version))
       (sha256
        (base32 "150bvmnv5myq856x19kg1ca9favcsh1cid70y8hv3wd9f25w9r8c"))))
    (properties `((upstream-name . "pawacc")))
    (build-system r-build-system)
    (propagated-inputs (list r-sparsem))
    (home-page "https://cran.r-project.org/package=pawacc")
    (synopsis "Physical Activity with Accelerometers")
    (description
     "This package provides functions to process, format and store @code{ActiGraph}
GT1M and GT3X accelerometer data.")
    (license license:gpl2+)))

(define-public r-pavo
  (package
    (name "r-pavo")
    (version "2.9.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pavo" version))
       (sha256
        (base32 "1kl3qd1q6p4rmfbml0lfjj3wi40w3kkb9gahvin8k62m5aa4hmq6"))))
    (properties `((upstream-name . "pavo")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridislite
                             r-sf
                             r-progressr
                             r-plot3d
                             r-magick
                             r-lightr
                             r-geometry
                             r-future-apply
                             r-farver
                             r-cluster))
    (native-inputs (list r-knitr))
    (home-page "http://pavo.colrverse.com")
    (synopsis
     "Perceptual Analysis, Visualization and Organization of Spectral Colour Data")
    (description
     "This package provides a cohesive framework for the spectral and spatial analysis
of colour described in Maia, Eliason, Bitton, Doucet & Shawkey (2013)
<doi:10.1111/2041-210X.12069> and Maia, Gruson, Endler & White (2019)
<doi:10.1111/2041-210X.13174>.")
    (license license:gpl2+)))

(define-public r-patterns
  (package
    (name "r-patterns")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Patterns" version))
       (sha256
        (base32 "1b4krl9gcwrxrslqyc0hhc6kqc7q34i70pyx9yb7xkdixs6dkr95"))))
    (properties `((upstream-name . "Patterns")))
    (build-system r-build-system)
    (propagated-inputs (list r-wgcna
                             r-vgam
                             r-tnet
                             r-selectboost
                             r-repmis
                             r-plotrix
                             r-nnls
                             r-movmf
                             r-mfuzz
                             r-limma
                             r-lattice
                             r-lars
                             r-jetset
                             r-igraph
                             r-gplots
                             r-e1071
                             r-cluster
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://fbertran.github.io/Patterns/")
    (synopsis
     "Deciphering Biological Networks with Patterned Heterogeneous Measurements")
    (description
     "This package provides a modeling tool dedicated to biological network modeling
(Bertrand and others 2020, <doi:10.1093/bioinformatics/btaa855>).  It allows for
single or joint modeling of, for instance, genes and proteins.  It starts with
the selection of the actors that will be the used in the reverse engineering
upcoming step.  An actor can be included in that selection based on its
differential measurement (for instance gene expression or protein abundance) or
on its time course profile.  Wrappers for actors clustering functions and
cluster analysis are provided.  It also allows reverse engineering of biological
networks taking into account the observed time course patterns of the actors.
Many inference functions are provided and dedicated to get specific features for
the inferred network such as sparsity, robust links, high confidence links or
stable through resampling links.  Some simulation and prediction tools are also
available for cascade networks (Jung and others 2014,
<doi:10.1093/bioinformatics/btt705>).  Example of use with microarray or RNA-Seq
data are provided.")
    (license license:gpl2+)))

(define-public r-patternplot
  (package
    (name "r-patternplot")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "patternplot" version))
       (sha256
        (base32 "0n4y23g894amwi62lx10k15i28rsmiaw52bds8dzmvkykkp9mm8q"))))
    (properties `((upstream-name . "patternplot")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppparallel
                             r-rcpp
                             r-r6
                             r-png
                             r-jpeg
                             r-gtable
                             r-gridextra
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=patternplot")
    (synopsis
     "Versatile Pie Charts, Ring Charts, Bar Charts and Box Plots using Patterns, Colors and Images")
    (description
     "This package creates aesthetically pleasing and informative pie charts, ring
charts, bar charts and box plots with colors, patterns, and images.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-patternize
  (package
    (name "r-patternize")
    (version "0.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "patternize" version))
       (sha256
        (base32 "1d0x27fy41sdxhmyj4cy01xxxcbyp4hvxjcybbmgg1r4ivi1sp08"))))
    (properties `((upstream-name . "patternize")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-sp
                             r-sf
                             r-rniftyreg
                             r-raster
                             r-purrr
                             r-morpho
                             r-magrittr
                             r-imager
                             r-geomorph
                             r-dplyr
                             r-clusterr
                             r-abind))
    (home-page "https://github.com/StevenVB12/patternize")
    (synopsis "Quantification of Color Pattern Variation")
    (description
     "Quantification of variation in organismal color patterns as obtained from image
data.  Patternize defines homology between pattern positions across images
either through fixed landmarks or image registration.  Pattern identification is
performed by categorizing the distribution of colors using RGB thresholds or
image segmentation.")
    (license license:gpl3)))

(define-public r-patterncausality
  (package
    (name "r-patterncausality")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "patterncausality" version))
       (sha256
        (base32 "01k4sg4vab8263vxyx7bc1phv1lj2x85wqw7nrpv4mzcq0pywr9d"))))
    (properties `((upstream-name . "patterncausality")))
    (build-system r-build-system)
    (propagated-inputs (list r-plot3d r-corrplot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/skstavroglou/pattern_causality/")
    (synopsis "Pattern Causality Algorithm")
    (description
     "The model proposes a robust methodology for detecting and reconstructing the
hidden structure of dynamic complex systems through short-term forecasts and
information embedded in reconstructed state spaces.  The approach not only
identifies critical components and causal interactions within these systems but
also provides a practical tool for optimizing system performance and stability.")
    (license license:expat)))

(define-public r-patternator
  (package
    (name "r-patternator")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "patternator" version))
       (sha256
        (base32 "0s4vrhdfblllnhpz2awhhrkwm8d4170xg7fpsp7kvvqy8z8biwvi"))))
    (properties `((upstream-name . "patternator")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table))
    (home-page "https://github.com/stathwang/patternator")
    (synopsis
     "Feature Extraction from Female Brown Anole Lizard Dorsal Patterns")
    (description
     "This package provides a set of functions to efficiently recognize and clean the
continuous dorsal pattern of a female brown anole lizard (Anolis sagrei) traced
from @code{ImageJ}', an open platform for scientific image analysis (see
<https://imagej.net> for more information), and extract common features such as
the pattern sinuosity indices, coefficient of variation, and max-min width.")
    (license license:gpl2+)))

(define-public r-patrick
  (package
    (name "r-patrick")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "patrick" version))
       (sha256
        (base32 "1ghjvm82hgal091hfjfyz3ndg32xsggip5pp4y0g7rmrzpfjmpjs"))))
    (properties `((upstream-name . "patrick")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-testthat r-rlang r-purrr r-dplyr))
    (home-page "https://github.com/google/patrick")
    (synopsis "Parameterized Unit Testing")
    (description
     "This is an extension of the testthat package that lets you add parameters to
your unit tests.  Parameterized unit tests are often easier to read and more
reliable, since they follow the DNRY (do not repeat yourself) rule.")
    (license license:asl2.0)))

(define-public r-patientprofilesvis
  (package
    (name "r-patientprofilesvis")
    (version "2.0.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "patientProfilesVis" version))
       (sha256
        (base32 "1jmf86dy5hvw81jc7n0hlhl6hwvcsi270bpq7vjj5r0zdlhgjgb7"))))
    (properties `((upstream-name . "patientProfilesVis")))
    (build-system r-build-system)
    (inputs (list cairo))
    (propagated-inputs (list r-stringr
                             r-scales
                             r-reshape2
                             r-plyr
                             r-knitr
                             r-gridextra
                             r-ggplot2
                             r-cowplot
                             r-clinutils))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/openanalytics/patientProfilesVis")
    (synopsis "Visualization of Patient Profiles")
    (description
     "Creation of patient profile visualizations for exploration, diagnostic or
monitoring purposes during a clinical trial.  These static visualizations
display a patient-specific overview of the evolution during the trial time frame
of parameters of interest (as laboratory, ECG, vital signs), presence of adverse
events, exposure to a treatment; associated with metadata patient information,
as demography, concomitant medication.  The visualizations can be tailored for
specific domain(s) or endpoint(s) of interest.  Visualizations are exported into
patient profile report(s) or can be embedded in custom report(s).")
    (license license:expat)))

(define-public r-patientprofiles
  (package
    (name "r-patientprofiles")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PatientProfiles" version))
       (sha256
        (base32 "0h3vcn17aa0gy0iff70grw4kd6mrmjzyq7d0iypzkxjzkrvjs16c"))))
    (properties `((upstream-name . "PatientProfiles")))
    (build-system r-build-system)
    (propagated-inputs (list r-visomopresults
                             r-tidyr
                             r-stringr
                             r-rlang
                             r-purrr
                             r-omopgenerics
                             r-magrittr
                             r-lifecycle
                             r-dplyr
                             r-cli
                             r-cdmconnector))
    (native-inputs (list r-knitr))
    (home-page "https://darwin-eu-dev.github.io/PatientProfiles/")
    (synopsis
     "Identify Characteristics of Patients in the OMOP Common Data Model")
    (description
     "Identify the characteristics of patients in data mapped to the Observational
Medical Outcomes Partnership (OMOP) common data model.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-pathwaytmb
  (package
    (name "r-pathwaytmb")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pathwayTMB" version))
       (sha256
        (base32 "1zrs7l9vifrsixk20p0nxs6s8kadc3l3mj933s7ribaby2fb0r8m"))))
    (properties `((upstream-name . "pathwayTMB")))
    (build-system r-build-system)
    (propagated-inputs (list r-survminer
                             r-survival
                             r-rcolorbrewer
                             r-randomforest
                             r-purrr
                             r-proc
                             r-maftools
                             r-glmnet
                             r-data-table
                             r-clusterprofiler
                             r-caret
                             r-biocgenerics))
    (native-inputs (list r-rmarkdown r-qpdf r-knitr))
    (home-page "https://cran.r-project.org/package=pathwayTMB")
    (synopsis "Pathway Based Tumor Mutational Burden")
    (description
     "This package provides a systematic bioinformatics tool to develop a new
pathway-based gene panel for tumor mutational burden (TMB) assessment
(pathway-based tumor mutational burden, PTMB), using somatic mutations files in
an efficient manner from either The Cancer Genome Atlas sources or any in-house
studies as long as the data is in mutation annotation file (MAF) format.
Besides, we develop a multiple machine learning method using the sample's PTMB
profiles to identify cancer-specific dysfunction pathways, which can be a
biomarker of prognostic and predictive for cancer immunotherapy.")
    (license license:gpl2+)))

(define-public r-pathwayspace
  (package
    (name "r-pathwayspace")
    (version "0.99.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PathwaySpace" version))
       (sha256
        (base32 "1wfml9krib7x0l4016hsn5n8s2p9v9v3sh7kfk0c4da5czsc52i0"))))
    (properties `((upstream-name . "PathwaySpace")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-rgraphspace
                             r-rann
                             r-igraph
                             r-ggrepel
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sysbiolab/PathwaySpace")
    (synopsis "Spatial Projection of Network Signals along Geodesic Paths")
    (description
     "For a given graph containing vertices, edges, and a signal associated with the
vertices, the @code{PathwaySpace} package performs a convolution operation,
which involves a weighted combination of neighboring vertices and their
associated signals.  The package then uses a decay function to project these
signals, creating geodesic paths on a 2D-image space. @code{PathwaySpace} could
have various applications, such as visualizing and analyzing network data in a
graphical format that highlights the relationships and signal strengths between
vertices.  It can be particularly useful for understanding the influence of
signals through complex networks.  By combining graph theory, signal processing,
and visualization, the @code{PathwaySpace} package provides a novel way of
representing and analyzing graph data.")
    (license license:artistic2.0)))

(define-public r-pathviewr
  (package
    (name "r-pathviewr")
    (version "1.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pathviewr" version))
       (sha256
        (base32 "0n3jv5zzwb7579ymv8s6vr7nzrc3gz5bi30yf6qvqixr8j0k4ggr"))))
    (properties `((upstream-name . "pathviewr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-r-matlab
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-ggplot2
                             r-fancova
                             r-dplyr
                             r-data-table
                             r-cowplot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ropensci/pathviewr/")
    (synopsis "Wrangle, Analyze, and Visualize Animal Movement Data")
    (description
     "This package provides tools to import, clean, and visualize movement data,
particularly from motion capture systems such as Optitrack's Motive', the Straw
Lab's Flydra', or from other sources.  We provide functions to remove artifacts,
standardize tunnel position and tunnel axes, select a region of interest,
isolate specific trajectories, fill gaps in trajectory data, and calculate 3D
and per-axis velocity.  For experiments of visual guidance, we also provide
functions that use subject position to estimate perception of visual stimuli.")
    (license license:gpl3)))

(define-public r-paths
  (package
    (name "r-paths")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paths" version))
       (sha256
        (base32 "19fl54r2myb9ky8ffnx71xw23a8dlg15sj9v36xcpv79z61ryws9"))))
    (properties `((upstream-name . "paths")))
    (build-system r-build-system)
    (propagated-inputs (list r-twang
                             r-tidyr
                             r-pryr
                             r-metr
                             r-ggplot2
                             r-gbm
                             r-boot
                             r-bart))
    (home-page "https://cran.r-project.org/package=paths")
    (synopsis
     "An Imputation Approach to Estimating Path-Specific Causal Effects")
    (description
     "In causal mediation analysis with multiple causally ordered mediators, a set of
path-specific effects are identified under standard ignorability assumptions.
This package implements an imputation approach to estimating these effects along
with a set of bias formulas for conducting sensitivity analysis (Zhou and
Yamamoto <doi:10.31235/osf.io/2rx6p>).  It contains two main functions: paths()
for estimating path-specific effects and sens() for conducting sensitivity
analysis.  Estimation uncertainty is quantified using the nonparametric
bootstrap.")
    (license license:gpl2+)))

(define-public r-pathmodelfit
  (package
    (name "r-pathmodelfit")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pathmodelfit" version))
       (sha256
        (base32 "1nxcnmz9r99znqmwibvvix7h3acp22cs6wp1jmp21p862ncabll8"))))
    (properties `((upstream-name . "pathmodelfit")))
    (build-system r-build-system)
    (propagated-inputs (list r-lavaan))
    (home-page "https://cran.r-project.org/package=pathmodelfit")
    (synopsis
     "Path Component Fit Indices for Latent Structural Equation Models")
    (description
     "This package provides functions for computing fit indices for evaluating the
path component of latent variable structural equation models.  Available fit
indices include RMSEA-P and NSCI-P originally presented and evaluated by
Williams and O'Boyle (2011) <doi:10.1177/1094428110391472> and demonstrated by
O'Boyle and Williams (2011) <doi:10.1037/a0020539> and Williams, O'Boyle, & Yu
(2020) <doi:10.1177/1094428117736137>.  Also included are fit indices described
by Hancock and Mueller (2011) <doi:10.1177/0013164410384856>.")
    (license license:gpl3)))

(define-public r-pathlit
  (package
    (name "r-pathlit")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pathlit" version))
       (sha256
        (base32 "0harvldkhq2c1wb9i4n72w1s3frhjzyn4cjpwk1n350zg12kh9gz"))))
    (properties `((upstream-name . "pathlit")))
    (build-system r-build-system)
    (propagated-inputs (list r-usethis r-timeseries r-testthat r-jsonlite
                             r-httr))
    (home-page "https://www.pathlit.io")
    (synopsis "An SDK for the PathLit Engine")
    (description
     "This wrapper houses @code{PathLit} API endpoints for R. The usage of these
endpoints require the use of an API key which can be obtained at
<https://www.pathlit.io/docs/cli/>.")
    (license license:gpl3+)))

(define-public r-pathling
  (package
    (name "r-pathling")
    (version "7.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pathling" version))
       (sha256
        (base32 "092pff81qg3pypygakihabi64kgw1fvdsn2pbkcvhnfa07yrirm1"))))
    (properties `((upstream-name . "pathling")))
    (build-system r-build-system)
    (propagated-inputs (list r-sparklyr r-rlang))
    (home-page "https://pathling.csiro.au/")
    (synopsis "Library for using 'Pathling'")
    (description
     "R API for Pathling', a tool for querying and transforming electronic health
record data that is represented using the Fast Healthcare Interoperability
Resources (FHIR) standard - see <https://pathling.csiro.au/docs>.")
    (license license:asl2.0)))

(define-public r-pathfindr-data
  (package
    (name "r-pathfindr-data")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pathfindR.data" version))
       (sha256
        (base32 "1plc83y35nbvn554kf52d3wc4q44fdm3rh5qs6asfcqif17g6k82"))))
    (properties `((upstream-name . "pathfindR.data")))
    (build-system r-build-system)
    (home-page "https://github.com/egeulgen/pathfindR.data")
    (synopsis "Data Package for 'pathfindR'")
    (description
     "This is a data-only package, containing data needed to run the CRAN package
@code{pathfindR}', a package for enrichment analysis utilizing active
subnetworks.  This package contains protein-protein interaction network data,
data related to gene sets and example input/output data.")
    (license license:expat)))

(define-public r-pathfindr
  (package
    (name "r-pathfindr")
    (version "2.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pathfindR" version))
       (sha256
        (base32 "0lwjnw6krqz2yi39as07zcpyh13c6b4d0qxxhhqbqcms6anms7q3"))))
    (properties `((upstream-name . "pathfindR")))
    (build-system r-build-system)
    (inputs (list openjdk))
    (propagated-inputs (list r-rmarkdown
                             r-r-utils
                             r-pathfindr-data
                             r-org-hs-eg-db
                             r-msigdbr
                             r-knitr
                             r-igraph
                             r-httr
                             r-ggupset
                             r-ggraph
                             r-ggplot2
                             r-fpc
                             r-foreach
                             r-doparallel
                             r-dbi
                             r-annotationdbi))
    (native-inputs (list r-knitr))
    (home-page "https://egeulgen.github.io/pathfindR/")
    (synopsis "Enrichment Analysis Utilizing Active Subnetworks")
    (description
     "Enrichment analysis enables researchers to uncover mechanisms underlying a
phenotype.  However, conventional methods for enrichment analysis do not take
into account protein-protein interaction information, resulting in incomplete
conclusions. @code{pathfindR} is a tool for enrichment analysis utilizing active
subnetworks.  The main function identifies active subnetworks in a
protein-protein interaction network using a user-provided list of genes and
associated p values.  It then performs enrichment analyses on the identified
subnetworks, identifying enriched terms (i.e.  pathways or, more broadly, gene
sets) that possibly underlie the phenotype of interest. @code{pathfindR} also
offers functionalities to cluster the enriched terms and identify representative
terms in each cluster, to score the enriched terms per sample and to visualize
analysis results.  The enrichment, clustering and other methods implemented in
@code{pathfindR} are described in detail in Ulgen E, Ozisik O, Sezerman OU.
2019. @code{pathfindR}: An R Package for Comprehensive Identification of
Enriched Pathways in Omics Data Through Active Subnetworks.  Front.  Genet.
<doi:10.3389/fgene.2019.00858>.")
    (license license:expat)))

(define-public r-path-chain
  (package
    (name "r-path-chain")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "path.chain" version))
       (sha256
        (base32 "16mpdms639hnzdarn8vkwr1rm8irjl55zgk0h3ia5y8r5yh1vpqh"))))
    (properties `((upstream-name . "path.chain")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringi r-rlang))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/krzjoa/path.chain")
    (synopsis "Concise Structure for Chainable Paths")
    (description
     "This package provides path_chain class and functions, which facilitates loading
and saving directory structure in YAML configuration files via config package.
The file structure you created during exploration can be transformed into
legible section in the config file, and then easily loaded for further usage.")
    (license license:expat)))

(define-public r-path-analysis
  (package
    (name "r-path-analysis")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Path.Analysis" version))
       (sha256
        (base32 "0xpr05qhlc20i2k8g61jq7grvdvlvai3fx827l77x6z1kcxcbzwa"))))
    (properties `((upstream-name . "Path.Analysis")))
    (build-system r-build-system)
    (propagated-inputs (list r-pastecs
                             r-metan
                             r-mathjaxr
                             r-hmisc
                             r-gplots
                             r-diagrammer
                             r-corrr
                             r-corrplot
                             r-complexheatmap))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/abeyran/Path.Analysis")
    (synopsis "Path Coefficient Analysis")
    (description
     "Facilitates the performance of several analyses, including simple and sequential
path coefficient analysis, correlation estimate, drawing correlogram, Heatmap,
and path diagram.  When working with raw data, that includes one or more
dependent variables along with one or more independent variables are available,
the path coefficient analysis can be conducted.  It allows for testing direct
effects, which can be a vital indicator in path coefficient analysis.  The
process of preparing the dataset rule is explained in detail in the vignette
file \"Path.Analysis_manual.Rmd\".  You can find this in the folders labelled
\"data\" and \"~/inst/extdata\".  Also see: 1)the lavaan', 2)a sample of sequential
path analysis in metan suggested by Olivoto and LÃºcio (2020)
<doi:10.1111/2041-210X.13384>, 3)the simple PATHSAS macro written in SAS by
Cramer et al. (1999) <doi:10.1093/jhered/90.1.260>, and 4)the @code{semPlot}()
function of @code{OpenMx} as initial tools for conducting path coefficient
analyses and SEM (Structural Equation Modeling).  To gain a comprehensive
understanding of path coefficient analysis, both in theory and practice, see a
Minitab macro developed by Arminian, A. in the paper by Arminian et al. (2008)
<doi:10.1080/15427520802043182>.")
    (license license:gpl3)))

(define-public r-patentsview
  (package
    (name "r-patentsview")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "patentsview" version))
       (sha256
        (base32 "1ysqarmqqbw7zy1c3nfa08i6byima31pmcvv60qz9qjpyd2np0fn"))))
    (properties `((upstream-name . "patentsview")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-httr))
    (home-page "https://docs.ropensci.org/patentsview/index.html")
    (synopsis "An R Client to the 'PatentsView' API")
    (description
     "This package provides functions to simplify the @code{PatentsView} API
(<https://patentsview.org/apis/purpose>) query language, send GET and POST
requests to the API's seven endpoints, and parse the data that comes back.")
    (license license:expat)))

(define-public r-patentr
  (package
    (name "r-patentr")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "patentr" version))
       (sha256
        (base32 "14jxqaq306mpppzz14f0qw1d0brrrkcs121h5fdiskvly3vjvs1j"))))
    (properties `((upstream-name . "patentr")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-rlang
                             r-rcpp
                             r-progress
                             r-magrittr
                             r-lubridate
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://JYProjs.github.io/patentr/")
    (synopsis "Access USPTO Bulk Data in Tidy Rectangular Format")
    (description
     "Converts TXT and XML data curated by the United States Patent and Trademark
Office (USPTO).  Allows conversion of bulk data after downloading directly from
the USPTO bulk data website, eliminating need for users to wrangle multiple data
formats to get large patent databases in tidy, rectangular format.  Data details
can be found on the USPTO website <https://bulkdata.uspto.gov/>.  Currently, all
3 formats: 1.  TXT data (1976-2001); 2.  XML format 1 data (2002-2004); and 3.
XML format 2 data (2005-current) can be converted to rectangular, CSV format.
Relevant literature that uses data from USPTO includes Wada (2020)
<doi:10.1007/s11192-020-03674-4> and Plaza & Albert (2008)
<doi:10.1007/s11192-007-1763-3>.")
    (license license:expat)))

