(define-module (guix-cran packages g)
  #: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 geo)
  #:use-module (gnu packages bioconductor)
  #:use-module (gnu packages python)
  #:use-module (gnu packages gcc)
  #:use-module (gnu packages pkg-config)
  #:use-module (gnu packages maths)
  #:use-module (gnu packages haskell-xyz)
  #:use-module (gnu packages web)
  #:use-module (gnu packages graphviz)
  #:use-module (gnu packages java)
  #:use-module (gnu packages machine-learning)
  #:use-module (gnu packages compression)
  #:use-module (gnu packages version-control)
  #:use-module (gnu packages language)
  #:use-module (gnu packages image)
  #:use-module (gnu packages multiprecision)
  #:use-module (gnu packages perl)
  #:use-module (gnu packages curl)
  #:use-module (gnu packages xml)
  #:use-module (gnu packages ssh)
  #:use-module (gnu packages tls)
  #:use-module (gnu packages pcre)
  #:use-module (gnu packages sqlite)
  #: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 p)
  #:use-module (guix-cran packages o)
  #:use-module (guix-cran packages n)
  #:use-module (guix-cran packages m)
  #:use-module (guix-cran packages l)
  #:use-module (guix-cran packages k)
  #:use-module (guix-cran packages j)
  #:use-module (guix-cran packages i)
  #:use-module (guix-cran packages h)
  #:use-module (guix-cran packages 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-gyro
  (package
    (name "r-gyro")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gyro" version))
       (sha256
        (base32 "0zgzqgfd97bxb4crijlc9byak4dl5zrhp6ynch8yda8m3rx1gz92"))))
    (properties `((upstream-name . "gyro")))
    (build-system r-build-system)
    (propagated-inputs (list r-rvcg
                             r-rstudioapi
                             r-rgl
                             r-rcpp
                             r-rcdt
                             r-purrr
                             r-polychrome
                             r-plotrix
                             r-morpho
                             r-cxhull
                             r-colorsgen
                             r-clipr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/stla/gyro")
    (synopsis "Hyperbolic Geometry")
    (description
     "Hyperbolic geometry in the Minkowski model and the PoincarÃ© model.  The methods
are based on the gyrovector space theory developed by A. A. Ungar that can be
found in the book Analytic Hyperbolic Geometry: Mathematical Foundations And
Applications <doi:10.1142/5914>.  The package provides functions to plot
three-dimensional hyperbolic polyhedra and to plot hyperbolic tilings of the
PoincarÃ© disk.")
    (license license:gpl3)))

(define-public r-gym
  (package
    (name "r-gym")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gym" version))
       (sha256
        (base32 "0vcwzgawqwjsf65hr1mbjkz3px8zsibfkn42jpsg39n13jpfjq8v"))))
    (properties `((upstream-name . "gym")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-httr))
    (home-page "https://github.com/paulhendricks/gym-R")
    (synopsis "Provides Access to the OpenAI Gym API")
    (description
     "@code{OpenAI} Gym is a open-source Python toolkit for developing and comparing
reinforcement learning algorithms.  This is a wrapper for the @code{OpenAI} Gym
API, and enables access to an ever-growing variety of environments.  For more
details on @code{OpenAI} Gym, please see here: <https://github.com/openai/gym>.
For more details on the @code{OpenAI} Gym API specification, please see here:
<https://github.com/openai/gym-http-api>.")
    (license license:expat)))

(define-public r-gxescanr
  (package
    (name "r-gxescanr")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GxEScanR" version))
       (sha256
        (base32 "0k3avad4b2x4sqv8iilx5a8gkybjlipx50mj0la4kl9xv0azshkd"))))
    (properties `((upstream-name . "GxEScanR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-prodlim))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GxEScanR")
    (synopsis "Run GWAS/GWEIS Scans Using Binary Dosage Files")
    (description
     "This package provides tools to run genome-wide association study (GWAS) and
genome-wide by environment interaction study (GWEIS) scans using the genetic
data stored in a binary dosage file.  The user provides a data frame with the
subject's covariate data and the information about the binary dosage file
returned by the @code{BinaryDosage::getbdinfo}() routine.")
    (license license:gpl3)))

(define-public r-gxeprs
  (package
    (name "r-gxeprs")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GxEprs" version))
       (sha256
        (base32 "1yv4b99mm1az298idvsb9czx0cgvq5032dv0k3wwcsgc5bk7hwqw"))))
    (properties `((upstream-name . "GxEprs")))
    (build-system r-build-system)
    (home-page "https://github.com/DoviniJ/GxEprs")
    (synopsis "Genotype-by-Environment Interaction in Polygenic Score Models")
    (description
     "This package provides a novel PRS model is introduced to enhance the prediction
accuracy by utilising @code{GxE} effects.  This package performs Genome Wide
Association Studies (GWAS) and Genome Wide Environment Interaction Studies
(GWEIS) using a discovery dataset.  The package has the ability to obtain
polygenic risk scores (PRSs) for a target sample.  Finally it predicts the risk
values of each individual in the target sample.  Users have the choice of using
existing models (Li et al., 2015) <doi:10.1093/annonc/mdu565>, (Pandis et al.,
2013) <doi:10.1093/ejo/cjt054>, (Peyrot et al., 2018)
<doi:10.1016/j.biopsych.2017.09.009> and (Song et al., 2022)
<doi:10.1038/s41467-022-32407-9>, as well as newly proposed models for genomic
risk prediction (refer to the URL for more details).")
    (license license:gpl3+)))

(define-public r-gwzinbr
  (package
    (name "r-gwzinbr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gwzinbr" version))
       (sha256
        (base32 "17i6y4zqr4p6zrp77pjhnpdq8wrannklc4ybmsbh8vmsq26in7d2"))))
    (properties `((upstream-name . "gwzinbr")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp))
    (home-page "https://cran.r-project.org/package=gwzinbr")
    (synopsis
     "Geographically Weighted Zero Inflated Negative Binomial Regression")
    (description
     "Fits a geographically weighted regression model using zero inflated probability
distributions.  Has the zero inflated negative binomial distribution (zinb) as
default, but also accepts the zero inflated Poisson (zip), negative binomial
(negbin) and Poisson distributions.  Can also fit the global versions of each
regression model.  Da Silva, A. R. & De Sousa, M. D. R. (2023). \"Geographically
weighted zero-inflated negative binomial regression: A general case for count
data\", Spatial Statistics <doi:10.1016/j.spasta.2023.100790>.  Brunsdon, C.,
Fotheringham, A. S., & Charlton, M. E. (1996). \"Geographically weighted
regression: a method for exploring spatial nonstationarity\", Geographical
Analysis, <doi:10.1111/j.1538-4632.1996.tb00936.x>.  Yau, K. K. W., Wang, K., &
Lee, A. H. (2003). \"Zero-inflated negative binomial mixed regression modeling of
over-dispersed count data with extra zeros\", Biometrical Journal,
<doi:10.1002/bimj.200390024>.")
    (license license:gpl3)))

(define-public r-gwsignif
  (package
    (name "r-gwsignif")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GWsignif" version))
       (sha256
        (base32 "0bss5s3ijnlckz44p7jj49bn2r8nwqckpzwzcv0vci915q8jfsj2"))))
    (properties `((upstream-name . "GWsignif")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GWsignif")
    (synopsis
     "Estimating Genome-Wide Significance for Whole Genome Sequencing Studies, Either Single SNP Tests or Region-Based Tests")
    (description
     "The correlations and linkage disequilibrium between tests can vary as a function
of minor allele frequency thresholds used to filter variants, and also varies
with different choices of test statistic for region-based tests.  Appropriate
genome-wide significance thresholds can be estimated empirically through
permutation on only a small proportion of the whole genome.")
    (license license:gpl2+)))

(define-public r-gwsdat
  (package
    (name "r-gwsdat")
    (version "3.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GWSDAT" version))
       (sha256
        (base32 "0zqln2wnmid2cq5727pkk9lyqs4pb5nml12b5cza1xg0yzp38b6h"))))
    (properties `((upstream-name . "GWSDAT")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-splancs
                             r-sp
                             r-sm
                             r-shinyjs
                             r-shinydashboard
                             r-shinycssloaders
                             r-shiny
                             r-sf
                             r-rhandsontable
                             r-readxl
                             r-raster
                             r-officer
                             r-matrix
                             r-mass
                             r-lubridate
                             r-lattice
                             r-kendall
                             r-geometry
                             r-digest
                             r-deldir))
    (home-page "https://cran.r-project.org/package=GWSDAT")
    (synopsis "GroundWater Spatiotemporal Data Analysis Tool (GWSDAT)")
    (description
     "Shiny application for the analysis of groundwater monitoring data, designed to
work with simple time-series data for solute concentration and ground water
elevation, but can also plot non-aqueous phase liquid (NAPL) thickness if
required.  Also provides the import of a site basemap in GIS shapefile format.")
    (license license:gpl3)))

(define-public r-gwrr
  (package
    (name "r-gwrr")
    (version "0.2-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gwrr" version))
       (sha256
        (base32 "1pn84056fdrrjq8m13671hn4gvz40z0jsi6r5kv5b82fwzj4g57y"))))
    (properties `((upstream-name . "gwrr")))
    (build-system r-build-system)
    (propagated-inputs (list r-lars r-fields))
    (home-page "https://cran.r-project.org/package=gwrr")
    (synopsis
     "Fits Geographically Weighted Regression Models with Diagnostic Tools")
    (description
     "Fits geographically weighted regression (GWR) models and has tools to diagnose
and remediate collinearity in the GWR models.  Also fits geographically weighted
ridge regression (GWRR) and geographically weighted lasso (GWL) models.  See
Wheeler (2009) <doi:10.1068/a40256> and Wheeler (2007) <doi:10.1068/a38325> for
more details.")
    (license license:gpl2+)))

(define-public r-gwrpvr
  (package
    (name "r-gwrpvr")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gwrpvr" version))
       (sha256
        (base32 "0x15nv2pfv99c04lgs6q6hgczarld34xc4aqw9bn179m7j02v6m3"))))
    (properties `((upstream-name . "gwrpvr")))
    (build-system r-build-system)
    (home-page "https://doi.org/10.1101/204727")
    (synopsis "Genome-Wide Regression P-Value (Gwrpv)")
    (description
     "Computes the sample probability value (p-value) for the estimated coefficient
from a standard genome-wide univariate regression.  It computes the exact
finite-sample p-value under the assumption that the measured phenotype (the
dependent variable in the regression) has a known Bernoulli-normal mixture
distribution.  Finite-sample genome-wide regression p-values (Gwrpv) with a
non-normally distributed phenotype (Gregory Connor and Michael O'Neill,
@code{bioRxiv} 204727 <doi:10.1101/204727>).")
    (license license:gpl3)))

(define-public r-gwrm
  (package
    (name "r-gwrm")
    (version "2.1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GWRM" version))
       (sha256
        (base32 "13g91bx7b0cf1rnyc6azkvjilvc3kjynb8nclmbh8n3ccm6b0v2r"))))
    (properties `((upstream-name . "GWRM")))
    (build-system r-build-system)
    (propagated-inputs (list r-foreach r-doparallel))
    (home-page "https://cran.r-project.org/package=GWRM")
    (synopsis "Generalized Waring Regression Model for Count Data")
    (description
     "Statistical functions to fit, validate and describe a Generalized Waring
Regression Model (GWRM).")
    (license (list license:gpl2 license:gpl3))))

(define-public r-gwrlasso
  (package
    (name "r-gwrlasso")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GWRLASSO" version))
       (sha256
        (base32 "172gjp4f3i2na53l04ddsz588qmhfs4fwdnbnvpjvkrc8xsp9yf2"))))
    (properties `((upstream-name . "GWRLASSO")))
    (build-system r-build-system)
    (propagated-inputs (list r-qpdf r-numbers r-matrix r-glmnet))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GWRLASSO")
    (synopsis "Hybrid Model for Spatial Prediction Through Local Regression")
    (description
     "It implements a hybrid spatial model for improved spatial prediction by
combining the variable selection capability of LASSO (Least Absolute Shrinkage
and Selection Operator) with the Geographically Weighted Regression (GWR) model
that captures the spatially varying relationship efficiently.  For method
details see, Wheeler, D.C.(2009).<DOI:10.1068/a40256>.  The developed hybrid
model efficiently selects the relevant variables by using LASSO as the first
step; these selected variables are then incorporated into the GWR framework,
allowing the estimation of spatially varying regression coefficients at unknown
locations and finally predicting the values of the response variable at unknown
test locations while taking into account the spatial heterogeneity of the data.
Integrating the LASSO and GWR models enhances prediction accuracy by considering
spatial heterogeneity and capturing the local relationships between the
predictors and the response variable.  The developed hybrid spatial model can be
useful for spatial modeling, especially in scenarios involving complex spatial
patterns and large datasets with multiple predictor variables.")
    (license license:gpl2+)))

(define-public r-gwqs
  (package
    (name "r-gwqs")
    (version "3.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gWQS" version))
       (sha256
        (base32 "11qs7vxal3br7rg379b5v2w06nvsipk7465c9l5lpifr8g0g561r"))))
    (properties `((upstream-name . "gWQS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlist
                             r-reshape2
                             r-pscl
                             r-plotroc
                             r-nnet
                             r-matrix
                             r-mass
                             r-knitr
                             r-kableextra
                             r-ggrepel
                             r-ggplot2
                             r-future-apply
                             r-future
                             r-cowplot
                             r-car
                             r-broom
                             r-bookdown))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gWQS")
    (synopsis "Generalized Weighted Quantile Sum Regression")
    (description
     "Fits Weighted Quantile Sum (WQS) regression (Carrico et al. (2014)
<doi:10.1007/s13253-014-0180-3>), a random subset implementation of WQS (Curtin
et al. (2019) <doi:10.1080/03610918.2019.1577971>), a repeated holdout
validation WQS (Tanner et al. (2019) <doi:10.1016/j.mex.2019.11.008>) and a WQS
with 2 indices (Renzetti et al. (2023) <doi:10.3389/fpubh.2023.1289579>) for
continuous, binomial, multinomial, Poisson, quasi-Poisson and negative binomial
outcomes.")
    (license license:gpl2+)))

(define-public r-gwpcormapper
  (package
    (name "r-gwpcormapper")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gwpcormapper" version))
       (sha256
        (base32 "0d8skx6rxas5qvhg7ykhymrzd6dawj45421vr66r4wi4hbdfjbzx"))))
    (properties `((upstream-name . "gwpcormapper")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-shinyjs
                             r-shinydashboard
                             r-shiny
                             r-sf
                             r-rcpp
                             r-processx
                             r-plotly
                             r-pkgload
                             r-leaflet
                             r-htmltools
                             r-golem
                             r-glue
                             r-geodist
                             r-dt
                             r-dplyr
                             r-crosstalk
                             r-corpcor
                             r-config
                             r-attempt))
    (home-page "https://github.com/gwpcor/gwpcormapper")
    (synopsis "Geographically Weighted Partial Correlation Mapper")
    (description
     "An interactive mapping tool for geographically weighted correlation and partial
correlation.  Geographically weighted partial correlation coefficients are
calculated following (Percival and Tsutsumida,
2017)<doi:10.1553/giscience2017_01_s36> and are described in greater detail in
(Tsutsumida et al., 2019)<doi:10.5194/ica-abs-1-372-2019> and (Percival et al.,
2021)<@code{arXiv:2101.03491>}.")
    (license license:gpl3)))

(define-public r-gwpcor
  (package
    (name "r-gwpcor")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GWpcor" version))
       (sha256
        (base32 "0q43673pmm2c6kss688fpz10b39y3jn1kblv13p8ky0bk82hmcvi"))))
    (properties `((upstream-name . "GWpcor")))
    (build-system r-build-system)
    (inputs (list proj geos gdal))
    (propagated-inputs (list r-sp
                             r-sf
                             r-pracma
                             r-geodist
                             r-foreach
                             r-dplyr
                             r-doparallel
                             r-corpcor))
    (home-page "https://cran.r-project.org/package=GWpcor")
    (synopsis "Geographically Weighted Partial Correlation Coefficient")
    (description
     "This package implements a geographically weighted partial correlation which is
an extension from gwss() function in the GWmodel package (Percival and
Tsutsumida (2017) <doi:10.1553/giscience2017_01_s36>).")
    (license license:gpl3)))

(define-public r-gwnnegpca
  (package
    (name "r-gwnnegpca")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GWnnegPCA" version))
       (sha256
        (base32 "1yinzlp22z9z5fk9f1nm14i408yfm5kfgkn3pqnh4bkghs569ivg"))))
    (properties `((upstream-name . "GWnnegPCA")))
    (build-system r-build-system)
    (inputs (list proj geos gdal))
    (propagated-inputs (list r-spdata
                             r-sp
                             r-sf
                             r-pracma
                             r-nsprcomp
                             r-geodist))
    (home-page "https://cran.r-project.org/package=GWnnegPCA")
    (synopsis
     "Geographically Weighted Non-Negative Principal Components Analysis")
    (description
     "This package implements a geographically weighted non-negative principal
components analysis, which consists of the fusion of geographically weighted and
sparse non-negative principal components analyses
<doi:10.17608/k6.auckland.9850826.v1>.")
    (license license:gpl2+)))

(define-public r-gwmodel
  (package
    (name "r-gwmodel")
    (version "2.4-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GWmodel" version))
       (sha256
        (base32 "1mlgcmr1s8gn52q32jxj2hfrymrc033w56bx0cm40m0mi657p3fb"))))
    (properties `((upstream-name . "GWmodel")))
    (build-system r-build-system)
    (propagated-inputs (list r-spdep
                             r-spatialreg
                             r-spacetime
                             r-sp
                             r-sf
                             r-robustbase
                             r-rcppeigen
                             r-rcpparmadillo
                             r-rcpp
                             r-fnn))
    (home-page "http://gwr.nuim.ie/")
    (synopsis "Geographically-Weighted Models")
    (description
     "Techniques from a particular branch of spatial statistics,termed
geographically-weighted (GW) models.  GW models suit situations when data are
not described well by some global model, but where there are spatial regions
where a suitably localised calibration provides a better description.  GWmodel
includes functions to calibrate: GW summary statistics (Brunsdon et al.,
2002)<doi: 10.1016/s0198-9715(01)00009-6>, GW principal components analysis
(Harris et al., 2011)<doi: 10.1080/13658816.2011.554838>, GW discriminant
analysis (Brunsdon et al., 2007)<doi: 10.1111/j.1538-4632.2007.00709.x> and
various forms of GW regression (Brunsdon et al., 1996)<doi:
10.1111/j.1538-4632.1996.tb00936.x>; some of which are provided in basic and
robust (outlier resistant) forms.")
    (license license:gpl2+)))

(define-public r-gwlelast
  (package
    (name "r-gwlelast")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GWLelast" version))
       (sha256
        (base32 "1nm4w5m4ca6npmpvh9nb0cnrbzlyv95nvvhgs3pqcrw4igagm4hj"))))
    (properties `((upstream-name . "GWLelast")))
    (build-system r-build-system)
    (propagated-inputs (list r-spgwr
                             r-sp
                             r-glmnet
                             r-geosphere
                             r-foreach
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=GWLelast")
    (synopsis "Geographically Weighted Logistic Elastic Net Regression")
    (description
     "Fit a geographically weighted logistic elastic net regression.  Detailed
explanations can be found in Yoneoka et al. (2016): New algorithm for
constructing area-based index with geographical heterogeneities and variable
selection: An application to gastric cancer screening <doi:10.1038/srep26582>.")
    (license license:expat)))

(define-public r-gwid
  (package
    (name "r-gwid")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gwid" version))
       (sha256
        (base32 "1rkq7pzqinsrjbidmjyjfkcwm2hldk8fnmkp4s222xlbj230ybwv"))))
    (properties `((upstream-name . "gwid")))
    (build-system r-build-system)
    (propagated-inputs (list r-snprelate
                             r-shiny
                             r-rcpproll
                             r-plotly
                             r-piggyback
                             r-matrix
                             r-lattice
                             r-ggplot2
                             r-gdsfmt
                             r-data-table))
    (home-page "https://github.com/soroushmdg/gwid")
    (synopsis "Genome-Wide Identity-by-Descent")
    (description
     "This package provides methods and tools for the analysis of Genome Wide
Identity-by-Descent ('gwid') mapping data, focusing on testing whether there is
a higher occurrence of Identity-By-Descent (IBD) segments around potential
causal variants in cases compared to controls, which is crucial for identifying
rare variants.  To enhance its analytical power, gwid incorporates a Sliding
Window Approach, allowing for the detection and analysis of signals from
multiple Single Nucleotide Polymorphisms (SNPs).")
    (license license:expat)))

(define-public r-gwi
  (package
    (name "r-gwi")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GWI" version))
       (sha256
        (base32 "0gwbidvdzk7b1w880baa338h7cikvlzvqmbmdxca4r83jzzwxlj1"))))
    (properties `((upstream-name . "GWI")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GWI")
    (synopsis "Count and Continuous Generalized Variability Indexes")
    (description
     "Firstly, both functions of the univariate Poisson dispersion index (DI) for
count data and the univariate exponential variation index (VI) for nonnegative
continuous data are performed.  Next, other functions of univariate indexes such
the binomial dispersion index (DIb), the negative binomial dispersion index
(DInb) and the inverse Gaussian variation index (V@code{IiG}) are given.
Finally, we are computed some multivariate versions of these functions such that
the generalized dispersion index (GDI) with its marginal one (MDI) and the
generalized variation index (GVI) with its marginal one (MVI) too.")
    (license license:gpl3)))

(define-public r-gwex
  (package
    (name "r-gwex")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GWEX" version))
       (sha256
        (base32 "17yd7c8qiqkfhzllrcrrf1az0rq1g6xkggp35zyd04idcjz4c6hf"))))
    (properties `((upstream-name . "GWEX")))
    (build-system r-build-system)
    (propagated-inputs (list r-renext
                             r-rcpparmadillo
                             r-rcpp
                             r-nleqslv
                             r-mvtnorm
                             r-mass
                             r-lmomco
                             r-foreach
                             r-fgarch
                             r-envstats
                             r-doparallel
                             r-abind))
    (home-page "https://cran.r-project.org/package=GWEX")
    (synopsis
     "Multi-Site Stochastic Models for Daily Precipitation and Temperature")
    (description
     "Application of multi-site models for daily precipitation and temperature data.
This package is designed for an application to 105 precipitation and 26
temperature gauges located in Switzerland.  It applies fitting procedures and
provides weather generators described in the following references: - Evin, G.,
A.-C. Favre, and B. Hingray. (2018) <doi:10.5194/hess-22-655-2018>. - Evin, G.,
A.-C. Favre, and B. Hingray. (2018) <doi:10.1007/s00704-018-2404-x>.")
    (license license:gpl3)))

(define-public r-gwbr
  (package
    (name "r-gwbr")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gwbr" version))
       (sha256
        (base32 "08yhg56i1xrkmfcyr87gz0w6mzsm0isg3fjm6kmmgsq522cj3jjb"))))
    (properties `((upstream-name . "gwbr")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gwbr")
    (synopsis "Local and Global Beta Regression")
    (description
     "Fit a regression model for when the response variable is presented as a ratio or
proportion.  This adjustment can occur globally, with the same estimate for the
entire study space, or locally, where a beta regression model is fitted for each
region, considering only influential locations for that area.  Da Silva, A. R.
and Lima, A. O. (2017) <doi:10.1016/j.spasta.2017.07.011>.")
    (license license:gpl3)))

(define-public r-gwavr
  (package
    (name "r-gwavr")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gwavr" version))
       (sha256
        (base32 "1hr1d66n5xf7ppmag70lzdnwp3m2jcs1qmh1nzrqq0nbqmqg8nck"))))
    (properties `((upstream-name . "gwavr")))
    (build-system r-build-system)
    (propagated-inputs (list r-whitebox
                             r-units
                             r-tidyr
                             r-terra
                             r-shinywidgets
                             r-shiny
                             r-sf
                             r-scales
                             r-purrr
                             r-promises
                             r-nhdplustools
                             r-miniui
                             r-leaflet-extras
                             r-leaflet
                             r-jsonlite
                             r-httr
                             r-htmlwidgets
                             r-elevatr
                             r-dplyr))
    (home-page "https://github.com/joshualerickson/gwavr/")
    (synopsis "Get Water Attributes Visually in R")
    (description
     "This package provides methods to Get Water Attributes Visually in R ('gwavr').
This allows the user to point and click on areas within the United States and
get back hydrological data, e.g. flowlines, catchments, basin boundaries,
comids, etc.")
    (license license:expat)))

(define-public r-gwasrapidd
  (package
    (name "r-gwasrapidd")
    (version "0.99.17")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gwasrapidd" version))
       (sha256
        (base32 "1fmymw7vm4ana86mwf8figda20l601c7c6nr14444647x8cimlhx"))))
    (properties `((upstream-name . "gwasrapidd")))
    (build-system r-build-system)
    (propagated-inputs (list r-writexl
                             r-urltools
                             r-tidyr
                             r-tibble
                             r-testthat
                             r-stringr
                             r-rlang
                             r-purrr
                             r-progress
                             r-plyr
                             r-pingr
                             r-magrittr
                             r-lubridate
                             r-jsonlite
                             r-httr
                             r-glue
                             r-dplyr
                             r-concatenate
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ramiromagno/gwasrapidd")
    (synopsis "'REST' 'API' Client for the 'NHGRI'-'EBI' 'GWAS' Catalog")
    (description
     "GWAS R API Data Download.  This package provides easy access to the NHGRI'-'EBI
GWAS Catalog data by accessing the REST API
<https://www.ebi.ac.uk/gwas/rest/docs/api/>.")
    (license license:expat)))

(define-public r-gwasinspector
  (package
    (name "r-gwasinspector")
    (version "1.7.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GWASinspector" version))
       (sha256
        (base32 "16q5r3fzli7vml481fl56mzhl0w8bp0bp70rlsw20a6vzhnk5xf2"))))
    (properties `((upstream-name . "GWASinspector")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsqlite
                             r-rmarkdown
                             r-r-utils
                             r-openxlsx
                             r-knitr
                             r-kableextra
                             r-ini
                             r-hash
                             r-gridextra
                             r-ggplot2
                             r-futile-logger
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "http://GWASinspector.com")
    (synopsis "Comprehensive and Easy to Use Quality Control of GWAS Results")
    (description
     "When evaluating the results of a genome-wide association study (GWAS), it is
important to perform a quality control to ensure that the results are valid,
complete, correctly formatted, and, in case of meta-analysis, consistent with
other studies that have applied the same analysis.  This package was developed
to facilitate and streamline this process and provide the user with a
comprehensive report.")
    (license license:gpl3)))

(define-public r-gwasinlps
  (package
    (name "r-gwasinlps")
    (version "2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GWASinlps" version))
       (sha256
        (base32 "1q5gaam554r2wxwsdgxjx6hngs2q850hbrkz8y5248i46pz16pcc"))))
    (properties `((upstream-name . "GWASinlps")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rcpparmadillo
                             r-rcpp
                             r-mombf
                             r-horseshoe
                             r-fastglm))
    (home-page "https://nilotpalsanyal.github.io/GWASinlps/")
    (synopsis
     "Non-Local Prior Based Iterative Variable Selection Tool for Genome-Wide Association Studies")
    (description
     "This package performs variable selection with data from Genome-wide association
studies (GWAS), or other high-dimensional data with continuous, binary or
survival outcomes, combining in an iterative framework the computational
efficiency of the structured screen-and-select variable selection strategy based
on some association learning and the parsimonious uncertainty quantification
provided by the use of non-local priors (see Sanyal et al., 2019
<DOI:10.1093/bioinformatics/bty472>).")
    (license license:gpl2+)))

(define-public r-gwasforest
  (package
    (name "r-gwasforest")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gwasforest" version))
       (sha256
        (base32 "122fg4pc409lvw4yvrc8ijm6d6j29b4aacd319a16caxkkydgwlb"))))
    (properties `((upstream-name . "gwasforest")))
    (build-system r-build-system)
    (propagated-inputs (list r-glue
                             r-ggrepel
                             r-ggplot2
                             r-dplyr
                             r-data-table
                             r-colorspace))
    (home-page "https://github.com/yilixu/gwasforest")
    (synopsis "Make Forest Plot with GWAS Data")
    (description
     "Extract and reform data from GWAS (genome-wide association study) results, and
then make a single integrated forest plot containing multiple windows of which
each shows the result of individual SNPs (or other items of interest).")
    (license license:expat)))

(define-public r-gwasbycluster
  (package
    (name "r-gwasbycluster")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GWASbyCluster" version))
       (sha256
        (base32 "0ivlkxbh8m23vbnjfmhk0vfczd5b8lzy9w4gm1m6jpvdm40kn70x"))))
    (properties `((upstream-name . "GWASbyCluster")))
    (build-system r-build-system)
    (propagated-inputs (list r-snpstats r-rootsolve r-limma r-biobase))
    (home-page "https://cran.r-project.org/package=GWASbyCluster")
    (synopsis
     "Identifying Significant SNPs in Genome Wide Association Studies (GWAS) via Clustering")
    (description
     "Identifying disease-associated significant SNPs using clustering approach.  This
package is implementation of method proposed in Xu et al (2019)
<DOI:10.1038/s41598-019-50229-6>.")
    (license license:gpl2+)))

(define-public r-gwars
  (package
    (name "r-gwars")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gwaRs" version))
       (sha256
        (base32 "1mjwsf1sbrd88psiz6f74zddv1wvhpxk5nxbfjpdrxs74rkc36fs"))))
    (properties `((upstream-name . "gwaRs")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-scales
                             r-rcolorbrewer
                             r-ggrepel
                             r-ggplot2
                             r-dplyr
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/LindoNkambule/gwaRs")
    (synopsis "Manhattan, Q-Q, and PCA Plots using 'ggplot2'")
    (description
     "Generate Manhattan, Q-Q, and PCA plots from GWAS and PCA results using ggplot2'.")
    (license (license:fsdg-compatible "MIT + file LICENCE"))))

(define-public r-gwalkr
  (package
    (name "r-gwalkr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GWalkR" version))
       (sha256
        (base32 "0d6f1br5g28z6qbi41y3sfglci941lzs38hm9cjr1vlxg65dnzav"))))
    (properties `((upstream-name . "GWalkR")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinycssloaders
                             r-shiny
                             r-openssl
                             r-jsonlite
                             r-htmlwidgets
                             r-duckdb
                             r-dbi))
    (home-page "https://github.com/Kanaries/GWalkR/")
    (synopsis "Interactive Exploratory Data Analysis Tool")
    (description
     "Simplify your R data analysis and data visualization workflow by turning your
data frame into an interactive Tableau'-like interface, leveraging the
graphic-walker @code{JavaScript} library and the htmlwidgets package.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-gvlma
  (package
    (name "r-gvlma")
    (version "1.0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gvlma" version))
       (sha256
        (base32 "16dhd407bwjs91c3p9kk43646197s7n9vbyghxb4ckrpv3fsaxp0"))))
    (properties `((upstream-name . "gvlma")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gvlma")
    (synopsis "Global Validation of Linear Models Assumptions")
    (description
     "This package provides methods from the paper: Pena, EA and Slate, EH, \"Global
Validation of Linear Model Assumptions,\" J. American Statistical Association,
101(473):341-354, 2006.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-gvcr
  (package
    (name "r-gvcr")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gvcR" version))
       (sha256
        (base32 "1gp13980yqfyqgjj19445vyva2769wsa1kfq7h7y366g4knygnlp"))))
    (properties `((upstream-name . "gvcR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-supernova
                             r-rlang
                             r-r6
                             r-magrittr
                             r-lme4
                             r-eda4treer
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=gvcR")
    (synopsis "Genotypic Variance Components")
    (description
     "Functionalities to compute model based genetic components i.e.  genotypic
variance, phenotypic variance and heritability for given traits of different
genotypes from replicated data using methodology explained by Burton, G. W. &
Devane, E. H. (1953) (<doi:10.2134/agronj1953.00021962004500100005x>) and
Allard, R.W. (2010, ISBN:8126524154).")
    (license license:gpl3)))

(define-public r-gvc
  (package
    (name "r-gvc")
    (version "6.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gvc" version))
       (sha256
        (base32 "1wwsl0vz801324558qwzy241ws6qics8nrd6yyzkwrfz2kyfmya0"))))
    (properties `((upstream-name . "gvc")))
    (build-system r-build-system)
    (propagated-inputs (list r-diagonals r-decompr))
    (native-inputs (list r-knitr))
    (home-page "https://qua.st/gvc")
    (synopsis "Global Value Chains Tools")
    (description
     "Several tools for Global Value Chain ('GVC') analysis are implemented.")
    (license license:gpl3)))

(define-public r-gvarx
  (package
    (name "r-gvarx")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GVARX" version))
       (sha256
        (base32 "0hafmq8m2pszmmj9yldrbab8670pfrm62x79g8lzac9rmhlz76w2"))))
    (properties `((upstream-name . "GVARX")))
    (build-system r-build-system)
    (propagated-inputs (list r-xts
                             r-vars
                             r-urca
                             r-tsdyn
                             r-strucchange
                             r-sandwich
                             r-lubridate
                             r-lmtest))
    (home-page "https://cran.r-project.org/package=GVARX")
    (synopsis "Perform Global Vector Autoregression Estimation and Inference")
    (description
     "Light procedures for learning Global Vector Autoregression model (GVAR) of
Pesaran, Schuermann and Weiner (2004) <DOI:10.1198/073500104000000019> and Dees,
di Mauro, Pesaran and Smith (2007) <DOI:10.1002/jae.932>.")
    (license license:gpl2+)))

(define-public r-guts
  (package
    (name "r-guts")
    (version "1.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GUTS" version))
       (sha256
        (base32 "1dx760bvc5nkdfhknj8ilqqrxwllzqg03rvg0ghd61xadmb3m64x"))))
    (properties `((upstream-name . "GUTS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GUTS")
    (synopsis
     "Fast Calculation of the Likelihood of a Stochastic Survival Model")
    (description
     "Given exposure and survival time series as well as parameter values, GUTS allows
for the fast calculation of the survival probabilities as well as the logarithm
of the corresponding likelihood (see Albert, C., Vogel, S. and Ashauer, R.
(2016) <doi:10.1371/journal.pcbi.1004978>).")
    (license license:gpl2+)))

(define-public r-gutenbergr
  (package
    (name "r-gutenbergr")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gutenbergr" version))
       (sha256
        (base32 "0hx9mrykkc7jqf5pl3k9y5i7pv8v9pwija8swv6s5p1gbmg56vg6"))))
    (properties `((upstream-name . "gutenbergr")))
    (build-system r-build-system)
    (propagated-inputs (list r-urltools
                             r-stringr
                             r-readr
                             r-purrr
                             r-magrittr
                             r-lazyeval
                             r-glue
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://docs.ropensci.org/gutenbergr/")
    (synopsis
     "Download and Process Public Domain Works from Project Gutenberg")
    (description
     "Download and process public domain works in the Project Gutenberg collection
<https://www.gutenberg.org/>.  Includes metadata for all Project Gutenberg
works, so that they can be searched and retrieved.")
    (license license:gpl2)))

(define-public r-gustave
  (package
    (name "r-gustave")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gustave" version))
       (sha256
        (base32 "0qja3f0925hhaq19xic0x9yygckjnym0daj2fzp36p7rsb4w0ga6"))))
    (properties `((upstream-name . "gustave")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (home-page "https://github.com/InseeFr/gustave")
    (synopsis
     "User-Oriented Statistical Toolkit for Analytical Variance Estimation")
    (description
     "This package provides a toolkit for analytical variance estimation in survey
sampling.  Apart from the implementation of standard variance estimators, its
main feature is to help the sampling expert produce easy-to-use variance
estimation \"wrappers\", where systematic operations (linearization, domain
estimation) are handled in a consistent and transparent way.")
    (license license:gpl3)))

(define-public r-gunsales
  (package
    (name "r-gunsales")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gunsales" version))
       (sha256
        (base32 "02vz16lhym72vvf48yai1g371wygdlmi63a6ylc147l0b69jgw31"))))
    (properties `((upstream-name . "gunsales")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-x13binary
                             r-seasonal
                             r-ggplot2
                             r-dplyr
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gunsales")
    (synopsis
     "Statistical Analysis of Monthly Background Checks of Gun Purchases")
    (description
     "Statistical analysis of monthly background checks of gun purchases for the New
York Times story \"What Drives Gun Sales: Terrorism, Obama and Calls for
Restrictions\" at
<http://www.nytimes.com/interactive/2015/12/10/us/gun-sales-terrorism-obama-restrictions.html?>
is provided.")
    (license (license:fsdg-compatible "Apache License (== 2)"))))

(define-public r-gunit
  (package
    (name "r-gunit")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gunit" version))
       (sha256
        (base32 "10n1mjhb3k0npcp3cmw3pm2035vda3341i0gfxni27xdhggpm0rk"))))
    (properties `((upstream-name . "gunit")))
    (build-system r-build-system)
    (propagated-inputs (list r-units r-tibble r-stringr r-magrittr))
    (home-page "https://github.com/cdmuir/gunit")
    (synopsis "Converts Conductance Units")
    (description
     "For plant physiologists, converts conductance (e.g. stomatal conductance) to
different units: m/s, mol/m^2/s, and umol/m^2/s/Pa.")
    (license license:expat)))

(define-public r-gumboot
  (package
    (name "r-gumboot")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gumboot" version))
       (sha256
        (base32 "12lbnhgcc6l76g6s3bcj46p1s4ddqazwspmkn97ybwcydma44x7m"))))
    (properties `((upstream-name . "gumboot")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-reshape2
                             r-ncdf4
                             r-lubridate
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gumboot")
    (synopsis
     "Bootstrap Analyses of Sampling Uncertainty in Goodness-of-Fit Statistics")
    (description
     "Uses jackknife and bootstrap methods to quantify the sampling uncertainty in
goodness-of-fit statistics.  Full details are in Clark et al. (2021), \"The abuse
of popular performance metrics in hydrologic modeling\", Water Resources
Research, <doi:10.1029/2020WR029001>.")
    (license license:gpl3)))

(define-public r-gumbel
  (package
    (name "r-gumbel")
    (version "1.10-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gumbel" version))
       (sha256
        (base32 "1ifvr284h1pwg2883qhzwi4a52l8sgdji1hk6la1h509aw2agv74"))))
    (properties `((upstream-name . "gumbel")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gumbel")
    (synopsis "The Gumbel-Hougaard Copula")
    (description
     "This package provides probability functions (cumulative distribution and density
functions), simulation function (Gumbel copula multivariate simulation) and
estimation functions (Maximum Likelihood Estimation, Inference For Margins,
Moment Based Estimation and Canonical Maximum Likelihood).")
    (license license:gpl2+)))

(define-public r-guiprofiler
  (package
    (name "r-guiprofiler")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GUIProfiler" version))
       (sha256
        (base32 "10m4d7f2rhw6cmkrnw3jh4iqlkfphf4v7mpfwzw17laq0ncmsx5r"))))
    (properties `((upstream-name . "GUIProfiler")))
    (build-system r-build-system)
    (propagated-inputs (list r-rstudioapi
                             r-rgraphviz
                             r-proftools
                             r-nozzle-r1
                             r-mass
                             r-graph))
    (home-page "https://cran.r-project.org/package=GUIProfiler")
    (synopsis "Graphical User Interface for Rprof()")
    (description
     "Show graphically the results of profiling R functions by tracking their
execution time.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-guiplot
  (package
    (name "r-guiplot")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "guiplot" version))
       (sha256
        (base32 "0w0airy3z9hskpsqsk136ipmj8wn9v9j5n6sicybwm0qvj83fzhj"))))
    (properties `((upstream-name . "guiplot")))
    (build-system r-build-system)
    (propagated-inputs (list r-svglite
                             r-shiny
                             r-rlang
                             r-r6
                             r-magrittr
                             r-jsonlite
                             r-ggplot2
                             r-excelr
                             r-dt))
    (home-page "https://s0521.github.io/guiplot/about/")
    (synopsis "User-Friendly GUI Plotting Tools")
    (description
     "Create a user-friendly plotting GUI for R'.  In addition, one purpose of
creating the R package is to facilitate third-party software to call R for
drawing, for example, Phoenix @code{WinNonlin} software calls R to draw the drug
concentration versus time curve.")
    (license license:expat)))

(define-public r-guilds
  (package
    (name "r-guilds")
    (version "1.4.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GUILDS" version))
       (sha256
        (base32 "0rjlx13shzmamxzqng0qjl5kwzg111lq245y9z6axb3al3ly59ij"))))
    (properties `((upstream-name . "GUILDS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-pracma r-nloptr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/thijsjanzen/GUILDS")
    (synopsis
     "Implementation of Sampling Formulas for the Unified Neutral Model of Biodiversity and Biogeography, with or without Guild Structure")
    (description
     "This package provides a collection of sampling formulas for the unified neutral
model of biogeography and biodiversity.  Alongside the sampling formulas, it
includes methods to perform maximum likelihood optimization of the sampling
formulas, methods to generate data given the neutral model, and methods to
estimate the expected species abundance distribution.  Sampling formulas
included in the GUILDS package are the Etienne Sampling Formula (Etienne 2005),
the guild sampling formula, where guilds are assumed to differ in dispersal
ability (Janzen et al.  2015), and the guilds sampling formula conditioned on
guild size (Janzen et al.  2015).")
    (license license:gpl2)))

(define-public r-guildai
  (package
    (name "r-guildai")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "guildai" version))
       (sha256
        (base32 "1y3m1756450dzcz1m8fdj53p2bw9zhisnp67fr2fhmv6kga37995"))))
    (properties `((upstream-name . "guildai")))
    (build-system r-build-system)
    (inputs (list python))
    (propagated-inputs (list r-yaml
                             r-tibble
                             r-rstudioapi
                             r-rlang
                             r-readr
                             r-rappdirs
                             r-processx
                             r-jsonlite
                             r-dplyr
                             r-config))
    (home-page "https://guildai.github.io/guildai-r/")
    (synopsis "Track Machine Learning Experiments")
    (description
     "Guild AI is an open-source tool for managing machine learning experiments.  It's
for scientists, engineers, and researchers who want to run scripts, compare
results, measure progress, and automate machine learning workflow.  Guild AI is
a light weight, external tool that runs locally.  It works with any framework,
doesn't require any changes to your code, or access to any web services.  Users
can easily record experiment metadata, track model changes, manage experiment
artifacts, tune hyperparameters, and share results.  Guild AI combines features
from Git', SQLite', and Make to provide a lab notebook for machine learning.")
    (license license:asl2.0)))

(define-public r-guidedpls
  (package
    (name "r-guidedpls")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "guidedPLS" version))
       (sha256
        (base32 "1gmprd6a5nyglm36sdyk9dqbgqavf3a7gzjbm5jzb5ybhj9r2w61"))))
    (properties `((upstream-name . "guidedPLS")))
    (build-system r-build-system)
    (propagated-inputs (list r-irlba))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rikenbit/guidedPLS")
    (synopsis
     "Supervised Dimensional Reduction by Guided Partial Least Squares")
    (description
     "Guided partial least squares (guided-PLS) is the combination of partial least
squares by singular value decomposition (PLS-SVD) and guided principal component
analysis (guided-PCA).  For the details of the methods, see the reference
section of @code{GitHub} README.md
<https://github.com/rikenbit/@code{guidedPLS>}.")
    (license license:expat)))

(define-public r-guide
  (package
    (name "r-guide")
    (version "1.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GUIDE" version))
       (sha256
        (base32 "0klaczmn3jnlzyh45yaqlc897irjfk467f4w03awmflaiwan3h6v"))))
    (properties `((upstream-name . "GUIDE")))
    (build-system r-build-system)
    (propagated-inputs (list r-tkrplot r-rpanel))
    (home-page "https://cran.r-project.org/package=GUIDE")
    (synopsis "GUI for DErivatives in R")
    (description
     "This package provides a nice GUI for financial DErivatives in R.")
    (license license:gpl2)))

(define-public r-guest
  (package
    (name "r-guest")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GUEST" version))
       (sha256
        (base32 "045cb9k99mrnx8c6xrshsca6dlyw3wqj57i5j34cjz39g4bmwxl6"))))
    (properties `((upstream-name . "GUEST")))
    (build-system r-build-system)
    (propagated-inputs (list r-xicor r-network r-ggally))
    (home-page "https://cran.r-project.org/package=GUEST")
    (synopsis
     "Graphical Models in Ultrahigh-Dimensional and Error-Prone Data via Boosting Algorithm")
    (description
     "We consider the ultrahigh-dimensional and error-prone data.  Our goal aims to
estimate the precision matrix and identify the graphical structure of the random
variables with measurement error corrected.  We further adopt the estimated
precision matrix to the linear discriminant function to do classification for
multi-label classes.")
    (license license:gpl2)))

(define-public r-guess
  (package
    (name "r-guess")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "guess" version))
       (sha256
        (base32 "198pxi0yipgm9wccpj3y4a0gkibhyxcmb7v5dz7ipzrk44ha5g6j"))))
    (properties `((upstream-name . "guess")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsolnp))
    (native-inputs (list r-knitr))
    (home-page "http://github.com/soodoku/guess")
    (synopsis "Adjust Estimates of Learning for Guessing")
    (description
     "Adjust Estimates of Learning for Guessing.  The package provides standard
guessing correction, and a latent class model that leverages informative
pre-post transitions.  For details of the latent class model, see
<http://gsood.com/research/papers/guess.pdf>.")
    (license license:expat)))

(define-public r-guescini
  (package
    (name "r-guescini")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "guescini" version))
       (sha256
        (base32 "05vz4lvwh01s24cdsklqypkxz64ixrj4la5l5x0jgg8vsshjc727"))))
    (properties `((upstream-name . "guescini")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble))
    (home-page "https://github.com/ramiromagno/guescini")
    (synopsis "Real-Time PCR Data Sets by Guescini et al. (2008)")
    (description
     "Real-time quantitative polymerase chain reaction (@code{qPCR}) data by Guescini
et al. (2008) <doi:10.1186/1471-2105-9-326> in tidy format.  This package
provides two data sets where the amplification efficiency has been modulated:
either by changing the amplification mix concentration, or by increasing the
concentration of @code{IgG}, a PCR inhibitor.  Original raw data files:
<https://static-content.springer.com/esm/art%3A10.1186%2F1471-2105-9-326/@code{MediaObjects/12859_2008_2311_MOESM1_ESM.xls>}
and
<https://static-content.springer.com/esm/art%3A10.1186%2F1471-2105-9-326/@code{MediaObjects/12859_2008_2311_MOESM5_ESM.xls>}.")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-guerry
  (package
    (name "r-guerry")
    (version "1.8.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Guerry" version))
       (sha256
        (base32 "01crjdgq7srn8a22a8cm4cbkxd4b0m7pm4fy5r5839ljy2k4dfs7"))))
    (properties `((upstream-name . "Guerry")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/friendly/Guerry")
    (synopsis
     "Maps, Data and Methods Related to Guerry (1833) \"Moral Statistics of France\"")
    (description
     "Maps of France in 1830, multivariate datasets from A.-M. Guerry and others, and
statistical and graphic methods related to Guerry's \"Moral Statistics of
France\".  The goal is to facilitate the exploration and development of
statistical and graphic methods for multivariate data in a geospatial context of
historical interest.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-gud
  (package
    (name "r-gud")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GUD" version))
       (sha256
        (base32 "0sbimcsclibjgilwy3p7h5lcz9dcvvfi36pcx9zkbdk765w5d5zg"))))
    (properties `((upstream-name . "GUD")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rdpack
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-posterior
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rh8liuqy/Bayesian_modal_regression")
    (synopsis "Bayesian Modal Regression Based on the GUD Family")
    (description
     "This package provides probability density functions and sampling algorithms for
three key distributions from the General Unimodal Distribution (GUD) family: the
Flexible Gumbel (FG) distribution, the Double Two-Piece (DTP) Student-t
distribution, and the Two-Piece Scale (TPSC) Student-t distribution.
Additionally, this package includes a function for Bayesian linear modal
regression, leveraging these three distributions for model fitting.  The details
of the Bayesian modal regression model based on the GUD family can be found at
Liu, Huang, and Bai (2024) <doi:10.1016/j.csda.2024.108012>.")
    (license license:gpl3+)))

(define-public r-guardianapi
  (package
    (name "r-guardianapi")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "guardianapi" version))
       (sha256
        (base32 "17xmmr6pfzhdnmf45jzvk84gbfnmlw1qfv1gqjaw85vgm8b6jn6l"))))
    (properties `((upstream-name . "guardianapi")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-rlang r-jsonlite r-httr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://docs.evanodell.com/guardianapi")
    (synopsis "Access 'The Guardian' Newspaper Open Data API")
    (description
     "Access to The Guardian newspaper's open API
<https://open-platform.theguardian.com/>, containing all articles published in
The Guardian from 1999 to the present, including article text, metadata, tags
and contributor information.  An API key and registration is required.")
    (license license:expat)))

(define-public r-guaguas
  (package
    (name "r-guaguas")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "guaguas" version))
       (sha256
        (base32 "0ngdwg297ndn763vx0qnvcsy5bhsr026hv8hvwgrz5w32mpw252d"))))
    (properties `((upstream-name . "guaguas")))
    (build-system r-build-system)
    (home-page "https://github.com/rivaquiroga/guaguas")
    (synopsis "Nombres Inscritos en Chile (1920 - 2021)")
    (description
     "Datos de nombres inscritos en Chile entre 1920 y 2021, de acuerdo al Servicio de
Registro Civil.  English: Chilean baby names registered from 1920 to 2021 by the
Civil Registry Service.")
    (license license:cc0)))

(define-public r-gtwas
  (package
    (name "r-gtwas")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gtWAS" version))
       (sha256
        (base32 "1kbkz0iwc3gfj982m93m2wdr1xm5rhy70k6bdc7kms9ivh5fh6sh"))))
    (properties `((upstream-name . "gtWAS")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gtWAS")
    (synopsis "Genome and Transcriptome Wide Association Study")
    (description
     "Quantitative trait loci mapping and genome wide association analysis are used to
find candidate molecular marker or region associated with phenotype based on
linkage analysis and linkage disequilibrium.  Gene expression quantitative trait
loci mapping is used to find candidate molecular marker or region associated
with gene expression.  In this package, we applied the method in Liu W. (2011)
<doi:10.1007/s00122-011-1631-7> and Gusev A. (2016) <doi:10.1038/ng.3506> to
genome and transcriptome wide association study, which is aimed at revealing the
association relationship between phenotype and molecular markers, expression
levels, molecular markers nested within different related expression effect and
expression effect nested within different related molecular marker effect.  F
test based on full and reduced model are performed to obtain p value or
likelihood ratio statistic.  The best linear model can be obtained by stepwise
regression analysis.")
    (license license:gpl2+)))

(define-public r-gtsummary
  (package
    (name "r-gtsummary")
    (version "2.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gtsummary" version))
       (sha256
        (base32 "1ihbh5gm029n7g8041l7wrsd1wph7cl4hvx3bmd9669lv9w8476d"))))
    (properties `((upstream-name . "gtsummary")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidyr
                             r-rlang
                             r-lifecycle
                             r-gt
                             r-glue
                             r-dplyr
                             r-cli
                             r-cards))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ddsjoberg/gtsummary")
    (synopsis "Presentation-Ready Data Summary and Analytic Result Tables")
    (description
     "This package creates presentation-ready tables summarizing data sets, regression
models, and more.  The code to create the tables is concise and highly
customizable.  Data frames can be summarized with any function, e.g. mean(),
median(), even user-written functions.  Regression models are summarized and
include the reference rows for categorical variables.  Common regression models,
such as logistic regression and Cox proportional hazards regression, are
automatically identified and the tables are pre-filled with appropriate column
headers.")
    (license license:expat)))

(define-public r-gtrendsr
  (package
    (name "r-gtrendsr")
    (version "1.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gtrendsR" version))
       (sha256
        (base32 "05bfcc77bg2ndl83l0lv7rs4slxcflv9h2pfij8a3j1k9r9lwp2x"))))
    (properties `((upstream-name . "gtrendsR")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-ggplot2 r-curl r-anytime))
    (home-page "https://github.com/PMassicotte/gtrendsR")
    (synopsis "Perform and Display Google Trends Queries")
    (description
     "An interface for retrieving and displaying the information returned online by
Google Trends is provided.  Trends (number of hits) over the time as well as
geographic representation of the results can be displayed.")
    (license license:gpl2+)))

(define-public r-gtreg
  (package
    (name "r-gtreg")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gtreg" version))
       (sha256
        (base32 "1ldlv43hgxb6dzs4d2ha1rd5qyyia5pyih2v7rfhmjdnz9qdwl6d"))))
    (properties `((upstream-name . "gtreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-purrr
                             r-gtsummary
                             r-glue
                             r-forcats
                             r-dplyr
                             r-cli
                             r-broom-helpers))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/shannonpileggi/gtreg")
    (synopsis "Regulatory Tables for Clinical Research")
    (description
     "This package creates tables suitable for regulatory agency submission by
leveraging the gtsummary package as the back end.  Tables can be exported to
HTML, Word, PDF and more.  Highly customized outputs are available by utilizing
existing styling functions from gtsummary as well as custom options designed for
regulatory tables.")
    (license license:gpl3+)))

(define-public r-gtranslate
  (package
    (name "r-gtranslate")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gtranslate" version))
       (sha256
        (base32 "1ag0qkp5dnsj6vnwjyf4lw7kr37xzj5lkkr0b2y24pfx7alb33sv"))))
    (properties `((upstream-name . "gtranslate")))
    (build-system r-build-system)
    (propagated-inputs (list r-rvest r-httr))
    (home-page "https://cran.r-project.org/package=gtranslate")
    (synopsis "Translate Between Different Languages")
    (description
     "The goal of this package is to translate between different languages without any
Google API authentication which is pain and you must pay for the key, This
package is free and lightweight.")
    (license license:expat)))

(define-public r-gto
  (package
    (name "r-gto")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gto" version))
       (sha256
        (base32 "0j8yflfibqa9lypvblb6l25wmwfi5mypr20gp7fqy5hadaliy3d6"))))
    (properties `((upstream-name . "gto")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-rlang r-officer r-magrittr r-gt))
    (home-page "https://cran.r-project.org/package=gto")
    (synopsis "Insert 'gt' Tables into Word Documents")
    (description
     "Insert tables created by the gt R package into Microsoft Word documents.  This
gives users the ability to add to their existing word documents the tables made
in gt using the familiar officer package and syntax from the officeverse'.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-gtheory
  (package
    (name "r-gtheory")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gtheory" version))
       (sha256
        (base32 "1xc1jl69z5csalqk3hx83s53awcl1fjq6p0x2pczhs7dsvwd6lks"))))
    (properties `((upstream-name . "gtheory")))
    (build-system r-build-system)
    (propagated-inputs (list r-lme4))
    (home-page "http://EvaluationDashboard.com")
    (synopsis "Apply Generalizability Theory with R")
    (description
     "Estimates variance components, generalizability coefficients, universe scores,
and standard errors when observed scores contain variation from one or more
measurement facets (e.g., items and raters).")
    (license license:gpl3)))

(define-public r-gtfstools
  (package
    (name "r-gtfstools")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gtfstools" version))
       (sha256
        (base32 "1gdizd3lkli60jkkfs7xd7yak8nc4w9sy21sjikdxbhh3xwks775"))))
    (properties `((upstream-name . "gtfstools")))
    (build-system r-build-system)
    (propagated-inputs (list r-zip
                             r-units
                             r-sfheaders
                             r-sf
                             r-processx
                             r-parallelly
                             r-gtfsio
                             r-data-table
                             r-curl
                             r-cli
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://ipeagit.github.io/gtfstools/")
    (synopsis
     "General Transit Feed Specification (GTFS) Editing and Analysing Tools")
    (description
     "Utility functions to read, manipulate, analyse and write transit feeds in the
General Transit Feed Specification (GTFS) data format.")
    (license license:expat)))

(define-public r-gtfsrouter
  (package
    (name "r-gtfsrouter")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gtfsrouter" version))
       (sha256
        (base32 "1v8b3yr7c7yaavy3ivj7ik2hmbdyjasmkxkbrs3f943m9fbkngwi"))))
    (properties `((upstream-name . "gtfsrouter")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-geodist r-data-table r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/UrbanAnalyst/gtfsrouter")
    (synopsis "Routing with 'GTFS' (General Transit Feed Specification) Data")
    (description
     "Use GTFS (General Transit Feed Specification) data for routing from nominated
start and end stations, for extracting isochrones', and travel times from any
nominated start station to all other stations.")
    (license license:gpl3)))

(define-public r-gtfsio
  (package
    (name "r-gtfsio")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gtfsio" version))
       (sha256
        (base32 "0dzmlq7x8zlvljfgyj5hs7f7ivgml0j4j9fl6zkdbg3dqwmrrysb"))))
    (properties `((upstream-name . "gtfsio")))
    (build-system r-build-system)
    (propagated-inputs (list r-zip r-jsonlite r-fs r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://r-transit.github.io/gtfsio/")
    (synopsis "Read and Write General Transit Feed Specification (GTFS) Files")
    (description
     "This package provides tools for the development of packages related to General
Transit Feed Specification (GTFS) files.  Establishes a standard for
representing GTFS feeds using R data types.  Provides fast and flexible
functions to read and write GTFS feeds while sticking to this standard.  Defines
a basic gtfs class which is meant to be extended by packages that depend on it.
And offers utility functions that support checking the structure of GTFS
objects.")
    (license license:expat)))

(define-public r-gtfs2gps
  (package
    (name "r-gtfs2gps")
    (version "2.1-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gtfs2gps" version))
       (sha256
        (base32 "0g1mklndk3y49h1ydxmzrmiz2098k1w4b053b6dcr8dsj2gga8wr"))))
    (properties `((upstream-name . "gtfs2gps")))
    (build-system r-build-system)
    (propagated-inputs (list r-units
                             r-terra
                             r-sfheaders
                             r-sf
                             r-rcpp
                             r-progressr
                             r-lwgeom
                             r-gtfstools
                             r-future
                             r-furrr
                             r-data-table
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ipeaGIT/gtfs2gps")
    (synopsis "Converting Transport Data from GTFS Format to GPS-Like Records")
    (description
     "Convert general transit feed specification (GTFS) data to global positioning
system (GPS) records in data.table format.  It also has some functions to subset
GTFS data in time and space and to convert both representations to simple
feature format.")
    (license license:expat)))

(define-public r-gtexture
  (package
    (name "r-gtexture")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gtexture" version))
       (sha256
        (base32 "1smlnnqsh1akjb2npn73qskq3dd3ypy40v3vkmqdf4qa2qx8yayk"))))
    (properties `((upstream-name . "gtexture")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rlang
                             r-magrittr
                             r-igraph
                             r-fitscape
                             r-dplyr
                             r-dlookr))
    (home-page "<https://rbarkerclarke.github.io/gtexture/>")
    (synopsis
     "Generalized Application of Co-Occurrence Matrices and Haralick Texture")
    (description
     "Generalizes application of gray-level co-occurrence matrix (GLCM) metrics to
objects outside of images.  The current focus is to apply GLCM metrics to the
study of biological networks and fitness landscapes that are used in studying
evolutionary medicine and biology, particularly the evolution of cancer
resistance.  The package was developed as part of the author's publication in
Physics in Medicine and Biology Barker-Clarke et al. (2023)
<doi:10.1088/1361-6560/ace305>.  A general reference to learn more about
mathematical oncology can be found at Rockne et al. (2019)
<doi:10.1088/1478-3975/ab1a09>.")
    (license license:expat)))

(define-public r-gtextras
  (package
    (name "r-gtextras")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gtExtras" version))
       (sha256
        (base32 "000vwq4k0vrn75vzy6iflqydxl81rgn06a8kqca99gwywcqr3gvv"))))
    (properties `((upstream-name . "gtExtras")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-rlang
                             r-paletteer
                             r-knitr
                             r-htmltools
                             r-gt
                             r-glue
                             r-ggplot2
                             r-fontawesome
                             r-dplyr
                             r-commonmark
                             r-cli))
    (home-page "https://github.com/jthomasmock/gtExtras")
    (synopsis "Extending 'gt' for Beautiful HTML Tables")
    (description
     "This package provides additional functions for creating beautiful tables with
gt'.  The functions are generally wrappers around boilerplate or adding
opinionated niche capabilities and helpers functions.")
    (license license:expat)))

(define-public r-gtexr
  (package
    (name "r-gtexr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gtexr" version))
       (sha256
        (base32 "18g7138mzr4hk5h6k13zkc3vy7pmidva0vplfd6vbgqbm9p79yf8"))))
    (properties `((upstream-name . "gtexr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-httr2
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://rmgpanw.github.io/gtexr/")
    (synopsis "Query the GTEx Portal API")
    (description
     "This package provides a convenient R interface to the Genotype-Tissue Expression
(GTEx) Portal API. For more information on the API, see
<https://gtexportal.org/api/v2/redoc>.")
    (license license:expat)))

(define-public r-gtestsmulti
  (package
    (name "r-gtestsmulti")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gTestsMulti" version))
       (sha256
        (base32 "126731bb0f454x0pr378x6ri71i8nl6i6zcqx9jdv68wj8f4g0zy"))))
    (properties `((upstream-name . "gTestsMulti")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-mass))
    (home-page "https://cran.r-project.org/package=gTestsMulti")
    (synopsis "New Graph-Based Multi-Sample Tests")
    (description
     "New multi-sample tests for testing whether multiple samples are from the same
distribution.  They work well particularly for high-dimensional data.  Song, H.
and Chen, H. (2022) <@code{arXiv:2205.13787>}.")
    (license license:gpl2+)))

(define-public r-gtests
  (package
    (name "r-gtests")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gTests" version))
       (sha256
        (base32 "1h1sd8mrzcniq7rx7frdlxwpnsn8lifng1x99fqq703hs3znl1yq"))))
    (properties `((upstream-name . "gTests")))
    (build-system r-build-system)
    (propagated-inputs (list r-ade4))
    (home-page "https://cran.r-project.org/package=gTests")
    (synopsis "Graph-Based Two-Sample Tests")
    (description
     "Four graph-based tests are provided for testing whether two samples are from the
same distribution.  It works for both continuous data and discrete data.")
    (license license:gpl2+)))

(define-public r-gte
  (package
    (name "r-gte")
    (version "1.2-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gte" version))
       (sha256
        (base32 "0fnrpgfji5f1n8nra0d8xghqgsjil7r2j6xnbmfm7mjafcav4wwk"))))
    (properties `((upstream-name . "gte")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "https://cran.r-project.org/package=gte")
    (synopsis "Generalized Turnbull's Estimator")
    (description
     "Generalized Turnbull's estimator proposed by Dehghan and Duchesne (2011).")
    (license license:gpl2)))

(define-public r-gtdl
  (package
    (name "r-gtdl")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GTDL" version))
       (sha256
        (base32 "1dryw7d9qnsa3wibk9kgkldwb660arzdw024p62a123v7i932jz0"))))
    (properties `((upstream-name . "GTDL")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "https://cran.r-project.org/package=GTDL")
    (synopsis "The Generalized Time-Dependent Logistic Family")
    (description
     "Computes the probability density, survival function, the hazard rate functions
and generates random samples from the GTDL distribution given by Mackenzie, G.
(1996) <doi:10.2307/2348408>.  The likelihood estimates, the randomized quantile
(Louzada, F., et al. (2020) <doi:10.1109/ACCESS.2020.3040525>) residuals and the
normally transformed randomized survival probability (Li,L., et al. (2021)
<doi:10.1002/sim.8852>) residuals are obtained for the GTDL model.")
    (license license:gpl3+)))

(define-public r-gtbasedim
  (package
    (name "r-gtbasedim")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GTbasedIM" version))
       (sha256
        (base32 "0im2s0n08kah2g9yg3f4l8y75jq1674liy6mffkd9xg7z6ad25q5"))))
    (properties `((upstream-name . "GTbasedIM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rweka r-coopgame))
    (home-page "https://cran.r-project.org/package=GTbasedIM")
    (synopsis "Game Theory-Based Influence Measures")
    (description
     "Understanding how features influence a specific response variable becomes
crucial in classification problems, with applications ranging from medical
diagnosis to customer behavior analysis.  This packages provides tools to
compute such an influence measure grounded on game theory concepts.  In
particular, the influence measures presented in Davila-Pena, Saavedra-Nieves,
and Casas-MÃ©ndez (2024) <doi:10.48550/@code{arXiv.2408.02481>} can be obtained.")
    (license license:gpl2)))

(define-public r-gt4ireval
  (package
    (name "r-gt4ireval")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gt4ireval" version))
       (sha256
        (base32 "1d567iz57xqszm45l1wq4n2qrrmyz8ssll1vllfmncb9ibbnx4f3"))))
    (properties `((upstream-name . "gt4ireval")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/julian-urbano/gt4ireval/")
    (synopsis "Generalizability Theory for Information Retrieval Evaluation")
    (description
     "This package provides tools to measure the reliability of an Information
Retrieval test collection.  It allows users to estimate reliability using
Generalizability Theory and map those estimates onto well-known indicators such
as Kendall tau correlation or sensitivity.")
    (license license:expat)))

(define-public r-gt
  (package
    (name "r-gt")
    (version "0.11.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gt" version))
       (sha256
        (base32 "0njzpj57afvbi7jsbpb0j1mgm1nz01bd25668gsmvpd7lajm7ig0"))))
    (properties `((upstream-name . "gt")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-vctrs
                             r-tidyselect
                             r-scales
                             r-sass
                             r-rlang
                             r-reactable
                             r-markdown
                             r-magrittr
                             r-juicyjuice
                             r-htmlwidgets
                             r-htmltools
                             r-glue
                             r-fs
                             r-dplyr
                             r-commonmark
                             r-cli
                             r-bitops
                             r-bigd
                             r-base64enc))
    (home-page "https://gt.rstudio.com")
    (synopsis "Easily Create Presentation-Ready Display Tables")
    (description
     "Build display tables from tabular data with an easy-to-use set of functions.
With its progressive approach, we can construct display tables with a cohesive
set of table parts.  Table values can be formatted using any of the included
formatting functions.  Footnotes and cell styles can be precisely added through
a location targeting system.  The way in which gt handles things for you means
that you don't often have to worry about the fine details.")
    (license license:expat)))

(define-public r-gsynth
  (package
    (name "r-gsynth")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gsynth" version))
       (sha256
        (base32 "1a4s8ghxc5zw295ys8hp22nx93gc1vyakn7r1dcq3ghikh02nzaa"))))
    (properties `((upstream-name . "gsynth")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-mvtnorm
                             r-mass
                             r-lfe
                             r-ggplot2
                             r-ggally
                             r-future
                             r-foreach
                             r-dorng
                             r-doparallel
                             r-abind))
    (home-page "https://yiqingxu.org/packages/gsynth/gsynth_examples.html")
    (synopsis "Generalized Synthetic Control Method")
    (description
     "This package provides causal inference with interactive fixed-effect models.  It
imputes counterfactuals for each treated unit using control group information
based on a linear interactive fixed effects model that incorporates
unit-specific intercepts interacted with time-varying coefficients.  This method
generalizes the synthetic control method to the case of multiple treated units
and variable treatment periods, and improves efficiency and interpretability.
This version supports unbalanced panels and implements the matrix completion
method.")
    (license license:expat)))

(define-public r-gsympoint
  (package
    (name "r-gsympoint")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GsymPoint" version))
       (sha256
        (base32 "0wifxgfj67wf1ax56a6j40vckrydrkw5qj7bk875gr0w0ah03a3g"))))
    (properties `((upstream-name . "GsymPoint")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm r-rsolnp r-rocr))
    (home-page "https://cran.r-project.org/package=GsymPoint")
    (synopsis
     "Estimation of the Generalized Symmetry Point, an Optimal Cutpoint in Continuous Diagnostic Tests")
    (description
     "Estimation of the cutpoint defined by the Generalized Symmetry point in a binary
classification setting based on a continuous diagnostic test or marker.  Two
methods have been implemented to construct confidence intervals for this optimal
cutpoint, one based on the Generalized Pivotal Quantity and the other based on
Empirical Likelihood.  Numerical and graphical outputs for these two methods are
easily obtained.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-gsw
  (package
    (name "r-gsw")
    (version "1.2-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gsw" version))
       (sha256
        (base32 "08lmw719mhdzg4g8384y3fbhm6lx8a1hzz5zdlfqq5xv3n4s974z"))))
    (properties `((upstream-name . "gsw")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "http://teos-10.github.io/GSW-R/")
    (synopsis "Gibbs Sea Water Functions")
    (description
     "This package provides an interface to the Gibbs @code{SeaWater} ('TEOS-10') C
library, version 3.06-16-0 (commit 657216dd4f5ea079b5f0e021a4163e2d26893371',
dated 2022-10-11, available at <https://github.com/TEOS-10/GSW-C>, which stems
from Matlab and other code written by members of Working Group 127 of
SCOR'/'IAPSO (Scientific Committee on Oceanic Research / International
Association for the Physical Sciences of the Oceans).")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-gstsm
  (package
    (name "r-gstsm")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gstsm" version))
       (sha256
        (base32 "1qkq9ghg49p09wx60k68nh871l1xz0qpzsls8lg4b038kngr9q6l"))))
    (properties `((upstream-name . "gstsm")))
    (build-system r-build-system)
    (propagated-inputs (list r-digest))
    (home-page "https://cran.r-project.org/package=gstsm")
    (synopsis "Generalized Spatial-Time Sequence Miner")
    (description
     "Implementations of the algorithms present article Generalized Spatial-Time
Sequence Miner, original title (Castro, Antonio; Borges, Heraldo ; Pacitti,
Esther ; Porto, Fabio ; Coutinho, Rafaelli ; Ogasawara, Eduardo .
GeneralizaÃ§Ã£o de MineraÃ§Ã£o de SequÃªncias Restritas no EspaÃ§o e no Tempo.
In: XXXVI SBBD - SimpÃ³sio Brasileiro de Banco de Dados, 2021
<doi:10.5753/sbbd.2021.17891>).")
    (license license:expat)))

(define-public r-gstream
  (package
    (name "r-gstream")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gStream" version))
       (sha256
        (base32 "1f4xnbavm99yhjmaahazigps41mqlfvvl29kf4fs1yjqr531y36b"))))
    (properties `((upstream-name . "gStream")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gStream")
    (synopsis
     "Graph-Based Sequential Change-Point Detection for Streaming Data")
    (description
     "Uses an approach based on k-nearest neighbor information to sequentially detect
change-points.  Offers analytic approximations for false discovery control given
user-specified average run length.  Can be applied to any type of data
(high-dimensional, non-Euclidean, etc.) as long as a reasonable similarity
measure is available.  See references (1) Chen, H. (2019) Sequential
change-point detection based on nearest neighbors.  The Annals of Statistics,
47(3):1381-1407. (2) Chu, L. and Chen, H. (2018) Sequential change-point
detection for high-dimensional and non-Euclidean data <@code{arXiv:1810.05973>}.")
    (license license:gpl2+)))

(define-public r-gstar
  (package
    (name "r-gstar")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gstar" version))
       (sha256
        (base32 "1582f7jcphb32j3wdc345qa5sgm2xyimlc6r5r5jay7ca5ll8c98"))))
    (properties `((upstream-name . "gstar")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-xts r-reshape2 r-ggplot2 r-dplyr))
    (home-page "https://cran.r-project.org/package=gstar")
    (synopsis "Generalized Space-Time Autoregressive Model")
    (description
     "Multivariate time series analysis based on Generalized Space-Time Autoregressive
Model by Ruchjana et al.(2012) <doi:10.1063/1.4724118>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-gsstda
  (package
    (name "r-gsstda")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GSSTDA" version))
       (sha256
        (base32 "0ah19qf6lfzlw52w43b5qszsxh2yvpz7vnspgvj4qidysbxkxd3q"))))
    (properties `((upstream-name . "GSSTDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-visnetwork
                             r-survival
                             r-devtools
                             r-complexheatmap
                             r-cluster
                             r-circlize))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GSSTDA")
    (synopsis
     "Progression Analysis of Disease with Survival using Topological Data Analysis")
    (description
     "Mapper-based survival analysis with transcriptomics data is designed to carry
out.  Mapper-based survival analysis is a modification of Progression Analysis
of Disease (PAD) where survival data is taken into account in the filtering
function.  More details in: J. Fores-Martos, B. Suay-Garcia, R. Bosch-Romeu,
M.C. Sanfeliu-Alonso, A. Falco, J. Climent, \"Progression Analysis of Disease
with Survival (PAD-S) by @code{SurvMap} identifies different prognostic
subgroups of breast cancer in a large combined set of transcriptomics and
methylation studies\" <doi:10.1101/2022.09.08.507080>.")
    (license license:gpl3)))