(define-public r-patchsynctex
  (package
    (name "r-patchsynctex")
    (version "0.1-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "patchSynctex" version))
       (sha256
        (base32 "1li3kw7a77sx6dss8pnxzb0p0sdy1kfm1zdnmhhj043zihrryd5p"))))
    (properties `((upstream-name . "patchSynctex")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr))
    (home-page "https://github.com/EmmanuelCharpentier/patchSynctex")
    (synopsis "Communication Between Editor and Viewer for Literate Programs")
    (description
     "This utility eases the debugging of literate documents ('noweb files) by
patching the synchronization information (the .synctex(.gz) file) produced by
pdflatex with concordance information produced by Sweave or knitr and Sweave or
knitr ; this allows for bilateral communication between a text editor
(visualizing the noweb source) and a viewer (visualizing the resultant PDF'),
thus bypassing the intermediate @code{TeX} file.")
    (license license:gpl2+)))

(define-public r-patchdvi
  (package
    (name "r-patchdvi")
    (version "1.11.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "patchDVI" version))
       (sha256
        (base32 "1cxgbrwb2qz39b9q1afz2i1pbnqyyi0hz8zc6j1i0llayyd6pgxk"))))
    (properties `((upstream-name . "patchDVI")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmdconcord))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/dmurdoch/patchDVI")
    (synopsis "Package to Patch '.dvi' or '.synctex' Files")
    (description
     "This package provides functions to patch specials in .dvi files, or entries in
.synctex files.  Works with concordance=TRUE in Sweave, knitr or R Markdown to
link sources to previews.")
    (license license:gpl2)))

(define-public r-paswr2
  (package
    (name "r-paswr2")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PASWR2" version))
       (sha256
        (base32 "124ybvqdz2ikwqagq6bw74487psrscwxkj0gqwz43p8kinkvx3ks"))))
    (properties `((upstream-name . "PASWR2")))
    (build-system r-build-system)
    (propagated-inputs (list r-lattice r-ggplot2 r-e1071))
    (home-page "https://github.com/alanarnholt/PASWR2")
    (synopsis "Probability and Statistics with R, Second Edition")
    (description
     "This package provides functions and data sets for the text Probability and
Statistics with R, Second Edition.")
    (license license:gpl2)))

(define-public r-paswr
  (package
    (name "r-paswr")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PASWR" version))
       (sha256
        (base32 "1p4896r4k6c0aaag4mwds2azp41amf726c4v62zca92cpsay4nml"))))
    (properties `((upstream-name . "PASWR")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-lattice r-e1071))
    (home-page "https://cran.r-project.org/package=PASWR")
    (synopsis "Probability and Statistics with R")
    (description
     "This package provides functions and data sets for the text Probability and
Statistics with R.")
    (license license:gpl2)))

(define-public r-pasteascomment
  (package
    (name "r-pasteascomment")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pasteAsComment" version))
       (sha256
        (base32 "165wsvgnrsbx9lh6bmffzyhdfhviap4vl556sbgr8f45lqcgsh69"))))
    (properties `((upstream-name . "pasteAsComment")))
    (build-system r-build-system)
    (propagated-inputs (list r-rstudioapi r-clipr))
    (home-page "https://github.com/stla/pasteAsComment")
    (synopsis
     "'RStudio' Addin to Paste the Clipboard as a Comment Block or a 'roxygen' Block")
    (description
     "This package provides a RStudio addin allowing to paste the content of the
clipboard as a comment block or as roxygen lines.  This is very useful to insert
an example in the roxygen block.")
    (license license:gpl3)))

(define-public r-pastclim
  (package
    (name "r-pastclim")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pastclim" version))
       (sha256
        (base32 "0wdccbnb808wc2fr2ysr7z5i9d3rjgfdd8lcmgkipdprq0ymvqak"))))
    (properties `((upstream-name . "pastclim")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-terra
                             r-sf
                             r-ncdf4
                             r-lubridate
                             r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/EvolEcolGroup/pastclim")
    (synopsis "Manipulate Time Series of Palaeoclimate Reconstructions")
    (description
     "This package provides methods to easily extract and manipulate palaeoclimate
reconstructions for ecological and anthropological analyses, as described in
Leonardi et al. (2023) <doi:10.1111/ecog.06481>.")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-pastboon
  (package
    (name "r-pastboon")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pastboon" version))
       (sha256
        (base32 "06xp6p4vrdkblhypz745r6sl0n1a7wxhinfb6sx18hiaaaw73ixh"))))
    (properties `((upstream-name . "pastboon")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pastboon")
    (synopsis "Simulation of Parameterized Stochastic Boolean Networks")
    (description
     "This package provides a Boolean network is a particular kind of discrete
dynamical system where the variables are simple binary switches.  Despite its
simplicity, Boolean network modeling has been a successful method to describe
the behavioral pattern of various phenomena.  Applying stochastic noise to
Boolean networks is a useful approach for representing the effects of various
perturbing stimuli on complex systems.  A number of methods have been developed
to control noise effects on Boolean networks using parameters integrated into
the update rules.  This package provides functions to examine three such
methods: Boolean network with perturbations (BNp), described by Trairatphisan et
al. (2013) <doi:10.1186/1478-811X-11-46>, stochastic discrete dynamical systems
(SDDS), proposed by Murrugarra et al. (2012) <doi:10.1186/1687-4153-2012-5>, and
Boolean network with probabilistic edge weights (PEW), presented by Deritei et
al. (2022) <doi:10.1371/journal.pcbi.1010536>.  This package includes source
code derived from the @code{BoolNet} package, which is licensed under the
Artistic License 2.0.")
    (license license:artistic2.0)))

(define-public r-pastaplot
  (package
    (name "r-pastaplot")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pastaPlot" version))
       (sha256
        (base32 "16a1rk903py7bm36wkywwspq927698k1kvv5h1h7ln6rcd2376r3"))))
    (properties `((upstream-name . "pastaPlot")))
    (build-system r-build-system)
    (propagated-inputs (list r-lme4 r-glmmtmb r-ggplot2 r-ggeffects))
    (home-page "https://cran.r-project.org/package=pastaPlot")
    (synopsis "Spaghetti-Plot Fixed and Random Effects of Linear Mixed Models")
    (description
     "Plot both fixed and random effects of linear mixed models, multilevel models in
a single spaghetti plot.  The package allows to visualize the effect of a
predictor on a criterion between different levels of a grouping variable.
Additionally, confidence intervals can be displayed for fixed effects.
Calculation of predicted values of random effects allows only models with one
random intercept and/or one random slope to be plotted.  Confidence intervals
and predicted values of fixed effects are computed using the ggpredict function
from the ggeffects package.  LÃ¼decke, D. (2018) <doi:10.21105/joss.00638>.")
    (license license:expat)))

(define-public r-password
  (package
    (name "r-password")
    (version "1.0-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "password" version))
       (sha256
        (base32 "1ijzqdw54l8wvpy6ys28njvhplzjxzzi5i9y41vjnrr88n13977v"))))
    (properties `((upstream-name . "password")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=password")
    (synopsis "Create Random Passwords")
    (description
     "Create random passwords of letters, numbers and punctuation.")
    (license license:gpl3)))

(define-public r-passt
  (package
    (name "r-passt")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "passt" version))
       (sha256
        (base32 "0yphnw38wwv303lkbfj2y3fwfjnd8l7gyi60c7xaj6qwy4y5ww8k"))))
    (properties `((upstream-name . "passt")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-rlang r-magrittr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/johannes-titz/passt")
    (synopsis "Probability Associator Time (PASS-T)")
    (description
     "Simulates judgments of frequency and duration based on the Probability
Associator Time (PASS-T) model.  PASS-T is a memory model based on a simple
competitive artificial neural network.  It can imitate human judgments of
frequency and duration, which have been extensively studied in cognitive
psychology (e.g. Hintzman (1970) <doi:10.1037/h0028865>, Betsch et al. (2010)
<https://psycnet.apa.org/record/2010-18204-003>).  The PASS-T model is an
extension of the PASS model (Sedlmeier, 2002, ISBN:0198508638).  The package
provides an easy way to run simulations, which can then be compared with
empirical data in human judgments of frequency and duration.")
    (license license:gpl3)))

(define-public r-passport
  (package
    (name "r-passport")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "passport" version))
       (sha256
        (base32 "176pkc7x76339bawdwywdcmiynzzlrwr91fgf5q0rq7asd9nc7d1"))))
    (properties `((upstream-name . "passport")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/alistaire47/passport")
    (synopsis "Travel Smoothly Between Country Name and Code Formats")
    (description
     "Smooths the process of working with country names and codes via powerful
parsing, standardization, and conversion utilities arranged in a simple,
consistent API. Country name formats include multiple sources including the
Unicode Common Locale Data Repository (CLDR, <http://cldr.unicode.org/>)
common-sense standardized names in hundreds of languages.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-passo
  (package
    (name "r-passo")
    (version "0.1.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PAsso" version))
       (sha256
        (base32 "1qfwwgcra86x4469bqrv3jdm3vyvdiialfgfy184d1l5294pwv4x"))))
    (properties `((upstream-name . "PAsso")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam
                             r-rcpp
                             r-progress
                             r-plotly
                             r-pcapp
                             r-mass
                             r-gridextra
                             r-ggplot2
                             r-ggally
                             r-foreach
                             r-dplyr
                             r-copula
                             r-copbasic))
    (home-page "https://cran.r-project.org/package=PAsso")
    (synopsis "Assessing the Partial Association Between Ordinal Variables")
    (description
     "An implementation of the unified framework for assessing partial association
between ordinal variables after adjusting for a set of covariates (Dungang Liu,
Shaobo Li, Yan Yu and Irini Moustaki (2020), accepted by the Journal of the
American Statistical Association).  This package provides a set of tools to
quantify, visualize, and test partial associations between multiple ordinal
variables.  It can produce a number of $phi$ measures, partial regression plots,
3-D plots, and $p$-values for testing $H_0: phi=0$ or $H_0: phi <= delta$.")
    (license license:gpl2+)))

(define-public r-passed
  (package
    (name "r-passed")
    (version "1.2-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PASSED" version))
       (sha256
        (base32 "000ha6q6cl79741rcri8pxirlps43vym1qwhkxwwpqz0b1dnbaws"))))
    (properties `((upstream-name . "PASSED")))
    (build-system r-build-system)
    (propagated-inputs (list r-rootsolve r-betareg))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://cran.r-project.org/package=PASSED")
    (synopsis "Calculate Power and Sample Size for Two Sample Mean Tests")
    (description
     "Power calculations are a critical component of any research study to determine
the minimum sample size necessary to detect differences between multiple groups.
 Here we present an R package, PASSED', that performs power and sample size
calculations for the test of two-sample means or ratios with data following
beta, gamma (Chang et al. (2011), <doi:10.1007/s00180-010-0209-1>), normal,
Poisson (Gu et al. (2008), <doi:10.1002/bimj.200710403>), binomial, geometric,
and negative binomial (Zhu and Lakkis (2014), <doi:10.1002/sim.5947>)
distributions.")
    (license license:gpl2+)))

(define-public r-pass-lme
  (package
    (name "r-pass-lme")
    (version "0.9.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pass.lme" version))
       (sha256
        (base32 "1rxm509vnkdvdxii4jwniirdb2pv90rjkf6wjc9zrjh88jrl678k"))))
    (properties `((upstream-name . "pass.lme")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pass.lme")
    (synopsis "Power and Sample Size for Linear Mixed Effect Models")
    (description
     "Power and sample size calculation for testing fixed effect coefficients in
multilevel linear mixed effect models with one or more than one independent
populations.  Laird, Nan M. and Ware, James H. (1982) <doi:10.2307/2529876>.")
    (license license:gpl3)))

(define-public r-pasensewear
  (package
    (name "r-pasensewear")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PASenseWear" version))
       (sha256
        (base32 "0x84j1vdds4g08birmjxb0c2g1ahwv83bk11532jkmg32dnhmj58"))))
    (properties `((upstream-name . "PASenseWear")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://cran.r-project.org/package=PASenseWear")
    (synopsis
     "Summarize Daily Physical Activity from 'SenseWear' Accelerometer Data")
    (description
     "Provide summary table of daily physical activity and per-person/grouped heat map
for accelerometer data from @code{SenseWear} Armband.  See
<https://templehealthcare.wordpress.com/the-sensewear-armband/> for more
information about @code{SenseWear} Armband.")
    (license license:gpl2)))

(define-public r-pasadr
  (package
    (name "r-pasadr")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pasadr" version))
       (sha256
        (base32 "0gc44jbqn96bn7f6xjmszz5lgsby24sxziixvhbkh5lgs4zap8i2"))))
    (properties `((upstream-name . "pasadr")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-pracma))
    (home-page "https://github.com/ainsuotain/pasadr")
    (synopsis
     "An Implementation of Process-Aware Stealthy Attack Detection(PASAD)")
    (description
     "Anomaly detection method based on the paper \"Truth will out: Departure-based
process-level detection of stealthy attacks on control systems\" from Wissam
Aoudi, Mikel Iturbe, and Magnus Almgren (2018) <DOI:10.1145/3243734.3243781>.
Also referred to the following implementation:
<https://github.com/rahulrajpl/@code{PyPASAD>}.")
    (license license:gpl3)))

(define-public r-pas
  (package
    (name "r-pas")
    (version "1.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PAS" version))
       (sha256
        (base32 "175p3bnpnhknfsc29hri0l2ipz1x6qcxs2bq7awns8lccam466r2"))))
    (properties `((upstream-name . "PAS")))
    (build-system r-build-system)
    (propagated-inputs (list r-glmnet))
    (home-page "http://statgen.ucr.edu")
    (synopsis "Polygenic Analysis System (PAS)")
    (description "An R package for polygenic trait analysis.")
    (license license:gpl2+)))

(define-public r-parzer
  (package
    (name "r-parzer")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "parzer" version))
       (sha256
        (base32 "0v97cfjkggkdhv8gc0ffgbijgwxzpz7n44z7gsmkvggjj3wsxvpr"))))
    (properties `((upstream-name . "parzer")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr r-rcpp))
    (native-inputs (list r-knitr))
    (home-page
     "https://github.com/ropensci/parzerhttps://docs.ropensci.org/parzer/")
    (synopsis "Parse Messy Geographic Coordinates")
    (description
     "Parse messy geographic coordinates from various character formats to decimal
degree numeric values.  Parse coordinates into their parts (degree, minutes,
seconds); calculate hemisphere from coordinates; pull out individually degrees,
minutes, or seconds; add and subtract degrees, minutes, and seconds.  C++ code
herein originally inspired from code written by Jeffrey D. Bogan, but then
completely re-written.")
    (license license:expat)))

(define-public r-party
  (package
    (name "r-party")
    (version "1.3-17")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "party" version))
       (sha256
        (base32 "0jvxy9v30aazh9rkvhwz95gaq7s64sasdlsp9ckm1ks3wyqpdq7h"))))
    (properties `((upstream-name . "party")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-survival
                             r-strucchange
                             r-sandwich
                             r-mvtnorm
                             r-modeltools
                             r-coin))
    (home-page "http://party.R-forge.R-project.org")
    (synopsis "Laboratory for Recursive Partytioning")
    (description
     "This package provides a computational toolbox for recursive partitioning.  The
core of the package is ctree(), an implementation of conditional inference trees
which embed tree-structured regression models into a well defined theory of
conditional inference procedures.  This non-parametric class of regression trees
is applicable to all kinds of regression problems, including nominal, ordinal,
numeric, censored as well as multivariate response variables and arbitrary
measurement scales of the covariates.  Based on conditional inference trees,
cforest() provides an implementation of Breiman's random forests.  The function
mob() implements an algorithm for recursive partitioning based on parametric
models (e.g. linear models, GLMs or survival regression) employing parameter
instability tests for split selection.  Extensible functionality for visualizing
tree-structured regression models is available.  The methods are described in
Hothorn et al. (2006) <doi:10.1198/106186006X133933>, Zeileis et al. (2008)
<doi:10.1198/106186008X319331> and Strobl et al. (2007)
<doi:10.1186/1471-2105-8-25>.")
    (license license:gpl2)))

(define-public r-parttime
  (package
    (name "r-parttime")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "parttime" version))
       (sha256
        (base32 "0n8yyi2c14b0rharb2afj9xkmjjfqalzyl164m5arzscwpxl9y2i"))))
    (properties `((upstream-name . "parttime")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs r-pillar r-lubridate r-crayon))
    (home-page "https://dgkf.github.io/parttime/")
    (synopsis "Partial Datetime Handling")
    (description
     "Datetimes and timestamps are invariably an imprecise notation, with any partial
representation implying some amount of uncertainty.  To handle this, parttime
provides classes for embedding partial missingness as a central part of its
datetime classes.  This central feature allows for more ergonomic use of
datetimes for challenging datetime computation, including calculations of
overlapping date ranges, imputations, and more thoughtful handling of ambiguity
that arises from uncertain time zones.  This package was developed first and
foremost with pharmaceutical applications in mind, but aims to be agnostic to
application to accommodate general use cases just as conveniently.")
    (license license:expat)))

(define-public r-partsm
  (package
    (name "r-partsm")
    (version "1.1-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "partsm" version))
       (sha256
        (base32 "17md3j41knscllckiqnvkqvf2v0fgsig101vgg6z5ic1yzvzfxgs"))))
    (properties `((upstream-name . "partsm")))
    (build-system r-build-system)
    (home-page "https://github.com/MatthieuStigler/partsm")
    (synopsis "Periodic Autoregressive Time Series Models")
    (description
     "Basic functions to fit and predict periodic autoregressive time series models.
These models are discussed in the book P.H. Franses (1996) \"Periodicity and
Stochastic Trends in Economic Time Series\", Oxford University Press.  Data set
analyzed in that book is also provided.  NOTE: the package was orphaned during
several years.  It is now only maintained, but no major enhancements are
expected, and the maintainer cannot provide any support.")
    (license license:gpl2)))

(define-public r-partools
  (package
    (name "r-partools")
    (version "1.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "partools" version))
       (sha256
        (base32 "0w7p88y4ab4v14k16k95cyb5f3yl2g6ban11775rmi2h9xqkfxk8"))))
    (properties `((upstream-name . "partools")))
    (build-system r-build-system)
    (propagated-inputs (list r-regtools r-pdist r-data-table))
    (home-page "https://github.com/matloff/partools")
    (synopsis "Tools for the 'Parallel' Package")
    (description
     "Miscellaneous utilities for parallelizing large computations.  Alternative to
@code{MapReduce}.  File splitting and distributed operations such as sort and
aggregate. \"Software Alchemy\" method for parallelizing most statistical methods,
presented in N. Matloff, Parallel Computation for Data Science, Chapman and
Hall, 2015.  Includes a debugging aid.")
    (license license:gpl2+)))

(define-public r-partitionmetric
  (package
    (name "r-partitionmetric")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "partitionMetric" version))
       (sha256
        (base32 "1wry9d3s814yp79ayab7rzf8z5l2mwpgnrc5j7d2sac24vp4pd48"))))
    (properties `((upstream-name . "partitionMetric")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=partitionMetric")
    (synopsis "Compute a distance metric between two partitions of a set")
    (description
     "@code{partitionMetric} computes a distance between two partitions of a set.")
    (license license:bsd-2)))

(define-public r-partitioncomparison
  (package
    (name "r-partitioncomparison")
    (version "0.2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "partitionComparison" version))
       (sha256
        (base32 "1z4ckjgjsq94wbnazb3335rcy6hhqh8pf9j5d22z6qkgfgxcv7w6"))))
    (properties `((upstream-name . "partitionComparison")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-lpsolve))
    (home-page "https://github.com/KIT-IISM-EM/partitionComparison")
    (synopsis "Implements Measures for the Comparison of Two Partitions")
    (description
     "This package provides several measures ((dis)similarity, distance/metric,
correlation, entropy) for comparing two partitions of the same set of objects.
The different measures can be assigned to three different classes: Pair
comparison (containing the famous Jaccard and Rand indices), set based, and
information theory based.  Many of the implemented measures can be found in
Albatineh AN, Niewiadomska-Bugaj M and Mihalko D (2006)
<doi:10.1007/s00357-006-0017-z> and Meila M (2007)
<doi:10.1016/j.jmva.2006.11.013>.  Partitions are represented by vectors of
class labels which allow a straightforward integration with existing clustering
algorithms (e.g. kmeans()).  The package is mostly based on the S4 object
system.")
    (license license:expat)))

(define-public r-partitionbefsp
  (package
    (name "r-partitionbefsp")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "partitionBEFsp" version))
       (sha256
        (base32 "0rirqk9v5jbjs2jpjjkg7kvay9f093q2gwx5fx3bm9b0f5869w1b"))))
    (properties `((upstream-name . "partitionBEFsp")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=partitionBEFsp")
    (synopsis "Methods for Calculating the Loreau & Hector 2001 BEF Partition")
    (description
     "This package provides a collection of functions that can be used to estimate
selection and complementarity effects, sensu Loreau & Hector (2001)
<doi:10.1038/35083573>, even in cases where data are only available for a random
subset of species (i.e.  incomplete sample-level data).  A full derivation and
explanation of the statistical corrections used here is available in Clark et
al. (2019) <doi:10.1111/2041-210X.13285>.")
    (license license:gpl3)))

(define-public r-partition
  (package
    (name "r-partition")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "partition" version))
       (sha256
        (base32 "1m02743l6sl33qyv862xm9c7g009n4c969rjbm74dd1svqk678f3"))))
    (properties `((upstream-name . "partition")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-rcpparmadillo
                             r-rcpp
                             r-purrr
                             r-progress
                             r-pillar
                             r-mass
                             r-magrittr
                             r-infotheo
                             r-ggplot2
                             r-forcats
                             r-dplyr
                             r-crayon))
    (native-inputs (list r-knitr))
    (home-page "https://uscbiostats.github.io/partition/")
    (synopsis "Agglomerative Partitioning Framework for Dimension Reduction")
    (description
     "This package provides a fast and flexible framework for agglomerative
partitioning.  partition uses an approach called Direct-Measure-Reduce to create
new variables that maintain the user-specified minimum level of information.
Each reduced variable is also interpretable: the original variables map to one
and only one variable in the reduced data set.  partition is flexible, as well:
how variables are selected to reduce, how information loss is measured, and the
way data is reduced can all be customized.  partition is based on the Partition
framework discussed in Millstein et al. (2020)
<doi:10.1093/bioinformatics/btz661>.")
    (license license:expat)))

(define-public r-partimeroc
  (package
    (name "r-partimeroc")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "parTimeROC" version))
       (sha256
        (base32 "0rshkjckv0llzgp2yywrbz55ahbzcq140k78wwwsjlwymjfxim9f"))))
    (properties `((upstream-name . "parTimeROC")))
    (build-system r-build-system)
    (propagated-inputs (list r-vinecopula
                             r-survival
                             r-stanheaders
                             r-sn
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-mvtnorm
                             r-moments
                             r-matrix
                             r-flexsurv
                             r-desctools
                             r-cubature
                             r-bh))
    (home-page "https://cran.r-project.org/package=parTimeROC")
    (synopsis "Parametric Time-Dependent Receiver Operating Characteristic")
    (description
     "Producing the time-dependent receiver operating characteristic (ROC) curve
through parametric approaches.  Tools for generating random data, fitting,
predicting and check goodness of fit are prepared.  The methods are developed
from the theoretical framework of proportional hazard model and copula
functions.  Using this package, users can now simulate parametric time-dependent
ROC and run experiment to understand the behavior of the curve under different
scenario.")
    (license license:expat)))

(define-public r-particles
  (package
    (name "r-particles")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "particles" version))
       (sha256
        (base32 "0x76q9qcfaraiy0w8hrdk4ws16w1a8m363a3l4cphpi6xi329027"))))
    (properties `((upstream-name . "particles")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidygraph
                             r-rlang
                             r-mgcv
                             r-igraph
                             r-dplyr
                             r-digest
                             r-cpp11))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/thomasp85/particles")
    (synopsis "Graph Based Particle Simulator Based on D3-Force")
    (description
     "Simulating particle movement in 2D space has many application.  The particles
package implements a particle simulator based on the ideas behind the d3-force
@code{JavaScript} library.  particles implements all forces defined in d3-force
as well as others such as vector fields, traps, and attractors.")
    (license license:expat)))

(define-public r-particle-swarm-optimisation
  (package
    (name "r-particle-swarm-optimisation")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "particle.swarm.optimisation" version))
       (sha256
        (base32 "0y3pq129irxifiph40mf027gbc02c4pwkchs305pcbj3vr9pwsyw"))))
    (properties `((upstream-name . "particle.swarm.optimisation")))
    (build-system r-build-system)
    (propagated-inputs (list r-rgl r-r6))
    (native-inputs (list r-knitr))
    (home-page
     "https://cran.r-project.org/package=particle.swarm.optimisation")
    (synopsis "Optimisation with Particle Swarm Optimisation")
    (description
     "This package provides a toolbox to create a particle swarm optimisation (PSO),
the package contains two classes: the Particle and the Particle Swarm, this two
class is used to run the PSO with methods to easily print, plot and save the
result.")
    (license license:gpl3)))

(define-public r-partialnetwork
  (package
    (name "r-partialnetwork")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PartialNetwork" version))
       (sha256
        (base32 "010q3d0r40kszdsyc6bish7vrzl98zxi47ppbnv5n86p0n3dckjn"))))
    (properties `((upstream-name . "PartialNetwork")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppprogress
                             r-rcppnumerical
                             r-rcppeigen
                             r-rcpparmadillo
                             r-rcpp
                             r-matrix
                             r-formula-tools
                             r-formula
                             r-foreach
                             r-dorng
                             r-doparallel
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ahoundetoungan/PartialNetwork")
    (synopsis "Estimating Peer Effects Using Partial Network Data")
    (description
     "This package implements IV-estimator and Bayesian estimator for linear-in-means
Spatial Autoregressive (SAR) model (see @code{LeSage}, 1997
<doi:10.1177/016001769702000107>; Lee, 2004
<doi:10.1111/j.1468-0262.2004.00558.x>; BramoullÃ© et al., 2009
<doi:10.1016/j.jeconom.2008.12.021>), while assuming that only a partial
information about the network structure is available.  Examples are when the
adjacency matrix is not fully observed or when only consistent estimation of the
network formation model is available (see Boucher and Houndetoungan
<https://ahoundetoungan.com/files/Papers/@code{PartialNetwork.pdf>}).")
    (license license:gpl3)))

(define-public r-partiallyoverlapping
  (package
    (name "r-partiallyoverlapping")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Partiallyoverlapping" version))
       (sha256
        (base32 "0pb0digyigqyzb0zqikgz002kqf1xprcjhxr62a7iqbzy1yycayf"))))
    (properties `((upstream-name . "Partiallyoverlapping")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=Partiallyoverlapping")
    (synopsis "Partially Overlapping Samples Tests")
    (description
     "Tests for a comparison of two partially overlapping samples.  A comparison of
means using the partially overlapping samples t-test: See Derrick, Russ, Toher
and White (2017), Test statistics for the comparison of means for two samples
which include both paired observations and independent observations, Journal of
Modern Applied Statistical Methods, 16(1).  A comparison of proportions using
the partially overlapping samples z-test: See Derrick, Dobson-Mckittrick, Toher
and White (2015), Test statistics for comparing two proportions with partially
overlapping samples.  Journal of Applied Quantitative Methods, 10(3).")
    (license license:gpl3)))

(define-public r-partialised
  (package
    (name "r-partialised")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "partialised" version))
       (sha256
        (base32 "0rb9d5p21f1y0c8arwh1lf516vw30qinf15rkrv49qw97dvvb9db"))))
    (properties `((upstream-name . "partialised")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs r-rlang r-purrr r-pillar))
    (home-page "https://github.com/UchidaMizuki/partialised")
    (synopsis "Partialised Functions")
    (description
     "This package provides a partialised class that extends the partialising function
of purrr by making it easier to change the arguments.  This is similar to the
function-like object in Julia
(<https://docs.julialang.org/en/v1/manual/methods/#Function-like-objects>).")
    (license license:expat)))

(define-public r-partdsa
  (package
    (name "r-partdsa")
    (version "0.9.14")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "partDSA" version))
       (sha256
        (base32 "1kp0cdsdjiay349jz22iqfzvspny8s343cfan8xahgf931k9h8p6"))))
    (properties `((upstream-name . "partDSA")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "https://cran.r-project.org/package=partDSA")
    (synopsis "Partitioning Using Deletion, Substitution, and Addition Moves")
    (description
     "This package provides a novel tool for generating a piecewise constant
estimation list of increasingly complex predictors based on an intensive and
comprehensive search over the entire covariate space.")
    (license license:gpl2)))

(define-public r-partcensreg
  (package
    (name "r-partcensreg")
    (version "1.39")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PartCensReg" version))
       (sha256
        (base32 "0blzv57cbxqghkz4fc3plvrcw80g0kx17dzmajkfv598m1wm6hc8"))))
    (properties `((upstream-name . "PartCensReg")))
    (build-system r-build-system)
    (propagated-inputs (list r-ssym r-optimx r-matrix))
    (home-page "https://cran.r-project.org/package=PartCensReg")
    (synopsis
     "Estimation and Diagnostics for Partially Linear Censored Regression Models Based on Heavy-Tailed Distributions")
    (description
     "It estimates the parameters of a partially linear regression censored model via
maximum penalized likelihood through of ECME algorithm.  The model belong to the
semiparametric class, that including a parametric and nonparametric component.
The error term considered belongs to the scale-mixture of normal (SMN)
distribution, that includes well-known heavy tails distributions as the
Student-t distribution, among others.  To examine the performance of the fitted
model, case-deletion and local influence techniques are provided to show its
robust aspect against outlying and influential observations.  This work is based
in Ferreira, C. S., & Paula, G. A. (2017) <doi:10.1080/02664763.2016.1267124>
but considering the SMN family.")
    (license license:gpl2+)))

(define-public r-parsim
  (package
    (name "r-parsim")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "parSim" version))
       (sha256
        (base32 "0gcycp0gcgz2a1w7nzrlwq41wvyg36931c9q4zjyq876nd43ah12"))))
    (properties `((upstream-name . "parSim")))
    (build-system r-build-system)
    (propagated-inputs (list r-snow r-pbapply r-dplyr r-data-table))
    (home-page "https://cran.r-project.org/package=parSim")
    (synopsis "Parallel Simulation Studies")
    (description
     "Perform flexible simulation studies using one or multiple computer cores.  The
package is set up to be usable on high-performance clusters in addition to being
run locally, see examples on <https://github.com/@code{SachaEpskamp/parSim>}.")
    (license license:gpl2)))

(define-public r-parserpdr
  (package
    (name "r-parserpdr")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "parseRPDR" version))
       (sha256
        (base32 "1ih4mrk0vfr8rkm55zgx4rlv3791asvsahk9w4pavns3r0wiq6av"))))
    (properties `((upstream-name . "parseRPDR")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-readr
                             r-progressr
                             r-parallelly
                             r-future
                             r-foreach
                             r-dofuture
                             r-data-table))
    (home-page "https://github.com/martonkolossvary/parseRPDR")
    (synopsis
     "Parse and Manipulate Research Patient Data Registry ('RPDR') Text Queries")
    (description
     "This package provides functions to load Research Patient Data Registry ('RPDR')
text queries from Partners Healthcare institutions into R. The package also
provides helper functions to manipulate data and execute common procedures such
as finding the closest radiological exams considering a given timepoint, or
creating a DICOM header database from the downloaded images.  All
functionalities are parallelized for fast and efficient analyses.")
    (license license:agpl3+)))

(define-public r-parsermd
  (package
    (name "r-parsermd")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "parsermd" version))
       (sha256
        (base32 "1xlqbgx65b137849f7w01xkkj2gi8sqfm0scjjzvxmi6kr0x5awh"))))
    (properties `((upstream-name . "parsermd")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-withr
                             r-tidyselect
                             r-tidyr
                             r-tibble
                             r-rmarkdown
                             r-rlang
                             r-readr
                             r-rcpp
                             r-purrr
                             r-pillar
                             r-magrittr
                             r-lifecycle
                             r-dplyr
                             r-cli
                             r-checkmate
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://rundel.github.io/parsermd/")
    (synopsis "Formal Parser and Related Tools for R Markdown Documents")
    (description
     "An implementation of a formal grammar and parser for R Markdown documents using
the Boost Spirit X3 library.  It also includes a collection of high level
functions for working with the resulting abstract syntax tree.")
    (license license:expat)))

(define-public r-parsel
  (package
    (name "r-parsel")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "parsel" version))
       (sha256
        (base32 "0d2sviaq94v5szd2qq1kdpirp3zyrx6r3kyins9xyfma9026770x"))))
    (properties `((upstream-name . "parsel")))
    (build-system r-build-system)
    (propagated-inputs (list r-rselenium r-rlang r-purrr r-lubridate))
    (home-page "https://github.com/till-tietz/parsel")
    (synopsis "Parallel Dynamic Web-Scraping Using 'RSelenium'")
    (description
     "This package provides a system to increase the efficiency of dynamic
web-scraping with RSelenium by leveraging parallel processing.  You provide a
function wrapper for your RSelenium scraping routine with a set of inputs, and
parsel runs it in several browser instances.  Chunked input processing as well
as error catching and logging ensures seamless execution and minimal data loss,
even when unforeseen RSelenium errors occur.  You can additionally build safe
scraping functions with minimal coding by utilizing constructor functions that
act as wrappers around RSelenium methods.")
    (license license:expat)))

(define-public r-parsec
  (package
    (name "r-parsec")
    (version "1.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "parsec" version))
       (sha256
        (base32 "0k6w4nd37yz9jlzwv0bkv90v72y5bjz7rhk8gjpbl6ym63brxps6"))))
    (properties `((upstream-name . "parsec")))
    (build-system r-build-system)
    (propagated-inputs (list r-netrankr r-igraph))
    (home-page "https://cran.r-project.org/package=parsec")
    (synopsis "Partial Orders in Socio-Economics")
    (description
     "This package implements tools for the analysis of partially ordered data, with a
particular focus on the evaluation of multidimensional systems of indicators and
on the analysis of poverty.  References, Fattore M. (2016)
<doi:10.1007/s11205-015-1059-6> Fattore M., Arcagni A. (2016)
<doi:10.1007/s11205-016-1501-4> Arcagni A. (2017)
<doi:10.1007/978-3-319-45421-4_19>.")
    (license license:gpl2+)))

(define-public r-parquetize
  (package
    (name "r-parquetize")
    (version "0.5.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "parquetize" version))
       (sha256
        (base32 "0vmvjycyw4kihdx13wgi8qbynnaic96wgvw2hhdjz8fjrg1lb9sy"))))
    (properties `((upstream-name . "parquetize")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-rsqlite
                             r-rlang
                             r-readr
                             r-lifecycle
                             r-jsonlite
                             r-haven
                             r-glue
                             r-fst
                             r-dplyr
                             r-dbi
                             r-curl
                             r-cli
                             r-arrow))
    (native-inputs (list r-knitr))
    (home-page "https://ddotta.github.io/parquetize/")
    (synopsis "Convert Files to Parquet Format")
    (description
     "Collection of functions to get files in parquet format.  Parquet is a columnar
storage file format <https://parquet.apache.org/>.  The files to convert can be
of several formats (\"csv\", \"RData\", \"rds\", \"RSQLite\", \"json\", \"ndjson\", \"SAS\",
\"SPSS\"...).")
    (license license:asl2.0)))

(define-public r-parqr
  (package
    (name "r-parqr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "parqr" version))
       (sha256
        (base32 "11956dvv46sgpmbv429c24wxdhfqvc07jxc1i4yymyz1l8wgr3yb"))))
    (properties `((upstream-name . "parqr")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr r-magrittr r-arrow))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jhnwllr/parqr")
    (synopsis "Read in Multi-Part Parquet Files")
    (description
     "Reads in multi-part parquet files.  Will read in parquet files that have not
been previously coalesced into one file.  Convenient for reading in moderately
sized, but split files.")
    (license license:cc0)))

(define-public r-paropt
  (package
    (name "r-paropt")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paropt" version))
       (sha256
        (base32 "0rmiamj3s0s9qcymx74lmk266mrja3mxkflf32dpahnric9rvpsl"))))
    (properties `((upstream-name . "paropt")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-rcppthread
                             r-rcpparmadillo
                             r-rcpp
                             r-dfdr
                             r-ast2ast))
    (native-inputs (list r-knitr gfortran))
    (home-page "https://cran.r-project.org/package=paropt")
    (synopsis "Parameter Optimizing of ODE-Systems")
    (description
     "Enable optimization of parameters of ordinary differential equations.
Therefore, using SUNDIALS to solve the ODE-System (see Hindmarsh, Alan C., Peter
N. Brown, Keith E. Grant, Steven L. Lee, Radu Serban, Dan E. Shumaker, and Carol
S. Woodward. (2005) <doi:10.1145/1089014.1089020>).  Furthermore, for
optimization the particle swarm algorithm is used (see: Akman, Devin, Olcay
Akman, and Elsa Schaefer. (2018) <doi:10.1155/2018/9160793> and Sengupta,
Saptarshi, Sanchita Basak, and Richard Peters. (2018)
<doi:10.3390/make1010010>).")
    (license license:gpl3)))

(define-public r-parmsurvfit
  (package
    (name "r-parmsurvfit")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "parmsurvfit" version))
       (sha256
        (base32 "0d3614q76dw3f7y9p8378hdny7bz5fymma5l0zpygr1cfnacdhh6"))))
    (properties `((upstream-name . "parmsurvfit")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-flexsurv r-fitdistrplus))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/apjacobson/parmsurvfit")
    (synopsis "Parametric Models for Survival Data")
    (description
     "Executes simple parametric models for right-censored survival data.
Functionality emulates capabilities in Minitab', including fitting
right-censored data, assessing fit, plotting survival functions, and summary
statistics and probabilities.")
    (license license:gpl2)))

(define-public r-parma
  (package
    (name "r-parma")
    (version "1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "parma" version))
       (sha256
        (base32 "0rv53d3ailqh5a0qarpw9967ghnvslr2kcn02yn8xbkpcvnb7lfk"))))
    (properties `((upstream-name . "parma")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm
                             r-slam
                             r-rglpk
                             r-quadprog
                             r-nloptr
                             r-corpcor))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/alexiosg/parma")
    (synopsis "Portfolio Allocation and Risk Management Applications")
    (description
     "Provision of a set of models and methods for use in the allocation and
management of capital in financial portfolios.")
    (license license:gpl3)))

(define-public r-paris2024colours
  (package
    (name "r-paris2024colours")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Paris2024Colours" version))
       (sha256
        (base32 "10qf6rrk4lsw6qx5lsr8331fnvdli7cc24phg7asal2w2mg0h07v"))))
    (properties `((upstream-name . "Paris2024Colours")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://github.com/maximekuntz/Paris2024Colours")
    (synopsis
     "Color Palettes Inspired by Paris 2024 Olympic and Paralympic Games")
    (description
     "Palettes inspired by Paris 2024 Olympic and Paralympic Games for data
visualizations.  Length of color palettes is configurable.")
    (license license:expat)))

(define-public r-pari
  (package
    (name "r-pari")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pARI" version))
       (sha256
        (base32 "0c6lbzam854j9c442jxgshkb18zr2jdwlc5kbddll64ijxxk5cym"))))
    (properties `((upstream-name . "pARI")))
    (build-system r-build-system)
    (propagated-inputs (list r-rnifti
                             r-rcpparmadillo
                             r-rcpp
                             r-plyr
                             r-matrixstats
                             r-aribrain))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/angeella/pARI")
    (synopsis "Permutation-Based All-Resolutions Inference")
    (description
     "Computes the All-Resolution Inference method in the permutation framework, i.e.,
simultaneous lower confidence bounds for the number of true discoveries.
<doi:10.1002/sim.9725>.")
    (license license:gpl2+)))

(define-public r-parglm
  (package
    (name "r-parglm")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "parglm" version))
       (sha256
        (base32 "11knspjg0s7x8c5dqrkdkr33af39rhyggs0s0dxv726zfmg1ldy3"))))
    (properties `((upstream-name . "parglm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-matrix))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/boennecd/parglm")
    (synopsis "Parallel GLM")
    (description
     "This package provides a parallel estimation method for generalized linear models
without compiling with a multithreaded LAPACK or BLAS.")
    (license license:gpl2)))

(define-public r-pargasite
  (package
    (name "r-pargasite")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pargasite" version))
       (sha256
        (base32 "0jna1mjq4k8zfnszw3yarn6s0bhc70awins3nz4kmply01jy5jfi"))))
    (properties `((upstream-name . "pargasite")))
    (build-system r-build-system)
    (propagated-inputs (list r-stars
                             r-shinycssloaders
                             r-shiny
                             r-sf
                             r-rlang
                             r-raqs
                             r-leafsync
                             r-leaflet
                             r-gstat
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pargasite")
    (synopsis "Pollution-Associated Risk Geospatial Analysis Site")
    (description
     "Offers tools to estimate and visualize levels of major pollutants (CO, NO2, SO2,
Ozone, PM2.5 and PM10) across the conterminous United States for user-defined
time ranges.  Provides functions to retrieve pollutant data from the U.S.
Environmental Protection Agencyâs Air Quality System (AQS) API service
<https://aqs.epa.gov/aqsweb/documents/data_api.html> for interactive
visualization through a shiny application, allowing users to explore pollutant
levels for a given location over time relative to the National Ambient Air
Quality Standards (NAAQS).")
    (license license:bsd-3)))

(define-public r-parfm
  (package
    (name "r-parfm")
    (version "2.7.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "parfm" version))
       (sha256
        (base32 "12kcvdpmp99lqgr1775xqjkcn3s5agng1hwhjq2sa3bgrjgmhsh5"))))
    (properties `((upstream-name . "parfm")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-sn r-optimx r-msm))
    (home-page "https://cran.r-project.org/package=parfm")
    (synopsis "Parametric Frailty Models")
    (description
     "Fits Parametric Frailty Models by maximum marginal likelihood.  Possible
baseline hazards: exponential, Weibull, inverse Weibull (FrÃ©chet), Gompertz,
lognormal, log-skew-normal, and loglogistic.  Possible Frailty distributions:
gamma, positive stable, inverse Gaussian and lognormal.")
    (license license:gpl2)))

(define-public r-paretoposstable
  (package
    (name "r-paretoposstable")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ParetoPosStable" version))
       (sha256
        (base32 "1fwji5wrhbxr089dll812csamvb5q2pxn1607rpirarifgfbj28m"))))
    (properties `((upstream-name . "ParetoPosStable")))
    (build-system r-build-system)
    (propagated-inputs (list r-lmom r-foreach r-doparallel r-adgoftest))
    (home-page "https://cran.r-project.org/package=ParetoPosStable")
    (synopsis "Computing, Fitting and Validating the PPS Distribution")
    (description
     "Statistical functions to describe a Pareto Positive Stable (PPS) distribution
and fit it to real data.  Graphical and statistical tools to validate the fits
are included.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-pareto
  (package
    (name "r-pareto")
    (version "2.4.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Pareto" version))
       (sha256
        (base32 "19mawxs743i7lb9inh51xdfpx0s72cvcw4iylr250r17r6qp4y8g"))))
    (properties `((upstream-name . "Pareto")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ulrichriegel/Pareto#pareto")
    (synopsis
     "The Pareto, Piecewise Pareto and Generalized Pareto Distribution")
    (description
     "Utilities for the Pareto, piecewise Pareto and generalized Pareto distribution
that are useful for reinsurance pricing.  In particular, the package provides a
non-trivial algorithm that can be used to match the expected losses of a tower
of reinsurance layers with a layer-independent collective risk model.  The
theoretical background of the matching algorithm and most other methods are
described in Ulrich Riegel (2018) <doi:10.1007/s13385-018-0177-3>.")
    (license license:gpl2+)))

(define-public r-pardnacopy
  (package
    (name "r-pardnacopy")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ParDNAcopy" version))
       (sha256
        (base32 "017xwznhfibi8kp0ifww02c0qcq0vxs06rjww4kcp2bvdmld8kc4"))))
    (properties `((upstream-name . "ParDNAcopy")))
    (build-system r-build-system)
    (propagated-inputs (list r-dnacopy))
    (home-page "https://cran.r-project.org/package=ParDNAcopy")
    (synopsis
     "Parallel implementation of the \"segment\" function of package \"DNAcopy\"")
    (description
     "Parallelized version of the \"segment\" function from Bioconductor package
\"DNAcopy\", utilizing multi-core computation on host CPU.")
    (license license:gpl2)))

(define-public r-parcr
  (package
    (name "r-parcr")
    (version "0.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "parcr" version))
       (sha256
        (base32 "0288l0jl246jp44zzi0avgz8yip23b64hg8pgnnadl7271xx0nvx"))))
    (properties `((upstream-name . "parcr")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/SystemsBioinformatics/parcr")
    (synopsis "Construct Parsers for Structured Text Files")
    (description
     "Construct parser combinator functions, higher order functions that parse input.
Construction of such parsers is transparent and easy.  Their main application is
the parsing of structured text files like those generated by laboratory
instruments.  Based on a paper by Hutton (1992) <doi:10.1017/S0956796800000411>.")
    (license license:expat)))

(define-public r-parcats
  (package
    (name "r-parcats")
    (version "0.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "parcats" version))
       (sha256
        (base32 "1imlifi6zdyv1wnvasw8n7cxwq06hj6rr1g2xc1d8j2ic3dbk4ig"))))
    (properties `((upstream-name . "parcats")))
    (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-tibble
                             r-stringr
                             r-purrr
                             r-magrittr
                             r-htmlwidgets
                             r-forcats
                             r-easyalluvial
                             r-dplyr))
    (native-inputs (list esbuild))
    (home-page "https://erblast.github.io/parcats/")
    (synopsis "Interactive Parallel Categories Diagrams for 'easyalluvial'")
    (description
     "Complex graphical representations of data are best explored using interactive
elements.  parcats adds interactive graphing capabilities to the easyalluvial
package.  The plotly.js parallel categories diagrams offer a good framework for
creating interactive flow graphs that allow manual drag and drop sorting of
dimensions and categories, highlighting single flows and displaying mouse over
information.  The plotly.js dependency is quite heavy and therefore is
outsourced into a separate package.")
    (license license:expat)))

(define-public r-parbayesianoptimization
  (package
    (name "r-parbayesianoptimization")
    (version "1.2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ParBayesianOptimization" version))
       (sha256
        (base32 "0g5i4908fl2j1a4jfsg1xs4q92mc29zp8dyf52a370ki2512w6h1"))))
    (properties `((upstream-name . "ParBayesianOptimization")))
    (build-system r-build-system)
    (propagated-inputs (list r-lhs
                             r-ggpubr
                             r-ggplot2
                             r-foreach
                             r-dicekriging
                             r-dbscan
                             r-data-table
                             r-crayon))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/AnotherSamWilson/ParBayesianOptimization")
    (synopsis "Parallel Bayesian Optimization of Hyperparameters")
    (description
     "Fast, flexible framework for implementing Bayesian optimization of model
hyperparameters according to the methods described in Snoek et al.
<@code{arXiv:1206.2944>}.  The package allows the user to run scoring function
in parallel, save intermediary results, and tweak other aspects of the process
to fully utilize the computing resources available to the user.")
    (license license:gpl2)))