(define-public r-gsse
  (package
    (name "r-gsse")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GSSE" version))
       (sha256
        (base32 "034mmxa6kjq5kgikhb5q75viagz5ck9irrjbxm26zq9099qxm13b"))))
    (properties `((upstream-name . "GSSE")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-iso))
    (home-page "https://cran.r-project.org/package=GSSE")
    (synopsis "Genotype-Specific Survival Estimation")
    (description
     "We propose a fully efficient sieve maximum likelihood method to estimate
genotype-specific distribution of time-to-event outcomes under a nonparametric
model.  We can handle missing genotypes in pedigrees.  We estimate the
time-dependent hazard ratio between two genetic mutation groups using B-splines,
while applying nonparametric maximum likelihood estimation to the reference
baseline hazard function.  The estimators are calculated via an
expectation-maximization algorithm.")
    (license license:gpl2+)))

(define-public r-gsrsb
  (package
    (name "r-gsrsb")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gsrsb" version))
       (sha256
        (base32 "0b2dzacd5axd4pfhv0q1gd7hk0fbfj738zdlabzs8dc9737bykiq"))))
    (properties `((upstream-name . "gsrsb")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable r-mvtnorm r-ldbounds))
    (home-page "https://cran.r-project.org/package=gsrsb")
    (synopsis "Group Sequential Refined Secondary Boundary")
    (description
     "This package provides a gate-keeping procedure to test a primary and a secondary
endpoint in a group sequential design with multiple interim looks.  Computations
related to group sequential primary and secondary boundaries.  Refined secondary
boundaries are calculated for a gate-keeping test on a primary and a secondary
endpoint in a group sequential design with multiple interim looks.  The choices
include both the standard boundaries and the boundaries using error spending
functions.  See Tamhane et al. (2018), \"A gatekeeping procedure to test a
primary and a secondary endpoint in a group sequential design with multiple
interim looks\", Biometrics, 74(1), 40-48.")
    (license license:gpl3)))

(define-public r-gsrs
  (package
    (name "r-gsrs")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gsrs" version))
       (sha256
        (base32 "0x5fll59l000dqszy0gmhsfqgpaljc57j1grqg5c7y1p3vji38in"))))
    (properties `((upstream-name . "gsrs")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-foreach r-doparallel))
    (home-page "https://cran.r-project.org/package=gsrs")
    (synopsis "Group-Specific Recommendation System")
    (description
     "This package provides a group-specific recommendation system to use dependency
information from users and items which share similar characteristics under the
singular value decomposition framework.  Refer to paper A Group-Specific
Recommender System <doi:10.1080/01621459.2016.1219261> for the details.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-gspcr
  (package
    (name "r-gspcr")
    (version "0.9.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gspcr" version))
       (sha256
        (base32 "19k07yg3z2p63dgq8hyyqpnq2r23rwbh1wqwna4d4w1bif9vgq8b"))))
    (properties `((upstream-name . "gspcr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-reshape2
                             r-pcamixdata
                             r-nnet
                             r-mlmetrics
                             r-mass
                             r-ggplot2
                             r-factominer
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gspcr")
    (synopsis "Generalized Supervised Principal Component Regression")
    (description
     "Generalization of supervised principal component regression (SPCR; Bair et al.,
2006, <doi:10.1198/016214505000000628>) to support continuous, binary, and
discrete variables as outcomes and predictors (inspired by the superpc R package
<https://cran.r-project.org/package=superpc>).")
    (license license:expat)))

(define-public r-gsparo
  (package
    (name "r-gsparo")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GSparO" version))
       (sha256
        (base32 "0xna2crxqwy8fj0s79rxbdcaz9x912rp1vdwqv1557fsnmfv2yf0"))))
    (properties `((upstream-name . "GSparO")))
    (build-system r-build-system)
    (propagated-inputs (list r-threeway r-ggplot2))
    (home-page "https://cran.r-project.org/package=GSparO")
    (synopsis "Group Sparse Optimization")
    (description
     "Approaches a group sparse solution of an underdetermined linear system.  It
implements the proximal gradient algorithm to solve a lower regularization model
of group sparse learning.  For details, please refer to the paper \"Y. Hu, C. Li,
K. Meng, J. Qin and X. Yang.  Group sparse optimization via l_{p,q}
regularization.  Journal of Machine Learning Research, to appear, 2017\".")
    (license license:gpl2+)))

(define-public r-gsodr
  (package
    (name "r-gsodr")
    (version "4.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GSODR" version))
       (sha256
        (base32 "0zqc0md2h2r0f0h9k8q70kdjnx35lvn8dnmzpxq0vwlkd0vzfyrn"))))
    (properties `((upstream-name . "GSODR")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr r-r-utils r-data-table r-curl
                             r-countrycode))
    (native-inputs (list r-knitr))
    (home-page "https://docs.ropensci.org/GSODR/")
    (synopsis "Global Surface Summary of the Day ('GSOD') Weather Data Client")
    (description
     "This package provides automated downloading, parsing, cleaning, unit conversion
and formatting of Global Surface Summary of the Day ('GSOD') weather data from
the from the USA National Centers for Environmental Information ('NCEI').  Units
are converted from from United States Customary System ('USCS') units to
International System of Units ('SI').  Stations may be individually checked for
number of missing days defined by the user, where stations with too many missing
observations are omitted.  Only stations with valid reported latitude and
longitude values are permitted in the final data.  Additional useful elements,
saturation vapour pressure ('es'), actual vapour pressure ('ea') and relative
humidity ('RH') are calculated from the original data using the improved
August-Roche-Magnus approximation (Alduchov & Eskridge 1996) and included in the
final data set.  The resulting metadata include station identification
information, country, state, latitude, longitude, elevation, weather
observations and associated flags.  For information on the GSOD data from NCEI',
please see the GSOD readme.txt file available from,
<https://www1.ncdc.noaa.gov/pub/data/gsod/readme.txt>.")
    (license license:expat)))

(define-public r-gsmx
  (package
    (name "r-gsmx")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GSMX" version))
       (sha256
        (base32 "1n1d7rixj14ari46snsmi48qfmy00ihmzvayk3hkrp2d2d0xi2gh"))))
    (properties `((upstream-name . "GSMX")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=GSMX")
    (synopsis "Multivariate Genomic Selection")
    (description
     "Estimating trait heritability and handling overfitting.  This package includes a
collection of functions for (1) estimating genetic variance-covariances and
calculate trait heritability; and (2) handling overfitting by calculating the
variance components and the heritability through cross validation.")
    (license license:gpl2+)))

(define-public r-gsmoothr
  (package
    (name "r-gsmoothr")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gsmoothr" version))
       (sha256
        (base32 "00z9852vn5pj04dhl3w36yk0xjawniay6iifw1i7fd8g98mgspxp"))))
    (properties `((upstream-name . "gsmoothr")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gsmoothr")
    (synopsis "Smoothing tools")
    (description
     "This package provides tools rewritten in C for various smoothing tasks.")
    (license license:lgpl2.0+)))

(define-public r-gsmams
  (package
    (name "r-gsmams")
    (version "0.7.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gsMAMS" version))
       (sha256
        (base32 "0n5f1mq81pzbzk1bly8pdbw7ryyb081sg02kc3wiyklhk071la3c"))))
    (properties `((upstream-name . "gsMAMS")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mvtnorm))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Tpatni719/gsMAMS")
    (synopsis "Group Sequential Designs of Multi-Arm Multi-Stage Trials")
    (description
     "It provides functions to generate operating characteristics and to calculate
Sequential Conditional Probability Ratio Tests(SCPRT) efficacy and futility
boundary values along with sample/event size of Multi-Arm Multi-Stage(MAMS)
trials for different outcomes.  The package is based on Jianrong Wu, Yimei Li,
Liang Zhu (2023) <doi:10.1002/sim.9682>, Jianrong Wu, Yimei Li (2023) \"Group
Sequential Multi-Arm Multi-Stage Survival Trial Design with Treatment
Selection\"(Manuscript accepted for publication) and Jianrong Wu, Yimei Li,
Shengping Yang (2023) \"Group Sequential Multi-Arm Multi-Stage Trial Design with
Ordinal Endpoints\"(In preparation).")
    (license license:gpl3)))

(define-public r-gsm
  (package
    (name "r-gsm")
    (version "1.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GSM" version))
       (sha256
        (base32 "04xjs9w4gaszwzxmsr7657ry2ywa9pvpwpczpvinxi8vpj347jbb"))))
    (properties `((upstream-name . "GSM")))
    (build-system r-build-system)
    (propagated-inputs (list r-gtools))
    (home-page "http://projecteuclid.org/euclid.aoas/1215118537")
    (synopsis "Gamma Shape Mixture")
    (description
     "Implementation of a Bayesian approach for estimating a mixture of gamma
distributions in which the mixing occurs over the shape parameter.  This family
provides a flexible and novel approach for modeling heavy-tailed distributions,
it is computationally efficient, and it only requires to specify a prior
distribution for a single parameter.")
    (license license:gpl2+)))

(define-public r-gsloid
  (package
    (name "r-gsloid")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gsloid" version))
       (sha256
        (base32 "1z9fci21870d8z7qsc8hzj3vxlxrr7gxy1vibga3acayxz21lhvs"))))
    (properties `((upstream-name . "gsloid")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/benmarwick/gsloid")
    (synopsis "Global Sea Level and Oxygen Isotope Data")
    (description
     "This package contains published data sets for global benthic d18O data for 0-5.3
Myr <doi:10.1029/2004PA001071> and global sea levels based on marine sediment
core data for 0-800 ka <doi:10.5194/cp-12-1-2016>.")
    (license license:expat)))

(define-public r-gslnls
  (package
    (name "r-gslnls")
    (version "1.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gslnls" version))
       (sha256
        (base32 "1rfzdd9qsiqam5m9f2pi909lchcmmb1ljqiiwd5dpblbk8nqhm82"))))
    (properties `((upstream-name . "gslnls")))
    (build-system r-build-system)
    (inputs (list gsl gsl))
    (propagated-inputs (list r-matrix))
    (native-inputs (list pkg-config gfortran))
    (home-page "https://github.com/JorisChau/gslnls")
    (synopsis "GSL Multi-Start Nonlinear Least-Squares Fitting")
    (description
     "An R interface to nonlinear least-squares optimization with the GNU Scientific
Library (GSL), see M. Galassi et al. (2009, ISBN:0954612078).  The available
trust region methods include the Levenberg-Marquardt algorithm with and without
geodesic acceleration, the Steihaug-Toint conjugate gradient algorithm for large
systems and several variants of Powell's dogleg algorithm.  Multi-start
optimization based on quasi-random samples is implemented using a modified
version of the algorithm in Hickernell and Yuan (1997, OR Transactions).
Bindings are provided to tune a number of parameters affecting the low-level
aspects of the trust region algorithms.  The interface mimics R's nls() function
and returns model objects inheriting from the same class.")
    (license license:lgpl3)))

(define-public r-gsisdecoder
  (package
    (name "r-gsisdecoder")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gsisdecoder" version))
       (sha256
        (base32 "1h7a9j4gsdk8nasfkzfizlarx335ra0j0yz98a2000v2qzm2a5qy"))))
    (properties `((upstream-name . "gsisdecoder")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://github.com/mrcaseb/gsisdecoder")
    (synopsis "High Efficient Functions to Decode NFL Player IDs")
    (description
     "This package provides a set of high efficient functions to decode identifiers of
National Football League players.")
    (license license:expat)))

(define-public r-gsignal
  (package
    (name "r-gsignal")
    (version "0.3-7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gsignal" version))
       (sha256
        (base32 "1chkzv3bx1nas1yc6l5i3hgc8m40n1lm1y3c8577l8sqi89hnhs3"))))
    (properties `((upstream-name . "gsignal")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-pracma))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/gjmvanboxtel/gsignal")
    (synopsis "Signal Processing")
    (description
     "R implementation of the Octave package signal', containing a variety of signal
processing tools, such as signal generation and measurement, correlation and
convolution, filtering, filter design, filter analysis and conversion, power
spectrum analysis, system identification, decimation and sample rate change, and
windowing.")
    (license license:gpl3)))

(define-public r-gsheet
  (package
    (name "r-gsheet")
    (version "0.4.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gsheet" version))
       (sha256
        (base32 "0b8q44cqkjfy6vnffm94czq4ag8xfbxik52026ahq95kcf9v2j91"))))
    (properties `((upstream-name . "gsheet")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-httr))
    (home-page "https://github.com/maxconway/gsheet")
    (synopsis "Download Google Sheets Using Just the URL")
    (description
     "Simple package to download Google Sheets using just the sharing link.
Spreadsheets can be downloaded as a data frame, or as plain text to parse
manually.  Google Sheets is the new name for Google Docs Spreadsheets
<https://www.google.com/sheets/about>.")
    (license license:gpl3)))

(define-public r-gsema
  (package
    (name "r-gsema")
    (version "0.99.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GSEMA" version))
       (sha256
        (base32 "1ynbjm61v1j3m06ixbzil0rxcmvfxpz27dcfiif8bkk9g5mkfs2d"))))
    (properties `((upstream-name . "GSEMA")))
    (build-system r-build-system)
    (propagated-inputs (list r-singscore
                             r-scales
                             r-rdpack
                             r-rcolorbrewer
                             r-progress
                             r-plyr
                             r-pheatmap
                             r-pbapply
                             r-metafor
                             r-limma
                             r-impute
                             r-gsva
                             r-doparallel
                             r-biocparallel
                             r-biobase))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GSEMA")
    (synopsis "Gene Set Enrichment Meta-Analysis")
    (description
     "Performing the different steps of gene set enrichment meta-analysis.  It
provides different functions that allow the application of meta-analysis based
on the combination of effect sizes from different pathways in different studies
to obtain significant pathways that are common to all of them.")
    (license license:gpl2)))

(define-public r-gselection
  (package
    (name "r-gselection")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GSelection" version))
       (sha256
        (base32 "052vh1haxlzbh3c5i24nrwphapik1y8ribmh6q4axd447ggc0ff9"))))
    (properties `((upstream-name . "GSelection")))
    (build-system r-build-system)
    (propagated-inputs (list r-sam r-penalized r-gdata))
    (home-page "https://cran.r-project.org/package=GSelection")
    (synopsis "Genomic Selection")
    (description
     "Genomic selection is a specialized form of marker assisted selection.  The
package contains functions to select important genetic markers and predict
phenotype on the basis of fitted training data using integrated model framework
(Guha Majumdar et.  al. (2019) <doi:10.1089/cmb.2019.0223>) developed by
combining one additive (sparse additive models by Ravikumar et.  al. (2009)
<doi:10.1111/j.1467-9868.2009.00718.x>) and one non-additive (hsic lasso by
Yamada et.  al. (2014) <doi:10.1162/NECO_a_00537>) model.")
    (license license:gpl3)))

(define-public r-gseg
  (package
    (name "r-gseg")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gSeg" version))
       (sha256
        (base32 "0lixlirisnxk29zlrragi3l8j1abb5cl7zh6vhx7l5hjdvh8ldq9"))))
    (properties `((upstream-name . "gSeg")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gSeg")
    (synopsis "Graph-Based Change-Point Detection (g-Segmentation)")
    (description
     "Using an approach based on similarity graph to estimate change-point(s) and the
corresponding p-values.  Can be applied to any type of data (high-dimensional,
non-Euclidean, etc.) as long as a reasonable similarity measure is available.")
    (license license:gpl2+)))

(define-public r-gsed
  (package
    (name "r-gsed")
    (version "2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GSED" version))
       (sha256
        (base32 "1k8axyzk55d8idn65qj3gxqjsk27n2r0q78v7524mg8n3fhxs0wj"))))
    (properties `((upstream-name . "GSED")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rootsolve r-r-utils r-memoise))
    (home-page "https://cran.r-project.org/package=GSED")
    (synopsis "Group Sequential Enrichment Design")
    (description
     "This package provides function to apply \"Group sequential enrichment design
incorporating subgroup selection\" (GSED) method proposed by Magnusson and
Turnbull (2013) <doi:10.1002/sim.5738>.")
    (license license:gpl3)))

(define-public r-gseavis
  (package
    (name "r-gseavis")
    (version "0.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GseaVis" version))
       (sha256
        (base32 "01c5rl05lf9abmasw9w8rxlgl55jr3faiwi1ndx993r3j8xvbcqj"))))
    (properties `((upstream-name . "GseaVis")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-reshape2
                             r-rcolorbrewer
                             r-purrr
                             r-magrittr
                             r-ggsci
                             r-ggrepel
                             r-ggpp
                             r-ggplot2
                             r-dplyr
                             r-dose
                             r-aplot))
    (home-page "https://github.com/junjunlab/GseaVis")
    (synopsis "Implement for 'GSEA' Enrichment Visualization")
    (description
     "Mark your interesting genes on plot and support more parameters to handle your
own gene set enrichment analysis plot.")
    (license license:expat)))

(define-public r-gseasy
  (package
    (name "r-gseasy")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gsEasy" version))
       (sha256
        (base32 "1mvqnyjzi5ik5bqisxpf6a592n9rnkv915kwnis238akwvkspspa"))))
    (properties `((upstream-name . "gsEasy")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-ontologyindex))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gsEasy")
    (synopsis "Gene Set Enrichment Analysis in R")
    (description
     "R-interface to C++ implementation of the rank/score permutation based GSEA test
(Subramanian et al 2005 <doi: 10.1073/pnas.0506580102>).")
    (license license:gpl2+)))

(define-public r-gse
  (package
    (name "r-gse")
    (version "4.2-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GSE" version))
       (sha256
        (base32 "0hxz2m8fn7zkxchvpn0kdw77mxgczff9mk1k0xy2ml50b9vzssm2"))))
    (properties `((upstream-name . "GSE")))
    (build-system r-build-system)
    (propagated-inputs (list r-rrcov
                             r-robustbase
                             r-rcpparmadillo
                             r-rcpp
                             r-mass
                             r-ggplot2
                             r-cellwise))
    (home-page "https://cran.r-project.org/package=GSE")
    (synopsis
     "Robust Estimation in the Presence of Cellwise and Casewise Contamination and Missing Data")
    (description
     "Robust Estimation of Multivariate Location and Scatter in the Presence of
Cellwise and Casewise Contamination and Missing Data.")
    (license license:gpl2+)))

(define-public r-gsdesign2
  (package
    (name "r-gsdesign2")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gsDesign2" version))
       (sha256
        (base32 "0kyj5pv5cr8ljbhzsly70115czhgx5wrpcgpd0jvv2djjf21klh2"))))
    (properties `((upstream-name . "gsDesign2")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-survival
                             r-rcpp
                             r-r2rtf
                             r-npsurvss
                             r-mvtnorm
                             r-gt
                             r-gsdesign
                             r-dplyr
                             r-data-table
                             r-corpcor))
    (native-inputs (list r-knitr))
    (home-page "https://merck.github.io/gsDesign2/")
    (synopsis "Group Sequential Design with Non-Constant Effect")
    (description
     "The goal of @code{gsDesign2} is to enable fixed or group sequential design under
non-proportional hazards.  To enable highly flexible enrollment, time-to-event
and time-to-dropout assumptions, @code{gsDesign2} offers piecewise constant
enrollment, failure rates, and dropout rates for a stratified population.  This
package includes three methods for designs: average hazard ratio, weighted
logrank tests in Yung and Liu (2019) <doi:10.1111/biom.13196>, and
@code{MaxCombo} tests.  Substantial flexibility on top of what is in the
@code{gsDesign} package is intended for selecting boundaries.")
    (license license:gpl3)))

(define-public r-gsdesign
  (package
    (name "r-gsdesign")
    (version "3.6.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gsDesign" version))
       (sha256
        (base32 "1lknw7n2zd9mfr13lyn9ny3d0lgla56hkdvqmgmvkjsv19jpaq5r"))))
    (properties `((upstream-name . "gsDesign")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-tidyr
                             r-tibble
                             r-rlang
                             r-r2rtf
                             r-magrittr
                             r-gt
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://keaven.github.io/gsDesign/")
    (synopsis "Group Sequential Design")
    (description
     "Derives group sequential clinical trial designs and describes their properties.
Particular focus on time-to-event, binary, and continuous outcomes.  Largely
based on methods described in Jennison, Christopher and Turnbull, Bruce W.,
2000, \"Group Sequential Methods with Applications to Clinical Trials\" ISBN:
0-8493-0316-8.")
    (license license:gpl3+)))

(define-public r-gsda
  (package
    (name "r-gsda")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GSDA" version))
       (sha256
        (base32 "1knzibpazyl0mp3c34hak634q4fb5cp250skkwgba92hj7hfhwvq"))))
    (properties `((upstream-name . "GSDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-msigdbr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GSDA")
    (synopsis "Gene Set Distance Analysis (GSDA)")
    (description
     "The gene-set distance analysis of omic data is implemented by generalizing
distance correlations to evaluate the association of a gene set with categorical
and censored event-time variables.")
    (license license:gpl2+)))

(define-public r-gsd
  (package
    (name "r-gsd")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GSD" version))
       (sha256
        (base32 "0qfn797wllkv44d55nhvm9mf4mxnxihckfbcd6l15irildnj9llg"))))
    (properties `((upstream-name . "GSD")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-igraph r-ggplot2 r-ebayesthresh))
    (home-page "https://cran.r-project.org/package=GSD")
    (synopsis "Graph Signal Decomposition")
    (description
     "Graph signals residing on the vertices of a graph have recently gained
prominence in research in various fields.  Many methodologies have been proposed
to analyze graph signals by adapting classical signal processing tools.
Recently, several notable graph signal decomposition methods have been proposed,
which include graph Fourier decomposition based on graph Fourier transform,
graph empirical mode decomposition, and statistical graph empirical mode
decomposition.  This package efficiently implements multiscale analysis
applicable to various fields, and offers an effective tool for visualizing and
decomposing graph signals.  For the detailed methodology, see Ortega et al.
(2018) <doi:10.1109/JPROC.2018.2820126>, Shuman et al. (2013)
<doi:10.1109/MSP.2012.2235192>, Tremblay et al. (2014)
<https://www.eurasip.org/Proceedings/Eusipco/Eusipco2014/HTML/papers/1569922141.pdf>,
and Cho et al. (2024) \"Statistical graph empirical mode decomposition by graph
denoising and boundary treatment\".")
    (license license:gpl2+)))

(define-public r-gscramble
  (package
    (name "r-gscramble")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gscramble" version))
       (sha256
        (base32 "0i62zngk2n4jx9bk378xakzr1fjb2f8p2x4larx38jg511is2lq1"))))
    (properties `((upstream-name . "gscramble")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-readr
                             r-purrr
                             r-magrittr
                             r-glue
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/eriqande/gscramble")
    (synopsis "Simulating Admixed Genotypes Without Replacement")
    (description
     "This package provides a genomic simulation approach for creating biologically
informed individual genotypes from empirical data that 1) samples alleles from
populations without replacement, 2) segregates alleles based on species-specific
recombination rates.  gscramble is a flexible simulation approach that allows
users to create pedigrees of varying complexity in order to simulate admixed
genotypes.  Furthermore, it allows users to track haplotype blocks from the
source populations through the pedigrees.")
    (license license:cc0)))

(define-public r-gscounts
  (package
    (name "r-gscounts")
    (version "0.1-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gscounts" version))
       (sha256
        (base32 "1wcna4bcm38ivmys5hv30b72bvn0ja0n30zhwns94n4v2z1d24k4"))))
    (properties `((upstream-name . "gscounts")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/tobiasmuetze/gscounts")
    (synopsis "Group Sequential Designs with Negative Binomial Outcomes")
    (description
     "Design and analysis of group sequential designs for negative binomial outcomes,
as described by T MÃ¼tze, E Glimm, H Schmidli, T Friede (2018)
<doi:10.1177/0962280218773115>.")
    (license license:gpl2+)))

(define-public r-gscalca
  (package
    (name "r-gscalca")
    (version "0.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gscaLCA" version))
       (sha256
        (base32 "12zksjp0wh95qg7vxh22z652hk35r9sp0qxrms4y1laixa8vr71x"))))
    (properties `((upstream-name . "gscaLCA")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-psych
                             r-progress
                             r-nnet
                             r-mass
                             r-gridextra
                             r-ggplot2
                             r-foreach
                             r-fclust
                             r-fastdummies
                             r-dosnow
                             r-devtools))
    (home-page "https://github.com/hee6904/gscaLCA")
    (synopsis
     "Generalized Structure Component Analysis- Latent Class Analysis & Latent Class Regression")
    (description
     "Execute Latent Class Analysis (LCA) and Latent Class Regression (LCR) by using
Generalized Structured Component Analysis (GSCA).  This is explained in Ryoo,
Park, and Kim (2019) <doi:10.1007/s41237-019-00084-6>.  It estimates the
parameters of latent class prevalence and item response probability in LCA with
a single line comment.  It also provides graphs of item response probabilities.
In addition, the package enables to estimate the relationship between the
prevalence and covariates.")
    (license license:gpl3)))

(define-public r-gsbm
  (package
    (name "r-gsbm")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gsbm" version))
       (sha256
        (base32 "0pvlb2vainsazcyh1713m11q387ykqkj11k8sg0s96ldnzv751lg"))))
    (properties `((upstream-name . "gsbm")))
    (build-system r-build-system)
    (propagated-inputs (list r-softimpute r-rspectra r-matrix r-foreach
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gsbm")
    (synopsis "Estimate Parameters in the Generalized SBM")
    (description
     "Given an adjacency matrix drawn from a Generalized Stochastic Block Model with
missing observations, this package robustly estimates the probabilities of
connection between nodes and detects outliers nodes, as describes in Gaucher,
Klopp and Robin (2019) <@code{arXiv:1911.13122>}.")
    (license license:gpl3)))

(define-public r-gsbdesign
  (package
    (name "r-gsbdesign")
    (version "1.0-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gsbDesign" version))
       (sha256
        (base32 "07a5dpwfw052hx0ryrl54z4w71l9q6grh9cx5nf2h3v0lz45n6bp"))))
    (properties `((upstream-name . "gsbDesign")))
    (build-system r-build-system)
    (propagated-inputs (list r-lattice r-gsdesign r-ggplot2))
    (home-page "https://cran.r-project.org/package=gsbDesign")
    (synopsis "Group Sequential Bayes Design")
    (description
     "Group Sequential Operating Characteristics for Clinical, Bayesian two-arm Trials
with known Sigma and Normal Endpoints, as described in Gerber and Gsponer (2016)
<doi: 10.18637/jss.v069.i11>.")
    (license license:gpl3)))

(define-public r-gsarima
  (package
    (name "r-gsarima")
    (version "0.1-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gsarima" version))
       (sha256
        (base32 "0nmn4hlvhl8dw7pkpf010x9qjd6w8drdlcj47qv0sap8wngrwhzg"))))
    (properties `((upstream-name . "gsarima")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://www.r-project.org")
    (synopsis "Two Functions for Generalized SARIMA Time Series Simulation")
    (description
     "Write SARIMA models in (finite) AR representation and simulate generalized
multiplicative seasonal autoregressive moving average (time) series with Normal
/ Gaussian, Poisson or negative binomial distribution.  The methodology of this
method is described in Briet OJT, Amerasinghe PH, and Vounatsou P (2013)
<doi:10.1371/journal.pone.0065761>.")
    (license license:gpl2+)))

(define-public r-gsaq
  (package
    (name "r-gsaq")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GSAQ" version))
       (sha256
        (base32 "1p1rab2dlh4h9bfg464nhx708p7kc4q01ifccdh5756lq1qh2kzv"))))
    (properties `((upstream-name . "GSAQ")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GSAQ")
    (synopsis "Gene Set Analysis with QTL")
    (description
     "Computation of Quantitative Trait Loci hits in the selected gene set.
Performing gene set validation with Quantitative Trait Loci information.
Performing gene set enrichment analysis with available Quantitative Trait Loci
data and computation of statistical significance value from gene set analysis.
Obtaining the list of Quantitative Trait Loci hit genes along with their
overlapped Quantitative Trait Loci names.")
    (license license:gpl2+)))

(define-public r-gsafishercombined
  (package
    (name "r-gsafishercombined")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GSAfisherCombined" version))
       (sha256
        (base32 "1xhq5nc3kbz3ml9qkdxm6m0c65fybabfhgqlwii2105jl0ll7qy5"))))
    (properties `((upstream-name . "GSAfisherCombined")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GSAfisherCombined")
    (synopsis "Gene Set Analysis with Fisher Combined Method")
    (description
     "This package provides the p-value for a joint test of association between a
phenotype and a set of genetic variants (SNPs) by combining marginal p-values
using the Fisher method.  See Fisher, R.A. (1925,ISBN:0-05-002170-2) Statistical
Methods for Research Workers.")
    (license license:gpl2+)))

(define-public r-gsa-un
  (package
    (name "r-gsa-un")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GSA.UN" version))
       (sha256
        (base32 "1jj9scv9x32zb24114a4sa43xvjmf3gmq8lnfablldjn7p7yx9cv"))))
    (properties `((upstream-name . "GSA.UN")))
    (build-system r-build-system)
    (propagated-inputs (list r-e1071))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GSA.UN")
    (synopsis "Global Sensitivity Analysis Tool")
    (description
     "This package provides a tool to sensitivity analysis using SOBOL (Sobol, 1993)
and AMA (Dell'Oca et al.  2017 <doi:10.5194/hess-21-6219-2017>) indices.  It
allows to identify the most sensitive parameter or parameters of a model.")
    (license license:gpl2)))

(define-public r-grwat
  (package
    (name "r-grwat")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grwat" version))
       (sha256
        (base32 "10sv1kzf9z1c27d99rvxvx1gmhga6rj1mcyfkvyg93gd8i1jwxg9"))))
    (properties `((upstream-name . "grwat")))
    (build-system r-build-system)
    (inputs (list pandoc))
    (propagated-inputs (list r-zoo
                             r-trend
                             r-tidyr
                             r-stringr
                             r-rlang
                             r-rcpp
                             r-r-utils
                             r-mblm
                             r-magrittr
                             r-lubridate
                             r-ggplot2
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/tsamsonov/grwat")
    (synopsis "River Hydrograph Separation and Analysis")
    (description
     "River hydrograph separation and daily runoff time series analysis.  Provides
various filters to separate baseflow and quickflow.  Implements advanced
separation technique by Rets et al. (2022) <doi:10.1134/S0097807822010146> which
involves meteorological data to reveal genetic components of the runoff: ground,
rain, thaw and spring (seasonal thaw).  High-performance C++17 computation,
annually aggregated variables, statistical testing and numerous plotting
functions for high-quality visualization.")
    (license license:expat)))

(define-public r-grto
  (package
    (name "r-grto")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GRTo" version))
       (sha256
        (base32 "1xkcx2agvrpfnmplgaqx70vz303v8rhwnxdyr4hmdlf4h92lbv8i"))))
    (properties `((upstream-name . "GRTo")))
    (build-system r-build-system)
    (propagated-inputs (list r-bootstrap))
    (home-page "https://cran.r-project.org/package=GRTo")
    (synopsis
     "Tools for the Analysis of Gutenberg-Richter Distributions of Earthquake Magnitudes")
    (description
     "Offers functions for the comparison of Gutenberg-Richter b-values.  Several
functions in GRTo are helpful for the assessment of the quality of seismicity
catalogs.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-grt
  (package
    (name "r-grt")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grt" version))
       (sha256
        (base32 "1dw5rjs82r82l6qryyyq9cczm770v4iv60d7pi1nb4b0733ndbd5"))))
    (properties `((upstream-name . "grt")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=grt")
    (synopsis "General Recognition Theory")
    (description
     "This package provides functions to generate and analyze data for psychology
experiments based on the General Recognition Theory.")
    (license license:gpl2+)))

(define-public r-grsxe
  (package
    (name "r-grsxe")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GRSxE" version))
       (sha256
        (base32 "0wzaddhqxygw7p81ni2dl2fwr5kwb4qjar126n9mq8n6bkbvq2yk"))))
    (properties `((upstream-name . "GRSxE")))
    (build-system r-build-system)
    (propagated-inputs (list r-ranger r-glmnet))
    (home-page "https://cran.r-project.org/package=GRSxE")
    (synopsis
     "Testing Gene-Environment Interactions Through Genetic Risk Scores")
    (description
     "Statistical testing procedures for detecting @code{GxE} (gene-environment)
interactions.  The main focus lies on GR@code{SxE} interaction tests that aim at
detecting @code{GxE} interactions through GRS (genetic risk scores).  Moreover,
a novel testing procedure based on bagging and OOB (out-of-bag) predictions is
implemented for incorporating all available observations at both GRS
construction and @code{GxE} testing (Lau et al., 2023,
<doi:10.1038/s41598-023-28172-4>).")
    (license license:expat)))

(define-public r-grshiny
  (package
    (name "r-grshiny")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GRShiny" version))
       (sha256
        (base32 "0i8qcqk97lm2pwjl2s6lfa4p6c69p8xl3q88bpn706ib687f5j3x"))))
    (properties `((upstream-name . "GRShiny")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-sirt
                             r-shinywidgets
                             r-shiny
                             r-sass
                             r-readr
                             r-purrr
                             r-openxlsx
                             r-officer
                             r-mirt
                             r-mass
                             r-magrittr
                             r-lavaan
                             r-gt
                             r-ggplot2
                             r-flextable
                             r-dt
                             r-dplyr
                             r-data-table
                             r-bslib))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sooyongl/GRShiny")
    (synopsis "Graded Response Model")
    (description
     "Simulation and analysis of graded response data with different types of
estimators.  Also, an interactive shiny application is provided with graphics
for characteristic and information curves.  Samejima (2018)
<doi:10.1007/978-1-4757-2691-6_5>.")
    (license license:gpl3+)))

(define-public r-grs-test
  (package
    (name "r-grs-test")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GRS.test" version))
       (sha256
        (base32 "1g560n81kqf81n1z3s4yxl24r386q21avjknz6msqzp4xhxhr4l6"))))
    (properties `((upstream-name . "GRS.test")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GRS.test")
    (synopsis
     "GRS Test for Portfolio Efficiency, Its Statistical Power Analysis, and Optimal Significance Level Calculation")
    (description
     "Computational resources for test proposed by Gibbons, Ross, Shanken
(1989)<DOI:10.2307/1913625>.  It also has the functions for the power analysis
and the choice of the optimal level of significance.  The optimal level is
determined by minimizing the expected loss from hypothesis testing.")
    (license license:gpl2)))

(define-public r-grpstring
  (package
    (name "r-grpstring")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GrpString" version))
       (sha256
        (base32 "14ss6assqk6s9kv4bvzlydm8qrbnslm4vj876q5dn2dw4k50dai1"))))
    (properties `((upstream-name . "GrpString")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-plyr r-cluster))
    (home-page "https://cran.r-project.org/package=GrpString")
    (synopsis
     "Patterns and Statistical Differences Between Two Groups of Strings")
    (description
     "This package provides methods include converting series of event names to
strings, finding common patterns in a group of strings, discovering featured
patterns when comparing two groups of strings as well as the number and starting
position of each pattern in each string, obtaining transition matrix, computing
transition entropy, statistically comparing the difference between two groups of
strings, and clustering string groups.  Event names can be any action names or
labels such as events in log files or areas of interest (AOIs) in eye tracking
research.")
    (license license:gpl2)))

(define-public r-grpslope
  (package
    (name "r-grpslope")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grpSLOPE" version))
       (sha256
        (base32 "05417f0pnp21svi30vcbkkw16zyg1kxynfigh5w2jdjmd12cb899"))))
    (properties `((upstream-name . "grpSLOPE")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/agisga/grpSLOPE")
    (synopsis "Group Sorted L1 Penalized Estimation")
    (description
     "Group SLOPE (Group Sorted L1 Penalized Estimation) is a penalized linear
regression method that is used for adaptive selection of groups of significant
predictors in a high-dimensional linear model.  The Group SLOPE method can
control the (group) false discovery rate at a user-specified level (i.e.,
control the expected proportion of irrelevant among all selected groups of
predictors).  For additional information about the implemented methods please
see Brzyski, Gossmann, Su, Bogdan (2018) <doi:10.1080/01621459.2017.1411269>.")
    (license license:gpl3)))

(define-public r-grpseq
  (package
    (name "r-grpseq")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grpseq" version))
       (sha256
        (base32 "187c3y3rsls1a66phb6kvndfh0rz7h8kl6w10gi9k6r8q6isvl9i"))))
    (properties `((upstream-name . "grpseq")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (native-inputs (list r-knitr))
    (home-page "https://sites.google.com/view/lmaowisc/")
    (synopsis "Group Sequential Analysis of Clinical Trials")
    (description
     "Design of group sequential trials, including non-binding futility analysis at
multiple time points (Gallo, Mao, and Shih, 2014,
<doi:10.1080/10543406.2014.932285>).")
    (license license:gpl2+)))

(define-public r-grpsel
  (package
    (name "r-grpsel")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grpsel" version))
       (sha256
        (base32 "0b901si4xm6306mzfk3nmwg2h8pk9rhvv2m08v3qnbnxaw06my2h"))))
    (properties `((upstream-name . "grpsel")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ryan-thompson/grpsel")
    (synopsis "Group Subset Selection")
    (description
     "This package provides tools for sparse regression modelling with grouped
predictors using the group subset selection penalty.  Uses coordinate descent
and local search algorithms to rapidly deliver near optimal estimates.  The
group subset penalty can be combined with a group lasso or ridge penalty for
added shrinkage.  Linear and logistic regression are supported, as are
overlapping groups.")
    (license license:gpl3)))

(define-public r-grpreg
  (package
    (name "r-grpreg")
    (version "3.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grpreg" version))
       (sha256
        (base32 "0344k07xy64byfp01qkp5fmwykl8nflz2l6zkk613f8bxqd342qn"))))
    (properties `((upstream-name . "grpreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (native-inputs (list r-knitr))
    (home-page "https://pbreheny.github.io/grpreg/")
    (synopsis
     "Regularization Paths for Regression Models with Grouped Covariates")
    (description
     "Efficient algorithms for fitting the regularization path of linear regression,
GLM, and Cox regression models with grouped penalties.  This includes group
selection methods such as group lasso, group MCP, and group SCAD as well as
bi-level selection methods such as the group exponential lasso, the composite
MCP, and the group bridge.  For more information, see Breheny and Huang (2009)
<doi:10.4310/sii.2009.v2.n3.a10>, Huang, Breheny, and Ma (2012)
<doi:10.1214/12-sts392>, Breheny and Huang (2015)
<doi:10.1007/s11222-013-9424-2>, and Breheny (2015) <doi:10.1111/biom.12300>, or
visit the package homepage <https://pbreheny.github.io/grpreg/>.")
    (license license:gpl3)))

(define-public r-grpnet
  (package
    (name "r-grpnet")
    (version "0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grpnet" version))
       (sha256
        (base32 "0ivkk669ycgghmbqzbkg6nz94y9gicixnyxd0np4bmrv62knz1h2"))))
    (properties `((upstream-name . "grpnet")))
    (build-system r-build-system)
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=grpnet")
    (synopsis "Group Elastic Net Regularized GLMs and GAMs")
    (description
     "Efficient algorithms for fitting generalized linear and additive models with
group elastic net penalties as described in Helwig (2024)
<doi:10.1080/10618600.2024.2362232>.  Implements group LASSO, group MCP, and
group SCAD with an optional group ridge penalty.  Computes the regularization
path for linear regression (gaussian), logistic regression (binomial),
multinomial logistic regression (multinomial), log-linear count regression
(poisson and negative.binomial), and log-linear continuous regression (gamma and
inverse gaussian).  Supports default and formula methods for model
specification, k-fold cross-validation for tuning the regularization parameters,
and nonparametric regression via tensor product reproducing kernel (smoothing
spline) basis function expansion.")
    (license license:gpl2+)))

(define-public r-grplasso
  (package
    (name "r-grplasso")
    (version "0.4-7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grplasso" version))
       (sha256
        (base32 "0jf48ahc6d6biwx235dlvbk1papxkqbcnvxsr93p5047pjm02y3a"))))
    (properties `((upstream-name . "grplasso")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=grplasso")
    (synopsis "Fitting User-Specified Models with Group Lasso Penalty")
    (description "Fits user-specified (GLM-) models with group lasso penalty.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-grpipe
  (package
    (name "r-grpipe")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grPipe" version))
       (sha256
        (base32 "0m9zxnmg8gfzridzk4lhhp1gw3nrygarn90ppwyi67bqdpp5hkfh"))))
    (properties `((upstream-name . "grPipe")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsvg
                             r-png
                             r-magrittr
                             r-dplyr
                             r-diagrammersvg
                             r-diagrammer))
    (home-page "https://cran.r-project.org/package=grPipe")
    (synopsis
     "Graphviz Pipeline Plot Based on Grids (grPipe: Graphviz Pipeline)")
    (description
     "Create a grid-based graphviz using the following functions: 1 - Creating the
data.frame where the nodes are; 2 - Adding and editing nodes; 3 - Plotting these
nodes.")
    (license license:gpl3+)))

(define-public r-grpcox
  (package
    (name "r-grpcox")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grpCox" version))
       (sha256
        (base32 "02x98zmaa7f73qv62wzr0vyyci7kcx7mmyw7cm3inlyphlkywvlw"))))
    (properties `((upstream-name . "grpCox")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp r-matrix r-mass r-colorspace))
    (home-page "https://cran.r-project.org/package=grpCox")
    (synopsis
     "Penalized Cox Model for High-Dimensional Data with Grouped Predictors")
    (description
     "Fit the penalized Cox models with both non-overlapping and overlapping grouped
penalties including the group lasso, group smoothly clipped absolute deviation,
and group minimax concave penalty.  The algorithms combine the MM approach and
group-wise descent with some computational tricks including the screening,
active set, and warm-start.  Different tuning regularization parameter methods
are provided.")
    (license license:gpl2+)))

(define-public r-growthrates
  (package
    (name "r-growthrates")
    (version "0.8.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "growthrates" version))
       (sha256
        (base32 "04q8psz4fiibjj0pl6n7wkq83qn1aizzixww8gqdxzzsq6kay81q"))))
    (properties `((upstream-name . "growthrates")))
    (build-system r-build-system)
    (propagated-inputs (list r-lattice r-fme r-desolve))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/tpetzoldt/growthrates")
    (synopsis "Estimate Growth Rates from Experimental Data")
    (description
     "This package provides a collection of methods to determine growth rates from
experimental data, in particular from batch experiments and plate reader trials.")
    (license license:gpl2+)))

(define-public r-growthrate
  (package
    (name "r-growthrate")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "growthrate" version))
       (sha256
        (base32 "1ak3yqlm7dnkdjlmikwa57qnf7yd9n1ixz36gv3shr252750x9cd"))))
    (properties `((upstream-name . "growthrate")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-matrix r-clime))
    (home-page "https://cran.r-project.org/package=growthrate")
    (synopsis "Bayesian reconstruction of growth velocity")
    (description
     "This package provides a nonparametric empirical Bayes method for recovering
gradients (or growth velocities) from observations of smooth functions (e.g.,
growth curves) at isolated time points.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-growthpheno
  (package
    (name "r-growthpheno")
    (version "2.1.25")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "growthPheno" version))
       (sha256
        (base32 "1bz1411ka1pghaa67ad0jkdzykadd64wzn8g37gp7z8zm1ablhzh"))))
    (properties `((upstream-name . "growthPheno")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringi
                             r-reshape
                             r-readxl
                             r-rcolorbrewer
                             r-jops
                             r-hmisc
                             r-ggplot2
                             r-ggally
                             r-dae))
    (native-inputs (list r-r-rsp))
    (home-page "http://chris.brien.name/")
    (synopsis
     "Functional Analysis of Phenotypic Growth Data to Smooth and Extract Traits")
    (description
     "Assists in the plotting and functional smoothing of traits measured over time
and the extraction of features from these traits, implementing the SET
(Smoothing and Extraction of Traits) method described in Brien et al. (2020)
Plant Methods, 16.  Smoothing of growth trends for individual plants using
natural cubic smoothing splines or P-splines is available for removing transient
effects and segmented smoothing is available to deal with discontinuities in
growth trends.  There are graphical tools for assessing the adequacy of trait
smoothing, both when using this and other packages, such as those that fit
nonlinear growth models.  A range of per-unit (plant, pot, plot) growth traits
or features can be extracted from the data, including single time points,
interval growth rates and other growth statistics, such as maximum growth or
days to maximum growth.  The package also has tools adapted to inputting data
from high-throughput phenotyping facilities, such from a Lemna-Tec Scananalyzer
3D (see <https://www.youtube.com/watch?v=MRAF_@code{mAEa7E/>} for more
information).  The package @code{growthPheno} can also be installed from
<http://chris.brien.name/rpackages/>.")
    (license license:gpl2+)))

(define-public r-growthmodels
  (package
    (name "r-growthmodels")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "growthmodels" version))
       (sha256
        (base32 "1lryxdjq5ns08sqfcrsi9q2h399xr6lhxgh7kasx6pajly35vg1r"))))
    (properties `((upstream-name . "growthmodels")))
    (build-system r-build-system)
    (home-page "https://github.com/drodriguezperez/growthmodels")
    (synopsis "Nonlinear Growth Models")
    (description
     "This package provides a compilation of nonlinear growth models.")
    (license license:gpl3)))

(define-public r-growthcurver
  (package
    (name "r-growthcurver")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "growthcurver" version))
       (sha256
        (base32 "1l3dqcl395nh406s7d5554r5ynk1a7v5ba5kmnv9r7lj7h9nfdjj"))))
    (properties `((upstream-name . "growthcurver")))
    (build-system r-build-system)
    (propagated-inputs (list r-minpack-lm))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sprouffske/growthcurver")
    (synopsis "Simple Metrics to Summarize Growth Curves")
    (description
     "Fits the logistic equation to microbial growth curve data (e.g., repeated
absorbance measurements taken from a plate reader over time).  From this fit, a
variety of metrics are provided, including the maximum growth rate, the doubling
time, the carrying capacity, the area under the logistic curve, and the time to
the inflection point.  Method described in Sprouffske and Wagner (2016)
<doi:10.1186/s12859-016-1016-7>.")
    (license license:gpl2+)))

(define-public r-growthcleanr
  (package
    (name "r-growthcleanr")
    (version "2.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "growthcleanr" version))
       (sha256
        (base32 "0wlpaqjw44xa7z95ngiihgvmcrif6d2j7mfzjlzjgc04sjsb6y1v"))))
    (properties `((upstream-name . "growthcleanr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-r-utils
                             r-plyr
                             r-magrittr
                             r-labelled
                             r-foreach
                             r-dplyr
                             r-doparallel
                             r-data-table))
    (home-page "https://carriedaymont.github.io/growthcleanr/index.html")
    (synopsis "Data Cleaner for Anthropometric Measurements")
    (description
     "Identifies implausible anthropometric (e.g., height, weight) measurements in
irregularly spaced longitudinal datasets, such as those from electronic health
records.")
    (license license:expat)))

(define-public r-growth
  (package
    (name "r-growth")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "growth" version))
       (sha256
        (base32 "05yxjlbv1i4ly8yp3aqbxzq1z2mp7sfx7xyqw48qsdv0acqai7jy"))))
    (properties `((upstream-name . "growth")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmutil))
    (native-inputs (list gfortran))
    (home-page "http://www.commanster.eu/rcode.html")
    (synopsis
     "Multivariate Normal and Elliptically-Contoured Repeated Measurements Models")
    (description
     "This package provides functions for fitting various normal theory (growth curve)
and elliptically-contoured repeated measurements models with ARMA and random
effects dependence.")
    (license license:gpl2+)))

(define-public r-growr
  (package
    (name "r-growr")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "growR" version))
       (sha256
        (base32 "0z2kz7gd5grpb5kan6j6nbjsgdii7is4bwyx3jzsziq74q239fig"))))
    (properties `((upstream-name . "growR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-rdpack r-r6))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/kuadrat/growR")
    (synopsis "Implementation of the Vegetation Model ModVege")
    (description
     "Run grass growth simulations using a grass growth model based on @code{ModVege}
(Jouven, M., P. CarrÃ¨re, and R. Baumont \"Model Predicting Dynamics of Biomass,
Structure and Digestibility of Herbage in Managed Permanent Pastures.  1.  Model
Description.\" (2006) <doi:10.1111/j.1365-2494.2006.00515.x>).  The
implementation in this package contains a few additions to the above cited
version of @code{ModVege}, such as simulations of management decisions, and
influences of snow cover.  As such, the model is fit to simulate grass growth in
mountainous regions, such as the Swiss Alps.  The package also contains routines
for calibrating the model and helpful tools for analysing model outputs and
performance.")
    (license license:expat)))

(define-public r-growfunctions
  (package
    (name "r-growfunctions")
    (version "0.16")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "growfunctions" version))
       (sha256
        (base32 "1v1sawv9fbb6lw4ynjdkj02z8rv5k8pkw55wmg2ws3jisy3xrcm7"))))
    (properties `((upstream-name . "growfunctions")))
    (build-system r-build-system)
    (propagated-inputs (list r-spam r-reshape2 r-rcpparmadillo r-rcpp
                             r-ggplot2))
    (home-page "https://cran.r-project.org/package=growfunctions")
    (synopsis
     "Bayesian Non-Parametric Dependent Models for Time-Indexed Functional Data")
    (description
     "Estimates a collection of time-indexed functions under either of Gaussian
process (GP) or intrinsic Gaussian Markov random field (@code{iGMRF}) prior
formulations where a Dirichlet process mixture allows sub-groupings of the
functions to share the same covariance or precision parameters.  The GP and
@code{iGMRF} formulations both support any number of additive covariance or
precision terms, respectively, expressing either or both of multiple trend and
seasonality.")
    (license license:gpl3+)))

(define-public r-grove
  (package
    (name "r-grove")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grove" version))
       (sha256
        (base32 "1flqjkmvl4wmz53hckn9j9w4lg4kjrjbx98hzlfkzc9ikkp68d7z"))))
    (properties `((upstream-name . "grove")))
    (build-system r-build-system)
    (propagated-inputs (list r-wavethresh r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=grove")
    (synopsis "Wavelet Functional ANOVA Through Markov Groves")
    (description
     "Functional denoising and functional ANOVA through wavelet-domain Markov groves.
Fore more details see: Ma L. and Soriano J. (2018) Efficient functional ANOVA
through wavelet-domain Markov groves. <@code{arXiv:1602.03990v2} [stat.ME]>.")
    (license license:gpl3+)))

(define-public r-groupwqs
  (package
    (name "r-groupwqs")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "groupWQS" version))
       (sha256
        (base32 "1c5yrsfgkhbg1fcjbrilfmf0phjry5z34536yfizc8wbc7mmmj0c"))))
    (properties `((upstream-name . "groupWQS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsolnp r-rjags r-mass r-glm2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=groupWQS")
    (synopsis "Grouped Weighted Quantile Sum Regression")
    (description
     "Fits weighted quantile sum (WQS) regressions for one or more chemical groups
with continuous or binary outcomes.  Wheeler D, Czarnota J.(2016)
<doi:10.1289/isee.2016.4698>.")
    (license license:gpl3)))

(define-public r-groupwalk
  (package
    (name "r-groupwalk")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "groupwalk" version))
       (sha256
        (base32 "1nhz9lyh863krxqqi189zpng12y658615w372km98scn1lwl6fi9"))))
    (properties `((upstream-name . "groupwalk")))
    (build-system r-build-system)
    (home-page "https://www.biorxiv.org/content/10.1101/2022.01.30.478144v1")
    (synopsis "Implement the Group Walk Algorithm")
    (description
     "This package provides a procedure that uses target-decoy competition (or
knockoffs) to reject multiple hypotheses in the presence of group structure.
The procedure controls the false discovery rate (FDR) at a user-specified
threshold.")
    (license license:expat)))

(define-public r-grouptesting
  (package
    (name "r-grouptesting")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "groupTesting" version))
       (sha256
        (base32 "10scrwi5g2i4g32wfksfhmmbhiddcnj4khh7ls32kx2m4lhmll53"))))
    (properties `((upstream-name . "groupTesting")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma r-bingroup2))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=groupTesting")
    (synopsis "Simulating and Modeling Group (Pooled) Testing Data")
    (description
     "This package provides an expectation-maximization (EM) algorithm using the
approach introduced in Warasi (2023) <doi:10.1080/03610918.2021.2009867>.  The
EM algorithm can be used to estimate the prevalence (overall proportion) of a
disease and to estimate a binary regression model from among the class of
generalized linear models based on group testing data.  The estimation framework
we consider offers a flexible and general approach; i.e., its application is not
limited to any specific group testing protocol.  Consequently, the EM algorithm
can model data arising from simple pooling as well as advanced pooling such as
hierarchical testing, array testing, and quality control pooling.  Also,
provided are functions that can be used to conduct the Wald tests described in
Buse (1982) <doi:10.1080/00031305.1982.10482817> and to simulate the group
testing data described in Kim et al. (2007)
<doi:10.1111/j.1541-0420.2007.00817.x>.  We offer a function to compute relative
efficiency measures, which can be used to optimize the maximum likelihood
estimator of disease prevalence.")
    (license license:gpl3)))

(define-public r-grouptest
  (package
    (name "r-grouptest")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GroupTest" version))
       (sha256
        (base32 "1v2230mw0irsr5y8n45g8sd362jp7f6dy2r532mhflfdqy6i2khs"))))
    (properties `((upstream-name . "GroupTest")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GroupTest")
    (synopsis "Multiple Testing Procedure for Grouped Hypotheses")
    (description
     "This package contains functions for a two-stage multiple testing procedure for
grouped hypothesis, aiming at controlling both the total posterior false
discovery rate and within-group false discovery rate.")
    (license license:gpl3)))

(define-public r-groupseq
  (package
    (name "r-groupseq")
    (version "1.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GroupSeq" version))
       (sha256
        (base32 "0051sihgnr0m60rkx3mqk2gsp2niff7bmd934czbxzrm3ki5jp6n"))))
    (properties `((upstream-name . "GroupSeq")))
    (build-system r-build-system)
    (propagated-inputs (list r-tcltk2 r-mvtnorm))
    (native-inputs (list r-knitr))
    (home-page "https://rpahl.github.io/GroupSeq/")
    (synopsis
     "Group Sequential Design Probabilities - With Graphical User Interface")
    (description
     "Computes probabilities related to group sequential designs for normally
distributed test statistics.  Enables to derive critical boundaries, power,
drift, and confidence intervals of such designs.  Supports the alpha spending
approach by Lan-@code{DeMets} (1994) <doi:10.1002/sim.4780131308>.")
    (license license:gpl3)))

(define-public r-grouprar
  (package
    (name "r-grouprar")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grouprar" version))
       (sha256
        (base32 "0n7pd93a3v3ii85g2ms02zdvy8rkb0j8kfp1bvmfy71kd1ghc7d5"))))
    (properties `((upstream-name . "grouprar")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-stringr r-gridextra r-ggplot2
                             r-extradistr))
    (home-page "https://cran.r-project.org/package=grouprar")
    (synopsis "Group Response Adaptive Randomization for Clinical Trials")
    (description
     "Implement group response-adaptive randomization procedures, which also
integrates standard non-group response-adaptive randomization methods as
specialized instances.  It is also uniquely capable of managing complex
scenarios, including those with delayed and missing responses, thereby expanding
its utility in real-world applications.  This package offers 16 functions for
simulating a variety of response adaptive randomization procedures.  These
functions are essential for guiding the selection of statistical methods in
clinical trials, providing a flexible and effective approach to trial design.
Some of the detailed methodologies and algorithms used in this package, please
refer to the following references: LJ Wei (1979) <doi:10.1214/aos/1176344614> L.
J. WEI and S. DURHAM (1978) <doi:10.1080/01621459.1978.10480109> Durham, S. D.,
@code{FlournoY}, N. AND LI, W. (1998) <doi:10.2307/3315771> Ivanova, A.,
Rosenberger, W. F., Durham, S. D. and Flournoy, N. (2000)
<https://www.jstor.org/stable/25053121> Bai Z D, Hu F, Shen L. (2002)
<doi:10.1006/jmva.2001.1987> Ivanova, A. (2003) <doi:10.1007/s001840200220> Hu,
F., & Zhang, L. X. (2004) <doi:10.1214/aos/1079120137> Hu, F., & Rosenberger, W.
F. (2006, ISBN:978-0-471-65396-7).  Zhang, L. X., Chan, W. S., Cheung, S. H., &
Hu, F. (2007) <https://www.jstor.org/stable/26432528> Zhang, L., & Rosenberger,
W. F. (2006) <doi:10.1111/j.1541-0420.2005.00496.x> Hu, F., Zhang, L. X.,
Cheung, S. H., & Chan, W. S. (2008) <doi:10.1002/cjs.5550360404>.")
    (license license:gpl2+)))

(define-public r-groupr
  (package
    (name "r-groupr")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "groupr" version))
       (sha256
        (base32 "1v4k8is6dkf7cl97z14i3j791a9xfcb1icm2ll5ss6h0d5ad3hym"))))
    (properties `((upstream-name . "groupr")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-pillar
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ngriffiths21/groupr")
    (synopsis "Groups with Inapplicable Values")
    (description
     "The groupr package provides a more powerful version of grouped tibbles from
dplyr'.  It allows groups to be marked inapplicable, which is a simple but
widely useful way to express structure in a dataset.  It also provides powerful
pivoting and other group manipulation functions.")
    (license license:expat)))

(define-public r-groupica
  (package
    (name "r-groupica")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "groupICA" version))
       (sha256
        (base32 "1wl828ligvvw55xic383gdn7aq4mbwsnkl4ym6f71jn5g3p36d0i"))))
    (properties `((upstream-name . "groupICA")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://github.com/sweichwald/groupICA-R")
    (synopsis "Independent Component Analysis for Grouped Data")
    (description
     "This package contains an implementation of an independent component analysis
(ICA) for grouped data.  The main function @code{groupICA}() performs a blind
source separation, by maximizing an independence across sources and allows to
adjust for varying confounding for user-specified groups.  Additionally, the
package contains the function uwedge() which can be used to approximately
jointly diagonalize a list of matrices.  For more details see the project
website <https://sweichwald.de/@code{groupICA/>}.")
    (license license:agpl3)))

(define-public r-grouphmap
  (package
    (name "r-grouphmap")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Grouphmap" version))
       (sha256
        (base32 "0wmzgd76adwq2gm8p1cxgy1kj1vl83bfsx4d9afk7wh3dxgq4c7l"))))
    (properties `((upstream-name . "Grouphmap")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-pheatmap
                             r-org-mm-eg-db
                             r-limma
                             r-dplyr
                             r-clusterprofiler))
    (home-page "https://cran.r-project.org/package=Grouphmap")
    (synopsis
     "'Grouphmap' is an Automated One-Step Common Analysis of Batch Expression Profile")
    (description
     "The Grouphmap was implemented in R, an open-source programming environment, and
was released under the provided website.  The difference analysis is based on
the limma package, which can cover gene and protein expression profiles
(Reference: Matthew E Ritchie , Belinda Phipson , Di Wu , Yifang Hu , Charity W
Law , Wei Shi , Gordon K Smyth (2015) <doi:10.1093/nar/gkv007>).  The GO
enrichment analysis is based on the @code{clusterProfiler} package and supports
three common species: human, mouse, and yeast (Reference: Guangchuang Yu, Li-Gen
Wang, Yanyan Han, Qing-Yu He (2012) <doi:10.1089/omi.2011.0118>).  The results
of batch difference analysis and enrichment analysis are output in separate
folders for easy viewing and further visualization of the results during the
process.  The results returned a heatmap in R and exported to 3 folders named
DEG, go, and merge.")
    (license license:artistic2.0)))

(define-public r-groupedsurv
  (package
    (name "r-groupedsurv")
    (version "1.0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "groupedSurv" version))
       (sha256
        (base32 "16qyvd8k7wdg0iafs5gxljx464nd79kwdl7g7g6hvhrhr621mgyq"))))
    (properties `((upstream-name . "groupedSurv")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen
                             r-rcpp
                             r-qvalue
                             r-foreach
                             r-doparallel
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=groupedSurv")
    (synopsis
     "Efficient Estimation of Grouped Survival Models Using the Exact Likelihood Function")
    (description
     "These Rcpp'-based functions compute the efficient score statistics for grouped
time-to-event data (Prentice and Gloeckler, 1978), with the optional inclusion
of baseline covariates.  Functions for estimating the parameter of interest and
nuisance parameters, including baseline hazards, using maximum likelihood are
also provided.  A parallel set of functions allow for the incorporation of
family structure of related individuals (e.g., trios).  Note that the current
implementation of the frailty model (Ripatti and Palmgren, 2000) is sensitive to
departures from model assumptions, and should be considered experimental.  For
these data, the exact proportional-hazards-model-based likelihood is computed by
evaluating multiple variable integration.  The integration is accomplished using
the Cuba library (Hahn, 2005), and the source files are included in this
package.  The maximization process is carried out using Brent's algorithm, with
the C++ code file from John Burkardt and John Denker (Brent, 2002).")
    (license license:gpl2+)))

(define-public r-groupdata2
  (package
    (name "r-groupdata2")
    (version "2.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "groupdata2" version))
       (sha256
        (base32 "0r126mq7cyc19nizyh8xk5a0pfi1sic8lxfvnlmhhhbpn97lqyqz"))))
    (properties `((upstream-name . "groupdata2")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rlang
                             r-rearrr
                             r-purrr
                             r-plyr
                             r-numbers
                             r-lifecycle
                             r-dplyr
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ludvigolsen/groupdata2")
    (synopsis "Creating Groups from Data")
    (description
     "This package provides methods for dividing data into groups.  Create balanced
partitions and cross-validation folds.  Perform time series windowing and
general grouping and splitting of data.  Balance existing groups with up- and
downsampling or collapse them to fewer groups.")
    (license license:expat)))

(define-public r-groupcomparisons
  (package
    (name "r-groupcomparisons")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GroupComparisons" version))
       (sha256
        (base32 "0fxgh1mksrfmd4yjrpxg3nfby5wyx75lj0shb5xrdzl7pxzh56ai"))))
    (properties `((upstream-name . "GroupComparisons")))
    (build-system r-build-system)
    (propagated-inputs (list r-car))
    (home-page "https://cran.r-project.org/package=GroupComparisons")
    (synopsis "Paired/Unpaired Parametric/Non-Parametric Group Comparisons")
    (description
     "Receives two vectors, computes appropriate function for group comparison (i.e.,
t-test, Mann-Whitney; equality of variances), and reports the findings
(mean/median, standard deviation, test statistic, p-value, effect size) in APA
format (Fay, M.P., & Proschan, M.A. (2010)<DOI: 10.1214/09-SS051>).")
    (license license:expat)))

(define-public r-groupbn
  (package
    (name "r-groupbn")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GroupBN" version))
       (sha256
        (base32 "0x41x7npaawnh6nxh4d4vmv2zwzc6c5ijrhh6fdpwzl4p75qlh1x"))))
    (properties `((upstream-name . "GroupBN")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-visnetwork
                             r-stringr
                             r-rlist
                             r-prroc
                             r-plyr
                             r-pcamixdata
                             r-mlmetrics
                             r-magrittr
                             r-clustofvar
                             r-bnlearn
                             r-arules))
    (home-page "https://www.r-project.org")
    (synopsis
     "Inferring Group Bayesian Networks using Hierarchical Feature Clustering")
    (description
     "Group Bayesian Networks: This package implements the inference of group Bayesian
networks based on hierarchical feature clustering, and the adaptive refinement
of the grouping regarding an outcome of interest, as described in Becker et.  al
(2021) <doi: 10.1371/journal.pcbi.1008735>.")
    (license license:gpl2+)))

(define-public r-groundhog
  (package
    (name "r-groundhog")
    (version "3.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "groundhog" version))
       (sha256
        (base32 "00lp733avwjxap5drc3p3763y4xhhphlck7zp7g5cijcbga4k54s"))))
    (properties `((upstream-name . "groundhog")))
    (build-system r-build-system)
    (home-page "https://groundhogr.com/")
    (synopsis "Version-Control for CRAN, GitHub, and GitLab Packages")
    (description
     "Make R scripts reproducible, by ensuring that every time a given script is run,
the same version of the used packages are loaded (instead of whichever version
the user running the script happens to have installed).  This is achieved by
using the command groundhog.library() instead of the base command library(), and
including a date in the call.  The date is used to call on the same version of
the package every time (the most recent version available at that date).  Load
packages from CRAN, @code{GitHub}, or Gitlab.")
    (license license:gpl3)))

(define-public r-groqr
  (package
    (name "r-groqr")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "groqR" version))
       (sha256
        (base32 "0q03yad36mlrm8yqavdssb9h55vphxnb3vp6kman78nm2nkf6zmq"))))
    (properties `((upstream-name . "groqR")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinywidgets
                             r-shiny
                             r-rstudioapi
                             r-miniui
                             r-jsonlite
                             r-httr
                             r-clipr))
    (home-page "https://github.com/GabrielKaiserQFin/groqR")
    (synopsis "Coding Assistant using the Fast AI Inference 'Groq'")
    (description
     "This package provides a comprehensive suite of functions and RStudio Add-ins
leveraging the capabilities of open-source Large Language Models (LLMs) to
support R developers.  These functions offer a range of utilities, including
text rewriting, translation, and general query capabilities.  Additionally, the
programming-focused functions provide assistance with debugging, translating,
commenting, documenting, and unit testing code, as well as suggesting variable
and function names, thereby streamlining the development process.")
    (license license:gpl3+)))

(define-public r-gromovlab
  (package
    (name "r-gromovlab")
    (version "0.8-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gromovlab" version))
       (sha256
        (base32 "0mm303iiaqfdvr94x765dbd1018cvvl0q4z2zppwk1aif0kb53x8"))))
    (properties `((upstream-name . "gromovlab")))
    (build-system r-build-system)
    (propagated-inputs (list r-quadprog r-igraph r-glpkapi r-cluster r-ape))
    (home-page "https://cran.r-project.org/package=gromovlab")
    (synopsis "Gromov-Hausdorff Type Distances for Labeled Metric Spaces")
    (description
     "Computes Gromov-Hausdorff type l^p distances for labeled metric spaces.  These
distances were introduced in V.Liebscher, Gromov meets Phylogenetics - new
Animals for the Zoo of Metrics on Tree Space <@code{arXiv:1504.05795>} for
phylogenetic trees, but may apply to a diversity of scenarios.")
    (license license:gpl2+)))

(define-public r-groc
  (package
    (name "r-groc")
    (version "1.0.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "groc" version))
       (sha256
        (base32 "1q95j7yp8iv1545gqgkq139i08ky5ga32krkr1vy67j3qs4zkiwm"))))
    (properties `((upstream-name . "groc")))
    (build-system r-build-system)
    (propagated-inputs (list r-rrcov r-robustbase r-pls r-mgcv r-mass))
    (home-page "https://cran.r-project.org/package=groc")
    (synopsis "Generalized Regression on Orthogonal Components")
    (description
     "Robust multiple or multivariate linear regression, nonparametric regression on
orthogonal components, classical or robust partial least squares models as
described in Bilodeau, Lafaye De Micheaux and Mahdi (2015)
<doi:10.18637/jss.v065.i01>.")
    (license license:gpl2+)))

(define-public r-grobblr
  (package
    (name "r-grobblr")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grobblR" version))
       (sha256
        (base32 "1l1msh900kmbbszn1f9vfdix4a6180lvs3gfidp9pgkvi2gv2g01"))))
    (properties `((upstream-name . "grobblR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-rcurl
                             r-purrr
                             r-png
                             r-magrittr
                             r-gridextra
                             r-glue
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=grobblR")
    (synopsis "Creating Flexible, Reproducible 'PDF' Reports")
    (description
     "This package provides a tool which allows users the ability to intuitively
create flexible, reproducible portable document format reports comprised of
aesthetically pleasing tables, images, plots and/or text.")
    (license license:expat)))

(define-public r-groan
  (package
    (name "r-groan")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GROAN" version))
       (sha256
        (base32 "1g6n00gnz47w06wkx0sha97qxxyhc8n2y8zaagqx7krgzx7lkha3"))))
    (properties `((upstream-name . "GROAN")))
    (build-system r-build-system)
    (propagated-inputs (list r-rrblup r-plyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GROAN")
    (synopsis "Genomic Regression Workbench")
    (description
     "Workbench for testing genomic regression accuracy on (optionally noisy)
phenotypes.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-grnns
  (package
    (name "r-grnns")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GRNNs" version))
       (sha256
        (base32 "1c36km6g4fcsvpirlh88fl3zq9pdpdyfilahfx949salj0gnqsn6"))))
    (properties `((upstream-name . "GRNNs")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan r-scales r-rdist r-cvtools))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GRNNs")
    (synopsis "General Regression Neural Networks Package")
    (description
     "This General Regression Neural Networks Package uses various distance functions.
 It was motivated by Specht (1991, ISBN:1045-9227), and updated from previous
published paper Li et al. (2016) <doi:10.1016/j.palaeo.2015.11.005>.  This
package includes various functions, although \"euclidean\" distance is used
traditionally.")
    (license license:gpl3+)))

(define-public r-grnn
  (package
    (name "r-grnn")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grnn" version))
       (sha256
        (base32 "1dxcmar42g9hz4zlyszlmmnnsnja0gxfggav5jxv0gkp32rkd0wh"))))
    (properties `((upstream-name . "grnn")))
    (build-system r-build-system)
    (home-page "http://flow.chasset.net/r-grnn/")
    (synopsis "General regression neural network")
    (description
     "The program GRNN implements the algorithm proposed by Specht (1991).")
    (license (license:fsdg-compatible "AGPL"))))

(define-public r-grmsem
  (package
    (name "r-grmsem")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grmsem" version))
       (sha256
        (base32 "0byyl1121acs90xbhgi4g4hnf4n259k7cg5grm6dyaxqkha3lyw7"))))
    (properties `((upstream-name . "grmsem")))
    (build-system r-build-system)
    (propagated-inputs (list r-optimparallel r-numderiv r-msm))
    (native-inputs (list r-knitr))
    (home-page "https://CRAN.R-project.org/package=grmsem")
    (synopsis
     "Genetic-Relationship-Matrix Structural Equation Modelling (GRMSEM)")
    (description
     "Quantitative genetics tool supporting the modelling of multivariate genetic
variance structures in quantitative data.  It allows fitting different models
through multivariate genetic-relationship-matrix (GRM) structural equation
modelling (SEM) in unrelated individuals, using a maximum likelihood approach.
Specifically, it combines genome-wide genotyping information, as captured by
GRMs, with twin-research-based SEM techniques, St Pourcain et al. (2017)
<doi:10.1016/j.biopsych.2017.09.020>, Shapland et al. (2020)
<doi:10.1101/2020.08.14.251199>.")
    (license license:gpl3)))

(define-public r-grizbayr
  (package
    (name "r-grizbayr")
    (version "1.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grizbayr" version))
       (sha256
        (base32 "04gka7q0wy1w4qg3wm4vnzc1q0g4zgvrvra1la2w1sggc5asdh32"))))
    (properties `((upstream-name . "grizbayr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rangi513/grizbayr")
    (synopsis "Bayesian Inference for A|B and Bandit Marketing Tests")
    (description
     "Uses simple Bayesian conjugate prior update rules to calculate the win
probability of each option, value remaining in the test, and percent lift over
the baseline for various marketing objectives.  References: Fink, Daniel (1997)
\"A Compendium of Conjugate Priors\"
<https://www.johndcook.com/@code{CompendiumOfConjugatePriors.pdf>}.  Stucchio,
Chris (2015) \"Bayesian A/B Testing at VWO\"
<https://vwo.com/downloads/VWO_@code{SmartStats_technical_whitepaper.pdf>}.")
    (license license:expat)))

(define-public r-gripp
  (package
    (name "r-gripp")
    (version "0.2.20")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gripp" version))
       (sha256
        (base32 "0rshipm8rdynl19a4dsvvvlhkd0n4vjfkidwnv5iilc5ini7c4qr"))))
    (properties `((upstream-name . "gripp")))
    (build-system r-build-system)
    (propagated-inputs (list r-usethis r-gensa r-ga))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gripp")
    (synopsis "General Inverse Problem Platform")
    (description
     "Set of functions designed to solve inverse problems.  The direct problem is used
to calculate a cost function to be minimized.  Here are listed some papers using
Inverse Problems solvers and sensitivity analysis: (Jader Lugon Jr.; Antonio J.
Silva Neto 2011) <doi:10.1590/S1678-58782011000400003>. (Jader Lugon Jr.;
Antonio J. Silva Neto; Pedro P.G.W. Rodrigues 2008)
<doi:10.1080/17415970802082864>. (Jader Lugon Jr.; Antonio J. Silva Neto; Cesar
C. Santana 2008) <doi:10.1080/17415970802082922>.")
    (license license:gpl3)))