(define-public r-paran
  (package
    (name "r-paran")
    (version "1.5.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paran" version))
       (sha256
        (base32 "0lfhizz1vdmlwwcnm3y1nm127rm02icdk56x2p0m7vrcqbxybyqp"))))
    (properties `((upstream-name . "paran")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://alexisdinno.com/Software/index.shtml#paran")
    (synopsis "Horn's Test of Principal Components/Factors")
    (description
     "An implementation of Horn's technique for numerically and graphically evaluating
the components or factors retained in a principle components analysis (PCA) or
common factor analysis (FA).  Horn's method contrasts eigenvalues produced
through a PCA or FA on a number of random data sets of uncorrelated variables
with the same number of variables and observations as the experimental or
observational data set to produce eigenvalues for components or factors that are
adjusted for the sample error-induced inflation.  Components with adjusted
eigenvalues greater than one are retained.  paran may also be used to conduct
parallel analysis following Glorfeld's (1995) suggestions to reduce the
likelihood of over-retention.")
    (license license:gpl2)))

(define-public r-paramtest
  (package
    (name "r-paramtest")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paramtest" version))
       (sha256
        (base32 "0yn1s1qkgby8x0qn7cy1zhxzc0wyi7bm30pkvijhn5h5afjpylsy"))))
    (properties `((upstream-name . "paramtest")))
    (build-system r-build-system)
    (propagated-inputs (list r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=paramtest")
    (synopsis "Run a Function Iteratively While Varying Parameters")
    (description
     "Run simulations or other functions while easily varying parameters from one
iteration to the next.  Some common use cases would be grid search for machine
learning algorithms, running sets of simulations (e.g., estimating statistical
power for complex models), or bootstrapping under various conditions.  See the
paramtest documentation for more information and examples.")
    (license license:gpl3)))

(define-public r-paramsim
  (package
    (name "r-paramsim")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paramsim" version))
       (sha256
        (base32 "1a227s1xglcc1skpsmwmmwr04mql73plc557sp8bl8444fhjz228"))))
    (properties `((upstream-name . "paramsim")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-future r-forecast r-foreach
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=paramsim")
    (synopsis "Parameterized Simulation")
    (description
     "This function obtains a Random Number Generator (RNG) or collection of RNGs that
replicate the required parameter(s) of a distribution for a time series of data.
 Consider the case of reproducing a time series data set of size 20 that uses an
autoregressive (AR) model with phi = 0.8 and standard deviation equal to 1.
When one checks the arima.sin() function's estimated parameters, it's possible
that after a single trial or a few more, one won't find the precise parameters.
This enables one to look for the ideal RNG setting for a simulation that will
accurately duplicate the desired parameters.")
    (license license:gpl2+)))

(define-public r-params
  (package
    (name "r-params")
    (version "0.7.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "params" version))
       (sha256
        (base32 "0zii9nxgl2z46vyqn2l0kdjmib90534ybpizck74mp2wp1vh9ndj"))))
    (properties `((upstream-name . "params")))
    (build-system r-build-system)
    (propagated-inputs (list r-whisker r-readr r-rcpptoml r-purrr r-glue))
    (home-page "https://github.com/sahilseth/params")
    (synopsis "Simplify Parameters")
    (description
     "An interface to simplify organizing parameters used in a package, using external
configuration files.  This attempts to provide a cleaner alternative to
options().")
    (license license:gpl2)))

(define-public r-paramlink2
  (package
    (name "r-paramlink2")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paramlink2" version))
       (sha256
        (base32 "1qdpwmrxjl5109wvgdc05yyifm9myhc852dzy96ypi6lqhyn5snr"))))
    (properties `((upstream-name . "paramlink2")))
    (build-system r-build-system)
    (propagated-inputs (list r-pedtools r-pedprobr))
    (home-page "https://github.com/magnusdv/paramlink2")
    (synopsis "Parametric Linkage Analysis")
    (description
     "Parametric linkage analysis of monogenic traits in medical pedigrees.  Features
include singlepoint analysis, multipoint analysis via MERLIN (Abecasis et al.
(2002) <doi:10.1038/ng786>), visualisation of log of the odds (LOD) scores and
summaries of linkage peaks.  Disease models may be specified to accommodate
phenocopies, reduced penetrance and liability classes.  paramlink2 is part of
the pedsuite package ecosystem, presented in Pedigree Analysis in R (Vigeland,
2021, ISBN:9780128244302).")
    (license license:gpl3)))

(define-public r-paramlink
  (package
    (name "r-paramlink")
    (version "1.1-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paramlink" version))
       (sha256
        (base32 "0a21cy8q3zv96zdq5q2hfkb2ga1fham00in7wfyyd9wpck9gp009"))))
    (properties `((upstream-name . "paramlink")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-maxlik r-kinship2 r-assertthat))
    (home-page "https://github.com/magnusdv/paramlink")
    (synopsis "Parametric Linkage and Other Pedigree Analysis in R")
    (description
     "NOTE: PARAMLINK HAS BEEN SUPERSEDED BY THE PED SUITE PACKAGES
(<https://magnusdv.github.io/pedsuite/>).  PARAMLINK IS MAINTAINED ONLY FOR
LEGACY PURPOSES AND SHOULD NOT BE USED IN NEW PROJECTS. A suite of tools for
analysing pedigrees with marker data, including parametric linkage analysis,
forensic computations, relatedness analysis and marker simulations.  The core of
the package is an implementation of the Elston-Stewart algorithm for pedigree
likelihoods, extended to allow mutations as well as complex inbreeding.
Features for linkage analysis include singlepoint LOD scores, power analysis,
and multipoint analysis (the latter through a wrapper to the MERLIN software).
Forensic applications include exclusion probabilities, genotype distributions
and conditional simulations.  Data from the Familias software can be imported
and analysed in paramlink'.  Finally, paramlink offers many utility functions
for creating, manipulating and plotting pedigrees with or without marker data
(the actual plotting is done by the kinship2 package).")
    (license license:gpl2+)))

(define-public r-paramhetero
  (package
    (name "r-paramhetero")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paramhetero" version))
       (sha256
        (base32 "1zkwzmh3igkwfq87v3rmp3axfspl20qn4qmpyqql6ain564yyabw"))))
    (properties `((upstream-name . "paramhetero")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-ggplot2))
    (home-page "https://cran.r-project.org/package=paramhetero")
    (synopsis
     "Numeric and Visual Comparisons of Heterogeneity in Parametric Models")
    (description
     "This package performs statistical tests to compare coefficients and residual
variance across models.  Also provides graphical methods for assessing
heterogeneity in coefficients and residuals.  Currently supports linear and
generalized linear models.")
    (license license:gpl3)))

(define-public r-paramgui
  (package
    (name "r-paramgui")
    (version "2.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paramGUI" version))
       (sha256
        (base32 "16z7fbrykyfysiqfrj00ks567wwrz2mpcwx75crbg45spdw0dck6"))))
    (properties `((upstream-name . "paramGUI")))
    (build-system r-build-system)
    (propagated-inputs (list r-timp r-shinydashboard r-shiny r-fields))
    (home-page "https://github.com/glotaran/paramGUI/")
    (synopsis "Shiny GUI for some Parameter Estimation Examples")
    (description
     "Allows specification and fitting of some parameter estimation examples inspired
by time-resolved spectroscopy via a Shiny GUI.")
    (license license:gpl2+)))

(define-public r-paramdemo
  (package
    (name "r-paramdemo")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paramDemo" version))
       (sha256
        (base32 "165fz12cqpc88cmfx0xq80dnbxgkmbn8v84dxv96ggh9jsxh06d9"))))
    (properties `((upstream-name . "paramDemo")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=paramDemo")
    (synopsis
     "Parametric and Non-Parametric Demographic Functions and Applications")
    (description
     "Calculate parametric mortality and Fertility models, following packages
@code{BaSTA} in Colchero, Jones and Rebke (2012)
<doi:10.1111/j.2041-210X.2012.00186.x> and @code{BaFTA}
<https://github.com/fercol/@code{BaFTA>}, summary statistics (e.g. ageing rates,
life expectancy, lifespan equality, etc.), life table and product limit
estimators from census data.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-param2moment
  (package
    (name "r-param2moment")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "param2moment" version))
       (sha256
        (base32 "1k5zgzi985hglk5xh0qan6f6bsayln6vq1jykrxyxlffvpbc4hg1"))))
    (properties `((upstream-name . "param2moment")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=param2moment")
    (synopsis
     "Raw, Central and Standardized Moments of Parametric Distributions")
    (description
     "To calculate the raw, central and standardized moments from distribution
parameters.  To solve the distribution parameters based on user-provided mean,
standard deviation, skewness and kurtosis.  Normal, skew-normal, skew-t and
Tukey g-&-h distributions are supported, for now.")
    (license license:gpl2)))

(define-public r-parallelplot
  (package
    (name "r-parallelplot")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "parallelPlot" version))
       (sha256
        (base32 "0lchizczzsn3b147lkq3ws7zlx2106fxn6776jqkvch5aph1mrqw"))))
    (properties `((upstream-name . "parallelPlot")))
    (build-system r-build-system)
    (propagated-inputs (list r-htmlwidgets))
    (native-inputs (list r-knitr))
    (home-page "https://gitlab.com/drti/parallelplot")
    (synopsis "`htmlwidget` for a Parallel Coordinates Plot")
    (description
     "Create a parallel coordinates plot, using `htmlwidgets` package and `d3.js`.")
    (license license:expat)))

(define-public r-parallelpc
  (package
    (name "r-parallelpc")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ParallelPC" version))
       (sha256
        (base32 "07y7xb16865khxkvwsk1yglzyy7ja4aj2wpkipaz48i77c3x8bi2"))))
    (properties `((upstream-name . "ParallelPC")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=ParallelPC")
    (synopsis
     "Paralellised Versions of Constraint Based Causal Discovery Algorithms")
    (description
     "Parallelise constraint based causality discovery and causal inference methods.
The parallelised algorithms in the package will generate the same results as
that of the pcalg package but will be much more efficient.")
    (license license:gpl2+)))

(define-public r-parallelpam
  (package
    (name "r-parallelpam")
    (version "1.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "parallelpam" version))
       (sha256
        (base32 "0dq50b3nrmzjjn1p05iykvvnl5g4pyx7jl552czfprsjw4wx7ans"))))
    (properties `((upstream-name . "parallelpam")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-memuse))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=parallelpam")
    (synopsis
     "Parallel Partitioning-Around-Medoids (PAM) for Big Sets of Data")
    (description
     "Application of the Partitioning-Around-Medoids (PAM) clustering algorithm
described in Schubert, E. and Rousseeuw, P.J.: \"Fast and eager k-medoids
clustering: O(k) runtime improvement of the PAM, CLARA, and CLARANS algorithms.\"
Information Systems, vol.  101, p.  101804, (2021).
<doi:10.1016/j.is.2021.101804>.  It uses a binary format for storing and
retrieval of matrices developed for the jmatrix package but the functionality of
jmatrix is included here, so you do not need to install it.  Also, it is used by
package scellpam', so if you have installed it, you do not need to install this
package.  PAM can be applied to sets of data whose dissimilarity matrix can be
very big.  It has been tested with up to 100.000 points.  It does this with the
help of the code developed for other package, jmatrix', which allows the matrix
not to be loaded in R memory (which would force it to be of double type) but it
gets from disk, which allows using float (or even smaller data types).
Moreover, the dissimilarity matrix is calculated in parallel if the computer has
several cores so it can open many threads.  The initial part of the PAM
algorithm can be done with the BUILD or LAB algorithms; the BUILD algorithm has
been implemented in parallel.  The optimization phase implements the
@code{FastPAM1} algorithm, also in parallel.  Finally, calculation of silhouette
is available and also implemented in parallel.")
    (license license:gpl2+)))

(define-public r-parallelmcmccombine
  (package
    (name "r-parallelmcmccombine")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "parallelMCMCcombine" version))
       (sha256
        (base32 "18xw0s9lga2w981cx4daywp6b6gxvgl9mh68r13c5mmhis3b8v14"))))
    (properties `((upstream-name . "parallelMCMCcombine")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://cran.r-project.org/package=parallelMCMCcombine")
    (synopsis "Combining Subset MCMC Samples to Estimate a Posterior Density")
    (description
     "See Miroshnikov and Conlon (2014) <doi:10.1371/journal.pone.0108425>.  Recent
Bayesian Markov chain Monto Carlo (MCMC) methods have been developed for big
data sets that are too large to be analyzed using traditional statistical
methods.  These methods partition the data into non-overlapping subsets, and
perform parallel independent Bayesian MCMC analyses on the data subsets,
creating independent subposterior samples for each data subset.  These
independent subposterior samples are combined through four functions in this
package, including averaging across subset samples, weighted averaging across
subsets samples, and kernel smoothing across subset samples.  The four functions
assume the user has previously run the Bayesian analysis and has produced the
independent subposterior samples outside of the package; the functions use as
input the array of subposterior samples.  The methods have been demonstrated to
be useful for Bayesian MCMC models including Bayesian logistic regression,
Bayesian Gaussian mixture models and Bayesian hierarchical Poisson-Gamma models.
 The methods are appropriate for Bayesian hierarchical models with
hyperparameters, as long as data values in a single level of the hierarchy are
not split into subsets.")
    (license license:gpl2+)))

(define-public r-parallellogger
  (package
    (name "r-parallellogger")
    (version "3.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ParallelLogger" version))
       (sha256
        (base32 "05f3ysa5vipjl43pmllyq1wypbc5ddn33ysmhcrh8h6sx3j36jc7"))))
    (properties `((upstream-name . "ParallelLogger")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-snow r-jsonlite))
    (native-inputs (list r-knitr))
    (home-page "https://ohdsi.github.io/ParallelLogger/")
    (synopsis
     "Support for Parallel Computation, Logging, and Function Automation")
    (description
     "Support for parallel computation with progress bar, and option to stop or
proceed on errors.  Also provides logging to console and disk, and the logging
persists in the parallel threads.  Additional functions support function call
automation with delayed execution (e.g. for executing functions in parallel).")
    (license license:asl2.0)))

(define-public r-parafac4microbiome
  (package
    (name "r-parafac4microbiome")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "parafac4microbiome" version))
       (sha256
        (base32 "1l60qpx5k6h7ba6g4crl5f3gclyrmn5xlvpp2jg0hbqav5qn4418"))))
    (properties `((upstream-name . "parafac4microbiome")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rtensor
                             r-rlang
                             r-pracma
                             r-multiway
                             r-mize
                             r-magrittr
                             r-lifecycle
                             r-ggpubr
                             r-ggplot2
                             r-foreach
                             r-dplyr
                             r-doparallel
                             r-cowplot
                             r-compositions))
    (native-inputs (list r-knitr))
    (home-page "https://grvanderploeg.github.io/parafac4microbiome/")
    (synopsis
     "Parallel Factor Analysis Modelling of Longitudinal Microbiome Data")
    (description
     "Creation and selection of PARAllel FACtor Analysis (PARAFAC) models of
longitudinal microbiome data.  You can import your own data with our import
functions or use one of the example datasets to create your own PARAFAC models.
Selection of the optimal number of components can be done using
@code{assessModelQuality}() and @code{assessModelStability}().  The selected
model can then be plotted using @code{plotPARAFACmodel}().  The Parallel Factor
Analysis method was originally described by Caroll and Chang (1970)
<doi:10.1007/BF02310791> and Harshman (1970)
<https://www.psychology.uwo.ca/faculty/harshman/wpppfac0.pdf>.")
    (license license:expat)))

(define-public r-parade
  (package
    (name "r-parade")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "parade" version))
       (sha256
        (base32 "07x02j9jlldz4p0cyhw292041l3pziv56bhbzp4f0qpxhcbn5zn4"))))
    (properties `((upstream-name . "parade")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=parade")
    (synopsis "Pen's Income Parades")
    (description
     "Tool for producing Pen's parade graphs, useful for visualizing inequalities in
income, wages or other variables, as proposed by Pen (1971, ISBN:
978-0140212594).  Income or another economic variable is captured by the
vertical axis, while the population is arranged in ascending order of income
along the horizontal axis.  Pen's income parades provide an easy-to-interpret
visualization of economic inequalities.")
    (license license:gpl2+)))

(define-public r-parabar
  (package
    (name "r-parabar")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "parabar" version))
       (sha256
        (base32 "0kxf9xnippw59pxcgww3aihk78h84vfs0jpmm5zh55fvw947bavn"))))
    (properties `((upstream-name . "parabar")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6 r-progress r-filelock r-callr))
    (native-inputs (list r-knitr))
    (home-page "https://parabar.mihaiconstantin.com")
    (synopsis "Progress Bar for Parallel Tasks")
    (description
     "This package provides a simple interface in the form of R6 classes for executing
tasks in parallel, tracking their progress, and displaying accurate progress
bars.")
    (license license:expat)))

(define-public r-paperplanes
  (package
    (name "r-paperplanes")
    (version "0.0.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paperplanes" version))
       (sha256
        (base32 "1d9grc95xqxn91lvk8v7w3z90bhl8savkhihwshyjp8ij2xpzfkl"))))
    (properties `((upstream-name . "paperplanes")))
    (build-system r-build-system)
    (home-page "https://gitlab.gwdg.de/aleha/paperplanes")
    (synopsis
     "Distance Recordings from a Paper Plane Folding/Flying Experiment")
    (description
     "This is a data only package, that provides distances from a paper plane
experiment.")
    (license license:gpl3)))

(define-public r-paper
  (package
    (name "r-paper")
    (version "1.0-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "papeR" version))
       (sha256
        (base32 "095qfgv6h3sl5p69gm1v0qh9axap8gq2a9a3v95688fia2ppym4x"))))
    (properties `((upstream-name . "papeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable r-gmodels r-car))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/hofnerb/papeR")
    (synopsis "Toolbox for Writing Pretty Papers and Reports")
    (description
     "This package provides a toolbox for writing knitr', Sweave or other
@code{LaTeX}'- or markdown'-based reports and to prettify the output of various
estimated models.")
    (license license:gpl2)))

(define-public r-papci
  (package
    (name "r-papci")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "papci" version))
       (sha256
        (base32 "0ma1pfwgmj6qcg23yzhbnqip4qnxhsmfnvrgrr3ln65q5plmvh58"))))
    (properties `((upstream-name . "papci")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyverse
                             r-shinythemes
                             r-shiny
                             r-readxl
                             r-ratesci
                             r-propcis
                             r-hmisc
                             r-dt
                             r-dplyr
                             r-binom))
    (home-page "https://cran.r-project.org/package=papci")
    (synopsis "Prevalence Adjusted PPV Confidence Interval")
    (description
     "Positive predictive value (PPV) defined as the conditional probability of
clinical trial assay (CTA) being positive given Companion diagnostic device
(CDx) being positive is a key performance parameter for evaluating the clinical
validity utility of a companion diagnostic test in clinical bridging studies.
When bridging study patients are enrolled based on CTA assay results,
Binomial-based confidence intervals (CI) may are not appropriate for PPV CI
estimation.  Bootstrap CIs which are not restricted by the Binomial assumption
may be used for PPV CI estimation only when PPV is not 100%.  Bootstrap CI is
not valid when PPV is 100% and becomes a single value of [1, 1].  We proposed a
risk ratio-based method for constructing CI for PPV. By simulation we
illustrated that the coverage probability of the proposed CI is close to the
nominal value even when PPV is high and negative percent agreement (NPA) is
close to 100%.  There is a lack of R package for PPV CI calculation.  we
developed a publicly available R package along with this shiny app to implement
the proposed approach and some other existing methods.")
    (license license:gpl3)))

(define-public r-pantarhei
  (package
    (name "r-pantarhei")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PantaRhei" version))
       (sha256
        (base32 "0lv04aa70yd5gr0zfh2gmah4d4h3kw0lym6284akcvll8mhdi8dd"))))
    (properties `((upstream-name . "PantaRhei")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PantaRhei")
    (synopsis "Plots Sankey Diagrams")
    (description
     "Sankey diagrams are a powerfull and visually attractive way to visualize the
flow of conservative substances through a system.  They typically consists of a
network of nodes, and fluxes between them, where the total balance in each
internal node is 0, i.e.  input equals output.  Sankey diagrams are typically
used to display energy systems, material flow accounts etc.  Unlike so-called
alluvial plots, Sankey diagrams also allow for cyclic flows: flows originating
from a single node can, either direct or indirect, contribute to the input of
that same node.  This package, named after the Greek aphorism Panta Rhei
(everything flows), provides functions to create publication-quality diagrams,
using data in tables (or spread sheets) and a simple syntax.")
    (license (license:fsdg-compatible "EUPL"))))

(define-public r-panstarrs
  (package
    (name "r-panstarrs")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "panstarrs" version))
       (sha256
        (base32 "0vynq6mycsirwb0kkl85xxijap0jk12k3m7lw7hljf6vwp2p2zc0"))))
    (properties `((upstream-name . "panstarrs")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite
                             r-httr
                             r-data-table
                             r-curl
                             r-checkmate
                             r-bit64))
    (native-inputs (list r-knitr))
    (home-page "https://uskovgs.github.io/PanSTARRS/")
    (synopsis "Interface to the Pan-STARRS API")
    (description
     "An interface to the API for Pan-STARRS1', a data archive of the PS1 wide-field
astronomical survey.  The package allows access to the PS1 catalog and to the
PS1 images. (see <https://outerspace.stsci.edu/display/PANSTARRS/> for more
information).  You can use it to plan astronomical observations, make guidance
pictures, find magnitudes in five broadband filters (g, r, i, z, y) and more.")
    (license license:expat)))

(define-public r-panjen
  (package
    (name "r-panjen")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PanJen" version))
       (sha256
        (base32 "18cl9rls98v286kk6lyxxws3w59zpzgmqi5250gdgcf71xy2lhvm"))))
    (properties `((upstream-name . "PanJen")))
    (build-system r-build-system)
    (propagated-inputs (list r-mgcv))
    (home-page "https://cran.r-project.org/package=PanJen")
    (synopsis "Semi-Parametric Test for Specifying Functional Form")
    (description
     "This package provides a central decision in a parametric regression is how to
specify the relation between an dependent variable and each explanatory
variable.  This package provides a semi-parametric tool for comparing different
transformations of an explanatory variables in a parametric regression.  The
functions is relevant in a situation, where you would use a box-cox or
Box-Tidwell transformations.  In contrast to the classic power-transformations,
the methods in this package allows for theoretical driven user input and the
possibility to compare with a non-parametric transformation.")
    (license license:gpl2+)))

(define-public r-pangaear
  (package
    (name "r-pangaear")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pangaear" version))
       (sha256
        (base32 "1x1dbf7jahzc5s4gs0hn6xpw35xxbgp61aym50k384pavsdm9xgw"))))
    (properties `((upstream-name . "pangaear")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tibble
                             r-png
                             r-oai
                             r-jsonlite
                             r-hoardr
                             r-crul))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ropensci/pangaear")
    (synopsis "Client for the 'Pangaea' Database")
    (description
     "This package provides tools to interact with the Pangaea Database
(<https://www.pangaea.de>), including functions for searching for data, fetching
datasets by dataset ID', and working with the Pangaea OAI-PMH service.")
    (license license:expat)))