(define-public r-grimr
  (package
    (name "r-grimr")
    (version "0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GrimR" version))
       (sha256
        (base32 "005ywc31yn1cs54kjlkrryw0s7zm8dqqfjkdlkm4s1sbc9r3mssz"))))
    (properties `((upstream-name . "GrimR")))
    (build-system r-build-system)
    (propagated-inputs (list r-car))
    (home-page "https://cran.r-project.org/package=GrimR")
    (synopsis "Calculate Optical Parameters from Spindle Stage Measurements")
    (description
     "Calculates optical parameters of crystals like the optical axes, the axis angle
2V, and the direction of the principal axes of the indicatrix from extinction
angles measured on a spindle stage mounted on a polarisation microscope stage.
Details of the method can be found in Dufey (2017) <@code{arXiv:1703.00070>}.")
    (license license:gpl3)))

(define-public r-grim
  (package
    (name "r-grim")
    (version "0.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gRim" version))
       (sha256
        (base32 "17rbvf69p5vzfb1s550sf6793bd7wgs8482hm60y9d6d10iicj60"))))
    (properties `((upstream-name . "gRim")))
    (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-rcppeigen
                             r-rcpparmadillo
                             r-rcpp
                             r-matrix
                             r-mass
                             r-igraph
                             r-grbase
                             r-grain
                             r-glue
                             r-doby))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://people.math.aau.dk/~sorenh/software/gR/")
    (synopsis "Graphical Interaction Models")
    (description
     "This package provides the following types of models: Models for contingency
tables (i.e.  log-linear models) Graphical Gaussian models for multivariate
normal data (i.e.  covariance selection models) Mixed interaction models.
Documentation about @code{gRim} is provided by vignettes included in this
package and the book by HÃ¸jsgaard, Edwards and Lauritzen (2012,
<doi:10.1007/978-1-4614-2299-0>); see citation(\"@code{gRim}\") for details.")
    (license license:gpl2+)))

(define-public r-gridstacker
  (package
    (name "r-gridstacker")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gridstackeR" version))
       (sha256
        (base32 "01yazynwprzjygkh0dwrlvbjsmxvfy1pxr0nldnbngmq10wvpp2n"))))
    (properties `((upstream-name . "gridstackeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinyjs r-shiny r-htmltools r-checkmate))
    (home-page "https://cran.r-project.org/package=gridstackeR")
    (synopsis "Wrapper for 'gridstack.js'")
    (description
     "An easy way to create responsive layouts with just a few lines of code.  You can
create boxes that are draggable and resizable and load predefined Layouts.  The
package serves as a wrapper to allow for easy integration of the gridstack.js
functionalities <https://github.com/gridstack/gridstack.js>.")
    (license license:gpl3)))

(define-public r-gridsampler
  (package
    (name "r-gridsampler")
    (version "0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gridsampler" version))
       (sha256
        (base32 "0wqpqg9c372sv8zqks6v93gawiyfghw58hn7m8q45dxpqm16ss3k"))))
    (properties `((upstream-name . "gridsampler")))
    (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-shinybs
                             r-shiny
                             r-reshape2
                             r-plyr
                             r-ggplot2
                             r-biasedurn))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://github.com/markheckmann/gridsampler")
    (synopsis
     "Simulation Tool to Determine the Required Sample Size for Repertory Grid Studies")
    (description
     "Simulation tool to facilitate determination of required sample size to achieve
category saturation for studies using multiple repertory grids in conjunction
with content analysis.")
    (license license:gpl3)))

(define-public r-gridot
  (package
    (name "r-gridot")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gridOT" version))
       (sha256
        (base32 "106h1lk2fyfl68iwmbqbm9h8ip75ibanr1dsi6nrmd54pz475ry8"))))
    (properties `((upstream-name . "gridOT")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=gridOT")
    (synopsis "Approximate Optimal Transport Between Two-Dimensional Grids")
    (description
     "Can be used for optimal transport between two-dimensional grids with respect to
separable cost functions of l^p form.  It utilizes the Frank-Wolfe algorithm to
approximate so-called pivot measures: one-dimensional transport plans that fully
describe the full transport, see G. Auricchio (2021) <@code{arXiv:2105.07278>}.
For these, it offers methods for visualization and to extract the corresponding
transport plans and costs.  Additionally, related functions for one-dimensional
optimal transport are available.")
    (license license:gpl3)))

(define-public r-gridonclusters
  (package
    (name "r-gridonclusters")
    (version "0.1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GridOnClusters" version))
       (sha256
        (base32 "1zanz8p5ghy7q7dsazv6wfkf5gl2dnbmchw88drbrip1h85gxyid"))))
    (properties `((upstream-name . "GridOnClusters")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack
                             r-rcpp
                             r-plotrix
                             r-mclust
                             r-fossil
                             r-dqrng
                             r-cluster
                             r-ckmeans-1d-dp))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GridOnClusters")
    (synopsis "Cluster-Preserving Multivariate Joint Grid Discretization")
    (description
     "Discretize multivariate continuous data using a grid that captures the joint
distribution via preserving clusters in the original data (Wang et al.  2020)
<doi:10.1145/3388440.3412415>.  Joint grid discretization is applicable as a
data transformation step to prepare data for model-free inference of
association, function, or causality.")
    (license license:lgpl3+)))

(define-public r-gridgraphviz
  (package
    (name "r-gridgraphviz")
    (version "0.3-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gridGraphviz" version))
       (sha256
        (base32 "0yzy7w4bk3rn9yjqy06gzkcs0dla3n49z3v1z0mjikg8cd97d5ni"))))
    (properties `((upstream-name . "gridGraphviz")))
    (build-system r-build-system)
    (inputs (list graphviz))
    (propagated-inputs (list r-rgraphviz r-graph))
    (home-page "https://r-forge.r-project.org/projects/gridgraph/")
    (synopsis "Drawing Graphs with 'grid'")
    (description
     "This package provides functions for drawing node-and-edge graphs that have been
laid out by graphviz'.  This provides an alternative rendering to that provided
by the Rgraphviz package, with two main advantages: the rendering provided by
@code{gridGraphviz} should be more similar to what graphviz itself would draw;
and rendering with grid allows for post-hoc customisations using the named
viewports and grobs that @code{gridGraphviz} produces.")
    (license license:gpl2+)))

(define-public r-gridgeometry
  (package
    (name "r-gridgeometry")
    (version "0.4-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gridGeometry" version))
       (sha256
        (base32 "1j19bwkc33r85xkc9vlphyyq3w8gwfigjkifikl7yi216fhgfrgr"))))
    (properties `((upstream-name . "gridGeometry")))
    (build-system r-build-system)
    (propagated-inputs (list r-polyclip))
    (home-page "https://github.com/pmur002/gridgeometry")
    (synopsis "Polygon Geometry in 'grid'")
    (description
     "This package provides functions for performing polygon geometry with grid grobs.
 This allows complex shapes to be defined by combining simpler shapes.")
    (license license:gpl2+)))

(define-public r-griddebug
  (package
    (name "r-griddebug")
    (version "0.5-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gridDebug" version))
       (sha256
        (base32 "1x1bbyfbyyn13sw5pvnzns6mq57fyq2fywj8hx5k9rhqclzjqxyr"))))
    (properties `((upstream-name . "gridDebug")))
    (build-system r-build-system)
    (inputs (list graphviz))
    (propagated-inputs (list r-gridsvg r-gridgraphviz r-graph))
    (home-page "https://cran.r-project.org/package=gridDebug")
    (synopsis "Debugging 'grid' Graphics")
    (description
     "This package provides functions for drawing scene trees representing scenes that
have been drawn using grid graphics.")
    (license license:gpl2+)))

(define-public r-gridcopula
  (package
    (name "r-gridcopula")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GRIDCOPULA" version))
       (sha256
        (base32 "0d8zibd27z0krn564ba906zlvnb1fx4mfvhpii7pxr0pzjir2a9c"))))
    (properties `((upstream-name . "GRIDCOPULA")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsolnp
                             r-reshape2
                             r-pracma
                             r-limsolve
                             r-ggplot2
                             r-fields
                             r-evmix))
    (home-page "https://cran.r-project.org/package=GRIDCOPULA")
    (synopsis "Bivariate Copula Functions Based on Regular Grid")
    (description
     "Estimates grid type bivariate copula functions, calculates some association
measures and provides several copula graphics.")
    (license license:gpl3)))

(define-public r-gridbezier
  (package
    (name "r-gridbezier")
    (version "1.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gridBezier" version))
       (sha256
        (base32 "1n5d6svxblcxpzf79px0qjxsydpwjpnfcagnwk35ljinyd0wv68z"))))
    (properties `((upstream-name . "gridBezier")))
    (build-system r-build-system)
    (home-page "https://github.com/pmur002/gridbezier")
    (synopsis "Bezier Curves in 'grid'")
    (description
     "This package provides functions for rendering Bezier curves (Pomax, 2018)
<https://pomax.github.io/bezierinfo/> in grid'.  There is support for both
quadratic and cubic Bezier curves.  There are also functions for calculating
points on curves, tangents to curves, and normals to curves.")
    (license license:gpl2+)))

(define-public r-grfa
  (package
    (name "r-grfa")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GrFA" version))
       (sha256
        (base32 "1qlj3zxv7fy7mwqrmpidn2z3ni8vwp1yvz8w5gvzrxac39xqc2dv"))))
    (properties `((upstream-name . "GrFA")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://cran.r-project.org/package=GrFA")
    (synopsis "Group Factor Analysis")
    (description
     "Several group factor analysis algorithms are implemented, including Canonical
Correlation-based Estimation by Choi et al. (2021)
<doi:10.1016/j.jeconom.2021.09.008> , Generalised Canonical Correlation
Estimation by Lin and Shin (2023) <doi:10.2139/ssrn.4295429>, Circularly
Projected Estimation by Chen (2022) <doi:10.1080/07350015.2022.2051520>, and
Aggregated projection method.")
    (license license:gpl3)))

(define-public r-grf
  (package
    (name "r-grf")
    (version "2.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grf" version))
       (sha256
        (base32 "0za2c9hlvp1s2h25vr6f0qlw92r9wp0vbmfdsp38q190drpjnff0"))))
    (properties `((upstream-name . "grf")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich
                             r-rcppeigen
                             r-rcpp
                             r-matrix
                             r-lmtest
                             r-dicekriging))
    (home-page "https://github.com/grf-labs/grf")
    (synopsis "Generalized Random Forests")
    (description
     "Forest-based statistical estimation and inference.  GRF provides non-parametric
methods for heterogeneous treatment effects estimation (optionally using
right-censored outcomes, multiple treatment arms or outcomes, or instrumental
variables), as well as least-squares regression, quantile regression, and
survival regression, all with support for missing covariates.")
    (license license:gpl3)))

(define-public r-greyzones
  (package
    (name "r-greyzones")
    (version "0.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GreyZones" version))
       (sha256
        (base32 "1mjnfiqc4zj08n8j899zqxkvxd6ryp6sg1n46isy0ni7ghbng38w"))))
    (properties `((upstream-name . "GreyZones")))
    (build-system r-build-system)
    (propagated-inputs (list r-irrcac))
    (home-page "https://cran.r-project.org/package=GreyZones")
    (synopsis
     "Detection of Grey Zones in Two-Way Inter-Rater Agreement Tables")
    (description
     "Grey zones locally occur in an agreement table due to the subjective evaluation
of raters based on various factors such as not having uniform guidelines, the
differences between the raters level of expertise or low variability among the
level of the categorical variable.  It is important to detect grey zones since
they cause a negative bias in the estimate of the agreement level.  This package
provides a function for detecting the existence of grey zones in two-way
inter-rater agreement tables (Demirhan and Yilmaz (2023)
<doi:10.1186/s12874-022-01759-7>).")
    (license license:gpl3)))

(define-public r-greymodels
  (package
    (name "r-greymodels")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Greymodels" version))
       (sha256
        (base32 "0gv4kqgn10l3pknx7w26yas1h70gsb2h8a82w2ykxs134xl4vpll"))))
    (properties `((upstream-name . "Greymodels")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinywidgets
                             r-shinydashboard
                             r-shiny
                             r-scales
                             r-readxl
                             r-plotly
                             r-particle-swarm-optimisation
                             r-metrics
                             r-ggplot2
                             r-expm
                             r-dplyr
                             r-cmna))
    (home-page "https://github.com/havishaJ/Greymodels")
    (synopsis "Shiny App for Grey Forecasting Model")
    (description
     "The Greymodels Shiny app is an interactive interface for statistical modelling
and forecasting using grey-based models.  It covers several state-of-the-art
univariate and multivariate grey models.  A user friendly interface allows users
to easily compare the performance of different models for prediction and among
others, visualize graphical plots of predicted values within user chosen
confidence intervals.  Chang, C. (2019) <doi:10.24818/18423264/53.1.19.11>, Li,
K., Zhang, T. (2019) <doi:10.1007/s12667-019-00344-0>, Ou, S. (2012)
<doi:10.1016/j.compag.2012.03.007>, Li, S., Zhou, M., Meng, W., Zhou, W. (2019)
<doi:10.1080/23307706.2019.1666310>, Xie, N., Liu, S. (2009)
<doi:10.1016/j.apm.2008.01.011>, Shao, Y., Su, H. (2012)
<doi:10.1016/j.aasri.2012.06.003>, Xie, N., Liu, S., Yang, Y., Yuan, C. (2013)
<doi:10.1016/j.apm.2012.10.037>, Li, S., Miao, Y., Li, G., Ikram, M. (2020)
<doi:10.1016/j.matcom.2019.12.020>, Che, X., Luo, Y., He, Z. (2013)
<doi:10.4028/www.scientific.net/AMM.364.207>, Zhu, J., Xu, Y., Leng, H., Tang,
H., Gong, H., Zhang, Z. (2016) <doi:10.1109/appeec.2016.7779929>, Luo, Y., Liao,
D. (2012) <doi:10.4028/www.scientific.net/AMR.507.265>, Bilgil, H. (2020)
<doi:10.3934/math.2021091>, Li, D., Chang, C., Chen, W., Chen, C. (2011)
<doi:10.1016/j.apm.2011.04.006>, Chen, C. (2008)
<doi:10.1016/j.chaos.2006.08.024>, Zhou, W., Pei, L. (2020)
<doi:10.1007/s00500-019-04248-0>, Xiao, X., Duan, H. (2020)
<doi:10.1016/j.engappai.2019.103350>, Xu, N., Dang, Y. (2015)
<doi:10.1155/2015/606707>, Chen, P., Yu, H.(2014) <doi:10.1155/2014/242809>,
Zeng, B., Li, S., Meng, W., Zhang, D. (2019) <doi:10.1371/journal.pone.0221333>,
Liu, L., Wu, L. (2021) <doi:10.1016/j.apm.2020.08.080>, Hu, Y. (2020)
<doi:10.1007/s00500-020-04765-3>, Zhou, P., Ang, B., Poh, K. (2006)
<doi:10.1016/j.energy.2005.12.002>, Cheng, M., Li, J., Liu, Y., Liu, B. (2020)
<doi:10.3390/su12020698>, Wang, H., Wang, P., Senel, M., Li, T. (2019)
<doi:10.1155/2019/9049815>, Ding, S., Li, R. (2020) <doi:10.1155/2020/4564653>,
Zeng, B., Li, C. (2018) <doi:10.1016/j.cie.2018.02.042>, Xie, N., Liu, S. (2015)
<doi:10.1109/JSEE.2015.00013>, Zeng, X., Yan, S., He, F., Shi, Y. (2019)
<doi:10.1016/j.apm.2019.11.032>.")
    (license license:gpl3)))

(define-public r-greymodel
  (package
    (name "r-greymodel")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GreyModel" version))
       (sha256
        (base32 "0vxl4kgihcyl96qiz6sz5icznaa1jpsqqbn1irwmzyngwsh1qvzw"))))
    (properties `((upstream-name . "GreyModel")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GreyModel")
    (synopsis "Fitting and Forecasting of Grey Model")
    (description
     "Testing, Implementation and Forecasting of Grey Model (GM(1, 1)).  For method
details see Hsu, L. and Wang, C. (2007). <doi:10.1016/j.techfore.2006.02.005>.")
    (license license:gpl3)))

(define-public r-greybox
  (package
    (name "r-greybox")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "greybox" version))
       (sha256
        (base32 "0qkydablpwjmp15pamdgh063pkq03zc2hyx9vnr08fyj9n2jdaaz"))))
    (properties `((upstream-name . "greybox")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xtable
                             r-texreg
                             r-statmod
                             r-rcpp
                             r-pracma
                             r-nloptr
                             r-generics))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/config-i1/greybox")
    (synopsis "Toolbox for Model Building and Forecasting")
    (description
     "This package implements functions and instruments for regression model building
and its application to forecasting.  The main scope of the package is in
variables selection and models specification for cases of time series data.
This includes promotional modelling, selection between different dynamic
regressions with non-standard distributions of errors, selection based on cross
validation, solutions to the fat regression model problem and more.  Models
developed in the package are tailored specifically for forecasting purposes.  So
as a results there are several methods that allow producing forecasts from these
models and visualising them.")
    (license license:lgpl2.1)))

(define-public r-grex
  (package
    (name "r-grex")
    (version "1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grex" version))
       (sha256
        (base32 "0s6nan76rrmh3yhgvzb7pqdrzx2w9px8ay4v9yiib4bamy9wmhpb"))))
    (properties `((upstream-name . "grex")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://nanx.me/grex/")
    (synopsis "Gene ID Mapping for Genotype-Tissue Expression (GTEx) Data")
    (description
     "Convert Ensembl gene identifiers from Genotype-Tissue Expression (GTEx) data to
identifiers in other annotation systems, including Entrez', HGNC', and
@code{UniProt}'.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-gretlr
  (package
    (name "r-gretlr")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gretlR" version))
       (sha256
        (base32 "094a4hy67j8bk6b0yfvzq5g0ydi69z0m9bqmvdgwv87b3llvpl1q"))))
    (properties `((upstream-name . "gretlR")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-rmarkdown r-magrittr r-knitr r-kableextra))
    (native-inputs (list r-knitr))
    (home-page "https://CRAN.R-project.org/package=gretlR")
    (synopsis "Seamless Integration of 'Gretl' and 'R'")
    (description
     "It allows running gretl (<http://gretl.sourceforge.net/index.html>) program from
R, R Markdown and Quarto.  gretl ('Gnu Regression, Econometrics', and
Time-series Library) is a statistical software for Econometric analysis.  This
package does not only integrate gretl and R but also serves as a gretl
Knit-Engine for knitr package.  Write all your gretl commands in R', R Markdown
chunk.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-gretel
  (package
    (name "r-gretel")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gretel" version))
       (sha256
        (base32 "1yyj4aing56f2vy0gshxwb623g9idg8k4701v3s53lqn4m2ybazk"))))
    (properties `((upstream-name . "gretel")))
    (build-system r-build-system)
    (propagated-inputs (list r-resistorarray r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/davidbuch/gretel")
    (synopsis "Generalized Path Analysis for Social Networks")
    (description
     "The social network literature features numerous methods for assigning value to
paths as a function of their ties.  gretel systemizes these approaches, casting
them as instances of a generalized path value function indexed by a penalty
parameter.  The package also calculates probabilistic path value and identifies
optimal paths in either value framework.  Finally, proximity matrices can be
generated in these frameworks that capture high-order connections overlooked in
primitive adjacency sociomatrices.  Novel methods are described in Buch (2019)
<https://davidbuch.github.io/analyzing-networks-with-gretel.html>.  More
traditional methods are also implemented, as described in Yang, Knoke (2001)
<doi:10.1016/S0378-8733(01)00043-0>.")
    (license license:gpl3)))

(define-public r-greta-gp
  (package
    (name "r-greta-gp")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "greta.gp" version))
       (sha256
        (base32 "0r33lbg9zjgq4pkzllr9vadfk8jm24avf68dnhm1n5j5ahzpc242"))))
    (properties `((upstream-name . "greta.gp")))
    (build-system r-build-system)
    (propagated-inputs (list r-tensorflow r-greta r-glue r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/greta-dev/greta.gp")
    (synopsis "Gaussian Process Modelling in 'greta'")
    (description
     "This package provides a syntax to create and combine Gaussian process kernels in
greta'.  You can then them to define either full rank or sparse Gaussian
processes.  This is an extension to the greta software, Golding (2019)
<doi:10.21105/joss.01601>.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-greta-dynamics
  (package
    (name "r-greta-dynamics")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "greta.dynamics" version))
       (sha256
        (base32 "143ixhi2hxxgb10lp9c8b2xdp63rpbhqbxzbbbzhigf9aflpmh62"))))
    (properties `((upstream-name . "greta.dynamics")))
    (build-system r-build-system)
    (propagated-inputs (list r-tensorflow r-greta r-glue r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/greta-dev/greta.dynamics")
    (synopsis "Modelling Structured Dynamical Systems in 'greta'")
    (description
     "This package provides a greta extension for analysing transition matrices and
ordinary differential equations representing dynamical systems.  Provides
functions for analysing transition matrices by iteration, and solving ordinary
differential equations.  This is an extension to the greta software, Golding
(2019) <doi:10.21105/joss.01601>.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-greta
  (package
    (name "r-greta")
    (version "0.4.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "greta" version))
       (sha256
        (base32 "0ky2d0i2s768na50940b733rr1fmmbpkdw1a8r21wbr5h43n8xf2"))))
    (properties `((upstream-name . "greta")))
    (build-system r-build-system)
    (propagated-inputs (list r-yesno
                             r-tensorflow
                             r-reticulate
                             r-r6
                             r-progress
                             r-parallelly
                             r-glue
                             r-future
                             r-coda
                             r-cli
                             r-callr
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://greta-stats.org")
    (synopsis "Simple and Scalable Statistical Modelling in R")
    (description
     "Write statistical models in R and fit them by MCMC and optimisation on CPUs and
GPUs, using Google @code{TensorFlow}'.  greta lets you write your own model like
in BUGS, JAGS and Stan, except that you write models right in R, it scales well
to massive datasets, and itâs easy to extend and build on.  See the website
for more information, including tutorials, examples, package documentation, and
the greta forum.")
    (license license:asl2.0)))

(define-public r-greport
  (package
    (name "r-greport")
    (version "0.7-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "greport" version))
       (sha256
        (base32 "1zyvw3npqz20mlfvmlrz742xxsigs21srn2df9yyjap93g7wqzy1"))))
    (properties `((upstream-name . "greport")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rms
                             r-latticeextra
                             r-lattice
                             r-hmisc
                             r-ggplot2
                             r-formula
                             r-data-table))
    (home-page "http://hbiostat.org/R/greport/")
    (synopsis "Graphical Reporting for Clinical Trials")
    (description
     "This package contains many functions useful for monitoring and reporting the
results of clinical trials and other experiments in which treatments are
compared. @code{LaTeX} is used to typeset the resulting reports, recommended to
be in the context of knitr'.  The Hmisc', ggplot2', and lattice packages are
used by greport for high-level graphics.")
    (license license:gpl2+)))

(define-public r-gremlins
  (package
    (name "r-gremlins")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GREMLINS" version))
       (sha256
        (base32 "16l2mzd6xm2yk1hs5333w9lfz7iymkwmi3k80yxal9dxqh5hajkb"))))
    (properties `((upstream-name . "GREMLINS")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6 r-pbmcapply r-igraph r-blockmodels r-aricode))
    (native-inputs (list r-knitr))
    (home-page "https://GrossSBM.github.io/GREMLINS/")
    (synopsis "Generalized Multipartite Networks")
    (description
     "We define generalized multipartite networks as the joint observation of several
networks implying some common pre-specified groups of individuals.  The aim is
to fit an adapted version of the popular stochastic block model to multipartite
networks, as described in Bar-hen, Barbillon and Donnet (2020)
<@code{arXiv:1807.10138>}.")
    (license license:gpl3)))

(define-public r-gremlin
  (package
    (name "r-gremlin")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gremlin" version))
       (sha256
        (base32 "1pi1bksxmjmzclppgbqqg93swf4d72d2mr8bsxv7ndcdjr69xd5r"))))
    (properties `((upstream-name . "gremlin")))
    (build-system r-build-system)
    (propagated-inputs (list r-nlme r-matrix))
    (home-page "http://github.com/matthewwolak/gremlin")
    (synopsis "Mixed-Effects REML Incorporating Generalized Inverses")
    (description
     "Fit linear mixed-effects models using restricted (or residual) maximum
likelihood (REML) and with generalized inverse matrices to specify covariance
structures for random effects.  In particular, the package is suited to fit
quantitative genetic mixed models, often referred to as animal models'.
Implements the average information algorithm as the main tool to maximize the
restricted log-likelihood, but with other algorithms available.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-grelevance
  (package
    (name "r-grelevance")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GRelevance" version))
       (sha256
        (base32 "1v1zcsk563cs6c1xn1qzm4v3yydmpr59hkz40xafkq4jbq9k2s9g"))))
    (properties `((upstream-name . "GRelevance")))
    (build-system r-build-system)
    (propagated-inputs (list r-philentropy r-mvtnorm r-mass))
    (home-page "https://cran.r-project.org/package=GRelevance")
    (synopsis
     "Graph-Based k-Sample Comparisons and Relevance Analysis in High Dimensions")
    (description
     "We propose two distribution-free test statistics based on between-sample edge
counts and measure the degree of relevance by standardized counts.  Users can
set edge costs in the graph to compare the parameters of the distributions.
Methods for comparing distributions are as described in: Xiaoping Shi (2021)
<@code{arXiv:2107.00728>}.")
    (license license:expat)))

(define-public r-gregry
  (package
    (name "r-gregry")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gregRy" version))
       (sha256
        (base32 "1b8c8wwp8r2v51ayy7rbav24wfz75z1pylg0p4vsasaw9p06dlbz"))))
    (properties `((upstream-name . "gregRy")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-purrr r-magrittr r-dplyr))
    (home-page "https://cran.r-project.org/package=gregRy")
    (synopsis "GREGORY Estimation")
    (description
     "This package provides functions which make using the Generalized Regression
Estimator(GREG) J.N.K. Rao, Isabel Molina, (2015) <doi:10.3390/f11020244> and
the Generalized Regression Estimator Operating on Resolutions of Y (GREGORY)
easier.  The functions are designed to work well within a forestry context, and
estimate multiple estimation units at once.  Compared to other survey estimation
packages, this function has greater flexibility when describing the linear
model.")
    (license license:expat)))

(define-public r-gregoryquadrature
  (package
    (name "r-gregoryquadrature")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GregoryQuadrature" version))
       (sha256
        (base32 "067plq78h4qkzsdwdwman4d4n66fx23q4cj21m8mm2zpwqknmnm7"))))
    (properties `((upstream-name . "GregoryQuadrature")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/dhetting/GregoryQuadrature")
    (synopsis "Gregory Weights for Function Integration")
    (description
     "Computes Gregory weights for a given number nodes and function order.  Anthony
Ralston and Philip Rabinowitz (2001) <ISBN:9780486414546>.")
    (license license:gpl3)))

(define-public r-greg
  (package
    (name "r-greg")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Greg" version))
       (sha256
        (base32 "13rsah8vf9s8wpkr5hlayyqa8v728cz80bvpg8vmlvnam8isb30s"))))
    (properties `((upstream-name . "Greg")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-sandwich
                             r-rms
                             r-rlang
                             r-purrr
                             r-nlme
                             r-knitr
                             r-htmltable
                             r-hmisc
                             r-gmisc
                             r-glue
                             r-forestplot
                             r-epi
                             r-dplyr
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "http://gforge.se")
    (synopsis "Regression Helper Functions")
    (description
     "This package provides methods for manipulating regression models and for
describing these in a style adapted for medical journals.  Contains functions
for generating an HTML table with crude and adjusted estimates, plotting hazard
ratio, plotting model estimates and confidence intervals using forest plots,
extending this to comparing multiple models in a single forest plots.  In
addition to the descriptive methods, there are functions for the robust
covariance matrix provided by the sandwich package, a function for adding
non-linearities to a model, and a wrapper around the Epi package's Lexis()
functions for time-splitting a dataset when modeling non-proportional hazards in
Cox regressions.")
    (license license:gpl3+)))

(define-public r-greener
  (package
    (name "r-greener")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GREENeR" version))
       (sha256
        (base32 "18li2xs12gi4c1wsjxdqbjfyw81xsmgnljdrlgj9fdw91w3f9bzw"))))
    (properties `((upstream-name . "GREENeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmap
                             r-tidyselect
                             r-sf
                             r-reshape2
                             r-parallelly
                             r-networkd3
                             r-magrittr
                             r-gridextra
                             r-ggplot2
                             r-fme
                             r-dplyr
                             r-data-table
                             r-classint))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/calfarog/GREENeR")
    (synopsis
     "Geospatial Regression Equation for European Nutrient Losses (GREEN)")
    (description
     "This package provides tools and methods to apply the model Geospatial Regression
Equation for European Nutrient losses (GREEN); Grizzetti et al. (2005)
<doi:10.1016/j.jhydrol.2004.07.036>; Grizzetti et al. (2008); Grizzetti et al.
(2012) <doi:10.1111/j.1365-2486.2011.02576.x>; Grizzetti et al. (2021)
<doi:10.1016/j.gloenvcha.2021.102281>.")
    (license license:gpl3)))

(define-public r-greencrab-toolkit
  (package
    (name "r-greencrab-toolkit")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "greencrab.toolkit" version))
       (sha256
        (base32 "11lrbjm91m2lbgr0wdy84mal4qxs9x0bmniaijxpw9fdwrkqq0aw"))))
    (properties `((upstream-name . "greencrab.toolkit")))
    (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=greencrab.toolkit")
    (synopsis
     "Run 'Stan' Models to Interpret Green Crab Monitoring Assessments")
    (description
     "These Bayesian models written in the Stan probabilistic language can be used to
interpret green crab trapping and environmental DNA monitoring data, either
independently or jointly.  Detailed model information is found in Keller (2022)
<doi:10.1002/eap.2561>.")
    (license license:gpl3)))

(define-public r-greenclust
  (package
    (name "r-greenclust")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "greenclust" version))
       (sha256
        (base32 "1wdz2lrrchwsxm68mvjcx3nlnr93n0jj8d5gr0j55idn7wn73gdq"))))
    (properties `((upstream-name . "greenclust")))
    (build-system r-build-system)
    (home-page "https://github.com/JeffJetton/greenclust")
    (synopsis "Combine Categories Using Greenacre's Method")
    (description
     "This package implements a method of iteratively collapsing the rows of a
contingency table, two at a time, by selecting the pair of categories whose
combination yields a new table with the smallest loss of chi-squared, as
described by Greenacre, M.J. (1988) <doi:10.1007/BF01901670>.  The result is
compatible with the class of object returned by the stats package's hclust()
function and can be used similarly (plotted as a dendrogram, cut, etc.).
Additional functions are provided for automatic cutting and diagnostic plotting.")
    (license license:expat)))

(define-public r-greeks
  (package
    (name "r-greeks")
    (version "1.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "greeks" version))
       (sha256
        (base32 "0vhb3k3fs57kx24bh3dnk3qgzrvn4qxnqakb2g8h1ah22i0qnvcs"))))
    (properties `((upstream-name . "greeks")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-shiny
                             r-rcpp
                             r-plotly
                             r-magrittr
                             r-ggplot2
                             r-dqrng))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ahudde/greeks")
    (synopsis
     "Sensitivities of Prices of Financial Options and Implied Volatilities")
    (description
     "This package provides methods to calculate sensitivities of financial option
prices for European, geometric and arithmetic Asian, and American options, with
various payoff functions in the Black Scholes model, and in more general jump
diffusion models.  A shiny app to interactively plot the results is included.
Furthermore, methods to compute implied volatilities are provided for a wide
range of option types and custom payoff functions.  Classical formulas are
implemented for European options in the Black Scholes Model, as is presented in
Hull, J. C. (2017), Options, Futures, and Other Derivatives.  In the case of
Asian options, Malliavin Monte Carlo Greeks are implemented, see Hudde, A. &
RÃ¼schendorf, L. (2023).  European and Asian Greeks for exponential LÃ©vy
processes. <doi:10.1007/s11009-023-10014-5>.  For American options, the Binomial
Tree Method is implemented, as is presented in Hull, J. C. (2017).")
    (license license:expat)))

(define-public r-greekletters
  (package
    (name "r-greekletters")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "greekLetters" version))
       (sha256
        (base32 "1bsdxzfz8ipa4c8xl2gjjymy20skqf5x52lqkn5wyxiln3dyx3vn"))))
    (properties `((upstream-name . "greekLetters")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-assertthat))
    (home-page "https://cran.r-project.org/package=greekLetters")
    (synopsis
     "Routines for Writing Greek Letters and Mathematical Symbols on the 'RStudio' and 'RGui'")
    (description
     "An implementation of functions to display Greek letters on the RStudio (include
subscript and superscript indexes) and RGui (without subscripts and only with
superscript 1, 2 or 3; because RGui doesn't support printing the corresponding
Unicode characters as a string: all subscripts ranging from 0 to 9 and
superscripts equal to 0, 4, 5, 6, 7, 8 or 9).  The functions in this package do
not work properly on the R console.  Characters are used via Unicode and encoded
as UTF-8 to ensure that they can be viewed on all operating systems.  Other
characters related to mathematics are included, such as the infinity symbol.
All this accessible from very simple commands.  This is a package that can be
used for teaching purposes, the statistical notation for hypothesis testing can
be written from this package and so it is possible to build a course from the
swirlify package.  Another utility of this package is to create new summary
functions that contain the functional form of the model adjusted with the Greek
letters, thus making the transition from statistical theory to practice easier.
In addition, it is a natural extension of the clisymbols package.")
    (license license:gpl2+)))

(define-public r-greedyexperimentaldesignjars
  (package
    (name "r-greedyexperimentaldesignjars")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GreedyExperimentalDesignJARs" version))
       (sha256
        (base32 "14i6m5qlh1fca9xmzid01hicd3bkf6rzl139ss78gvw544zrapw7"))))
    (properties `((upstream-name . "GreedyExperimentalDesignJARs")))
    (build-system r-build-system)
    (inputs (list openjdk))
    (propagated-inputs (list r-rjava))
    (home-page
     "https://cran.r-project.org/package=GreedyExperimentalDesignJARs")
    (synopsis "GreedyExperimentalDesign JARs")
    (description
     "These are @code{GreedyExperimentalDesign} Java dependency libraries.  Note: this
package has no functionality of its own and should not be installed as a
standalone package without @code{GreedyExperimentalDesign}.")
    (license license:gpl3)))

(define-public r-greedyexperimentaldesign
  (package
    (name "r-greedyexperimentaldesign")
    (version "1.5.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GreedyExperimentalDesign" version))
       (sha256
        (base32 "057wpfg5kfgcvfgry12agn7arg917jwgvxhjz6q1na13y8pmqnrv"))))
    (properties `((upstream-name . "GreedyExperimentalDesign")))
    (build-system r-build-system)
    (inputs (list openjdk))
    (propagated-inputs (list r-survey
                             r-stringr
                             r-stringi
                             r-rlist
                             r-rjava
                             r-rcpp
                             r-nbpmatching
                             r-kernlab
                             r-checkmate))
    (home-page "https://github.com/kapelner/GreedyExperimentalDesign")
    (synopsis "Greedy Experimental Design Construction")
    (description
     "Computes experimental designs for a two-arm experiment with covariates via a
number of methods: (0) complete randomization and randomization with
forced-balance, (1) Greedily optimizing a balance objective function via
pairwise switching.  This optimization provides lower variance for the treatment
effect estimator (and higher power) while preserving a design that is close to
complete randomization.  We return all iterations of the designs for use in a
permutation test, (2) The second is via numerical optimization (via gurobi which
must be installed, see
<https://www.gurobi.com/documentation/9.1/quickstart_windows/r_ins_the_r_package.html>)
a la Bertsimas and Kallus, (3) rerandomization, (4) Karp's method for one
covariate, (5) exhaustive enumeration to find the optimal solution (only for
small sample sizes), (6) Binary pair matching using the @code{nbpMatching}
library, (7) Binary pair matching plus design number (1) to further optimize
balance, (8) Binary pair matching plus design number (3) to further optimize
balance, (9) Hadamard designs, (10) Simultaneous Multiple Kernels.  In (1-9) we
allow for three objective functions: Mahalanobis distance, Sum of absolute
differences standardized and Kernel distances via the kernlab library.  This
package is the result of a stream of research that can be found in Krieger, A,
Azriel, D and Kapelner, A \"Nearly Random Designs with Greatly Improved Balance\"
(2016) <@code{arXiv:1612.02315>}, Krieger, A, Azriel, D and Kapelner, A \"Better
Experimental Design by Hybridizing Binary Matching with Imbalance Optimization\"
(2021) <@code{arXiv:2012.03330>}.")
    (license license:gpl3)))

(define-public r-greedyepl
  (package
    (name "r-greedyepl")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GreedyEPL" version))
       (sha256
        (base32 "19bgrgzb7jdqpljcbhr3d98y331k1b8fbwgcipak6zkhk2vr6vqz"))))
    (properties `((upstream-name . "GreedyEPL")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=GreedyEPL")
    (synopsis "Greedy Expected Posterior Loss")
    (description
     "Summarises a collection of partitions into a single optimal partition.  The
objective function is the expected posterior loss, and the minimisation is
performed through a greedy algorithm described in Rastelli, R. and Friel, N.
(2017) \"Optimal Bayesian estimators for latent variable cluster models\"
<DOI:10.1007/s11222-017-9786-y>.")
    (license license:gpl3)))

(define-public r-greed
  (package
    (name "r-greed")
    (version "0.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "greed" version))
       (sha256
        (base32 "05p24991s97h0hmvv88s3hca9b50384hm6qm771dqznqs5m4ggrq"))))
    (properties `((upstream-name . "greed")))
    (build-system r-build-system)
    (propagated-inputs (list r-rspectra
                             r-rcpparmadillo
                             r-rcpp
                             r-matrix
                             r-listenv
                             r-gtable
                             r-gridextra
                             r-ggplot2
                             r-future
                             r-cli
                             r-cba))
    (native-inputs (list r-knitr))
    (home-page "https://comeetie.github.io/greed/")
    (synopsis
     "Clustering and Model Selection with the Integrated Classification Likelihood")
    (description
     "An ensemble of algorithms that enable the clustering of networks and data
matrices (such as counts, categorical or continuous) with different type of
generative models.  Model selection and clustering is performed in combination
by optimizing the Integrated Classification Likelihood (which is equivalent to
minimizing the description length).  Several models are available such as:
Stochastic Block Model, degree corrected Stochastic Block Model, Mixtures of
Multinomial, Latent Block Model.  The optimization is performed thanks to a
combination of greedy local search and a genetic algorithm (see
<@code{arXiv:2002:11577>} for more details).")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-grec
  (package
    (name "r-grec")
    (version "1.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grec" version))
       (sha256
        (base32 "1zv71cb6kj4bm11ihygzxhmc5hcxn7m4pmyga4fpxmc2xz8pv1sz"))))
    (properties `((upstream-name . "grec")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-raster r-lifecycle r-imagine r-abind))
    (home-page "https://github.com/LuisLauM/grec")
    (synopsis
     "Gradient-Based Recognition of Spatial Patterns in Environmental Data")
    (description
     "This package provides algorithms for detection of spatial patterns from
oceanographic data using image processing methods based on Gradient Recognition.")
    (license license:gpl3+)))

(define-public r-greatr
  (package
    (name "r-greatr")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "greatR" version))
       (sha256
        (base32 "0fb065y4k7zsxb91kswssfq8zlg1p9780mx8wnzxn1pydckn4z3n"))))
    (properties `((upstream-name . "greatR")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-patchwork
                             r-optimization
                             r-neldermead
                             r-ggplot2
                             r-future
                             r-furrr
                             r-data-table
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://ruthkr.github.io/greatR/")
    (synopsis "Gene Registration from Expression and Time-Courses in R")
    (description
     "This package provides a tool for registering (aligning) gene expression profiles
between reference and query data.")
    (license license:gpl3+)))

(define-public r-grcregression
  (package
    (name "r-grcregression")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GRCRegression" version))
       (sha256
        (base32 "05ll1ncxqnkyp5231xlpkmzd07m2rnmcdzcc68mq1gqfcahakdl1"))))
    (properties `((upstream-name . "GRCRegression")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma r-mass))
    (home-page "https://cran.r-project.org/package=GRCRegression")
    (synopsis
     "Modified Poisson Regression of Grouped and Right-Censored Counts")
    (description
     "Implement maximum likelihood estimation for Poisson generalized linear models
with grouped and right-censored count data.  Intended to be used for analyzing
grouped and right-censored data, which is widely applied in many branches of
social sciences.  The algorithm implemented is described in Fu et al., (2021)
<doi:10.1111/rssa.12678>.")
    (license license:gpl3+)))

(define-public r-grcdesigns
  (package
    (name "r-grcdesigns")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GRCdesigns" version))
       (sha256
        (base32 "0fa1c9sf2sb085izcimwg78s7a7zgflncx0wbzhh8i8vfmi5xvi9"))))
    (properties `((upstream-name . "GRCdesigns")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GRCdesigns")
    (synopsis "Generalized Row-Column Designs")
    (description
     "When the number of treatments is large with limited experimental resources then
Row-Column(RC) designs with multiple units per cell can be used.  These designs
are called Generalized Row-Column (GRC) designs and are defined as designs with
v treatments in p rows and q columns such that the intersection of each row and
column (cell) consists of k experimental units.  For example (Bailey & Monod
(2001)<doi:10.1111/1467-9469.00235>), to conduct an experiment for comparing 4
treatments using 4 plants with leaves at 2 different heights row-column design
with two units per cell can be used.  A GRC design is said to be structurally
complete if corresponding to the intersection of each row and column, there
appears at least two treatments.  A GRC design is said to be structurally
incomplete if corresponding to the intersection of any row and column, there is
at least one cell which does not contain any treatment.")
    (license license:gpl2+)))

(define-public r-grcdata
  (package
    (name "r-grcdata")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GRCdata" version))
       (sha256
        (base32 "0nshii6kfvffncgcrmm7wvniq94j9djj84jikcb6ck49viikkrky"))))
    (properties `((upstream-name . "GRCdata")))
    (build-system r-build-system)
    (propagated-inputs (list r-nloptr r-cubature))
    (home-page "https://cran.r-project.org/package=GRCdata")
    (synopsis
     "Parameter Inference and Optimal Designs for Grouped and/or Right-Censored Count Data")
    (description
     "We implement two main functions.  The first function uses a given grouped and/or
right-censored grouping scheme and empirical data to infer parameters, and
implements chi-square goodness-of-fit tests.  The second function searches for
the global optimal grouping scheme of grouped and/or right-censored count
responses in surveys.")
    (license license:gpl3+)))

(define-public r-grc
  (package
    (name "r-grc")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gRc" version))
       (sha256
        (base32 "1l067jz2631x7kbmzr1zjyy3bbzr5qcyrrvw52sdplg1flald22c"))))
    (properties `((upstream-name . "gRc")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mass r-igraph r-grbase))
    (home-page "https://people.math.aau.dk/~sorenh/software/gR/")
    (synopsis
     "Inference in Graphical Gaussian Models with Edge and Vertex Symmetries")
    (description
     "Estimation, model selection and other aspects of statistical inference in
Graphical Gaussian models with edge and vertex symmetries (Graphical Gaussian
models with colours).  Documentation about @code{gRc} is provided in the paper
by Hojsgaard and Lauritzen (2007, <doi:10.18637/jss.v023.i06>) and the paper by
Hojsgaard and Lauritzen (2008, <doi:10.1111/j.1467-9868.2008.00666.x>).")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-grbase
  (package
    (name "r-grbase")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gRbase" version))
       (sha256
        (base32 "15rfl3wib8gq90fy1rr1p7cvdbiqg3qkp6vc71k02difp14hwwin"))))
    (properties `((upstream-name . "gRbase")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpparmadillo r-rcpp r-matrix
                             r-igraph))
    (native-inputs (list r-knitr))
    (home-page "https://people.math.aau.dk/~sorenh/software/gR/")
    (synopsis "Package for Graphical Modelling in R")
    (description
     "The @code{gRbase} package provides graphical modelling features used by e.g. the
packages @code{gRain}', @code{gRim} and @code{gRc}'. @code{gRbase} implements
graph algorithms including (i) maximum cardinality search (for marked and
unmarked graphs). (ii) moralization, (iii) triangulation, (iv) creation of
junction tree. @code{gRbase} facilitates array operations, @code{gRbase}
implements functions for testing for conditional independence. @code{gRbase}
illustrates how hierarchical log-linear models may be implemented and describes
concept of graphical meta data.  The facilities of the package are documented in
the book by HÃ¸jsgaard, Edwards and Lauritzen (2012,
<doi:10.1007/978-1-4614-2299-0>) and in the paper by Dethlefsen and HÃ¸jsgaard,
(2005, <doi:10.18637/jss.v014.i17>).  Please see citation(\"@code{gRbase}\") for
citation details.")
    (license license:gpl2+)))

(define-public r-gravmagsubs
  (package
    (name "r-gravmagsubs")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gravmagsubs" version))
       (sha256
        (base32 "1kc3dm2wmbwbad6sdhzs6bnpmri66142rc552cd75pijzbnymqvf"))))
    (properties `((upstream-name . "gravmagsubs")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://code.usgs.gov/gmegsc/gravmagsubs")
    (synopsis
     "Gravitational and Magnetic Attraction of 3-D Vertical Rectangular Prisms")
    (description
     "Computes the gravitational and magnetic anomalies generated by 3-D vertical
rectangular prisms at specific observation points using the method of Plouff
(1976) <doi:10.1190/1.1440645>.")
    (license license:cc0)))

(define-public r-gravity
  (package
    (name "r-gravity")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gravity" version))
       (sha256
        (base32 "05kkprv63x9m715yv9kx2k70bqnkacc72zr0aljcj0ry10nl40c6"))))
    (properties `((upstream-name . "gravity")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-survival
                             r-sandwich
                             r-rlang
                             r-rdpack
                             r-purrr
                             r-multiwayvcov
                             r-mass
                             r-magrittr
                             r-lmtest
                             r-glm2
                             r-dplyr
                             r-censreg))
    (native-inputs (list r-knitr))
    (home-page "https://pacha.dev/gravity/")
    (synopsis "Estimation Methods for Gravity Models")
    (description
     "This package provides a wrapper of different standard estimation methods for
gravity models.  This package provides estimation methods for log-log models and
multiplicative models.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-gravitas
  (package
    (name "r-gravitas")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gravitas" version))
       (sha256
        (base32 "11n85bdmigqasbq8c1amj1k7manfvs3gnax2ihsf2vp56s9c463p"))))
    (properties `((upstream-name . "gravitas")))
    (build-system r-build-system)
    (propagated-inputs (list r-tsibble
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-shiny
                             r-rlang
                             r-rcolorbrewer
                             r-magrittr
                             r-lvplot
                             r-lubridate
                             r-ineq
                             r-ggridges
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Sayani07/gravitas/")
    (synopsis
     "Explore Probability Distributions for Bivariate Temporal Granularities")
    (description
     "This package provides tools for systematically exploring large quantities of
temporal data across cyclic temporal granularities (deconstructions of time) by
visualizing probability distributions.  Cyclic time granularities can be
circular, quasi-circular or aperiodic.  gravitas computes cyclic single-order-up
or multiple-order-up granularities, check the feasibility of creating plots for
any two cyclic granularities and recommend probability distributions plots for
exploring periodicity in the data.")
    (license license:gpl3)))

(define-public r-graven
  (package
    (name "r-graven")
    (version "1.1.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gRaven" version))
       (sha256
        (base32 "136s54cgxi8dyy0zpjcag9b02lgqgdxa39lpzsk4fndz9h45wp9c"))))
    (properties `((upstream-name . "gRaven")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-grbase r-grain))
    (home-page "https://cran.r-project.org/package=gRaven")
    (synopsis "Bayes Nets: 'RHugin' Emulation with 'gRain'")
    (description
     "Wrappers for functions in the @code{gRain} package to emulate some RHugin
functionality, allowing the building of Bayesian networks consisting on discrete
chance nodes incrementally, through adding nodes, edges and conditional
probability tables, the setting of evidence, both hard (boolean) or soft
(likelihoods), querying marginal probabilities and normalizing constants, and
generating sets of high-probability configurations.  Computations will typically
not be so fast as they are with RHugin', but this package should assist users
without access to Hugin to use code written to use RHugin'.")
    (license license:gpl2+)))

(define-public r-grattaninflators
  (package
    (name "r-grattaninflators")
    (version "0.5.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grattanInflators" version))
       (sha256
        (base32 "1mls2pnnds5c2ys5wsiki0xrwcn9l4bfpl785817cbfw61rwwi3h"))))
    (properties `((upstream-name . "grattanInflators")))
    (build-system r-build-system)
    (propagated-inputs (list r-hutils r-fy r-data-table))
    (home-page "https://cran.r-project.org/package=grattanInflators")
    (synopsis "Inflators for Australian Policy Analysis")
    (description
     "Using Australian Bureau of Statistics indices, provides functions that convert
historical, nominal statistics to real, contemporary values without worrying
about date input quality, performance, or the ABS catalogue.")
    (license license:gpl2)))

(define-public r-grattan
  (package
    (name "r-grattan")
    (version "2024.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grattan" version))
       (sha256
        (base32 "0cxgj20c7gk55p22rmy1al44if4pk90yf9zify2jz9h7pviwgpjx"))))
    (properties `((upstream-name . "grattan")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr
                             r-ineq
                             r-hutilscpp
                             r-hutils
                             r-grattaninflators
                             r-fy
                             r-forecast
                             r-fastmatch
                             r-data-table
                             r-checkmate
                             r-assertthat))
    (home-page "https://github.com/HughParsonage/grattan")
    (synopsis "Australian Tax Policy Analysis")
    (description
     "Utilities to cost and evaluate Australian tax policy, including fast projections
of personal income tax collections, high-performance tax and transfer
calculators, and an interface to common indices from the Australian Bureau of
Statistics.  Written to support Grattan Institute's Australian Perspectives
program, and related projects.  Access to the Australian Taxation Office's
sample files of personal income tax returns is assumed.")
    (license license:gpl2)))

(define-public r-gratis
  (package
    (name "r-gratis")
    (version "1.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gratis" version))
       (sha256
        (base32 "0y9gblkcn145r8kx4447waqwc59iibm6nlmdykf6vrwn1y5vssa3"))))
    (properties `((upstream-name . "gratis")))
    (build-system r-build-system)
    (propagated-inputs (list r-tsibble
                             r-tsfeatures
                             r-tibble
                             r-shiny
                             r-purrr
                             r-polynom
                             r-mvtnorm
                             r-magrittr
                             r-generics
                             r-ga
                             r-forecast
                             r-foreach
                             r-fgarch
                             r-feasts
                             r-dplyr
                             r-dorng))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ykang/gratis")
    (synopsis
     "Generating Time Series with Diverse and Controllable Characteristics")
    (description
     "Generates synthetic time series based on various univariate time series models
including MAR and ARIMA processes.  Kang, Y., Hyndman, R.J., Li, F.(2020)
<doi:10.1002/sam.11461>.")
    (license license:gpl3)))

(define-public r-graticule
  (package
    (name "r-graticule")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "graticule" version))
       (sha256
        (base32 "0i2nhsrhixbwisgx8354aqsnik5sax9m63fzagmj4k3fcnpry7b0"))))
    (properties `((upstream-name . "graticule")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp r-reproj r-raster r-geosphere))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/hypertidy/graticule")
    (synopsis "Meridional and Parallel Lines for Maps")
    (description
     "Create graticule lines and labels for maps.  Control the creation of lines or
tiles by setting their placement (at particular meridians and parallels) and
extent (along parallels and meridians).  Labels are created independently of
lines.")
    (license license:gpl3)))

(define-public r-gratia
  (package
    (name "r-gratia")
    (version "0.9.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gratia" version))
       (sha256
        (base32 "0jjjncs5c3zd3jlxk5ywkg31lhxafdj5b58k664yd29193f2w3m0"))))
    (properties `((upstream-name . "gratia")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-vctrs
                             r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-purrr
                             r-pillar
                             r-patchwork
                             r-nlme
                             r-mvnfast
                             r-mgcv
                             r-lifecycle
                             r-ggplot2
                             r-ggokabeito
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://gavinsimpson.github.io/gratia/")
    (synopsis
     "Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv'")
    (description
     "Graceful ggplot'-based graphics and utility functions for working with
generalized additive models (GAMs) fitted using the mgcv package.  Provides a
reimplementation of the plot() method for GAMs that mgcv provides, as well as
tidyverse compatible representations of estimated smooths.")
    (license license:expat)))

(define-public r-grates
  (package
    (name "r-grates")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grates" version))
       (sha256
        (base32 "11014vf4w7l5hsx9r9c6r6y5vn1i3avrgs58wsymxrq9nhpphcbk"))))
    (properties `((upstream-name . "grates")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://www.reconverse.org/grates/")
    (synopsis "Grouped Date Classes")
    (description
     "This package provides a coherent interface and implementation for creating
grouped date classes.  This package is part of the RECON
(<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.")
    (license license:expat)))

(define-public r-grateful
  (package
    (name "r-grateful")
    (version "0.2.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grateful" version))
       (sha256
        (base32 "02flwjchvqr7wx5rsbiq3l8pgkmg6anw30piydnw0dsp5pmxrq1b"))))
    (properties `((upstream-name . "grateful")))
    (build-system r-build-system)
    (propagated-inputs (list r-rstudioapi r-rmarkdown r-renv r-remotes r-knitr))
    (home-page "https://pakillo.github.io/grateful/")
    (synopsis "Facilitate Citation of R Packages")
    (description
     "Facilitates the citation of R packages used in analysis projects.  Scans project
for packages used, gets their citations, and produces a document with citations
in the preferred bibliography format, ready to be pasted into reports or
manuscripts.  Alternatively, grateful can be used directly within an R Markdown
or Quarto document.")
    (license license:expat)))

(define-public r-graposas
  (package
    (name "r-graposas")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "graposas" version))
       (sha256
        (base32 "0wyrzhmkswx7s2a78lzphqm3jz9akxc462gy0icpvxn2hff9wvki"))))
    (properties `((upstream-name . "graposas")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-ga))
    (home-page "https://cran.r-project.org/package=graposas")
    (synopsis "Graphical Approach Optimal Sample Size")
    (description
     "Graphical approach provides a useful framework for multiplicity adjustment in
clinical trials with multiple endpoints.  This package includes statistical
methods to optimize sample size over initial weight and transition probability
in a graphical approach under a common setting, which is to use marginal power
for each endpoint in a trial design.  See Zhang, F. and Gou, J. (2023).  Sample
size optimization for clinical trials using graphical approaches for
multiplicity adjustment, Technical Report.")
    (license license:gpl3)))

(define-public r-graphtweets
  (package
    (name "r-graphtweets")
    (version "0.5.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "graphTweets" version))
       (sha256
        (base32 "0jf52lclwvqgybdj6fknzx046bh6jgwxvqs4c5g1ii8f2lsz9y07"))))
    (properties `((upstream-name . "graphTweets")))
    (build-system r-build-system)
    (propagated-inputs (list r-zeallot
                             r-tidyr
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-igraph
                             r-dplyr
                             r-combinat))
    (home-page "http://graphTweets.john-coene.com")
    (synopsis "Visualise Twitter Interactions")
    (description
     "Allows building an edge table from data frame of tweets, also provides function
to build nodes and another create a temporal graph.")
    (license license:expat)))

(define-public r-graphsim
  (package
    (name "r-graphsim")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "graphsim" version))
       (sha256
        (base32 "16djk5zm2pnvfyb2783rdyi6a0gspm23br7xxgb2c8wfd9gf430n"))))
    (properties `((upstream-name . "graphsim")))
    (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-mvtnorm r-matrixcalc r-matrix r-igraph r-gplots))
    (native-inputs (list r-r-rsp esbuild))
    (home-page "https://github.com/TomKellyGenetics/graphsim/")
    (synopsis "Simulate Expression Data from 'igraph' Networks")
    (description
     "This package provides functions to develop simulated continuous data (e.g., gene
expression) from a sigma covariance matrix derived from a graph structure in
igraph objects.  Intended to extend mvtnorm to take igraph structures rather
than sigma matrices as input.  This allows the use of simulated data that
correctly accounts for pathway relationships and correlations.  This allows the
use of simulated data that correctly accounts for pathway relationships and
correlations.  Here we present a versatile statistical framework to simulate
correlated gene expression data from biological pathways, by sampling from a
multivariate normal distribution derived from a graph structure.  This package
allows the simulation of biological pathways from a graph structure based on a
statistical model of gene expression.  For example methods to infer biological
pathways and gene regulatory networks from gene expression data can be tested on
simulated datasets using this framework.  This also allows for pathway
structures to be considered as a confounding variable when simulating gene
expression data to test the performance of genomic analyses.")
    (license license:gpl3)))

(define-public r-graphql
  (package
    (name "r-graphql")
    (version "1.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "graphql" version))
       (sha256
        (base32 "0b9j7hv45ml7xl29w7s2m63qc96rm6v746yy6ckpz25y1zv3j2aq"))))
    (properties `((upstream-name . "graphql")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-jsonlite))
    (home-page "https://cran.r-project.org/package=graphql")
    (synopsis "GraphQL Query Parser")
    (description
     "Bindings to the libgraphqlparser C++ library.  Parses @code{GraphQL}
<https://graphql.org> syntax and exports the AST in JSON format.")
    (license license:expat)))

(define-public r-graphpaf
  (package
    (name "r-graphpaf")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "graphPAF" version))
       (sha256
        (base32 "0m6v796hwxdv7nan3157x7b8xpph605ihj6gnmlh3v1p95dygsy7"))))
    (properties `((upstream-name . "graphPAF")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-reshape2
                             r-mass
                             r-madness
                             r-gtools
                             r-gridextra
                             r-ggrepel
                             r-ggplot2
                             r-dplyr
                             r-boot))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/johnfergusonNUIG/graphPAF")
    (synopsis "Estimating and Displaying Population Attributable Fractions")
    (description
     "Estimation and display of various types of population attributable fraction and
impact fractions.  As well as the usual calculations of attributable fractions
and impact fractions, functions are provided for attributable fraction nomograms
and fan plots, continuous exposures, for pathway specific population
attributable fractions, and for joint, average and sequential population
attributable fractions.")
    (license license:expat)))

(define-public r-graphon
  (package
    (name "r-graphon")
    (version "0.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "graphon" version))
       (sha256
        (base32 "1x61pdsb3s1vi1iiy9pjq0iz0m5j5b6q3rqhwvx9kvmg4r8yifpv"))))
    (properties `((upstream-name . "graphon")))
    (build-system r-build-system)
    (propagated-inputs (list r-roptspace r-rdpack))
    (home-page "https://cran.r-project.org/package=graphon")
    (synopsis "Collection of Graphon Estimation Methods")
    (description
     "This package provides a not-so-comprehensive list of methods for estimating
graphon, a symmetric measurable function, from a single or multiple of observed
networks.  For a detailed introduction on graphon and popular estimation
techniques, see the paper by Orbanz, P. and Roy, D.M.(2014)
<doi:10.1109/TPAMI.2014.2334607>.  It also contains several auxiliary functions
for generating sample networks using various network models and graphons.")
    (license license:expat)))

(define-public r-graphkernels
  (package
    (name "r-graphkernels")
    (version "1.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "graphkernels" version))
       (sha256
        (base32 "1fwl5a13mdmia374zas0vvd4dlfnp4in9gj42c0iq01icy1wq0la"))))
    (properties `((upstream-name . "graphkernels")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp r-igraph))
    (home-page "https://cran.r-project.org/package=graphkernels")
    (synopsis "Graph Kernels")
    (description
     "This package provides a fast C++ implementation for computing various graph
kernels including (1) simple kernels between vertex and/or edge label
histograms, (2) graphlet kernels, (3) random walk kernels (popular baselines),
and (4) the Weisfeiler-Lehman graph kernel (state-of-the-art).")
    (license license:gpl2+)))

(define-public r-graphicalvar
  (package
    (name "r-graphicalvar")
    (version "0.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "graphicalVAR" version))
       (sha256
        (base32 "1p5x77ck3prs8782kv176qq9d5ngw3zz3w6qg7aq5658pwc87gd3"))))
    (properties `((upstream-name . "graphicalVAR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-rcpparmadillo
                             r-rcpp
                             r-qgraph
                             r-mvtnorm
                             r-matrix
                             r-igraph
                             r-glmnet
                             r-glasso
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=graphicalVAR")
    (synopsis "Graphical VAR for Experience Sampling Data")
    (description
     "Estimates within and between time point interactions in experience sampling
data, using the Graphical vector autoregression model in combination with
regularization.  See also Epskamp, Waldorp, Mottus & Borsboom (2018)
<doi:10.1080/00273171.2018.1454823>.")
    (license license:gpl2+)))

(define-public r-graphicalmcp
  (package
    (name "r-graphicalmcp")
    (version "0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "graphicalMCP" version))
       (sha256
        (base32 "0bs4d1fz6l2rwssh8wq5sakkl478lr6vsx6lb1jz8qnryk6cb3wy"))))
    (properties `((upstream-name . "graphicalMCP")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-matrixstats))
    (native-inputs (list r-knitr))
    (home-page "https://gilead-biostats.github.io/graphicalMCP/")
    (synopsis "Graphical Multiple Comparison Procedures")
    (description
     "Multiple comparison procedures (MCPs) control the familywise error rate in
clinical trials.  Graphical MCPs include many commonly used procedures as
special cases; see Bretz et al. (2011) <doi:10.1002/bimj.201000239>, Lu (2016)
<doi:10.1002/sim.6985>, and Xi et al. (2017) <doi:10.1002/bimj.201600233>.  This
package is a low-dependency implementation of graphical MCPs which allow mixed
types of tests.  It also includes power simulations and visualization of
graphical MCPs.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-graphicalextremes
  (package
    (name "r-graphicalextremes")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "graphicalExtremes" version))
       (sha256
        (base32 "18gyfynx9rd06168vv1qxa8hg7ibwv0h2mx1ppizrnrl7ciwwis1"))))
    (properties `((upstream-name . "graphicalExtremes")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack
                             r-osqp
                             r-mvtnorm
                             r-igraph
                             r-glmnet
                             r-glassofast
                             r-edmcr
                             r-cvxr
                             r-corpcor))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sebastian-engelke/graphicalExtremes")
    (synopsis "Statistical Methodology for Graphical Extreme Value Models")
    (description
     "Statistical methodology for sparse multivariate extreme value models.  Methods
are provided for exact simulation and statistical inference for multivariate
Pareto distributions on graphical structures as described in the paper Graphical
Models for Extremes by Engelke and Hitz (2020) <doi:10.1111/rssb.12355>.")
    (license license:gpl3)))

(define-public r-graphhopper
  (package
    (name "r-graphhopper")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "graphhopper" version))
       (sha256
        (base32 "1zrnwh86jv37z07b6l1i94dgjn8k3b3a3f76gfjsyyxvlc64i76x"))))
    (properties `((upstream-name . "graphhopper")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-magrittr
                             r-jsonlite
                             r-httr
                             r-googlepolylines
                             r-dplyr))
    (home-page "https://github.com/crazycapivara/graphhopper-r")
    (synopsis "An R Interface to the 'GraphHopper' Directions API")
    (description
     "This package provides a quick and easy access to the @code{GraphHopper}
Directions API. @code{GraphHopper} <https://www.graphhopper.com/> itself is a
routing engine based on @code{OpenStreetMap} data.  API responses can be
converted to simple feature (sf) objects in a convenient way.")
    (license license:expat)))

(define-public r-graphframes
  (package
    (name "r-graphframes")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "graphframes" version))
       (sha256
        (base32 "1bv7d0sggwab4rd486ayw9gkb5ja5p5fin5pn92xnrmz8cgwsfxq"))))
    (properties `((upstream-name . "graphframes")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-sparklyr r-forge))
    (home-page "https://github.com/rstudio/graphframes")
    (synopsis "Interface for 'GraphFrames'")
    (description
     "This package provides a sparklyr <https://spark.rstudio.com/> extension that
provides an R interface for @code{GraphFrames} <https://graphframes.github.io/>.
@code{GraphFrames} is a package for Apache Spark that provides a
@code{DataFrame-based} API for working with graphs.  Functionality includes
motif finding and common graph algorithms, such as @code{PageRank} and
Breadth-first search.")
    (license (list license:asl2.0
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-grapherator
  (package
    (name "r-grapherator")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grapherator" version))
       (sha256
        (base32 "071by9b8figbsf4g0f8a8mgcdksjksc57mnlgggilw7py1yjnjlh"))))
    (properties `((upstream-name . "grapherator")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-reshape2
                             r-lhs
                             r-ggplot2
                             r-deldir
                             r-checkmate
                             r-bbmisc))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jakobbossek/grapherator")
    (synopsis "Modular Multi-Step Graph Generator")
    (description
     "Set of functions for step-wise generation of (weighted) graphs.  Aimed for
research in the field of single- and multi-objective combinatorial optimization.
 Graphs are generated adding nodes, edges and weights.  Each step may be
repeated multiple times with different predefined and custom generators
resulting in high flexibility regarding the graph topology and structure of edge
weights.")
    (license license:bsd-2)))

(define-public r-grapher
  (package
    (name "r-grapher")
    (version "1.9-86-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GrapheR" version))
       (sha256
        (base32 "1cjll41857dh5r1jb3zkbxpkqj2qj9s7867nwfdn0h3ws52fb304"))))
    (properties `((upstream-name . "GrapheR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GrapheR")
    (synopsis "Multi-Platform GUI for Drawing Customizable Graphs in R")
    (description
     "This package provides a multi-platform user interface for drawing highly
customizable graphs in R. It aims to be a valuable help to quickly draw
publishable graphs without any knowledge of R commands.  Six kinds of graph are
available: histogram, box-and-whisker plot, bar plot, pie chart, curve and
scatter plot.")
    (license license:gpl2)))

(define-public r-graphclust
  (package
    (name "r-graphclust")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "graphclust" version))
       (sha256
        (base32 "1crzsc05qi4i5q78w4z6s4a2s5y8fb3msr9443bnjv121sj7zn6f"))))
    (properties `((upstream-name . "graphclust")))
    (build-system r-build-system)
    (propagated-inputs (list r-sclust r-igraph r-blockmodels))
    (home-page "https://cran.r-project.org/package=graphclust")
    (synopsis "Hierarchical Graph Clustering for a Collection of Networks")
    (description
     "Graph clustering using an agglomerative algorithm to maximize the integrated
classification likelihood criterion and a mixture of stochastic block models.
The method is described in the article \"Model-based clustering of multiple
networks with a hierarchical algorithm\" by T. Rebafka (2022)
<@code{arXiv:2211.02314>}.")
    (license license:gpl2)))

(define-public r-graph4lg
  (package
    (name "r-graph4lg")
    (version "1.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "graph4lg" version))
       (sha256
        (base32 "07a109jjn1s36x1hpb1zvgb2wq41f11l8cv3car1lr6lw5g3rr3x"))))
    (properties `((upstream-name . "graph4lg")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-tidyr
                             r-stringr
                             r-spatstat-linnet
                             r-spatstat-geom
                             r-sp
                             r-sf
                             r-rdpack
                             r-raster
                             r-rappdirs
                             r-pegas
                             r-matrix
                             r-mass
                             r-igraph
                             r-hierfstat
                             r-ggplot2
                             r-gdistance
                             r-foreign
                             r-ecodist
                             r-adegenet))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://cran.r-project.org/package=graph4lg")
    (synopsis "Build Graphs for Landscape Genetics Analysis")
    (description
     "Build graphs for landscape genetics analysis.  This set of functions can be used
to import and convert spatial and genetic data initially in different formats,
import landscape graphs created with GRAPHAB software (Foltete et al., 2012)
<doi:10.1016/j.envsoft.2012.07.002>, make diagnosis plots of isolation by
distance relationships in order to choose how to build genetic graphs, create
graphs with a large range of pruning methods, weight their links with several
genetic distances, plot and analyse graphs,	compare them with other graphs.  It
uses functions from other packages such as adegenet (Jombart, 2008)
<doi:10.1093/bioinformatics/btn129> and igraph (Csardi et Nepusz, 2006)
<https://igraph.org/>.  It also implements methods commonly used in landscape
genetics to create graphs, described by Dyer et Nason (2004)
<doi:10.1111/j.1365-294X.2004.02177.x> and Greenbaum et Fefferman (2017)
<doi:10.1111/mec.14059>, and to analyse distance data (van Strien et al., 2015)
<doi:10.1038/hdy.2014.62>.")
    (license license:gpl2)))

(define-public r-graph3d
  (package
    (name "r-graph3d")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "graph3d" version))
       (sha256
        (base32 "1c4pmx37nf4nm6svrn9dqr50gb2fckx8635zg0n4gnzgdca1iyv4"))))
    (properties `((upstream-name . "graph3d")))
    (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-lazyeval r-htmlwidgets))
    (native-inputs (list esbuild))
    (home-page "https://github.com/stla/graph3d")
    (synopsis "Wrapper of the JavaScript Library 'vis-graph3d'")
    (description
     "Create interactive visualization charts to draw data in three dimensional
graphs.  The graphs can be included in Shiny apps and R markdown documents, or
viewed from the R console and RStudio Viewer.  Based on the vis.js Graph3d
module and the htmlwidgets R package.")
    (license license:gpl3)))

(define-public r-grapesagri1
  (package
    (name "r-grapesagri1")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grapesAgri1" version))
       (sha256
        (base32 "0bsnxk7fqick6y76z6s1kwrg1y8spvg0awmdg5hk37g0f3wxgyas"))))
    (properties `((upstream-name . "grapesAgri1")))
    (build-system r-build-system)
    (propagated-inputs (list r-summarytools
                             r-shinywidgets
                             r-shiny
                             r-rmarkdown
                             r-reshape2
                             r-rdpack
                             r-rcolorbrewer
                             r-pastecs
                             r-paireddata
                             r-magrittr
                             r-knitr
                             r-kableextra
                             r-hmisc
                             r-gtools
                             r-gridgraphics
                             r-ggpubr
                             r-ggplot2
                             r-dplyr
                             r-desplot
                             r-corrplot
                             r-agricolae))
    (home-page "https://cran.r-project.org/package=grapesAgri1")
    (synopsis
     "Collection of Shiny Apps for Agricultural Research Data Analysis")
    (description
     "Allows user to have graphical user interface to perform analysis of Agricultural
experimental data.  On using the functions in this package a Interactive User
Interface will pop up.  Apps Works by simple upload of files in CSV format.")
    (license license:gpl3)))

(define-public r-grapes
  (package
    (name "r-grapes")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grapes" version))
       (sha256
        (base32 "079b7w2xhb0a481s7qihhka0l9ydkjdph0865wjjkcbpr4775p26"))))
    (properties `((upstream-name . "grapes")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/wlandau/grapes")
    (synopsis "Make Binary Operators")
    (description "Turn arbitrary functions into binary operators.")
    (license license:gpl3+)))

(define-public r-grape
  (package
    (name "r-grape")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GRAPE" version))
       (sha256
        (base32 "1jzvf45ch8x8f2x9kmvv02xz9bix5vplh8143vc4m1yyq42x09c6"))))
    (properties `((upstream-name . "GRAPE")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GRAPE")
    (synopsis "Gene-Ranking Analysis of Pathway Expression")
    (description
     "Gene-Ranking Analysis of Pathway Expression (GRAPE) is a tool for summarizing
the consensus behavior of biological pathways in the form of a template, and for
quantifying the extent to which individual samples deviate from the template.
GRAPE templates are based only on the relative rankings of the genes within the
pathway and can be used for classification of tissue types or disease subtypes.
GRAPE can be used to represent gene-expression samples as vectors of pathway
scores, where each pathway score indicates the departure from a given collection
of reference samples.  The resulting pathway- space representation can be used
as the feature set for various applications, including survival analysis and
drug-response prediction.  Users of GRAPE should use the following citation:
Klein MI, Stern DF, and Zhao H. GRAPE: A pathway template method to characterize
tissue-specific functionality from gene expression profiles.  BMC
Bioinformatics, 18:317 (June 2017).")
    (license license:gpl2)))

(define-public r-grantham
  (package
    (name "r-grantham")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grantham" version))
       (sha256
        (base32 "1bcycvf1sjzwxsmdbscj1q8i7kigy6gx5r202ihb1ilvdi1nmx7l"))))
    (properties `((upstream-name . "grantham")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs r-tibble r-dplyr))
    (home-page "https://www.pattern.institute/grantham/")
    (synopsis "Calculate the Grantham Distance")
    (description
     "This package provides a minimal set of routines to calculate the Grantham
distance <doi:10.1126/science.185.4154.862>.  The Grantham distance attempts to
provide a proxy for the evolutionary distance between two amino acids based on
three key chemical properties: composition, polarity and molecular volume.  In
turn, evolutionary distance is used as a proxy for the impact of missense
mutations.  The higher the distance, the more deleterious the substitution is
expected to be.")
    (license license:expat)))