(define-public r-panelwranglr
  (package
    (name "r-panelwranglr")
    (version "1.2.13")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "panelWranglR" version))
       (sha256
        (base32 "0zj0m08518zi6xsa7qik61ys1qqa2lqiar4l7acclkw58w5zrmbc"))))
    (properties `((upstream-name . "panelWranglR")))
    (build-system r-build-system)
    (propagated-inputs (list r-hmisc r-data-table r-caret))
    (home-page "https://github.com/JSzitas/panelWranglR")
    (synopsis "Panel Data Wrangling Tools")
    (description
     "Leading/lagging a panel, creating dummy variables, taking panel differences,
looking for panel autocorrelations, and more.  Implemented via a data.table back
end.")
    (license license:gpl3)))

(define-public r-panelview
  (package
    (name "r-panelview")
    (version "1.1.18")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "panelView" version))
       (sha256
        (base32 "1kp8cvc0qklbdxmjh5n4sxzv3ncvrykcyvdsnvil40pmikmd0599"))))
    (properties `((upstream-name . "panelView")))
    (build-system r-build-system)
    (propagated-inputs (list r-gridextra r-ggplot2 r-dplyr))
    (home-page "https://yiqingxu.org/packages/panelview/index.html")
    (synopsis "Visualizing Panel Data")
    (description
     "Visualizes panel data.  It has three main functionalities: (1) it plots the
treatment status and missing values in a panel dataset; (2) it visualizes the
temporal dynamics of a main variable of interest; (3) it depicts the bivariate
relationships between a treatment variable and an outcome variable either by
unit or in aggregate.  For details, see <doi:10.18637/jss.v107.i07>.")
    (license license:expat)))

(define-public r-panelvar
  (package
    (name "r-panelvar")
    (version "0.5.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "panelvar" version))
       (sha256
        (base32 "15bks8aqy29rslgbjp4w8xw7w2mcpgcc77bya7a9jcs5715j9xdb"))))
    (properties `((upstream-name . "panelvar")))
    (build-system r-build-system)
    (propagated-inputs (list r-texreg
                             r-reshape2
                             r-progress
                             r-matrixcalc
                             r-matrix
                             r-mass
                             r-knitr
                             r-ggplot2))
    (home-page "https://cran.r-project.org/package=panelvar")
    (synopsis "Panel Vector Autoregression")
    (description
     "We extend two general methods of moment estimators to panel vector
autoregression models (PVAR) with p lags of endogenous variables, predetermined
and strictly exogenous variables.  This general PVAR model contains the first
difference GMM estimator by Holtz-Eakin et al. (1988) <doi:10.2307/1913103>,
Arellano and Bond (1991) <doi:10.2307/2297968> and the system GMM estimator by
Blundell and Bond (1998) <doi:10.1016/S0304-4076(98)00009-8>.  We also provide
specification tests (Hansen overidentification test, lag selection criterion and
stability test of the PVAR polynomial) and classical structural analysis for
PVAR models such as orthogonal and generalized impulse response functions,
bootstrapped confidence intervals for impulse response analysis and forecast
error variance decompositions.")
    (license license:gpl2+)))

(define-public r-paneltm
  (package
    (name "r-paneltm")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PanelTM" version))
       (sha256
        (base32 "1x9zzs88cprwjlwwgnfvv7fmx12p57s5wwxsgjch8i11fzyjkw48"))))
    (properties `((upstream-name . "PanelTM")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma))
    (home-page "https://cran.r-project.org/package=PanelTM")
    (synopsis
     "Two- And Three-Way Dynamic Panel Threshold Regression Model for Change Point Detection")
    (description
     "Estimation of two- and three-way dynamic panel threshold regression models (Di
Lascio and Perazzini (2024) <https://repec.unibz.it/bemps104.pdf>; Di Lascio and
Perazzini (2022, ISBN:978-88-9193-231-0); Seo and Shin (2016)
<doi:10.1016/j.jeconom.2016.03.005>) through the generalized method of moments
based on the first difference transformation and the use of instrumental
variables.  The models can be used to find a change point detection in the time
series.  In addition, random number generation is also implemented.")
    (license license:gpl2+)))

(define-public r-panelsur
  (package
    (name "r-panelsur")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "panelSUR" version))
       (sha256
        (base32 "025p6dqd7fxa0nh07mwrkbvi5cb6jsfx28kskqvzf8zvar6rbr41"))))
    (properties `((upstream-name . "panelSUR")))
    (build-system r-build-system)
    (propagated-inputs (list r-plm r-matlib r-mass r-formula-tools
                             r-fastmatrix))
    (home-page "https://cran.r-project.org/package=panelSUR")
    (synopsis
     "Two-Way Error Component SUR Systems Estimation on Unbalanced Panel Data")
    (description
     "Generalized Least Squares (GLS) estimation of Seemingly Unrelated Regression
(SUR) systems on unbalanced panel in the one/two-way cases also taking into
account the possibility of cross equation restrictions.  Methodological details
can be found in BiÃ¸rn (2004) <doi:10.1016/j.jeconom.2003.10.023> and Platoni,
Sckokai, Moro (2012) <doi:10.1080/07474938.2011.607098>.")
    (license license:gpl3+)))

(define-public r-panelsummary
  (package
    (name "r-panelsummary")
    (version "0.1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "panelsummary" version))
       (sha256
        (base32 "00rscxkblqkhyx2kdnl78hy82y9bjwdzhiia7bdx9qbjf0g2qjkl"))))
    (properties `((upstream-name . "panelsummary")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-stringr
                             r-rlang
                             r-modelsummary
                             r-kableextra
                             r-fixest
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/michaeltopper1/panelsummary")
    (synopsis "Create Publication-Ready Regression Tables with Panels")
    (description
     "Create an automated regression table that is well-suited for models that are
estimated with multiple dependent variables.  panelsummary extends modelsummary
(Arel-Bundock, V. (2022) <doi:10.18637/jss.v103.i01>) by allowing regression
tables to be split into multiple sections with a simple function call.  Utilize
familiar arguments such as fmt, estimate, statistic, vcov, conf_level, stars,
coef_map, coef_omit, coef_rename, gof_map, and gof_omit from modelsummary to
clean the table, and additionally, add a row for the mean of the dependent
variable without external manipulation.")
    (license license:gpl3+)))

(define-public r-panelr
  (package
    (name "r-panelr")
    (version "0.7.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "panelr" version))
       (sha256
        (base32 "0vq7f91lqpd0n507lswgj7zpp56dzps3zcvbgsp2ivaj3c9vy0s5"))))
    (properties `((upstream-name . "panelr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-lmertest
                             r-lme4
                             r-jtools
                             r-ggplot2
                             r-formula
                             r-dplyr
                             r-crayon))
    (native-inputs (list r-knitr))
    (home-page "https://panelr.jacob-long.com")
    (synopsis
     "Regression Models and Utilities for Repeated Measures and Panel Data")
    (description
     "This package provides an object type and associated tools for storing and
wrangling panel data.  Implements several methods for creating regression models
that take advantage of the unique aspects of panel data.  Among other
capabilities, automates the \"within-between\" (also known as \"between-within\" and
\"hybrid\") panel regression specification that combines the desirable aspects of
both fixed effects and random effects econometric models and fits them as
multilevel models (Allison, 2009 <doi:10.4135/9781412993869.d33>; Bell & Jones,
2015 <doi:10.1017/psrm.2014.7>).  These models can also be estimated via
generalized estimating equations (GEE; @code{McNeish}, 2019
<doi:10.1080/00273171.2019.1602504>) and Bayesian estimation is (optionally)
supported via Stan'.  Supports estimation of asymmetric effects models via first
differences (Allison, 2019 <doi:10.1177/2378023119826441>) as well as a
generalized linear model extension thereof using GEE.")
    (license license:expat)))

(define-public r-panelpomp
  (package
    (name "r-panelpomp")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "panelPomp" version))
       (sha256
        (base32 "1ld0f2pf24lf20xalgapkid9zh7gmc202lkmnqih2sx4gh4l1086"))))
    (properties `((upstream-name . "panelPomp")))
    (build-system r-build-system)
    (propagated-inputs (list r-pomp r-lifecycle))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=panelPomp")
    (synopsis "Inference for Panel Partially Observed Markov Processes")
    (description
     "Data analysis based on panel partially-observed Markov process
(@code{PanelPOMP}) models.  To implement such models, simulate them and fit them
to panel data, @code{panelPomp} extends some of the facilities provided for time
series data by the pomp package.  Implemented methods include filtering (panel
particle filtering) and maximum likelihood estimation (Panel Iterated Filtering)
as proposed in Breto, Ionides and King (2020) \"Panel Data Analysis via
Mechanistic Models\" <doi:10.1080/01621459.2019.1604367>.")
    (license license:gpl3)))

(define-public r-panelmatch
  (package
    (name "r-panelmatch")
    (version "2.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PanelMatch" version))
       (sha256
        (base32 "14y6msvs4h3xdpi83yi6q4a58xqbf1nxs5m0wbbzszzj7jdjiq79"))))
    (properties `((upstream-name . "PanelMatch")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen
                             r-rcpparmadillo
                             r-rcpp
                             r-matrix
                             r-mass
                             r-ggplot2
                             r-foreach
                             r-doparallel
                             r-data-table
                             r-cbps))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PanelMatch")
    (synopsis
     "Matching Methods for Causal Inference with Time-Series Cross-Sectional Data")
    (description
     "This package implements a set of methodological tools that enable researchers to
apply matching methods to time-series cross-sectional data.  Imai, Kim, and Wang
(2023) <http://web.mit.edu/insong/www/pdf/tscs.pdf> proposes a nonparametric
generalization of the difference-in-differences estimator, which does not rely
on the linearity assumption as often done in practice.  Researchers first select
a method of matching each treated observation for a given unit in a particular
time period with control observations from other units in the same time period
that have a similar treatment and covariate history.  These methods include
standard matching methods based on propensity score and Mahalanobis distance, as
well as weighting methods.  Once matching and refinement is done, treatment
effects can be estimated with standard errors.  The package also offers
diagnostics for researchers to assess the quality of their results.")
    (license license:gpl3+)))

(define-public r-panelhetero
  (package
    (name "r-panelhetero")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "panelhetero" version))
       (sha256
        (base32 "12csjgcjxwl7fxlqib0vgywf32frd7jri3dj9lyq5fmz9riqkdix"))))
    (properties `((upstream-name . "panelhetero")))
    (build-system r-build-system)
    (propagated-inputs (list r-rearrangement r-kernsmooth r-ggplot2 r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://tkhdyanagi.github.io/panelhetero/")
    (synopsis "Panel Data Analysis with Heterogeneous Dynamics")
    (description
     "Understanding the dynamics of potentially heterogeneous variables is important
in statistical applications.  This package provides tools for estimating the
degree of heterogeneity across cross-sectional units in the panel data analysis.
 The methods are developed by Okui and Yanagi (2019)
<doi:10.1016/j.jeconom.2019.04.036> and Okui and Yanagi (2020)
<doi:10.1093/ectj/utz019>.")
    (license license:expat)))

(define-public r-panelcount
  (package
    (name "r-panelcount")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PanelCount" version))
       (sha256
        (base32 "07dn8a86q51gvy4xkqz2mfmqkc59cf4zcwcqrw366b791y5g182y"))))
    (properties `((upstream-name . "PanelCount")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod r-rcpparmadillo r-rcpp r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PanelCount")
    (synopsis
     "Random Effects and/or Sample Selection Models for Panel Count Data")
    (description
     "This package provides a high performance package implementing random effects
and/or sample selection models for panel count data.  The details of the models
are discussed in Peng and Van den Bulte (2023) <doi:10.2139/ssrn.2702053>.")
    (license license:expat)))

(define-public r-panelaggregation
  (package
    (name "r-panelaggregation")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "panelaggregation" version))
       (sha256
        (base32 "0x8ldqb9216pclfvs4ymdpian43v2ydkyflpf0k6lcn35r04xfr6"))))
    (properties `((upstream-name . "panelaggregation")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table))
    (home-page "https://cran.r-project.org/package=panelaggregation")
    (synopsis "Aggregate Longitudinal Survey Data")
    (description
     "Aggregate Business Tendency Survey Data (and other qualitative surveys) to time
series at various aggregation levels.  Run aggregation of survey data in a
speedy, re-traceable and a easily deployable way.  Aggregation is substantially
accelerated by use of data.table.  This package intends to provide an interface
that is less general and abstract than data.table but rather geared towards
survey researchers.")
    (license license:gpl2)))

(define-public r-pandora
  (package
    (name "r-pandora")
    (version "24.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Pandora" version))
       (sha256
        (base32 "0zqsqc1wqmps3dd79w4qwxyl4idmrl1zn24s5nmsazk5l1i984p1"))))
    (properties `((upstream-name . "Pandora")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-rlang
                             r-readxl
                             r-readr
                             r-readods
                             r-openxlsx
                             r-magrittr
                             r-jsonlite
                             r-dplyr
                             r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Pandora-IsoMemo/pandora-data")
    (synopsis "Retrieve Data using the API of the 'Pandora' Data Platform")
    (description
     "API wrapper that contains functions to retrieve data from the Pandora databases.
 Web services for API: <https://pandora.earth/>.")
    (license license:gpl3+)))

(define-public r-pandocfilters
  (package
    (name "r-pandocfilters")
    (version "0.1-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pandocfilters" version))
       (sha256
        (base32 "0y2n49n4ln3ac45176nkdd7407bk5vdc0fpl872nasgsvia0zknk"))))
    (properties `((upstream-name . "pandocfilters")))
    (build-system r-build-system)
    (inputs (list pandoc))
    (propagated-inputs (list r-jsonlite))
    (native-inputs (list r-knitr))
    (home-page "https://pandoc.org/")
    (synopsis "Pandoc Filters for R")
    (description
     "The document converter pandoc <https://pandoc.org/> is widely used in the R
community.  One feature of pandoc is that it can produce and consume
JSON-formatted abstract syntax trees (AST).  This allows to transform a given
source document into JSON-formatted AST, alter it by so called filters and pass
the altered JSON-formatted AST back to pandoc'.  This package provides functions
which allow to write such filters in native R code.  Although this package is
inspired by the Python package pandocfilters
<https://github.com/jgm/pandocfilters/>, it provides additional convenience
functions which make it simple to use the pandocfilters package as a report
generator.  Since pandocfilters inherits most of it's functionality from pandoc
it can create documents in many formats (for more information see
<https://pandoc.org/>) but is also bound to the same limitations as pandoc'.")
    (license license:gpl3)))

(define-public r-pandoc
  (package
    (name "r-pandoc")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pandoc" version))
       (sha256
        (base32 "00bmvxvp4rskbjphp93lpwkp58bkqzlglir7my3icdgrbcyxagj3"))))
    (properties `((upstream-name . "pandoc")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-rappdirs r-fs))
    (home-page "https://github.com/cderv/pandoc")
    (synopsis "Manage and Run Universal Converter 'Pandoc' from 'R'")
    (description
     "This package provides a set of tools to install, manage and run several Pandoc
versions.")
    (license license:expat)))

(define-public r-pandemics
  (package
    (name "r-pandemics")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pandemics" version))
       (sha256
        (base32 "17rivm44crgws0pcpw7ii8kqzq7r18bwawpfchx4fzgql8647d8d"))))
    (properties `((upstream-name . "pandemics")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pandemics")
    (synopsis "Monitoring a Developing Pandemic with Available Data")
    (description
     "Full dynamic system to describe and forecast the spread and the severity of a
developing pandemic, based on available data.  These data are number of
infections, hospitalizations, deaths and recoveries notified each day.  The
system consists of three transitions, infection-infection, infection-hospital
and hospital-death/recovery.  The intensities of these transitions are dynamic
and estimated using non-parametric local linear estimators.  The package can be
used to provide forecasts and survival indicators such as the median time spent
in hospital and the probability that a patient who has been in hospital for a
number of days can leave it alive.  Methods are described in GÃ¡miz, Mammen,
MartÃ­nez-Miranda, and Nielsen (2024) <doi:10.48550/@code{arXiv.2308.09918>} and
<doi:10.48550/@code{arXiv.2308.09919>}.")
    (license license:gpl2)))

(define-public r-pancanvarsel
  (package
    (name "r-pancanvarsel")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PanCanVarSel" version))
       (sha256
        (base32 "06xkzg2kvyr6ivm1z71x2pk20j37h36sx6kf4kmc8q50cq73k9jl"))))
    (properties `((upstream-name . "PanCanVarSel")))
    (build-system r-build-system)
    (propagated-inputs (list r-smoothmest r-mvtnorm r-msm r-matrix))
    (home-page "https://cran.r-project.org/package=PanCanVarSel")
    (synopsis "Pan-Cancer Variable Selection")
    (description
     "This package provides function for performing Bayesian survival regression using
Horseshoe prior in the accelerated failure time model with log normal assumption
in order to achieve high dimensional pan-cancer variable selection as developed
in Maity et.  al. (2019) <doi:10.1111/biom.13132>.")
    (license license:gpl3)))

(define-public r-panalysis
  (package
    (name "r-panalysis")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pAnalysis" version))
       (sha256
        (base32 "0pykdlbynzgcbnjs8xs8frgncf53l8qgf6na34adq7da76n570hi"))))
    (properties `((upstream-name . "pAnalysis")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-coin))
    (home-page "https://cran.r-project.org/package=pAnalysis")
    (synopsis "Benchmarking and Rescaling R2 using Noise Percentile Analysis")
    (description
     "This package provides the tools needed to benchmark the R2 value corresponding
to a certain acceptable noise level while also providing a rescaling function
based on that noise level yielding a new value of R2 we refer to as R2k which is
independent of both the number of degrees of freedom and the noise distribution
function.")
    (license license:gpl2)))

(define-public r-panacea
  (package
    (name "r-panacea")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PANACEA" version))
       (sha256
        (base32 "0pq5kq0i87yrdfs6id2lc6gg4dqsa07fsas13yc8jw6bd9lcf6rh"))))
    (properties `((upstream-name . "PANACEA")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-org-hs-eg-db r-igraph r-dbi))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/egeulgen/PANACEA")
    (synopsis "Personalized Network-Based Anti-Cancer Therapy Evaluation")
    (description
     "Identification of the most appropriate pharmacotherapy for each patient based on
genomic alterations is a major challenge in personalized oncology.  PANACEA is a
collection of personalized anti-cancer drug prioritization approaches utilizing
network methods.  The methods utilize personalized \"driverness\" scores from
@code{driveR} to rank drugs, mapping these onto a protein-protein interaction
network.  The \"distance-based\" method scores each drug based on these scores and
distances between drugs and genes to rank given drugs.  The \"RWR\" method
propagates these scores via a random-walk with restart framework to rank the
drugs.  The methods are described in detail in Ulgen E, Ozisik O, Sezerman OU.
2023.  PANACEA: network-based methods for pharmacotherapy prioritization in
personalized oncology.  Bioinformatics <doi:10.1093/bioinformatics/btad022>.")
    (license license:expat)))

(define-public r-pamscapes
  (package
    (name "r-pamscapes")
    (version "0.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PAMscapes" version))
       (sha256
        (base32 "1wk827dlpp4266mpvm7zhw94dxmm95xam4xx8znp7ricsr0fz1x3"))))
    (properties `((upstream-name . "PAMscapes")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tdigest
                             r-shiny
                             r-sf
                             r-scales
                             r-rlang
                             r-purrr
                             r-pammisc
                             r-ncdf4
                             r-lubridate
                             r-httr
                             r-ggplot2
                             r-geosphere
                             r-dplyr
                             r-data-table))
    (home-page "https://cran.r-project.org/package=PAMscapes")
    (synopsis "Tools for Summarising and Analysing Soundscape Data")
    (description
     "This package provides a variety of tools relevant to the analysis of marine
soundscape data.  There are tools for downloading AIS (automatic identification
system) data from Marine Cadastre <https://hub.marinecadastre.gov>, connecting
AIS data to GPS coordinates, plotting summaries of various soundscape
measurements, and downloading relevant environmental variables (wind, swell
height) from the National Center for Atmospheric Research data server
<https://rda.ucar.edu/datasets/ds084.1/>.  Most tools were developed to work
well with output from Triton software, but can be adapted to work with any
similar measurements.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-pampe
  (package
    (name "r-pampe")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pampe" version))
       (sha256
        (base32 "092n04nrp886kd163v32f5vhp9r7gnayxzqb6pj57ilm5w1yrcsk"))))
    (properties `((upstream-name . "pampe")))
    (build-system r-build-system)
    (propagated-inputs (list r-leaps))
    (home-page "https://cran.r-project.org/package=pampe")
    (synopsis
     "Implementation of the Panel Data Approach Method for Program Evaluation")
    (description
     "This package implements the Panel Data Approach Method for program evaluation as
developed in Hsiao, Ching and Ki Wan (2012).  pampe estimates the effect of an
intervention by comparing the evolution of the outcome for a unit affected by an
intervention or treatment to the evolution of the unit had it not been affected
by the intervention.")
    (license license:gpl2)))

(define-public r-pampal
  (package
    (name "r-pampal")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PAMpal" version))
       (sha256
        (base32 "1jb89rl1apb1nb1yfhq0knqkw7n6jcb2l4aqq61qy28iwl7vanr5"))))
    (properties `((upstream-name . "PAMpal")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tuner
                             r-tidyr
                             r-signal
                             r-shiny
                             r-seewave
                             r-rsqlite
                             r-rlang
                             r-reticulate
                             r-purrr
                             r-pammisc
                             r-pambinaries
                             r-lubridate
                             r-knitr
                             r-ggplot2
                             r-geosphere
                             r-gam
                             r-dplyr
                             r-data-table))
    (home-page "https://cran.r-project.org/package=PAMpal")
    (synopsis "Load and Process Passive Acoustic Data")
    (description
     "This package provides tools for loading and processing passive acoustic data.
Read in data that has been processed in Pamguard (<https://www.pamguard.org/>),
apply a suite processing functions, and export data for reports or external
modeling tools.  Parameter calculations implement methods by Oswald et al (2007)
<doi:10.1121/1.2743157>, Griffiths et al (2020) <doi:10.1121/10.0001229> and
Baumann-Pickering et al (2010) <doi:10.1121/1.3479549>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-pammtools
  (package
    (name "r-pammtools")
    (version "0.5.93")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pammtools" version))
       (sha256
        (base32 "0xihvszndsnzzh3kzn67yqqdxms5pba609lvlg68k3gfxbxk104l"))))
    (properties `((upstream-name . "pammtools")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidyr
                             r-tibble
                             r-survival
                             r-rlang
                             r-purrr
                             r-pec
                             r-mvtnorm
                             r-mgcv
                             r-magrittr
                             r-lazyeval
                             r-ggplot2
                             r-formula
                             r-dplyr
                             r-checkmate))
    (home-page "https://adibender.github.io/pammtools/")
    (synopsis
     "Piece-Wise Exponential Additive Mixed Modeling Tools for Survival Analysis")
    (description
     "The Piece-wise exponential (Additive Mixed) Model (PAMM; Bender and others
(2018) <doi: 10.1177/1471082X17748083>) is a powerful model class for the
analysis of survival (or time-to-event) data, based on Generalized Additive
(Mixed) Models (GA(M)Ms).  It offers intuitive specification and robust
estimation of complex survival models with stratified baseline hazards, random
effects, time-varying effects, time-dependent covariates and cumulative effects
(Bender and others (2019)), as well as support for left-truncated, competing
risks and recurrent events data.  pammtools provides tidy workflow for survival
analysis with PAMMs, including data simulation, transformation and other
functions for data preprocessing and model post-processing as well as
visualization.")
    (license license:expat)))

(define-public r-pammisc
  (package
    (name "r-pammisc")
    (version "1.12.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PAMmisc" version))
       (sha256
        (base32 "0xv4bm1gzvhybgaww24nikwcbn8wq8299gbr1n831gscnzablzp3"))))
    (properties `((upstream-name . "PAMmisc")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tuner
                             r-suncalc
                             r-signal
                             r-seewave
                             r-scales
                             r-rsqlite
                             r-rjson
                             r-rerddap
                             r-rcpproll
                             r-purrr
                             r-pambinaries
                             r-ncdf4
                             r-magrittr
                             r-lubridate
                             r-httr
                             r-ggplot2
                             r-geosphere
                             r-fftw
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=PAMmisc")
    (synopsis "Miscellaneous Functions for Passive Acoustic Analysis")
    (description
     "This package provides a collection of miscellaneous functions for passive
acoustics.  Much of the content here is adapted to R from code written by other
people.  If you have any ideas of functions to add, please contact Taiki Sakai.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-pamm
  (package
    (name "r-pamm")
    (version "1.122")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pamm" version))
       (sha256
        (base32 "0jd0v3vlq1hrhlyhw008h1jlx9f2nd72ngpyn6km0ay04pnbhkvs"))))
    (properties `((upstream-name . "pamm")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-lmertest r-lme4 r-lattice))
    (home-page "https://github.com/JulienGAMartin/pamm_R")
    (synopsis "Power Analysis for Random Effects in Mixed Models")
    (description
     "Simulation functions to assess or explore the power of a dataset to estimates
significant random effects (intercept or slope) in a mixed model.  The functions
are based on the \"lme4\" and \"@code{lmerTest}\" packages.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-pamhm
  (package
    (name "r-pamhm")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PAMhm" version))
       (sha256
        (base32 "0k0n3ni0ypcizri39v76x1vv7hlxlmbpfi7w81yxy9inz7zz4zfw"))))
    (properties `((upstream-name . "PAMhm")))
    (build-system r-build-system)
    (propagated-inputs (list r-robusthd
                             r-readxl
                             r-readmore
                             r-rcolorbrewer
                             r-r-utils
                             r-plyr
                             r-heatmapflex
                             r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PAMhm")
    (synopsis "Generate Heatmaps Based on Partitioning Around Medoids (PAM)")
    (description
     "Data are partitioned (clustered) into k clusters \"around medoids\", which is a
more robust version of K-means implemented in the function pam() in the cluster
package.  The PAM algorithm is described in Kaufman and Rousseeuw (1990)
<doi:10.1002/9780470316801>.  Please refer to the pam() function documentation
for more references.  Clustered data is plotted as a split heatmap allowing
visualisation of representative \"group-clusters\" (medoids) in the data as
separated fractions of the graph while those \"sub-clusters\" are visualised as a
traditional heatmap based on hierarchical clustering.")
    (license license:gpl3)))

(define-public r-pameasures
  (package
    (name "r-pameasures")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PAmeasures" version))
       (sha256
        (base32 "0c2pkms06lijly9clxp6zkasqy4zjicdwnvbqwvhp45yavwmkkkj"))))
    (properties `((upstream-name . "PAmeasures")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "https://cran.r-project.org/package=PAmeasures")
    (synopsis
     "Prediction and Accuracy Measures for Nonlinear Models and for Right-Censored Time-to-Event Data")
    (description
     "We propose a pair of summary measures for the predictive power of a prediction
function based on a regression model.  The regression model can be linear or
nonlinear, parametric, semi-parametric, or nonparametric, and correctly
specified or mis-specified.  The first measure, R-squared, is an extension of
the classical R-squared statistic for a linear model, quantifying the prediction
function's ability to capture the variability of the response.  The second
measure, L-squared, quantifies the prediction function's bias for predicting the
mean regression function.  When used together, they give a complete summary of
the predictive power of a prediction function.  Please refer to Gang Li and
Xiaoyan Wang (2016) <@code{arXiv:1611.03063>} for more details.")
    (license license:gpl3)))

(define-public r-pambinaries
  (package
    (name "r-pambinaries")
    (version "1.9.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PamBinaries" version))
       (sha256
        (base32 "1anxn06l714ila4kjdbvrmfnmg7k7rwbscvzdd0z768h84s34v19"))))
    (properties `((upstream-name . "PamBinaries")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-dplyr))
    (home-page "https://cran.r-project.org/package=PamBinaries")
    (synopsis "Read and Process 'Pamguard' Binary Data")
    (description
     "This package provides functions for easily reading and processing binary data
files created by Pamguard (<https://www.pamguard.org/>).  All functions for
directly reading the binary data files are based on MATLAB code written by
Michael Oswald.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-palr
  (package
    (name "r-palr")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "palr" version))
       (sha256
        (base32 "1day74mapad08x95vpdd56qg74yizfna6rdap3y7rwbk3yk3spf6"))))
    (properties `((upstream-name . "palr")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/AustralianAntarcticDivision/palr")
    (synopsis "Colour Palettes for Data")
    (description
     "Colour palettes for data, based on some well known public data sets.  Includes
helper functions to map absolute values to known palettes, and capture the work
of image colour mapping as raster data sets.")
    (license license:gpl3)))

(define-public r-palmtree
  (package
    (name "r-palmtree")
    (version "0.9-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "palmtree" version))
       (sha256
        (base32 "06438wla5zm2s8vgip15lfy5gmhwh3wkiak8hsylc8zzkvn698cn"))))
    (properties `((upstream-name . "palmtree")))
    (build-system r-build-system)
    (propagated-inputs (list r-partykit r-formula))
    (home-page "https://cran.r-project.org/package=palmtree")
    (synopsis "Partially Additive (Generalized) Linear Model Trees")
    (description
     "This is an implementation of model-based trees with global model parameters
(PALM trees).  The PALM tree algorithm is an extension to the MOB algorithm
(implemented in the partykit package), where some parameters are fixed across
all groups.  Details about the method can be found in Seibold, Hothorn, Zeileis
(2016) <@code{arXiv:1612.07498>}.  The package offers coef(), @code{logLik}(),
plot(), and predict() functions for PALM trees.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-palmr
  (package
    (name "r-palmr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PaLMr" version))
       (sha256
        (base32 "1i1kzd41payn0vpplnj5wdf83y00ga5r4y86vc38xkpxymaa7i4r"))))
    (properties `((upstream-name . "PaLMr")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-httr))
    (home-page "https://palmr.ly.gd.edu.kg/")
    (synopsis "Interface for 'Google Pathways Language Model 2 (PaLM 2)'")
    (description
     "Google Pathways Language Model 2 (@code{PaLM} 2) as a coding and writing
assistant designed for R'.  With a range of functions, including natural
language processing and coding optimization, to assist R developers in
simplifying tedious coding tasks and content searching.")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-palm
  (package
    (name "r-palm")
    (version "1.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "palm" version))
       (sha256
        (base32 "108w8vsb41j1kwvymjcf5123xi8qiprcpiqynlvvfk1bp24jaz45"))))
    (properties `((upstream-name . "palm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-r6 r-mvtnorm r-minqa r-gsl))
    (home-page "https://github.com/b-steve/palm")
    (synopsis "Fitting Point Process Models via the Palm Likelihood")
    (description
     "This package provides functions to fit point process models using the Palm
likelihood.  First proposed by Tanaka, Ogata, and Stoyan (2008)
<DOI:10.1002/bimj.200610339>, maximisation of the Palm likelihood can provide
computationally efficient parameter estimation for point process models in
situations where the full likelihood is intractable.  This package is chiefly
focused on Neyman-Scott point processes, but can also fit the void processes
proposed by Jones-Todd et al. (2019) <DOI:10.1002/sim.8046>.  The development of
this package was motivated by the analysis of capture-recapture surveys on which
individuals cannot be identified---the data from which can conceptually be seen
as a clustered point process (Stevenson, Borchers, and Fewster, 2019
<DOI:10.1111/biom.12983>).  As such, some of the functions in this package are
specifically for the estimation of cetacean density from two-camera aerial
surveys.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-palinsol
  (package
    (name "r-palinsol")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "palinsol" version))
       (sha256
        (base32 "1xxpi81gf8wb2a3a42akvibrk8319h4x13k19zxjg02qyrq9gsfx"))))
    (properties `((upstream-name . "palinsol")))
    (build-system r-build-system)
    (home-page "https://github.com/mcrucifix/palinsol")
    (synopsis "Insolation for Palaeoclimate Studies")
    (description
     "R package to compute Incoming Solar Radiation (insolation) for palaeoclimate
studies.  Features three solutions: Berger (1978), Berger and Loutre (1991) and
Laskar et al. (2004).  Computes daily-mean, season-averaged and annual means and
for all latitudes, and polar night dates.")
    (license (license:fsdg-compatible "file://LICENSE"))))

(define-public r-palettetown
  (package
    (name "r-palettetown")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "palettetown" version))
       (sha256
        (base32 "1kjj1sqib1ns7895plp8c7h317pxwbyxi2shjkcgadkcsv2yjsxn"))))
    (properties `((upstream-name . "palettetown")))
    (build-system r-build-system)
    (home-page "https://github.com/timcdlucas/palettetown")
    (synopsis "Use Pokemon Inspired Colour Palettes")
    (description
     "Use Pokemon(R) inspired palettes with additional ggplot2 scales.  Palettes are
the colours in each Pokemon's sprite, ordered by how common they are in the
image.  The first 386 Pokemon are currently provided.")
    (license license:expat)))

(define-public r-palettesforr
  (package
    (name "r-palettesforr")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "palettesForR" version))
       (sha256
        (base32 "0nkb0dszj3a9ba7w6kfyn8lxacqsjw60i87p3g2gyl098kjwv7qv"))))
    (properties `((upstream-name . "palettesForR")))
    (build-system r-build-system)
    (home-page "https://github.com/frareb/palettesForR")
    (synopsis "GPL Palettes Copied from 'Gimp' and 'Inkscape'")
    (description
     "This package provides a set of palettes imported from Gimp distributed under
GPL3 (<https://www.gimp.org/about/COPYING>), and Inkscape distributed under GPL2
(<https://inkscape.org/about/license/>).")
    (license license:gpl2)))

(define-public r-palettes
  (package
    (name "r-palettes")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "palettes" version))
       (sha256
        (base32 "0s0pdza87jq5p7biq4vnajl59gnw77m2yi0x05gb31nmvcicxf2b"))))
    (properties `((upstream-name . "palettes")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tibble
                             r-scales
                             r-rlang
                             r-purrr
                             r-prismatic
                             r-pillar
                             r-ggplot2
                             r-farver
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://mccarthy-m-g.github.io/palettes/")
    (synopsis "Methods for Colour Vectors and Colour Palettes")
    (description
     "This package provides a comprehensive library for colour vectors and colour
palettes using a new family of colour classes (palettes_colour and
palettes_palette) that always print as hex codes with colour previews.
Capabilities include: formatting, casting and coercion, extraction and updating
of components, plotting, colour mixing arithmetic, and colour interpolation.")
    (license license:expat)))

(define-public r-paletteknife
  (package
    (name "r-paletteknife")
    (version "0.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paletteknife" version))
       (sha256
        (base32 "1m86xzx4qa3hlcp9pqmzd7n9js4nq2xap3lpsjxjqm0w3xsvb65s"))))
    (properties `((upstream-name . "paletteknife")))
    (build-system r-build-system)
    (home-page "https://github.com/johnxhobbs/paletteknife")
    (synopsis
     "Create Colour Scales and Legend from Continuous or Categorical Vectors")
    (description
     "Streamlines the steps for adding colour scales and associated legends when
working with base R graphics, especially for interactive use.  Popular palettes
are included and pretty legends produced when mapping a large variety of vector
classes to a colour scale.  An additional helper for adding axes and grid lines
complements the base::plot() work flow.")
    (license license:expat)))

(define-public r-palette
  (package
    (name "r-palette")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "palette" version))
       (sha256
        (base32 "1a19pf7aq6fj1v15almnin8rmz3xwf34ddxg81887ljd9sp7j8vg"))))
    (properties `((upstream-name . "palette")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs r-pillar r-cli))
    (home-page "https://github.com/christopherkenny/palette")
    (synopsis "Color Scheme Helpers")
    (description
     "Hexadecimal codes are typically used to represent colors in R. Connecting these
codes to their colors requires practice or memorization.  palette provides a
vctrs class for working with color palettes, including printing and plotting
functions.  The goal of the class is to place visual representations of color
palettes directly on or, at least, next to their corresponding character
representations.  Palette extensions also are provided for data frames using
pillar'.")
    (license license:expat)))

(define-public r-paleots
  (package
    (name "r-paleots")
    (version "0.6.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paleoTS" version))
       (sha256
        (base32 "1cz390nbiy28zgi4nc8w57bkljrx5sz5l7kcwbrvyya1n83r59dz"))))
    (properties `((upstream-name . "paleoTS")))
    (build-system r-build-system)
    (propagated-inputs (list r-mnormt r-foreach r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=paleoTS")
    (synopsis "Analyze Paleontological Time-Series")
    (description
     "Facilitates analysis of paleontological sequences of trait values.  Functions
are provided to fit, using maximum likelihood, simple evolutionary models
(including unbiased random walks, directional evolution,stasis,
Ornstein-Uhlenbeck, covariate-tracking) and complex models (punctuation, mode
shifts).")
    (license license:gpl3)))

(define-public r-paleotree
  (package
    (name "r-paleotree")
    (version "3.4.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paleotree" version))
       (sha256
        (base32 "05v35w84xm186mc0f9k53fpgfx4ihfvj7523jh0ba1crjakwha6b"))))
    (properties `((upstream-name . "paleotree")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcurl
                             r-png
                             r-phytools
                             r-phangorn
                             r-jsonlite
                             r-ape))
    (home-page "https://github.com/dwbapst/paleotree")
    (synopsis "Paleontological and Phylogenetic Analyses of Evolution")
    (description
     "This package provides tools for transforming, a posteriori time-scaling, and
modifying phylogenies containing extinct (i.e.  fossil) lineages.  In
particular, most users are interested in the functions @code{timePaleoPhy},
bin_@code{timePaleoPhy}, cal3@code{TimePaleoPhy} and
bin_cal3@code{TimePaleoPhy}, which date cladograms of fossil taxa using
stratigraphic data.  This package also contains a large number of likelihood
functions for estimating sampling and diversification rates from different types
of data available from the fossil record (e.g. range data, occurrence data,
etc).  paleotree users can also simulate diversification and sampling in the
fossil record using the function @code{simFossilRecord}, which is a detailed
simulator for branching birth-death-sampling processes composed of discrete
taxonomic units arranged in ancestor-descendant relationships.  Users can use
@code{simFossilRecord} to simulate diversification in incompletely sampled
fossil records, under various models of morphological differentiation (i.e.  the
various patterns by which morphotaxa originate from one another), and with
time-dependent, longevity-dependent and/or diversity-dependent rates of
diversification, extinction and sampling.  Additional functions allow users to
translate simulated ancestor-descendant data from @code{simFossilRecord} into
standard time-scaled phylogenies or unscaled cladograms that reflect the
relationships among taxon units.")
    (license license:cc0)))

(define-public r-paleopop
  (package
    (name "r-paleopop")
    (version "2.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paleopop" version))
       (sha256
        (base32 "0cxwx159krfdhf1ifdaidhsqq27y6ladmi1s70cw27aipmylrlrn"))))
    (properties `((upstream-name . "paleopop")))
    (build-system r-build-system)
    (propagated-inputs (list r-trend r-sf r-r6 r-poems))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/GlobalEcologyLab/paleopop/")
    (synopsis
     "Pattern-Oriented Modeling Framework for Coupled Niche-Population Paleo-Climatic Models")
    (description
     "This extension of the poems pattern-oriented modeling (POM) framework provides a
collection of modules and functions customized for paleontological time-scales,
and optimized for single-generation transitions and large populations, across
multiple generations.")
    (license license:gpl3+)))

(define-public r-paleomorph
  (package
    (name "r-paleomorph")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paleomorph" version))
       (sha256
        (base32 "05l55miahkmj8ikq8qz20y6kgxvxmdf04kji898i7fp8qyj4vfpa"))))
    (properties `((upstream-name . "paleomorph")))
    (build-system r-build-system)
    (home-page "https://github.com/timcdlucas/paleomorph/")
    (synopsis "Geometric Morphometric Tools for Paleobiology")
    (description
     "Fill missing symmetrical data with mirroring, calculate Procrustes alignments
with or without scaling, and compute standard or vector correlation and
covariance matrices (congruence coefficients) of 3D landmarks.  Tolerates
missing data for all analyses.")
    (license license:expat)))

(define-public r-paleodiv
  (package
    (name "r-paleodiv")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paleoDiv" version))
       (sha256
        (base32 "1v4dmx00vh3qpbw7pwvrr3sh4v6262b5y2h5nz2kgg9n4mfakc7n"))))
    (properties `((upstream-name . "paleoDiv")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=paleoDiv")
    (synopsis "Extracting and Visualizing Paleobiodiversity")
    (description
     "This package contains various tools for conveniently downloading and editing
taxon-specific datasets from the Paleobiology Database <https://paleobiodb.org>,
extracting information on abundance, temporal distribution of subtaxa and
taxonomic diversity through deep time, and visualizing these data in relation to
phylogeny and stratigraphy.")
    (license license:gpl3+)))

(define-public r-paleobuddy
  (package
    (name "r-paleobuddy")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paleobuddy" version))
       (sha256
        (base32 "0kaq6vvzw5h0fpvggmvf6v2zg6a06kxcyajv1krzx0p9482nwyzi"))))
    (properties `((upstream-name . "paleobuddy")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/brpetrucci/paleobuddy")
    (synopsis "Simulating Diversification Dynamics")
    (description
     "Simulation of species diversification, fossil records, and phylogenies.  While
the literature on species birth-death simulators is extensive, including
important software like paleotree and APE', we concluded there were interesting
gaps to be filled regarding possible diversification scenarios.  Here we strove
for flexibility over focus, implementing a large array of regimens for users to
experiment with and combine.  In this way, paleobuddy can be used in complement
to other simulators as a flexible jack of all trades, or, in the case of
scenarios implemented only here, can allow for robust and easy simulations for
novel situations.  Environmental data modified from that in RPANDA': Morlon H.
et al (2016) <doi:10.1111/2041-210X.12526>.")
    (license license:gpl3)))

(define-public r-paleobiodb
  (package
    (name "r-paleobiodb")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paleobioDB" version))
       (sha256
        (base32 "146p6pn3y6ca9vrdbkg3gyqfpb9drvbkkzq50y3qnzwiz15hmxq5"))))
    (properties `((upstream-name . "paleobioDB")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-rjson r-maps r-gtools r-curl))
    (home-page "https://docs.ropensci.org/paleobioDB/")
    (synopsis "Download and Process Data from the Paleobiology Database")
    (description
     "Includes functions to wrap most endpoints of the @code{PaleobioDB} API and
functions to visualize and process the fossil data.  The API documentation for
the Paleobiology Database can be found at <https://paleobiodb.org/data1.2/>.")
    (license license:gpl2)))

(define-public r-paleoam
  (package
    (name "r-paleoam")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paleoAM" version))
       (sha256
        (base32 "1svl2nqdflk65ppvlrdr07w66lzgj9g5mx8n2mamwrs9k1pv9gg8"))))
    (properties `((upstream-name . "paleoAM")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan))
    (home-page "https://cran.r-project.org/package=paleoAM")
    (synopsis
     "Simulating Assemblage Models of Abundance for the Fossil Record")
    (description
     "This package provides functions for fitting abundance distributions over
environmental gradients to the species in ecological communities, and tools for
simulating the fossil assemblages from those abundance models for such
communities, as well as simulating assemblages across various patterns of
sedimentary history and sampling.  These tools are for particular use with
fossil records with detailed age models and abundance distributions used for
calculating environmental gradients from ordinations or other indices based on
fossil assemblages.")
    (license license:cc0)))

(define-public r-pald
  (package
    (name "r-pald")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pald" version))
       (sha256
        (base32 "1b24z6lls2z2c3mwmnbp3qjr63hc1css8d2zy61fzrc78v6v42am"))))
    (properties `((upstream-name . "pald")))
    (build-system r-build-system)
    (propagated-inputs (list r-igraph r-glue))
    (home-page "https://github.com/LucyMcGowan/pald")
    (synopsis "Partitioned Local Depth for Community Structure in Data")
    (description
     "Implementation of the Partitioned Local Depth (@code{PaLD}) approach which
provides a measure of local depth and the cohesion of a point to another which
(together with a universal threshold for distinguishing strong and weak ties)
may be used to reveal local and global structure in data, based on methods
described in Berenhaut, Moore, and Melvin (2022) <doi:10.1073/pnas.2003634119>.
No extraneous inputs, distributional assumptions, iterative procedures nor
optimization criteria are employed.  This package includes functions for
computing local depths and cohesion as well as flexible functions for plotting
community networks and displays of cohesion against distance.")
    (license license:expat)))

(define-public r-palasso
  (package
    (name "r-palasso")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "palasso" version))
       (sha256
        (base32 "1lc258dfigfnafafm1h4v935wl0gjdw3ay14g1y0iw06b4r7x436"))))
    (properties `((upstream-name . "palasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-matrix r-glmnet))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rauschenberger/palasso")
    (synopsis "Sparse Regression with Paired Covariates")
    (description
     "This package implements sparse regression with paired covariates
(<doi:10.1007/s11634-019-00375-6>).  The paired lasso is designed for settings
where each covariate in one set forms a pair with a covariate in the other set
(one-to-one correspondence).  For the optional correlation shrinkage, install
ashr (<https://github.com/stephens999/ashr>) and @code{CorShrink}
(<https://github.com/kkdey/@code{CorShrink>}) from @code{GitHub} (see README).")
    (license license:gpl3)))

(define-public r-palaeoverse
  (package
    (name "r-palaeoverse")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "palaeoverse" version))
       (sha256
        (base32 "1i2dqlql24nsgnz0i3lc9f0s1lr2wig6xyp7la9dk2pzm16ars7g"))))
    (properties `((upstream-name . "palaeoverse")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringdist
                             r-sf
                             r-pbapply
                             r-lifecycle
                             r-httr
                             r-h3jsr
                             r-geosphere
                             r-curl
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://palaeoverse.palaeoverse.org")
    (synopsis "Prepare and Explore Data for Palaeobiological Analyses")
    (description
     "This package provides functionality to support data preparation and exploration
for palaeobiological analyses, improving code reproducibility and accessibility.
 The wider aim of palaeoverse is to bring the palaeobiological community
together to establish agreed standards.  The package currently includes
functionality for data cleaning, binning (time and space), exploration,
summarisation and visualisation.  Reference datasets (i.e.  Geological Time
Scales <https://stratigraphy.org/chart>) and auxiliary functions are also
provided.  Details can be found in: Jones et al., (2023) <doi:
10.1111/2041-210X.14099>.")
    (license license:gpl3+)))

(define-public r-palaeosig
  (package
    (name "r-palaeosig")
    (version "2.1-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "palaeoSig" version))
       (sha256
        (base32 "121akb42lqzp2vvfj6kjlaxsivxd71r7vnwyg6pcymkin94r8c3k"))))
    (properties `((upstream-name . "palaeoSig")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-tidyr
                             r-tibble
                             r-teachingdemos
                             r-rlang
                             r-rioja
                             r-purrr
                             r-mgcv
                             r-mass
                             r-magrittr
                             r-ggrepel
                             r-ggplot2
                             r-forcats
                             r-dplyr
                             r-assertr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/richardjtelford/palaeoSig")
    (synopsis "Significance Tests for Palaeoenvironmental Reconstructions")
    (description
     "Several tests of quantitative palaeoenvironmental reconstructions from
microfossil assemblages, including the null model tests of the statistically
significant of reconstructions developed by Telford and Birks (2011)
<doi:10.1016/j.quascirev.2011.03.002>, and tests of the effect of spatial
autocorrelation on transfer function model performance using methods from
Telford and Birks (2009) <doi:10.1016/j.quascirev.2008.12.020> and Trachsel and
Telford (2016) <doi:10.5194/cp-12-1215-2016>.  Age-depth models with generalized
mixed-effect regression from Heegaard et al (2005)
<doi:10.1191/0959683605hl836rr> are also included.")
    (license license:gpl3)))