(define-public r-granovagg
  (package
    (name "r-granovagg")
    (version "1.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "granovaGG" version))
       (sha256
        (base32 "1w75d878p6izdr88j836vc7753c0mkjj5k3s04f09938gxda8a8w"))))
    (properties `((upstream-name . "granovaGG")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rcolorbrewer
                             r-magrittr
                             r-ggplot2
                             r-dplyr))
    (home-page "https://github.com/briandk/granovaGG")
    (synopsis "Graphical Analysis of Variance Using ggplot2")
    (description
     "Create what we call Elemental Graphics for display of anova results.  The term
elemental derives from the fact that each function is aimed at construction of
graphical displays that afford direct visualizations of data with respect to the
fundamental questions that drive the particular anova methods.  This package
represents a modification of the original granova package; the key change is to
use ggplot2', Hadley Wickham's package based on Grammar of Graphics concepts
(due to Wilkinson).  The main function is granovagg.1w() (a graphic for one way
ANOVA); two other functions (granovagg.ds() and granovagg.contr()) are to
construct graphics for dependent sample analyses and contrast-based analyses
respectively. (The function granova.2w(), which entails dynamic displays of
data, is not currently part of @code{granovaGG}'.) The @code{granovaGG}
functions are to display data for any number of groups, regardless of their
sizes (however, very large data sets or numbers of groups can be problematic).
For granovagg.1w() a specialized approach is used to construct data-based
contrast vectors for which anova data are displayed.  The result is that the
graphics use a straight line to facilitate clear interpretations while being
faithful to the standard effect test in anova.  The graphic results are
complementary to standard summary tables; indeed, numerical summary statistics
are provided as side effects of the graphic constructions.  granovagg.ds() and
granovagg.contr() provide graphic displays and numerical outputs for a dependent
sample and contrast-based analyses.  The graphics based on these functions can
be especially helpful for learning how the respective methods work to answer the
basic question(s) that drive the analyses.  This means they can be particularly
helpful for students and non-statistician analysts.  But these methods can be of
assistance for work-a-day applications of many kinds, as they can help to
identify outliers, clusters or patterns, as well as highlight the role of
non-linear transformations of data.  In the case of granovagg.1w() and
granovagg.ds() several arguments are provided to facilitate flexibility in the
construction of graphics that accommodate diverse features of data, according to
their corresponding display requirements.  See the help files for individual
functions.")
    (license license:expat)))

(define-public r-granova
  (package
    (name "r-granova")
    (version "2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "granova" version))
       (sha256
        (base32 "0fs8r0q5gvdyppsiil5p3dmfqyn2xygkqarnbmyhr0znbcxzkwxp"))))
    (properties `((upstream-name . "granova")))
    (build-system r-build-system)
    (propagated-inputs (list r-car))
    (home-page "https://cran.r-project.org/package=granova")
    (synopsis "Graphical Analysis of Variance")
    (description
     "This small collection of functions provides what we call elemental graphics for
display of analysis of variance results, David C. Hoaglin, Frederick Mosteller
and John W. Tukey (1991, ISBN:978-0-471-52735-0), Paul R. Rosenbaum (1989)
<doi:10.2307/2684513>, Robert M. Pruzek and James E. Helmreich
<https://jse.amstat.org/v17n1/helmreich.html>.  The term elemental derives from
the fact that each function is aimed at construction of graphical displays that
afford direct visualizations of data with respect to the fundamental questions
that drive the particular analysis of variance methods.  These functions can be
particularly helpful for students and non-statistician analysts.  But these
methods should be quite generally helpful for work-a-day applications of all
kinds, as they can help to identify outliers, clusters or patterns, as well as
highlight the role of non-linear transformations of data.")
    (license license:gpl2+)))

(define-public r-grangers
  (package
    (name "r-grangers")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grangers" version))
       (sha256
        (base32 "15q8hv7p5g5m7v6lx6nq5394fa6p05xgawnsrn1v5aslmmbbzyvl"))))
    (properties `((upstream-name . "grangers")))
    (build-system r-build-system)
    (propagated-inputs (list r-vars r-tseries))
    (home-page "https://github.com/MatFar88/grangers")
    (synopsis "Inference on Granger-Causality in the Frequency Domain")
    (description
     "This package contains five functions performing the calculation of unconditional
and conditional Granger-causality spectra, bootstrap inference on both, and
inference on the difference between them via the bootstrap approach of Farne and
Montanari, 2018 <@code{arXiv:1803.00374>}.")
    (license license:gpl2+)))

(define-public r-grandr
  (package
    (name "r-grandr")
    (version "0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grandR" version))
       (sha256
        (base32 "1cb8mkckbwkdi3zy108z3yhq3mssshhx1l9yb1j8rhgfg9abirly"))))
    (properties `((upstream-name . "grandR")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-rlang
                             r-reshape2
                             r-rcurl
                             r-plyr
                             r-patchwork
                             r-numderiv
                             r-minpack-lm
                             r-matrix
                             r-mass
                             r-lfc
                             r-labeling
                             r-ggplot2
                             r-cowplot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/erhard-lab/grandR")
    (synopsis
     "Comprehensive Analysis of Nucleotide Conversion Sequencing Data")
    (description
     "Nucleotide conversion sequencing experiments have been developed to add a
temporal dimension to RNA-seq and single-cell RNA-seq.  Such experiments require
specialized tools for primary processing such as GRAND-SLAM, (see JÃ¼rges et al
<doi:10.1093/bioinformatics/bty256>) and specialized tools for downstream
analyses. @code{grandR} provides a comprehensive toolbox for quality control,
kinetic modeling, differential gene expression analysis and visualization of
such data.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-grand
  (package
    (name "r-grand")
    (version "0.9.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grand" version))
       (sha256
        (base32 "08lgwpkbamb7p5a59q0dp1n7np9kmmvccvg97bl7937ishx50mg5"))))
    (properties `((upstream-name . "grand")))
    (build-system r-build-system)
    (propagated-inputs (list r-igraph))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/zpneal/grand")
    (synopsis "Guidelines for Reporting About Network Data")
    (description
     "Interactively applies the Guidelines for Reporting About Network Data (GRAND) to
an igraph object, and generates a uniform narrative or tabular description of
the object.")
    (license license:gpl3)))

(define-public r-gramquad
  (package
    (name "r-gramquad")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GramQuad" version))
       (sha256
        (base32 "02bpxs7yyd9805h8whvzfaxg43z4n5zxdj57hx36r3cx03hh26zd"))))
    (properties `((upstream-name . "GramQuad")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma))
    (home-page "https://gitlab.com/iagogv/GramQuad")
    (synopsis "Gram Quadrature")
    (description
     "Numerical integration with Gram polynomials (based on <@code{arXiv:2106.14875>}
[math.NA] 28 Jun 2021, by Irfan Muhammad [School of Computer Science, University
of Birmingham, UK]).")
    (license license:gpl3)))

(define-public r-gramevol
  (package
    (name "r-gramevol")
    (version "2.1-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gramEvol" version))
       (sha256
        (base32 "1am5nlnyawi4lybi10ag4fdhddvbwsdzrmr6f66bzspdpl4s26g9"))))
    (properties `((upstream-name . "gramEvol")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/fnoorian/gramEvol/")
    (synopsis "Grammatical Evolution for R")
    (description
     "This package provides a native R implementation of grammatical evolution (GE).
GE facilitates the discovery of programs that can achieve a desired goal.  This
is done by performing an evolutionary optimisation over a population of R
expressions generated via a user-defined context-free grammar (CFG) and cost
function.")
    (license license:gpl2+)))

(define-public r-grainscape
  (package
    (name "r-grainscape")
    (version "0.4.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grainscape" version))
       (sha256
        (base32 "0c9ms97xnz7z5r2hhwbw0qpcmknp2wk327z95x751yjvgbps3lkg"))))
    (properties `((upstream-name . "grainscape")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-sf
                             r-rcpp
                             r-raster
                             r-igraph
                             r-ggplot2))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://www.alexchubaty.com/grainscape/")
    (synopsis "Landscape Connectivity, Habitat, and Protected Area Networks")
    (description
     "Given a landscape resistance surface, creates minimum planar graph (Fall et al.
(2007) <doi:10.1007/s10021-007-9038-7>) and grains of connectivity (Galpern et
al. (2012) <doi:10.1111/j.1365-294X.2012.05677.x>) models that can be used to
calculate effective distances for landscape connectivity at multiple scales.
Documentation is provided by several vignettes, and a paper (Chubaty, Galpern &
Doctolero (2020) <doi:10.1111/2041-210X.13350>).")
    (license license:gpl2+)))

(define-public r-grain
  (package
    (name "r-grain")
    (version "1.4.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gRain" version))
       (sha256
        (base32 "02wh9lwg7ky8yl58rxj2f7fay1gg0q0wf3j8gkb7381cn7d4c6xk"))))
    (properties `((upstream-name . "gRain")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen
                             r-rcpparmadillo
                             r-rcpp
                             r-igraph
                             r-grbase
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://people.math.aau.dk/~sorenh/software/gR/")
    (synopsis "Bayesian Networks")
    (description
     "Probability propagation in Bayesian networks, also known as graphical
independence networks.  Documentation of the package is provided in vignettes
included in the package and in the paper by HÃ¸jsgaard (2012,
<doi:10.18637/jss.v046.i10>).  See citation(\"@code{gRain}\") for details.")
    (license license:gpl2+)))

(define-public r-grafzahl
  (package
    (name "r-grafzahl")
    (version "0.0.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grafzahl" version))
       (sha256
        (base32 "0zx6fqwklyi3sc1p59nb71mgrfnp7bqw5axmddj3706cawlrqrb8"))))
    (properties `((upstream-name . "grafzahl")))
    (build-system r-build-system)
    (propagated-inputs (list r-reticulate r-quanteda r-lime r-jsonlite))
    (native-inputs (list r-knitr))
    (home-page "https://gesistsa.github.io/grafzahl/")
    (synopsis
     "Supervised Machine Learning for Textual Data Using Transformers and 'Quanteda'")
    (description
     "Duct tape the quanteda ecosystem (Benoit et al., 2018) <doi:10.21105/joss.00774>
to modern Transformer-based text classification models (Wolf et al., 2020)
<doi:10.18653/v1/2020.emnlp-demos.6>, in order to facilitate supervised machine
learning for textual data.  This package mimics the behaviors of
quanteda.textmodels and provides a function to setup the Python environment to
use the pretrained models from Hugging Face <https://huggingface.co/>.  More
information: <doi:10.5117/CCR2023.1.003.CHAN>.")
    (license license:gpl3+)))

(define-public r-grafify
  (package
    (name "r-grafify")
    (version "4.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grafify" version))
       (sha256
        (base32 "0349sz0gykm69gf7n54g8snvjb8fkfx4cgfwp8bpvib4r9dbsllp"))))
    (properties `((upstream-name . "grafify")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-purrr
                             r-patchwork
                             r-mgcv
                             r-magrittr
                             r-lmertest
                             r-lme4
                             r-hmisc
                             r-ggplot2
                             r-emmeans
                             r-car))
    (home-page "https://github.com/ashenoy-cmbi/grafify")
    (synopsis "Easy Graphs for Data Visualisation and Linear Models for ANOVA")
    (description
     "Easily explore data by plotting graphs with a few lines of code.  Use these
ggplot() wrappers to quickly draw graphs of scatter/dots with box-whiskers,
violins or SD error bars, data distributions, before-after graphs, factorial
ANOVA and more.  Customise graphs in many ways, for example, by choosing from
colour blind-friendly palettes (12 discreet, 3 continuous and 2 divergent
palettes).  Use the simple code for ANOVA as ordinary (lm()) or mixed-effects
linear models (lmer()), including randomised-block or repeated-measures designs,
and fit non-linear outcomes as a generalised additive model (gam) using mgcv().
Obtain estimated marginal means and perform post-hoc comparisons on fitted
models (via emmeans()).  Also includes small datasets for practising code and
teaching basics before users move on to more complex designs.  See vignettes for
details on usage <https://grafify-vignettes.netlify.app/>.  Citation:
<doi:10.5281/zenodo.5136508>.")
    (license license:gpl2+)))

(define-public r-gradientpickerd3
  (package
    (name "r-gradientpickerd3")
    (version "0.1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gradientPickerD3" version))
       (sha256
        (base32 "0b20iqfipr7migwk1fl4lvdf4ghprqivcgmchfshhr8q82hyd1q0"))))
    (properties `((upstream-name . "gradientPickerD3")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-jsonlite r-htmlwidgets))
    (home-page "https://github.com/peikert/gradientPickerD3")
    (synopsis
     "Interactive Color Gradient Picker Using 'htmlwidgets' and the Modified JS Script 'jquery-gradient-picker'")
    (description
     "Widget for an interactive selection and modification of a color gradient.
@code{gradientPickerD3} allows addition, removement and replacement of color
ticks.  List of numeric values will automatically translate in their
corresponding tick position within the numeric range.  App returns a data.frame
containing tick values, colors and the positions in percent (0.0 to 1.0) for
each color tick in the gradient.  The original JS jquery-gradient-picker was
implemented by Matt Crinklaw-Vogt (nick: tantaman)
<https://github.com/tantaman/>.  Widget and JS modifications were done by CD.
Peikert.")
    (license license:gpl3)))

(define-public r-gradient
  (package
    (name "r-gradient")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "graDiEnt" version))
       (sha256
        (base32 "102nf21a410rsfxqs7dv88kxxr6ya76kyyj95bkjjkn4bz8dykyc"))))
    (properties `((upstream-name . "graDiEnt")))
    (build-system r-build-system)
    (propagated-inputs (list r-doparallel))
    (home-page "https://github.com/bmgaldo/graDiEnt")
    (synopsis "Stochastic Quasi-Gradient Differential Evolution Optimization")
    (description
     "An optim-style implementation of the Stochastic Quasi-Gradient Differential
Evolution (SQG-DE) optimization algorithm first published by Sala, Baldanzini,
and Pierini (2018; <doi:10.1007/978-3-319-72926-8_27>).  This optimization
algorithm fuses the robustness of the population-based global optimization
algorithm \"Differential Evolution\" with the efficiency of gradient-based
optimization.  The derivative-free algorithm uses population members to build
stochastic gradient estimates, without any additional objective function
evaluations.  Sala, Baldanzini, and Pierini argue this algorithm is useful for
difficult optimization problems under a tight function evaluation budget.  This
package can run SQG-DE in parallel and sequentially.")
    (license license:expat)))

(define-public r-grader
  (package
    (name "r-grader")
    (version "1.0.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gradeR" version))
       (sha256
        (base32 "1ngfrminhsk3155da0vjmvrk66nbnak8p1ns20cw32qayq6wyrr2"))))
    (properties `((upstream-name . "gradeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-testthat r-jsonlite r-callr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gradeR")
    (synopsis "Helps Grade Assignment Submissions that are R Scripts")
    (description
     "After being given the location of your students submissions and a test file, the
function runs each .R file, and evaluates the results from all the given tests.
Results are neatly returned in a data frame that has a row for each student, and
a column for each test.")
    (license license:expat)))

(define-public r-grade
  (package
    (name "r-grade")
    (version "0.2-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grade" version))
       (sha256
        (base32 "085hfvqn880yk19axdjv3z9jr33kls212vs172a8mzhnkallph1r"))))
    (properties `((upstream-name . "grade")))
    (build-system r-build-system)
    (home-page "https://github.com/ltjohnson/grade")
    (synopsis "Binary Grading functions for R")
    (description
     "This package provides functions for matching student-answers to teacher answers
for a variety of data types.")
    (license license:gpl2)))

(define-public r-grace
  (package
    (name "r-grace")
    (version "0.5.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Grace" version))
       (sha256
        (base32 "0r41zvgdd5rqm15axqqssik6plwy7snpgw8m32labkfn3f0pp7n0"))))
    (properties `((upstream-name . "Grace")))
    (build-system r-build-system)
    (propagated-inputs (list r-scalreg r-mass r-glmnet))
    (home-page
     "http://onlinelibrary.wiley.com/doi/10.1111/biom.12418/abstract")
    (synopsis "Graph-Constrained Estimation and Hypothesis Tests")
    (description
     "Use the graph-constrained estimation (Grace) procedure (Zhao and Shojaie, 2016
<doi:10.1111/biom.12418>) to estimate graph-guided linear regression
coefficients and use the Grace/@code{GraceI/GraceR} tests to perform
graph-guided hypothesis tests on the association between the response and the
predictors.")
    (license license:gpl3)))

(define-public r-grabsvg
  (package
    (name "r-grabsvg")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GrabSVG" version))
       (sha256
        (base32 "0w53wwaaap90shm17jjkyvcx55cpvpmd9cp9f7cbf36v73n74wjm"))))
    (properties `((upstream-name . "GrabSVG")))
    (build-system r-build-system)
    (propagated-inputs (list r-sparsematrixstats r-spam r-rann r-matrix
                             r-fitdistrplus))
    (home-page "https://cran.r-project.org/package=GrabSVG")
    (synopsis "Granularity-Based Spatially Variable Genes Identifications")
    (description
     "Identifying spatially variable genes is critical in linking molecular cell
functions with tissue phenotypes.  This package implemented a granularity-based
dimension-agnostic tool for the identification of spatially variable genes.  The
detailed description of this method is available at Wang, J. and Li, J. et al.
2023 (Wang, J. and Li, J. (2023), <doi:10.1038/s41467-023-43256-5>).")
    (license license:gpl2+)))

(define-public r-grabsampling
  (package
    (name "r-grabsampling")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "grabsampling" version))
       (sha256
        (base32 "031a4ak84k2mbhrmjbai3q8qnf2z3d9rp0xra67x74a99av9lzs9"))))
    (properties `((upstream-name . "grabsampling")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-plyr r-ggthemes r-ggplot2
                             r-extradistr))
    (home-page "https://github.com/Mayooran1987/grabsampling")
    (synopsis "Probability of Detection for Grab Sample Selection")
    (description
     "This package provides functions for obtaining the probability of detection, for
grab samples selection by using two different methods such as systematic or
random based on two-state Markov chain model.  For detection probability
calculation, we used results from Bhat, U. and Lal, R. (1988)
<doi:10.2307/1427041>.")
    (license license:gpl2+)))

(define-public r-gquad
  (package
    (name "r-gquad")
    (version "2.1-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gquad" version))
       (sha256
        (base32 "09hcs2lgqnim29gpd805b5ndxi22z2xwsf25rb1zckxf9c7bn7sj"))))
    (properties `((upstream-name . "gquad")))
    (build-system r-build-system)
    (propagated-inputs (list r-seqinr r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gquad")
    (synopsis "Prediction of G Quadruplexes and Other Non-B DNA Motifs")
    (description
     "Genomic biology is not limited to the confines of the canonical B-forming DNA
duplex, but includes over ten different types of other secondary structures that
are collectively termed non-B DNA structures.  Of these non-B DNA structures,
the G-quadruplexes are highly stable four-stranded structures that are
recognized by distinct subsets of nuclear factors.  This package provide
functions for predicting intramolecular G quadruplexes.  In addition, functions
for predicting other intramolecular @code{nonB} DNA structures are included.")
    (license license:artistic2.0)))

(define-public r-gqlr
  (package
    (name "r-gqlr")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gqlr" version))
       (sha256
        (base32 "0gzhqyrbnz3qjzkr4slryrlfhlaxm474y9fahlj0sb0hvcx68pxj"))))
    (properties `((upstream-name . "gqlr")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6 r-pryr r-magrittr r-jsonlite r-graphql))
    (home-page "https://github.com/schloerke/gqlr")
    (synopsis "'GraphQL' Server in R")
    (description
     "Server implementation of @code{GraphQL}
<http://graphql.github.io/graphql-spec/>, a query language originally created by
Facebook for describing data requirements on complex application data models.
Visit <http://graphql.org> to learn more about @code{GraphQL}'.")
    (license license:expat)))

(define-public r-gpx
  (package
    (name "r-gpx")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gpx" version))
       (sha256
        (base32 "0x9b3mnbrim690l4wnyzk47bgywvnw4h4l8qznnx7zkpqib2hn5i"))))
    (properties `((upstream-name . "gpx")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-rvest r-lubridate))
    (home-page "https://cran.r-project.org/package=gpx")
    (synopsis "Process GPX Files into R Data Structures")
    (description
     "Process open standard GPX files into data.frames for further use and analysis in
R.")
    (license license:expat)))

(define-public r-gpvecchia
  (package
    (name "r-gpvecchia")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GPvecchia" version))
       (sha256
        (base32 "1dvixffrn3vz98n8p7vqqr9l77m8n1gij5za8z2hfpg78pipa0mi"))))
    (properties `((upstream-name . "GPvecchia")))
    (build-system r-build-system)
    (propagated-inputs (list r-sparseinv
                             r-rcpparmadillo
                             r-rcpp
                             r-matrix
                             r-gpgp
                             r-fnn
                             r-fields
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GPvecchia")
    (synopsis "Scalable Gaussian-Process Approximations")
    (description
     "Fast scalable Gaussian process approximations, particularly well suited to
spatial (aerial, remote-sensed) and environmental data, described in more detail
in Katzfuss and Guinness (2017) <@code{arXiv:1708.06302>}.  Package also
contains a fast implementation of the incomplete Cholesky decomposition (IC0),
based on Schaefer et al. (2019) <@code{arXiv:1706.02205>} and @code{MaxMin}
ordering proposed in Guinness (2018) <@code{arXiv:1609.05372>}.")
    (license license:gpl2+)))

(define-public r-gpvam
  (package
    (name "r-gpvam")
    (version "3.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GPvam" version))
       (sha256
        (base32 "0qd71rab04qnk8z1x6p4p9y3yx9vs2dn7xm5l44xmq2xyv6bcyab"))))
    (properties `((upstream-name . "GPvam")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-patchwork
                             r-numderiv
                             r-matrix
                             r-mass
                             r-ggplot2))
    (home-page "https://cran.r-project.org/package=GPvam")
    (synopsis
     "Maximum Likelihood Estimation of Multiple Membership Mixed Models Used in Value-Added Modeling")
    (description
     "An EM algorithm, Karl et al. (2013) <doi:10.1016/j.csda.2012.10.004>, is used to
estimate the generalized, variable, and complete persistence models, Mariano et
al. (2010) <doi:10.3102/1076998609346967>.  These are multiple-membership linear
mixed models with teachers modeled as \"G-side\" effects and students modeled with
either \"G-side\" or \"R-side\" effects.")
    (license license:gpl2)))

(define-public r-gpur
  (package
    (name "r-gpur")
    (version "2.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gpuR" version))
       (sha256
        (base32 "1z9s3c4lspbm29bp1l2sixlfps04aif70rv1p1db1f1xwk6vw3bn"))))
    (properties `((upstream-name . "gpuR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rviennacl r-rcppeigen r-rcpp r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/eborgnine/gpuR")
    (synopsis "GPU Functions for R Objects")
    (description
     "This package provides GPU enabled functions for R objects in a simple and
approachable manner.  New gpu* and vcl* classes have been provided to wrap
typical R objects (e.g. vector, matrix), in both host and device spaces, to
mirror typical R syntax without the need to know @code{OpenCL}'.")
    (license license:gpl2+)))

(define-public r-gpumatrix
  (package
    (name "r-gpumatrix")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GPUmatrix" version))
       (sha256
        (base32 "07s5p6miidc8sldnjmn11zwg4rahx43skwjzw4bhwdvbd5fssd20"))))
    (properties `((upstream-name . "GPUmatrix")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GPUmatrix")
    (synopsis "Basic Linear Algebra with GPU")
    (description
     "GPUs are great resources for data analysis, especially in statistics and linear
algebra.  Unfortunately, very few packages connect R to the GPU, and none of
them are transparent enough to run the computations on the GPU without
substantial changes to the code.  The maintenance of these packages is
cumbersome: several of the earlier attempts have been removed from their
respective repositories.  It would be desirable to have a properly maintained R
package that takes advantage of the GPU with minimal changes to the existing
code.  We have developed the GPUmatrix package (available on CRAN).  GPUmatrix
mimics the behavior of the Matrix package and extends R to use the GPU for
computations.  It includes single(FP32) and double(FP64) precision data types,
and provides support for sparse matrices.  It is easy to learn, and requires
very few code changes to perform the operations on the GPU. GPUmatrix relies on
either the Torch or Tensorflow R packages to perform the GPU operations.  We
have demonstrated its usefulness for several statistical applications and
machine learning applications: non-negative matrix factorization, logistic
regression and general linear models.  We have also included a comparison of GPU
and CPU performance on different matrix operations.")
    (license license:artistic2.0)))

(define-public r-gptzeror
  (package
    (name "r-gptzeror")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gptzeror" version))
       (sha256
        (base32 "1xdzg0xazim7qn54zh4wkix0xn0z49b3grrazr7xhjbqx29aca48"))))
    (properties `((upstream-name . "gptzeror")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-lifecycle
                             r-httr2
                             r-dplyr
                             r-curl
                             r-cli))
    (home-page "https://github.com/christopherkenny/gptzeror")
    (synopsis "Identify Text Written by Large Language Models using 'GPTZero'")
    (description
     "An R interface to the GPTZero API (<https://gptzero.me/docs>).  Allows users to
classify text into human and computer written with probabilities.  Formats the
data into data frames where each sentence is an observation.  Paragraph-level
and document-level predictions are organized to align with the sentences.")
    (license license:expat)))

(define-public r-gptstudio
  (package
    (name "r-gptstudio")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gptstudio" version))
       (sha256
        (base32 "0hlmpysw3wlz2nvixbdz2gd9nk290jpfj5jcjidi6cn3skykvkdv"))))
    (properties `((upstream-name . "gptstudio")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-waiter
                             r-stringr
                             r-sseparser
                             r-shiny-i18n
                             r-shiny
                             r-rvest
                             r-rstudioapi
                             r-rlang
                             r-r6
                             r-purrr
                             r-magrittr
                             r-jsonlite
                             r-ids
                             r-httr2
                             r-htmlwidgets
                             r-htmltools
                             r-glue
                             r-fontawesome
                             r-curl
                             r-colorspace
                             r-cli
                             r-bslib
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MichelNivard/gptstudio")
    (synopsis
     "Use Large Language Models Directly in your Development Environment")
    (description
     "Large language models are readily accessible via API. This package lowers the
barrier to use the API inside of your development environment.  For more on the
API, see <https://platform.openai.com/docs/introduction>.")
    (license license:expat)))

(define-public r-gptreeo
  (package
    (name "r-gptreeo")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GPTreeO" version))
       (sha256
        (base32 "0526qy3d067nh5753rqyfgmylz7fc27hc0nfvzxisx8kifmjbzcv"))))
    (properties `((upstream-name . "GPTreeO")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6 r-mlegp r-hash r-dicekriging))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GPTreeO")
    (synopsis
     "Dividing Local Gaussian Processes for Online Learning Regression")
    (description
     "We implement and extend the Dividing Local Gaussian Process algorithm by Lederer
et al. (2020) <doi:10.48550/@code{arXiv.2006.09446>}.  Its main use case is in
online learning where it is used to train a network of local GPs (referred to as
tree) by cleverly partitioning the input space.  In contrast to a single GP,
GP@code{TreeO} is able to deal with larger amounts of data.  The package
includes methods to create the tree and set its parameter, incorporating data
points from a data stream as well as making joint predictions based on all
relevant local GPs.")
    (license license:expat)))

(define-public r-gptr
  (package
    (name "r-gptr")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gptr" version))
       (sha256
        (base32 "17kf5azrm8xhc26pkv7fsqlfw1430h6024n3nz01kfmjyjqs348m"))))
    (properties `((upstream-name . "gptr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcurl r-jsonlite))
    (home-page "https://cran.r-project.org/package=gptr")
    (synopsis "Convenient R Interface with the OpenAI 'ChatGPT' API")
    (description
     "This package provides a convenient interface with the @code{OpenAI}
@code{ChatGPT} API <https://openai.com/api>.  gptr allows you to interact with
@code{ChatGPT}', a powerful language model, for various natural language
processing tasks.  The gptr R package makes talking to @code{ChatGPT} in R super
easy.  It helps researchers and data folks by simplifying the complicated stuff,
like asking questions and getting answers.  With gptr', you can use
@code{ChatGPT} in R without any hassle, making it simpler for everyone to do
cool things with language!")
    (license license:expat)))

(define-public r-gptoolsstan
  (package
    (name "r-gptoolsstan")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gptoolsStan" version))
       (sha256
        (base32 "1f2aigkqpmbi8hxcivrbbfrpl3qs8fi2hcbvmcdc3k7j6znv4al3"))))
    (properties `((upstream-name . "gptoolsStan")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gptoolsStan")
    (synopsis "Gaussian Processes on Graphs and Lattices in 'Stan'")
    (description
     "Gaussian processes are flexible distributions to model functional data.  Whilst
theoretically appealing, they are computationally cumbersome except for small
datasets.  This package implements two methods for scaling Gaussian process
inference in Stan'.  First, a sparse approximation of the likelihood that is
generally applicable and, second, an exact method for regularly spaced data
modeled by stationary kernels using fast Fourier methods.  Utility functions are
provided to compile and fit Stan models using the cmdstanr interface.
References: Hoffmann and Onnela (2022) <@code{arXiv:2301.08836>}.")
    (license license:expat)))

(define-public r-gpseqclus
  (package
    (name "r-gpseqclus")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GPSeqClus" version))
       (sha256
        (base32 "0bs6swzj2zh2dgyiwxirimz5gdk7vfssb7ga0cqmr9s5ckvgh8ir"))))
    (properties `((upstream-name . "GPSeqClus")))
    (build-system r-build-system)
    (propagated-inputs (list r-suncalc
                             r-sp
                             r-sf
                             r-purrr
                             r-plyr
                             r-leaflet-extras
                             r-leaflet
                             r-htmlwidgets
                             r-geosphere))
    (home-page "https://cran.r-project.org/package=GPSeqClus")
    (synopsis "Sequential Clustering Algorithm for Location Data")
    (description
     "Applies sequential clustering algorithm to animal location data based on
user-defined parameters.  Plots interactive cluster maps and provides a summary
dataframe with attributes for each cluster commonly used as covariates in
subsequent modeling efforts.  Additional functions provide individual keyhole
markup language plots for quick assessment, and export of global positioning
system exchange format files for navigation purposes.  Methods can be found at
<doi:10.1111/2041-210X.13572>.")
    (license license:gpl3)))

(define-public r-gpscdf
  (package
    (name "r-gpscdf")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GPSCDF" version))
       (sha256
        (base32 "1l50a6p81lykiqlqiwb6kix5s6wzwdi8vddqhl418dlwlsh83ybf"))))
    (properties `((upstream-name . "GPSCDF")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-nnet r-nbpmatching r-mass r-dplyr))
    (home-page "https://cran.r-project.org/package=GPSCDF")
    (synopsis "Generalized Propensity Score Cumulative Distribution Function")
    (description
     "This package implements the generalized propensity score cumulative distribution
function proposed by Greene (2017)
<https://digitalcommons.library.tmc.edu/dissertations/AAI10681743/>.  A single
scalar balancing score is calculated for any generalized propensity score vector
with three or more treatments.  This balancing score is used for propensity
score matching and stratification in outcome analyses when analyzing either
ordinal or multinomial treatments.")
    (license license:gpl3+)))

(define-public r-gps-track
  (package
    (name "r-gps-track")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gps.track" version))
       (sha256
        (base32 "064l4pi44n3vwwz04rd6fs74lja2yvsr38nf911w7ighafp7s7ph"))))
    (properties `((upstream-name . "gps.track")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp r-sf r-raster r-nngeo))
    (home-page "https://cran.r-project.org/package=gps.track")
    (synopsis "GPS Track Point Information Extractor")
    (description
     "Focused on extracting important data from track points such as speed, distance,
elevation difference and azimuth.(PLAZA, J. et al., 2022)
<doi:10.1016/j.applanim.2022.105643>.")
    (license license:gpl3)))

(define-public r-gps
  (package
    (name "r-gps")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gps" version))
       (sha256
        (base32 "06vz0vs95z7cbmgsf71114d2gaqx7bn4zl6m33qg22aflna4shsr"))))
    (properties `((upstream-name . "gps")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (home-page "https://github.com/ZheyuanLi/gps")
    (synopsis "General P-Splines")
    (description
     "General P-splines are non-uniform B-splines penalized by a general difference
penalty, proposed by Li and Cao (2022) <@code{arXiv:2201.06808>}.  Constructible
on arbitrary knots, they extend the standard P-splines of Eilers and Marx (1996)
<doi:10.1214/ss/1038425655>.  They are also related to the O-splines of
O'Sullivan (1986) <doi:10.1214/ss/1177013525> via a sandwich formula that links
a general difference penalty to a derivative penalty.  The package includes
routines for setting up and handling difference and derivative penalties.  It
also fits P-splines and O-splines to (x, y) data (optionally weighted) for a
grid of smoothing parameter values in the automatic search intervals of Li and
Cao (2023) <doi:10.1007/s11222-022-10178-z>.  It aims to facilitate other
packages to implement P-splines or O-splines as a smoothing tool in their model
estimation framework.")
    (license license:gpl3)))

(define-public r-gprmortality
  (package
    (name "r-gprmortality")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GPRMortality" version))
       (sha256
        (base32 "0k5pfyl7r5vsac927aqx19lz93vyk5l3ydqkazn2hwwaw1lh2wav"))))
    (properties `((upstream-name . "GPRMortality")))
    (build-system r-build-system)
    (propagated-inputs (list r-rstan))
    (home-page "https://cran.r-project.org/package=GPRMortality")
    (synopsis "Gaussian Process Regression for Mortality Rates")
    (description
     "This package provides a Bayesian statistical model for estimating child
(under-five age group) and adult (15-60 age group) mortality.  The main
challenge is how to combine and integrate these different time series and how to
produce unified estimates of mortality rates during a specified time span.  GPR
is a Bayesian statistical model for estimating child and adult mortality rates
which its data likelihood is mortality rates from different data sources such
as: Death Registration System, Censuses or surveys.  There are also various
hyper-parameters for completeness of DRS, mean, covariance functions and
variances as priors.  This function produces estimations and uncertainty (95% or
any desirable percentiles) based on sampling and non-sampling errors due to
variation in data sources.  The GP model utilizes Bayesian inference to update
predicted mortality rates as a posterior in Bayes rule by combining data and a
prior probability distribution over parameters in mean, covariance function, and
the regression model.  This package uses Markov Chain Monte Carlo (MCMC) to
sample from posterior probability distribution by rstan package in R. Details
are given in Wang H, Dwyer-Lindgren L, Lofgren KT, et al. (2012)
<doi:10.1016/S0140-6736(12)61719-X>, Wang H, Liddell CA, Coates MM, et al.
(2014) <doi:10.1016/S0140-6736(14)60497-9> and Mohammadi, Parsaeian, Mehdipour
et al. (2017) <doi:10.1016/S2214-109X(17)30105-5>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-gpp
  (package
    (name "r-gpp")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GPP" version))
       (sha256
        (base32 "1qryza8mamwzwqiy31drf176w192ksw34kpsa0wc8gsp3ylkm2f9"))))
    (properties `((upstream-name . "GPP")))
    (build-system r-build-system)
    (propagated-inputs (list r-rstan))
    (home-page "https://cran.r-project.org/package=GPP")
    (synopsis "Gaussian Process Projection")
    (description
     "Estimates a counterfactual using Gaussian process projection.  It takes a
dataframe, creates missingness in the desired outcome variable and estimates
counterfactual values based on all information in the dataframe.  The package
writes Stan code, checks it for convergence and adds artificial noise to prevent
overfitting and returns a plot of actual values and estimated counterfactual
values using r-base plot.")
    (license license:gpl2+)))

(define-public r-gpom
  (package
    (name "r-gpom")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GPoM" version))
       (sha256
        (base32 "01j9v4a8ahfddd2mibqs44wjv5q7vm1rsa3nbswfm8x0vg7qv70c"))))
    (properties `((upstream-name . "GPoM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rgl r-float r-desolve))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GPoM")
    (synopsis "Generalized Polynomial Modelling")
    (description
     "Platform dedicated to the Global Modelling technique.  Its aim is to obtain
ordinary differential equations of polynomial form directly from time series.
It can be applied to single or multiple time series under various conditions of
noise, time series lengths, sampling, etc.  This platform is developped at the
Centre d'Etudes Spatiales de la Biosphere (CESBIO), UMR 5126 UPS/CNRS/CNES/IRD,
18 av.  Edouard Belin, 31401 TOULOUSE, FRANCE. The developments were funded by
the French program Les Enveloppes Fluides et l'Environnement (LEFE, MANU,
projets @code{GloMo}, @code{SpatioGloMo} and @code{MoMu}).  The French program
Defi @code{InFiNiTi} (CNRS) and PNTS are also acknowledged (projects
Crops'IChaos and Musc & @code{SlowFast}).  The method is described in the
article : Mangiarotti S. and Huc M. (2019) <doi:10.1063/1.5081448>.")
    (license (license:fsdg-compatible "CeCILL-2"))))

(define-public r-gpmap
  (package
    (name "r-gpmap")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gpmap" version))
       (sha256
        (base32 "1n1r4rda9v4zw8zz4vcagmkkpgirwydhqz36pyq0assxwclnkcz0"))))
    (properties `((upstream-name . "gpmap")))
    (build-system r-build-system)
    (propagated-inputs (list r-plyr r-isotone r-ggplot2 r-foreach))
    (home-page "https://cran.r-project.org/package=gpmap")
    (synopsis "Analysing and Plotting Genotype-Phenotype Maps")
    (description
     "This package provides tools for studying genotype-phenotype maps for bi-allelic
loci underlying quantitative phenotypes.  The 0.1 version is released in
connection with the publication of Gjuvsland et al (2013) and implements basic
line plots and the monotonicity measures for GP maps presented in the paper.
Reference: Gjuvsland AB, Wang Y, Plahte E and Omholt SW (2013) Monotonicity is a
key feature of genotype-phenotype maps.  Frontier in Genetics 4:216
<doi:10.3389/fgene.2013.00216>.")
    (license license:gpl3)))

(define-public r-gpm
  (package
    (name "r-gpm")
    (version "3.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GPM" version))
       (sha256
        (base32 "01vd68w5pnwhng83x5i0k9c5fhsn9siyzzndkvkmpis4mjm42sgv"))))
    (properties `((upstream-name . "GPM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-randtoolbox
                             r-pracma
                             r-lhs
                             r-lattice
                             r-iterators
                             r-foreach
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=GPM")
    (synopsis
     "Gaussian Process Modeling of Multi-Response and Possibly Noisy Datasets")
    (description
     "This package provides a general and efficient tool for fitting a response
surface to a dataset via Gaussian processes.  The dataset can have multiple
responses and be noisy (with stationary variance).  The fitted GP model can
predict the gradient as well.  The package is based on the work of Bostanabad,
R., Kearney, T., Tao, S. Y., Apley, D. W. & Chen, W. (2018) Leveraging the
nugget parameter for efficient Gaussian process modeling.  International Journal
for Numerical Methods in Engineering, 114, 501-516.")
    (license license:gpl2)))

(define-public r-gpltr
  (package
    (name "r-gpltr")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GPLTR" version))
       (sha256
        (base32 "0rlaryw8l1hj15x9s57w7rif063njcc72l42bpc9lm8r5knjyjpb"))))
    (properties `((upstream-name . "GPLTR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rpart))
    (home-page "https://cran.r-project.org/package=GPLTR")
    (synopsis "Generalized Partially Linear Tree-Based Regression Model")
    (description
     "Combining a generalized linear model with an additional tree part on the same
scale.  A four-step procedure is proposed to fit the model and test the joint
effect of the selected tree part while adjusting on confounding factors.  We
also proposed an ensemble procedure based on the bagging to improve prediction
accuracy and computed several scores of importance for variable selection.  See
Cyprien Mbogning et al.'(2014)<doi:10.1186/2043-9113-4-6> and Cyprien Mbogning
et al.'(2015)<doi:10.1159/000380850> for an overview of all the methods
implemented in this package.")
    (license license:gpl2+)))

(define-public r-gplsim
  (package
    (name "r-gplsim")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gplsim" version))
       (sha256
        (base32 "1lqw5vk23r86cks5g6vx6m8bag7pafg2w755gx16rvm5ja9l7rkj"))))
    (properties `((upstream-name . "gplsim")))
    (build-system r-build-system)
    (propagated-inputs (list r-minpack-lm r-mgcv))
    (home-page "https://cran.r-project.org/package=gplsim")
    (synopsis "Spline Estimation for GPLSIM")
    (description
     "We provides functions that employ splines to estimate generalized partially
linear single index models (GPLSIM), which extend the generalized linear models
to include nonlinear effect for some predictors.  Please see Y. (2017) at
<doi:10.1007/s11222-016-9639-0> and Y., and R. (2002) at
<doi:10.1198/016214502388618861> for more details.")
    (license license:gpl2)))

(define-public r-gplm
  (package
    (name "r-gplm")
    (version "0.7-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gplm" version))
       (sha256
        (base32 "0apvj14nl5qbi4dhhdx5nih5lvjwfcipvr8cyk6xsz4r5gfr2iw4"))))
    (properties `((upstream-name . "gplm")))
    (build-system r-build-system)
    (propagated-inputs (list r-aer))
    (home-page "https://cran.r-project.org/package=gplm")
    (synopsis "Generalized Partial Linear Models (GPLM)")
    (description
     "This package provides functions for estimating a generalized partial linear
model, a semiparametric variant of the generalized linear model (GLM) which
replaces the linear predictor by the sum of a linear and a nonparametric
function.")
    (license license:gpl2+)))

(define-public r-gplite
  (package
    (name "r-gplite")
    (version "0.13.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gplite" version))
       (sha256
        (base32 "0nxms34dfmbi3rz6x0yz8pn9a1v6w4k2w6jz1qrbqclmlg66g8vs"))))
    (properties `((upstream-name . "gplite")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-matrix))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gplite")
    (synopsis "General Purpose Gaussian Process Modelling")
    (description
     "This package implements the most common Gaussian process (GP) models using
Laplace and expectation propagation (EP) approximations, maximum marginal
likelihood (or posterior) inference for the hyperparameters, and sparse
approximations for larger datasets.")
    (license license:gpl3)))

(define-public r-gpl2025
  (package
    (name "r-gpl2025")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GPL2025" version))
       (sha256
        (base32 "0af42g3v91fbcmla2yq98v8rd4ifaznb1xvw7ny41r3843d8appc"))))
    (properties `((upstream-name . "GPL2025")))
    (build-system r-build-system)
    (propagated-inputs (list r-dplyr))
    (home-page "https://cran.r-project.org/package=GPL2025")
    (synopsis
     "Convert Chip ID of the GPL2015 into GeneBank Accession and ENTREZID")
    (description
     "Convert the chip ID of GPL2025
<https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL2025> to @code{GeneBank}
Accession and ENTREZID <http://www.ncbi.nlm.nih.gov/gene>.")
    (license license:artistic2.0)))

(define-public r-gpkg
  (package
    (name "r-gpkg")
    (version "0.0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gpkg" version))
       (sha256
        (base32 "0bx6mjvbzlli3fykrakkm8554hi4arf1ym1z9gcagybcjp4dbia1"))))
    (properties `((upstream-name . "gpkg")))
    (build-system r-build-system)
    (propagated-inputs (list r-dbi))
    (native-inputs (list r-knitr))
    (home-page "https://humus.rocks/gpkg/")
    (synopsis
     "Utilities for the Open Geospatial Consortium 'GeoPackage' Format")
    (description
     "Build Open Geospatial Consortium @code{GeoPackage} files
(<https://www.geopackage.org/>).  GDAL utilities for reading and writing spatial
data are provided by the terra package.  Additional @code{GeoPackage} and SQLite
features for attributes and tabular data are implemented with the RSQLite
package.")
    (license license:cc0)))

(define-public r-gpk
  (package
    (name "r-gpk")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gpk" version))
       (sha256
        (base32 "1zfhkqyypb24mhbj2zi9qy3gw0kqxvlp8j5ni3zm7k5rz1bnrygg"))))
    (properties `((upstream-name . "gpk")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gpk")
    (synopsis "100 Data Sets for Statistics Education")
    (description
     "Collection of datasets as prepared by Profs.  A.P. Gore, S.A. Paranjape, and
M.B. Kulkarni of Department of Statistics, Poona University, India.  With their
permission, first letter of their names forms the name of this package, the
package has been built by me and made available for the benefit of R users.
This collection requires a rich class of models and can be a very useful
building block for a beginner.")
    (license license:gpl2)))

(define-public r-gpindex
  (package
    (name "r-gpindex")
    (version "0.6.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gpindex" version))
       (sha256
        (base32 "1hif169xsvfvsnbgmad8v9qwq1d6hxvn74zyqkbmic8v3yxifs56"))))
    (properties `((upstream-name . "gpindex")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://marberts.github.io/gpindex/")
    (synopsis "Generalized Price and Quantity Indexes")
    (description
     "This package provides tools to build and work with bilateral generalized-mean
price indexes (and by extension quantity indexes), and indexes composed of
generalized-mean indexes (e.g., superlative quadratic-mean indexes, GEKS).
Covers the core mathematical machinery for making bilateral price indexes,
computing price relatives, detecting outliers, and decomposing indexes, with
wrappers for all common (and many uncommon) index-number formulas.  Implements
and extends many of the methods in 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>).")
    (license license:expat)))

(define-public r-gpic
  (package
    (name "r-gpic")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GPIC" version))
       (sha256
        (base32 "1v44hkzf3wh09j5pcfmsc8h5jzmqa501bwkmdwwjb7pz64976r23"))))
    (properties `((upstream-name . "GPIC")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GPIC")
    (synopsis "Quantifying Group Performance in Individual Competitions")
    (description
     "Compute the GPIC index as described in Pham (2020) <doi:10.35542/osf.io/ajz5v>.")
    (license license:gpl3)))

(define-public r-gpgp
  (package
    (name "r-gpgp")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GpGp" version))
       (sha256
        (base32 "13hkni4w39rkb6ki03i9nfz2cgz8yr7kxcha3n8ivz834gsfzj77"))))
    (properties `((upstream-name . "GpGp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-fnn r-bh))
    (home-page "https://cran.r-project.org/package=GpGp")
    (synopsis
     "Fast Gaussian Process Computation Using Vecchia's Approximation")
    (description
     "This package provides functions for fitting and doing predictions with Gaussian
process models using Vecchia's (1988) approximation.  Package also includes
functions for reordering input locations, finding ordered nearest neighbors
(with help from FNN package), grouping operations, and conditional simulations.
Covariance functions for spatial and spatial-temporal data on Euclidean domains
and spheres are provided.  The original approximation is due to Vecchia (1988)
<http://www.jstor.org/stable/2345768>, and the reordering and grouping methods
are from Guinness (2018) <doi:10.1080/00401706.2018.1437476>.  Model fitting
employs a Fisher scoring algorithm described in Guinness (2019)
<doi:10.48550/@code{arXiv.1905.08374>}.")
    (license license:expat)))

(define-public r-gpgame
  (package
    (name "r-gpgame")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GPGame" version))
       (sha256
        (base32 "1xxilr1ify9ip3vs000jawxplcbf1vqli40frhnwwjqf01kj8jq5"))))
    (properties `((upstream-name . "GPGame")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp
                             r-mvtnorm
                             r-mnormt
                             r-matrixstats
                             r-mass
                             r-kriginv
                             r-gpareto
                             r-dicekriging
                             r-dicedesign))
    (home-page "https://github.com/vpicheny/GPGame")
    (synopsis "Solving Complex Game Problems using Gaussian Processes")
    (description
     "Sequential strategies for finding a game equilibrium are proposed in a black-box
setting (expensive pay-off evaluations, no derivatives).  The algorithm handles
noiseless or noisy evaluations.  Two acquisition functions are available.
Graphical outputs can be generated automatically.  V. Picheny, M. Binois, A.
Habbal (2018) <doi:10.1007/s10898-018-0688-0>.  M. Binois, V. Picheny, P.
Taillandier, A. Habbal (2020) <@code{arXiv:1902.06565v2>}.")
    (license license:gpl3)))

(define-public r-gpg
  (package
    (name "r-gpg")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gpg" version))
       (sha256
        (base32 "04hnmxvnxligd93vzvp38wpxpyxvqz5qd5084awklim846v3dg03"))))
    (properties `((upstream-name . "gpg")))
    (build-system r-build-system)
    (propagated-inputs (list r-curl r-askpass))
    (native-inputs (list pkg-config r-knitr))
    (home-page "https://github.com/jeroen/gpg")
    (synopsis "GNU Privacy Guard for R")
    (description
     "Bindings to @code{GnuPG} for working with @code{OpenGPG} (RFC4880) cryptographic
methods.  Includes utilities for public key encryption, creating and verifying
digital signatures, and managing your local keyring.  Some functionality depends
on the version of @code{GnuPG} that is installed on the system.  On Windows this
package can be used together with GPG4Win which provides a GUI for managing keys
and entering passphrases.")
    (license license:expat)))

(define-public r-gpfda
  (package
    (name "r-gpfda")
    (version "3.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GPFDA" version))
       (sha256
        (base32 "0s5jq2vmz02yr1wd79s6h5c8rvpbkdlnhdy5yfckk5nxddabcymb"))))
    (properties `((upstream-name . "GPFDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-mgcv
                             r-interp
                             r-fields
                             r-fda-usc
                             r-fda))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GPFDA")
    (synopsis "Gaussian Process for Functional Data Analysis")
    (description
     "Functionalities for modelling functional data with multidimensional inputs,
multivariate functional data, and non-separable and/or non-stationary covariance
structure of function-valued processes.  In addition, there are functionalities
for functional regression models where the mean function depends on scalar
and/or functional covariates and the covariance structure depends on functional
covariates.  The development version of the package can be found on
<https://github.com/gpfda/GPFDA-dev>.")
    (license license:gpl3)))

(define-public r-gpemr
  (package
    (name "r-gpemr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GPEMR" version))
       (sha256
        (base32 "174zhr0nj6z4myvy24j1dx6j44fn30nwbjcpl95wpsz9rsrl41rm"))))
    (properties `((upstream-name . "GPEMR")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GPEMR")
    (synopsis "Growth Parameter Estimation Method")
    (description
     "This package provides functions for simulating and estimating parameters of
various growth models, including Logistic, Exponential, Theta-logistic,
Von-Bertalanffy, and Gompertz models.  The package supports both simulated and
real data analysis, including parameter estimation, visualization, and
calculation of global and local estimates.  The methods are based on research
described by Md Aktar Ul Karim and Amiya Ranjan Bhowmick (2022) in
(<https://www.researchsquare.com/article/rs-2363586/v1>).  An interactive web
application is also available at [GPEMR Web
App](<https://gpem-r.shinyapps.io/GPEM-R/>).")
    (license license:expat)))

(define-public r-gpcsign
  (package
    (name "r-gpcsign")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GPCsign" version))
       (sha256
        (base32 "0qijvdk6l8gjmikvq8bgglj5kp83b1qv7d860hrd2kw22s0gv0l7"))))
    (properties `((upstream-name . "GPCsign")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncatednormal r-tmvtnorm r-future-apply
                             r-future r-dicekriging))
    (home-page "https://cran.r-project.org/package=GPCsign")
    (synopsis
     "Gaussian Process Classification as Described in Bachoc et al. (2020)")
    (description
     "Parameter estimation and prediction of Gaussian Process Classifier models as
described in Bachoc et al. (2020) <doi:10.1007/S10898-020-00920-0>.  Important
functions : gpcm(), predict.gpcm(), update.gpcm().")
    (license license:gpl3)))

(define-public r-gpcmlasso
  (package
    (name "r-gpcmlasso")
    (version "0.1-7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GPCMlasso" version))
       (sha256
        (base32 "0hlhm780xyila4idk5r87pw046xi3lxhhpr0a22smhf4klvnlfgc"))))
    (properties `((upstream-name . "GPCMlasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-teachingdemos
                             r-statmod
                             r-rcpparmadillo
                             r-rcpp
                             r-mvtnorm
                             r-mirt
                             r-ltm
                             r-cubature
                             r-caret))
    (home-page "https://cran.r-project.org/package=GPCMlasso")
    (synopsis
     "Differential Item Functioning in Generalized Partial Credit Models")
    (description
     "This package provides a framework to detect Differential Item Functioning (DIF)
in Generalized Partial Credit Models (GPCM) and special cases of the GPCM as
proposed by Schauberger and Mair (2019) <doi:10.3758/s13428-019-01224-2>.  A
joint model is set up where DIF is explicitly parametrized and penalized
likelihood estimation is used for parameter selection.  The big advantage of the
method called GPCMlasso is that several variables can be treated simultaneously
and that both continuous and categorical variables can be used to detect DIF.")
    (license license:gpl2+)))

(define-public r-gpcerf
  (package
    (name "r-gpcerf")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GPCERF" version))
       (sha256
        (base32 "0nzfd0sz73sb4903vpgix9m767np1prcmi3m1y3pq6dzmxmqzh6a"))))
    (properties `((upstream-name . "GPCERF")))
    (build-system r-build-system)
    (propagated-inputs (list r-xgboost
                             r-wcorr
                             r-superlearner
                             r-spatstat-geom
                             r-rlang
                             r-rfast
                             r-rcpparmadillo
                             r-rcpp
                             r-mass
                             r-logger
                             r-ggplot2
                             r-cowplot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/NSAPH-Software/GPCERF")
    (synopsis
     "Gaussian Processes for Estimating Causal Exposure Response Curves")
    (description
     "This package provides a non-parametric Bayesian framework based on Gaussian
process priors for estimating causal effects of a continuous exposure and
detecting change points in the causal exposure response curves using
observational data.  Ren, B., Wu, X., Braun, D., Pillai, N., & Dominici,
F.(2021). \"Bayesian modeling for exposure response curve via gaussian processes:
Causal effects of exposure to air pollution on health outcomes.\" @code{arXiv}
preprint <doi:10.48550/@code{arXiv.2105.03454>}.")
    (license license:gpl3+)))

(define-public r-gpbstat
  (package
    (name "r-gpbstat")
    (version "0.4.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gpbStat" version))
       (sha256
        (base32 "18yzwa07grj5mqhl4y5p4gq9fqi8mrvv8mp886fjw6c3r5s84c6j"))))
    (properties `((upstream-name . "gpbStat")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-tibble r-purrr r-magrittr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/nandp1/gpbStat/")
    (synopsis
     "Comprehensive Statistical Analysis of Plant Breeding Experiments")
    (description
     "This package performs statistical data analysis of various Plant Breeding
experiments.  Contains functions for Line by Tester analysis as per Arunachalam,
V.(1974) <http://repository.ias.ac.in/89299/> and Diallel analysis as per
Griffing, B. (1956) <https://www.publish.csiro.au/bi/pdf/BI9560463>.")
    (license license:gpl2)))

(define-public r-gpboost
  (package
    (name "r-gpboost")
    (version "1.5.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gpboost" version))
       (sha256
        (base32 "04vq8vic040id09052l732pw231maghpja9j01whil9cvj5k97sj"))))
    (properties `((upstream-name . "gpboost")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjsonio r-r6 r-matrix r-data-table))
    (home-page "https://github.com/fabsig/GPBoost")
    (synopsis
     "Combining Tree-Boosting with Gaussian Process and Mixed Effects Models")
    (description
     "An R package that allows for combining tree-boosting with Gaussian process and
mixed effects models.  It also allows for independently doing tree-boosting as
well as inference and prediction for Gaussian process and mixed effects models.
See <https://github.com/fabsig/GPBoost> for more information on the software and
Sigrist (2022, JMLR) <https://www.jmlr.org/papers/v23/20-322.html> and Sigrist
(2023, TPAMI) <doi:10.1109/TPAMI.2022.3168152> for more information on the
methodology.")
    (license (list license:asl2.0
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-gpbayes
  (package
    (name "r-gpbayes")
    (version "0.1.0-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GPBayes" version))
       (sha256
        (base32 "0qrpybxvwajmwq8ybllk298wbc6q6h937b93q2az5d0bw4nqsiq2"))))
    (properties `((upstream-name . "GPBayes")))
    (build-system r-build-system)
    (inputs (list gsl))
    (propagated-inputs (list r-rcppprogress r-rcppeigen r-rcpp))
    (home-page "https://cran.r-project.org/package=GPBayes")
    (synopsis
     "Tools for Gaussian Process Modeling in Uncertainty Quantification")
    (description
     "Gaussian processes ('GPs') have been widely used to model spatial data,
spatio'-temporal data, and computer experiments in diverse areas of statistics
including spatial statistics, spatio'-temporal statistics, uncertainty
quantification, and machine learning.  This package creates basic tools for
fitting and prediction based on GPs with spatial data, spatio'-temporal data,
and computer experiments.  Key characteristics for this GP tool include: (1) the
comprehensive implementation of various covariance functions including the
MatÃ©rn family and the Confluent Hypergeometric family with isotropic form,
tensor form, and automatic relevance determination form, where the isotropic
form is widely used in spatial statistics, the tensor form is widely used in
design and analysis of computer experiments and uncertainty quantification, and
the automatic relevance determination form is widely used in machine learning;
(2) implementations via Markov chain Monte Carlo ('MCMC') algorithms and
optimization algorithms for GP models with all the implemented covariance
functions.  The methods for fitting and prediction are mainly implemented in a
Bayesian framework; (3) model evaluation via Fisher information and predictive
metrics such as predictive scores; (4) built-in functionality for simulating GPs
with all the implemented covariance functions; (5) unified implementation to
allow easy specification of various GPs'.")
    (license license:gpl2+)))

(define-public r-gparotatedf
  (package
    (name "r-gparotatedf")
    (version "2023.11-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GPArotateDF" version))
       (sha256
        (base32 "161mml6pwcqza6asn7315lqkn69k9k1l9rwa59f9m68v427a5lr0"))))
    (properties `((upstream-name . "GPArotateDF")))
    (build-system r-build-system)
    (propagated-inputs (list r-gparotation))
    (home-page "https://cran.r-project.org/package=GPArotateDF")
    (synopsis "Derivative Free Gradient Projection Factor Rotation")
    (description
     "Derivative Free Gradient Projection Algorithms for Factor Rotation.  For more
details see ?GP@code{ArotateDF}.  Theory for these functions can be found in the
following publications: Jennrich (2004) <doi:10.1007/BF02295647>.  Bernaards and
Jennrich (2005) <doi:10.1177/0013164404272507>.")
    (license license:gpl2+)))

(define-public r-gpareto
  (package
    (name "r-gpareto")
    (version "1.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GPareto" version))
       (sha256
        (base32 "1grs7pxxl9nymmg1qx8a7ln25c2bz47bv06pahq7ackhsh6zlvk9"))))
    (properties `((upstream-name . "GPareto")))
    (build-system r-build-system)
    (propagated-inputs (list r-rgl
                             r-rgenoud
                             r-rcpp
                             r-randtoolbox
                             r-pso
                             r-pbivnorm
                             r-mass
                             r-ks
                             r-kriginv
                             r-emoa
                             r-dicekriging
                             r-dicedesign))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mbinois/GPareto")
    (synopsis
     "Gaussian Processes for Pareto Front Estimation and Optimization")
    (description
     "Gaussian process regression models, a.k.a.  Kriging models, are applied to
global multi-objective optimization of black-box functions.  Multi-objective
Expected Improvement and Step-wise Uncertainty Reduction sequential infill
criteria are available.  A quantification of uncertainty on Pareto fronts is
provided using conditional simulations.")
    (license license:gpl3)))

(define-public r-gpairs
  (package
    (name "r-gpairs")
    (version "1.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gpairs" version))
       (sha256
        (base32 "08m1ydrr239vnhjbl47mng3i2w9bqmd8fijrwsraq5ysvn826n27"))))
    (properties `((upstream-name . "gpairs")))
    (build-system r-build-system)
    (propagated-inputs (list r-vcd r-mass r-lattice r-colorspace r-barcode))
    (home-page "https://cran.r-project.org/package=gpairs")
    (synopsis "The Generalized Pairs Plot")
    (description
     "Offers a generalization of the scatterplot matrix based on the recognition that
most datasets include both categorical and quantitative information.
Traditional grids of scatterplots often obscure important features of the data
when one or more variables are categorical but coded as numerical.  The
generalized pairs plot offers a range of displays of paired combinations of
categorical and quantitative variables.  Emerson et al. (2013)
<DOI:10.1080/10618600.2012.694762>.")
    (license license:gpl2+)))

(define-public r-gp
  (package
    (name "r-gp")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gp" version))
       (sha256
        (base32 "0w5wcjzgjajfx29whqkn3xv3v9bn4q0almmmsb7qw79dnbv5c16s"))))
    (properties `((upstream-name . "gp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rngforgpd r-rfast))
    (home-page "https://cran.r-project.org/package=gp")
    (synopsis
     "Maximum Likelihood Estimation of the Generalized Poisson Distribution")
    (description
     "This package provides functions to estimate the parameters of the generalized
Poisson distribution with or without covariates using maximum likelihood.  The
references include Nikoloulopoulos A.K. & Karlis D. (2008). \"On modeling count
data: a comparison of some well-known discrete distributions\".  Journal of
Statistical Computation and Simulation, 78(3): 437--457,
<doi:10.1080/10629360601010760> and Consul P.C. & Famoye F. (1992). \"Generalized
Poisson regression model\".  Communications in Statistics - Theory and Methods,
21(1): 89--109, <doi:10.1080/03610929208830766>.")
    (license license:gpl2+)))

(define-public r-goxygen
  (package
    (name "r-goxygen")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "goxygen" version))
       (sha256
        (base32 "0825zl16ap0k3jqhrjw4hfsw97ils77jmf4yqq347k3bb8i3dm79"))))
    (properties `((upstream-name . "goxygen")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml r-stringi r-pander r-gms r-citation))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/pik-piam/goxygen")
    (synopsis "In-Code Documentation for 'GAMS'")
    (description
     "This package provides a collection of tools which extract a model documentation
from GAMS code and comments.  In order to use the package you need to install
pandoc and pandoc-citeproc first (<https://pandoc.org/>).")
    (license license:bsd-2)))

(define-public r-goxplorer
  (package
    (name "r-goxplorer")
    (version "1.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GOxploreR" version))
       (sha256
        (base32 "0878czn9fyjh52a8qwmffl47x1jy8pw4m35wzicxj7k7ck58ws9k"))))
    (properties `((upstream-name . "GOxploreR")))
    (build-system r-build-system)
    (propagated-inputs (list r-network
                             r-igraph
                             r-gontr
                             r-go-db
                             r-ggraph
                             r-ggplot2
                             r-dplyr
                             r-biomart
                             r-annotate))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GOxploreR")
    (synopsis
     "Structural Exploration of the Gene Ontology (GO) Knowledge Base")
    (description
     "It provides an effective, efficient, and fast way to explore the Gene Ontology
(GO).  Given a set of genes, the package contains functions to assess the GO and
obtain the terms associated with the genes and the levels of the GO terms.  The
package provides functions for the three different GO ontology.  We discussed
the methods explicitly in the following article
<doi:10.1038/s41598-020-73326-3>.")
    (license license:gpl2)))

(define-public r-goweragreement
  (package
    (name "r-goweragreement")
    (version "1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "goweragreement" version))
       (sha256
        (base32 "0jcnl8snw6chz6hy5a7hnmzsxkzf4hhimrnp3igpg23gl0pgc2yx"))))
    (properties `((upstream-name . "goweragreement")))
    (build-system r-build-system)
    (home-page "http://www.johnhughes.org")
    (synopsis "Bayesian Gower Agreement for Categorical Data")
    (description
     "This package provides tools for applying the Bayesian Gower agreement
methodology (presented in the package vignette) to nominal or ordinal data.  The
framework can accommodate any number of units, any number of coders, and
missingness; and can handle both one-way and two-way random study designs.
Influential units and/or coders can be identified easily using leave-one-out
statistics.")
    (license license:gpl2+)))

(define-public r-govstatjpn
  (package
    (name "r-govstatjpn")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "govStatJPN" version))
       (sha256
        (base32 "03sywa7rl5rblvv370mfszz5ngp850qf32yydy1fdx10lv5amrfl"))))
    (properties `((upstream-name . "govStatJPN")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=govStatJPN")
    (synopsis "functions to get public survey data in Japan")
    (description
     "This package purposes to deal with public survey data of Japanese government via
their Application Programming Interface (http://statdb.nstac.go.jp/).")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-govinfor
  (package
    (name "r-govinfor")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "govinfoR" version))
       (sha256
        (base32 "0rxlk5wib9l03g7lm97z3n3dcywzs86h8yv9hh0w0f4h0ya9zcj4"))))
    (properties `((upstream-name . "govinfoR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-lubridate
                             r-janitor
                             r-httr2
                             r-dplyr
                             r-cli))
    (home-page "https://github.com/blackerby/govinfoR")
    (synopsis "'GovInfo' API Wrapper")
    (description
     "Access data provided by the United States Government Publishing Office (GPO)
@code{GovInfo} API (<https://github.com/usgpo/api>).")
    (license license:expat)))

(define-public r-governor
  (package
    (name "r-governor")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "governor" version))
       (sha256
        (base32 "0nm6bdlzm0qbk5pdy77iahq44ya86dkfkabx0hyalprdrn3cbpm2"))))
    (properties `((upstream-name . "governor")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/coolbutuseless/governor")
    (synopsis "Speed Limiter to Control Rate of Execution of Loops")
    (description
     "It can be necessary to limit the rate of execution of a loop or repeated
function call e.g. to show or gather data only at particular intervals.  This
package includes two methods for limiting this execution rate; speed governors
and timers.  A speed governor will insert pauses during execution to meet a
user-specified loop time.  Timers are alarm clocks which will indicate whether a
certain time has passed.  These mechanisms are implemented in C to minimize
processing overhead.")
    (license license:expat)))

(define-public r-govdown
  (package
    (name "r-govdown")
    (version "0.10.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "govdown" version))
       (sha256
        (base32 "00ndpp3v6z2497xlhhc92pa7w93qk9x8m6ibswj9dfh978d07028"))))
    (properties `((upstream-name . "govdown")))
    (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-rmarkdown))
    (native-inputs (list esbuild))
    (home-page "https://ukgovdatascience.github.io/govdown/")
    (synopsis "GOV.UK Style Templates for R Markdown")
    (description
     "This package provides a suite of custom R Markdown formats and templates for
authoring web pages styled with the GOV.UK Design System.")
    (license license:expat)))

(define-public r-gotop
  (package
    (name "r-gotop")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gotop" version))
       (sha256
        (base32 "0q65qvzg5f36hp1f3yigmysy83x40jgz4c1ksch56b7svmfbrq0s"))))
    (properties `((upstream-name . "gotop")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-htmltools))
    (home-page "https://felixluginbuhl.com/gotop/")
    (synopsis "Scroll Back to Top Icon in Shiny and R Markdown")
    (description
     "Add a scroll back to top Font Awesome icon <https://fontawesome.com/> in
rmarkdown documents and shiny apps thanks to @code{jQuery} @code{GoTop}
<https://scottdorman.blog/jquery-gotop/>.")
    (license license:expat)))

(define-public r-gosset
  (package
    (name "r-gosset")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gosset" version))
       (sha256
        (base32 "0k50vjfrqqkw9anq012pbq861lnq3ihjh310rp12xyvzbzf6d6c7"))))
    (properties `((upstream-name . "gosset")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-qvcalc
                             r-psychotools
                             r-plackettluce
                             r-patchwork
                             r-partykit
                             r-ggrepel
                             r-ggplot2
                             r-ggparty
                             r-desctools
                             r-bradleyterry2))
    (native-inputs (list r-knitr))
    (home-page "https://agrdatasci.github.io/gosset/")
    (synopsis "Tools for Data Analysis in Experimental Agriculture")
    (description
     "This package provides methods to analyse experimental agriculture data, from
data synthesis to model selection and visualisation.  The package is named after
W.S. Gosset aka âStudentâ, a pioneer of modern statistics in small sample
experimental design and analysis.")
    (license license:expat)))

(define-public r-gorica
  (package
    (name "r-gorica")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gorica" version))
       (sha256
        (base32 "1r8dg9wcqxd53zm27mf0554x2zlsclbfmqqwjbkywa3np6gi358y"))))
    (properties `((upstream-name . "gorica")))
    (build-system r-build-system)
    (propagated-inputs (list r-quadprog
                             r-mvtnorm
                             r-mass
                             r-lme4
                             r-limsolve
                             r-lavaan
                             r-bain))
    (home-page "https://informative-hypotheses.sites.uu.nl/software/goric/")
    (synopsis "Evaluation of Inequality Constrained Hypotheses Using GORICA")
    (description
     "This package implements the generalized order-restricted information criterion
approximation (GORICA), an AIC-like information criterion that can be utilized
to evaluate informative hypotheses specifying directional relationships between
model parameters in terms of (in)equality constraints (see Altinisik, Van Lissa,
Hoijtink, Oldehinkel, & Kuiper, 2021), <doi:10.31234/osf.io/t3c8g>.  The GORICA
is applicable not only to normal linear models, but also to generalized linear
models (GLMs), generalized linear mixed models (GLMMs), structural equation
models (SEMs), and contingency tables.  For contingency tables, restrictions on
cell probabilities can be non-linear.")
    (license license:gpl3+)))

(define-public r-goric
  (package
    (name "r-goric")
    (version "1.1-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "goric" version))
       (sha256
        (base32 "004nx29wz191rp0naa73r762bf6lap931lwrxifhz7izl47jzfj0"))))
    (properties `((upstream-name . "goric")))
    (build-system r-build-system)
    (propagated-inputs (list r-quadprog r-nlme r-mvtnorm r-matrix r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=goric")
    (synopsis "Generalized Order-Restricted Information Criterion")
    (description
     "Generalized Order-Restricted Information Criterion (GORIC) value for a set of
hypotheses in multivariate linear models and generalised linear models.")
    (license license:gpl2+)))

(define-public r-gorcure
  (package
    (name "r-gorcure")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GORCure" version))
       (sha256
        (base32 "1hazin0q74hf16azxa5h08kfj7g1079q77n2fn4fahpfxb8hmvwm"))))
    (properties `((upstream-name . "GORCure")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-pracma r-mass r-icsurv))
    (home-page "https://cran.r-project.org/package=GORCure")
    (synopsis
     "Fit Generalized Odds Rate Mixture Cure Model with Interval Censored Data")
    (description
     "Generalized Odds Rate Mixture Cure (GORMC) model is a flexible model of fitting
survival data with a cure fraction, including the Proportional Hazards Mixture
Cure (PHMC) model and the Proportional Odds Mixture Cure Model as special cases.
 This package fit the GORMC model with interval censored data.")
    (license license:gpl2+)))

(define-public r-gor
  (package
    (name "r-gor")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gor" version))
       (sha256
        (base32 "05wjmp8r58jv0ppq54vjw8xbwdlmksh5zch8cjm3ss20fsvqxdsj"))))
    (properties `((upstream-name . "gor")))
    (build-system r-build-system)
    (propagated-inputs (list r-igraph))
    (home-page "https://cran.r-project.org/package=gor")
    (synopsis "Algorithms for the Subject Graphs and Network Optimization")
    (description
     "Informal implementation of some algorithms from Graph Theory and Combinatorial
Optimization which arise in the subject \"Graphs and Network Optimization\" from
first course of the EUPLA (Escuela Universitaria Politecnica de La Almunia)
degree of Data Engineering in Industrial Processes.  References used are: Cook
et al (1998, ISBN:0-471-55894-X), Korte, Vygen (2018)
<doi:10.1007/978-3-662-56039-6>, Hromkovic (2004)
<doi:10.1007/978-3-662-05269-3>, Hartmann, Weigt (2005, ISBN:978-3-527-40473-5).")
    (license license:gpl3)))

(define-public r-googleway
  (package
    (name "r-googleway")
    (version "2.7.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "googleway" version))
       (sha256
        (base32 "17mihhyavl07aiyhm5pcsihmwznbdca5yfcaq4qk8kiyh81nnml0"))))
    (properties `((upstream-name . "googleway")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridislite
                             r-shiny
                             r-scales
                             r-magrittr
                             r-jsonlite
                             r-jqr
                             r-jpeg
                             r-htmlwidgets
                             r-htmltools
                             r-googlepolylines
                             r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=googleway")
    (synopsis "Accesses Google Maps APIs to Retrieve Data and Plot Maps")
    (description
     "This package provides a mechanism to plot a Google Map from R and overlay it
with shapes and markers.  Also provides access to Google Maps APIs, including
places, directions, roads, distances, geocoding, elevation and timezone.")
    (license license:expat)))