(define-public r-pakret
  (package
    (name "r-pakret")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pakret" version))
       (sha256
        (base32 "05qdw4yv8w9wxgbdh3xqr540svhszl74m06c0v6xnpkhsnvd578g"))))
    (properties `((upstream-name . "pakret")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr r-rmarkdown r-readr r-knitr))
    (home-page "https://arnaudgallou.github.io/pakret/")
    (synopsis "Cite 'R' Packages on the Fly in 'R Markdown' and 'Quarto'")
    (description
     "References and cites R and R packages on the fly in R Markdown and Quarto'.
pakret provides a minimalistic API that generates preformatted citations of R
and R packages, and adds their reference to a .bib file directly from within
your document.")
    (license license:gpl3+)))

(define-public r-pakpmics2018mn
  (package
    (name "r-pakpmics2018mn")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PakPMICS2018mn" version))
       (sha256
        (base32 "1r5cn1mx7i7hif64lhfcdz4xqqfj3j45i092370xhhi19pqq217n"))))
    (properties `((upstream-name . "PakPMICS2018mn")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/myaseen208/PakPMICS2018mn")
    (synopsis
     "Multiple Indicator Cluster Survey (MICS) 2017-18 Men Questionnaire Data for Punjab, Pakistan")
    (description
     "This package provides data set and function for exploration of Multiple
Indicator Cluster Survey (MICS) 2017-18 Men questionnaire data for Punjab,
Pakistan.  The results of the present survey are critically important for the
purposes of Sustainable Development Goals (SDGs) monitoring, as the survey
produces information on 32 global Sustainable Development Goals (SDGs)
indicators.  The data was collected from 53,840 households selected at the
second stage with systematic random sampling out of a sample of 2,692 clusters
selected using probability proportional to size sampling.  Six questionnaires
were used in the survey: (1) a household questionnaire to collect basic
demographic information on all de jure household members (usual residents), the
household, and the dwelling; (2) a water quality testing questionnaire
administered in three households in each cluster of the sample; (3) a
questionnaire for individual women administered in each household to all women
age 15-49 years; (4) a questionnaire for individual men administered in every
second household to all men age 15-49 years; (5) an under-5 questionnaire,
administered to mothers (or caretakers) of all children under 5 living in the
household; and (6) a questionnaire for children age 5-17 years, administered to
the mother (or caretaker) of one randomly selected child age 5-17 years living
in the household (<http://www.mics.unicef.org/surveys>).")
    (license license:gpl2)))

(define-public r-pakpmics2018mm
  (package
    (name "r-pakpmics2018mm")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PakPMICS2018mm" version))
       (sha256
        (base32 "08f2n134q545kwysy705mgsy6319ns4l9pwxldlxvbzq0lha4hf9"))))
    (properties `((upstream-name . "PakPMICS2018mm")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/myaseen208/PakPMICS2018mm")
    (synopsis
     "Multiple Indicator Cluster Survey (MICS) 2017-18 Maternal Mortality Questionnaire Data for Punjab, Pakistan")
    (description
     "This package provides data set and function for exploration of Multiple
Indicator Cluster Survey (MICS) 2017-18 Maternal Mortality questionnaire data
for Punjab, Pakistan.  The results of the present survey are critically
important for the purposes of Sustainable Development Goals (SDGs) monitoring,
as the survey produces information on 32 global Sustainable Development Goals
(SDGs) indicators.  The data was collected from 53,840 households selected at
the second stage with systematic random sampling out of a sample of 2,692
clusters selected using probability proportional to size sampling.  Six
questionnaires were used in the survey: (1) a household questionnaire to collect
basic demographic information on all de jure household members (usual
residents), the household, and the dwelling; (2) a water quality testing
questionnaire administered in three households in each cluster of the sample;
(3) a questionnaire for individual women administered in each household to all
women age 15-49 years; (4) a questionnaire for individual men administered in
every second household to all men age 15-49 years; (5) an under-5 questionnaire,
administered to mothers (or caretakers) of all children under 5 living in the
household; and (6) a questionnaire for children age 5-17 years, administered to
the mother (or caretaker) of one randomly selected child age 5-17 years living
in the household (<http://www.mics.unicef.org/surveys>).")
    (license license:gpl2)))

(define-public r-pakpmics2018hh
  (package
    (name "r-pakpmics2018hh")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PakPMICS2018hh" version))
       (sha256
        (base32 "0wkjxy8qkndhnyf1w03cr3g7s03cm60yn0lxn5j6hvh019b28nmz"))))
    (properties `((upstream-name . "PakPMICS2018hh")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/myaseen208/PakPMICS2018hh")
    (synopsis
     "Multiple Indicator Cluster Survey (MICS) 2017-18 Household Questionnaire Data for Punjab, Pakistan")
    (description
     "This package provides data set and function for exploration of Multiple
Indicator Cluster Survey (MICS) 2017-18 Household questionnaire data for Punjab,
Pakistan.  The results of the present survey are critically important for the
purposes of Sustainable Development Goals (SDGs) monitoring, as the survey
produces information on 32 global Sustainable Development Goals (SDGs)
indicators.  The data was collected from 53,840 households selected at the
second stage with systematic random sampling out of a sample of 2,692 clusters
selected using probability proportional to size sampling.  Six questionnaires
were used in the survey: (1) a household questionnaire to collect basic
demographic information on all de jure household members (usual residents), the
household, and the dwelling; (2) a water quality testing questionnaire
administered in three households in each cluster of the sample; (3) a
questionnaire for individual women administered in each household to all women
age 15-49 years; (4) a questionnaire for individual men administered in every
second household to all men age 15-49 years; (5) an under-5 questionnaire,
administered to mothers (or caretakers) of all children under 5 living in the
household; and (6) a questionnaire for children age 5-17 years, administered to
the mother (or caretaker) of one randomly selected child age 5-17 years living
in the household (<http://www.mics.unicef.org/surveys>).")
    (license license:gpl2)))

(define-public r-pakpmics2018fs
  (package
    (name "r-pakpmics2018fs")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PakPMICS2018fs" version))
       (sha256
        (base32 "009hx1r9a0q0xxrk5zsy39sj1ynj5qjvci37i82yihjvjxb3rj0g"))))
    (properties `((upstream-name . "PakPMICS2018fs")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/myaseen208/PakPMICS2018fs")
    (synopsis
     "Multiple Indicator Cluster Survey (MICS) 2017-18 Children Age 5-17 Questionnaire Data for Punjab, Pakistan")
    (description
     "This package provides data set and function for exploration of Multiple
Indicator Cluster Survey (MICS) 2017-18 Children Age 5-17 questionnaire data for
Punjab, Pakistan.  The results of the present survey are critically important
for the purposes of Sustainable Development Goals (SDGs) monitoring, as the
survey produces information on 32 global Sustainable Development Goals (SDGs)
indicators.  The data was collected from 53,840 households selected at the
second stage with systematic random sampling out of a sample of 2,692 clusters
selected using probability proportional to size sampling.  Six questionnaires
were used in the survey: (1) a household questionnaire to collect basic
demographic information on all de jure household members (usual residents), the
household, and the dwelling; (2) a water quality testing questionnaire
administered in three households in each cluster of the sample; (3) a
questionnaire for individual women administered in each household to all women
age 15-49 years; (4) a questionnaire for individual men administered in every
second household to all men age 15-49 years; (5) an under-5 questionnaire,
administered to mothers (or caretakers) of all children under 5 living in the
household; and (6) a questionnaire for children age 5-17 years, administered to
the mother (or caretaker) of one randomly selected child age 5-17 years living
in the household (<http://www.mics.unicef.org/surveys>).")
    (license license:gpl2)))

(define-public r-pakpmics2018bh
  (package
    (name "r-pakpmics2018bh")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PakPMICS2018bh" version))
       (sha256
        (base32 "0c1fy4mp45nay15plpfzv4xjx1dw17zrrild70sfbrmdfdwnjgcl"))))
    (properties `((upstream-name . "PakPMICS2018bh")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/myaseen208/PakPMICS2018bh")
    (synopsis
     "Multiple Indicator Cluster Survey (MICS) 2017-18 Birth History of Children Questionnaire Data for Punjab, Pakistan")
    (description
     "This package provides data set and function for exploration of Multiple
Indicator Cluster Survey (MICS) 2017-18 Household questionnaire data for Punjab,
Pakistan.  The results of the present survey are critically important for the
purposes of SDG monitoring, as the survey produces information on 32 global SDG
indicators.  The data was collected from 53,840 households selected at the
second stage with systematic random sampling out of a sample of 2,692 clusters
selected using Probability Proportional to size sampling.  Six questionnaires
were used in the survey: (1) a household questionnaire to collect basic
demographic information on all de jure household members (usual residents), the
household, and the dwelling; (2) a water quality testing questionnaire
administered in three households in each cluster of the sample; (3) a
questionnaire for individual women administered in each household to all women
age 15-49 years; (4) a questionnaire for individual men administered in every
second household to all men age 15-49 years; (5) an under-5 questionnaire,
administered to mothers (or caretakers) of all children under 5 living in the
household; and (6) a questionnaire for children age 5-17 years, administered to
the mother (or caretaker) of one randomly selected child age 5-17 years living
in the household (<http://www.mics.unicef.org/surveys>).")
    (license license:gpl2)))

(define-public r-pakpmics2018
  (package
    (name "r-pakpmics2018")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PakPMICS2018" version))
       (sha256
        (base32 "0r72gbsaprcxnw88z4gg852gg8k18bg6xid3y5rh5y9dr988mnvk"))))
    (properties `((upstream-name . "PakPMICS2018")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=PakPMICS2018")
    (synopsis
     "Multiple Indicator Cluster Survey (MICS) 2017-18 Data for Punjab, Pakistan")
    (description
     "This package provides data set and function for exploration of Multiple
Indicator Cluster Survey (MICS) 2017-18 data for Punjab, Pakistan.  The results
of the present survey are critically important for the purposes of SDG
monitoring, as the survey produces information on 32 global SDG indicators.  The
data was collected from 53,840 households selected at the second stage with
systematic random sampling out of a sample of 2,692 clusters selected using
Probability Proportional to size sampling.  Six questionnaires were used in the
survey: (1) a household questionnaire to collect basic demographic information
on all de jure household members (usual residents), the household, and the
dwelling; (2) a water quality testing questionnaire administered in three
households in each cluster of the sample; (3) a questionnaire for individual
women administered in each household to all women age 15-49 years; (4) a
questionnaire for individual men administered in every second household to all
men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or
caretakers) of all children under 5 living in the household; and (6) a
questionnaire for children age 5-17 years, administered to the mother (or
caretaker) of one randomly selected child age 5-17 years living in the
household.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-pakpmics2014wm
  (package
    (name "r-pakpmics2014wm")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PakPMICS2014Wm" version))
       (sha256
        (base32 "1ni3a487chaixhbyzd5frr4rfjmf4vgnp1gbkqgqmir8a5m53rkf"))))
    (properties `((upstream-name . "PakPMICS2014Wm")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table))
    (home-page "https://github.com/MYaseen208/PakPMICS2014Wm")
    (synopsis
     "Multiple Indicator Cluster Survey (MICS) 2014 Women Questionnaire Data for Punjab, Pakistan")
    (description
     "This package provides data set and function for exploration of Multiple
Indicator Cluster Survey 2014 Women (age 15-49 years) questionnaire data for
Punjab, Pakistan.")
    (license license:gpl2)))

(define-public r-pakpmics2014hl
  (package
    (name "r-pakpmics2014hl")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PakPMICS2014HL" version))
       (sha256
        (base32 "0famy09rh8grcyymh1nlrxk35cdkxcwipqbvf4jcixkfvzw8fhjk"))))
    (properties `((upstream-name . "PakPMICS2014HL")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table))
    (home-page "https://github.com/MYaseen208/PakPMICS2014HL")
    (synopsis
     "Multiple Indicator Cluster Survey (MICS) 2014 Household Listing Questionnaire Data for Punjab, Pakistan")
    (description
     "This package provides data set and function for exploration of Multiple
Indicator Cluster Survey 2014 Household Listing questionnaire data for Punjab,
Pakistan.")
    (license license:gpl2)))

(define-public r-pakpmics2014hh
  (package
    (name "r-pakpmics2014hh")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PakPMICS2014HH" version))
       (sha256
        (base32 "1n6cyanqafscizgi1xqmc19w59yigxk5mvqinbivpdr7rydhrac6"))))
    (properties `((upstream-name . "PakPMICS2014HH")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table))
    (home-page "https://github.com/MYaseen208/PakPMICS2014HH")
    (synopsis
     "Multiple Indicator Cluster Survey (MICS) 2014 Household Questionnaire Data for Punjab, Pakistan")
    (description
     "This package provides data set and function for exploration of Multiple
Indicator Cluster Survey (MICS) 2014 Household questionnaire data for Punjab,
Pakistan (<http://www.mics.unicef.org/surveys>).")
    (license license:gpl2)))

(define-public r-pakpmics2014ch
  (package
    (name "r-pakpmics2014ch")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PakPMICS2014Ch" version))
       (sha256
        (base32 "0gjrx34613d9wzal292zfzh8hmkav271fqb0skdxd1f8rs2c79dg"))))
    (properties `((upstream-name . "PakPMICS2014Ch")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table))
    (home-page "https://github.com/MYaseen208/PakPMICS2014Ch")
    (synopsis
     "Multiple Indicator Cluster Survey (MICS) 2014 Child Questionnaire Data for Punjab, Pakistan")
    (description
     "This package provides data set and functions for exploration of Multiple
Indicator Cluster Survey (MICS) 2014 Child questionnaire data for Punjab,
Pakistan (<http://www.mics.unicef.org/surveys>).")
    (license license:gpl2)))

(define-public r-pakpc2023
  (package
    (name "r-pakpc2023")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PakPC2023" version))
       (sha256
        (base32 "1vih3bkz6sy7bkw18c1d7a85xj112zagg65i7sfhy8dgwx7m78f1"))))
    (properties `((upstream-name . "PakPC2023")))
    (build-system r-build-system)
    (propagated-inputs (list r-htmltools r-dt))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=PakPC2023")
    (synopsis "Pakistan Population Census 2023")
    (description
     "This package provides data sets and functions for exploration of Pakistan
Population Census 2023 (<https://www.pbs.gov.pk/>).")
    (license license:gpl2)))

(define-public r-pakpc2017
  (package
    (name "r-pakpc2017")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PakPC2017" version))
       (sha256
        (base32 "196jvhv7pv4pvnnz9izk6icxv3aml26mif0w6g4907izyszf8cal"))))
    (properties `((upstream-name . "PakPC2017")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr r-dplyr))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/MYaseen208/PakPC2017")
    (synopsis "Pakistan Population Census 2017")
    (description
     "This package provides data sets and functions for exploration of Pakistan
Population Census 2017 (<http://www.pbscensus.gov.pk/>).")
    (license license:gpl2)))

(define-public r-pak
  (package
    (name "r-pak")
    (version "0.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pak" version))
       (sha256
        (base32 "0rr8ikhy0zg2n0dqjrdmjdb8qdmzncrpizy1dqzqqlmfrpmxsgw5"))))
    (properties `((upstream-name . "pak")))
    (build-system r-build-system)
    (inputs (list zlib openssl openssh curl))
    (native-inputs (list pkg-config))
    (home-page "https://pak.r-lib.org/")
    (synopsis "Another Approach to Package Installation")
    (description
     "The goal of pak is to make package installation faster and more reliable.  In
particular, it performs all HTTP operations in parallel, so metadata resolution
and package downloads are fast.  Metadata and package files are cached on the
local disk as well.  pak has a dependency solver, so it finds version conflicts
before performing the installation.  This version of pak supports CRAN,
Bioconductor and @code{GitHub} packages as well.")
    (license license:gpl3)))

(define-public r-pairwiseci
  (package
    (name "r-pairwiseci")
    (version "0.1-27")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pairwiseCI" version))
       (sha256
        (base32 "0yp9nibdrsddjqq6vdlfbpvmfgm209h99b6qqqd6wgfzng327822"))))
    (properties `((upstream-name . "pairwiseCI")))
    (build-system r-build-system)
    (propagated-inputs (list r-mcprofile r-mcpan r-mass r-coin r-boot))
    (home-page "https://cran.r-project.org/package=pairwiseCI")
    (synopsis "Confidence Intervals for Two Sample Comparisons")
    (description
     "Calculation of the parametric, nonparametric confidence intervals for the
difference or ratio of location parameters, nonparametric confidence interval
for the Behrens-Fisher problem and for the difference, ratio and odds-ratio of
binomial proportions for comparison of independent samples.  Common wrapper
functions to split data sets and apply confidence intervals or tests to these
subsets.  A by-statement allows calculation of CI separately for the levels of
further factors.  CI are not adjusted for multiplicity.")
    (license license:gpl2)))

(define-public r-pairwise
  (package
    (name "r-pairwise")
    (version "0.6.1-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pairwise" version))
       (sha256
        (base32 "03dvpyr0hdz5a4wdim791m69825q762pwahlv3vwlnqas7niw3mf"))))
    (properties `((upstream-name . "pairwise")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pairwise")
    (synopsis "Rasch Model Parameters by Pairwise Algorithm")
    (description
     "This package performs the explicit calculation -- not estimation! -- of the
Rasch item parameters for dichotomous and polytomous item responses, using a
pairwise comparison approach.  Person parameters (WLE) are calculated according
to Warm's weighted likelihood approach.")
    (license license:gpl3)))

(define-public r-pairviz
  (package
    (name "r-pairviz")
    (version "1.3.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PairViz" version))
       (sha256
        (base32 "12rg1ml401c9zz1q6qy2ghs18x9vdb8smrkb2bcp34afbg5dvdd3"))))
    (properties `((upstream-name . "PairViz")))
    (build-system r-build-system)
    (propagated-inputs (list r-tsp r-gtools r-graph))
    (native-inputs (list r-knitr))
    (home-page "https://cbhurley.github.io/PairViz/")
    (synopsis "Visualization using Graph Traversal")
    (description
     "Improving graphics by ameliorating order effects, using Eulerian tours and
Hamiltonian decompositions of graphs.  References for the methods presented here
are C.B. Hurley and R.W. Oldford (2010) <doi:10.1198/jcgs.2010.09136> and C.B.
Hurley and R.W. Oldford (2011) <doi:10.1007/s00180-011-0229-5>.")
    (license license:gpl2)))

(define-public r-pairsd3
  (package
    (name "r-pairsd3")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pairsD3" version))
       (sha256
        (base32 "1w1i9d5pls07zs95ikfzmdm3dr6yw3dy6k1rbkhfqb5g2y9g82zr"))))
    (properties `((upstream-name . "pairsD3")))
    (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))
    (native-inputs (list esbuild))
    (home-page "https://github.com/garthtarr/pairsD3/")
    (synopsis "D3 Scatterplot Matrices")
    (description
     "This package creates an interactive scatterplot matrix using the D3
@code{JavaScript} library.  See <https://d3js.org/> for more information on D3.")
    (license license:gpl3+)))

(define-public r-paireddata
  (package
    (name "r-paireddata")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PairedData" version))
       (sha256
        (base32 "07y5fd4fkwfp0lbacf41ya8cn7x4n1haxl0cln9iibjffc1mbr5q"))))
    (properties `((upstream-name . "PairedData")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-mass r-lattice r-gld r-ggplot2))
    (home-page "https://cran.r-project.org/package=PairedData")
    (synopsis "Paired Data Analysis")
    (description
     "Many datasets and a set of graphics (based on ggplot2), statistics, effect sizes
and hypothesis tests are provided for analysing paired data with S4 class.")
    (license license:gpl2+)))

(define-public r-paintmap
  (package
    (name "r-paintmap")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paintmap" version))
       (sha256
        (base32 "1qw5725yjymxmfhffqjx4lbmzj437c7qwr8xrzmf52f0m28nl3vk"))))
    (properties `((upstream-name . "paintmap")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=paintmap")
    (synopsis "Plotting Paintmaps")
    (description
     "Plots matrices of colours as grids of coloured squares - aka heatmaps,
guaranteeing legible row and column names, without transformation of values,
without re-ordering rows or columns, and without dendrograms.")
    (license license:gpl2+)))

(define-public r-paintingr
  (package
    (name "r-paintingr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paintingr" version))
       (sha256
        (base32 "0km21rv6f137nngx4nzamplzgzhfgijlh1skfvvkp0cbjxv17cpi"))))
    (properties `((upstream-name . "paintingr")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=paintingr")
    (synopsis "Painting Palettes Generator")
    (description "Color palettes generated from paintings.")
    (license license:expat)))

(define-public r-painter
  (package
    (name "r-painter")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "painter" version))
       (sha256
        (base32 "0qr6p13h50rlavcsamxmijfr4dfrbv1k088m28qf8a4gam3ap85b"))))
    (properties `((upstream-name . "painter")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=painter")
    (synopsis "Creation and Manipulation of Color Palettes")
    (description
     "This package provides functions for creating color palettes, visualizing
palettes, modifying colors, and assigning colors for plotting.")
    (license license:gpl3)))

(define-public r-painbow
  (package
    (name "r-painbow")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "painbow" version))
       (sha256
        (base32 "10c8spknh7sqa4vl0q4bjdwd03nl4qk28hj2qa9sbyb1ryc7p4j9"))))
    (properties `((upstream-name . "painbow")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://github.com/steveharoz/painbow")
    (synopsis "Use XKCD's \"Painbow\" Colormap in ggplot2")
    (description
     "XKCD described a supposedly \"bad\" colormap that it called a \"Painbow\" (see
<https://xkcd.com/2537/>).  But simple tests demonstrate that under some
circumstances, the colormap can perform very well, and people can find
information that is difficult to detect with the ggplot2 default and even
supposedly \"good\" colormaps like viridis.  This library let's you use the
Painbow in your own ggplot graphs.")
    (license license:expat)))

(define-public r-paice
  (package
    (name "r-paice")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PAICE" version))
       (sha256
        (base32 "04zqyi5zvigg21pa4mqz948gxiqixc8j12n47c1ndjsd2ras98wb"))))
    (properties `((upstream-name . "PAICE")))
    (build-system r-build-system)
    (home-page "<https://github.com/PAICEcode/PAICE>")
    (synopsis "Phylogeographic Analysis of Island Colonization Events")
    (description
     "Estimation of the number of colonization events between islands of the same
archipelago for a species.  It uses rarefaction curves to control for both field
and genetic sample sizes as it was described in Coello et al. (2022)
<doi:10.1111/jbi.14341>.")
    (license license:gpl2)))

(define-public r-pagwas
  (package
    (name "r-pagwas")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PAGWAS" version))
       (sha256
        (base32 "0bz47ivd32kx1amgqllqbxyyvj773q7wasgk924hmibabiixa8nx"))))
    (properties `((upstream-name . "PAGWAS")))
    (build-system r-build-system)
    (propagated-inputs (list r-mnormt r-lars r-foreach))
    (home-page "https://cran.r-project.org/package=PAGWAS")
    (synopsis "Pathway Analysis Methods for Genomewide Association Data")
    (description
     "Bayesian hierarchical methods for pathway analysis of genomewide association
data: Normal/Bayes factors and Sparse Normal/Adaptive lasso.  The Frequentist
Fisher's product method is included as well.")
    (license license:gpl2+)))

(define-public r-pagfl
  (package
    (name "r-pagfl")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PAGFL" version))
       (sha256
        (base32 "0as4azrg4hmh5slljjasvq248r0qylvgyqcz1myfxgja9lpzd604"))))
    (properties `((upstream-name . "PAGFL")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppparallel r-rcpparmadillo r-rcpp r-lifecycle
                             r-ggplot2))
    (home-page "https://github.com/Paul-Haimerl/PAGFL")
    (synopsis
     "Joint Estimation of Latent Groups and Group-Specific Coefficients in Panel Data Models")
    (description
     "Latent group structures are a common challenge in panel data analysis.
Disregarding group-level heterogeneity can introduce bias.  Conversely,
estimating individual coefficients for each cross-sectional unit is inefficient
and may lead to high uncertainty.  This package addresses the issue of
unobservable group structures by implementing the pairwise adaptive group fused
Lasso (PAGFL) by Mehrabani (2023) <doi:10.1016/j.jeconom.2022.12.002>.  PAGFL
identifies latent group structures and group-specific coefficients in a single
step.  On top of that, we extend the PAGFL to time-varying coefficient
functions.")
    (license license:agpl3+)))

(define-public r-pageviews
  (package
    (name "r-pageviews")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pageviews" version))
       (sha256
        (base32 "187gy6czxkicxghhklma87pfa23xcm07c4n9jfsr5yl9hwwfcna6"))))
    (properties `((upstream-name . "pageviews")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-httr r-curl))
    (home-page "https://github.com/ironholds/pageviews")
    (synopsis "An API Client for Wikimedia Traffic Data")
    (description
     "Pageview data from the Wikimedia sites, such as Wikipedia
<https://www.wikipedia.org/>, from entire projects to per-article levels of
granularity, through the new RESTful API and data source
<https://wikimedia.org/api/rest_v1/?doc>.")
    (license license:expat)))

(define-public r-pagenum
  (package
    (name "r-pagenum")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pagenum" version))
       (sha256
        (base32 "0gcy9gsjb75v9wc03n1yazxnf1b4v5x8w4mwlsvv9d42kbx1ib3r"))))
    (properties `((upstream-name . "pagenum")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://kwstat.github.io/pagenum/")
    (synopsis "Put Page Numbers on Graphics")
    (description
     "This package provides a simple way to add page numbers to base/ggplot/lattice
graphics.")
    (license license:expat)))

(define-public r-pagemap
  (package
    (name "r-pagemap")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pagemap" version))
       (sha256
        (base32 "1rwspbk7xmp27iadrd1471a7z71qyzlb0vnkw4ssjk7wi8q48fnj"))))
    (properties `((upstream-name . "pagemap")))
    (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 r-knitr esbuild))
    (home-page "https://github.com/swsoyee/pagemapR")
    (synopsis "Create Mini Map for Web Pages")
    (description
     "Quickly and easily add a mini map to your rmarkdown html documents.")
    (license license:expat)))

(define-public r-pagedown
  (package
    (name "r-pagedown")
    (version "0.21")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pagedown" version))
       (sha256
        (base32 "1hb4h2bq3gvlmbwbbhkn2c5cbdfi8jmbv4gb4xssc9z37297v56k"))))
    (properties `((upstream-name . "pagedown")))
    (build-system r-build-system)
    (inputs (list pandoc))
    (propagated-inputs (list r-xfun
                             r-websocket
                             r-servr
                             r-rmarkdown
                             r-processx
                             r-later
                             r-jsonlite
                             r-httpuv
                             r-htmltools
                             r-bookdown))
    (home-page "https://github.com/rstudio/pagedown")
    (synopsis "Paginate the HTML Output of R Markdown with CSS for Print")
    (description
     "Use the paged media properties in CSS and the @code{JavaScript} library paged.js
to split the content of an HTML document into discrete pages.  Each page can
have its page size, page numbers, margin boxes, and running headers, etc.
Applications of this package include books, letters, reports, papers, business
cards, resumes, and posters.")
    (license license:expat)))

(define-public r-pafr
  (package
    (name "r-pafr")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pafr" version))
       (sha256
        (base32 "0ali4m1pv73y88x1dk5rvmg1ysy48janjnc1hnqfcndszfz2b0wm"))))
    (properties `((upstream-name . "pafr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-stringr r-rlang r-ggplot2 r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://dwinter.github.io/pafr/")
    (synopsis "Read, Manipulate and Visualize 'Pairwise mApping Format' Data")
    (description
     "This package provides functions to read, process and visualize pairwise sequence
alignments in the PAF format used by minimap2 and other whole-genome aligners.
minimap2 is described by Li H. (2018) <doi:10.1093/bioinformatics/bty191>.")
    (license license:expat)))

(define-public r-pafit
  (package
    (name "r-pafit")
    (version "1.2.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PAFit" version))
       (sha256
        (base32 "10p2q90jbjyzbvn8kgx1i8if144gq2zylqh8h3x4sg2ajqbcv8kg"))))
    (properties `((upstream-name . "PAFit")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam
                             r-rcpp
                             r-rcolorbrewer
                             r-plyr
                             r-networkdynamic
                             r-network
                             r-mass
                             r-mapproj
                             r-magicaxis
                             r-knitr
                             r-igraph
                             r-ggplot2))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/thongphamthe/PAFit")
    (synopsis "Generative Mechanism Estimation in Temporal Complex Networks")
    (description
     "Statistical methods for estimating preferential attachment and node fitness
generative mechanisms in temporal complex networks are provided.  Thong Pham et
al. (2015) <doi:10.1371/journal.pone.0137796>.  Thong Pham et al. (2016)
<doi:10.1038/srep32558>.  Thong Pham et al. (2020) <doi:10.18637/jss.v092.i03>.
Thong Pham et al. (2021) <doi:10.1093/comnet/cnab024>.")
    (license license:gpl3)))

(define-public r-pafdr
  (package
    (name "r-pafdr")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pafdR" version))
       (sha256
        (base32 "1yimsd4h23hcf752p5flda3dqk8hgn6qm9k0pmbapxj4jbsw14w5"))))
    (properties `((upstream-name . "pafdR")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-exams r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=pafdR")
    (synopsis
     "Book Companion for Processing and Analyzing Financial Data with R")
    (description
     "This package provides access to material from the book \"Processing and Analyzing
Financial Data with R\" by Marcelo Perlin (2017) available at
<https://sites.google.com/view/pafdr/home>.")
    (license license:gpl2)))

(define-public r-padr
  (package
    (name "r-padr")
    (version "0.6.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "padr" version))
       (sha256
        (base32 "0mgjdva2yj9m0mps69gafy3w1c4zhik002b5b2j2zvbv3m34mw97"))))
    (properties `((upstream-name . "padr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-rcpp r-lubridate r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/EdwinTh/padr")
    (synopsis "Quickly Get Datetime Data Ready for Analysis")
    (description
     "Transforms datetime data into a format ready for analysis.  It offers two core
functionalities; aggregating data to a higher level interval (thicken) and
imputing records where observations were absent (pad).")
    (license license:expat)))

(define-public r-pade
  (package
    (name "r-pade")
    (version "1.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Pade" version))
       (sha256
        (base32 "1rs3wap5rx4kq1vabhcfj2qbzfq2b78nvs4bc5h0jg3z32g99220"))))
    (properties `((upstream-name . "Pade")))
    (build-system r-build-system)
    (home-page "https://github.com/aadler/Pade")
    (synopsis "PadÃ© Approximant Coefficients")
    (description
     "Given a vector of Taylor series coefficients of sufficient length as input, the
function returns the numerator and denominator coefficients for the PadÃ©
approximant of appropriate order (Baker, 1975) <ISBN:9780120748556>.")
    (license (list license:gpl2+ license:bsd-2))))

(define-public r-pacvr
  (package
    (name "r-pacvr")
    (version "1.0.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PACVr" version))
       (sha256
        (base32 "0wj32rghf040539z6lld4zspyalz3c4ag1syr940arvilvyv5wav"))))
    (properties `((upstream-name . "PACVr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-read-gb
                             r-rcircos
                             r-r6
                             r-logger
                             r-iranges
                             r-genomicranges
                             r-genomicalignments
                             r-dplyr
                             r-biostrings))
    (home-page "https://github.com/michaelgruenstaeudl/PACVr")
    (synopsis "Plastome Assembly Coverage Visualization")
    (description
     "Visualizes the coverage depth of a complete plastid genome as well as the
equality of its inverted repeat regions in relation to the circular,
quadripartite genome structure and the location of individual genes.  For more
information, please see Gruenstaeudl and Jenke (2020)
<doi:10.1186/s12859-020-3475-0>.")
    (license (license:fsdg-compatible "BSD 3-clause License + file LICENSE"))))

(define-public r-pacs
  (package
    (name "r-pacs")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pacs" version))
       (sha256
        (base32 "0afsci8qknvhg0zlc6fli87dn1pcs5jhyzddg7fad4qqyyy41sqx"))))
    (properties `((upstream-name . "pacs")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-stringi r-memoise r-jsonlite r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Polkas/pacs")
    (synopsis "Supplementary Tools for R Packages Developers")
    (description
     "Supplementary utils for CRAN maintainers and R packages developers.  Validating
the library, packages and lock files.  Exploring a complexity of a specific
package like evaluating its size in bytes with all dependencies.  The shiny app
complexity could be explored too.  Assessing the life duration of a specific
package version.  Checking a CRAN package check page status for any errors and
warnings.  Retrieving a DESCRIPTION or NAMESPACE file for any package version.
Comparing DESCRIPTION or NAMESPACE files between different package versions.
Getting a list of all releases for a specific package.  The Bioconductor is
partly supported.")
    (license license:gpl3+)))

(define-public r-pacotest
  (package
    (name "r-pacotest")
    (version "0.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pacotest" version))
       (sha256
        (base32 "0zdrvcrf6ynid2wpngdslxzkpc4grcvrf6bzbrni9rvp94sfzjlf"))))
    (properties `((upstream-name . "pacotest")))
    (build-system r-build-system)
    (propagated-inputs (list r-vinecopula
                             r-rcpparmadillo
                             r-rcpp
                             r-numderiv
                             r-gridextra
                             r-ggplot2))
    (home-page "https://cran.r-project.org/package=pacotest")
    (synopsis
     "Testing for Partial Copulas and the Simplifying Assumption in Vine Copulas")
    (description
     "Routines for two different test types, the Constant Conditional Correlation
(CCC) test and the Vectorial Independence (VI) test are provided (Kurz and
Spanhel (2022) <doi:10.1214/22-EJS2051>).  The tests can be applied to check
whether a conditional copula coincides with its partial copula.  Functions to
test whether a regular vine copula satisfies the so-called simplifying
assumption or to test a single copula within a regular vine copula to be a
(j-1)-th order partial copula are available.  The CCC test comes with a decision
tree approach to allow testing in high-dimensional settings.")
    (license license:expat)))

(define-public r-paco
  (package
    (name "r-paco")
    (version "0.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paco" version))
       (sha256
        (base32 "0p02fdc5f7d7sbdvxill8jln76p1ndiw95k6s9j41slfriq647vd"))))
    (properties `((upstream-name . "paco")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan r-plyr r-ape))
    (home-page "https://www.uv.es/cophylpaco/")
    (synopsis "Procrustes Application to Cophylogenetic Analysis")
    (description
     "Procrustes analyses to infer co-phylogenetic matching between pairs of
phylogenetic trees.")
    (license license:expat)))

(define-public r-paclasso
  (package
    (name "r-paclasso")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PACLasso" version))
       (sha256
        (base32 "1qzzymhfsjy8mk0r0xpd5h4wrx4wk8gq3vi6fnawkq5xkrg26rld"))))
    (properties `((upstream-name . "PACLasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-quadprog r-penalized r-mass r-limsolve r-lars))
    (home-page "http://www-bcf.usc.edu/~gareth/research/PAC.pdf")
    (synopsis "Penalized and Constrained Lasso Optimization")
    (description
     "An implementation of both the equality and inequality constrained lasso
functions for the algorithm described in \"Penalized and Constrained
Optimization\" by James, Paulson, and Rusmevichientong (Journal of the American
Statistical Association, 2019; see
<http://www-bcf.usc.edu/~gareth/research/PAC.pdf> for a full-text version of the
paper).  The algorithm here is designed to allow users to define linear
constraints (either equality or inequality constraints) and use a penalized
regression approach to solve the constrained problem.  The functions here are
used specifically for constraints with the lasso formulation, but the method
described in the @code{PaC} paper can be used for a variety of scenarios.  In
addition to the simple examples included here with the corresponding functions,
complete code to entirely reproduce the results of the paper is available online
through the Journal of the American Statistical Association.")
    (license license:gpl3)))

(define-public r-packmbplsda
  (package
    (name "r-packmbplsda")
    (version "0.9.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "packMBPLSDA" version))
       (sha256
        (base32 "1d9plb88d4hic6881ibij888ic3p65d0ijlsyxcghi93ag3wsfj3"))))
    (properties `((upstream-name . "packMBPLSDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-proc
                             r-mass
                             r-foreach
                             r-factominer
                             r-doparallel
                             r-ade4))
    (home-page "https://cran.r-project.org/package=packMBPLSDA")
    (synopsis "Multi-Block Partial Least Squares Discriminant Analysis")
    (description
     "Several functions are provided to implement a MBPLSDA : components search,
optimal model components number search, optimal model validity test by
permutation tests, observed values evaluation of optimal model parameters and
predicted categories, bootstrap values evaluation of optimal model parameters
and predicted cross-validated categories.  The use of this package is described
in Brandolini-Bunlon et al (2019.  Multi-block PLS discriminant analysis for the
joint analysis of metabolomic and epidemiological data.  Metabolomics,
15(10):134).")
    (license license:gpl2+)))

(define-public r-packhv
  (package
    (name "r-packhv")
    (version "2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "packHV" version))
       (sha256
        (base32 "0bj7zhlz2283y1hfdqdn7xil4wscwri1f5hszarwjwczx4n5sv81"))))
    (properties `((upstream-name . "packHV")))
    (build-system r-build-system)
    (propagated-inputs (list r-writexls r-survival))
    (home-page "https://cran.r-project.org/package=packHV")
    (synopsis "few Useful Functions for Statisticians")
    (description
     "Various useful functions for statisticians: describe data, plot Kaplan-Meier
curves with numbers of subjects at risk, compare data sets, display
spaghetti-plot, build multi-contingency tables...")
    (license license:gpl2+)))

(define-public r-packer
  (package
    (name "r-packer")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "packer" version))
       (sha256
        (base32 "0b51nag4dmjxb8fb6z79j92dx1s6427rhmr2piaqdzrfbk9kd7dh"))))
    (properties `((upstream-name . "packer")))
    (build-system r-build-system)
    (propagated-inputs (list r-usethis
                             r-rstudioapi
                             r-rprojroot
                             r-roxygen2
                             r-jsonlite
                             r-htmlwidgets
                             r-fs
                             r-cli
                             r-assertthat))
    (home-page "https://github.com/JohnCoene/packer")
    (synopsis "An Opinionated Framework for Using 'JavaScript'")
    (description
     "Enforces good practice and provides convenience functions to make work with
@code{JavaScript} not just easier but also scalable.  It is a robust wrapper to
NPM', yarn', and webpack that enables to compartmentalize @code{JavaScript}
code, leverage NPM and yarn packages, include @code{TypeScript}', React', or Vue
in web applications, and much more.")
    (license license:expat)))

(define-public r-packagerank
  (package
    (name "r-packagerank")
    (version "0.9.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "packageRank" version))
       (sha256
        (base32 "0psm8y3x4ndcjppbxhgznvvv8rlgipabnklg13jkds05dzdlq316"))))
    (properties `((upstream-name . "packageRank")))
    (build-system r-build-system)
    (propagated-inputs (list r-sugrrants
                             r-rversions
                             r-rlang
                             r-rcurl
                             r-r-utils
                             r-pkgsearch
                             r-memoise
                             r-isocodes
                             r-ggplot2
                             r-data-table
                             r-curl
                             r-cranlogs))
    (home-page "https://github.com/lindbrook/packageRank")
    (synopsis
     "Computation and Visualization of Package Download Counts and Percentile Ranks")
    (description
     "Compute and visualize the cross-sectional and longitudinal number and rank
percentile of package downloads from Posit/RStudio's CRAN mirror.")
    (license license:gpl2+)))

(define-public r-packager
  (package
    (name "r-packager")
    (version "1.15.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "packager" version))
       (sha256
        (base32 "0kvlc0cn3v1wpffh4hn29q8jgfxxg4pcxnfnnhykbx13wilrvrph"))))
    (properties `((upstream-name . "packager")))
    (build-system r-build-system)
    (propagated-inputs (list r-whoami
                             r-whisker
                             r-tinytest
                             r-rprojroot
                             r-rhub
                             r-remotes
                             r-rcmdcheck
                             r-pkgload
                             r-pkgbuild
                             r-httr
                             r-gert
                             r-fs
                             r-fritools
                             r-fakemake
                             r-desc
                             r-cyclocomp
                             r-crayon
                             r-codetools
                             r-checkmate
                             r-callr))
    (native-inputs (list r-rasciidoc))
    (home-page "https://gitlab.com/fvafrcu/packager")
    (synopsis "Create, Build and Maintain Packages")
    (description
     "Helper functions for package creation, building and maintenance.  Designed to
work with a build system such as GNU make or package fakemake to help you to
conditionally work through the stages of package development (such as spell
checking, linting, testing, before building and checking a package).")
    (license license:bsd-2)))

(define-public r-packagepal
  (package
    (name "r-packagepal")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "packagepal" version))
       (sha256
        (base32 "0l49qfxdf54m07dpbj3gb3safh4nbc5hz1vgh4dyqxnkrvmh49xl"))))
    (properties `((upstream-name . "packagepal")))
    (build-system r-build-system)
    (propagated-inputs (list r-usethis r-cli r-available))
    (home-page "https://github.com/lddurbin/packagepal")
    (synopsis "Guidelines and Checklists for Building CRAN-Worthy Packages")
    (description
     "This package provides essential checklists for R package developers, whether
you're creating your first package or beginning a new project.  This tool guides
you through each step of the development process, including specific
considerations for submitting your package to the Comprehensive R Archive
Network (CRAN).  Simplify your workflow and ensure adherence to best practices
with packagepal'.")
    (license license:expat)))

(define-public r-packagefinder
  (package
    (name "r-packagefinder")
    (version "0.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "packagefinder" version))
       (sha256
        (base32 "1fxv6q6pla3yfr32z6j5g4f0c0f1f9gsyn4w8bp23692lfpip721"))))
    (properties `((upstream-name . "packagefinder")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-textutils
                             r-stringr
                             r-shinyjs
                             r-shinybusy
                             r-shiny
                             r-reactable
                             r-pander
                             r-lubridate
                             r-jsonlite
                             r-httr
                             r-htmltable
                             r-formattable
                             r-crayon
                             r-clipr))
    (home-page "https://github.com/jsugarelli/packagefinder/")
    (synopsis
     "Comfortable Search for R Packages on CRAN, Either Directly from the R Console or with an R Studio Add-in")
    (description
     "Search for R packages on CRAN directly from the R console, based on the packages
titles, short and long descriptions, or other fields.  Combine multiple keywords
with logical operators ('and', or'), view detailed information on any package
and keep track of the latest package contributions to CRAN. If you don't want to
search from the R console, use the comfortable R Studio add-in.")
    (license license:gpl3)))

(define-public r-packagediff
  (package
    (name "r-packagediff")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "packageDiff" version))
       (sha256
        (base32 "0qxgx2ac0x7p9j9clj7hgjahf7xwzcjkcw0d2n6w0r9c4g0mw9w2"))))
    (properties `((upstream-name . "packageDiff")))
    (build-system r-build-system)
    (propagated-inputs (list r-htmlwidgets r-diffr))
    (home-page "https://github.com/couthcommander/packageDiff")
    (synopsis "Compare R Package Differences")
    (description
     "It provides utility functions for investigating changes within R packages.  The
@code{pkgInfo}() function extracts package information such as exported and
non-exported functions as well as their arguments.  The @code{pkgDiff}()
function compares this information for two versions of a package and creates a
diff file viewable in a browser.")
    (license license:gpl2+)))

(define-public r-pack
  (package
    (name "r-pack")
    (version "0.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pack" version))
       (sha256
        (base32 "0x4p8clwp49s2y67y7in530xwhjngnqwagf9xnyb1jp0z3myd3r7"))))
    (properties `((upstream-name . "pack")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=pack")
    (synopsis "Convert values to/from raw vectors")
    (description
     "This package provides functions to easily convert data to binary formats other
programs/machines can understand.")
    (license license:gpl3)))

(define-public r-pac
  (package
    (name "r-pac")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "PAC" version))
       (sha256
        (base32 "0h7381mr3jgzl7fi6v7amgmkf5agpkgy068c6ysgh3adlwsh6gs6"))))
    (properties `((upstream-name . "PAC")))
    (build-system r-build-system)
    (propagated-inputs (list r-rtsne
                             r-rcpp
                             r-parmigene
                             r-infotheo
                             r-igraph
                             r-ggrepel
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://doi.org/10.1371/journal.pcbi.1005875")
    (synopsis
     "Partition-Assisted Clustering and Multiple Alignments of Networks")
    (description
     "This package implements partition-assisted clustering and multiple alignments of
networks.  It 1) utilizes partition-assisted clustering to find robust and
accurate clusters and 2) discovers coherent relationships of clusters across
multiple samples.  It is particularly useful for analyzing single-cell data set.
 Please see Li et al. (2017) <doi:10.1371/journal.pcbi.1005875> for detail
method description.")
    (license license:gpl3)))

(define-public r-paar
  (package
    (name "r-paar")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "paar" version))
       (sha256
        (base32 "1yc5yqx1rdvmkwz0pmzbdlpbnb4vxxhzyp0h5j74nzk22j9332f3"))))
    (properties `((upstream-name . "paar")))
    (build-system r-build-system)
    (propagated-inputs (list r-spdep r-sf r-gstat r-e1071 r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://ppaccioretti.github.io/paar/")
    (synopsis "Precision Agriculture Data Analysis")
    (description
     "Precision agriculture spatial data depuration and homogeneous zones (management
zone) delineation.  The package includes functions that performs protocols for
data cleaning management zone delineation and zone comparison; protocols are
described in Paccioretti et al., (2020) <doi:10.1016/j.compag.2020.105556>.")
    (license license:expat)))

(define-public r-pa
  (package
    (name "r-pa")
    (version "1.2-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "pa" version))
       (sha256
        (base32 "0rjq0b68qh5ayn5nd174bw6h7d707fy4kli1zi74vj4yc51z0las"))))
    (properties `((upstream-name . "pa")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://cran.r-project.org/package=pa")
    (synopsis "Performance Attribution for Equity Portfolios")
    (description
     "It provides tools for conducting performance attribution for equity portfolios.
The package uses two methods: the Brinson method and a regression-based
analysis.")
    (license license:gpl2)))

(define-public r-p3state-msm
  (package
    (name "r-p3state-msm")
    (version "1.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "p3state.msm" version))
       (sha256
        (base32 "1gklkgimpmhyxkga0g4s3m7ap3c5lwg2wyl4gvvfv4n4vbnfazis"))))
    (properties `((upstream-name . "p3state.msm")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "https://cran.r-project.org/package=p3state.msm")
    (synopsis "Analyzing Survival Data from an Illness-Death Model")
    (description
     "This package contains functions for data preparation, prediction of transition
probabilities, estimating semi-parametric regression models and for implementing
nonparametric estimators for other quantities.  See Meira-Machado and
Roca-PardiÃ±as (2011) <doi:10.18637/jss.v038.i03>.")
    (license license:gpl3)))