(define-public r-googletraffic
  (package
    (name "r-googletraffic")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "googletraffic" version))
       (sha256
        (base32 "0zy3dn8rzhdwlxpxmpj8j3m5nzx1hkk733lcvfl6dl7z61n72s7a"))))
    (properties `((upstream-name . "googletraffic")))
    (build-system r-build-system)
    (propagated-inputs (list r-webshot2
                             r-stringr
                             r-sp
                             r-sf
                             r-schemr
                             r-raster
                             r-png
                             r-plotwidgets
                             r-htmlwidgets
                             r-googleway
                             r-dplyr
                             r-colornamer))
    (home-page "https://dime-worldbank.github.io/googletraffic/")
    (synopsis "Google Traffic")
    (description
     "Create geographically referenced traffic data from the Google Maps
@code{JavaScript} API
<https://developers.google.com/maps/documentation/javascript/examples/layer-traffic>.")
    (license license:expat)))

(define-public r-googletagmanager
  (package
    (name "r-googletagmanager")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "googleTagManageR" version))
       (sha256
        (base32 "0ia3ndz5vf3bc5kckyxwn71nngml6p67v8r3n6h79s12xn9jzylg"))))
    (properties `((upstream-name . "googleTagManageR")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr
                             r-jsonlite
                             r-httr
                             r-googleauthr
                             r-future
                             r-dplyr
                             r-assertthat))
    (home-page "https://cran.r-project.org/package=googleTagManageR")
    (synopsis "Access the 'Google Tag Manager' API using R")
    (description
     "Interact with the Google Tag Manager API
<https://developers.google.com/tag-platform/tag-manager/api/v2>, enabling
scripted deployments and updates across multiple tags, triggers, variables and
containers.")
    (license license:expat)))

(define-public r-googler
  (package
    (name "r-googler")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "googler" version))
       (sha256
        (base32 "1aayji3ncqxl58k29d93dbx05s9rp9bc63fl2cpl1cys7gilblxx"))))
    (properties `((upstream-name . "googler")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-jsonlite))
    (home-page "https://github.com/mkearney/googler")
    (synopsis "Google from the R Console")
    (description
     "This is a wrapper for the command line tool googler', which can be found at the
following URL: <https://github.com/jarun/googler>.")
    (license license:expat)))

(define-public r-googlepubsubr
  (package
    (name "r-googlepubsubr")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "googlePubsubR" version))
       (sha256
        (base32 "1f9nd93x2z82jwjw2nha2czcnix7r97h1g3llgfx1b970gsm3pk1"))))
    (properties `((upstream-name . "googlePubsubR")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr r-googleauthr r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/andodet/googlePubsubR")
    (synopsis "R Interface for Google 'Cloud Pub/Sub' REST API")
    (description
     "This package provides an easy to use interface to the Google Pub/Sub REST API
<https://cloud.google.com/pubsub/docs/reference/rest>.")
    (license license:expat)))

(define-public r-googlepublicdata
  (package
    (name "r-googlepublicdata")
    (version "0.16.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "googlePublicData" version))
       (sha256
        (base32 "15hyslpqvncg21q581h061rfyg99y8lavqw1x8hzl7xzd49bn9r5"))))
    (properties `((upstream-name . "googlePublicData")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml r-readxl))
    (home-page "http://github.com/gvegayon/googlePublicData/")
    (synopsis
     "Working with Google's 'Public Data Explorer' DSPL Metadata Files")
    (description
     "This package provides a collection of functions to set up Google Public Data
Explorer <https://www.google.com/publicdata/> data visualization tool with your
own data, building automatically the corresponding @code{DataSet} Publishing
Language file, or DSPL (XML), metadata file jointly with the CSV files.  All
zip-up and ready to be published in Public Data Explorer'.")
    (license license:expat)))

(define-public r-googlepolylines
  (package
    (name "r-googlepolylines")
    (version "0.8.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "googlePolylines" version))
       (sha256
        (base32 "1qrigccfnv6g2jii2kfdhnxnvx0mjpxq78pg8g8nza33w9lrmrrq"))))
    (properties `((upstream-name . "googlePolylines")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=googlePolylines")
    (synopsis "Encoding Coordinates into 'Google' Polylines")
    (description
     "Encodes simple feature ('sf') objects and coordinates, and decodes polylines
using the Google polyline encoding algorithm
(<https://developers.google.com/maps/documentation/utilities/polylinealgorithm>).")
    (license license:expat)))

(define-public r-googlenlp
  (package
    (name "r-googlenlp")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "googlenlp" version))
       (sha256
        (base32 "0d0g18i3im2s5f14k69ym3vwf9a7zkaval3nwrip97xcl6kf4x9h"))))
    (properties `((upstream-name . "googlenlp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-readr
                             r-purrr
                             r-jsonlite
                             r-httr
                             r-dplyr))
    (home-page "https://github.com/BrianWeinstein/googlenlp")
    (synopsis "An Interface to Google's Cloud Natural Language API")
    (description
     "Interact with Google's Cloud Natural Language API
<https://cloud.google.com/natural-language/> (v1) via R. The API has four main
features, all of which are available through this R package: syntax analysis and
part-of-speech tagging, entity analysis, sentiment analysis, and language
identification.")
    (license license:expat)))

(define-public r-googlelanguager
  (package
    (name "r-googlelanguager")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "googleLanguageR" version))
       (sha256
        (base32 "0lm50g3gshp18nvygi6is2rayzhcx0rw2rvb4lvm0jlx5m96xgxb"))))
    (properties `((upstream-name . "googleLanguageR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-purrr
                             r-magrittr
                             r-jsonlite
                             r-googleauthr
                             r-base64enc
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "http://code.markedmondson.me/googleLanguageR/")
    (synopsis
     "Call Google's 'Natural Language' API, 'Cloud Translation' API, 'Cloud Speech' API and 'Cloud Text-to-Speech' API")
    (description
     "Call Google Cloud machine learning APIs for text and speech tasks.  Call the
Cloud Translation API <https://cloud.google.com/translate/> for detection and
translation of text, the Natural Language API
<https://cloud.google.com/natural-language/> to analyse text for sentiment,
entities or syntax, the Cloud Speech API <https://cloud.google.com/speech/> to
transcribe sound files to text and the Cloud Text-to-Speech API
<https://cloud.google.com/text-to-speech/> to turn text into sound files.")
    (license license:expat)))

(define-public r-googleknowledgegraphr
  (package
    (name "r-googleknowledgegraphr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GoogleKnowledgeGraphR" version))
       (sha256
        (base32 "1jxiq1s48skn43pydsw8s67hf70v9fhhw2drcx9vni3c3szs44qd"))))
    (properties `((upstream-name . "GoogleKnowledgeGraphR")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-curl))
    (home-page "https://cran.r-project.org/package=GoogleKnowledgeGraphR")
    (synopsis "Retrieve Information from 'Google Knowledge Graph' API")
    (description
     "Allows you to retrieve information from the Google Knowledge Graph API
<https://www.google.com/intl/bn/insidesearch/features/search/knowledge.html> and
process it in R in various forms.  The Knowledge Graph Search API lets you find
entities in the Google Knowledge Graph'.  The API uses standard schema.org types
and is compliant with the JSON-LD specification.")
    (license license:expat)))

(define-public r-googleimage2array
  (package
    (name "r-googleimage2array")
    (version "0.99.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GoogleImage2Array" version))
       (sha256
        (base32 "0ycv7lr1p2rsfln5829yk6nk9h1yk24rirjr1fk27999n42iy1pk"))))
    (properties `((upstream-name . "GoogleImage2Array")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-rvest r-magrittr r-ebimage))
    (home-page "https://github.com/kumeS/GoogleImage2Array")
    (synopsis
     "Create Array Data from 2D Image Thumbnails via Google Image Search")
    (description
     "Images are provided as an array dataset of 2D image thumbnails from Google Image
Search <https://www.google.com/search>.  This array data may be suitable for a
training data of machine learning or deep learning as a first trial.")
    (license license:artistic2.0)))

(define-public r-googleerrorreportingr
  (package
    (name "r-googleerrorreportingr")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "googleErrorReportingR" version))
       (sha256
        (base32 "0x8pzj43apkk7qkx3fv2a8531lp94ir5l83vj3ksmvzrvqjars3l"))))
    (properties `((upstream-name . "googleErrorReportingR")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr r-jsonlite r-httr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ixpantia/googleErrorReportingR")
    (synopsis "Send Error Reports to the Google Error Reporting Service API")
    (description
     "Send error reports to the Google Error Reporting service
<https://cloud.google.com/error-reporting/> and view errors and assign error
status in the Google Error Reporting user interface.")
    (license license:expat)))

(define-public r-googlecomputeenginer
  (package
    (name "r-googlecomputeenginer")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "googleComputeEngineR" version))
       (sha256
        (base32 "1s0qvnijzkja645lim373swk815n3s1292q6sk3q36ss70q42cfg"))))
    (properties `((upstream-name . "googleComputeEngineR")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-httr r-googleauthr r-future
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://cloudyr.github.io/googleComputeEngineR/")
    (synopsis "R Interface with Google Compute Engine")
    (description
     "Interact with the Google Compute Engine API in R. Lets you create, start and
stop instances in the Google Cloud'.  Support for preconfigured instances, with
templates for common R needs.")
    (license license:expat)))

(define-public r-googlecloudvisionr
  (package
    (name "r-googlecloudvisionr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "googleCloudVisionR" version))
       (sha256
        (base32 "1n4gkmnwp9wadxz1izggxnpcpm4bz3imhq5y3i2szr8zwafcqqjv"))))
    (properties `((upstream-name . "googleCloudVisionR")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr r-jsonlite r-googleauthr r-glue
                             r-data-table))
    (home-page "https://cran.r-project.org/package=googleCloudVisionR")
    (synopsis
     "Access to the 'Google Cloud Vision' API for Image Recognition, OCR and Labeling")
    (description
     "Interact with the Google Cloud Vision <https://cloud.google.com/vision/> API in
R. Part of the cloudyr <https://cloudyr.github.io/> project.")
    (license license:expat)))

(define-public r-googlecloudstorager
  (package
    (name "r-googlecloudstorager")
    (version "0.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "googleCloudStorageR" version))
       (sha256
        (base32 "1yp8h87i17kald9pkr9icxd918n5vywk0h9q8lclip2slx9bry1p"))))
    (properties `((upstream-name . "googleCloudStorageR")))
    (build-system r-build-system)
    (propagated-inputs (list r-zip
                             r-yaml
                             r-openssl
                             r-jsonlite
                             r-httr
                             r-googleauthr
                             r-curl
                             r-cli
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://code.markedmondson.me/googleCloudStorageR/")
    (synopsis "Interface with Google Cloud Storage API")
    (description
     "Interact with Google Cloud Storage <https://cloud.google.com/storage/> API in R.
Part of the cloudyr <https://cloudyr.github.io/> project.")
    (license license:expat)))

(define-public r-googlecloudrunner
  (package
    (name "r-googlecloudrunner")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "googleCloudRunner" version))
       (sha256
        (base32 "14nynl56gc8aj03wxbj243fz80j1wxd93ywfaivy1cgyfi3rid6z"))))
    (properties `((upstream-name . "googleCloudRunner")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-withr
                             r-usethis
                             r-plumber
                             r-openssl
                             r-jsonlite
                             r-jose
                             r-httr
                             r-googlepubsubr
                             r-googlecloudstorager
                             r-googleauthr
                             r-curl
                             r-cli
                             r-assertthat))
    (home-page "https://code.markedmondson.me/googleCloudRunner/")
    (synopsis
     "R Scripts in the Google Cloud via Cloud Run, Cloud Build and Cloud Scheduler")
    (description
     "This package provides tools to easily enable R scripts in the Google Cloud
Platform.  Utilise cloud services such as Cloud Run
<https://cloud.google.com/run/> for R over HTTP, Cloud Build
<https://cloud.google.com/build> for Continuous Delivery and Integration
services and Cloud Scheduler <https://cloud.google.com/scheduler/> for scheduled
scripts.")
    (license license:expat)))

(define-public r-googleauthr
  (package
    (name "r-googleauthr")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "googleAuthR" version))
       (sha256
        (base32 "15ir747qmxdcfqvdpj48famrgd1c2r1l3gfawnv55akqbxdwhmgx"))))
    (properties `((upstream-name . "googleAuthR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-memoise
                             r-jsonlite
                             r-httr
                             r-gargle
                             r-digest
                             r-cli
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://code.markedmondson.me/googleAuthR/")
    (synopsis "Authenticate and Create Google APIs")
    (description
     "Create R functions that interact with OAuth2 Google APIs
<https://developers.google.com/apis-explorer/> easily, with auto-refresh and
Shiny compatibility.")
    (license license:expat)))

(define-public r-googleanalyticsr
  (package
    (name "r-googleanalyticsr")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "googleAnalyticsR" version))
       (sha256
        (base32 "0syqlmrq4l8qnqzh6csfyf075fkfv1bmgglrmgynsbs16f4x195v"))))
    (properties `((upstream-name . "googleAnalyticsR")))
    (build-system r-build-system)
    (propagated-inputs (list r-whisker
                             r-usethis
                             r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-memoise
                             r-measurementprotocol
                             r-magrittr
                             r-jsonlite
                             r-httr
                             r-googleauthr
                             r-gargle
                             r-dplyr
                             r-cli
                             r-assertthat))
    (home-page "https://github.com/8-bit-sheep/googleAnalyticsR/")
    (synopsis "Google Analytics API into R")
    (description
     "Interact with the Google Analytics APIs
<https://developers.google.com/analytics/>, including the Core Reporting API (v3
and v4), Management API, User Activity API GA4's Data API and Admin API and
Multi-Channel Funnel API.")
    (license license:expat)))

(define-public r-googleadsr
  (package
    (name "r-googleadsr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "googleadsR" version))
       (sha256
        (base32 "02zr7y485bpp11z9njcb3c07aw454pvf8zsdqb4qswps6l1ni0gc"))))
    (properties `((upstream-name . "googleadsR")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=googleadsR")
    (synopsis "Access to 'Google Ads' via the 'Windsor.ai' API")
    (description
     "Collect marketing data from Google Ads using the Windsor.ai API
<https://windsor.ai/api-fields/>.")
    (license license:expat)))

(define-public r-goodreader
  (package
    (name "r-goodreader")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Goodreader" version))
       (sha256
        (base32 "14bsgjqy2is5wimrz0biaw882p2zyna1p6lv1py8p0xgcxadg4a1"))))
    (properties `((upstream-name . "Goodreader")))
    (build-system r-build-system)
    (propagated-inputs (list r-wordcloud2
                             r-topicmodels
                             r-tm
                             r-tidytext
                             r-tidyr
                             r-stringr
                             r-rvest
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-httr
                             r-ggplot2
                             r-dplyr
                             r-cld2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=Goodreader")
    (synopsis "Scrape and Analyze 'Goodreads' Book Data")
    (description
     "This package provides a comprehensive toolkit for scraping and analyzing book
data from <https://www.goodreads.com/>.  This package provides functions to
search for books, scrape book details and reviews, perform sentiment analysis on
reviews, and conduct topic modeling.  It's designed for researchers, data
analysts, and book enthusiasts who want to gain insights from Goodreads data.")
    (license license:gpl3)))

(define-public r-goodpractice
  (package
    (name "r-goodpractice")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "goodpractice" version))
       (sha256
        (base32 "1sz5np8lgs2s1rlswrjfw20x8c9lxdz1r0wxybrgakzlfpj1i85w"))))
    (properties `((upstream-name . "goodpractice")))
    (build-system r-build-system)
    (propagated-inputs (list r-xmlparsedata
                             r-xml2
                             r-withr
                             r-whoami
                             r-rstudioapi
                             r-rcmdcheck
                             r-praise
                             r-lintr
                             r-jsonlite
                             r-desc
                             r-cyclocomp
                             r-crayon
                             r-covr
                             r-clisymbols))
    (native-inputs (list r-knitr))
    (home-page "https://docs.ropensci.org/goodpractice/")
    (synopsis "Advice on R Package Building")
    (description
     "Give advice about good practices when building R packages.  Advice includes
functions and syntax to avoid, package structure, code complexity, code
formatting, etc.")
    (license license:expat)))

(define-public r-goodmankruskal
  (package
    (name "r-goodmankruskal")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GoodmanKruskal" version))
       (sha256
        (base32 "1d0xkmkcfcwx0qs8m4b9dgrbkg1qpb859mmzg4d1rww8rc0wxh9h"))))
    (properties `((upstream-name . "GoodmanKruskal")))
    (build-system r-build-system)
    (propagated-inputs (list r-corrplot r-classint))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GoodmanKruskal")
    (synopsis "Association Analysis for Categorical Variables")
    (description
     "Association analysis between categorical variables using the Goodman and Kruskal
tau measure.  This asymmetric association measure allows the detection of
asymmetric relations between categorical variables (e.g., one variable obtained
by re-grouping another).")
    (license license:expat)))

(define-public r-goodfitsbm
  (package
    (name "r-goodfitsbm")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GoodFitSBM" version))
       (sha256
        (base32 "1na4q7b963iayhfyrxxq2k57ph8ldxg7q8d5njplfx3ph1f0fzds"))))
    (properties `((upstream-name . "GoodFitSBM")))
    (build-system r-build-system)
    (propagated-inputs (list r-irlba r-igraph))
    (home-page "https://github.com/Roy-SR-007/GoodFitSBM")
    (synopsis "Monte Carlo Goodness-of-Fit Tests for Stochastic Block Models")
    (description
     "Performing goodness-of-fit tests for stochastic block models used to fit network
data.  Among the three variants discussed in Karwa et al. (2023)
<doi:10.1093/jrsssb/qkad084>, goodness-of-fit test has been performed for the
Erdos-Renyi (ER) and Beta versions.")
    (license license:gpl3+)))

(define-public r-goodfibes
  (package
    (name "r-goodfibes")
    (version "0.1.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GoodFibes" version))
       (sha256
        (base32 "0pcjb1266zhwwxqhyn9jlq2mpqwxg77aka5k29zc84izyz6ba3nk"))))
    (properties `((upstream-name . "GoodFibes")))
    (build-system r-build-system)
    (propagated-inputs (list r-splines2 r-rgl r-prodlim r-imager r-concaveman))
    (home-page "https://cran.r-project.org/package=GoodFibes")
    (synopsis
     "Detection and Reconstruction of Muscle Fibers from diceCT Image Data")
    (description
     "Reconstruction of muscle fibers from image stacks using textural analysis.
Includes functions for tracking, smoothing, cleaning, plotting and exporting
muscle fibers.  Also calculates basic fiber properties (e.g., length and
curvature).")
    (license license:gpl2+)))

(define-public r-good
  (package
    (name "r-good")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "good" version))
       (sha256
        (base32 "06lzr8kb0pb5k10md23dlzkzx4qk8ajriwinj4mqhgd51manm3nk"))))
    (properties `((upstream-name . "good")))
    (build-system r-build-system)
    (propagated-inputs (list r-plyr r-maxlik r-copula))
    (home-page "https://cran.r-project.org/package=good")
    (synopsis "Good Regression")
    (description
     "Fit Good regression models to count data (Tur et al., 2021)
<doi:10.48550/@code{arXiv.2105.01557>}.  The package provides functions for
model estimation and model prediction.  Density, distribution function, quantile
function and random generation for the Good distribution are also provided.")
    (license license:gpl2+)))

(define-public r-gontr
  (package
    (name "r-gontr")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gontr" version))
       (sha256
        (base32 "0asvpc3nizcn7cqdxr1j9hvhprmrazd8c7a2svhmln4vg9x1zzkp"))))
    (properties `((upstream-name . "gontr")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gontr")
    (synopsis "Dataset for 'GOxploreR'")
    (description
     "This package contains the Gene ontology terms and skeleton for the reduced GO
directed acyclic graph (DAG) for the organisms Rat and Mouse.  The methods are
explicitly discussed in the following article : Manjang et al (2020)
<doi:10.1038/s41598-020-73326-3>.")
    (license license:gpl2)))

(define-public r-gompertztrunc
  (package
    (name "r-gompertztrunc")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gompertztrunc" version))
       (sha256
        (base32 "0m1xnjn5fm3s475gjqpkdc589z3qbplzjd4f8ay3sy38pg8j48gz"))))
    (properties `((upstream-name . "gompertztrunc")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-modelr
                             r-magrittr
                             r-ggsci
                             r-ggplot2
                             r-flexsurv
                             r-dplyr
                             r-data-table
                             r-cowplot
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://caseybreen.github.io/gompertztrunc/")
    (synopsis
     "Conducting Maximum Likelihood Estimation with Truncated Mortality Data")
    (description
     "Estimates hazard ratios and mortality differentials for doubly-truncated data
without population denominators.  This method is described in Goldstein et al.
(2023) <doi:10.1007/s11113-023-09785-z>.")
    (license license:gpl3+)))

(define-public r-gomogomonomi
  (package
    (name "r-gomogomonomi")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GomoGomonoMi" version))
       (sha256
        (base32 "16kvf355yzpv0z2rndc5f50pxjnav6ld48j40vyip4h353wpwaks"))))
    (properties `((upstream-name . "GomoGomonoMi")))
    (build-system r-build-system)
    (propagated-inputs (list r-htmltools r-glue))
    (home-page "https://github.com/feddelegrand7/GomoGomonoMi")
    (synopsis "Animate Text using the 'Animate.css' Library")
    (description
     "Allows the user to animate text within rmarkdown documents and shiny
applications.  The animations are activated using the Animate.css library.  See
<https://animate.style/> for more information.")
    (license license:expat)))

(define-public r-gomms
  (package
    (name "r-gomms")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gomms" version))
       (sha256
        (base32 "1i9d5y28wrhal990x7xxy99v3z9y2pzlakj7wnynd3dpx5pqr0jj"))))
    (properties `((upstream-name . "gomms")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gomms")
    (synopsis "GLM-Based Ordination Method")
    (description
     "This package provides a zero-inflated quasi-Poisson factor model to display
similarity between samples visually in a low (2 or 3) dimensional space.")
    (license license:gpl2+)))

(define-public r-goldfish
  (package
    (name "r-goldfish")
    (version "1.6.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "goldfish" version))
       (sha256
        (base32 "0y6x3zlsgq48kv5mhgcaaczjz8zlf8yvs5z7hgdc8jdzi1dl0vw8"))))
    (properties `((upstream-name . "goldfish")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-rlang
                             r-rcpparmadillo
                             r-rcpp
                             r-ggplot2
                             r-generics
                             r-changepoint))
    (native-inputs (list r-knitr))
    (home-page "https://stocnet.github.io/goldfish/")
    (synopsis "Statistical Network Models for Dynamic Network Data")
    (description
     "This package provides tools for fitting statistical network models to dynamic
network data.  Can be used for fitting both dynamic network actor models
('@code{DyNAMs}') and relational event models ('REMs').  Stadtfeld, Hollway, and
Block (2017a) <doi:10.1177/0081175017709295>, Stadtfeld, Hollway, and Block
(2017b) <doi:10.1177/0081175017733457>, Stadtfeld and Block (2017)
<doi:10.15195/v4.a14>, Hoffman et al. (2020) <doi:10.1017/nws.2020.3>.")
    (license license:gpl3+)))

(define-public r-gogarch
  (package
    (name "r-gogarch")
    (version "0.7-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gogarch" version))
       (sha256
        (base32 "01ikd7ngzgyrgkr637cxnj5xqmdmsj6szcr9f8wmrxvly0b3qf1i"))))
    (properties `((upstream-name . "gogarch")))
    (build-system r-build-system)
    (propagated-inputs (list r-fgarch r-fastica))
    (home-page "https://cran.r-project.org/package=gogarch")
    (synopsis "Generalized Orthogonal GARCH (GO-GARCH) Models")
    (description
     "Provision of classes and methods for estimating generalized orthogonal GARCH
models.  This is an alternative approach to CC-GARCH models in the context of
multivariate volatility modeling.")
    (license license:gpl2+)))

(define-public r-goft
  (package
    (name "r-goft")
    (version "1.3.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "goft" version))
       (sha256
        (base32 "0ag1bks44hm787sgbab70ab9q0dzbfsx70ldprafjx92gg1cqv5s"))))
    (properties `((upstream-name . "goft")))
    (build-system r-build-system)
    (propagated-inputs (list r-sn r-fitdistrplus))
    (home-page "https://cran.r-project.org/package=goft")
    (synopsis "Tests of Fit for some Probability Distributions")
    (description
     "Goodness-of-fit tests for skew-normal, gamma, inverse Gaussian, log-normal,
Weibull', Frechet', Gumbel, normal, multivariate normal, Cauchy, Laplace or
double exponential, exponential and generalized Pareto distributions.  Parameter
estimators for gamma, inverse Gaussian and generalized Pareto distributions.")
    (license license:gpl3+)))

(define-public r-gofshiny
  (package
    (name "r-gofshiny")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GOFShiny" version))
       (sha256
        (base32 "04m08d9w8qqbql86c18qpnwym7agry8wspfwrnbbyp52h2pn2gxb"))))
    (properties `((upstream-name . "GOFShiny")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-rmarkdown r-rhandsontable))
    (home-page "https://cran.r-project.org/package=GOFShiny")
    (synopsis "Interactive Document for Working with Goodness of Fit Analysis")
    (description
     "An interactive document on the topic of goodness of fit analysis using rmarkdown
and shiny packages.  Runtime examples are provided in the package function as
well as at <https://predanalyticssessions1.shinyapps.io/@code{ChiSquareGOF/>}.")
    (license license:gpl2)))

(define-public r-gofreg
  (package
    (name "r-gofreg")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gofreg" version))
       (sha256
        (base32 "0jy3vb8xm35waqzbj1px5nnm9ld7hdni28qwwciqsxsgpyb1n92z"))))
    (properties `((upstream-name . "gofreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-r6 r-ggplot2 r-dplyr r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/gkremling/gofreg")
    (synopsis
     "Bootstrap-Based Goodness-of-Fit Tests for Parametric Regression")
    (description
     "This package provides statistical methods to check if a parametric family of
conditional density functions fits to some given dataset of covariates and
response variables.  Different test statistics can be used to determine the
goodness-of-fit of the assumed model, see Andrews (1997) <doi:10.2307/2171880>,
Bierens & Wang (2012) <doi:10.1017/S0266466611000168>, Dikta & Scheer (2021)
<doi:10.1007/978-3-030-73480-0> and Kremling & Dikta (2024)
<doi:10.48550/@code{arXiv.2409.20262>}.  As proposed in these papers, the
corresponding p-values are approximated using a parametric bootstrap method.")
    (license license:expat)))

(define-public r-gofkmt
  (package
    (name "r-gofkmt")
    (version "2.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GofKmt" version))
       (sha256
        (base32 "1v9n1hfracincgaip7pym2l9h54azajw4fmsld0ff2rsd5np362z"))))
    (properties `((upstream-name . "GofKmt")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsolnp r-rcpparmadillo r-rcpp r-ggplot2))
    (home-page "https://cran.r-project.org/package=GofKmt")
    (synopsis "Khmaladze Martingale Transformation Goodness-of-Fit Test")
    (description
     "Consider a goodness-of-fit (GOF) problem of testing whether a random sample
comes from one sample location-scale model where location and scale parameters
are unknown.  It is well known that Khmaladze martingale transformation method -
which was proposed by Khmaladze (1981) <DOI:10.1137/1126027> - provides
asymptotic distribution free test for the GOF problem.  This package contains
one function: @code{KhmaladzeTrans}().  In this version, @code{KhmaladzeTrans}()
provides test statistic and critical value of GOF test for normal, Cauchy, and
logistic distributions.  This package used the main algorithm proposed by Kim
(2020) <DOI:10.1007/s00180-020-00971-7> and tests for other distributions will
be available at the later version.")
    (license license:gpl2)))

(define-public r-gofkernel
  (package
    (name "r-gofkernel")
    (version "2.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GoFKernel" version))
       (sha256
        (base32 "0xygsdmggl35fafyp431mkwalwixw2r3f32qll1pf72dfwd8y8d3"))))
    (properties `((upstream-name . "GoFKernel")))
    (build-system r-build-system)
    (propagated-inputs (list r-kernsmooth))
    (home-page "https://cran.r-project.org/package=GoFKernel")
    (synopsis "Testing Goodness-of-Fit with the Kernel Density Estimator")
    (description
     "Tests of goodness-of-fit based on a kernel smoothing of the data.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-gofgamma
  (package
    (name "r-gofgamma")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gofgamma" version))
       (sha256
        (base32 "00wda3fandrd1kkm20fb615g5fd0wykk3s0xn0xh9da28q08yh2m"))))
    (properties `((upstream-name . "gofgamma")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gofgamma")
    (synopsis "Goodness-of-Fit Tests for the Gamma Distribution")
    (description
     "We implement various classical tests for the composite hypothesis of testing the
fit to the family of gamma distributions as the Kolmogorov-Smirnov test, the
Cramer-von Mises test, the Anderson Darling test and the Watson test.  For each
test a parametric bootstrap procedure is implemented, as considered in Henze,
Meintanis & Ebner (2012) <doi:10.1080/03610926.2010.542851>.  The recent
procedures presented in Henze, Meintanis & Ebner (2012)
<doi:10.1080/03610926.2010.542851> and Betsch & Ebner (2019)
<doi:10.1007/s00184-019-00708-7> are implemented.  Estimation of parameters of
the gamma law are implemented using the method of Bhattacharya (2001)
<doi:10.1080/00949650108812100>.")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-goffda
  (package
    (name "r-goffda")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "goffda" version))
       (sha256
        (base32 "0d4w2rz7024j4vky1nynqfanfgp8rvgivl2p6b8l2ws98brlhy24"))))
    (properties `((upstream-name . "goffda")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-ks r-glmnet r-fda-usc))
    (home-page "https://github.com/egarpor/goffda")
    (synopsis "Goodness-of-Fit Tests for Functional Data")
    (description
     "Implementation of several goodness-of-fit tests for functional data.  Currently,
mostly related with the functional linear model with functional/scalar response
and functional/scalar predictor.  The package allows for the replication of the
data applications considered in GarcÃ­a-PortuguÃ©s, Ãlvarez-LiÃ©bana,
Ãlvarez-PÃ©rez and GonzÃ¡lez-Manteiga (2021) <doi:10.1111/sjos.12486>.")
    (license license:gpl3)))

(define-public r-gofedf
  (package
    (name "r-gofedf")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gofedf" version))
       (sha256
        (base32 "12y6lhrpldj46adn3a7jz4snx79y9xdrzbgr5qa4gjl00w3nx6cn"))))
    (properties `((upstream-name . "gofedf")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod r-mass r-glm2 r-compquadform))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/pnickchi/gofedf")
    (synopsis
     "Goodness of Fit Tests Based on Empirical Distribution Functions")
    (description
     "Routines that allow the user to run goodness of fit tests based on empirical
distribution functions for formal model evaluation in a general likelihood
model.  In addition, functions are provided to test a sample against Normal or
Gamma distributions, validate the normality assumptions in a linear model, and
examine the appropriateness of a Gamma distribution in generalized linear models
with various link functions.  Michael Arthur Stephens (1976)
<http://www.jstor.org/stable/2958206>.")
    (license license:gpl3+)))

(define-public r-gofcopula
  (package
    (name "r-gofcopula")
    (version "0.4-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gofCopula" version))
       (sha256
        (base32 "17bdrn8xsqjz64caa5wdigkv798i18gc2f9jiin1bz840bqn1abg"))))
    (properties `((upstream-name . "gofCopula")))
    (build-system r-build-system)
    (propagated-inputs (list r-yarrr
                             r-vinecopula
                             r-sparsegrid
                             r-r-utils
                             r-progress
                             r-numderiv
                             r-mass
                             r-foreach
                             r-dosnow
                             r-crayon
                             r-copula))
    (home-page "https://cran.r-project.org/package=gofCopula")
    (synopsis "Goodness-of-Fit Tests for Copulae")
    (description
     "Several Goodness-of-Fit (@code{GoF}) tests for Copulae are provided.  A new
hybrid test, Zhang et al. (2016) <doi:10.1016/j.jeconom.2016.02.017> is
implemented which supports all of the individual tests in the package, e.g.
Genest et al. (2009) <doi:10.1016/j.insmatheco.2007.10.005>.  Estimation methods
for the margins are provided and all the tests support parameter estimation and
predefined values.  The parameters are estimated by pseudo maximum likelihood
but if it fails the estimation switches automatically to inversion of Kendall's
tau.  For reproducibility of results, the functions support the definition of
seeds.  Also all the tests support automatized parallelization of the
bootstrapping tasks.  The package provides an interface to perform new
@code{GoF} tests by submitting the test statistic.")
    (license license:gpl3+)))

(define-public r-gofcens
  (package
    (name "r-gofcens")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GofCens" version))
       (sha256
        (base32 "1xixv05wlwlyga5br9jzrg68rhc71ibhfbyjkmiy0dh9xwyrygsy"))))
    (properties `((upstream-name . "GofCens")))
    (build-system r-build-system)
    (propagated-inputs (list r-survminer
                             r-survival
                             r-gridextra
                             r-ggplot2
                             r-fitdistrplus
                             r-boot
                             r-actuar))
    (home-page "https://cran.r-project.org/package=GofCens")
    (synopsis "Goodness-of-Fit Methods for Complete and Right-Censored Data")
    (description
     "Graphical tools and goodness-of-fit tests for complete and right-censored data:
1.  Kolmogorov-Smirnov, CramÃ©r-von Mises, and Anderson-Darling tests, which use
the empirical distribution function for complete data and are extended for
right-censored data.  2.  Generalized chi-squared-type test, which is based on
the squared differences between observed and expected counts using random cells
with right-censored data.  3.  A series of graphical tools such as probability
or cumulative hazard plots to guide the decision about the most suitable
parametric model for the data.")
    (license license:gpl2+)))

(define-public r-gofcat
  (package
    (name "r-gofcat")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gofcat" version))
       (sha256
        (base32 "0jkkavws3p336i3gzlkd24q467zd1clfndcpimwc6scy8zh67q9l"))))
    (properties `((upstream-name . "gofcat")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam
                             r-stringr
                             r-reshape
                             r-matrix
                             r-epir
                             r-crayon))
    (home-page "https://cran.r-project.org/package=gofcat")
    (synopsis "Goodness-of-Fit Measures for Categorical Response Models")
    (description
     "This package provides a post-estimation method for categorical response models
(CRM).  Inputs from objects of class serp(), clm(), polr(), multinom(),
mlogit(), vglm() and glm() are currently supported.  Available tests include the
Hosmer-Lemeshow tests for the binary, multinomial and ordinal logistic
regression; the Lipsitz and the Pulkstenis-Robinson tests for the ordinal
models.  The proportional odds, adjacent-category, and constrained
continuation-ratio models are particularly supported at ordinal level.  Tests
for the proportional odds assumptions in ordinal models are also possible with
the Brant and the Likelihood-Ratio tests.  Moreover, several summary measures of
predictive strength (Pseudo R-squared), and some useful error metrics,
including, the brier score, misclassification rate and logloss are also
available for the binary, multinomial and ordinal models.  Ugba, E. R. and
Gertheiss, J. (2018)
<http://www.statmod.org/workshops_archive_proceedings_2018.html>.")
    (license license:gpl2)))

(define-public r-gofar
  (package
    (name "r-gofar")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gofar" version))
       (sha256
        (base32 "10c1qgmkdhzxk9m2sd0xxww453y9lf5vjsrbmrbzbln91i6svy8j"))))
    (properties `((upstream-name . "gofar")))
    (build-system r-build-system)
    (propagated-inputs (list r-rrpack
                             r-rcpparmadillo
                             r-rcpp
                             r-mass
                             r-magrittr
                             r-glmnet))
    (home-page "https://github.com/amishra-stats/gofar")
    (synopsis "Generalized Co-Sparse Factor Regression")
    (description
     "Divide and conquer approach for estimating low-rank and sparse coefficient
matrix in the generalized co-sparse factor regression.  Please refer the
manuscript Mishra, Aditya, Dipak K. Dey, Yong Chen, and Kun Chen.  Generalized
co-sparse factor regression.  Computational Statistics & Data Analysis 157
(2021): 107127 for more details.")
    (license license:gpl3+)))

(define-public r-goeveg
  (package
    (name "r-goeveg")
    (version "0.7.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "goeveg" version))
       (sha256
        (base32 "1pdbl044xjganm8y7jg3j877bj1rinsr6k22b5b8ngm2gbdmril3"))))
    (properties `((upstream-name . "goeveg")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan r-mgcv r-hmisc r-fields))
    (home-page "https://github.com/fvlampe/goeveg/")
    (synopsis "Functions for Community Data and Ordinations")
    (description
     "This package provides a collection of functions useful in (vegetation) community
analyses and ordinations.  Includes automatic species selection for ordination
diagrams, NMDS stress/scree plots, species response curves, merging of taxa as
well as calculation and sorting of synoptic tables.")
    (license license:gpl2+)))

(define-public r-gocompare
  (package
    (name "r-gocompare")
    (version "1.0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GOCompare" version))
       (sha256
        (base32 "0h4235bs9aszil20bxyscxv2z96rw9gdp87va8bmvj15cg2lfvgv"))))
    (properties `((upstream-name . "GOCompare")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-stringr
                             r-mathjaxr
                             r-igraph
                             r-ggrepel
                             r-ggplot2
                             r-ape))
    (home-page "https://github.com/ccsosa/GOCompare")
    (synopsis "Comprehensive GO Terms Comparison Between Species")
    (description
     "Supports the assessment of functional enrichment analyses obtained for several
lists of genes and provides a workflow to analyze them between two species via
weighted graphs.  Methods are described in Sosa et al. (2023)
<doi:10.1016/j.ygeno.2022.110528>.")
    (license license:gpl3+)))

(define-public r-goat
  (package
    (name "r-goat")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "goat" version))
       (sha256
        (base32 "154x643dlh614nasx8q77rksp0q9n19a6qnmc5gki0pxv6ysgyj6"))))
    (properties `((upstream-name . "goat")))
    (build-system r-build-system)
    (propagated-inputs (list r-writexl
                             r-vctrs
                             r-treemap
                             r-tidyselect
                             r-tidyr
                             r-tibble
                             r-readxl
                             r-rcpp
                             r-pheatmap
                             r-monopoly
                             r-matrix
                             r-igraph
                             r-ggraph
                             r-ggplot2
                             r-dplyr
                             r-data-table))
    (home-page "https://github.com/ftwkoopmans/goat/")
    (synopsis "Gene Set Analysis Using the Gene Set Ordinal Association Test")
    (description
     "Perform gene set enrichment analyses using the Gene set Ordinal Association Test
(GOAT) algorithm and visualize your results.  Koopmans, F. (2024)
<doi:10.1101/2023.12.10.570979>.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-goalp
  (package
    (name "r-goalp")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "goalp" version))
       (sha256
        (base32 "1xr9bgizzsjr4nn41q1zi049wg9cw6qril60bri96x0xgc00qdpz"))))
    (properties `((upstream-name . "goalp")))
    (build-system r-build-system)
    (propagated-inputs (list r-lpsolve))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=goalp")
    (synopsis "Weighted and Lexicographic Goal Programming Interface")
    (description
     "Solves goal programming problems of the weighted and lexicographic type, as well
as combinations of the two, as described by Ignizio (1983)
<doi:10.1016/0305-0548(83)90003-5>.  Allows for a simple human-readable input
describing the problem as a series of equations.  Relies on the @code{lpSolve}
package to solve the underlying linear optimisation problem.")
    (license license:gpl3+)))

(define-public r-go2bigq
  (package
    (name "r-go2bigq")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "go2bigq" version))
       (sha256
        (base32 "0xz6im5gc214m11w70m83mnas09sql9i0lvbgyfy0qvizn6zhyl3"))))
    (properties `((upstream-name . "go2bigq")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmpfr r-gmp))
    (home-page "https://cran.r-project.org/package=go2bigq")
    (synopsis "Convert Large Numbers to Bigq Format")
    (description
     "This function converts mfpr, numeric, or character strings representing numbers
to bigq format without loss of precision.")
    (license license:lgpl3)))

(define-public r-gnumeric
  (package
    (name "r-gnumeric")
    (version "0.7-10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gnumeric" version))
       (sha256
        (base32 "0x8w35wj81yx7ri2jvkafkmg8d68il6m5fl6gh97g9rg5w0xkz7n"))))
    (properties `((upstream-name . "gnumeric")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gnumeric")
    (synopsis "Read Data from Files Readable by 'gnumeric'")
    (description
     "Read data files readable by gnumeric into R'.  Can read whole sheet or a range,
from several file formats, including the native format of gnumeric'.  Reading is
done by using ssconvert (a file converter utility included in the gnumeric
distribution <http://www.gnumeric.org>) to convert the requested part to CSV.
From gnumeric files (but not other formats) can list sheet names and sheet sizes
or read all sheets.")
    (license license:gpl2+)))

(define-public r-gnssseg
  (package
    (name "r-gnssseg")
    (version "6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GNSSseg" version))
       (sha256
        (base32 "1zkj98xh63wfyyygg2ahq3m54xlvi3x9sy0rhcn2dlfvbxaxfl4k"))))
    (properties `((upstream-name . "GNSSseg")))
    (build-system r-build-system)
    (propagated-inputs (list r-robustbase r-capushe))
    (home-page "https://cran.r-project.org/package=GNSSseg")
    (synopsis "Homogenization of GNSS Series")
    (description
     "Homogenize GNSS (Global Navigation Satellite System) time-series.  The general
model is a segmentation in the mean model including a periodic function and
considering monthly variances, see Quarello (2020) <@code{arXiv:2005.04683>}.")
    (license license:gpl3)))

(define-public r-gnrs
  (package
    (name "r-gnrs")
    (version "0.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GNRS" version))
       (sha256
        (base32 "0aq6rq5b3jq9yj8hhyxjin38f2008qsix4zm20nyhqx0k0hizql1"))))
    (properties `((upstream-name . "GNRS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcurl r-jsonlite r-httr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GNRS")
    (synopsis "Access the 'Geographic Name Resolution Service'")
    (description
     "This package provides tools for interacting with the geographic name resolution
service ('GNRS') API <https://github.com/ojalaquellueva/gnrs> and associated
functionality.  The GNRS is a batch application for resolving & standardizing
political division names against standard name in the geonames database
<http://www.geonames.org/>.  The GNRS resolves political division names at three
levels: country, state/province and county/parish.  Resolution is performed in a
series of steps, beginning with direct matching to standard names, followed by
direct matching to alternate names in different languages, followed by direct
matching to standard codes (such as ISO and FIPS codes).  If direct matching
fails, the GNRS attempts to match to standard and then alternate names using
fuzzy matching, but does not perform fuzzing matching of political division
codes.  The GNRS works down the political division hierarchy, stopping at the
current level if all matches fail.  In other words, if a country cannot be
matched, the GNRS does not attempt to match state or county.")
    (license license:expat)))

(define-public r-gnrprod
  (package
    (name "r-gnrprod")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gnrprod" version))
       (sha256
        (base32 "1bk0cqpz0ng8pn2iss2fbbrprfm099fmy0nafa2flqwi7m04swp7"))))
    (properties `((upstream-name . "gnrprod")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table))
    (home-page "https://cran.r-project.org/package=gnrprod")
    (synopsis "Estimates Gross Output Functions")
    (description
     "Estimation of gross output production functions and productivity in the presence
of numerous fixed (nonflexible) and a single flexible input using the
nonparametric identification strategy specified in Gandhi, Navarro, and Rivers
(2020) <doi:10.1086/707736>.  Monte Carlo evidence from the paper demonstrates
high performance in estimating production function elasticities.")
    (license license:gpl3)))

(define-public r-gnorm
  (package
    (name "r-gnorm")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gnorm" version))
       (sha256
        (base32 "17h43qwb07wk5hiif89k6dgn6bbdsqfy2jy3k5blbdhhp3b9jcqp"))))
    (properties `((upstream-name . "gnorm")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "http://github.com/maryclare/gnorm")
    (synopsis "Generalized Normal/Exponential Power Distribution")
    (description
     "This package provides functions for obtaining generalized normal/exponential
power distribution probabilities, quantiles, densities and random deviates.  The
generalized normal/exponential power distribution was introduced by Subbotin
(1923) and rediscovered by Nadarajah (2005).  The parametrization given by
Nadarajah (2005) <doi:10.1080/02664760500079464> is used.")
    (license license:gpl2+)))

(define-public r-gnonadd
  (package
    (name "r-gnonadd")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gnonadd" version))
       (sha256
        (base32 "1yhh6s5hzvpdy6qrvnjx84basrbvi1r7dcgapwf7g8677c8f5fdb"))))
    (properties `((upstream-name . "gnonadd")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://github.com/DecodeGenetics/gnonadd")
    (synopsis "Various Non-Additive Models for Genetic Associations")
    (description
     "The goal of gnonadd is to simplify workflows in the analysis of non-additive
effects of sequence variants.  This includes variance effects (Ivarsdottir et.
al (2017) <doi:10.1038/ng.3928>), correlation effects, interaction effects and
dominance effects.  The package also includes convenience functions for
visualization.")
    (license license:expat)))

(define-public r-gnomonicm
  (package
    (name "r-gnomonicm")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gnomonicM" version))
       (sha256
        (base32 "1yv5ry21rv69hfd4wmhnwhvarnp49hn200aws428ahs5wvx3md1v"))))
    (properties `((upstream-name . "gnomonicM")))
    (build-system r-build-system)
    (propagated-inputs (list r-triangle r-minqa r-kableextra))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gnomonicM")
    (synopsis "Estimate Natural Mortality for Different Life Stages")
    (description
     "Estimate natural mortality (M) throughout the life history for organisms, mainly
fish and invertebrates, based on gnomonic interval approach proposed by Caddy
(1996) <doi:10.1051/alr:1996023> and Martinez-Aguilar et al. (2005)
<doi:10.1016/j.fishres.2004.04.008>.  It includes estimation of duration of each
gnomonic interval (life stage), the constant probability of death (G), and some
basic plots.")
    (license license:gpl2)))

(define-public r-gnn
  (package
    (name "r-gnn")
    (version "0.0-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gnn" version))
       (sha256
        (base32 "0kwizsqzrb0q6nailnh6k3mzqwxv0d0phrqn7dp97rkgg2qq1kxa"))))
    (properties `((upstream-name . "gnn")))
    (build-system r-build-system)
    (inputs (list tensorflow))
    (propagated-inputs (list r-tensorflow r-r6 r-qrng r-keras r-copula))
    (home-page "https://cran.r-project.org/package=gnn")
    (synopsis "Generative Neural Networks")
    (description
     "This package provides tools to set up, train, store, load, investigate and
analyze generative neural networks.  In particular, functionality for generative
moment matching networks is provided.")
    (license license:gpl3+)))

(define-public r-gnm
  (package
    (name "r-gnm")
    (version "1.1-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gnm" version))
       (sha256
        (base32 "0igqzm6sdmk1wqc3rdy6pc3ba9n7bs76j5nql5qa1y2wg3rlxna4"))))
    (properties `((upstream-name . "gnm")))
    (build-system r-build-system)
    (propagated-inputs (list r-relimp r-qvcalc r-nnet r-matrix r-mass))
    (home-page "https://github.com/hturner/gnm")
    (synopsis "Generalized Nonlinear Models")
    (description
     "This package provides functions to specify and fit generalized nonlinear models,
including models with multiplicative interaction terms such as the UNIDIFF model
from sociology and the AMMI model from crop science, and many others.
Over-parameterized representations of models are used throughout; functions are
provided for inference on estimable parameter combinations, as well as standard
methods for diagnostics etc.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-gnlm
  (package
    (name "r-gnlm")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gnlm" version))
       (sha256
        (base32 "1krbz791l5qc06sh6my0bkq420b5afncw87lhyx1yiml2yg46zrk"))))
    (properties `((upstream-name . "gnlm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmutil))
    (home-page "http://www.commanster.eu/rcode.html")
    (synopsis "Generalized Nonlinear Regression Models")
    (description
     "This package provides a variety of functions to fit linear and nonlinear
regression with a large selection of distributions.")
    (license license:gpl2+)))

(define-public r-gnfit
  (package
    (name "r-gnfit")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gnFit" version))
       (sha256
        (base32 "0cji4zjslh18dfj0l827r6br4hx2f0akiaxrg7fhhyyyhgdcjnsm"))))
    (properties `((upstream-name . "gnFit")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmutil r-ismev))
    (home-page "https://cran.r-project.org/package=gnFit")
    (synopsis "Goodness of Fit Test for Continuous Distribution Functions")
    (description
     "Computes the test statistic and p-value of the Cramer-von Mises and
Anderson-Darling test for some continuous distribution functions proposed by
Chen and Balakrishnan (1995)
<http://asq.org/qic/display-item/index.html?item=11407>.  In addition to our
classic distribution functions here, we calculate the Goodness of Fit
(@code{GoF}) test to dataset which follows the extreme value distribution
function, without remembering the formula of distribution/density functions.
Calculates the Value at Risk (@code{VaR}) and Average @code{VaR} are another
important risk factors which are estimated by using well-known distribution
functions.  Pflug and Romisch (2007, ISBN: 9812707409) is a good reference to
study the properties of risk measures.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-gne
  (package
    (name "r-gne")
    (version "0.99-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GNE" version))
       (sha256
        (base32 "163jhkz2gxzq8ji3fgk1337rhnjhmwz55ai5mhgsbyyrgd5dayii"))))
    (properties `((upstream-name . "GNE")))
    (build-system r-build-system)
    (propagated-inputs (list r-squarem r-nleqslv r-bb r-alabama))
    (native-inputs (list r-knitr))
    (home-page "https://r-forge.r-project.org/projects/optimizer/")
    (synopsis "Computation of Generalized Nash Equilibria")
    (description
     "Compute standard and generalized Nash Equilibria of non-cooperative games.
Optimization methods available are nonsmooth reformulation, fixed-point
formulation, minimization problem and constrained-equation reformulation.  See
e.g. Kanzow and Facchinei (2010), <doi:10.1007/s10479-009-0653-x>.")
    (license license:gpl2+)))

(define-public r-gnar
  (package
    (name "r-gnar")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GNAR" version))
       (sha256
        (base32 "0ma7cp0ikpihmjmi3m8h51pg897287xbkwiinpairrsmiz0l711i"))))
    (properties `((upstream-name . "GNAR")))
    (build-system r-build-system)
    (propagated-inputs (list r-wordcloud
                             r-viridis
                             r-rlang
                             r-matrixcalc
                             r-igraph
                             r-ggpubr
                             r-ggplot2
                             r-ggforce))
    (home-page "https://cran.r-project.org/package=GNAR")
    (synopsis "Methods for Fitting Network Time Series Models")
    (description
     "Simulation of, and fitting models for, Generalised Network Autoregressive (GNAR)
time series models which take account of network structure, potentially with
exogenous variables.  Such models are described in Knight et al. (2020)
<doi:10.18637/jss.v096.i05> and Nason and Wei (2021) <doi:10.1111/rssa.12875>.
Diagnostic tools for GNAR(X) models can be found in Nason et al. (2023)
<doi:10.48550/@code{arXiv.2312.00530>}.")
    (license license:gpl2)))

(define-public r-gmwt
  (package
    (name "r-gmwt")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gMWT" version))
       (sha256
        (base32 "1iv7dav6l9gx6vhghcyasdizjsz2f8yzcgkj2i50kpgw74ljg5ia"))))
    (properties `((upstream-name . "gMWT")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-clinfun))
    (home-page "https://cran.r-project.org/package=gMWT")
    (synopsis "Generalized Mann-Whitney Type Tests")
    (description
     "Generalized Mann-Whitney type tests based on probabilistic indices and new
diagnostic plots, for the underlying manuscript see Fischer, Oja (2015)
<doi:10.18637/jss.v065.i09>.")
    (license license:gpl2+)))

(define-public r-gmwmx
  (package
    (name "r-gmwmx")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gmwmx" version))
       (sha256
        (base32 "1jp3jjg0znvqmir83ygvi1nnws6xs29cfccd79vbk8702k4i9y61"))))
    (properties `((upstream-name . "gmwmx")))
    (build-system r-build-system)
    (propagated-inputs (list r-wv
                             r-stringi
                             r-rjson
                             r-rcpparmadillo
                             r-rcpp
                             r-matrix
                             r-ltsa
                             r-longmemo
                             r-fs))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gmwmx")
    (synopsis
     "Estimate Functional and Stochastic Parameters of Linear Models with Correlated Residuals")
    (description
     "This package implements the Generalized Method of Wavelet Moments with Exogenous
Inputs estimator (GMWMX) presented in Cucci, D. A., Voirol, L., Kermarrec, G.,
Montillet, J. P., and Guerrier, S. (2023) <doi:10.1007/s00190-023-01702-8>.  The
GMWMX estimator allows to estimate functional and stochastic parameters of
linear models with correlated residuals.  The gmwmx package provides functions
to estimate, compare and analyze models, utilities to load and work with Global
Navigation Satellite System (GNSS) data as well as methods to compare results
with the Maximum Likelihood Estimator (MLE) implemented in Hector.")
    (license license:agpl3)))

(define-public r-gmvjoint
  (package
    (name "r-gmvjoint")
    (version "0.4.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gmvjoint" version))
       (sha256
        (base32 "1kalq3w70ixzw7anwbalfnyzxa0vap81bk05b91sk2aw98n36qq1"))))
    (properties `((upstream-name . "gmvjoint")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-survival
                             r-statmod
                             r-reformulas
                             r-rcpparmadillo
                             r-rcpp
                             r-pracma
                             r-mvtnorm
                             r-mass
                             r-glmmtmb))
    (home-page "https://github.com/jamesmurray7/gmvjoint")
    (synopsis "Joint Models of Survival and Multivariate Longitudinal Data")
    (description
     "Fit joint models of survival and multivariate longitudinal data.  The
longitudinal data is specified by generalised linear mixed models.  The joint
models are fit via maximum likelihood using an approximate expectation
maximisation algorithm.  Bernhardt (2015) <doi:10.1016/j.csda.2014.11.011>.")
    (license license:gpl3)))

(define-public r-gmvarkit
  (package
    (name "r-gmvarkit")
    (version "2.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gmvarkit" version))
       (sha256
        (base32 "06w822gq2zigynzmqndd3wilqyh1clbx2a52c9x5mg9km4pp95mb"))))
    (properties `((upstream-name . "gmvarkit")))
    (build-system r-build-system)
    (propagated-inputs (list r-pbapply r-mvnfast r-gsl r-brobdingnag))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gmvarkit")
    (synopsis
     "Estimate Gaussian and Student's t Mixture Vector Autoregressive Models")
    (description
     "Unconstrained and constrained maximum likelihood estimation of structural and
reduced form Gaussian mixture vector autoregressive, Student's t mixture vector
autoregressive, and Gaussian and Student's t mixture vector autoregressive
models, quantile residual tests, graphical diagnostics, simulations,
forecasting, and estimation of generalized impulse response function and
generalized forecast error variance decomposition.  Leena Kalliovirta, Mika
Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>, Savi
Virolainen (forthcoming) <doi:10.1080/07350015.2024.2322090>, Savi Virolainen
(2022) <@code{arXiv:2109.13648>}.")
    (license license:gpl3)))

(define-public r-gmtfd
  (package
    (name "r-gmtfd")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gmtFD" version))
       (sha256
        (base32 "0qayzxy2aqikndcmk25m26cjxdqf62d4pdk9aiclhw4acvq9awvz"))))
    (properties `((upstream-name . "gmtFD")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-matrix
                             r-mass
                             r-gfdmcv
                             r-foreach
                             r-fda
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=gmtFD")
    (synopsis
     "General Multiple Tests for Univariate and Multivariate Functional Data")
    (description
     "The multiple contrast tests for univariate were proposed by Munko, Ditzhaus,
Pauly, Smaga, and Zhang (2023) <doi:10.48550/@code{arXiv.2306.15259>}.
Recently, they were extended to the multivariate functional data in Munko,
Ditzhaus, Pauly, and Smaga (2024) <doi:10.48550/@code{arXiv.2406.01242>}.  These
procedures enable us to evaluate the overall hypothesis regarding equality, as
well as specific hypotheses defined by contrasts.  In particular, we can perform
post hoc tests to examine particular comparisons of interest.  Different
experimental designs are supported, e.g., one-way and multi-way analysis of
variance for functional data.")
    (license (list license:lgpl2.0 license:lgpl3 license:gpl2 license:gpl3))))

(define-public r-gmt
  (package
    (name "r-gmt")
    (version "2.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gmt" version))
       (sha256
        (base32 "1clnhwxn1z1iyzx9ps9rkfv2xqdyqc73mcc77ysmlg5cncvm1hac"))))
    (properties `((upstream-name . "gmt")))
    (build-system r-build-system)
    (home-page "https://www.generic-mapping-tools.org")
    (synopsis "Interface Between GMT Map-Making Software and R")
    (description
     "Interface between the GMT map-making software and R, enabling the user to
manipulate geographic data within R and call GMT commands to draw and annotate
maps in postscript format.  The gmt package is about interactive data analysis,
rapidly visualizing subsets and summaries of geographic data, while performing
statistical analysis in the R console.")
    (license license:gpl3)))

(define-public r-gmsimpute
  (package
    (name "r-gmsimpute")
    (version "0.0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GMSimpute" version))
       (sha256
        (base32 "1hmn5ss81df9j9ibgibs95j98irqzif94vq0c861mq4a65y59fcp"))))
    (properties `((upstream-name . "GMSimpute")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-glmnet r-ggplot2))
    (home-page "https://cran.r-project.org/package=GMSimpute")
    (synopsis "Generalized Mass Spectrum Missing Peaks Abundance Imputation")
    (description
     "Two-Step Lasso (TS-Lasso) and compound minimum methods to recover the abundance
of missing peaks in mass spectrum analysis.  TS-Lasso is an imputation method
that handles various types of missing peaks simultaneously.  This package
provides the procedure to generate missing peaks (or data) for simulation study,
as well as a tool to estimate and visualize the proportion of missing at random.")
    (license license:gpl2+)))

(define-public r-gmse
  (package
    (name "r-gmse")
    (version "1.0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GMSE" version))
       (sha256
        (base32 "08564m2j6xlrh658syczxcl0149g54wkqc6irgsafqxvl3fdjs52"))))
    (properties `((upstream-name . "GMSE")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinyjs r-shinydashboard r-shinycssloaders
                             r-shiny))
    (native-inputs (list r-r-rsp))
    (home-page "https://confoobio.github.io/gmse/")
    (synopsis "Generalised Management Strategy Evaluation Simulator")
    (description
     "Integrates game theory and ecological theory to construct social-ecological
models that simulate the management of populations and stakeholder actions.
These models build off of a previously developed management strategy evaluation
(MSE) framework to simulate all aspects of management: population dynamics,
manager observation of populations, manager decision making, and stakeholder
responses to management decisions.  The newly developed generalised management
strategy evaluation (GMSE) framework uses genetic algorithms to mimic the
decision-making process of managers and stakeholders under conditions of change,
uncertainty, and conflict.  Simulations can be run using gmse(), gmse_apply(),
and gmse_gui() functions.")
    (license license:gpl2+)))

(define-public r-gms
  (package
    (name "r-gms")
    (version "0.31.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gms" version))
       (sha256
        (base32 "1mf1pxxi9c62zvz3mnl0s616ps9cjhkm6xm3c2ak25saxmw5kfnv"))))
    (properties `((upstream-name . "gms")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-withr
                             r-stringr
                             r-rlang
                             r-filelock
                             r-dplyr))
    (home-page "https://github.com/pik-piam/gms")
    (synopsis "'GAMS' Modularization Support Package")
    (description
     "This package provides a collection of tools to create, use and maintain
modularized model code written in the modeling language GAMS
(<https://www.gams.com/>).  Out-of-the-box GAMS does not come with support for
modularized model code.  This package provides the tools necessary to convert a
standard GAMS model to a modularized one by introducing a modularized code
structure together with a naming convention which emulates local environments.
In addition, this package provides tools to monitor the compliance of the model
code with modular coding guidelines.")
    (license license:bsd-2)))

(define-public r-gmresls
  (package
    (name "r-gmresls")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gmresls" version))
       (sha256
        (base32 "1xzry0z02qb54ygvrykq3jyfrrs39zq0as8f54i7sdzx7nryxas3"))))
    (properties `((upstream-name . "gmresls")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gmresls")
    (synopsis "Solve Least Squares with GMRES(k)")
    (description
     "Solves a least squares system Ax~=b (dim(A)=(m,n) with m >= n) with a
precondition matrix B: BAx=Bb (dim(B)=(n,m)).  Implemented method is based on
GMRES (Saad, Youcef; Schultz, Martin H. (1986). \"GMRES: A Generalized Minimal
Residual Algorithm for Solving Nonsymmetric Linear Systems\"
<doi:10.1137/0907058>) with callback functions, i.e.  no explicit A, B or b are
required.")
    (license license:gpl3+)))

(define-public r-gmptzcurve
  (package
    (name "r-gmptzcurve")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GmptzCurve" version))
       (sha256
        (base32 "00n28difj8qs3i539fsd2x07qakyjxf77l8rq5fy07h4f4cpyd0x"))))
    (properties `((upstream-name . "GmptzCurve")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GmptzCurve")
    (synopsis "Gompertz Curve Fitting")
    (description
     "This package provides a system for fitting Gompertz Curve in a Time Series Data.")
    (license license:gpl3)))

(define-public r-gmpro
  (package
    (name "r-gmpro")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GMPro" version))
       (sha256
        (base32 "0xhja221wz89cm76qbq01nqz73jx7d2zx9i2ky5vd31zkspmlqkm"))))
    (properties `((upstream-name . "GMPro")))
    (build-system r-build-system)
    (propagated-inputs (list r-transport r-igraph r-combinat))
    (home-page "https://arxiv.org/abs/2006.03284")
    (synopsis "Graph Matching with Degree Profiles")
    (description
     "This package provides functions for graph matching via nodes degree profiles are
provided in this package.  The models we can handle include Erdos-Renyi random
graphs and stochastic block models(SBM).  More details are in the reference
paper: Yaofang Hu, Wanjie Wang and Yi Yu (2020) <@code{arXiv:2006.03284>}.")
    (license license:gpl2)))

(define-public r-gmpoly
  (package
    (name "r-gmpoly")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gmpoly" version))
       (sha256
        (base32 "0rlmwlxyg9b8gqyrc7mbqv7x86v9pqj51pd847raq7x1smjnz1kg"))))
    (properties `((upstream-name . "gmpoly")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr r-mvp r-gmp r-english))
    (home-page "https://github.com/stla/gmpoly")
    (synopsis "Multivariate Polynomials with Rational Coefficients")
    (description
     "Symbolic calculation (addition or multiplication) and evaluation of multivariate
polynomials with rational coefficients.")
    (license license:gpl3)))

(define-public r-gmotree
  (package
    (name "r-gmotree")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gmoTree" version))
       (sha256
        (base32 "07n3qy3i7yyb0z3i49x0pa3z697gppixk7jz31zk5d0144hxl4zi"))))
    (properties `((upstream-name . "gmoTree")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rmarkdown
                             r-rlist
                             r-rlang
                             r-plyr
                             r-pander
                             r-openxlsx
                             r-knitr
                             r-dplyr
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://zauchnerp.github.io/gmoTree/")
    (synopsis "Get and Modify 'oTree' Data")
    (description
     "Efficiently manage and process data from @code{oTree} experiments.  Import
@code{oTree} data and clean them by using functions that handle messy data,
dropouts, and other problematic cases.  Create IDs, calculate the time, transfer
variables between app data frames, and delete sensitive information.  Review
your experimental data prior to running the experiment and automatically
generate a detailed summary of the variables used in your @code{oTree} code.
Information on @code{oTree} is found in Chen, D. L., Schonger, M., & Wickens, C.
(2016) <doi:10.1016/j.jbef.2015.12.001>.")
    (license license:gpl3+)))

(define-public r-gmoog
  (package
    (name "r-gmoog")
    (version "0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GmooG" version))
       (sha256
        (base32 "019vxm1vwlsjkw6zgvmkg87bhnikl0i3mqhwqzm94vr4p406dgaz"))))
    (properties `((upstream-name . "GmooG")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GmooG")
    (synopsis "Datasets for the Book 'Getting (more out of) Graphics'")
    (description
     "Datasets analysed in the book Antony Unwin (2024, ISBN:978-0367674007) \"Getting
(more out of) Graphics\".")
    (license license:gpl2+)))

(define-public r-gmoip
  (package
    (name "r-gmoip")
    (version "1.5.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gMOIP" version))
       (sha256
        (base32 "0ssmrc25caqdwcs16msippdbakd1q8i8fss7jx20dphr5by7rhdl"))))
    (properties `((upstream-name . "gMOIP")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-sp
                             r-rlang
                             r-rgl
                             r-rfast
                             r-purrr
                             r-png
                             r-plyr
                             r-matrix
                             r-mass
                             r-ggrepel
                             r-ggplot2
                             r-geometry
                             r-eaf
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://relund.github.io/gMOIP/")
    (synopsis
     "Tools for 2D and 3D Plots of Single and Multi-Objective Linear/Integer Programming Models")
    (description
     "Make 2D and 3D plots of linear programming (LP), integer linear programming
(ILP), or mixed integer linear programming (MILP) models with up to three
objectives.  Plots of both the solution and criterion space are possible.  For
instance the non-dominated (Pareto) set for bi-objective LP/ILP/MILP programming
models (see vignettes for an overview).  The package also contains an function
for checking if a point is inside the convex hull.")
    (license (license:fsdg-compatible "GPL (>= 3.3.2)"))))

(define-public r-gmnl
  (package
    (name "r-gmnl")
    (version "1.1-3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gmnl" version))
       (sha256
        (base32 "01pviab3gyvjdlv5jy7wzp33909znhpn47kar55bwkm6gm3bdlg8"))))
    (properties `((upstream-name . "gmnl")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm
                             r-plotrix
                             r-msm
                             r-mlogit
                             r-maxlik
                             r-formula))
    (home-page "https://msarrias.com/description.html")
    (synopsis "Multinomial Logit Models with Random Parameters")
    (description
     "An implementation of maximum simulated likelihood method for the estimation of
multinomial logit models with random coefficients as presented by Sarrias and
Daziano (2017) <doi:10.18637/jss.v079.i02>.  Specifically, it allows estimating
models with continuous heterogeneity such as the mixed multinomial logit and the
generalized multinomial logit.  It also allows estimating models with discrete
heterogeneity such as the latent class and the mixed-mixed multinomial logit
model.")
    (license license:gpl2+)))

(define-public r-gmmsslm
  (package
    (name "r-gmmsslm")
    (version "1.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gmmsslm" version))
       (sha256
        (base32 "1cbpgl5w4zkgci77lbx2ji73z3hvzj6kyr6bi79a8l65d303fblk"))))
    (properties `((upstream-name . "gmmsslm")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://cran.r-project.org/package=gmmsslm")
    (synopsis
     "Semi-Supervised Gaussian Mixture Model with a Missing-Data Mechanism")
    (description
     "The algorithm of semi-supervised learning is based on finite Gaussian mixture
models and includes a mechanism for handling missing data.  It aims to fit a
g-class Gaussian mixture model using maximum likelihood.  The algorithm treats
the labels of unclassified features as missing data, building on the framework
introduced by Rubin (1976) <doi:10.2307/2335739> for missing data analysis.  By
taking into account the dependencies in the missing pattern, the algorithm
provides more information for determining the optimal classifier, as specified
by Bayes rule.")
    (license license:gpl3)))

(define-public r-gmmboost
  (package
    (name "r-gmmboost")
    (version "1.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GMMBoost" version))
       (sha256
        (base32 "0ysy5av49fmswhn66b67lvbvzb5k6x6i9y0h9yrkkllssrghd7dr"))))
    (properties `((upstream-name . "GMMBoost")))
    (build-system r-build-system)
    (propagated-inputs (list r-minqa r-magic))
    (home-page "https://cran.r-project.org/package=GMMBoost")
    (synopsis "Likelihood-Based Boosting for Generalized Mixed Models")
    (description
     "Likelihood-based boosting approaches for generalized mixed models are provided.")
    (license license:gpl2)))

(define-public r-gmmat
  (package
    (name "r-gmmat")
    (version "1.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GMMAT" version))
       (sha256
        (base32 "151bc1mn69kmswkn6i5czmadsnymkw4yvgph8s7z3i6xc66r8s5y"))))
    (properties `((upstream-name . "GMMAT")))
    (build-system r-build-system)
    (inputs (list zlib))
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-matrix
                             r-foreach
                             r-data-table
                             r-compquadform))
    (home-page "https://cran.r-project.org/package=GMMAT")
    (synopsis "Generalized Linear Mixed Model Association Tests")
    (description
     "Perform association tests using generalized linear mixed models (GLMMs) in
genome-wide association studies (GWAS) and sequencing association studies.
First, GMMAT fits a GLMM with covariate adjustment and random effects to account
for population structure and familial or cryptic relatedness.  For GWAS, GMMAT
performs score tests for each genetic variant as proposed in Chen et al. (2016)
<DOI:10.1016/j.ajhg.2016.02.012>.  For candidate gene studies, GMMAT can also
perform Wald tests to get the effect size estimate for each genetic variant.
For rare variant analysis from sequencing association studies, GMMAT performs
the variant Set Mixed Model Association Tests (SMMAT) as proposed in Chen et al.
(2019) <DOI:10.1016/j.ajhg.2018.12.012>, including the burden test, the sequence
kernel association test (SKAT), SKAT-O and an efficient hybrid test of the
burden test and SKAT, based on user-defined variant sets.")
    (license license:gpl3+)))

(define-public r-gmm
  (package
    (name "r-gmm")
    (version "1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gmm" version))
       (sha256
        (base32 "1n45dls7632b11bg6pipamp2zmp8j1hp4nlrj91r4schd9fgr6bh"))))
    (properties `((upstream-name . "gmm")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich))
    (native-inputs (list r-knitr gfortran))
    (home-page "https://cran.r-project.org/package=gmm")
    (synopsis
     "Generalized Method of Moments and Generalized Empirical Likelihood")
    (description
     "It is a complete suite to estimate models based on moment conditions.  It
includes the two step Generalized method of moments (Hansen 1982;
<doi:10.2307/1912775>), the iterated GMM and continuous updated estimator
(Hansen, Eaton and Yaron 1996; <doi:10.2307/1392442>) and several methods that
belong to the Generalized Empirical Likelihood family of estimators (Smith 1997;
<doi:10.1111/j.0013-0133.1997.174.x>, Kitamura 1997;
<doi:10.1214/aos/1069362388>, Newey and Smith 2004;
<doi:10.1111/j.1468-0262.2004.00482.x>, and Anatolyev 2005
<doi:10.1111/j.1468-0262.2005.00601.x>).")
    (license license:gpl2+)))

(define-public r-gmkmcharlie
  (package
    (name "r-gmkmcharlie")
    (version "1.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GMKMcharlie" version))
       (sha256
        (base32 "1c7zb83p4r46h42xsjaj3c8zrnhrrhvggz4nqmasp52fh6gdarz6"))))
    (properties `((upstream-name . "GMKMcharlie")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppparallel r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=GMKMcharlie")
    (synopsis
     "Unsupervised Gaussian Mixture and Minkowski and Spherical K-Means with Constraints")
    (description
     "High performance trainers for parameterizing and clustering weighted data.  The
Gaussian mixture (GM) module includes the conventional EM (expectation
maximization) trainer, the component-wise EM trainer, the minimum-message-length
EM trainer by Figueiredo and Jain (2002) <doi:10.1109/34.990138>.  These
trainers accept additional constraints on mixture weights, covariance eigen
ratios and on which mixture components are subject to update.  The K-means (KM)
module offers clustering with the options of (i) deterministic and stochastic
K-means++ initializations, (ii) upper bounds on cluster weights (sizes), (iii)
Minkowski distances, (iv) cosine dissimilarity, (v) dense and sparse
representation of data input.  The package improved the typical implementations
of GM and KM algorithms in various aspects.  It is carefully crafted in
multithreaded C++ for modeling large data for industry use.")
    (license license:gpl3)))

(define-public r-gmisc
  (package
    (name "r-gmisc")
    (version "3.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Gmisc" version))
       (sha256
        (base32 "1s8gkqg54622xhsmhl7n5pff2b6xfij111m7h3i4inzfjfmziq43"))))
    (properties `((upstream-name . "Gmisc")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-xml
                             r-stringr
                             r-rmarkdown
                             r-rlang
                             r-rcpp
                             r-magrittr
                             r-lubridate
                             r-lattice
                             r-knitr
                             r-htmltable
                             r-hmisc
                             r-glue
                             r-forestplot
                             r-checkmate
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://gforge.se")
    (synopsis "Descriptive Statistics, Transition Plots, and More")
    (description
     "This package provides tools for making the descriptive \"Table 1\" used in medical
articles, a transition plot for showing changes between categories (also known
as a Sankey diagram), flow charts by extending the grid package, a method for
variable selection based on the SVD, BÃ©zier lines with arrows complementing the
ones in the grid package, and more.")
    (license license:gpl3+)))

(define-public r-gmgm
  (package
    (name "r-gmgm")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gmgm" version))
       (sha256
        (base32 "18q119v8wi4l5i8r6qdsdwih3jj71iidwgbjjvqp2bh37wp58wq3"))))
    (properties `((upstream-name . "gmgm")))
    (build-system r-build-system)
    (propagated-inputs (list r-visnetwork
                             r-tidyr
                             r-stringr
                             r-rlang
                             r-purrr
                             r-ggplot2
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=gmgm")
    (synopsis "Gaussian Mixture Graphical Model Learning and Inference")
    (description
     "Gaussian mixture graphical models include Bayesian networks and dynamic Bayesian
networks (their temporal extension) whose local probability distributions are
described by Gaussian mixture models.  They are powerful tools for graphically
and quantitatively representing nonlinear dependencies between continuous
variables.  This package provides a complete framework to create, manipulate,
learn the structure and the parameters, and perform inference in these models.
Most of the algorithms are described in the @code{PhD} thesis of Roos (2018)
<https://tel.archives-ouvertes.fr/tel-01943718>.")
    (license license:gpl3)))

(define-public r-gmgeostats
  (package
    (name "r-gmgeostats")
    (version "0.11.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gmGeostats" version))
       (sha256
        (base32 "19kf409yhq13689akqz88dj0fq02falbh0j0kkvh0l0vymx4hj7y"))))
    (properties `((upstream-name . "gmGeostats")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-rcolorbrewer
                             r-gstat
                             r-foreach
                             r-compositions
                             r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://codebase.helmholtz.cloud/geomet/gmGeostats")
    (synopsis "Geostatistics for Compositional Analysis")
    (description
     "Support for geostatistical analysis of multivariate data, in particular data
with restrictions, e.g. positive amounts, compositions, distributional data,
microstructural data, etc.  It includes descriptive analysis and modelling for
such data, both from a two-point Gaussian perspective and multipoint
perspective.  The methods mainly follow Tolosana-Delgado, Mueller and van den
Boogaart (2018) <doi:10.1007/s11004-018-9769-3>.")
    (license (list license:cc-by-sa4.0 license:gpl2+))))

(define-public r-gmfd
  (package
    (name "r-gmfd")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gmfd" version))
       (sha256
        (base32 "03sag09x7pjyzsp6w1z06108ig068krmw75064cnl3pqfvfyjfgr"))))
    (properties `((upstream-name . "gmfd")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gmfd")
    (synopsis "Inference and Clustering of Functional Data")
    (description
     "Some methods for the inference and clustering of univariate and multivariate
functional data, using a generalization of Mahalanobis distance, along with some
functions useful for the analysis of functional data.  For further details, see
Martino A., Ghiglietti, A., Ieva, F. and Paganoni A. M. (2017)
<@code{arXiv:1708.00386>}.")
    (license license:gpl3)))

(define-public r-gmfamm
  (package
    (name "r-gmfamm")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gmfamm" version))
       (sha256
        (base32 "0l08x6qajr7ancf1dijayayycbs9p1xir4f78fxgicifszcdak6r"))))
    (properties `((upstream-name . "gmfamm")))
    (build-system r-build-system)
    (propagated-inputs (list r-mgcv r-matrix r-mass r-bamlss))
    (home-page "https://cran.r-project.org/package=gmfamm")
    (synopsis "Generalized Multivariate Functional Additive Models")
    (description
     "Supply implementation to model generalized multivariate functional data using
Bayesian additive mixed models of R package bamlss via a latent Gaussian process
(see Umlauf, Klein, Zeileis (2018) <doi:10.1080/10618600.2017.1407325>).")
    (license license:gpl3)))

(define-public r-gmedian
  (package
    (name "r-gmedian")
    (version "1.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Gmedian" version))
       (sha256
        (base32 "0m9fp54y4m1xxjqphpnxhw2hspzdca9qc9l60znpnynyj5m6cy2k"))))
    (properties `((upstream-name . "Gmedian")))
    (build-system r-build-system)
    (propagated-inputs (list r-rspectra r-robustbase r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=Gmedian")
    (synopsis "Geometric Median, k-Medians Clustering and Robust Median PCA")
    (description
     "Fast algorithms for robust estimation with large samples of multivariate
observations.  Estimation of the geometric median, robust k-Gmedian clustering,
and robust PCA based on the Gmedian covariation matrix.")
    (license license:gpl2+)))

(define-public r-gmdhreg
  (package
    (name "r-gmdhreg")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GMDHreg" version))
       (sha256
        (base32 "1h0ysi0wkigjysqgzzw3za7nyfpmw71abdfl4hxanwihm7gaayxy"))))
    (properties `((upstream-name . "GMDHreg")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GMDHreg")
    (synopsis "Regression using GMDH Algorithms")
    (description
     "Regression using GMDH algorithms from Prof. Alexey G. Ivakhnenko.  Group Method
of Data Handling (GMDH), or polynomial neural networks, is a family of inductive
algorithms that performs gradually complicated polynomial models and selecting
the best solution by an external criterion.  In other words, inductive GMDH
algorithms give possibility finding automatically interrelations in data, and
selecting an optimal structure of model or network.  The package includes GMDH
Combinatorial, GMDH MIA (Multilayered Iterative Algorithm), GMDH GIA
(Generalized Iterative Algorithm) and GMDH Combinatorial with Active Neurons.")
    (license license:gpl3)))

(define-public r-gmdh2
  (package
    (name "r-gmdh2")
    (version "1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GMDH2" version))
       (sha256
        (base32 "1cqpwnncxir9gc6vwzclm0av9hzd2a74xvl3w73dpzafvzkg4x67"))))
    (properties `((upstream-name . "GMDH2")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-randomforest
                             r-plotly
                             r-nnet
                             r-mass
                             r-magrittr
                             r-glmnet
                             r-e1071))
    (home-page "http://www.softmed.hacettepe.edu.tr/GMDH2")
    (synopsis "Binary Classification via GMDH-Type Neural Network Algorithms")
    (description
     "This package performs binary classification via Group Method of Data Handling
(GMDH) - type neural network algorithms.  There exist two main algorithms
available in GMDH() and @code{dceGMDH}() functions.  GMDH() performs
classification via GMDH algorithm for a binary response and returns important
variables. @code{dceGMDH}() performs classification via diverse classifiers
ensemble based on GMDH (dce-GMDH) algorithm.  Also, the package produces a
well-formatted table of descriptives for a binary response.  Moreover, it
produces confusion matrix, its related statistics and scatter plot (2D and 3D)
with classification labels of binary classes to assess the prediction
performance.  All GMDH2 functions are designed for a binary response (Dag et
al., 2019, <https://download.atlantis-press.com/article/125911202.pdf>).")
    (license license:gpl2+)))

(define-public r-gmdh
  (package
    (name "r-gmdh")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GMDH" version))
       (sha256
        (base32 "093glyz73246m5f2xb2xgbgi80haj4fanmr325byr99dnf4x35yn"))))
    (properties `((upstream-name . "GMDH")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=GMDH")
    (synopsis "Short Term Forecasting via GMDH-Type Neural Network Algorithms")
    (description
     "Group method of data handling (GMDH) - type neural network algorithm is the
heuristic self-organization method for modelling the complex systems.  In this
package, GMDH-type neural network algorithms are applied to make short term
forecasting for a univariate time series.")
    (license license:gpl2+)))

(define-public r-gmdatabase
  (package
    (name "r-gmdatabase")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gmDatabase" version))
       (sha256
        (base32 "0prap4a8pvylmvakd2ii87jz9bqf0vvfsxdi4iwa40nx444hqhx2"))))
    (properties `((upstream-name . "gmDatabase")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-rmysql r-foreach r-digest r-dbi))
    (home-page "http://www.r-project.org")
    (synopsis "Accessing a Geometallurgical Database with R")
    (description
     "This package provides a template for a geometallurgical database and a fast and
easy interface for accessing it is provided in this package.")
    (license (list license:gpl2+ license:lgpl2.0+))))

(define-public r-gmcplite
  (package
    (name "r-gmcplite")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gMCPLite" version))
       (sha256
        (base32 "1qykizcq42hj6v9fb9hyi59zi1m4hpk10il7why9s9cwyii1m69z"))))
    (properties `((upstream-name . "gMCPLite")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-mass r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://merck.github.io/gMCPLite/")
    (synopsis "Lightweight Graph Based Multiple Comparison Procedures")
    (description
     "This package provides a lightweight fork of @code{gMCP} with functions for
graphical described multiple test procedures introduced in Bretz et al. (2009)
<doi:10.1002/sim.3495> and Bretz et al. (2011) <doi:10.1002/bimj.201000239>.
Implements a flexible function using ggplot2 to create multiplicity graph
visualizations.  Contains instructions of multiplicity graph and graphical
testing for group sequential design, described in Maurer and Bretz (2013)
<doi:10.1080/19466315.2013.807748>, with necessary unit testing using testthat'.")
    (license license:gpl3)))

(define-public r-gmcp
  (package
    (name "r-gmcp")
    (version "0.8-17")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gMCP" version))
       (sha256
        (base32 "0w5qxjyn5z7mygsvrmqk5ab827abhp7v4d8ha8279gjjkfswq5fi"))))
    (properties `((upstream-name . "gMCP")))
    (build-system r-build-system)
    (inputs (list openjdk))
    (propagated-inputs (list r-xlsxjars
                             r-rjava
                             r-polynomf
                             r-mvtnorm
                             r-multcomp
                             r-matrix
                             r-mass
                             r-javagd
                             r-commonjavajars))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/kornl/gMCP")
    (synopsis "Graph Based Multiple Comparison Procedures")
    (description
     "This package provides functions and a graphical user interface for graphical
described multiple test procedures.")
    (license license:gpl2+)))

(define-public r-gmcm
  (package
    (name "r-gmcm")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GMCM" version))
       (sha256
        (base32 "13ds8da7lqkvm1mz75440hmn9smi9pznb8bknmckg8vd3fc70f6x"))))
    (properties `((upstream-name . "GMCM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-ellipse))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/AEBilgrau/GMCM")
    (synopsis "Fast Estimation of Gaussian Mixture Copula Models")
    (description
     "Unsupervised Clustering and Meta-analysis using Gaussian Mixture Copula Models.")
    (license license:gpl2+)))

(define-public r-gmapsdistance
  (package
    (name "r-gmapsdistance")
    (version "4.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gmapsdistance" version))
       (sha256
        (base32 "0gvpmannzhzxrgihi7fvj2kww637hq3zjvl52f8zp4psjpyd752s"))))
    (properties `((upstream-name . "gmapsdistance")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml r-rcurl))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jlacko/gmapsdistance")
    (synopsis "Distance and Travel Time Between Two Points from Google Maps")
    (description
     "Get distance and travel time between two points from Google Maps.  Four possible
modes of transportation (bicycling, walking, driving and public transportation).")
    (license license:gpl3+)))

(define-public r-gmailr
  (package
    (name "r-gmailr")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gmailr" version))
       (sha256
        (base32 "0bn4605mjirhw1kmdnaa14rqqzrvxckfgi1ys597zwrclw601x59"))))
    (properties `((upstream-name . "gmailr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-rematch2
                             r-rappdirs
                             r-mime
                             r-lifecycle
                             r-jsonlite
                             r-httr
                             r-glue
                             r-gargle
                             r-crayon
                             r-cli
                             r-base64enc))
    (native-inputs (list r-knitr))
    (home-page "https://gmailr.r-lib.org")
    (synopsis "Access the 'Gmail' 'RESTful' API")
    (description
     "An interface to the Gmail RESTful API. Allows access to your Gmail messages,
threads, drafts and labels.")
    (license license:expat)))

(define-public r-gmac
  (package
    (name "r-gmac")
    (version "3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GMAC" version))
       (sha256
        (base32 "1vrx6lqnm60g8nk44yhl55q2b7xqchxq3qzfrrf59sjk7whpcy5h"))))
    (properties `((upstream-name . "GMAC")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GMAC")
    (synopsis
     "Genomic Mediation Analysis with Adaptive Confounding Adjustment")
    (description
     "This package performs genomic mediation analysis with adaptive confounding
adjustment (GMAC) proposed by Yang et al. (2017) <doi:10.1101/078683>.  It
implements large scale mediation analysis and adaptively selects potential
confounding variables to adjust for each mediation test from a pool of candidate
confounders.  The package is tailored for but not limited to genomic mediation
analysis (e.g., cis-gene mediating trans-gene regulation pattern where an
@code{eQTL}, its cis-linking gene transcript, and its trans-gene transcript play
the roles as treatment, mediator and the outcome, respectively), restricting to
scenarios with the presence of cis-association (i.e., treatment-mediator
association) and random @code{eQTL} (i.e., treatment).")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-gma
  (package
    (name "r-gma")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gma" version))
       (sha256
        (base32 "08hxbs9z4vq5zjis0lgdcvlysaj1k7i0icdk3wsyqf3wd9znsibi"))))
    (properties `((upstream-name . "gma")))
    (build-system r-build-system)
    (propagated-inputs (list r-nlme r-mass r-car))
    (home-page "https://cran.r-project.org/package=gma")
    (synopsis "Granger Mediation Analysis")
    (description
     "This package performs Granger mediation analysis (GMA) for time series.  This
package includes a single level GMA model and a two-level GMA model, for time
series with hierarchically nested structure.  The single level GMA model for the
time series of a single participant performs the causal mediation analysis which
integrates the structural equation modeling and the Granger causality
frameworks.  A vector autoregressive model of order p is employed to account for
the spatiotemporal dependencies in the data.  Meanwhile, the model introduces
the unmeasured confounding effect through a nonzero correlation parameter.
Under the two-level model, by leveraging the variabilities across participants,
the parameters are identifiable and consistently estimated based on a full
conditional likelihood or a two-stage method.  See Zhao, Y., & Luo, X. (2017),
Granger Mediation Analysis of Multiple Time Series with an Application to
@code{fMRI}, <@code{arXiv:1709.05328>} for details.")
    (license license:gpl2+)))

(define-public r-gm
  (package
    (name "r-gm")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gm" version))
       (sha256
        (base32 "0vjq3nj1wv4jy0rqi4f03kq3bdamfw63vncbhfmih063rpf7i69h"))))
    (properties `((upstream-name . "gm")))
    (build-system r-build-system)
    (propagated-inputs (list r-htmltools r-erify r-base64enc))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/flujoo/gm")
    (synopsis "Create Music with Ease")
    (description
     "This package provides a simple and intuitive high-level language for music
representation.  Generates and embeds music scores and audio files in RStudio',
R Markdown documents, and R Jupyter Notebooks'.  Internally, uses
@code{MusicXML} <https://github.com/w3c/musicxml> to represent music, and
@code{MuseScore} <https://musescore.org/> to convert @code{MusicXML}'.")
    (license license:expat)))

(define-public r-glycanr
  (package
    (name "r-glycanr")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glycanr" version))
       (sha256
        (base32 "1ic2v2dim0a8kzl38vx8an80jrbzimng0plrx62a1ps1jnczihl9"))))
    (properties `((upstream-name . "glycanr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-ggplot2 r-dplyr r-coin))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/iugrina/glycanr")
    (synopsis "Tools for Analysing N-Glycan Data")
    (description
     "Useful utilities in N-glycan data analysis.  This package tries to fill the gap
in N-glycan data analysis by providing easy to use functions for basic
operations on data (see <https://en.wikipedia.org/wiki/Glycomics> for more
details on Glycomics).  At the moment glycanr is mostly oriented to data
obtained by UPLC (Ultra Performance Liquid Chromatography) and LCMS (Liquid
chromatographyâmass spectrometry) analysis of Plasma and @code{IgG} glycome.")
    (license license:expat)))

(define-public r-glvmfit
  (package
    (name "r-glvmfit")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glvmfit" version))
       (sha256
        (base32 "1nb34dm4m0nq7cr9f9jsacnk0xjv451m419sdwbk11ykpqkh9cdk"))))
    (properties `((upstream-name . "glvmfit")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=glvmfit")
    (synopsis "Methods to Assess Generalized Latent Variable Model Fit")
    (description
     "This package provides residual global fit indices for generalized latent
variable models.")
    (license license:gpl3)))

(define-public r-gluvarpro
  (package
    (name "r-gluvarpro")
    (version "7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gluvarpro" version))
       (sha256
        (base32 "04npfllnl0x7w8z693p4zixgkrcvvvjbp9047b231pbldfirx0hy"))))
    (properties `((upstream-name . "gluvarpro")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-tidyr
                             r-scales
                             r-pracma
                             r-gridextra
                             r-ggplot2))
    (home-page "https://cran.r-project.org/package=gluvarpro")
    (synopsis
     "Glucose Variability Measures from Continuous Glucose Monitoring Data")
    (description
     "Calculate different glucose variability measures, including average measures of
glycemia, measures of glycemic variability and measures of glycemic risk, from
continuous glucose monitoring data.  Boris P. Kovatchev, Erik Otto, Daniel Cox,
Linda Gonder-Frederick, and William Clarke (2006) <doi:10.2337/dc06-1085>.
Jean-Pierre Le Floch, Philippe Escuyer, Eric Baudin, Dominique Baudon, and Leon
Perlemuter (1990) <doi:10.2337/diacare.13.2.172>.  C.M. @code{McDonnell}, S.M.
Donath, S.I. Vidmar, G.A. Werther, and F.J. Cameron (2005)
<doi:10.1089/dia.2005.7.253>.  Everitt, Brian (1998)
<doi:10.1111/j.1751-5823.2011.00149_2.x>.  Becker, R. A., Chambers, J. M. and
Wilks, A. R. (1988) <doi:10.2307/2234167>.  Dougherty, R. L., Edelman, A. and
Hyman, J. M. (1989) <doi:10.1090/S0025-5718-1989-0962209-1>.  Tukey, J. W.
(1977) <doi:10.1016/0377-2217(86)90209-2>.  F. John Service (2013)
<doi:10.2337/db12-1396>.  Edmond A. Ryan, Tami Shandro, Kristy Green, Breay W.
Paty, Peter A. Senior, David Bigam, A.M. James Shapiro, and Marie-Christine
Vantyghem (2004) <doi:10.2337/diabetes.53.4.955>.  F. John Service, George D.
Molnar, John W. Rosevear, Eugene Ackerman, Leal C. Gatewood, William F. Taylor
(1970) <doi:10.2337/diab.19.9.644>.  Sarah E. Siegelaar, Frits Holleman, Joost
B. L. Hoekstra, and J. Hans @code{DeVries} (2010) <doi:10.1210/er.2009-0021>.
Gabor Marics, Zsofia Lendvai, Csaba Lodi, Levente Koncz, David Zakarias, Gyorgy
Schuster, Borbala Mikos, Csaba Hermann, Attila J. Szabo, and Peter Toth-Heyn
(2015) <doi:10.1186/s12938-015-0035-3>.  Thomas Danne, Revital Nimri, Tadej
Battelino, Richard M. Bergenstal, Kelly L. Close, J. Hans @code{DeVries},
@code{SatishGarg}, Lutz Heinemann, Irl Hirsch, Stephanie A. Amiel, Roy Beck,
Emanuele Bosi, Bruce Buckingham, @code{ClaudioCobelli}, Eyal Dassau, Francis J.
Doyle, Simon Heller, Roman Hovorka, Weiping Jia, Tim Jones, Olga
Kordonouri,Boris Kovatchev, Aaron Kowalski, Lori Laffel, David Maahs, Helen R.
Murphy, Kirsten NÃ¸rgaard, Christopher G.Parkin, Eric Renard, Banshi Saboo,
Mauro Scharf, William V. Tamborlane, Stuart A. Weinzimer, and Moshe
Phillip.International consensus on use of continuous glucose monitoring.Diabetes
Care, 2017 <doi:10.2337/dc17-1600>.")
    (license license:gpl2)))

(define-public r-gluedown
  (package
    (name "r-gluedown")
    (version "1.0.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gluedown" version))
       (sha256
        (base32 "0hnjzml5ag0xfdb9kab5fg1dfp2ldcj8i7ggfqssn4ifgyxk3c89"))))
    (properties `((upstream-name . "gluedown")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr r-glue))
    (native-inputs (list r-knitr))
    (home-page "https://k5cents.github.io/gluedown/")
    (synopsis "Wrap Vectors in Markdown Formatting")
    (description
     "Ease the transition between R vectors and markdown text.  With gluedown and
rmarkdown', users can create traditional vectors in R, glue those strings
together with the markdown syntax, and print those formatted vectors directly to
the document.  This package primarily uses @code{GitHub} Flavored Markdown
(GFM), an offshoot of the unambiguous @code{CommonMark} specification by John
@code{MacFarlane} (2019) <https://spec.commonmark.org/>.")
    (license license:gpl3)))

(define-public r-glsme
  (package
    (name "r-glsme")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GLSME" version))
       (sha256
        (base32 "18gixzg8b8yql91dhv8xsrcrxdix0kr0qgmiva0hwmyzpsn6rqam"))))
    (properties `((upstream-name . "GLSME")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-corpcor))
    (home-page "https://cran.r-project.org/package=GLSME")
    (synopsis "Generalized Least Squares with Measurement Error")
    (description
     "This package performs linear regression with correlated predictors, responses
and correlated measurement errors in predictors and responses, correcting for
biased caused by these.")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file LICENCE")))))

(define-public r-glrth
  (package
    (name "r-glrth")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gLRTH" version))
       (sha256
        (base32 "1drmmr576n3pbr0q1cnnx4k7r4iz061n2cizv1kpr3wcc1g291pn"))))
    (properties `((upstream-name . "gLRTH")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gLRTH")
    (synopsis
     "Genome-Wide Association and Linkage Analysis under Heterogeneity")
    (description
     "Likelihood ratio tests for genome-wide association and genome-wide linkage
analysis under heterogeneity.")
    (license license:gpl3)))

(define-public r-glpkapi
  (package
    (name "r-glpkapi")
    (version "1.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glpkAPI" version))
       (sha256
        (base32 "1cr40jksm27h0j1j0q1ngcf4cbrhrjz48m6z3c3jns8h17h8g8qh"))))
    (properties `((upstream-name . "glpkAPI")))
    (build-system r-build-system)
    (inputs (list glpk))
    (home-page "https://cran.r-project.org/package=glpkAPI")
    (synopsis "R Interface to C API of GLPK")
    (description
     "R Interface to C API of GLPK, depends on GLPK Version >= 4.42.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-glow
  (package
    (name "r-glow")
    (version "0.13.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glow" version))
       (sha256
        (base32 "0hzjvc70syb0n771fnvxayfgjcpqxbp4pm5c9451n519m9j0srk1"))))
    (properties `((upstream-name . "glow")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppparallel r-rcppeigen r-rcpp r-r6 r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/traversc/glow")
    (synopsis "Make Plots that Glow")
    (description
     "This package provides a framework for creating plots with glowing points.")
    (license license:gpl3)))

(define-public r-glottospace
  (package
    (name "r-glottospace")
    (version "0.0.112")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glottospace" version))
       (sha256
        (base32 "1bgyxki16mh38pw1476hq9f9r2899vs6ax7dsyrn1xzcad6x91y3"))))
    (properties `((upstream-name . "glottospace")))
    (build-system r-build-system)
    (propagated-inputs (list r-writexl
                             r-units
                             r-tmap
                             r-tidyr
                             r-tibble
                             r-sf
                             r-rnaturalearthdata
                             r-rnaturalearth
                             r-rlang
                             r-readxl
                             r-purrr
                             r-magrittr
                             r-ggplot2
                             r-dplyr))
    (home-page "https://github.com/SietzeN/glottospace")
    (synopsis
     "Language Mapping and Geospatial Analysis of Linguistic and Cultural Data")
    (description
     "Streamlined workflows for geolinguistic analysis, including: accessing global
linguistic and cultural databases, data import, data entry, data cleaning, data
exploration, mapping, visualization and export.")
    (license license:gpl3+)))

(define-public r-glossr
  (package
    (name "r-glossr")
    (version "0.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glossr" version))
       (sha256
        (base32 "1ip7s2whnid0ycdd554ncyvnazd2g7zmv16qfgfhjb0iy8ny3mc0"))))
    (properties `((upstream-name . "glossr")))
    (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-tibble
                             r-systemfonts
                             r-rlang
                             r-purrr
                             r-knitr
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://montesmariana.github.io/glossr/")
    (synopsis "Use Interlinear Glosses in R Markdown")
    (description
     "Read examples with interlinear glosses from files or from text and print them in
a way compatible with both Latex and HTML outputs.")
    (license license:expat)))

(define-public r-glossary
  (package
    (name "r-glossary")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glossary" version))
       (sha256
        (base32 "0p3sn30gpi1zbh498iv08n55sjpls0120cxcg9dkz0yjs60y2snl"))))
    (properties `((upstream-name . "glossary")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-xml2
                             r-rvest
                             r-markdown
                             r-knitr
                             r-kableextra))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/debruine/glossary")
    (synopsis "Glossaries for Markdown and Quarto Documents")
    (description
     "Add glossaries to markdown and quarto documents by tagging individual words.
Definitions can be provided inline or in a separate file.")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-glossa
  (package
    (name "r-glossa")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glossa" version))
       (sha256
        (base32 "17b9iir51a9vavd3bpg3448wpcmb9ib2kcf08qa29f68xy8fglgz"))))
    (properties `((upstream-name . "glossa")))
    (build-system r-build-system)
    (propagated-inputs (list r-zip
                             r-waiter
                             r-tidyterra
                             r-terra
                             r-svglite
                             r-sparkline
                             r-shinywidgets
                             r-shiny
                             r-sf
                             r-proc
                             r-mcp
                             r-markdown
                             r-leaflet
                             r-jsonlite
                             r-htmltools
                             r-ggplot2
                             r-geothinner
                             r-dt
                             r-dplyr
                             r-dbarts
                             r-bs4dash))
    (home-page "https://github.com/iMARES-group/glossa")
    (synopsis
     "User-Friendly 'shiny' App for Bayesian Species Distribution Models")
    (description
     "This package provides a user-friendly shiny application for Bayesian machine
learning analysis of marine species distributions.  GLOSSA (Global Species
Spatiotemporal Analysis) uses Bayesian Additive Regression Trees (BART; Chipman,
George, and @code{McCulloch} (2010) <doi:10.1214/09-AOAS285>) to model species
distributions with intuitive workflows for data upload, processing, model
fitting, and result visualization.  It supports presence-absence and
presence-only data (with pseudo-absence generation), spatial thinning,
cross-validation, and scenario-based projections.  GLOSSA is designed to
facilitate ecological research by providing easy-to-use tools for analyzing and
visualizing marine species distributions across different spatial and temporal
scales.")
    (license license:gpl3)))

(define-public r-glogis
  (package
    (name "r-glogis")
    (version "1.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glogis" version))
       (sha256
        (base32 "15zk11p10v8fmqvmg9gdhxa0h6s5axvmdkxkpwrg8sgp2ln94fr9"))))
    (properties `((upstream-name . "glogis")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-sandwich))
    (home-page "https://cran.r-project.org/package=glogis")
    (synopsis "Fitting and Testing Generalized Logistic Distributions")
    (description
     "This package provides tools for the generalized logistic distribution (Type I,
also known as skew-logistic distribution), encompassing basic distribution
functions (p, q, d, r, score), maximum likelihood estimation, and structural
change methods.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-glober
  (package
    (name "r-glober")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glober" version))
       (sha256
        (base32 "05syd93995pcvzvrwd8hchs2dgklv627a7pias806vsbs8xbp2vg"))))
    (properties `((upstream-name . "glober")))
    (build-system r-build-system)
    (propagated-inputs (list r-plot3d r-matrix r-ggplot2 r-genlasso r-fda))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=glober")
    (synopsis "Estimating Functions with Multivariate B-Splines")
    (description
     "Generalized @code{LassO} applied to knot selection in multivariate
B-@code{splinE} Regression (GLOBER) implements a novel approach for estimating
functions in a multivariate nonparametric regression model based on an adaptive
knot selection for B-splines using the Generalized Lasso.  For further details
we refer the reader to the paper Savino, M. E. and LÃ©vy-Leduc, C. (2023),
<@code{arXiv:2306.00686>}.")
    (license license:gpl2)))

(define-public r-globe
  (package
    (name "r-globe")
    (version "1.2-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "globe" version))
       (sha256
        (base32 "0xa0q5dac0g7xdlr679f3km8nqxlm7gi4msp9fbfivfvy0rmri96"))))
    (properties `((upstream-name . "globe")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=globe")
    (synopsis "Plot 2D and 3D Views of the Earth, Including Major Coastline")
    (description
     "Basic functions for plotting 2D and 3D views of a sphere, by default the Earth
with its major coastline, and additional lines and points.")
    (license license:gpl2+)))

(define-public r-globaltrends
  (package
    (name "r-globaltrends")
    (version "0.0.14")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "globaltrends" version))
       (sha256
        (base32 "13alq7vwfa2awg7xk9plna0sygsm65yni0ffkncznfpkhk5ff63q"))))
    (properties `((upstream-name . "globaltrends")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rsqlite
                             r-rlang
                             r-purrr
                             r-lubridate
                             r-gtrendsr
                             r-ggplot2
                             r-forecast
                             r-forcats
                             r-dplyr
                             r-dbplyr
                             r-dbi))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ha-pu/globaltrends/")
    (synopsis
     "Download and Measure Global Trends Through Google Search Volumes")
    (description
     "Google offers public access to global search volumes from its search engine
through the Google Trends portal.  The package downloads these search volumes
provided by Google Trends and uses them to measure and analyze the distribution
of search scores across countries or within countries.  The package allows
researchers and analysts to use these search scores to investigate global trends
based on patterns within these scores.  This offers insights such as degree of
internationalization of firms and organizations or dissemination of political,
social, or technological trends across the globe or within single countries.  An
outline of the package's methodological foundations and potential applications
is available as a working paper:
<https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3969013>.")
    (license license:expat)))

(define-public r-globalopttests
  (package
    (name "r-globalopttests")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "globalOptTests" version))
       (sha256
        (base32 "0yf4p82dpjh36ddpfrby7m3fnj2blf5s76lncflch917sq251h4f"))))
    (properties `((upstream-name . "globalOptTests")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=globalOptTests")
    (synopsis
     "Objective functions for benchmarking the performance of global optimization algorithms")
    (description
     "This package makes available 50 objective functions for benchmarking the
performance of global optimization algorithms.")
    (license license:gpl3+)))

(define-public r-globalkinhom
  (package
    (name "r-globalkinhom")
    (version "0.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "globalKinhom" version))
       (sha256
        (base32 "0j07vlr8df9amb90cdg09yypz41878nhrbxv5mplyyjj76d8szlq"))))
    (properties `((upstream-name . "globalKinhom")))
    (build-system r-build-system)
    (propagated-inputs (list r-spatstat-univar r-spatstat-random
                             r-spatstat-geom r-spatstat-explore))
    (home-page "https://cran.r-project.org/package=globalKinhom")
    (synopsis
     "Inhomogeneous K- And Pair Correlation Functions Using Global Estimators")
    (description
     "Second-order summary statistics K- and pair-correlation functions describe
interactions in point pattern data.  This package provides computations to
estimate those statistics on inhomogeneous point processes, using the methods of
in T Shaw, J MÃ¸ller, R Waagepetersen, 2020
<doi:10.48550/@code{arXiv.2004.00527>}.")
    (license license:gpl2+)))

(define-public r-glmxdiag
  (package
    (name "r-glmxdiag")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmxdiag" version))
       (sha256
        (base32 "0m077j7prjc38yshq8ahprfsai0rc20qma2njjbdi975mrwxi25c"))))
    (properties `((upstream-name . "glmxdiag")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam))
    (home-page "https://cran.r-project.org/package=glmxdiag")
    (synopsis
     "Collection of Graphic Tools for GLM Diagnostics and some Extensions")
    (description
     "This package provides diagnostic graphic tools for GLMs, beta-binomial
regression model (estimated by VGAM package), beta regression model (estimated
by betareg package) and negative binomial regression model (estimated by MASS
package).  Since most of functions implemented in glmxdiag already exist in
other packages, the aim is to provide the user unique functions that work on
almost all regression models previously specified.  Details about some of the
implemented functions can be found in Brown (1992) <doi:10.2307/2347617>, Dunn
and Smyth (1996) <doi:10.2307/1390802>, O'Hara Hines and Carter (1993)
<doi:10.2307/2347405>, Wang (1985) <doi:10.2307/1269708>.")
    (license license:gpl2+)))

(define-public r-glmx
  (package
    (name "r-glmx")
    (version "0.2-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmx" version))
       (sha256
        (base32 "0mg7m981fvnywm4kv2v4hq9r7y18pjcmhzxy7w7lfi8gak75wcpg"))))
    (properties `((upstream-name . "glmx")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich r-mass r-lmtest r-formula))
    (home-page "https://cran.r-project.org/package=glmx")
    (synopsis "Generalized Linear Models Extended")
    (description
     "Extended techniques for generalized linear models (GLMs), especially for binary
responses, including parametric links and heteroscedastic latent variables.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-glmvsd
  (package
    (name "r-glmvsd")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmvsd" version))
       (sha256
        (base32 "0im3143xvlsgkknsjnifx9zh7jsdm9bsmgiwrk0r2iil2ns22wgy"))))
    (properties `((upstream-name . "glmvsd")))
    (build-system r-build-system)
    (propagated-inputs (list r-ncvreg r-mass r-glmnet r-brglm))
    (home-page "https://github.com/emeryyi/glmvsd")
    (synopsis
     "Variable Selection Deviation Measures and Instability Tests for High-Dimensional Generalized Linear Models")
    (description
     "Variable selection deviation (VSD) measures and instability tests for
high-dimensional model selection methods such as LASSO, SCAD and MCP, etc., to
decide whether the sparse patterns identified by those methods are reliable.")
    (license license:gpl2)))

(define-public r-glmulti
  (package
    (name "r-glmulti")
    (version "1.0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmulti" version))
       (sha256
        (base32 "0zviahj99645s1xs2f9z7rnv95x4rdz7mfwzy3qjfw46yqpfj5cn"))))
    (properties `((upstream-name . "glmulti")))
    (build-system r-build-system)
    (inputs (list openjdk))
    (propagated-inputs (list r-rjava r-leaps))
    (home-page "https://cran.r-project.org/package=glmulti")
    (synopsis "Model Selection and Multimodel Inference Made Easy")
    (description
     "Automated model selection and model-averaging.  Provides a wrapper for glm and
other functions, automatically generating all possible models (under constraints
set by the user) with the specified response and explanatory variables, and
finding the best models in terms of some Information Criterion (AIC, AICc or
BIC).  Can handle very large numbers of candidate models.  Features a Genetic
Algorithm to find the best models when an exhaustive screening of the candidates
is not feasible.")
    (license license:gpl2+)))

(define-public r-glmtree
  (package
    (name "r-glmtree")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmtree" version))
       (sha256
        (base32 "0d0i9qhmib0rr15jm9kwl9jcfwhsagdr56jqgan5sr0hb4wbzzvw"))))
    (properties `((upstream-name . "glmtree")))
    (build-system r-build-system)
    (propagated-inputs (list r-partykit r-magrittr r-dplyr r-caret))
    (native-inputs (list r-knitr))
    (home-page "https://adimajo.github.io")
    (synopsis "Logistic Regression Trees")
    (description
     "This package provides a logistic regression tree is a decision tree with
logistic regressions at its leaves.  A particular stochastic expectation
maximization algorithm is used to draw a few good trees, that are then assessed
via the user's criterion of choice among BIC / AIC / test set Gini.  The formal
development is given in a @code{PhD} chapter, see Ehrhardt (2019)
<https://github.com/adimajo/manuscrit_these/releases/>.")
    (license license:gpl2+)))

(define-public r-glmtrans
  (package
    (name "r-glmtrans")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmtrans" version))
       (sha256
        (base32 "10d9z1rrjnxkw3nmim01ql5yfn1w62sncgclmprznr5kbna49yj3"))))
    (properties `((upstream-name . "glmtrans")))
    (build-system r-build-system)
    (propagated-inputs (list r-glmnet
                             r-ggplot2
                             r-formatr
                             r-foreach
                             r-doparallel
                             r-caret
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=glmtrans")
    (synopsis "Transfer Learning under Regularized Generalized Linear Models")
    (description
     "We provide an efficient implementation for two-step multi-source transfer
learning algorithms in high-dimensional generalized linear models (GLMs).  The
elastic-net penalized GLM with three popular families, including linear,
logistic and Poisson regression models, can be fitted.  To avoid negative
transfer, a transferable source detection algorithm is proposed.  We also
provides visualization for the transferable source detection results.  The
relevant paper is available on @code{arXiv}: <@code{arXiv:2105.14328>}.")
    (license license:gpl2)))

(define-public r-glmtoolbox
  (package
    (name "r-glmtoolbox")
    (version "0.1.12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmtoolbox" version))
       (sha256
        (base32 "1nzn5nqicry2687d0nx6dacy0kbl0fzsw2wqwwi07mc12b84m7j9"))))
    (properties `((upstream-name . "glmtoolbox")))
    (build-system r-build-system)
    (propagated-inputs (list r-suppdists
                             r-statmod
                             r-rfast
                             r-numderiv
                             r-mass
                             r-formula
                             r-broom))
    (home-page "https://mlgs.netlify.app/")
    (synopsis "Set of Tools to Data Analysis using Generalized Linear Models")
    (description
     "Set of tools for the statistical analysis of data using: (1) normal linear
models; (2) generalized linear models; (3) negative binomial regression models
as alternative to the Poisson regression models under the presence of
overdispersion; (4) beta-binomial and random-clumped binomial regression models
as alternative to the binomial regression models under the presence of
overdispersion; (5) Zero-inflated and zero-altered regression models to deal
with zero-excess in count data; (6) generalized nonlinear models; (7)
generalized estimating equations for cluster correlated data.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-glmtlp
  (package
    (name "r-glmtlp")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmtlp" version))
       (sha256
        (base32 "1kdjhm29wglpn3xpr10ypx953m4y82ipf2q3qqfqk50k2g0gcy0k"))))
    (properties `((upstream-name . "glmtlp")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-foreach r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://yuyangyy.com/glmtlp/")
    (synopsis "Generalized Linear Models with Truncated Lasso Penalty")
    (description
     "Extremely efficient procedures for fitting regularization path with l0, l1, and
truncated lasso penalty for linear regression and logistic regression models.
This version is a completely new version compared with our previous version,
which was mainly based on R. New core algorithms are developed and are now
written in C++ and highly optimized.")
    (license license:gpl3)))

(define-public r-glmsimulator
  (package
    (name "r-glmsimulator")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GlmSimulatoR" version))
       (sha256
        (base32 "1xfbmxsdlgpwlss5bfb4arlnblj4s6pzyn9npy04v5aara1m04na"))))
    (properties `((upstream-name . "GlmSimulatoR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tweedie
                             r-stringr
                             r-statmod
                             r-mass
                             r-magrittr
                             r-ggplot2
                             r-dplyr
                             r-cplm
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GlmSimulatoR")
    (synopsis "Creates Ideal Data for Generalized Linear Models")
    (description
     "This package creates ideal data for all distributions in the generalized linear
model framework.")
    (license license:gpl3)))

(define-public r-glmsdata
  (package
    (name "r-glmsdata")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GLMsData" version))
       (sha256
        (base32 "1jk2avzp66nz8gxrqcynhrhwkdywr0mjv2r1smrfkxmwxjpdgm4i"))))
    (properties `((upstream-name . "GLMsData")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GLMsData")
    (synopsis "Generalized Linear Model Data Sets")
    (description
     "Data sets from the book Generalized Linear Models with Examples in R by Dunn and
Smyth.")
    (license license:gpl2+)))

(define-public r-glmpermu
  (package
    (name "r-glmpermu")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmpermu" version))
       (sha256
        (base32 "09jm71izzdj4bhhd3fmcxjwc1b6q9qhbynx9bzm1g18zwr8kck7k"))))
    (properties `((upstream-name . "glmpermu")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=glmpermu")
    (synopsis "Permutation-Based Inference for Generalized Linear Models")
    (description
     "In practical applications, the assumptions underlying generalized linear models
frequently face violations, including incorrect specifications of the outcome
variable's distribution or omitted predictors.  These deviations can render the
results of standard generalized linear models unreliable.  As the sample size
increases, what might initially appear as minor issues can escalate to critical
concerns.  To address these challenges, we adopt a permutation-based inference
method tailored for generalized linear models.  This approach offers robust
estimations that effectively counteract the mentioned problems, and its
effectiveness remains consistent regardless of the sample size.")
    (license license:expat)))

(define-public r-glmpathcr
  (package
    (name "r-glmpathcr")
    (version "1.0.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmpathcr" version))
       (sha256
        (base32 "03kjj1wcx8p2jcdqb70fsdz2ljiz37xaaahqadawr3vjs7x441q3"))))
    (properties `((upstream-name . "glmpathcr")))
    (build-system r-build-system)
    (propagated-inputs (list r-glmpath))
    (home-page "https://cran.r-project.org/package=glmpathcr")
    (synopsis
     "Fit a Penalized Continuation Ratio Model for Predicting an Ordinal Response")
    (description
     "This package provides a function for fitting a penalized constrained
continuation ratio model using the glmpath algorithm and methods for extracting
coefficient estimates, predicted class, class probabilities, and plots as
described by Archer and Williams (2012) <doi:10.1002/sim.4484>.")
    (license license:gpl2)))

(define-public r-glmpath
  (package
    (name "r-glmpath")
    (version "0.98")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmpath" version))
       (sha256
        (base32 "1jbiqqd1s93i941dhyyx50zlgwavhyjfw8bx13z4gz05rdvi4gn8"))))
    (properties `((upstream-name . "glmpath")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "https://cran.r-project.org/package=glmpath")
    (synopsis
     "L1 Regularization Path for Generalized Linear Models and Cox Proportional Hazards Model")
    (description
     "This package provides a path-following algorithm for L1 regularized generalized
linear models and Cox proportional hazards model.")
    (license license:gpl2+)))

(define-public r-glmpack
  (package
    (name "r-glmpack")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GLMpack" version))
       (sha256
        (base32 "15bpw42y999ljdw8ka2ysrrm0y73xz37wwi3j67i01wafipq82xy"))))
    (properties `((upstream-name . "GLMpack")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich
                             r-pscl
                             r-plm
                             r-pbrackets
                             r-nnet
                             r-matrix
                             r-mass
                             r-lmtest
                             r-lme4
                             r-foreign
                             r-effects
                             r-censreg
                             r-aer))
    (home-page "https://cran.r-project.org/package=GLMpack")
    (synopsis
     "Data and Code to Accompany Generalized Linear Models, 2nd Edition")
    (description
     "This package contains all the data and functions used in Generalized Linear
Models, 2nd edition, by Jeff Gill and Michelle Torres.  Examples to create all
models, tables, and plots are included for each data set.")
    (license license:gpl3+)))

(define-public r-glmnetse
  (package
    (name "r-glmnetse")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmnetSE" version))
       (sha256
        (base32 "0qj3c8qrx3vl0dr9prravfjwzqv1yfi771xfss9d44wlvjz5f4gp"))))
    (properties `((upstream-name . "glmnetSE")))
    (build-system r-build-system)
    (propagated-inputs (list r-glmnet r-boot))
    (home-page "https://github.com/sebastianbahr/glmnetSE")
    (synopsis
     "Add Nonparametric Bootstrap SE to 'glmnet' for Selected Coefficients (No Shrinkage)")
    (description
     "Builds a LASSO, Ridge, or Elastic Net model with glmnet or cv.glmnet with
bootstrap inference statistics (SE, CI, and p-value) for selected coefficients
with no shrinkage applied for them.  Model performance can be evaluated on test
data and an automated alpha selection is implemented for Elastic Net.
Parallelized computation is used to speed up the process.  The methods are
described in Friedman et al. (2010) <doi:10.18637/jss.v033.i01> and Simon et al.
(2011) <doi:10.18637/jss.v039.i05>.")
    (license license:gpl3)))

(define-public r-glmnetr
  (package
    (name "r-glmnetr")
    (version "0.5-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmnetr" version))
       (sha256
        (base32 "1kk0wf59blxjr35wcamb7wfggnskk3q02mx3z8lcjlrhmbdl6mm4"))))
    (properties `((upstream-name . "glmnetr")))
    (build-system r-build-system)
    (propagated-inputs (list r-xgboost
                             r-torch
                             r-survival
                             r-smoof
                             r-rpart
                             r-rgenoud
                             r-randomforestsrc
                             r-paramhelpers
                             r-mlrmbo
                             r-matrix
                             r-glmnet
                             r-dicekriging
                             r-aorsf))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=glmnetr")
    (synopsis
     "Nested Cross Validation for the Relaxed Lasso and Other Machine Learning Models")
    (description
     "Cross validation informed Relaxed LASSO, Artificial Neural Network (ANN),
gradient boosting machine ('xgboost'), Random Forest ('@code{RandomForestSRC}'),
Oblique Random Forest ('aorsf'), Recursive Partitioning ('RPART') or step wise
regression models are fit.  Cross validation leave out samples (leading to
nested cross validation) or bootstrap out-of-bag samples are used to evaluate
and compare performances between these models with results presented in tabular
or graphical means.  Calibration plots can also be generated, again based upon
(outer nested) cross validation or bootstrap leave out (out of bag) samples.
For some datasets, for example when the design matrix is not of full rank,
glmnet may have very long run times when fitting the relaxed lasso model, from
our experience when fitting Cox models on data with many predictors and many
patients, making it difficult to get solutions from either glmnet() or
cv.glmnet().  This may be remedied by using the path=TRUE option when calling
glmnet() and cv.glmnet().  Within the glmnetr package the approach of path=TRUE
is taken by default.  When fitting not a relaxed lasso model but an elastic-net
model, then the R-packages nestedcv
<https://cran.r-project.org/package=nestedcv>, @code{glmnetSE}
<https://cran.r-project.org/package=@code{glmnetSE>} or others may provide
greater functionality when performing a nested CV. Use of the glmnetr has many
similarities to the glmnet package and it is recommended that the user of
glmnetr also become familiar with the glmnet package
<https://cran.r-project.org/package=glmnet>, with the \"An Introduction to
glmnet'\" and \"The Relaxed Lasso\" being especially useful in this regard.")
    (license license:gpl3)))

(define-public r-glmnetcr
  (package
    (name "r-glmnetcr")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmnetcr" version))
       (sha256
        (base32 "0cs0yj2js0920iggcw2vci5y9hnsm3hd7wpd7mwnx8mszwrklawg"))))
    (properties `((upstream-name . "glmnetcr")))
    (build-system r-build-system)
    (propagated-inputs (list r-glmnet))
    (home-page "https://cran.r-project.org/package=glmnetcr")
    (synopsis
     "Fit a Penalized Constrained Continuation Ratio Model for Predicting an Ordinal Response")
    (description
     "Penalized methods are useful for fitting over-parameterized models.  This
package includes functions for restructuring an ordinal response dataset for
fitting continuation ratio models for datasets where the number of covariates
exceeds the sample size or when there is collinearity among the covariates.  The
glmnet fitting algorithm is used to fit the continuation ratio model after data
restructuring.")
    (license license:gpl2)))

(define-public r-glmmseq
  (package
    (name "r-glmmseq")
    (version "0.5.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmmSeq" version))
       (sha256
        (base32 "182pgwqqbgpkcvapkps7ahxa4ppic44j10081q0v9lp2cjdiv4qy"))))
    (properties `((upstream-name . "glmmSeq")))
    (build-system r-build-system)
    (propagated-inputs (list r-qvalue
                             r-plotly
                             r-pbmcapply
                             r-pbapply
                             r-mass
                             r-lmertest
                             r-lme4
                             r-glmmtmb
                             r-ggpubr
                             r-ggplot2
                             r-car))
    (native-inputs (list r-knitr))
    (home-page "https://myles-lewis.github.io/glmmSeq/")
    (synopsis
     "General Linear Mixed Models for Gene-Level Differential Expression")
    (description
     "Using mixed effects models to analyse longitudinal gene expression can highlight
differences between sample groups over time.  The most widely used differential
gene expression tools are unable to fit linear mixed effect models, and are less
optimal for analysing longitudinal data.  This package provides negative
binomial and Gaussian mixed effects models to fit gene expression and other
biological data across repeated samples.  This is particularly useful for
investigating changes in RNA-Sequencing gene expression between groups of
individuals over time, as described in: Rivellese, F., Surace, A. E., Goldmann,
K., Sciacca, E., Cubuk, C., Giorli, G., ...  Lewis, M. J., & Pitzalis, C. (2022)
Nature medicine <doi:10.1038/s41591-022-01789-0>.")
    (license license:expat)))

(define-public r-glmmselect
  (package
    (name "r-glmmselect")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GLMMselect" version))
       (sha256
        (base32 "19yvfnmiw98fyixw8nms158y4bwhzzf0bfsi0dcxg651rv4vrvla"))))
    (properties `((upstream-name . "GLMMselect")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GLMMselect")
    (synopsis "Bayesian Model Selection for Generalized Linear Mixed Models")
    (description
     "This package provides a Bayesian model selection approach for generalized linear
mixed models.  Currently, GLMMselect can be used for Poisson GLMM and Bernoulli
GLMM. GLMMselect can select fixed effects and random effects simultaneously.
Covariance structures for the random effects are a product of a unknown scalar
and a known semi-positive definite matrix.  GLMMselect can be widely used in
areas such as longitudinal studies, genome-wide association studies, and spatial
statistics.  GLMMselect is based on Xu, Ferreira, Porter, and Franck (202X),
Bayesian Model Selection Method for Generalized Linear Mixed Models, Biometrics,
under review.")
    (license license:gpl3)))

(define-public r-glmmrr
  (package
    (name "r-glmmrr")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GLMMRR" version))
       (sha256
        (base32 "072zwi1rs6d84iszn09v5g19bkpla19nfqk4pq40ad8368zkbhcm"))))
    (properties `((upstream-name . "GLMMRR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer r-lme4 r-lattice))
    (home-page "https://cran.r-project.org/package=GLMMRR")
    (synopsis
     "Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data")
    (description
     "Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data.
Includes Cauchit, Compl.  Log-Log, Logistic, and Probit link functions for
Bernoulli Distributed RR data.  RR Designs: Warner, Forced Response, Unrelated
Question, Kuk, Crosswise, and Triangular.  Reference: Fox, J-P, Veen, D. and
Klotzke, K. (2018).  Generalized Linear Mixed Models for Randomized Responses.
Methodology. <doi:10.1027/1614-2241/a000153>.")
    (license license:gpl3)))

(define-public r-glmmroptim
  (package
    (name "r-glmmroptim")
    (version "0.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmmrOptim" version))
       (sha256
        (base32 "0aic2wc1f1qdj2g9g7zsaz4hckrgpy7w293harpjmq9ll3gxpfcz"))))
    (properties `((upstream-name . "glmmrOptim")))
    (build-system r-build-system)
    (propagated-inputs (list r-sparsechol
                             r-rminqa
                             r-rcppprogress
                             r-rcppeigen
                             r-rcpp
                             r-matrix
                             r-glmmrbase
                             r-digest
                             r-bh))
    (home-page "https://github.com/samuel-watson/glmmrOptim")
    (synopsis
     "Approximate Optimal Experimental Designs Using Generalised Linear Mixed Models")
    (description
     "Optimal design analysis algorithms for any study design that can be represented
or modelled as a generalised linear mixed model including cluster randomised
trials, cohort studies, spatial and temporal epidemiological studies, and
split-plot designs.  See
<https://github.com/samuel-watson/@code{glmmrBase/blob/master/README.md>} for a
detailed manual on model specification.  A detailed discussion of the methods in
this package can be found in Watson, Hemming, and Girling (2023)
<doi:10.1177/09622802231202379>.")
    (license license:gpl2+)))

(define-public r-glmmrbase
  (package
    (name "r-glmmrbase")
    (version "0.10.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmmrBase" version))
       (sha256
        (base32 "1yq80s4j7riw2hrgcxjys5ljx32vm6ckhx0mcbxjqq6wggkwhzzl"))))
    (properties `((upstream-name . "glmmrBase")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-sparsechol
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-r6
                             r-matrix
                             r-bh))
    (home-page "https://github.com/samuel-watson/glmmrBase")
    (synopsis "Generalised Linear Mixed Models in R")
    (description
     "Specification, analysis, simulation, and fitting of generalised linear mixed
models.  Includes Markov Chain Monte Carlo Maximum likelihood and Laplace
approximation model fitting for a range of models, non-linear fixed effect
specifications, a wide range of flexible covariance functions that can be
combined arbitrarily, robust and bias-corrected standard error estimation, power
calculation, data simulation, and more.  See
<https://samuel-watson.github.io/glmmr-web/> for a detailed manual.")
    (license license:gpl2+)))

(define-public r-glmmpen
  (package
    (name "r-glmmpen")
    (version "1.5.4.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmmPen" version))
       (sha256
        (base32 "1rlwfw54scd0l9n1jisf9dxjddc5fzw8ryakgdxk8k9zs5jzscbd"))))
    (properties `((upstream-name . "glmmPen")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-stringr
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-reshape2
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpparmadillo
                             r-rcpp
                             r-ncvreg
                             r-mvtnorm
                             r-matrix
                             r-mass
                             r-lme4
                             r-ggplot2
                             r-bigmemory
                             r-bh))
    (home-page "https://cran.r-project.org/package=glmmPen")
    (synopsis
     "High Dimensional Penalized Generalized Linear Mixed Models (pGLMM)")
    (description
     "Fits high dimensional penalized generalized linear mixed models using the Monte
Carlo Expectation Conditional Minimization (MCECM) algorithm.  The purpose of
the package is to perform variable selection on both the fixed and random
effects simultaneously for generalized linear mixed models.  The package
supports fitting of Binomial, Gaussian, and Poisson data with canonical links,
and supports penalization using the MCP, SCAD, or LASSO penalties.  The MCECM
algorithm is described in Rashid et al. (2020)
<doi:10.1080/01621459.2019.1671197>.  The techniques used in the minimization
portion of the procedure (the M-step) are derived from the procedures of the
ncvreg package (Breheny and Huang (2011) <doi:10.1214/10-AOAS388>) and grpreg
package (Breheny and Huang (2015) <doi:10.1007/s11222-013-9424-2>), with
appropriate modifications to account for the estimation and penalization of the
random effects.  The ncvreg and grpreg packages also describe the MCP, SCAD, and
LASSO penalties.")
    (license license:gpl2+)))

(define-public r-glmmml
  (package
    (name "r-glmmml")
    (version "1.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmmML" version))
       (sha256
        (base32 "07w6b1i9fkv8dxcmvky81f87ysbrrry3d4jczwkg953g9lankr5j"))))
    (properties `((upstream-name . "glmmML")))
    (build-system r-build-system)
    (native-inputs (list r-knitr gfortran))
    (home-page "https://cran.r-project.org/package=glmmML")
    (synopsis "Generalized Linear Models with Clustering")
    (description
     "Binomial and Poisson regression for clustered data, fixed and random effects
with bootstrapping.")
    (license license:gpl3+)))

(define-public r-glmmlasso
  (package
    (name "r-glmmlasso")
    (version "1.6.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmmLasso" version))
       (sha256
        (base32 "1b2wkmsmnhmkql0kin56faf64abc6hijrs6dlz8qvwcbqpgbpcig"))))
    (properties `((upstream-name . "glmmLasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp r-minqa r-matrix))
    (home-page "https://cran.r-project.org/package=glmmLasso")
    (synopsis
     "Variable Selection for Generalized Linear Mixed Models by L1-Penalized Estimation")
    (description
     "This package provides a variable selection approach for generalized linear mixed
models by L1-penalized estimation is provided, see Groll and Tutz (2014)
<doi:10.1007/s11222-012-9359-z>.  See also Groll and Tutz (2017)
<doi:10.1007/s10985-016-9359-y> for discrete survival models including
heterogeneity.")
    (license license:gpl2)))

(define-public r-glmmisrep
  (package
    (name "r-glmmisrep")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmMisrep" version))
       (sha256
        (base32 "0ymcgn42r3n6fhfp7rmkz033bmnqdshx71957zvbrmxjphkmr37b"))))
    (properties `((upstream-name . "glmMisrep")))
    (build-system r-build-system)
    (propagated-inputs (list r-poisson-glm-mix r-mass))
    (home-page "https://cran.r-project.org/package=glmMisrep")
    (synopsis "Generalized Linear Models Adjusting for Misrepresentation")
    (description
     "Fit Generalized Linear Models to continuous and count outcomes, as well as
estimate the prevalence of misrepresentation of an important binary predictor.
Misrepresentation typically arises when there is an incentive for the binary
factor to be misclassified in one direction (e.g., in insurance settings where
policy holders may purposely deny a risk status in order to lower the insurance
premium).  This is accomplished by treating a subset of the response variable as
resulting from a mixture distribution.  Model parameters are estimated via the
Expectation Maximization algorithm and standard errors of the estimates are
obtained from closed forms of the Observed Fisher Information.  For an
introduction to the models and the misrepresentation framework, see Xia et.
al., (2023)
<https://variancejournal.org/article/73151-maximum-likelihood-approaches-to-misrepresentation-models-in-glm-ratemaking-model-comparisons>.")
    (license license:gpl2+)))

(define-public r-glmmfields
  (package
    (name "r-glmmfields")
    (version "0.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmmfields" version))
       (sha256
        (base32 "0nn328dn7fbh3h0wnl4kahwrbq9ddr3hyqq7930raa0abimg8ykq"))))
    (properties `((upstream-name . "glmmfields")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-reshape2
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-nlme
                             r-mvtnorm
                             r-loo
                             r-ggplot2
                             r-forcats
                             r-dplyr
                             r-cluster
                             r-broom-mixed
                             r-broom
                             r-bh
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/seananderson/glmmfields")
    (synopsis
     "Generalized Linear Mixed Models with Robust Random Fields for Spatiotemporal Modeling")
    (description
     "This package implements Bayesian spatial and spatiotemporal models that
optionally allow for extreme spatial deviations through time.  glmmfields uses a
predictive process approach with random fields implemented through a
multivariate-t distribution instead of the usual multivariate normal.  Sampling
is conducted with Stan'.  References: Anderson and Ward (2019)
<doi:10.1002/ecy.2403>.")
    (license license:gpl3+)))

(define-public r-glmmep
  (package
    (name "r-glmmep")
    (version "1.0-3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmmEP" version))
       (sha256
        (base32 "0fql85m0mxn58jh3mgrcw56bb4dx7amrz39ngqrljwi5pv5i7aj4"))))
    (properties `((upstream-name . "glmmEP")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrixcalc r-lme4))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=glmmEP")
    (synopsis
     "Generalized Linear Mixed Model Analysis via Expectation Propagation")
    (description
     "Approximate frequentist inference for generalized linear mixed model analysis
with expectation propagation used to circumvent the need for multivariate
integration.  In this version, the random effects can be any reasonable
dimension.  However, only probit mixed models with one level of nesting are
supported.  The methodology is described in Hall, Johnstone, Ormerod, Wand and
Yu (2018) <@code{arXiv:1805.08423v1>}.")
    (license license:gpl2+)))

(define-public r-glmmcosinor
  (package
    (name "r-glmmcosinor")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GLMMcosinor" version))
       (sha256
        (base32 "098r9l5m26ld8frxjzc6rr7ahw2z22njnskn06kqchxjqxv6amqw"))))
    (properties `((upstream-name . "GLMMcosinor")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-rlang
                             r-lme4
                             r-glmmtmb
                             r-ggplot2
                             r-ggforce
                             r-cowplot
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ropensci/GLMMcosinor")
    (synopsis
     "Fit a Cosinor Model Using a Generalised Mixed Modelling Framework")
    (description
     "Allows users to fit a cosinor model using the @code{glmmTMB} framework.  This
extends on existing cosinor modelling packages, including cosinor and
circacompare', by including a wide range of available link functions and the
capability to fit mixed models.  The cosinor model is described by Cornelissen
(2014) <doi:10.1186/1742-4682-11-16>.")
    (license license:gpl3+)))

(define-public r-glmmadaptive
  (package
    (name "r-glmmadaptive")
    (version "0.9-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GLMMadaptive" version))
       (sha256
        (base32 "1gzm61a7qy4mvw1zaa4n01a5si7vawq2w0ppwk6pfsd06ffh5g2r"))))
    (properties `((upstream-name . "GLMMadaptive")))
    (build-system r-build-system)
    (propagated-inputs (list r-nlme r-matrixstats r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://drizopoulos.github.io/GLMMadaptive/")
    (synopsis
     "Generalized Linear Mixed Models using Adaptive Gaussian Quadrature")
    (description
     "Fits generalized linear mixed models for a single grouping factor under maximum
likelihood approximating the integrals over the random effects with an adaptive
Gaussian quadrature rule; Jose C. Pinheiro and Douglas M. Bates (1995)
<doi:10.1080/10618600.1995.10474663>.")
    (license license:gpl3+)))

(define-public r-glmm-hp
  (package
    (name "r-glmm-hp")
    (version "0.1-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmm.hp" version))
       (sha256
        (base32 "0jmjh219klgmz28ds0kjy1kzcz9jwjcz12bnp5l9cmh4l1jhzpmb"))))
    (properties `((upstream-name . "glmm.hp")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan r-mumin r-lme4 r-ggplot2))
    (home-page "https://github.com/laijiangshan/glmm.hp")
    (synopsis
     "Hierarchical Partitioning of Marginal R2 for Generalized Mixed-Effect Models")
    (description
     "Conducts hierarchical partitioning to calculate individual contributions of each
predictor (fixed effects) towards marginal R2 for generalized linear
mixed-effect model (including lm, glm and glmm) based on output of
r.@code{squaredGLMM}() in @code{MuMIn}', applying the algorithm of Lai J.,Zou
Y., Zhang S.,Zhang X.,Mao L.(2022)glmm.hp: an R package for computing individual
effect of predictors in generalized linear mixed models.Journal of Plant
Ecology,15(6)1302-1307<doi:10.1093/jpe/rtac096>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-glmm
  (package
    (name "r-glmm")
    (version "1.4.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmm" version))
       (sha256
        (base32 "0wl1ca4brlqp5dn2zi5c49y2i21b22ag88cxzzwb8nx10snlr9p1"))))
    (properties `((upstream-name . "glmm")))
    (build-system r-build-system)
    (propagated-inputs (list r-trust
                             r-mvtnorm
                             r-matrix
                             r-itertools
                             r-foreach
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=glmm")
    (synopsis
     "Generalized Linear Mixed Models via Monte Carlo Likelihood Approximation")
    (description
     "Approximates the likelihood of a generalized linear mixed model using Monte
Carlo likelihood approximation.  Then maximizes the likelihood approximation to
return maximum likelihood estimates, observed Fisher information, and other
model information.")
    (license license:gpl2)))

(define-public r-glmlep
  (package
    (name "r-glmlep")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmlep" version))
       (sha256
        (base32 "0rq2vxbr732r725pn8qdvppxcrhjhyvlr550svfgy1inq985c1w4"))))
    (properties `((upstream-name . "glmlep")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=glmlep")
    (synopsis "Fit GLM with LEP-Based Penalized Maximum Likelihood")
    (description
     "Efficient algorithms for fitting regularization paths for linear or logistic
regression models penalized by LEP.")
    (license license:gpl2)))

(define-public r-glmglrt
  (package
    (name "r-glmglrt")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmglrt" version))
       (sha256
        (base32 "1954ywshnqzr7h77wshf63y3zx3ra8mrfabg5dqjhkgv4rsk3h1y"))))
    (properties `((upstream-name . "glmglrt")))
    (build-system r-build-system)
    (propagated-inputs (list r-parameters r-mass))
    (home-page "https://cran.r-project.org/package=glmglrt")
    (synopsis "GLRT P-Values in Generalized Linear Models")
    (description
     "This package provides functions to compute Generalized Likelihood Ratio Tests
(GLRT) also known as Likelihood Ratio Tests (LRT) and Rao's score tests of
simple and complex contrasts of Generalized Linear Models (GLMs).  It provides
the same interface as summary.glm(), adding GLRT P-values, less biased than
Wald's P-values and consistent with profile-likelihood confidence interval
generated by confint().  See Wilks (1938) <doi:10.1214/aoms/1177732360> for the
LRT chi-square approximation.  See Rao (1948) <doi:10.1017/S0305004100023987>
for Rao's score test.  See Wald (1943) <doi:10.2307/1990256> for Wald's test.")
    (license license:gpl2)))

(define-public r-glmertree
  (package
    (name "r-glmertree")
    (version "0.2-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmertree" version))
       (sha256
        (base32 "0qwfracqrxs471fmz2rcnaw6kc0b2csldm2n4vfc5jlq1k46fljk"))))
    (properties `((upstream-name . "glmertree")))
    (build-system r-build-system)
    (propagated-inputs (list r-partykit r-lme4 r-formula))
    (home-page "https://cran.r-project.org/package=glmertree")
    (synopsis "Generalized Linear Mixed Model Trees")
    (description
     "Recursive partitioning based on (generalized) linear mixed models (GLMMs)
combining lmer()/glmer() from lme4 and lmtree()/glmtree() from partykit'.  The
fitting algorithm is described in more detail in Fokkema, Smits, Zeileis,
Hothorn & Kelderman (2018; <DOI:10.3758/s13428-017-0971-x>).")
    (license (list license:gpl2 license:gpl3))))

(define-public r-glme
  (package
    (name "r-glme")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glme" version))
       (sha256
        (base32 "1ysg1yx0pdyxlbkn36023230yky8wj3rvi3skrdqdjbnnyzhn49b"))))
    (properties `((upstream-name . "glme")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape r-nlme r-dplyr))
    (home-page "https://cran.r-project.org/package=glme")
    (synopsis "Generalized Linear Mixed Effects Models")
    (description
     "This package provides Generalized Inferences based on exact distributions and
exact probability statements for mixed effect models, provided by such papers as
Weerahandi and Yu (2020) <doi:10.1186/s40488-020-00105-w> under the widely used
Compound Symmetric Covariance structure.  The package returns the estimation of
the coefficients in random and fixed part of the mixed models by generalized
inference.")
    (license license:gpl2+)))

(define-public r-glmcat
  (package
    (name "r-glmcat")
    (version "0.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GLMcat" version))
       (sha256
        (base32 "0a9gvnrzrhpywmy4zzm0pyah6dflim4zfg4hqj8kh1ks0wynmjbr"))))
    (properties `((upstream-name . "GLMcat")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rcppeigen r-rcpp r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ylleonv/GLMcat")
    (synopsis "Generalized Linear Models for Categorical Responses")
    (description
     "In statistical modeling, there is a wide variety of regression models for
categorical dependent variables (nominal or ordinal data); yet, there is no
software embracing all these models together in a uniform and generalized
format.  Following the methodology proposed by Peyhardi, Trottier, and GuÃ©don
(2015) <doi:10.1093/biomet/asv042>, we introduce GLMcat', an R package to
estimate generalized linear models implemented under the unified specification
(r, F, Z).  Where r represents the ratio of probabilities (reference,
cumulative, adjacent, or sequential), F the cumulative cdf function for the
linkage, and Z, the design matrix.")
    (license license:gpl3)))

(define-public r-glmc
  (package
    (name "r-glmc")
    (version "0.3-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmc" version))
       (sha256
        (base32 "0x6jk2l5rpjkp5x815gpiwv8maa7mhmy6m8gaxbv1cbn3zyccynn"))))
    (properties `((upstream-name . "glmc")))
    (build-system r-build-system)
    (propagated-inputs (list r-emplik))
    (home-page "https://cran.r-project.org/package=glmc")
    (synopsis "Fitting Generalized Linear Models Subject to Constraints")
    (description
     "Fits generalized linear models where the parameters are subject to linear
constraints.  The model is specified by giving a symbolic description of the
linear predictor, a description of the error distribution, and a matrix of
constraints on the parameters.")
    (license license:gpl2+)))

(define-public r-glmbb
  (package
    (name "r-glmbb")
    (version "0.5-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glmbb" version))
       (sha256
        (base32 "1jy77z5fr781rm5mx6kszwi2zlvnpfwi24fn9vw1kza4jzcw9pjw"))))
    (properties `((upstream-name . "glmbb")))
    (build-system r-build-system)
    (propagated-inputs (list r-digest))
    (home-page "https://github.com/cjgeyer/glmbb")
    (synopsis
     "All Hierarchical or Graphical Models for Generalized Linear Model")
    (description
     "Find all hierarchical models of specified generalized linear model with
information criterion (AIC, BIC, or AICc) within specified cutoff of minimum
value.  Alternatively, find all such graphical models.  Use branch and bound
algorithm so we do not have to fit all models.")
    (license license:expat)))

(define-public r-glmaspu
  (package
    (name "r-glmaspu")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GLMaSPU" version))
       (sha256
        (base32 "0zx2bza5v5cfp9v7hf42s57522b6prawcwl77aa3kvjcnb0ish6g"))))
    (properties `((upstream-name . "GLMaSPU")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mvtnorm r-mnormt r-mass))
    (home-page "https://cran.r-project.org/package=GLMaSPU")
    (synopsis
     "An Adaptive Test on High Dimensional Parameters in Generalized Linear Models")
    (description
     "Several tests for high dimensional generalized linear models have been proposed
recently.  In this package, we implemented a new test called adaptive sum of
powered score (@code{aSPU}) for high dimensional generalized linear models,
which is often more powerful than the existing methods in a wide scenarios.  We
also implemented permutation based version of several existing methods for
research purpose.  We recommend users use the @code{aSPU} test for their real
testing problem.  You can learn more about the tests implemented in the package
via the following papers: 1.  Pan, W., Kim, J., Zhang, Y., Shen, X. and Wei, P.
(2014) <DOI:10.1534/genetics.114.165035> A powerful and adaptive association
test for rare variants, Genetics, 197(4).  2.  Guo, B., and Chen, S. X. (2016)
<DOI:10.1111/rssb.12152>.  Tests for high dimensional generalized linear models.
 Journal of the Royal Statistical Society: Series B. 3.  Goeman, J. J., Van
Houwelingen, H. C., and Finos, L. (2011) <DOI:10.1093/biomet/asr016>.  Testing
against a high-dimensional alternative in the generalized linear model:
asymptotic type I error control.  Biometrika, 98(2).")
    (license license:gpl2)))

(define-public r-glm2
  (package
    (name "r-glm2")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glm2" version))
       (sha256
        (base32 "1byqlvpls23gzy7xlbs1kgfr4bsrmsrpc8rzzacf9czap1nlw39y"))))
    (properties `((upstream-name . "glm2")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=glm2")
    (synopsis "Fitting Generalized Linear Models")
    (description
     "Fits generalized linear models using the same model specification as glm in the
stats package, but with a modified default fitting method that provides greater
stability for models that may fail to converge using glm.")
    (license license:gpl2+)))

(define-public r-glm-predict
  (package
    (name "r-glm-predict")
    (version "4.3-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glm.predict" version))
       (sha256
        (base32 "18cs6z6573vq8sdjb4kgz8kahizs9njdbip4cm1dk63ldjjrl0kf"))))
    (properties `((upstream-name . "glm.predict")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam
                             r-survival
                             r-survey
                             r-nnet
                             r-mlogit
                             r-mass
                             r-lme4
                             r-dfidx
                             r-aer))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/benjaminschlegel/glm.predict/")
    (synopsis "Predicted Values and Discrete Changes for Regression Models")
    (description
     "This package provides functions to calculate predicted values and the difference
between the two cases with confidence interval for lm() [linear model], glm()
[generalized linear model], glm.nb() [negative binomial model], polr() [ordinal
logistic model], vglm() [generalized ordinal logistic model],	multinom()
[multinomial model], tobit() [tobit model], svyglm() [survey-weighted
generalised linear models] and lmer() [linear multilevel models] using Monte
Carlo simulations or bootstrap.  Reference: Bennet A. Zelner (2009)
<doi:10.1002/smj.783>.")
    (license license:gpl2+)))

(define-public r-glm-deploy
  (package
    (name "r-glm-deploy")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glm.deploy" version))
       (sha256
        (base32 "1akklvbhz2pp0sv1hax1jq0s9ajbfv1597z643gh5l9hwizj2h9m"))))
    (properties `((upstream-name . "glm.deploy")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://github.com/oscarcastrolopez/glm.deploy")
    (synopsis "'C' and 'Java' Source Code Generator for Fitted Glm Objects")
    (description
     "This package provides two functions that generate source code implementing the
predict function of fitted glm objects.  In this version, code can be generated
for either C or Java'.  The idea is to provide a tool for the easy and fast
deployment of glm predictive models into production.  The source code generated
by this package implements two function/methods.  One of such functions
implements the equivalent to predict(type=\"response\"), while the second
implements predict(type=\"link\").  Source code is written to disk as a .c or
.java file in the specified path.  In the case of c, an .h file is also
generated.")
    (license (list license:gpl3+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-gllvm
  (package
    (name "r-gllvm")
    (version "1.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gllvm" version))
       (sha256
        (base32 "1k3yw1flqzj151kqx8wyh54a7crh62ya700x5n4989ngx5071dv3"))))
    (properties `((upstream-name . "gllvm")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmb
                             r-statmod
                             r-rcppeigen
                             r-nloptr
                             r-mvabund
                             r-mgcv
                             r-matrix
                             r-mass
                             r-fishmod
                             r-alabama))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/JenniNiku/gllvm")
    (synopsis "Generalized Linear Latent Variable Models")
    (description
     "Analysis of multivariate data using generalized linear latent variable models
(gllvm).  Estimation is performed using either Laplace approximation method or
variational approximation method implemented via TMB (Kristensen et al., (2016),
<doi:10.18637/jss.v070.i05>).  For details see Niku et al. (2019a)
<doi:10.1371/journal.pone.0216129> and Niku et al. (2019b)
<doi:10.1111/2041-210X.13303>.")
    (license license:gpl2)))

(define-public r-gllm
  (package
    (name "r-gllm")
    (version "0.38")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gllm" version))
       (sha256
        (base32 "06sq7yk11rbqj8i900ss987fg9b232cirjxhdm1yl9xqz4inxvlg"))))
    (properties `((upstream-name . "gllm")))
    (build-system r-build-system)
    (home-page "https://genepi.qimr.edu.au/Staff/davidD/#loglin")
    (synopsis "Generalised log-Linear Model")
    (description
     "Routines for log-linear models of incomplete contingency tables, including some
latent class models, via EM and Fisher scoring approaches.  Allows
bootstrapping.  See Espeland and Hui (1987) <doi:10.2307/2531553> for general
approach.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-glinvci
  (package
    (name "r-glinvci")
    (version "1.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glinvci" version))
       (sha256
        (base32 "050rpdyp3xawkyjxcimjpbvn24677whlfhfmkyxijz7zqnvl7i8p"))))
    (properties `((upstream-name . "glinvci")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-plyr
                             r-optimx
                             r-numderiv
                             r-lbfgsb3c
                             r-generics
                             r-bb
                             r-ape))
    (native-inputs (list pkg-config gfortran))
    (home-page "https://git.sr.ht/~hckiang/glinvci")
    (synopsis "Phylogenetic Comparative Methods with Uncertainty Estimates")
    (description
     "This package provides a framework for analytically computing the asymptotic
confidence intervals and maximum-likelihood estimates of a class of
continuous-time Gaussian branching processes defined by Mitov V, Bartoszek K,
Asimomitis G, Stadler T (2019) <doi:10.1016/j.tpb.2019.11.005>.  The class of
model includes the widely used Ornstein-Uhlenbeck and Brownian motion branching
processes.  The framework is designed to be flexible enough so that the users
can easily specify their own sub-models, or re-parameterizations, and obtain the
maximum-likelihood estimates and confidence intervals of their own custom
models.")
    (license license:gpl3)))

(define-public r-gldrm
  (package
    (name "r-gldrm")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gldrm" version))
       (sha256
        (base32 "02xgkrh2wybf9prcrb55w5ingf6x8f5xzdcnh2fv4skr0ixn0gp8"))))
    (properties `((upstream-name . "gldrm")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gldrm")
    (synopsis "Generalized Linear Density Ratio Models")
    (description
     "Fits a generalized linear density ratio model (GLDRM).  A GLDRM is a
semiparametric generalized linear model.  In contrast to a GLM, which assumes a
particular exponential family distribution, the GLDRM uses a semiparametric
likelihood to estimate the reference distribution.  The reference distribution
may be any discrete, continuous, or mixed exponential family distribution.  The
model parameters, which include both the regression coefficients and the cdf of
the unspecified reference distribution, are estimated by maximizing a
semiparametric likelihood.  Regression coefficients are estimated with no loss
of efficiency, i.e.  the asymptotic variance is the same as if the true
exponential family distribution were known.  Huang (2014)
<doi:10.1080/01621459.2013.824892>.  Huang and Rathouz (2012)
<doi:10.1093/biomet/asr075>.  Rathouz and Gao (2008)
<doi:10.1093/biostatistics/kxn030>.")
    (license license:expat)))

(define-public r-gldreg
  (package
    (name "r-gldreg")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GLDreg" version))
       (sha256
        (base32 "1cp5mgbvq0g9ss2cv5yincfjvs8jmciz0h3g5jrr8gda1gvf357i"))))
    (properties `((upstream-name . "GLDreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-gldex r-ddst))
    (home-page "https://cran.r-project.org/package=GLDreg")
    (synopsis "Fit GLD Regression/Quantile/AFT Model to Data")
    (description
     "Owing to the rich shapes of Generalised Lambda Distributions (GLDs), GLD
standard/quantile/Accelerated Failure Time (AFT) regression is a competitive
flexible model compared to standard/quantile/AFT regression.  The proposed
method has some major advantages: 1) it provides a reference line which is very
robust to outliers with the attractive property of zero mean residuals and 2) it
gives a unified, elegant quantile regression model from the reference line with
smooth regression coefficients across different quantiles.  For AFT model, it
also eliminates the needs to try several different AFT models, owing to the
flexible shapes of GLD. The goodness of fit of the proposed model can be
assessed via QQ plots and Kolmogorov-Smirnov tests and data driven smooth test,
to ensure the appropriateness of the statistical inference under consideration.
Statistical distributions of coefficients of the GLD regression line are
obtained using simulation, and interval estimates are obtained directly from
simulated data.  References include the following: Su (2015) \"Flexible
Parametric Quantile Regression Model\" <doi:10.1007/s11222-014-9457-1>, Su (2021)
\"Flexible parametric accelerated failure time
model\"<doi:10.1080/10543406.2021.1934854>.")
    (license license:gpl3+)))

(define-public r-glcmtextures
  (package
    (name "r-glcmtextures")
    (version "0.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GLCMTextures" version))
       (sha256
        (base32 "0sfa05nm2riv8mnldq9hf7p0rcwwycc627ccwjkgh9a754fzxhcv"))))
    (properties `((upstream-name . "GLCMTextures")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-rcpparmadillo r-rcpp r-raster))
    (native-inputs (list r-knitr))
    (home-page "https://ailich.github.io/GLCMTextures/")
    (synopsis "GLCM Textures of Raster Layers")
    (description
     "Calculates grey level co-occurrence matrix (GLCM) based texture measures
(Hall-Beyer (2017)
<https://prism.ucalgary.ca/bitstream/handle/1880/51900/texture%20tutorial%20v%203_0%20180206.pdf>;
Haralick et al. (1973) <doi:10.1109/TSMC.1973.4309314>) of raster layers using a
sliding rectangular window.  It also includes functions to quantize a raster
into grey levels as well as tabulate a glcm and calculate glcm texture metrics
for a matrix.")
    (license license:gpl3+)))

(define-public r-glcm
  (package
    (name "r-glcm")
    (version "1.6.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glcm" version))
       (sha256
        (base32 "13my6y9jqd86cg5n74p4b8mqbdgwnanxx4qf1rrhm5jxf6sa31qm"))))
    (properties `((upstream-name . "glcm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-raster))
    (home-page "http://www.azvoleff.com/glcm")
    (synopsis
     "Calculate Textures from Grey-Level Co-Occurrence Matrices (GLCMs)")
    (description
     "Enables calculation of image textures (Haralick 1973)
<doi:10.1109/TSMC.1973.4309314> from grey-level co-occurrence matrices (GLCMs).
Supports processing images that cannot fit in memory.")
    (license license:gpl3+)))

(define-public r-glca
  (package
    (name "r-glca")
    (version "1.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glca" version))
       (sha256
        (base32 "0vvd8m015g5rabpmkmfp20kh15d7ck8aamr9l9mbs2vapqs5k9c7"))))
    (properties `((upstream-name . "glca")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://kim0sun.github.io/glca/")
    (synopsis "An R Package for Multiple-Group Latent Class Analysis")
    (description
     "Fits multiple-group latent class analysis (LCA) for exploring differences
between populations in the data with a multilevel structure.  There are two
approaches to reflect group differences in glca: fixed-effect LCA (Bandeen-Roche
et al (1997) <doi:10.1080/01621459.1997.10473658>; Clogg and Goodman (1985)
<doi:10.2307/270847>) and nonparametric random-effect LCA (Vermunt (2003)
<doi:10.1111/j.0081-1750.2003.t01-1-00131.x>).")
    (license license:gpl3)))

(define-public r-glba
  (package
    (name "r-glba")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glba" version))
       (sha256
        (base32 "0dilf144xfn454xfsndbddhc28gzf36fiknc2qwcx3bp7z5caqjn"))))
    (properties `((upstream-name . "glba")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=glba")
    (synopsis "General Linear Ballistic Accumulator Models")
    (description
     "Analyses response times and accuracies from psychological experiments with the
linear ballistic accumulator (LBA) model from Brown and Heathcote (2008).  The
LBA model is optionally fitted with explanatory variables on the parameters such
as the drift rate, the boundary and the starting point parameters.  A log-link
function on the linear predictors can be used to ensure that parameters remain
positive when needed.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-glassdoor
  (package
    (name "r-glassdoor")
    (version "0.8.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glassdoor" version))
       (sha256
        (base32 "0r85k1fyk8979ahfmd4vmzkhyvrlnpkgjwg1yi7092kpx8877lj0"))))
    (properties `((upstream-name . "glassdoor")))
    (build-system r-build-system)
    (propagated-inputs (list r-httr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=glassdoor")
    (synopsis "Interface to 'Glassdoor' API")
    (description
     "Interacts with the Glassdoor API
<https://www.glassdoor.com/developer/index.htm>.  Allows the user to search job
statistics, employer statistics, and job progression, where Glassdoor provides a
breakdown of other jobs a person did after their current one.")
    (license license:gpl2)))

(define-public r-glarmavarsel
  (package
    (name "r-glarmavarsel")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GlarmaVarSel" version))
       (sha256
        (base32 "080v2qckihhacclxi2zscslbwbc6179pc7g02lyqaicf9wrsr8ff"))))
    (properties `((upstream-name . "GlarmaVarSel")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-glmnet r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GlarmaVarSel")
    (synopsis "Variable Selection in Sparse GLARMA Models")
    (description
     "This package performs variable selection in high-dimensional sparse GLARMA
models.  For further details we refer the reader to the paper Gomtsyan et al.
(2020), <@code{arXiv:2007.08623v1>}.")
    (license license:gpl2)))

(define-public r-glarmadillo
  (package
    (name "r-glarmadillo")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Glarmadillo" version))
       (sha256
        (base32 "14k24f6yjx9jlvmd7q1lw6nyajzpsmsbdj8jq3v8ad1kdrip05x7"))))
    (properties `((upstream-name . "Glarmadillo")))
    (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=Glarmadillo")
    (synopsis "Solve the Graphical Lasso Problem with 'Armadillo'")
    (description
     "Efficiently implements the Graphical Lasso algorithm, utilizing the Armadillo
C++ library for rapid computation.  This algorithm introduces an L1 penalty to
derive sparse inverse covariance matrices from observations of multivariate
normal distributions.  Features include the generation of random and structured
sparse covariance matrices, beneficial for simulations, statistical method
testing, and educational purposes in graphical modeling.  A unique function for
regularization parameter selection based on predefined sparsity levels is also
offered, catering to users with specific sparsity requirements in their models.
The methodology for sparse inverse covariance estimation implemented in this
package is based on the work of Friedman, Hastie, and Tibshirani (2008)
<doi:10.1093/biostatistics/kxm045>.")
    (license license:gpl3+)))

(define-public r-glarma
  (package
    (name "r-glarma")
    (version "1.6-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glarma" version))
       (sha256
        (base32 "0n0mv4xvh5q3y383cs224x6r608ciw4822pkanvmcjsc9zl0z1ar"))))
    (properties `((upstream-name . "glarma")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=glarma")
    (synopsis "Generalized Linear Autoregressive Moving Average Models")
    (description
     "This package provides functions are provided for estimation, testing, diagnostic
checking and forecasting of generalized linear autoregressive moving average
(GLARMA) models for discrete valued time series with regression variables.
These are a class of observation driven non-linear non-Gaussian state space
models.  The state vector consists of a linear regression component plus an
observation driven component consisting of an autoregressive-moving average
(ARMA) filter of past predictive residuals.  Currently three distributions
(Poisson, negative binomial and binomial) can be used for the response series.
Three options (Pearson, score-type and unscaled) for the residuals in the
observation driven component are available.  Estimation is via maximum
likelihood (conditional on initializing values for the ARMA process) optimized
using Fisher scoring or Newton Raphson iterative methods.  Likelihood ratio and
Wald tests for the observation driven component allow testing for serial
dependence in generalized linear model settings.  Graphical diagnostics
including model fits, autocorrelation functions and probability integral
transform residuals are included in the package.  Several standard data sets are
included in the package.")
    (license license:gpl2+)))

(define-public r-glamlasso
  (package
    (name "r-glamlasso")
    (version "3.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glamlasso" version))
       (sha256
        (base32 "0vzkszam7nz40v8pxq06jn3fgwa1nd3s485hgfkcv7r8pg2rp56q"))))
    (properties `((upstream-name . "glamlasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=glamlasso")
    (synopsis "Penalization in Large Scale Generalized Linear Array Models")
    (description
     "Efficient design matrix free lasso penalized estimation in large scale 2 and
3-dimensional generalized linear array model framework.  The procedure is based
on the gdpg algorithm from Lund et al. (2017)
<doi:10.1080/10618600.2017.1279548>.  Currently Lasso or Smoothly Clipped
Absolute Deviation (SCAD) penalized estimation is possible for the following
models: The Gaussian model with identity link, the Binomial model with logit
link, the Poisson model with log link and the Gamma model with log link.  It is
also possible to include a component in the model with non-tensor design e.g an
intercept.  Also provided are functions, @code{glamlassoRR}() and
@code{glamlassoS}(), fitting special cases of GLAMs.")
    (license license:gpl3)))

(define-public r-glam
  (package
    (name "r-glam")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "glam" version))
       (sha256
        (base32 "0fwh6x1y1d7fi0d4qh0vdmlr324zqil91zmi79w2mxs9q1w9a2ws"))))
    (properties `((upstream-name . "glam")))
    (build-system r-build-system)
    (propagated-inputs (list r-gam))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=glam")
    (synopsis "Generalized Additive and Linear Models (GLAM)")
    (description
     "This package contains methods for fitting Generalized Linear Models (GLMs) and
Generalized Additive Models (GAMs).  Generalized regression models are common
methods for handling data for which assuming Gaussian-distributed errors is not
appropriate.  For instance, if the response of interest is binary, count, or
proportion data, one can instead model the expectation of the response based on
an appropriate data-generating distribution.  This package provides methods for
fitting GLMs and GAMs under Beta regression, Poisson regression, Gamma
regression, and Binomial regression (currently GLM only) settings.  Models are
fit using local scoring algorithms described in Hastie and Tibshirani (1990)
<doi:10.1214/ss/1177013604>.")
    (license license:expat)))

(define-public r-gkrls
  (package
    (name "r-gkrls")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gKRLS" version))
       (sha256
        (base32 "0di8hczllxrww6xhcl6hi67p8ihxa09dgd9nbkcx39akv3cwxa8q"))))
    (properties `((upstream-name . "gKRLS")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich
                             r-rcppeigen
                             r-rcpp
                             r-r6
                             r-mlr3
                             r-mgcv
                             r-matrix))
    (home-page "https://github.com/mgoplerud/gKRLS")
    (synopsis "Generalized Kernel Regularized Least Squares")
    (description
     "Kernel regularized least squares, also known as kernel ridge regression, is a
flexible machine learning method.  This package implements this method by
providing a smooth term for use with mgcv and uses random sketching to
facilitate scalable estimation on large datasets.  It provides additional
functions for calculating marginal effects after estimation and for use with
ensembles ('@code{SuperLearning}'), double/debiased machine learning
('@code{DoubleML}'), and robust/clustered standard errors ('sandwich').  Chang
and Goplerud (2024) <doi:10.1017/pan.2023.27> provide further details.")
    (license license:gpl2+)))

(define-public r-gkgraphr
  (package
    (name "r-gkgraphr")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gkgraphR" version))
       (sha256
        (base32 "1sb52hhpwm3bq7rn2xdq5vly6z1c5yab9a26xzgj3c5s47vk02pa"))))
    (properties `((upstream-name . "gkgraphR")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-isocodes r-httr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/racorreia/gkgraphR")
    (synopsis "Accessing the Official 'Google Knowledge Graph' API")
    (description
     "This package provides a simple way to interact with and extract data from the
official Google Knowledge Graph API
<https://developers.google.com/knowledge-graph/>.")
    (license license:expat)))

(define-public r-gk2011
  (package
    (name "r-gk2011")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GK2011" version))
       (sha256
        (base32 "13vafhbgcsj485f12qv962y07v3hil3pla51vkl2b030amzy86jv"))))
    (properties `((upstream-name . "GK2011")))
    (build-system r-build-system)
    (home-page "https://github.com/leeper/GK2011")
    (synopsis "Gaines and Kuklinski (2011) Estimators for Hybrid Experiments")
    (description
     "Implementations of the treatment effect estimators for hybrid (self-selection)
experiments, as developed by Brian J. Gaines and James H. Kuklinski, (2011),
\"Experimental Estimation of Heterogeneous Treatment Effects Related to
Self-Selection,\" American Journal of Political Science 55(3): 724-736.")
    (license license:gpl2+)))

(define-public r-gk
  (package
    (name "r-gk")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gk" version))
       (sha256
        (base32 "1dwv6389w9cgavwkzznbdzf92d3y0s07s74a5ccwiy1zf48jwwar"))))
    (properties `((upstream-name . "gk")))
    (build-system r-build-system)
    (propagated-inputs (list r-progress r-lubridate r-ecdat))
    (home-page "https://github.com/dennisprangle/gk")
    (synopsis "g-and-k and g-and-h Distribution Functions")
    (description
     "This package provides functions for the g-and-k and generalised g-and-h
distributions.")
    (license license:gpl2)))

(define-public r-gjrm
  (package
    (name "r-gjrm")
    (version "0.2-6.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GJRM" version))
       (sha256
        (base32 "0j69lh8hbdcmgj6bwg67s1mlgffcmc045z6sm6p3jifcg8ri18sw"))))
    (properties `((upstream-name . "GJRM")))
    (build-system r-build-system)
    (propagated-inputs (list r-vinecopula
                             r-vgam
                             r-trust
                             r-survival
                             r-survey
                             r-scam
                             r-rmpfr
                             r-psych
                             r-numderiv
                             r-mnormt
                             r-mgcv
                             r-matrixstats
                             r-magic
                             r-ismev
                             r-ggplot2
                             r-gamlss-dist
                             r-evd
                             r-copula))
    (home-page "https://www.ucl.ac.uk/statistics/people/giampieromarra")
    (synopsis "Generalised Joint Regression Modelling")
    (description
     "Routines for fitting various joint (and univariate) regression models, with
several types of covariate effects, in the presence of equations errors
association, endogeneity, non-random sample selection or partial observability.")
    (license license:gpl2+)))

(define-public r-gjls2
  (package
    (name "r-gjls2")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gJLS2" version))
       (sha256
        (base32 "112fyy76ry6g0xinhy09s5vdcd11jw7sivadlx05ali89lm5nigg"))))
    (properties `((upstream-name . "gJLS2")))
    (build-system r-build-system)
    (propagated-inputs (list r-quantreg
                             r-plyr
                             r-nlme
                             r-moments
                             r-mcmcpack
                             r-mass
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gJLS2")
    (synopsis
     "Generalized Joint Location and Scale Framework for Association Testing")
    (description
     "An update to the Joint Location-Scale (JLS) testing framework that identifies
associated SNPs, gene-sets and pathways with main and/or interaction effects on
quantitative traits (Soave et al., 2015; <doi:10.1016/j.ajhg.2015.05.015>).  The
JLS method simultaneously tests the null hypothesis of equal mean and equal
variance across genotypes, by aggregating association evidence from the
individual location/mean-only and scale/variance-only tests using Fisher's
method.  The generalized joint location-scale (@code{gJLS}) framework has been
developed to deal specifically with sample correlation and group uncertainty
(Soave and Sun, 2017; <doi:10.1111/biom.12651>).  The current release:
@code{gJLS2}, include additional functionalities that enable analyses of
X-chromosome genotype data through novel methods for location (Chen et al.,
2021; <doi:10.1002/gepi.22422>) and scale (Deng et al., 2019;
<doi:10.1002/gepi.22247>).")
    (license license:gpl3+)))

(define-public r-gjam
  (package
    (name "r-gjam")
    (version "2.6.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gjam" version))
       (sha256
        (base32 "1jzgh5hp112gjgqyyhpd4fv1yizb56596wl3izb066rzhhyc4wim"))))
    (properties `((upstream-name . "gjam")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-rann r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gjam")
    (synopsis "Generalized Joint Attribute Modeling")
    (description
     "Analyzes joint attribute data (e.g., species abundance) that are combinations of
continuous and discrete data with Gibbs sampling.  Full model and computation
details are described in Clark et al. (2018) <doi:10.1002/ecm.1241>.")
    (license license:gpl2+)))

(define-public r-givitir
  (package
    (name "r-givitir")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "givitiR" version))
       (sha256
        (base32 "0y2slhxhj7k413kjaydin9hq3x2xgx4cgjshdplwnmg3x2nakn31"))))
    (properties `((upstream-name . "givitiR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rootsolve r-alabama))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=givitiR")
    (synopsis "The GiViTI Calibration Test and Belt")
    (description
     "This package provides functions to assess the calibration of logistic regression
models with the @code{GiViTI} (Gruppo Italiano per la Valutazione degli
interventi in Terapia Intensiva, Italian Group for the Evaluation of the
Interventions in Intensive Care Units - see <http://www.giviti.marionegri.it/>)
approach.  The approach consists in a graphical tool, namely the @code{GiViTI}
calibration belt, and in the associated statistical test.  These tools can be
used both to evaluate the internal calibration (i.e.  the goodness of fit) and
to assess the validity of an externally developed model.")
    (license license:gpl3)))

(define-public r-gittargets
  (package
    (name "r-gittargets")
    (version "0.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gittargets" version))
       (sha256
        (base32 "0kgr3rcy261zlsc4a5sqvar6kwb23wcfdwv2rpjcfwdvghz3afzc"))))
    (properties `((upstream-name . "gittargets")))
    (build-system r-build-system)
    (inputs (list git))
    (propagated-inputs (list r-uuid
                             r-tibble
                             r-targets
                             r-processx
                             r-gert
                             r-data-table
                             r-cli
                             r-callr))
    (native-inputs (list r-knitr))
    (home-page "https://docs.ropensci.org/gittargets/")
    (synopsis "Data Version Control for the Targets Package")
    (description
     "In computationally demanding data analysis pipelines, the targets R package
(2021, <doi:10.21105/joss.02959>) maintains an up-to-date set of results while
skipping tasks that do not need to rerun.  This process increases speed and
increases trust in the final end product.  However, it also overwrites old
output with new output, and past results disappear by default.  To preserve
historical output, the gittargets package captures version-controlled snapshots
of the data store, and each snapshot links to the underlying commit of the
source code.  That way, when the user rolls back the code to a previous branch
or commit, gittargets can recover the data contemporaneous with that commit so
that all targets remain up to date.")
    (license license:expat)))

(define-public r-gitr
  (package
    (name "r-gitr")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gitr" version))
       (sha256
        (base32 "09l22wv2r7l1ax17y1q1xmasibyqm71jk0nyvmvlawavkw6by658"))))
    (properties `((upstream-name . "gitr")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://stufield.github.io/gitr/")
    (synopsis "Lightweight API for 'Git'")
    (description
     "This package provides a light-weight, dependency-free, application programming
interface (API) to access system-level Git commands from within R'.  Contains
wrappers and defaults for common data science workflows as well as Zsh plugin
aliases.  A generalized API syntax is also available.  A system installation of
Git <https://git-scm.com/downloads> is required.")
    (license license:expat)))

(define-public r-gitlink
  (package
    (name "r-gitlink")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gitlink" version))
       (sha256
        (base32 "1g34cyx35wlpq42alsayk69x708zxqgvbdndb7j6n1q0rm4vncvd"))))
    (properties `((upstream-name . "gitlink")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-htmltools))
    (home-page "https://github.com/colearendt/gitlink")
    (synopsis "Add 'Git' Links to Your Web Based Assets")
    (description
     "This package provides helpers to add Git links to shiny applications, rmarkdown
documents, and other HTML based resources.  This is most commonly used for
@code{GitHub} ribbons.")
    (license license:expat)))

(define-public r-gitlabr
  (package
    (name "r-gitlabr")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gitlabr" version))
       (sha256
        (base32 "1mlzg17pvbxgq8n09lajsb4qyfyl4ksyn7c2v5vlda94brvgspkd"))))
    (properties `((upstream-name . "gitlabr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-purrr
                             r-magrittr
                             r-httr
                             r-dplyr
                             r-base64enc
                             r-arpr))
    (native-inputs (list r-knitr))
    (home-page "https://thinkr-open.github.io/gitlabr/")
    (synopsis "Access to the 'GitLab' API")
    (description
     "This package provides R functions to access the API of the project and
repository management web application @code{GitLab}'.  For many common tasks
(repository file access, issue assignment and status, commenting) convenience
wrappers are provided, and in addition the full API can be used by specifying
request locations. @code{GitLab} is open-source software and can be self-hosted
or used on <https://about.gitlab.com>.")
    (license license:gpl3+)))

(define-public r-gitignore
  (package
    (name "r-gitignore")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gitignore" version))
       (sha256
        (base32 "047626a31rksyqcxfkvn1rpyv6gbls0b72blaifmy37nhrmwgykz"))))
    (properties `((upstream-name . "gitignore")))
    (build-system r-build-system)
    (propagated-inputs (list r-xfun
                             r-purrr
                             r-jsonlite
                             r-here
                             r-glue
                             r-curl
                             r-clipr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://docs.ropensci.org/gitignore/")
    (synopsis "Create Useful .gitignore Files for your Project")
    (description
     "Simple interface to query gitignore.io to fetch gitignore templates that can be
included in the .gitignore file.  More than 450 templates are currently
available.")
    (license license:gpl3)))

(define-public r-githubr
  (package
    (name "r-githubr")
    (version "0.9.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "githubr" version))
       (sha256
        (base32 "1h3yaz2snrlibl707rnv2l5y5r879dsprcr0a0vc8aliwdsmydvi"))))
    (properties `((upstream-name . "githubr")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr r-httr r-gitcreds r-gh r-dplyr))
    (home-page "https://github.com/fhdsl/githubr")
    (synopsis "Easier to Use API Wrapper for 'GitHub'")
    (description
     "This is a @code{GitHub} API wrapper for R. <https://docs.github.com/en/rest> It
uses the gh package but has things wrapped up for convenient use cases.")
    (license license:gpl3)))

(define-public r-githubinstall
  (package
    (name "r-githubinstall")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "githubinstall" version))
       (sha256
        (base32 "0hqh86r2007hzdbm8rr0fwqhhsna7ji8sdgmdnrxkxraa5f2pfz3"))))
    (properties `((upstream-name . "githubinstall")))
    (build-system r-build-system)
    (propagated-inputs (list r-mockery
                             r-jsonlite
                             r-httr
                             r-devtools
                             r-data-table
                             r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/hoxo-m/githubinstall")
    (synopsis "Helpful Way to Install R Packages Hosted on GitHub")
    (description
     "This package provides an helpful way to install packages hosted on
@code{GitHub}.")
    (license license:expat)))

(define-public r-gitgpt
  (package
    (name "r-gitgpt")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gitGPT" version))
       (sha256
        (base32 "1jznhl0ljcang10s4k9338fy5qsp0pxg4kv482v93milszib3vy7"))))
    (properties `((upstream-name . "gitGPT")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-httr))
    (home-page "https://github.com/stevecondylios/gitGPT")
    (synopsis "Automated Git Commit Messages using the 'OpenAI' 'GPT' Model")
    (description
     "Automates the process of adding, committing, and pushing changes to a git
repository using commit messages generated by passing the git diff output to the
@code{OpenAI} GPT-3.5 Turbo model
(<https://platform.openai.com/docs/models/gpt-3>).")
    (license license:expat)))

(define-public r-gitgadget
  (package
    (name "r-gitgadget")
    (version "0.8.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gitgadget" version))
       (sha256
        (base32 "1kcky3yxj4f9rgp2dliwavajwy2784ynplnmvcyizwj83l07zjql"))))
    (properties `((upstream-name . "gitgadget")))
    (build-system r-build-system)
    (propagated-inputs (list r-usethis
                             r-shinyfiles
                             r-shiny
                             r-rstudioapi
                             r-miniui
                             r-markdown
                             r-jsonlite
                             r-dplyr
                             r-curl
                             r-callr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gitgadget")
    (synopsis
     "'Rstudio' Addin for Version Control and Assignment Management using Git")
    (description
     "An Rstudio addin for version control that allows users to clone repositories,
create and delete branches, and sync forks on @code{GitHub}, @code{GitLab}, etc.
 Furthermore, the addin uses the @code{GitLab} API to allow instructors to
create forks and merge requests for all students/teams with one click of a
button.")
    (license license:gpl3)))

(define-public r-gitear
  (package
    (name "r-gitear")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gitear" version))
       (sha256
        (base32 "139h19yyi7bpkps2vskdnga43ggpgb9fg8jdr7n8vgadxjamv98p"))))
    (properties `((upstream-name . "gitear")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rcpp
                             r-mockery
                             r-magrittr
                             r-jsonlite
                             r-httr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://ixpantia.github.io/gitear/")
    (synopsis "Client to the 'gitea' API")
    (description
     "Gitea is a community managed, lightweight code hosting solution were projects
and their respective git repositories can be managed <https://gitea.io>.  This
package gives an interface to the Gitea API to access and manage repositories,
issues and organizations directly in R.")
    (license license:gpl3)))

(define-public r-gitdown
  (package
    (name "r-gitdown")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gitdown" version))
       (sha256
        (base32 "16g4kngal0w0vjrympxmnq16rpxks4wbj35aasvr3p2srgqn86by"))))
    (properties `((upstream-name . "gitdown")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringi
                             r-rmarkdown
                             r-purrr
                             r-magrittr
                             r-knitr
                             r-git2r
                             r-dplyr
                             r-bookdown
                             r-attempt))
    (native-inputs (list r-knitr))
    (home-page "https://thinkr-open.github.io/gitdown/")
    (synopsis "Turn Your Git Commit Messages into a HTML Book")
    (description
     "Read all commit messages of your local git repository and sort them according to
tags or specific text pattern into chapters of a HTML book using bookdown'.  The
git history book presentation helps organisms required to testify for every
changes in their source code, in relation to features requests.")
    (license license:expat)))

(define-public r-git4r
  (package
    (name "r-git4r")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "git4r" version))
       (sha256
        (base32 "1kfx95y0kp656y8306jz75q4zmd8dswfmrb5jr0wihlhnz182g2h"))))
    (properties `((upstream-name . "git4r")))
    (build-system r-build-system)
    (propagated-inputs (list r-git2r r-diffr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/johnxhobbs/git4r")
    (synopsis "Interactive Git for R")
    (description
     "An interactive git user interface from the R command line.  Intuitive tools to
make commits, branches, remotes, and diffs an integrated part of R coding.
Built on git2r, a system installation of git is not required and has default
on-premises remote option.")
    (license license:expat)))

(define-public r-git2rdata
  (package
    (name "r-git2rdata")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "git2rdata" version))
       (sha256
        (base32 "0vj308ilycfv9jhi6ixk7n3pg6m8dh86rzcjnh78rzrd72fvcg9h"))))
    (properties `((upstream-name . "git2rdata")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml r-git2r r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://ropensci.github.io/git2rdata/")
    (synopsis "Store and Retrieve Data.frames in a Git Repository")
    (description
     "The git2rdata package is an R package for writing and reading dataframes as
plain text files.  A metadata file stores important information.  1) Storing
metadata allows to maintain the classes of variables.  By default, git2rdata
optimizes the data for file storage.  The optimization is most effective on data
containing factors.  The optimization makes the data less human readable.  The
user can turn this off when they prefer a human readable format over smaller
files.  Details on the implementation are available in vignette(\"plain_text\",
package = \"git2rdata\").  2) Storing metadata also allows smaller row based diffs
between two consecutive commits.  This is a useful feature when storing data as
plain text files under version control.  Details on this part of the
implementation are available in vignette(\"version_control\", package =
\"git2rdata\").  Although we envisioned git2rdata with a git workflow in mind, you
can use it in combination with other version control systems like subversion or
mercurial.  3) git2rdata is a useful tool in a reproducible and traceable
workflow.  vignette(\"workflow\", package = \"git2rdata\") gives a toy example.  4)
vignette(\"efficiency\", package = \"git2rdata\") provides some insight into the
efficiency of file storage, git repository size and speed for writing and
reading.")
    (license license:gpl3)))

(define-public r-gistools
  (package
    (name "r-gistools")
    (version "1.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GISTools" version))
       (sha256
        (base32 "024ngx3r0c38ss9qzali6nw55sgl2jqksspjz6v2drgaadkxczlz"))))
    (properties `((upstream-name . "GISTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp r-sf r-rcolorbrewer r-mass))
    (home-page "https://cran.r-project.org/package=GISTools")
    (synopsis "Further Capabilities in Geographic Information Science")
    (description
     "Mapping and spatial data manipulation tools - in particular drawing thematic
maps with nice looking legends, and aggregation of point data to polygons.")
    (license license:gpl2+)))

(define-public r-gissb
  (package
    (name "r-gissb")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GISSB" version))
       (sha256
        (base32 "0clhch4xvklrmk7h6b7vz83zzlj5r1lzrszy73agmpdbs5cax5sy"))))
    (properties `((upstream-name . "GISSB")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidygraph
                             r-tibble
                             r-stringr
                             r-sf
                             r-rlang
                             r-reshape2
                             r-rcurl
                             r-purrr
                             r-nabor
                             r-magrittr
                             r-leaflet
                             r-igraph
                             r-httr
                             r-here
                             r-dplyr
                             r-cpprouting))
    (native-inputs (list r-knitr))
    (home-page "https://statisticsnorway.github.io/GISSB/")
    (synopsis "Network Analysis on the Norwegian Road Network")
    (description
     "This package provides a collection of GIS (Geographic Information System)
functions in R, created for use in Statistics Norway.  The functions are
primarily related to network analysis on the Norwegian road network.")
    (license license:expat)))

(define-public r-gisintegration
  (package
    (name "r-gisintegration")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GISINTEGRATION" version))
       (sha256
        (base32 "1nl1cbjcjkyd67i0qk0skbrk9ya9fydw9iiq4ip11bjg86661jjb"))))
    (properties `((upstream-name . "GISINTEGRATION")))
    (build-system r-build-system)
    (propagated-inputs (list r-tm
                             r-syn
                             r-stringr
                             r-shapefiles
                             r-sf
                             r-recordlinkage))
    (home-page "https://cran.r-project.org/package=GISINTEGRATION")
    (synopsis "GIS Integration")
    (description
     "Designed to facilitate the preprocessing and linking of GIS (Geographic
Information System) databases
<https://www.sciencedirect.com/topics/computer-science/gis-database>, the R
package GISINTEGRATION offers a robust solution for efficiently preparing GIS
data for advanced spatial analyses.  This package excels in simplifying intrica
procedures like data cleaning, normalization, and format conversion, ensuring
that the data are optimally primed for precise and thorough analysis.")
    (license license:gpl3)))

(define-public r-giscor
  (package
    (name "r-giscor")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "giscoR" version))
       (sha256
        (base32 "1zdm79h9mxwv72y9s1nznr39zxrpl4jmqyb6b9nc7nnij9w145yf"))))
    (properties `((upstream-name . "giscoR")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-rappdirs r-jsonlite r-geojsonsf
                             r-countrycode))
    (native-inputs (list r-knitr))
    (home-page "https://ropengov.github.io/giscoR/")
    (synopsis "Download Map Data from GISCO API - Eurostat")
    (description
     "This package provides tools to download data from the GISCO (Geographic
Information System of the Commission) Eurostat database
<https://ec.europa.eu/eurostat/web/gisco>.  Global and European map data
available.  This package is in no way officially related to or endorsed by
Eurostat.")
    (license license:gpl3)))

(define-public r-giraf
  (package
    (name "r-giraf")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GiRaF" version))
       (sha256
        (base32 "1m19jxr6h485v32iqq260cizf30cgrx3pk802r73fbp3ssj7nmvh"))))
    (properties `((upstream-name . "GiRaF")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GiRaF")
    (synopsis "Gibbs Random Fields Analysis")
    (description
     "Allows calculation on, and sampling from Gibbs Random Fields, and more precisely
general homogeneous Potts model.  The primary tool is the exact computation of
the intractable normalising constant for small rectangular lattices.  Beside the
latter function, it contains method that give exact sample from the likelihood
for small enough rectangular lattices or approximate sample from the likelihood
using MCMC samplers for large lattices.")
    (license license:gpl2+)))

(define-public r-gips
  (package
    (name "r-gips")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gips" version))
       (sha256
        (base32 "0mvjgzqy3sqa5p1263v042k20j6hmlgli9llgq89y17d47njmfw4"))))
    (properties `((upstream-name . "gips")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-permutations r-numbers))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/PrzeChoj/gips")
    (synopsis "Gaussian Model Invariant by Permutation Symmetry")
    (description
     "Find the permutation symmetry group such that the covariance matrix of the given
data is approximately invariant under it.  Discovering such a permutation
decreases the number of observations needed to fit a Gaussian model, which is of
great use when it is smaller than the number of variables.  Even if that is not
the case, the covariance matrix found with gips approximates the actual
covariance with less statistical error.  The methods implemented in this package
are described in Graczyk et al. (2022) <doi:10.1214/22-AOS2174>.")
    (license license:gpl3+)))

(define-public r-giplot
  (package
    (name "r-giplot")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GIplot" version))
       (sha256
        (base32 "1asyd4f76xg84jnwa8kxcw5z6ch3kpy0p7yigmyga94908gylpxj"))))
    (properties `((upstream-name . "GIplot")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GIplot")
    (synopsis "Gaussian Interval Plot (GIplot)")
    (description
     "The Gaussian Interval Plot (GIplot) is a pictorial representation of the mean
and the standard deviation of a quantitative variable.  It also flags potential
outliers (together with their frequencies) that are c standard deviations away
from the mean.")
    (license license:gpl3)))

(define-public r-gipfrm
  (package
    (name "r-gipfrm")
    (version "3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gIPFrm" version))
       (sha256
        (base32 "08rfdac442picbw1r3xyxjzf2dc57svg44am0714z4r72mshvj04"))))
    (properties `((upstream-name . "gIPFrm")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gIPFrm")
    (synopsis
     "Generalized Iterative Proportional Fitting for Relational Models")
    (description
     "Maximum likelihood estimation under relational models, with or without the
overall effect.")
    (license license:gpl2)))

(define-public r-ginsarcorw
  (package
    (name "r-ginsarcorw")
    (version "1.15.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GInSARCorW" version))
       (sha256
        (base32 "0hxcaixr8f3pq4kpzhfrbm7blvkq6sk3pz63bfjy6n1lhlashb13"))))
    (properties `((upstream-name . "GInSARCorW")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp r-raster r-circular))
    (home-page
     "<https://subhadipdatta.wixsite.com/profile/post/ginsarcorw-gacos-insar-correction-workflow>")
    (synopsis "GACOS InSAR Correction Workflow")
    (description
     "This package provides a workflow for correction of Differential Interferometric
Synthetic Aperture Radar (D@code{InSAR}) atmospheric delay base on Generic
Atmospheric Correction Online Service for @code{InSAR} (GACOS) data and
correction algorithms proposed by Chen Yu.  This package calculate the Both
Zenith and LOS direction (User Depend).  You have to just download GACOS product
on your area and preprocessed D-@code{InSAR} unwrapped images.  Cite those
references and this package in your work, when using this framework.
References: Yu, C., N. T. Penna, and Z. Li (2017)
<doi:10.1016/j.rse.2017.10.038>.  Yu, C., Li, Z., & Penna, N. T. (2017)
<doi:10.1016/j.rse.2017.10.038>.  Yu, C., Penna, N. T., and Li, Z. (2017)
<doi:10.1002/2016JD025753>.")
    (license license:gpl3)))

(define-public r-ginormal
  (package
    (name "r-ginormal")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ginormal" version))
       (sha256
        (base32 "0b37np2h03m82dhww9slnchm3dk4qwfmj6v7q13li788cw7whid1"))))
    (properties `((upstream-name . "ginormal")))
    (build-system r-build-system)
    (propagated-inputs (list r-bas))
    (native-inputs (list gfortran))
    (home-page "https://github.com/smonto2/ginormal")
    (synopsis "Generalized Inverse Normal Distribution Density and Generation")
    (description
     "Density function and generation of random variables from the Generalized Inverse
Normal (GIN) distribution from Robert (1991) <doi:10.1016/0167-7152(91)90174-P>.
 Also provides density functions and generation from the GIN distribution
truncated to positive or negative reals.  Theoretical guarantees supporting the
sampling algorithms and an application to Bayesian estimation of network
formation models can be found in the working paper Ding, Estrada and
Montoya-BlandÃ³n (2023)
<https://www.smontoyablandon.com/publication/networks/network_externalities.pdf>.")
    (license license:gpl3+)))

(define-public r-ginivarci
  (package
    (name "r-ginivarci")
    (version "0.0.1-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "giniVarCI" version))
       (sha256
        (base32 "1yv97q6l8imd7z8zqykymmsx6905wrygj5l3arwdz9irfgvw55lx"))))
    (properties `((upstream-name . "giniVarCI")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=giniVarCI")
    (synopsis
     "Gini Indices, Variances and Confidence Intervals for Finite and Infinite Populations")
    (description
     "Estimates the Gini index and computes variances and confidence intervals for
finite and infinite populations, using different methods; also computes Gini
index for continuous probability distributions, draws samples from continuous
probability distributions with Gini indices set by the user; uses Rcpp'.
References: MuÃ±oz et al. (2023) <doi:10.1177/00491241231176847>.  Ãlvarez et
al. (2021) <doi:10.3390/math9243252>.  Giorgi and Gigliarano (2017)
<doi:10.1111/joes.12185>.  Langel and TillÃ© (2013)
<doi:10.1111/j.1467-985X.2012.01048.x>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-ginidistance
  (package
    (name "r-ginidistance")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GiniDistance" version))
       (sha256
        (base32 "1x9a59x60a2jdnql3wn4za510rv9qp84j6b9m6la81j3mmy92vzz"))))
    (properties `((upstream-name . "GiniDistance")))
    (build-system r-build-system)
    (propagated-inputs (list r-readxl r-rcpparmadillo r-rcpp r-randomforest
                             r-energy))
    (home-page "https://cran.r-project.org/package=GiniDistance")
    (synopsis
     "New Gini Correlation Between Quantitative and Qualitative Variables")
    (description
     "An implementation of a new Gini covariance and correlation to measure dependence
between a categorical and numerical variables.  Dang, X., Nguyen, D., Chen, Y.
and Zhang, J., (2018) <@code{arXiv:1809.09793>}.")
    (license license:gpl2+)))

(define-public r-ginidecomply
  (package
    (name "r-ginidecomply")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GiniDecompLY" version))
       (sha256
        (base32 "0fvsj9z8hhaayi6k9rmcym9vda6vc0hfn59fmvk3qjj8ga8bsxaq"))))
    (properties `((upstream-name . "GiniDecompLY")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-magrittr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GiniDecompLY")
    (synopsis "Gini Decomposition by Income Sources")
    (description
     "Estimation of the effect of each income source on income inequalities based on
the decomposition of Lerman and Yitzhaki (1985) <doi:10.2307/1928447>.")
    (license license:gpl3)))

(define-public r-gini
  (package
    (name "r-gini")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Gini" version))
       (sha256
        (base32 "0858mlh8pwylnnb13y40wscq08gcx1cgwjn88wv5vkim5jn4bigi"))))
    (properties `((upstream-name . "Gini")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=Gini")
    (synopsis "Gini Coefficient")
    (description
     "Providing various equations to calculate Gini coefficients.  The methods used in
this package can be referenced from Brown MC (1994) <doi:
10.1016/0277-9536(94)90189-9>.")
    (license license:gpl3)))

(define-public r-gina
  (package
    (name "r-gina")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GiNA" version))
       (sha256
        (base32 "0his7wsgnggyv9526blxzxa1ni8hwq2ws64wkmha45nvvnm3dsiw"))))
    (properties `((upstream-name . "GiNA")))
    (build-system r-build-system)
    (propagated-inputs (list r-png r-foreach r-ebimage r-doparallel))
    (home-page "http://www.wisc.edu")
    (synopsis "High Throughput Phenotyping")
    (description
     "This package performs image segmentation in fruit or seeds pictures in order to
measure physical features in a high-throughput manner for genome-wide
association (GWAS) and genomic selection programs.")
    (license license:gpl3)))

(define-public r-gimms
  (package
    (name "r-gimms")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gimms" version))
       (sha256
        (base32 "0dhvi05mpwyx6cpcyk2nzvgd6c8byd7cmrd9crx83lvfa03smpng"))))
    (properties `((upstream-name . "gimms")))
    (build-system r-build-system)
    (propagated-inputs (list r-zyp r-raster r-ncdf4 r-kendall r-curl))
    (home-page "https://github.com/environmentalinformatics-marburg/gimms")
    (synopsis "Download and Process GIMMS NDVI3g Data")
    (description
     "This is a set of functions to retrieve information about GIMMS NDVI3g files
currently available online; download (and re-arrange, in the case of NDVI3g.v0)
the half-monthly data sets; import downloaded files from ENVI binary (NDVI3g.v0)
or @code{NetCDF} format (NDVI3g.v1) directly into R based on the widespread
raster package; conduct quality control; and generate monthly composites (e.g.,
maximum values) from the half-monthly input data.  As a special gimmick, a
method is included to conveniently apply the Mann-Kendall trend test upon
Raster* images, optionally featuring trend-free pre-whitening to account for
lag-1 autocorrelation.")
    (license license:expat)))

(define-public r-gimmetools
  (package
    (name "r-gimmetools")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gimmeTools" version))
       (sha256
        (base32 "1g170rz3c3qx27yy67f7xab4gkm0pbbvwlwkcsczfxphad609ryl"))))
    (properties `((upstream-name . "gimmeTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinywidgets
                             r-shinyjs
                             r-shiny
                             r-rstudioapi
                             r-rintrojs
                             r-rhandsontable
                             r-miniui
                             r-magrittr
                             r-easycsv))
    (home-page "https://github.com/stlane")
    (synopsis "Supplemental Tools for the 'gimme' R Package")
    (description
     "Supplemental tools for the gimme R package.  It contains an interactive
graphical user interface, allowing for the flexible specification of a variety
of both basic and advanced options.  It will expand to include a variety of
tools for navigating output.")
    (license license:gpl2)))

(define-public r-gimmemyplot
  (package
    (name "r-gimmemyplot")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GimmeMyPlot" version))
       (sha256
        (base32 "18k3z6ynzpmdg029sspl8as0bkifqf8ff6hf9l2yblls7sb64id0"))))
    (properties `((upstream-name . "GimmeMyPlot")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-scales
                             r-rstatix
                             r-rcolorbrewer
                             r-magrittr
                             r-ggpubr
                             r-ggplot2
                             r-ggforce
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GimmeMyPlot")
    (synopsis "Graphical Utilities for Visualizing and Exploring Data")
    (description
     "Simplifies the process of creating essential visualizations in R, offering a
range of plotting functions for common chart types like violin plots, pie
charts, and histograms.  With an intuitive interface, users can effortlessly
customize colors, labels, and styles, making it an ideal tool for both beginners
and experienced data analysts.  Whether exploring datasets or producing quick
visual summaries, this package provides a streamlined solution for fundamental
graphics in R.")
    (license license:gpl3)))

(define-public r-gimmegvar
  (package
    (name "r-gimmegvar")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GIMMEgVAR" version))
       (sha256
        (base32 "1r5j5s8pmjrndr4qsi4ivrqfyj9rlmw72ya850ig387z9gl84n47"))))
    (properties `((upstream-name . "GIMMEgVAR")))
    (build-system r-build-system)
    (propagated-inputs (list r-qgraph r-png r-here r-graphicalvar))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GIMMEgVAR")
    (synopsis "Group Iterative Multiple Model Estimation with 'graphicalVAR'")
    (description
     "Data-driven approach for arriving at person-specific time series models from
within a Graphical Vector Autoregression (VAR) framework.  The method first
identifies which relations replicate across the majority of individuals to
detect signal from noise.  These group-level relations are then used as a
foundation for starting the search for person-specific (or individual-level)
relations.  All estimates are obtained uniquely for each individual in the final
models.  The method for the @code{graphicalVAR} approach is found in Epskamp,
Waldorp, Mottus & Borsboom (2018) <doi:10.1080/00273171.2018.1454823>.")
    (license license:gpl2)))

(define-public r-gimme
  (package
    (name "r-gimme")
    (version "0.7-18")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gimme" version))
       (sha256
        (base32 "16xb0182nh2p2cbr1iw74q1biif240jcyckal3mf6zdq703vgy4j"))))
    (properties `((upstream-name . "gimme")))
    (build-system r-build-system)
    (propagated-inputs (list r-tseries
                             r-qgraph
                             r-nloptr
                             r-miivsem
                             r-mass
                             r-lavaan
                             r-imputets
                             r-igraph
                             r-data-tree))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/GatesLab/gimme/")
    (synopsis "Group Iterative Multiple Model Estimation")
    (description
     "Data-driven approach for arriving at person-specific time series models.  The
method first identifies which relations replicate across the majority of
individuals to detect signal from noise.  These group-level relations are then
used as a foundation for starting the search for person-specific (or
individual-level) relations.  See Gates & Molenaar (2012)
<doi:10.1016/j.neuroimage.2012.06.026>.")
    (license license:gpl2)))

(define-public r-gim
  (package
    (name "r-gim")
    (version "0.33.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gim" version))
       (sha256
        (base32 "1km8pyr1qfzffm33ihhlgvc0qxgxxfi161vkrz77d1b7jkpdyxjj"))))
    (properties `((upstream-name . "gim")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/zhangh12/gim")
    (synopsis "Generalized Integration Model")
    (description
     "This package implements the generalized integration model, which integrates
individual-level data and summary statistics under a generalized linear model
framework.  It supports continuous and binary outcomes to be modeled by the
linear and logistic regression models.  For binary outcome, data can be sampled
in prospective cohort studies or case-control studies.  Described in Zhang et
al. (2020)<doi:10.1093/biomet/asaa014>.")
    (license license:expat)))

(define-public r-gillespiessa
  (package
    (name "r-gillespiessa")
    (version "0.6.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GillespieSSA" version))
       (sha256
        (base32 "1hk7fnv3gdfvx9q4ssw0jgph59l7h2dgl7lv8pivkm678i53n8zl"))))
    (properties `((upstream-name . "GillespieSSA")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rcannood/GillespieSSA")
    (synopsis "Gillespie's Stochastic Simulation Algorithm (SSA)")
    (description
     "This package provides a simple to use, intuitive, and extensible interface to
several stochastic simulation algorithms for generating simulated trajectories
of finite population continuous-time model.  Currently it implements Gillespie's
exact stochastic simulation algorithm (Direct method) and several approximate
methods (Explicit tau-leap, Binomial tau-leap, and Optimized tau-leap).  The
package also contains a library of template models that can be run as demo
models and can easily be customized and extended.  Currently the following
models are included, Decaying-Dimerization reaction set, linear chain system,
logistic growth model, Lotka predator-prey model, Rosenzweig-@code{MacArthur}
predator-prey model, Kermack-@code{McKendrick} SIR model, and a metapopulation
SIRS model.  Pineda-Krch et al. (2008) <doi:10.18637/jss.v025.i12>.")
    (license license:gpl3+)))

(define-public r-gigrvg
  (package
    (name "r-gigrvg")
    (version "0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GIGrvg" version))
       (sha256
        (base32 "09n0nhsv3v61asfzghzxa9sr3ryl25mlvj73gk9ql9wlrynd3wrl"))))
    (properties `((upstream-name . "GIGrvg")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GIGrvg")
    (synopsis "Random Variate Generator for the GIG Distribution")
    (description
     "Generator and density function for the Generalized Inverse Gaussian (GIG)
distribution.")
    (license license:gpl2+)))

(define-public r-gigg
  (package
    (name "r-gigg")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gigg" version))
       (sha256
        (base32 "0qb8w455a8wxc5ljmydq4xag2kbj5yk06an0pd9hd4k48wssg8la"))))
    (properties `((upstream-name . "gigg")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-bh))
    (home-page "https://github.com/umich-cphds/gigg")
    (synopsis
     "Group Inverse-Gamma Gamma Shrinkage for Sparse Regression with Grouping Structure")
    (description
     "This package provides a Gibbs sampler corresponding to a Group Inverse-Gamma
Gamma (GIGG) regression model with adjustment covariates.  Hyperparameters in
the GIGG prior specification can either be fixed by the user or can be estimated
via Marginal Maximum Likelihood Estimation.  Jonathan Boss, Jyotishka Datta, Xin
Wang, Sung Kyun Park, Jian Kang, Bhramar Mukherjee (2021)
<@code{arXiv:2102.10670>}.")
    (license license:gpl2)))

(define-public r-giftr
  (package
    (name "r-giftr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GIFTr" version))
       (sha256
        (base32 "101vr2qasky8hnxp5i3lhlnymwzciypqwvhpl6mrr0r0czgpcz2f"))))
    (properties `((upstream-name . "GIFTr")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-glue))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/omarelashkar/GIFTr")
    (synopsis "GIFT Questions Format Generator from Dataframes")
    (description
     "This package provides a framework and functions to create MOODLE quizzes.  GIFTr
takes dataframe of questions of four types: multiple choices, numerical, true or
false and short answer questions, and exports a text file formatted in MOODLE
GIFT format.  You can prepare a spreadsheet in any software and import it into R
to generate any number of questions with HTML', markdown and @code{LaTeX}
support.")
    (license license:gpl3)))

(define-public r-gifti
  (package
    (name "r-gifti")
    (version "0.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gifti" version))
       (sha256
        (base32 "1gb3x4rln9vayjd9fgzb434mm3dwcqgrvlv6rmh4b39mjfm73sly"))))
    (properties `((upstream-name . "gifti")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-r-utils r-base64enc))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gifti")
    (synopsis
     "Reads in 'Neuroimaging' 'GIFTI' Files with Geometry Information")
    (description
     "This package provides functions to read in the geometry format under the
Neuroimaging Informatics Technology Initiative ('N@code{IfTI}'), called GIFTI
<https://www.nitrc.org/projects/gifti/>.  These files contain surfaces of brain
imaging data.")
    (license license:gpl2)))

(define-public r-gift
  (package
    (name "r-gift")
    (version "1.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GIFT" version))
       (sha256
        (base32 "0ngrci818f0k21aiy7dnzpip33sikjf2kp7j9l3r7mwp35h6q2qd"))))
    (properties `((upstream-name . "GIFT")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-sf
                             r-purrr
                             r-phytools
                             r-jsonlite
                             r-httr2
                             r-dplyr
                             r-curl
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/BioGeoMacro/GIFT")
    (synopsis "Access to the Global Inventory of Floras and Traits (GIFT)")
    (description
     "Retrieving regional plant checklists, species traits and distributions, and
environmental data from the Global Inventory of Floras and Traits (GIFT).  More
information about the GIFT database can be found at
<https://gift.uni-goettingen.de/about> and the map of available floras can be
visualized at <https://gift.uni-goettingen.de/map>.  The API and associated
queries can be accessed according the following scheme:
<https://gift.uni-goettingen.de/api/extended/index2.0.php?query=env_raster>.")
    (license license:gpl2+)))

(define-public r-gifski
  (package
    (name "r-gifski")
    (version "1.32.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gifski" version))
       (sha256
        (base32 "1vak4chf3cxy6dq7i7asxbs6byn6gqwl8iafrfpg12bkc71b4rhb"))))
    (properties `((upstream-name . "gifski")))
    (build-system r-build-system)
    (inputs (list))
    (home-page "https://r-rust.r-universe.dev/gifski")
    (synopsis "Highest Quality GIF Encoder")
    (description
     "Multi-threaded GIF encoder written in Rust: <https://gif.ski/>.  Converts images
to GIF animations using pngquant's efficient cross-frame palettes and temporal
dithering with thousands of colors per frame.")
    (license license:expat)))

(define-public r-gifi
  (package
    (name "r-gifi")
    (version "0.4-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Gifi" version))
       (sha256
        (base32 "0a8xxr0qn72x2kkzlk9pm9nqp1pr74qh6p2g6l63km6c1imhc4i7"))))
    (properties `((upstream-name . "Gifi")))
    (build-system r-build-system)
    (propagated-inputs (list r-colorspace))
    (native-inputs (list gfortran))
    (home-page "https://r-forge.r-project.org/projects/psychor/")
    (synopsis "Multivariate Analysis with Optimal Scaling")
    (description
     "This package implements categorical principal component analysis ('PRINCALS'),
multiple correspondence analysis ('HOMALS'), monotone regression analysis
('MORALS').  It replaces the homals package.")
    (license license:gpl3)))

(define-public r-gif
  (package
    (name "r-gif")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gif" version))
       (sha256
        (base32 "1gh6h3cxf27x6wa3g28fi8famb1vvf69qgq8qw3ypdamc58avmp9"))))
    (properties `((upstream-name . "gif")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp r-matrix r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gif")
    (synopsis "Graphical Independence Filtering")
    (description
     "This package provides a method of recovering the precision matrix for Gaussian
graphical models efficiently.  Our approach could be divided into three
categories.  First of all, we use Hard Graphical Thresholding for best subset
selection problem of Gaussian graphical model, and the core concept of this
method was proposed by Luo et al. (2014) <@code{arXiv:1407.7819>}.  Secondly, a
closed form solution for graphical lasso under acyclic graph structure is
implemented in our package (Fattahi and Sojoudi (2019)
<https://jmlr.org/papers/v20/17-501.html>).  Furthermore, we implement block
coordinate descent algorithm to efficiently solve the covariance selection
problem (Dempster (1972) <doi:10.2307/2528966>).  Our package is computationally
efficient and can solve ultra-high-dimensional problems, e.g. p > 10,000, in a
few minutes.")
    (license license:gpl2+)))

(define-public r-gie
  (package
    (name "r-gie")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gie" version))
       (sha256
        (base32 "0dgzmlza4mxghs3f9d81p03f91bz7v07af1d6zpfmaif3lzc2q77"))))
    (properties `((upstream-name . "gie")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-httr
                             r-dplyr
                             r-curl))
    (home-page "https://cran.r-project.org/package=gie")
    (synopsis
     "API Wrapper for the Natural Gas Transparency Platforms of Gas Infrastructure Europe")
    (description
     "Providing access to the API for Gas Infrastructure Europe's natural gas
transparency platforms <https://agsi.gie.eu/> and <https://alsi.gie.eu/>.  Lets
the user easily download metadata on companies and gas storage units covered by
the API as well as the respective data on regional, country, company or facility
level.")
    (license license:expat)))

(define-public r-gic
  (package
    (name "r-gic")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GIC" version))
       (sha256
        (base32 "0wjrv5rpzcyng0vvpr59l148lxzqnqq4c045fw2x9ynffyb71h99"))))
    (properties `((upstream-name . "GIC")))
    (build-system r-build-system)
    (propagated-inputs (list r-randomforest r-ggplot2 r-cluster))
    (home-page "https://cran.r-project.org/package=GIC")
    (synopsis "General Iterative Clustering Algorithm")
    (description
     "An iterative algorithm that improves the proximity matrix (PM) from a random
forest (RF) and the resulting clusters as measured by the silhouette score.")
    (license license:gpl3)))

(define-public r-gibble
  (package
    (name "r-gibble")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gibble" version))
       (sha256
        (base32 "19il4si7ch5jr7plr8qwv7jrc5kfkxmh5jrcdvbn688gxcnv7bs0"))))
    (properties `((upstream-name . "gibble")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-rlang r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mdsumner/gibble")
    (synopsis "Geometry Decomposition")
    (description
     "Build a map of path-based geometry, this is a simple description of the number
of parts in an object and their basic structure.  Translation and restructuring
operations for planar shapes and other hierarchical types require a data model
with a record of the underlying relationships between elements.  The gibble()
function creates a geometry map, a simple record of the underlying structure in
path-based hierarchical types.  There are methods for the planar shape types in
the sf and sp packages and for types in the trip and silicate packages.")
    (license license:gpl3)))

(define-public r-gibasa
  (package
    (name "r-gibasa")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gibasa" version))
       (sha256
        (base32 "1j6k11vq9n9bh8f1sdc14maj88nd5vacl3xq9v54nb2phi55rp72"))))
    (properties `((upstream-name . "gibasa")))
    (build-system r-build-system)
    (inputs (list mecab))
    (propagated-inputs (list r-stringi
                             r-rlang
                             r-readr
                             r-rcppparallel
                             r-rcpp
                             r-purrr
                             r-matrix
                             r-dplyr))
    (home-page "https://paithiov909.github.io/gibasa/")
    (synopsis "An Alternative 'Rcpp' Wrapper of 'MeCab'")
    (description
     "This package provides a plain Rcpp wrapper of @code{MeCab} that can segment
Chinese, Japanese, and Korean text into tokens.  The main goal of this package
is to provide an alternative to tidytext using morphological analysis.")
    (license license:gpl3+)))

(define-public r-giant
  (package
    (name "r-giant")
    (version "1.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GiANT" version))
       (sha256
        (base32 "01gvaian8a1dnlfvvbbxshfvfwr50dl9l1d41cq1nmy37gilj0pk"))))
    (properties `((upstream-name . "GiANT")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GiANT")
    (synopsis "Gene Set Uncertainty in Enrichment Analysis")
    (description
     "Toolbox for various enrichment analysis methods and quantification of
uncertainty of gene sets, Schmid et al. (2016)
<doi:10.1093/bioinformatics/btw030>.")
    (license license:artistic2.0)))

(define-public r-giacr
  (package
    (name "r-giacr")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "giacR" version))
       (sha256
        (base32 "0y7vcca2im6q70jxsjnf4a7jg66y66g941ad0d7iykw3c2cwx6g4"))))
    (properties `((upstream-name . "giacR")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6 r-processx r-pingr r-jsonlite r-chromote))
    (home-page "https://github.com/stla/giacR")
    (synopsis "Interface to the Computer Algebra System 'Giac'")
    (description
     "Giac
<https://www-fourier.ujf-grenoble.fr/~parisse/giac/doc/en/cascmd_en/cascmd_en.html>
is a general purpose symbolic algebra software.  It powers the graphical
interface Xcas'.  This package allows to execute Giac commands in R'.")
    (license license:gpl3)))

(define-public r-ghypernet
  (package
    (name "r-ghypernet")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ghypernet" version))
       (sha256
        (base32 "0k4w37vnhpanpa80la8rscgimnnzzid5v59109xfbbz1xxx1bb49"))))
    (properties `((upstream-name . "ghypernet")))
    (build-system r-build-system)
    (propagated-inputs (list r-texreg
                             r-rootsolve
                             r-rlang
                             r-reshape2
                             r-purrr
                             r-plyr
                             r-pbmcapply
                             r-numbers
                             r-extradistr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://ghyper.net")
    (synopsis
     "Fit and Simulate Generalised Hypergeometric Ensembles of Graphs")
    (description
     "This package provides functions for model fitting and selection of generalised
hypergeometric ensembles of random graphs (@code{gHypEG}).  To learn how to use
it, check the vignettes for a quick tutorial.  Please reference its use as
Casiraghi, G., Nanumyan, V. (2019) <doi:10.5281/zenodo.2555300> together with
those relevant references from the one listed below.  The package is based on
the research developed at the Chair of Systems Design, ETH Zurich.  Casiraghi,
G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2016) <@code{arXiv:1607.02441>}.
 Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2017)
<doi:10.1007/978-3-319-67256-4_11>.  Casiraghi, G., (2017)
<@code{arXiv:1702.02048>} Brandenberger, L., Casiraghi, G., Nanumyan, V.,
Schweitzer, F. (2019) <doi:10.1145/3341161.3342926> Casiraghi, G. (2019)
<doi:10.1007/s41109-019-0241-1>.  Casiraghi, G., Nanumyan, V. (2021)
<doi:10.1038/s41598-021-92519-y>.  Casiraghi, G. (2021)
<doi:10.1088/2632-072X/ac0493>.")
    (license license:agpl3)))

(define-public r-ghyp
  (package
    (name "r-ghyp")
    (version "1.6.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ghyp" version))
       (sha256
        (base32 "10w3wlr9wk6z4ph2m8qip6bdsr3gg4nl7v5k277zswkyz26fg135"))))
    (properties `((upstream-name . "ghyp")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv r-mass))
    (home-page "https://cran.r-project.org/package=ghyp")
    (synopsis "Generalized Hyperbolic Distribution and Its Special Cases")
    (description
     "Detailed functionality for working with the univariate and multivariate
Generalized Hyperbolic distribution and its special cases (Hyperbolic (hyp),
Normal Inverse Gaussian (NIG), Variance Gamma (VG), skewed Student-t and
Gaussian distribution).  Especially, it contains fitting procedures, an
AIC-based model selection routine, and functions for the computation of density,
quantile, probability, random variates, expected shortfall and some portfolio
optimization and plotting routines as well as the likelihood ratio test.  In
addition, it contains the Generalized Inverse Gaussian distribution.  See
Chapter 3 of A. J. @code{McNeil}, R. Frey, and P. Embrechts.  Quantitative risk
management: Concepts, techniques and tools.  Princeton University Press,
Princeton (2005).")
    (license license:gpl2+)))

(define-public r-ghs
  (package
    (name "r-ghs")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GHS" version))
       (sha256
        (base32 "1khjfy62a67r59s2rax9mmnqy5xnnwhbydzhqwwmyspl48mxycdj"))))
    (properties `((upstream-name . "GHS")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=GHS")
    (synopsis
     "Graphical Horseshoe MCMC Sampler Using Data Augmented Block Gibbs Sampler")
    (description
     "Draw posterior samples to estimate the precision matrix for multivariate
Gaussian data.  Posterior means of the samples is the graphical horseshoe
estimate by Li, Bhadra and Craig(2017) <@code{arXiv:1707.06661>}.  The function
uses matrix decomposition and variable change from the Bayesian graphical lasso
by Wang(2012) <doi:10.1214/12-BA729>, and the variable augmentation for sampling
under the horseshoe prior by Makalic and Schmidt(2016)
<@code{arXiv:1508.03884>}.  Structure of the graphical horseshoe function was
inspired by the Bayesian graphical lasso function using blocked sampling,
authored by Wang(2012) <doi:10.1214/12-BA729>.")
    (license license:gpl2)))

(define-public r-ghql
  (package
    (name "r-ghql")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ghql" version))
       (sha256
        (base32 "19awg98r2hwwwwrwh0166y8ij1clf58vrf02i8fhhfzddx3an6bz"))))
    (properties `((upstream-name . "ghql")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6 r-jsonlite r-graphql r-crul))
    (home-page
     "https://github.com/ropensci/ghqlhttps://docs.ropensci.org/ghql")
    (synopsis "General Purpose 'GraphQL' Client")
    (description
     "This package provides a @code{GraphQL} client, with an R6 interface for
initializing a connection to a @code{GraphQL} instance, and methods for
constructing queries, including fragments and parameterized queries.  Queries
are checked with the libgraphqlparser C++ parser via the gaphql package.")
    (license license:expat)))

(define-public r-ghostknockoff
  (package
    (name "r-ghostknockoff")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GhostKnockoff" version))
       (sha256
        (base32 "0vp9f8k31yclxvmcjgzkg6xl3rdv49anjmwdhcrhcxv8x9f3bdrj"))))
    (properties `((upstream-name . "GhostKnockoff")))
    (build-system r-build-system)
    (propagated-inputs (list r-seqminer
                             r-rspectra
                             r-rdsdp
                             r-matrix
                             r-gtools
                             r-cvxr
                             r-corpcor))
    (home-page "https://cran.r-project.org/package=GhostKnockoff")
    (synopsis "The Knockoff Inference Using Summary Statistics")
    (description
     "This package provides functions for multiple knockoff inference using summary
statistics, e.g. Z-scores.  The knockoff inference is a general procedure for
controlling the false discovery rate (FDR) when performing variable selection.
This package provides a procedure which performs knockoff inference without ever
constructing individual knockoffs (@code{GhostKnockoff}).  It additionally
supports multiple knockoff inference for improved stability and reproducibility.
 Moreover, it supports meta-analysis of multiple overlapping studies.")
    (license license:gpl3)))

(define-public r-ghost
  (package
    (name "r-ghost")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Ghost" version))
       (sha256
        (base32 "0anrb1n3f3jnq2p0dgqzw5a0gby2slafv2bybgy5gm3f9fsj831w"))))
    (properties `((upstream-name . "Ghost")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6))
    (home-page
     "https://www.researchgate.net/publication/332779980_Ghost_Imputation_Accurately_Reconstructing_Missing_Data_of_the_Off_Period")
    (synopsis "Missing Data Segments Imputation in Multivariate Streams")
    (description
     "Helper functions provide an accurate imputation algorithm for reconstructing the
missing segment in a multi-variate data streams.  Inspired by single-shot
learning, it reconstructs the missing segment by identifying the first similar
segment in the stream.  Nevertheless, there should be one column of data
available, i.e.  a constraint column.  The values of columns can be characters
(A, B, C, etc.).  The result of the imputed dataset will be returned a .csv
file.  For more details see Reza Rawassizadeh (2019)
<doi:10.1109/TKDE.2019.2914653>.")
    (license license:gpl3)))

(define-public r-ghibli
  (package
    (name "r-ghibli")
    (version "0.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ghibli" version))
       (sha256
        (base32 "1qj5cysgbh9hq7q17p3vvaqychg3cyhyzlv1hs4jqqlb9z2cz1gr"))))
    (properties `((upstream-name . "ghibli")))
    (build-system r-build-system)
    (propagated-inputs (list r-prismatic))
    (native-inputs (list r-knitr))
    (home-page "https://ewenme.github.io/ghibli/")
    (synopsis "Studio Ghibli Colour Palettes")
    (description
     "Colour palettes inspired by Studio Ghibli
<https://en.wikipedia.org/wiki/Studio_Ghibli> films, ported to R for your
enjoyment.")
    (license license:expat)))

(define-public r-ghcm
  (package
    (name "r-ghcm")
    (version "3.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ghcm" version))
       (sha256
        (base32 "18cp82bniz1mdrkfi6y6mrrjxz5jhkz4w21939c5wsz9mlp9d2bj"))))
    (properties `((upstream-name . "ghcm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-compquadform))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/arlundborg/ghcm")
    (synopsis "Functional Conditional Independence Testing with the GHCM")
    (description
     "This package provides a statistical hypothesis test for conditional
independence.  Given residuals from a sufficiently powerful regression, it tests
whether the covariance of the residuals is vanishing.  It can be applied to both
discretely-observed functional data and multivariate data.  Details of the
method can be found in Anton Rask Lundborg, Rajen D. Shah and Jonas Peters
(2022) <doi:10.1111/rssb.12544>.")
    (license license:expat)))

(define-public r-ghclass
  (package
    (name "r-ghclass")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ghclass" version))
       (sha256
        (base32 "10sq9rissf3w7rp8449vzigl7bj7y3iqx0vci2m559ihjmsdkn6i"))))
    (properties `((upstream-name . "ghclass")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-whisker
                             r-tibble
                             r-rlang
                             r-purrr
                             r-lubridate
                             r-lifecycle
                             r-httr
                             r-glue
                             r-gh
                             r-fs
                             r-dplyr
                             r-cli
                             r-base64enc))
    (home-page "https://github.com/rundel/ghclass")
    (synopsis "Tools for Managing Classes on GitHub")
    (description
     "Interface for the @code{GitHub} API that enables efficient management of courses
on @code{GitHub}.  It has a functionality for managing organizations, teams,
repositories, and users on @code{GitHub} and helps automate most of the tedious
and repetitive tasks around creating and distributing assignments.")
    (license license:gpl3)))

(define-public r-ghat
  (package
    (name "r-ghat")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Ghat" version))
       (sha256
        (base32 "0xvr3xpi3d00ns55bwbj7k74ccs0lf0vf8f74qxhi5389bf3l0zr"))))
    (properties `((upstream-name . "Ghat")))
    (build-system r-build-system)
    (propagated-inputs (list r-rrblup))
    (home-page "https://academic.oup.com/genetics/article/209/1/321/5931021")
    (synopsis "Quantifying Evolution and Selection on Complex Traits")
    (description
     "This package provides functions are provided for quantifying evolution and
selection on complex traits.  The package implements effective handling and
analysis algorithms scaled for genome-wide data and calculates a composite
statistic, denoted Ghat, which is used to test for selection on a trait.  The
package provides a number of simple examples for handling and analysing the
genome data and visualising the output and results.  Beissinger et al., (2018)
<doi:10.1534/genetics.118.300857>.")
    (license license:expat)))

(define-public r-ghapps
  (package
    (name "r-ghapps")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ghapps" version))
       (sha256
        (base32 "18m4kzz3vhkqbsdgj3k0cywsl7lvf6r6whwsaww480fazx7vwfk5"))))
    (properties `((upstream-name . "ghapps")))
    (build-system r-build-system)
    (propagated-inputs (list r-openssl r-jose r-gh))
    (home-page "https://r-lib.r-universe.dev/ghapps")
    (synopsis "Authenticate as a 'GitHub' App")
    (description
     "@code{GitHub} apps provide a powerful way to manage fine grained programmatic
access to specific git repositories, without having to create dummy users, and
which are safer than a personal access token for automated tasks.  This package
extends the gh package to let you authenticate and interact with @code{GitHub}
<https://docs.github.com/en/rest/overview> in R as an app.")
    (license license:expat)))

(define-public r-ghap
  (package
    (name "r-ghap")
    (version "3.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GHap" version))
       (sha256
        (base32 "1fyxgnn6b474rx8cqg9lgdybq5j4nriq2axwgmhx7m8f7mysrqny"))))
    (properties `((upstream-name . "GHap")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringi
                             r-sparseinv
                             r-pedigreemm
                             r-matrix
                             r-e1071
                             r-data-table
                             r-class))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=GHap")
    (synopsis "Genome-Wide Haplotyping")
    (description
     "Haplotype calling from phased marker data.  Given user-defined haplotype blocks
(@code{HapBlock}), the package identifies the different haplotype alleles
(@code{HapAllele}) present in the data and scores sample haplotype allele
genotypes (@code{HapGenotype}) based on @code{HapAllele} dose (i.e.  0, 1 or 2
copies).  The output is not only useful for analyses that can handle
multi-allelic markers, but is also conveniently formatted for existing pipelines
intended for bi-allelic markers.  The package was first described in
Bioinformatics by Utsunomiya et al. (2016, <doi:10.1093/bioinformatics/btw356>).
 Since the v2 release, the package provides functions for unsupervised and
supervised detection of ancestry tracks.  The methods implemented in these
functions were described in an article published in Methods in Ecology and
Evolution by Utsunomiya et al. (2020, <doi:10.1111/2041-210X.13467>).  The
source code for v3 was modified for improved performance and inclusion of new
functionality, including analysis of unphased data, runs of homozygosity,
sampling methods for virtual gamete mating, mixed model fitting and GWAS.")
    (license license:gpl2+)))

(define-public r-ggx
  (package
    (name "r-ggx")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggx" version))
       (sha256
        (base32 "13yvckny2b7b7rhgi28xm8379paskin78wm5095s1ily9a2dxgkg"))))
    (properties `((upstream-name . "ggx")))
    (build-system r-build-system)
    (propagated-inputs (list r-sets r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ggx")
    (synopsis "Natural Language Interface to 'ggplot2'")
    (description
     "The ggplot2 package is the state-of-the-art toolbox for creating and formatting
graphs.  However, it is easy to forget how certain formatting commands are named
and sometimes users find themselves asking: How do you rotate the x-axis labels
again? Or how do you hide the legend...? This package allows users to issue
natural language commands related to theme-related styling of plots (colors,
font size and such), which then are translated into valid ggplot2 commands.")
    (license license:gpl3)))

(define-public r-ggwordcloud
  (package
    (name "r-ggwordcloud")
    (version "0.6.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggwordcloud" version))
       (sha256
        (base32 "0zl50rksfxiw3ab1j26w27rypygrr28yysflg507djamnc2371gj"))))
    (properties `((upstream-name . "ggwordcloud")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-rcpp
                             r-png
                             r-gridtext
                             r-ggplot2
                             r-colorspace))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/lepennec/ggwordcloud")
    (synopsis "Word Cloud Geom for 'ggplot2'")
    (description
     "This package provides a word cloud text geom for ggplot2'.  Texts are placed so
that they do not overlap as in ggrepel'.  The algorithm used is a variation
around the one of wordcloud2.js'.")
    (license license:gpl3)))

(define-public r-ggvolcano
  (package
    (name "r-ggvolcano")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggvolcano" version))
       (sha256
        (base32 "1sj5g3gsdk3bi9vvnjmsfx6c68dhg0b4lpsq9ljxsx2xzapmilmm"))))
    (properties `((upstream-name . "ggvolcano")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-golem r-ggrepel r-ggplot2 r-config))
    (home-page "https://cran.r-project.org/package=ggvolcano")
    (synopsis "Publication-Ready Volcano Plots")
    (description
     "This package provides publication-ready volcano plots for visualizing
differential expression results, commonly used in RNA-seq and similar analyses.
This tool helps create high-quality visual representations of data using the
ggplot2 framework Wickham (2016) <doi:10.1007/978-3-319-24277-4>.")
    (license license:expat)))

(define-public r-ggview
  (package
    (name "r-ggview")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggview" version))
       (sha256
        (base32 "0jdpr4bmw9kzq41h42r1i4ravfvkv65n2xkmqn6gm23bhdwyngmj"))))
    (properties `((upstream-name . "ggview")))
    (build-system r-build-system)
    (propagated-inputs (list r-rstudioapi r-ggplot2))
    (home-page "https://github.com/idmn/ggview")
    (synopsis "'ggplot2' Picture Previewer")
    (description
     "Preview what a ggplot2 plot would look like if you save it to a file.  Attach
picture dimensions as a canvas() element and get an instant preview.  These
dimensions will then be used when you save the plot.")
    (license (list license:gpl2
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-ggversa
  (package
    (name "r-ggversa")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggversa" version))
       (sha256
        (base32 "0xqn8iznrm8q30pxwka88wi1z7lndi6lm12rq6ngcaxf294fbz01"))))
    (properties `((upstream-name . "ggversa")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=ggversa")
    (synopsis "Graficas Versatiles Con 'ggplot2'")
    (description
     "This package provides a collection of datasets for the upcoming book \"Graficas
versatiles con ggplot: Analisis visuales de datos\", by Raymond L. Tremblay and
Julian Hernandez-Serano.")
    (license license:gpl2)))

(define-public r-ggupset
  (package
    (name "r-ggupset")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggupset" version))
       (sha256
        (base32 "0cycnnycn1qazy35s4w746zlfraayf109bq40fglklmick6kfwsz"))))
    (properties `((upstream-name . "ggupset")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-scales r-rlang r-gtable r-ggplot2))
    (home-page "https://github.com/const-ae/ggupset")
    (synopsis "Combination Matrix Axis for 'ggplot2' to Create 'UpSet' Plots")
    (description
     "Replace the standard x-axis in ggplots with a combination matrix to visualize
complex set overlaps. @code{UpSet} has introduced a new way to visualize the
overlap of sets as an alternative to Venn diagrams.  This package provides a
simple way to produce such plots using ggplot2'.  In addition it can convert any
categorical axis into a combination matrix axis.")
    (license license:gpl3)))

(define-public r-ggum
  (package
    (name "r-ggum")
    (version "0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GGUM" version))
       (sha256
        (base32 "1xbbapam5whnsa0f2fpvj2v56c7g5wm4jcs0xwzrcjlp9g293vqj"))))
    (properties `((upstream-name . "GGUM")))
    (build-system r-build-system)
    (propagated-inputs (list r-xlsx r-viridis r-rdpack r-psych r-abind))
    (home-page "https://github.com/jorgetendeiro/GGUM/")
    (synopsis "Generalized Graded Unfolding Model")
    (description
     "An implementation of the generalized graded unfolding model (GGUM) in R, see
Roberts, Donoghue, and Laughlin (2000) <doi:10.1177/01466216000241001>).  It
allows to simulate data sets based on the GGUM. It fits the GGUM and the GUM,
and it retrieves item and person parameter estimates.  Several plotting
functions are available (item and test information functions; item and test
characteristic curves; item category response curves).  Additionally, there are
some functions that facilitate the communication between R and GGUM2004'.
Finally, a model-fit checking utility, MODFIT(), is also available.")
    (license license:gpl2+)))

(define-public r-ggtricks
  (package
    (name "r-ggtricks")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggtricks" version))
       (sha256
        (base32 "1bpf3a5da8csqzwyqw0xqkbki4jgyhsybj2lw8zs00vcdsg9bhhv"))))
    (properties `((upstream-name . "ggtricks")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-cli))
    (home-page "https://github.com/AbdoulMa/ggtricks")
    (synopsis
     "Create Sector and Other Charts Easily Using Grammar of Graphics")
    (description
     "This package provides a collection of several geoms to create graphics, using
ggplot2 and the Cartesian coordinate system.  You use the familiar mapping
Grammar of Graphics without the need to do another transformation into polar
coordinates.")
    (license license:expat)))

(define-public r-ggtrendline
  (package
    (name "r-ggtrendline")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggtrendline" version))
       (sha256
        (base32 "10ml5mdijh27b1js67zwaywn81bvz6aja1bn60k3rnr964nsljsj"))))
    (properties `((upstream-name . "ggtrendline")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://github.com/PhDMeiwp/ggtrendline")
    (synopsis "Add Trendline and Confidence Interval to 'ggplot'")
    (description
     "Add trendline and confidence interval of linear or nonlinear regression model
and show equation to ggplot as simple as possible.  For a general overview of
the methods used in this package, see Ritz and Streibig (2008)
<doi:10.1007/978-0-387-09616-2> and Greenwell and Schubert Kabban (2014)
<doi:10.32614/RJ-2014-009>.")
    (license license:gpl3)))

(define-public r-ggtrace
  (package
    (name "r-ggtrace")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggtrace" version))
       (sha256
        (base32 "1zzdamzxkwimzjhrjf5nijj0r94l03nabny5gjv49kqlncjicr7r"))))
    (properties `((upstream-name . "ggtrace")))
    (build-system r-build-system)
    (inputs (list pandoc))
    (propagated-inputs (list r-rlang r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rnabioco/ggtrace")
    (synopsis "Trace and Highlight Groups of Data Points")
    (description
     "This package provides ggplot2 geoms that allow groups of data points to be
outlined or highlighted for emphasis.  This is particularly useful when working
with dense datasets that are prone to overplotting.")
    (license license:expat)))

(define-public r-ggtimeseries
  (package
    (name "r-ggtimeseries")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggTimeSeries" version))
       (sha256
        (base32 "0sd39svwi3ai81szahd49a8qvsydi1922z4q6r2r70fpbfwi4qrq"))))
    (properties `((upstream-name . "ggTimeSeries")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-data-table))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/thecomeonman/ggTimeSeries")
    (synopsis "Time Series Visualisations Using the Grammar of Graphics")
    (description
     "This package provides additional display mediums for time series visualisations.")
    (license license:expat)))

(define-public r-ggtikz
  (package
    (name "r-ggtikz")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggtikz" version))
       (sha256
        (base32 "07af96qx8n4mal4jnnn8br3gf7bw22mk82c24csvqhki6q5w460z"))))
    (properties `((upstream-name . "ggtikz")))
    (build-system r-build-system)
    (propagated-inputs (list r-tikzdevice r-stringr r-ggplot2 r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/osthomas/ggtikz")
    (synopsis
     "Post-Process 'ggplot2' Plots with 'TikZ' Code Using Plot Coordinates")
    (description
     "Annotation of ggplot2 plots with arbitrary @code{TikZ} code, using absolute data
or relative plot coordinates.")
    (license license:expat)))

(define-public r-ggtibble
  (package
    (name "r-ggtibble")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggtibble" version))
       (sha256
        (base32 "1f9i81vmdkcahxsxqzwgkrjfqchk3jr14h2w8i5gsyhink271kxj"))))
    (properties `((upstream-name . "ggtibble")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-knitr
                             r-glue
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://billdenney.github.io/ggtibble/")
    (synopsis "Create Tibbles and Lists of 'ggplot' Figures for Reporting")
    (description
     "Create tibbles and lists of ggplot figures that can be modified as easily as
regular ggplot figures.  Typical use cases are for creating reports or web pages
where many figures are needed with different data and similar formatting.")
    (license license:gpl3+)))

(define-public r-ggthemeul
  (package
    (name "r-ggthemeul")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggthemeUL" version))
       (sha256
        (base32 "0nv11db6ds7xshh7dkpz1agfq0r8a9z3qvgd3m1icqcg3w60as0y"))))
    (properties `((upstream-name . "ggthemeUL")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ggthemeUL")
    (synopsis "'ggplot' Theme for University of Ljubljana")
    (description
     "Designed to customize ggplot graphics according to the institutional identity of
the University of Ljubljana.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-ggtea
  (package
    (name "r-ggtea")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggtea" version))
       (sha256
        (base32 "04g8g17ziw13rxjzkvgy5yd0z15ypxk0b8fq2y1vk8acsqr2dv11"))))
    (properties `((upstream-name . "ggtea")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://cran.r-project.org/package=ggtea")
    (synopsis "Palettes and Themes for 'ggplot2'")
    (description
     "This package provides a collection of palettes and themes for ggplot2', offering
a light, pastel aesthetic.  Syntax follows the viridis package.")
    (license license:gpl3+)))

(define-public r-ggtangle
  (package
    (name "r-ggtangle")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggtangle" version))
       (sha256
        (base32 "0wgz6hffpmnvrs0kkxmgc6mnmjh4w6a5ifik8bz7axc3adlwyk5z"))))
    (properties `((upstream-name . "ggtangle")))
    (build-system r-build-system)
    (propagated-inputs (list r-yulab-utils r-rlang r-igraph r-ggplot2 r-ggfun))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ggtangle")
    (synopsis "Draw Network with Data")
    (description
     "Extends the ggplot2 plotting system to support network visualization.  Inspired
by ggtree', ggtangle is designed to work with network associated data.")
    (license license:artistic2.0)))

(define-public r-ggswissmaps
  (package
    (name "r-ggswissmaps")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggswissmaps" version))
       (sha256
        (base32 "0is48x6k2p5dgj9q4km0dv33a9pcpfhlai9vz295y3acpyrkmnn4"))))
    (properties `((upstream-name . "ggswissmaps")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/gibonet/ggswissmaps")
    (synopsis "Offers Various Swiss Maps as Data Frames and 'ggplot2' Objects")
    (description
     "Offers various swiss maps as data frames and ggplot2 objects and gives the
possibility to add layers of data on the maps.  Data are publicly available from
the swiss federal statistical office.")
    (license license:gpl2)))

(define-public r-ggsurvfit
  (package
    (name "r-ggsurvfit")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggsurvfit" version))
       (sha256
        (base32 "0c3qv9gii3ndqip3nxs08bx7lvh6sm46mxz8nyi627zbgqmhwrr5"))))
    (properties `((upstream-name . "ggsurvfit")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-survival
                             r-rlang
                             r-patchwork
                             r-gtable
                             r-glue
                             r-ggplot2
                             r-dplyr
                             r-cli
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/pharmaverse/ggsurvfit")
    (synopsis "Flexible Time-to-Event Figures")
    (description
     "Ease the creation of time-to-event (i.e.  survival) endpoint figures.  The
modular functions create figures ready for publication.  Each of the functions
that add to or modify the figure are written as proper ggplot2 geoms or stat
methods, allowing the functions from this package to be combined with any
function or customization from ggplot2 and other ggplot2 extension packages.")
    (license license:expat)))

(define-public r-ggsurvey
  (package
    (name "r-ggsurvey")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggsurvey" version))
       (sha256
        (base32 "1a50ac2546pxa35k59j4ih0csiyfjdgqfqwm3fb5ny7jx1mk0hmi"))))
    (properties `((upstream-name . "ggsurvey")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey r-hexbin r-ggplot2 r-dplyr))
    (home-page "https://cran.r-project.org/package=ggsurvey")
    (synopsis "Simplifying 'ggplot2' for Survey Data")
    (description
     "This package provides functions for survey data including svydesign objects from
the survey package that call ggplot2 to make bar charts, histograms, boxplots,
and hexplots of survey data.")
    (license license:expat)))

(define-public r-ggstudent
  (package
    (name "r-ggstudent")
    (version "0.1.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggstudent" version))
       (sha256
        (base32 "0qs27b4lksaaaspavkycbzf8cfjxjli7xrfjby71sl0lca3fwm1g"))))
    (properties `((upstream-name . "ggstudent")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-dplyr))
    (home-page "https://cran.r-project.org/package=ggstudent")
    (synopsis "Continuous Confidence Interval Plots using t-Distribution")
    (description
     "This package provides an extension to ggplot2 (Wickham, 2016,
<doi:10.1007/978-3-319-24277-4>) for creating two types of continuous confidence
interval plots (Violin CI and Gradient CI plots), typically for the sample mean.
 These plots contain multiple user-defined confidence areas with varying
colours, defined by the underlying t-distribution used to compute standard
confidence intervals for the mean of the normal distribution when the variance
is unknown.  Two types of plots are available, a gradient plot with rectangular
areas, and a violin plot where the shape (horizontal width) is defined by the
probability density function of the t-distribution.")
    (license license:gpl2+)))

(define-public r-ggstream
  (package
    (name "r-ggstream")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggstream" version))
       (sha256
        (base32 "0d6g5916mfrg2srcqjhlpm4acp4g16vq9ks4x37yzkpr34vi5x7j"))))
    (properties `((upstream-name . "ggstream")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-purrr
                             r-magrittr
                             r-ggplot2
                             r-forcats
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=ggstream")
    (synopsis "Create Streamplots in 'ggplot2'")
    (description
     "Make smoothed stacked area charts in ggplot2'.  Stream plots are useful to show
magnitude trends over time.")
    (license license:expat)))

(define-public r-ggstar
  (package
    (name "r-ggstar")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggstar" version))
       (sha256
        (base32 "0m2knp1jf1x62nqd8ln4haw90jv69l7vjrhn4pdrywgmwhdrp8gd"))))
    (properties `((upstream-name . "ggstar")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-gridextra r-ggplot2 r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/xiangpin/ggstar/")
    (synopsis "Multiple Geometric Shape Point Layer for 'ggplot2'")
    (description
     "To create the multiple polygonal point layer for easily discernible shapes, we
developed the package, it is like the geom_point of ggplot2'.  It can be used to
draw the scatter plot.")
    (license license:artistic2.0)))

(define-public r-ggspectra
  (package
    (name "r-ggspectra")
    (version "0.3.13")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggspectra" version))
       (sha256
        (base32 "08kvym2h07093jfnc12ivr04275931qrczvfqyrsfp718s8nwnn7"))))
    (properties `((upstream-name . "ggspectra")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-scales
                             r-rlang
                             r-photobiologywavebands
                             r-photobiology
                             r-lubridate
                             r-ggrepel
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://docs.r4photobiology.info/ggspectra/")
    (synopsis "Extensions to 'ggplot2' for Radiation Spectra")
    (description
     "Additional annotations, stats, geoms and scales for plotting \"light\" spectra
with ggplot2', together with specializations of ggplot() and autoplot() methods
for spectral data and waveband definitions stored in objects of classes defined
in package photobiology'.  Part of the r4photobiology suite, Aphalo P. J. (2015)
<doi:10.19232/uv4pb.2015.1.14>.")
    (license license:gpl2+)))

(define-public r-ggspatial
  (package
    (name "r-ggspatial")
    (version "1.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggspatial" version))
       (sha256
        (base32 "14q3fc33cjy8vc85mb9a6h8fgciypnlcxjihbgc6jbzjksd0l38p"))))
    (properties `((upstream-name . "ggspatial")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-sf
                             r-scales
                             r-rosm
                             r-rlang
                             r-glue
                             r-ggplot2
                             r-abind))
    (home-page "https://paleolimbot.github.io/ggspatial/")
    (synopsis "Spatial Data Framework for ggplot2")
    (description
     "Spatial data plus the power of the ggplot2 framework means easier mapping when
input data are already in the form of spatial objects.")
    (license license:gpl3)))

(define-public r-ggspark
  (package
    (name "r-ggspark")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggspark" version))
       (sha256
        (base32 "09xdjiizx31sj8vahjdk12p7yhmxll8z61nirr13myj0wffrj79x"))))
    (properties `((upstream-name . "ggspark")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://github.com/marcboschmatas/ggspark")
    (synopsis "'ggplot2' Functions to Create Tufte Style Sparklines")
    (description
     "This package provides functions to help with creating sparklines in the style of
Edward Tufte
<https://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0001OR&topic_id=1>
in ggplot2'.  It computes ribbon geoms with the interquartile ranges and points
and/or labels at the beginning, end, max, and min points.")
    (license license:gpl2+)))

(define-public r-ggsom
  (package
    (name "r-ggsom")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggsom" version))
       (sha256
        (base32 "0qsih00rm3ng2zh6p2s7a35rh1qlvbkn82s53v765w60fjmhwkwf"))))
    (properties `((upstream-name . "ggsom")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-magrittr
                             r-kohonen
                             r-ggplot2
                             r-entropy
                             r-dplyr
                             r-data-table
                             r-assertthat))
    (home-page "https://github.com/oldlipe/ggsom")
    (synopsis "New Data Visualisations for SOMs Networks")
    (description
     "The aim of this package is to offer more variability of graphics based on the
self-organizing maps.")
    (license license:expat)))

(define-public r-ggsolvencyii
  (package
    (name "r-ggsolvencyii")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggsolvencyii" version))
       (sha256
        (base32 "0jzj4iglgzwp6pfs5zr7mirdiqrrmbwdvl245b1fwf42rnxbqrq6"))))
    (properties `((upstream-name . "ggsolvencyii")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-magrittr r-ggplot2 r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/vanzanden/ggsolvencyii")
    (synopsis "'ggplot2'-Plot of Composition of Solvency II SCR: SF and IM")
    (description
     "An implementation of ggplot2'-methods to present the composition of Solvency II
Solvency Capital Requirement (SCR) as a series of concentric circle-parts.
Solvency II (Solvency 2) is European insurance legislation, coming in force by
the delegated acts of October 10, 2014.
<https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=OJ%3AL%3A2015%3A012%3ATOC>.
 Additional files, defining the structure of the Standard Formula (SF) method of
the SCR-calculation are provided.  The structure files can be adopted for
localization or for insurance companies who use Internal Models (IM).  Options
are available for combining smaller components, horizontal and vertical scaling,
rotation, and plotting only some circle-parts.  With outlines and connectors
several SCR-compositions can be compared, for example in ORSA-scenarios (Own
Risk and Solvency Assessment).")
    (license license:gpl3)))

(define-public r-ggsoccer
  (package
    (name "r-ggsoccer")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggsoccer" version))
       (sha256
        (base32 "0ps1xr878arwyy0h6py62ibnhrrq3wimqkyay1acxb1pl2dfz26d"))))
    (properties `((upstream-name . "ggsoccer")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-ggplot2))
    (home-page "https://torvaney.github.io/ggsoccer/")
    (synopsis "Plot Soccer Event Data")
    (description
     "The ggplot2 package provides a powerful set of tools for visualising and
investigating data.  The ggsoccer package provides a set of functions for
elegantly displaying and exploring soccer event data with ggplot2'.  Providing
extensible layers and themes, it is designed to work smoothly with a variety of
popular sports data providers.")
    (license license:expat)))

(define-public r-ggsmc
  (package
    (name "r-ggsmc")
    (version "0.1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggsmc" version))
       (sha256
        (base32 "1wgb5ml1bgfi6rddbvm3rfk6di9imyx17iflg8h42hhbvbvm93iy"))))
    (properties `((upstream-name . "ggsmc")))
    (build-system r-build-system)
    (propagated-inputs (list r-poorman r-ggplot2 r-gganimate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/richardgeveritt/ggsmc")
    (synopsis
     "Visualising Output from Sequential Monte Carlo Samplers and Ensemble-Based Methods")
    (description
     "This package provides functions for plotting, and animating, the output of
importance samplers, sequential Monte Carlo samplers (SMC) and ensemble-based
methods.  The package can be used to plot and animate histograms, densities,
scatter plots and time series, and to plot the genealogy of an SMC or
ensemble-based algorithm.  These functions all rely on algorithm output to be
supplied in tidy format.  A function is provided to transform algorithm output
from matrix format (one Monte Carlo point per row) to the tidy format required
by the plotting and animating functions.")
    (license license:expat)))

(define-public r-ggshadow
  (package
    (name "r-ggshadow")
    (version "0.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggshadow" version))
       (sha256
        (base32 "0cakz31hp97kdvdcyrp9p03nz81kypsn9sn6nxl7kb0n3rrrlav0"))))
    (properties `((upstream-name . "ggshadow")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-scales
                             r-rlang
                             r-glue
                             r-ggplot2
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/marcmenem/ggshadow/")
    (synopsis "Shadow and Glow Geoms for 'ggplot2'")
    (description
     "This package provides a collection of Geoms for R's ggplot2 library.
geom_shadowpath(), geom_shadowline(), geom_shadowstep() and geom_shadowpoint()
functions draw a shadow below lines to make busy plots more aesthetically
pleasing.  geom_glowpath(), geom_glowline(), geom_glowstep() and
geom_glowpoint() add a neon glow around lines to get a steampunk style.")
    (license license:gpl2)))

(define-public r-ggseqplot
  (package
    (name "r-ggseqplot")
    (version "0.8.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggseqplot" version))
       (sha256
        (base32 "0h4v5lxwr8rflxmxrv324axz6m1girib6gdh4p3k5hk48ilglwj0"))))
    (properties `((upstream-name . "ggseqplot")))
    (build-system r-build-system)
    (propagated-inputs (list r-usethis
                             r-traminer
                             r-tidyr
                             r-rlang
                             r-rdpack
                             r-purrr
                             r-patchwork
                             r-haven
                             r-glue
                             r-ggtext
                             r-ggrepel
                             r-ggplot2
                             r-ggh4x
                             r-forcats
                             r-dplyr
                             r-colorspace
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://maraab23.github.io/ggseqplot/")
    (synopsis "Render Sequence Plots using 'ggplot2'")
    (description
     "This package provides a set of wrapper functions that mainly re-produces most of
the sequence plots rendered with @code{TraMineR::seqplot}().  Whereas
@code{TraMineR} uses base R to produce the plots this library draws on ggplot2'.
 The plots are produced on the basis of a sequence object defined with
@code{TraMineR::seqdef}().  The package automates the reshaping and plotting of
sequence data.  Resulting plots are of class ggplot', i.e.  components can be
added and tweaked using + and regular ggplot2 functions.")
    (license license:gpl3+)))

(define-public r-ggseg3d
  (package
    (name "r-ggseg3d")
    (version "1.6.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggseg3d" version))
       (sha256
        (base32 "1rpq2wgyyladz5nkngkvbf3cy4pw212dj1vfh7ksh0bxi7v1k6mh"))))
    (properties `((upstream-name . "ggseg3d")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-scales r-plotly r-magrittr r-dplyr))
    (home-page "https://github.com/ggseg/ggseg3d/")
    (synopsis "Tri-Surface Mesh Plots for Brain Atlases")
    (description
     "Mainly contains a plotting function ggseg3d(), and data of two standard brain
atlases (Desikan-Killiany and aseg).  By far, the largest bit of the package is
the data for each of the atlases.  The functions and data enable users to plot
tri-surface mesh plots of brain atlases, and customise these by projecting
colours onto the brain segments based on values in their own data sets.
Functions are wrappers for plotly'.  Mowinckel & Vidal-PiÃ±eiro (2020)
<doi:10.1177/2515245920928009>.")
    (license license:expat)))

(define-public r-ggseg
  (package
    (name "r-ggseg")
    (version "1.6.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggseg" version))
       (sha256
        (base32 "0495g3l71lagrmz3s3lxy4rkfr7gin8vn1m59fvcv7hmycn39i0k"))))
    (properties `((upstream-name . "ggseg")))
    (build-system r-build-system)
    (inputs (list proj geos gdal))
    (propagated-inputs (list r-vctrs r-tidyr r-sf r-ggplot2 r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ggseg/ggseg")
    (synopsis "Plotting Tool for Brain Atlases")
    (description
     "This package contains ggplot2 geom for plotting brain atlases using simple
features.  The largest component of the package is the data for the two built-in
atlases.  Mowinckel & Vidal-PiÃ±eiro (2020) <doi:10.1177/2515245920928009>.")
    (license license:expat)))

(define-public r-ggsector
  (package
    (name "r-ggsector")
    (version "1.6.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggsector" version))
       (sha256
        (base32 "0qaf8wl1mmwl3razyif08sh329p9j1jgqc0ls5szqirq3db4315i"))))
    (properties `((upstream-name . "ggsector")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-seurat
                             r-rlang
                             r-prettydoc
                             r-matrix
                             r-magrittr
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ggsector")
    (synopsis "Draw Sectors")
    (description
     "Some useful functions that can use grid and ggplot2 to plot sectors and interact
with Seurat to plot gene expression percentages.  Also, there are some examples
of how to draw sectors in @code{ComplexHeatmap}'.")
    (license license:artistic2.0)))

(define-public r-ggseas
  (package
    (name "r-ggseas")
    (version "0.5.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggseas" version))
       (sha256
        (base32 "1jpdijwll5l7bg7cmjnklkxffysi9ckzg50rw2a2cd00zwby11q9"))))
    (properties `((upstream-name . "ggseas")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-seasonal r-rlang r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ggseas")
    (synopsis "'stats' for Seasonal Adjustment on the Fly with 'ggplot2'")
    (description
     "This package provides ggplot2 stats that estimate seasonally adjusted series and
rolling summaries such as rolling average on the fly for time series.")
    (license license:gpl3)))

(define-public r-ggscidca
  (package
    (name "r-ggscidca")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggscidca" version))
       (sha256
        (base32 "1ggjakfafq6gib7yn684ssv6wdad68an0p13wxl232ikwmw4b66w"))))
    (properties `((upstream-name . "ggscidca")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-reshape2
                             r-randomforest
                             r-kernlab
                             r-ggplot2
                             r-e1071
                             r-cmprsk))
    (home-page "https://cran.r-project.org/package=ggscidca")
    (synopsis "Plotting Decision Curve Analysis with Coloured Bars")
    (description
     "Decision curve analysis is a method for evaluating and comparing prediction
models that incorporates clinical consequences, requires only the data set on
which the models are tested, and can be applied to models that have either
continuous or dichotomous results.  The ggscidca package adds coloured bars of
discriminant relevance to the traditional decision curve.  Improved practicality
and aesthetics.  This method was described by Balachandran VP (2015)
<doi:10.1016/S1470-2045(14)71116-7>.")
    (license license:gpl3)))

(define-public r-ggscatridges
  (package
    (name "r-ggscatridges")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggScatRidges" version))
       (sha256
        (base32 "1blwzynrzywdgqllcgjjb0j0xc8fwplybnx3mfmiysppcvrk47p9"))))
    (properties `((upstream-name . "ggScatRidges")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-hrbrthemes
                             r-ggridges
                             r-ggrepel
                             r-ggpubr
                             r-ggplot2
                             r-cowplot))
    (home-page "https://github.com/matbou85/ggScatRidges")
    (synopsis "Scatter Plot Combined with Ridgelines in 'ggplot2'")
    (description
     "The function combines a scatter plot with ridgelines to better visualise the
distribution between sample groups.  The plot is created with ggplot2'.")
    (license license:gpl3)))

(define-public r-ggsankeyfier
  (package
    (name "r-ggsankeyfier")
    (version "0.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggsankeyfier" version))
       (sha256
        (base32 "1sn7gpkqbgk3adkflnzak29d0zk57d0x6hlv2wm1shq45nawjmcg"))))
    (properties `((upstream-name . "ggsankeyfier")))
    (build-system r-build-system)
    (propagated-inputs (list r-vwline
                             r-tidyr
                             r-scales
                             r-rlang
                             r-gridbezier
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://pepijn-devries.github.io/ggsankeyfier/")
    (synopsis "Create Sankey and Alluvial Diagrams Using 'ggplot2'")
    (description
     "Sankey and alluvial diagrams visualise flows of quantities across stages in
stacked bars.  This package makes it easy to create such diagrams using
ggplot2'.")
    (license license:gpl3+)))

(define-public r-ggrtsy
  (package
    (name "r-ggrtsy")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggRtsy" version))
       (sha256
        (base32 "04q9674z0wgpd5igjhggih348jd6lv6nbwz4na597pn5d3pirhvl"))))
    (properties `((upstream-name . "ggRtsy")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-purrr
                             r-magrittr
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ggRtsy")
    (synopsis "Add Some Van Gogh Colors and Overlay Colors on Your 'ggplot()'")
    (description
     "Works with ggplot2 to add a Van Gogh color palette to the userâs repertoire.
It also has a function that work alongside ggplot2 to create more interesting
data visualizations and add contextual information to the userâs plots.")
    (license license:cc0)))

(define-public r-ggroups
  (package
    (name "r-ggroups")
    (version "2.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggroups" version))
       (sha256
        (base32 "0fb6awfm4vn67pgmz50fljvp9mfxx1q29pirgv04dya8751srxgi"))))
    (properties `((upstream-name . "ggroups")))
    (build-system r-build-system)
    (home-page "https://github.com/nilforooshan/ggroups")
    (synopsis "Pedigree and Genetic Groups")
    (description
     "Calculates additive and dominance genetic relationship matrices and their
inverses, in matrix and tabular-sparse formats.  It includes functions for
checking and processing pedigree, calculating inbreeding coefficients (Meuwissen
& Luo, 1992 <doi:10.1186/1297-9686-24-4-305>), as well as functions to calculate
the matrix of genetic group contributions (Q), and adding those contributions to
the genetic merit of animals (Quaas (1988)
<doi:10.3168/jds.S0022-0302(88)79691-5>).  Calculation of Q is computationally
extensive.  There are computationally optimized functions to calculate Q.")
    (license license:gpl3)))

(define-public r-ggrounded
  (package
    (name "r-ggrounded")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggrounded" version))
       (sha256
        (base32 "124khfv4fl3xgylq4s1pc3wzld0n68iclyl0pm19jcw97bmf1cxl"))))
    (properties `((upstream-name . "ggrounded")))
    (build-system r-build-system)
    (propagated-inputs (list r-gridgeometry r-ggplot2))
    (home-page "https://github.com/botan/ggrounded")
    (synopsis "Rounded Bar Plots")
    (description
     "This package creates bar plots with rounded corners using ggplot2'.  The code in
this package was adapted from a solution provided by Stack Overflow user sthoch
in the following post
<https://stackoverflow.com/questions/62176038/r-ggplot2-bar-chart-with-round-corners-on-top-of-bar>.")
    (license license:expat)))

(define-public r-ggrisk
  (package
    (name "r-ggrisk")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggrisk" version))
       (sha256
        (base32 "02df3gcviax747d33v30m97jsazj7zdpjnhypfy38cg70yxidwg9"))))
    (properties `((upstream-name . "ggrisk")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-set
                             r-rms
                             r-reshape2
                             r-nomogramformula
                             r-ggplot2
                             r-egg
                             r-do
                             r-cutoff))
    (home-page "https://github.com/yikeshu0611/ggrisk")
    (synopsis "Risk Score Plot for Cox Regression")
    (description
     "The risk plot may be one of the most commonly used figures in tumor genetic data
analysis.  We can conclude the following two points: Comparing the prediction
results of the model with the real survival situation to see whether the
survival rate of the high-risk group is lower than that of the low-level group,
and whether the survival time of the high-risk group is shorter than that of the
low-risk group.  The other is to compare the heat map and scatter plot to see
the correlation between the predictors and the outcome.")
    (license license:gpl2)))

(define-public r-ggridge
  (package
    (name "r-ggridge")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GGRidge" version))
       (sha256
        (base32 "08gj61ahk7ilvsz93xcdv7yknkmfz2spbym6c2c62gq8cva9zg86"))))
    (properties `((upstream-name . "GGRidge")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-grbase r-cvglasso))
    (home-page "https://cran.r-project.org/package=GGRidge")
    (synopsis "Graphical Group Ridge")
    (description
     "The Graphical Group Ridge GGRidge package package classifies ridge regression
predictors in disjoint groups of conditionally correlated variables and derives
different penalties (shrinkage parameters) for these groups of predictors.  It
combines the ridge regression method with the graphical model for
high-dimensional data (i.e.  the number of predictors exceeds the number of
cases) or ill-conditioned data (e.g. in the presence of multicollinearity among
predictors).  The package reduces the mean square errors and the extent of
over-shrinking of predictors as compared to the ridge method.Aldahmani, S. and
Zoubeidi, T. (2020) <DOI:10.1080/00949655.2020.1803320>.")
    (license license:gpl2)))

(define-public r-ggreveal
  (package
    (name "r-ggreveal")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggreveal" version))
       (sha256
        (base32 "1dgm33y6swc75p6d992wv5m47kgl36g0fvp2s79byl51llm6940k"))))
    (properties `((upstream-name . "ggreveal")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-rlang
                             r-lemon
                             r-ggplotify
                             r-ggplot2
                             r-dplyr
                             r-cli))
    (home-page "http://www.weverthon.com/ggreveal/")
    (synopsis "Reveal a 'ggplot' Incrementally")
    (description
     "This package provides functions that make it easy to reveal ggplot2 graphs
incrementally.  The functions take a plot produced with ggplot2 and return a
list of plots showing data incrementally by panels, layers, groups, the values
in an axis or any arbitrary aesthetic.")
    (license license:expat)))

(define-public r-ggresidpanel
  (package
    (name "r-ggresidpanel")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggResidpanel" version))
       (sha256
        (base32 "1vsjw229pd36100ix1sgfmk5lb9b0fx3l6pxg1irsfhm2ypbg1ig"))))
    (properties `((upstream-name . "ggResidpanel")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-qqplotr
                             r-plotly
                             r-mass
                             r-ggplot2
                             r-cowplot))
    (native-inputs (list r-knitr))
    (home-page "https://goodekat.github.io/ggResidpanel/")
    (synopsis
     "Panels and Interactive Versions of Diagnostic Plots using 'ggplot2'")
    (description
     "An R package for creating panels of diagnostic plots for residuals from a model
using ggplot2 and plotly to analyze residuals and model assumptions from a
variety of viewpoints.  It also allows for the creation of interactive
diagnostic plots.")
    (license license:expat)))

(define-public r-ggredist
  (package
    (name "r-ggredist")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggredist" version))
       (sha256
        (base32 "1p5bzqi4g5ri0m0nlwra6vps4sil7qc56cd8nzp0xh7k1gl598ig"))))
    (properties `((upstream-name . "ggredist")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-ggplot2))
    (home-page "https://github.com/alarm-redist/ggredist")
    (synopsis
     "Scales, Geometries, and Extensions of 'ggplot2' for Election Mapping")
    (description
     "This package provides ggplot2 extensions for political map making.  Implements
new geometries for groups of simple feature geometries.  Adds palettes and
scales for red to blue color mapping and for discrete maps.  Implements tools
for easy label generation and placement, automatic map coloring, and themes.")
    (license license:expat)))

(define-public r-ggrcs
  (package
    (name "r-ggrcs")
    (version "0.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggrcs" version))
       (sha256
        (base32 "16ywi8dgsp000w0869mwrn61lwk3gb40bb0i7yh9i1ch2cyc05v1"))))
    (properties `((upstream-name . "ggrcs")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-rms r-ggplot2 r-cowplot))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ggrcs")
    (synopsis "Draw Histograms and Restricted Cubic Splines (RCS)")
    (description
     "You can use this function to easily draw a combined histogram and restricted
cubic spline.  The function draws the graph through ggplot2'.  RCS fitting
requires the use of the rcs() function of the rms package.  Can fit cox
regression, logistic regression.  This method was described by Per Kragh (2003)
<doi:10.1002/sim.1497>.")
    (license license:gpl3)))

(define-public r-ggrasp
  (package
    (name "r-ggrasp")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggrasp" version))
       (sha256
        (base32 "0jl4bmhsisqc7nam8r027lr1qp0yr6bvdiyycknp7bkl65cg2x1z"))))
    (properties `((upstream-name . "ggrasp")))
    (build-system r-build-system)
    (propagated-inputs (list r-mixtools r-ggplot2 r-colorspace r-bgmm r-ape))
    (home-page "https://cran.r-project.org/package=ggrasp")
    (synopsis
     "Gaussian-Based Genome Representative Selector with Prioritization")
    (description
     "Given a group of genomes and their relationship with each other, the package
clusters the genomes and selects the most representative members of each
cluster.  Additional data can be provided to the prioritize certain genomes.
The results can be printed out as a list or a new phylogeny with graphs of the
trees and distance distributions also available.  For detailed introduction see:
Thomas H Clarke, Lauren M Brinkac, Granger Sutton, and Derrick E Fouts (2018),
GG@code{RaSP}: a R-package for selecting representative genomes using Gaussian
mixture models, Bioinformatics, bty300, <doi:10.1093/bioinformatics/bty300>.")
    (license license:gpl2)))

(define-public r-ggraptr
  (package
    (name "r-ggraptr")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggraptR" version))
       (sha256
        (base32 "1i6jp0n1sz5b4sv3d4h5624zhlknih2qp6p2m2ri6s3gsh1pl9zz"))))
    (properties `((upstream-name . "ggraptR")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-purrr r-pacman r-ggplot2 r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ggraptR")
    (synopsis
     "Allows Interactive Visualization of Data Through a Web Browser GUI")
    (description
     "Intended for both technical and non-technical users to create interactive data
visualizations through a web browser GUI without writing any code.")
    (license license:gpl2+)))

(define-public r-ggrandomforests
  (package
    (name "r-ggrandomforests")
    (version "2.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggRandomForests" version))
       (sha256
        (base32 "05w1rs0mg2nj5j1rd32s1mcj294p4zm24p2d87535rmslqmya9c7"))))
    (properties `((upstream-name . "ggRandomForests")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-survival r-randomforestsrc
                             r-randomforest r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ehrlinger/ggRandomForests")
    (synopsis "Visually Exploring Random Forests")
    (description
     "Graphic elements for exploring Random Forests using the @code{randomForest} or
@code{randomForestSRC} package for survival, regression and classification
forests and ggplot2 package plotting.")
    (license license:gpl3+)))

(define-public r-ggrain
  (package
    (name "r-ggrain")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggrain" version))
       (sha256
        (base32 "0l7042f4xh37vw9skhnagz5ncn8a0aflsz53x6rm3n1f6y2mi1gc"))))
    (properties `((upstream-name . "ggrain")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-rlang
                             r-ggpp
                             r-ggplot2
                             r-gghalves
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/njudd/ggrain")
    (synopsis "Rainclouds Geom for 'ggplot2'")
    (description
     "The geom_rain() function adds different geoms together using ggplot2 to create
raincloud plots.")
    (license license:expat)))

(define-public r-ggragged
  (package
    (name "r-ggragged")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggragged" version))
       (sha256
        (base32 "0b97k8cs5v0zlny6spcn4amgvih6px5n6w8lmm8068siah1d1bzy"))))
    (properties `((upstream-name . "ggragged")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs r-rlang r-gtable r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mikmart/ggragged")
    (synopsis "Ragged Grids for 'ggplot2'")
    (description
     "Extend ggplot2 facets to panel layouts arranged in a grid with ragged edges.
facet_ragged_rows() groups panels into rows that can vary in length,
facet_ragged_cols() does the same but for columns.  These can be useful, for
example, to represent nested or partially crossed relationships between faceting
variables.")
    (license license:expat)))

(define-public r-ggquiver
  (package
    (name "r-ggquiver")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggquiver" version))
       (sha256
        (base32 "1ywpcbm6rg2f44iiw2j07p7zwfgjrnli6wscrfx93wsqaa55xyfi"))))
    (properties `((upstream-name . "ggquiver")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://github.com/mitchelloharawild/ggquiver")
    (synopsis "Quiver Plots for 'ggplot2'")
    (description
     "An extension of ggplot2 to provide quiver plots to visualise vector fields.
This functionality is implemented using a geom to produce a new graphical layer,
which allows aesthetic options.  This layer can be overlaid on a map to improve
visualisation of mapped data.")
    (license license:gpl3)))

(define-public r-ggquickeda
  (package
    (name "r-ggquickeda")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggquickeda" version))
       (sha256
        (base32 "00m4qf51q40aknb20mj08n8s2fpym3qfcbcb37mpm2pdbsqgrzan"))))
    (properties `((upstream-name . "ggquickeda")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-tidyr
                             r-tibble
                             r-table1
                             r-survminer
                             r-survival
                             r-stringr
                             r-shinyjs
                             r-shinyjqui
                             r-shinyfiles
                             r-shiny
                             r-scales
                             r-rpostgres
                             r-rms
                             r-rlang
                             r-quantreg
                             r-plotly
                             r-patchwork
                             r-markdown
                             r-hmisc
                             r-gridextra
                             r-glue
                             r-ggstance
                             r-ggridges
                             r-ggrepel
                             r-ggpubr
                             r-ggpmisc
                             r-ggplot2
                             r-ggh4x
                             r-ggbeeswarm
                             r-ggally
                             r-formula
                             r-forcats
                             r-dt
                             r-dplyr
                             r-data-table
                             r-colourpicker))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/smouksassi/ggquickeda")
    (synopsis
     "Quickly Explore Your Data Using 'ggplot2' and 'table1' Summary Tables")
    (description
     "Quickly and easily perform exploratory data analysis by uploading your data as a
csv file.  Start generating insights using ggplot2 plots and table1 tables with
descriptive stats, all using an easy-to-use point and click Shiny interface.")
    (license license:expat)))

(define-public r-ggqqunif
  (package
    (name "r-ggqqunif")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggQQunif" version))
       (sha256
        (base32 "0vrxmqxy946mwdq0mb2m1ch41r0chrw7hcn18dr3mp10bv7pl7wj"))))
    (properties `((upstream-name . "ggQQunif")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-ggplot2 r-dplyr))
    (home-page "https://cran.r-project.org/package=ggQQunif")
    (synopsis "Compare Big Datasets to the Uniform Distribution")
    (description
     "This package provides a quantile-quantile plot can be used to compare a sample
of p-values to the uniform distribution.  But when the dataset is big (i.e. >
1e4 p-values), plotting the quantile-quantile plot can be slow.  geom_QQ uses
all the data to calculate the quantiles, but thins it out in a way that focuses
on points near zero before plotting to speed up plotting and decrease file size,
when vector graphics are stored.")
    (license license:gpl3)))

(define-public r-ggqc
  (package
    (name "r-ggqc")
    (version "0.0.31")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggQC" version))
       (sha256
        (base32 "1j9cs97hcj5zmqvn5rjr3gl0b9748s3pv2nb7v66dsyjdwvj7wyy"))))
    (properties `((upstream-name . "ggQC")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-ggplot2 r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ggQC")
    (synopsis "Quality Control Charts for 'ggplot'")
    (description
     "Plot single and faceted type quality control charts for ggplot'.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-ggpval
  (package
    (name "r-ggpval")
    (version "0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggpval" version))
       (sha256
        (base32 "09wjfkmsh165xiw1dab6c8qb9wrnzm1q1sbfnbzyg99wsk480bgy"))))
    (properties `((upstream-name . "ggpval")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/s6juncheng/ggpval")
    (synopsis "Annotate Statistical Tests for 'ggplot2'")
    (description
     "Automatically performs desired statistical tests (e.g. wilcox.test(), t.test())
to compare between groups, and adds the resulting p-values to the plot with an
annotation bar.  Visualizing group differences are frequently performed by
boxplots, bar plots, etc.  Statistical test results are often needed to be
annotated on these plots.  This package provides a convenient function that
works on ggplot2 objects, performs the desired statistical test between groups
of interest and annotates the test results on the plot.")
    (license license:gpl3)))

(define-public r-ggpolypath
  (package
    (name "r-ggpolypath")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggpolypath" version))
       (sha256
        (base32 "0ipn1lhmpcdxim3235kxsw6vkj7cf7smqdm534jfa75ski4q9xpy"))))
    (properties `((upstream-name . "ggpolypath")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://mdsumner.github.io/ggpolypath/")
    (synopsis "Polygons with Holes for the Grammar of Graphics")
    (description
     "This package provides tools for working with polygons with holes in ggplot2',
with a new geom for drawing a polypath applying the evenodd or winding rules.")
    (license license:gpl3)))

(define-public r-ggpolar
  (package
    (name "r-ggpolar")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggpolar" version))
       (sha256
        (base32 "18m0nd74cz30gr961404wnib5v2wddpf1iv453s5dri0vxlmdl6w"))))
    (properties `((upstream-name . "ggpolar")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ShixiangWang/polar")
    (synopsis "Dots and Their Connections in Polar Coordinate System")
    (description
     "This package provides basic graphing functions to fully demonstrate
point-to-point connections in a polar coordinate space.")
    (license license:gpl3+)))

(define-public r-ggpol
  (package
    (name "r-ggpol")
    (version "0.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggpol" version))
       (sha256
        (base32 "11xr26kwmkjjb51wm44ydv0vcinc6k6faqwx4s2faj4iwidlys1m"))))
    (properties `((upstream-name . "ggpol")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-rlang
                             r-plyr
                             r-gtable
                             r-glue
                             r-ggplot2
                             r-dplyr))
    (home-page "https://github.com/erocoar/ggpol")
    (synopsis "Visualizing Social Science Data with 'ggplot2'")
    (description
     "This package provides a ggplot2 extension for implementing parliament charts and
several other useful visualizations.")
    (license license:expat)))

(define-public r-ggpointless
  (package
    (name "r-ggpointless")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggpointless" version))
       (sha256
        (base32 "1qzd5jcac3syl9pgxzq2r7vmrp7kzd2h56xa1y6hich2rhzywva9"))))
    (properties `((upstream-name . "ggpointless")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-lifecycle r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://flrd.github.io/ggpointless/")
    (synopsis "Additional Geometries and Stats for 'ggplot2'")
    (description
     "An (aspirational) collection of additional geometries and statistics for
ggplot2'.")
    (license license:expat)))

(define-public r-ggpmx
  (package
    (name "r-ggpmx")
    (version "1.2.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggPMX" version))
       (sha256
        (base32 "04381iaa52ljpkbi4sy5xgyiv0vznd2k7vnwvsdyk4qkggxxyqw9"))))
    (properties `((upstream-name . "ggPMX")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-yaml
                             r-tidyr
                             r-stringr
                             r-scales
                             r-rmarkdown
                             r-rlang
                             r-readr
                             r-r6
                             r-purrr
                             r-magrittr
                             r-knitr
                             r-gtable
                             r-ggplot2
                             r-ggforce
                             r-ggally
                             r-dplyr
                             r-data-table
                             r-checkmate
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ggPMXdevelopment/ggPMX")
    (synopsis
     "'ggplot2' Based Tool to Facilitate Diagnostic Plots for NLME Models")
    (description
     "At Novartis, we aimed at standardizing the set of diagnostic plots used for
modeling activities in order to reduce the overall effort required for
generating such plots.  For this, we developed a guidance that proposes an
adequate set of diagnostics and a toolbox, called @code{ggPMX} to execute them.
@code{ggPMX} is a toolbox that can generate all diagnostic plots at a quality
sufficient for publication and submissions using few lines of code.  This
package focuses on plots recommended by I@code{SoP} <doi:10.1002/psp4.12161>.
While not required, you can get/install the R @code{lixoftConnectors} package in
the Monolix installation, as described at the following url
<https://monolix.lixoft.com/monolix-api/lixoftconnectors_installation/>.  When
@code{lixoftConnectors} is available, R can use Monolix directly to create the
required Chart Data instead of exporting it from the Monolix gui.")
    (license license:gpl2)))

(define-public r-ggplotlyextra
  (package
    (name "r-ggplotlyextra")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggplotlyExtra" version))
       (sha256
        (base32 "1m2rhylpzj934gb50lxjp47rxz0kwl9i1p3n7w1rz4qi96pr5p1c"))))
    (properties `((upstream-name . "ggplotlyExtra")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-plotly r-ggplot2))
    (home-page "https://cran.r-project.org/package=ggplotlyExtra")
    (synopsis "Extra Convenience Functions for 'Plotly'")
    (description
     "Convenience functions for smooth conversion from ggplot to plotly where the
conversion using ggplotly() usually gives an unexpected labels.  The package
ease the process of making a plotly figures generated from ggplot2 object more
aesthetic in terms of labels and customizability.")
    (license license:gpl3)))

(define-public r-ggplotgui
  (package
    (name "r-ggplotgui")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggplotgui" version))
       (sha256
        (base32 "1yfglg5438mwyml845isimzma5q6824x19jka509ng49bzmf1za0"))))
    (properties `((upstream-name . "ggplotgui")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-shiny
                             r-readxl
                             r-readr
                             r-rcolorbrewer
                             r-plotly
                             r-haven
                             r-ggplot2))
    (home-page "https://github.com/gertstulp/ggplotgui/")
    (synopsis "Create Ggplots via a Graphical User Interface")
    (description
     "Easily explore data by creating ggplots through a (shiny-)GUI. R-code to
recreate graph provided.")
    (license license:gpl3)))

(define-public r-ggplotassist
  (package
    (name "r-ggplotassist")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggplotAssist" version))
       (sha256
        (base32 "1g0s1dkbrxmwqjqppagdbnx24s0nbjn09xwxix3dkfssz6drhala"))))
    (properties `((upstream-name . "ggplotAssist")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyverse
                             r-tibble
                             r-stringr
                             r-shinywidgets
                             r-shinyace
                             r-shiny
                             r-scales
                             r-rstudioapi
                             r-moonbook
                             r-miniui
                             r-magrittr
                             r-ggthemes
                             r-ggplot2
                             r-gcookbook
                             r-editdata
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/cardiomoon/ggplotAssist")
    (synopsis "'RStudio' Addin for Teaching and Learning 'ggplot2'")
    (description
     "An RStudio addin for teaching and learning making plot using the ggplot2
package.  You can learn each steps of making plot by clicking your mouse without
coding.  You can get resultant code for the plot.")
    (license license:gpl3)))

(define-public r-ggplot2movies
  (package
    (name "r-ggplot2movies")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggplot2movies" version))
       (sha256
        (base32 "067ld6djxcpbliv70r2c1pp4z50rvwmn1xbvxfcqdi9s3k9a2v8q"))))
    (properties `((upstream-name . "ggplot2movies")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=ggplot2movies")
    (synopsis "Movies Data")
    (description
     "This package provides a dataset about movies.  This was previously contained in
ggplot2, but has been moved its own package to reduce the download size of
ggplot2.")
    (license license:gpl3)))

(define-public r-ggplot2-utils
  (package
    (name "r-ggplot2-utils")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggplot2.utils" version))
       (sha256
        (base32 "07svk16j9mc3d1fgkd5zpilnr82f7r40y9nachkp32s2bsg1a29g"))))
    (properties `((upstream-name . "ggplot2.utils")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-ggstats
                             r-ggpp
                             r-ggplot2
                             r-envstats
                             r-checkmate))
    (home-page "https://insightsengineering.github.io/ggplot2.utils/")
    (synopsis "Selected Utilities Extending 'ggplot2'")
    (description
     "Selected utilities, in particular geoms and stats functions, extending the
ggplot2 package.  This package imports functions from @code{EnvStats}
<doi:10.1007/978-1-4614-8456-1> by Millard (2013), ggpp
<https://CRAN.R-project.org/package=ggpp> by Aphalo et al. (2023) and ggstats
<doi:10.5281/zenodo.10183964> by Larmarange (2023), and then exports them.  This
package also contains modified code from ggquickeda
<https://CRAN.R-project.org/package=ggquickeda> by Mouksassi et al. (2023) for
Kaplan-Meier lines and ticks additions to plots.  All functions are tested to
make sure that they work reliably.")
    (license license:asl2.0)))

(define-public r-ggplate
  (package
    (name "r-ggplate")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggplate" version))
       (sha256
        (base32 "15q78hl9fngjx1xyccwzny78rg169zv10fvzl6an9silcgwrw2ak"))))
    (properties `((upstream-name . "ggplate")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-scales
                             r-rlang
                             r-purrr
                             r-ggplot2
                             r-forcats
                             r-farver
                             r-dplyr))
    (home-page "https://github.com/jpquast/ggplate")
    (synopsis
     "Create Layout Plots of Biological Culture Plates and Microplates")
    (description
     "Enables users to create simple plots of biological culture plates as well as
microplates.  Both continuous and discrete values can be plotted onto the plate
layout.")
    (license license:expat)))

(define-public r-ggpie
  (package
    (name "r-ggpie")
    (version "0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggpie" version))
       (sha256
        (base32 "11c431ggrhyxiql1v9qajp568jri6hlp1kgl334j4f7sgyh73ng5"))))
    (properties `((upstream-name . "ggpie")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-scales
                             r-rlang
                             r-rcolorbrewer
                             r-magrittr
                             r-ggrepel
                             r-ggplot2
                             r-ggnewscale
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/showteeth/ggpie")
    (synopsis "Pie, Donut and Rose Pie Plots")
    (description "Create pie, donut and rose pie plot with ggplot2'.")
    (license license:expat)))

(define-public r-ggperiodic
  (package
    (name "r-ggperiodic")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggperiodic" version))
       (sha256
        (base32 "03gsbdqqxi0j4slp61bzrn4m2sjhh9fvcy1a1h5vcg4ax233j270"))))
    (properties `((upstream-name . "ggperiodic")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect r-sticky r-ggplot2 r-dplyr
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/eliocamp/ggperiodic")
    (synopsis "Easy Plotting of Periodic Data with 'ggplot2'")
    (description
     "This package implements methods to plot periodic data in any arbitrary range on
the fly.")
    (license license:gpl3)))

(define-public r-ggpcp
  (package
    (name "r-ggpcp")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggpcp" version))
       (sha256
        (base32 "1xkxsxnxcyy8rrazzbl38g405cxjjjz6a720cdm4bsk03q2q4rar"))))
    (properties `((upstream-name . "ggpcp")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-rlang
                             r-ggplot2
                             r-dplyr
                             r-assertthat))
    (home-page "https://github.com/heike/ggpcp")
    (synopsis "Parallel Coordinate Plots in the 'ggplot2' Framework")
    (description
     "Modern Parallel Coordinate Plots have been introduced in the 1980s as a way to
visualize arbitrarily many numeric variables.  This Grammar of Graphics
implementation also incorporates categorical variables into the plots in a
principled manner.  By separating the data managing part from the visual
rendering, we give full access to the users while keeping the number of
parameters manageably low.")
    (license license:gpl3)))

(define-public r-ggpath
  (package
    (name "r-ggpath")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggpath" version))
       (sha256
        (base32 "19rsshpcgdwccv790bydvp68p4797p5d6zyqwqbahd7xa1bwmg86"))))
    (properties `((upstream-name . "ggpath")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-memoise
                             r-magick
                             r-ggplot2
                             r-cli
                             r-cachem
                             r-backports))
    (home-page "https://github.com/mrcaseb/ggpath")
    (synopsis "Robust Image Rendering Support for 'ggplot2'")
    (description
     "This package provides a ggplot2 extension that enables robust image grobs in
panels and theme elements.")
    (license license:expat)))

(define-public r-ggparty
  (package
    (name "r-ggparty")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggparty" version))
       (sha256
        (base32 "0s6hr5p930kl3pj6ajwgwqz6yikc3l9hhzy1yn0nqc0r8pp2jyqf"))))
    (properties `((upstream-name . "ggparty")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rlang
                             r-partykit
                             r-gtable
                             r-ggplot2
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/martin-borkovec/ggparty")
    (synopsis "'ggplot' Visualizations for the 'partykit' Package")
    (description
     "Extends ggplot2 functionality to the partykit package.  ggparty provides the
necessary tools to create clearly structured and highly customizable
visualizations for tree-objects of the class party'.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-ggparallel
  (package
    (name "r-ggparallel")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggparallel" version))
       (sha256
        (base32 "1flhy0731phknzxwmr6lza7a6r0mqy7j8z93p4aala3d3cg2x1nw"))))
    (properties `((upstream-name . "ggparallel")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-plyr r-ggplot2))
    (home-page "https://github.com/heike/ggparallel/")
    (synopsis "Variations of Parallel Coordinate Plots for Categorical Data")
    (description
     "Create hammock plots, parallel sets, and common angle plots with ggplot2'.")
    (license license:expat)))

(define-public r-ggpage
  (package
    (name "r-ggpage")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggpage" version))
       (sha256
        (base32 "1qi8ydq3fxb1smk0jg9z1cv2xjq8c1lsnmz8l7c61rvdbjm3710v"))))
    (properties `((upstream-name . "ggpage")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidytext
                             r-stringr
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/EmilHvitfeldt/ggpage")
    (synopsis "Creates Page Layout Visualizations")
    (description
     "Facilitates the creation of page layout visualizations in which words are
represented as rectangles with sizes relating to the length of the words.  Which
then is divided in lines and pages for easy overview of up to quite large texts.")
    (license license:expat)))

(define-public r-ggpackets
  (package
    (name "r-ggpackets")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggpackets" version))
       (sha256
        (base32 "1fdyr0m5rf1wy2pydvk12g7lw338iw026rxz98mv8kryrs32q7kh"))))
    (properties `((upstream-name . "ggpackets")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/dgkf/ggpackets")
    (synopsis "Package Plot Layers for Easier Portability and Modularization")
    (description
     "Create groups of ggplot2 layers that can be easily migrated from one plot to
another, reducing redundant code and improving the ability to format many plots
that draw from the same source ggpacket layers.")
    (license license:expat)))

(define-public r-ggoutlier
  (package
    (name "r-ggoutlier")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GGoutlieR" version))
       (sha256
        (base32 "18ass4fxdsvcn9j103dixar0grrh5w3nh6icydh1w5myrd455z9l"))))
    (properties `((upstream-name . "GGoutlieR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-sf
                             r-scales
                             r-rnaturalearth
                             r-rlang
                             r-rcolorbrewer
                             r-ggplot2
                             r-ggforce
                             r-foreach
                             r-fastknn
                             r-doparallel
                             r-cowplot))
    (home-page "https://cran.r-project.org/package=GGoutlieR")
    (synopsis "Identify Individuals with Unusual Geo-Genetic Patterns")
    (description
     "Identify and visualize individuals with unusual association patterns of genetics
and geography using the approach of Chang and Schmid (2023)
<doi:10.1101/2023.04.06.535838>.  It detects potential outliers that violate the
isolation-by-distance assumption using the K-nearest neighbor approach.  You can
obtain a table of outliers with statistics and visualize unusual geo-genetic
patterns on a geographical map.  This is useful for landscape genomics studies
to discover individuals with unusual geography and genetics associations from a
large biological sample.")
    (license license:expat)))

(define-public r-ggordiplots
  (package
    (name "r-ggordiplots")
    (version "0.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggordiplots" version))
       (sha256
        (base32 "0ncwkahwdl6mg27a7cg6363d51jqr03cxsn5ww4ainq679lw8fmc"))))
    (properties `((upstream-name . "ggordiplots")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan r-glue r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jfq3/ggordiplots")
    (synopsis "Make 'ggplot2' Versions of Vegan's Ordiplots")
    (description
     "The vegan package includes several functions for adding features to ordination
plots: ordiarrows(), ordiellipse(), ordihull(), ordispider() and ordisurf().
This package adds these same features to ordination plots made with ggplot2'.
In addition, gg_ordibubble() sizes points relative to the value of an
environmental variable.")
    (license license:gpl2+)))

(define-public r-ggokabeito
  (package
    (name "r-ggokabeito")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggokabeito" version))
       (sha256
        (base32 "07lbzq87zl2yswidp0f90l8szl7y4wwn8psgacyl90wn2gd9mdcp"))))
    (properties `((upstream-name . "ggokabeito")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://github.com/malcolmbarrett/ggokabeito")
    (synopsis "'Okabe-Ito' Scales for 'ggplot2' and 'ggraph'")
    (description
     "Discrete scales for the colorblind-friendly Okabe-Ito palette, including color',
fill', and edge_colour'.  ggokabeito provides ggplot2 and ggraph scales to
easily use the Okabe-Ito palette in your data visualizations.")
    (license license:expat)))

(define-public r-ggoceanmaps
  (package
    (name "r-ggoceanmaps")
    (version "2.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggOceanMaps" version))
       (sha256
        (base32 "1wrr92srwrg7fx0iz4qnmn89mhhwfbrx5z2rk8vfy3clh8i65x23"))))
    (properties `((upstream-name . "ggOceanMaps")))
    (build-system r-build-system)
    (propagated-inputs (list r-units r-stars r-smoothr r-sf r-ggplot2))
    (home-page "https://mikkovihtakari.github.io/ggOceanMaps/")
    (synopsis "Plot Data on Oceanographic Maps using 'ggplot2'")
    (description
     "Allows plotting data on bathymetric maps using ggplot2'.  Plotting oceanographic
spatial data is made as simple as feasible, but also flexible for custom
modifications.  Data that contain geographic information from anywhere around
the globe can be plotted on maps generated by the basemap() or qmap() functions
using ggplot2 layers separated by the + operator.  The package uses spatial
shape- ('sf') and raster ('stars') files, geospatial packages for R to
manipulate, and the ggplot2 package to plot these files.  The package ships with
low-resolution spatial data files and higher resolution files for detailed maps
are stored in the @code{ggOceanMapsLargeData} repository on @code{GitHub} and
downloaded automatically when needed.")
    (license license:gpl3)))

(define-public r-ggnuplot
  (package
    (name "r-ggnuplot")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggnuplot" version))
       (sha256
        (base32 "0i09ybmxdjyr15z1a651k3bkbqskri4ipcyjap93020zbfv05r9c"))))
    (properties `((upstream-name . "ggnuplot")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://github.com/hriebl/ggnuplot")
    (synopsis "Make 'ggplot2' Look Like 'gnuplot'")
    (description
     "This package provides a theme, a discrete color palette, and continuous scales
to make ggplot2 look like gnuplot'.  This may be helpful if you use both ggplot2
and gnuplot in one project.")
    (license license:expat)))

(define-public r-ggnormalviolin
  (package
    (name "r-ggnormalviolin")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggnormalviolin" version))
       (sha256
        (base32 "1gbv2b86nznw5bs05c4ns96b8hsxkhikhcf8syl2qbbafzy8vx6y"))))
    (properties `((upstream-name . "ggnormalviolin")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-magrittr r-ggplot2 r-dplyr))
    (home-page "https://github.com/wjschne/ggnormalviolin")
    (synopsis "'ggplot2' Extension to Make Normal Violin Plots")
    (description
     "Uses ggplot2 to create normally distributed violin plots with specified means
and standard deviations.  This function can be useful in showing hypothetically
normal distributions and confidence intervals.")
    (license license:cc0)))

(define-public r-ggmulti
  (package
    (name "r-ggmulti")
    (version "1.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggmulti" version))
       (sha256
        (base32 "071h76imfsm6p9ml9pkfsar3d6z1nawd9m7f82smy4csb58iz5i3"))))
    (properties `((upstream-name . "ggmulti")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-ggplot2 r-dplyr r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ggmulti")
    (synopsis "High Dimensional Data Visualization")
    (description
     "It provides materials (i.e.  serial axes objects, Andrew's plot, various glyphs
for scatter plot) to visualize high dimensional data.")
    (license license:gpl2)))

(define-public r-ggmuller
  (package
    (name "r-ggmuller")
    (version "0.5.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggmuller" version))
       (sha256
        (base32 "069y6wf9lbkz32kmkrc61vjc0p9fpr5191103dvaz0nj6gpd686a"))))
    (properties `((upstream-name . "ggmuller")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-dplyr r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ggmuller")
    (synopsis "Create Muller Plots of Evolutionary Dynamics")
    (description
     "Create plots that combine a phylogeny and frequency dynamics.  Phylogenetic
input can be a generic adjacency matrix or a tree of class \"phylo\".  Inspired by
similar plots in publications of the labs of RE Lenski and JE Barrick.  Named
for HJ Muller (who popularised such plots) and H Wickham (whose code this
package exploits).")
    (license license:expat)))

(define-public r-ggmugs
  (package
    (name "r-ggmugs")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggmugs" version))
       (sha256
        (base32 "1qky7ywk8np24hgn1lj4mpd4snwlvngx8ix7ack4gxblixl9s0z0"))))
    (properties `((upstream-name . "ggmugs")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-purrr
                             r-ggplot2
                             r-dplyr
                             r-data-table))
    (home-page "https://cran.r-project.org/package=ggmugs")
    (synopsis
     "Visualization of Multiple Genome-Wide Association Study Summary Statistics")
    (description
     "This package provides a grammar of graphics approach for visualizing summary
statistics from multiple Genome-wide Association Studies (GWAS).  It offers
geneticists, bioinformaticians, and researchers a powerful yet flexible tool for
illustrating complex genetic associations using data from various GWAS datasets.
 The visualizations can be extensively customized, facilitating detailed
comparative analysis across different genetic studies.  Reference: Uffelmann, E.
et al. (2021) <doi:10.1038/s43586-021-00056-9>.")
    (license license:expat)))

(define-public r-ggmselect
  (package
    (name "r-ggmselect")
    (version "0.1-12.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GGMselect" version))
       (sha256
        (base32 "1l90viinjfbjwl7229aqbzv72d0jgjbcidv0ll770qsd1hc1p70r"))))
    (properties `((upstream-name . "GGMselect")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-lars r-gtools))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=GGMselect")
    (synopsis "Gaussian Graphs Models Selection")
    (description
     "Graph estimation in Gaussian Graphical Models, following the method developed by
C. Giraud, S. Huet and N. Verzelen (2012) <doi:10.1515/1544-6115.1625>.  The
main functions return the adjacency matrix of an undirected graph estimated from
a data matrix.")
    (license license:gpl3+)))

(define-public r-ggmridge
  (package
    (name "r-ggmridge")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GGMridge" version))
       (sha256
        (base32 "179g82w7vrbkvq0b8bcaakcjhqz1gsyc2qyh70ldglbj0sj5yh4m"))))
    (properties `((upstream-name . "GGMridge")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-mass))
    (home-page "https://cran.r-project.org/package=GGMridge")
    (synopsis
     "Gaussian Graphical Models Using Ridge Penalty Followed by Thresholding and Reestimation")
    (description
     "Estimation of partial correlation matrix using ridge penalty followed by
thresholding and reestimation.  Under multivariate Gaussian assumption, the
matrix constitutes an Gaussian graphical model (GGM).")
    (license license:gpl2)))

(define-public r-ggmr
  (package
    (name "r-ggmr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggmr" version))
       (sha256
        (base32 "0941rvh7m5knlhp0m0mhm4j8spmch0pyvwhzs3bycq3vzlvzv1w7"))))
    (properties `((upstream-name . "ggmr")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=ggmr")
    (synopsis "Generalized Gauss Markov Regression")
    (description
     "This package implements the generalized Gauss Markov regression, this is useful
when both predictor and response have uncertainty attached to them and also when
covariance within the predictor, within the response and between the predictor
and the response is present.  Base on the results published in guide ISO/TS
28037 (2010) <https://www.iso.org/standard/44473.html>.")
    (license license:gpl2+)))

(define-public r-ggmnonreg
  (package
    (name "r-ggmnonreg")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GGMnonreg" version))
       (sha256
        (base32 "1bhqk172ddc7xq1hqj1i6q8np6jvmf2xypvd8kp3w1bcsafsbms8"))))
    (properties `((upstream-name . "GGMnonreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-sna
                             r-rdpack
                             r-psych
                             r-poibin
                             r-network
                             r-matrix
                             r-mass
                             r-ggplot2
                             r-ggmncv
                             r-ggally
                             r-foreach
                             r-doparallel
                             r-corpcor
                             r-bestglm))
    (home-page "https://cran.r-project.org/package=GGMnonreg")
    (synopsis "Non-Regularized Gaussian Graphical Models")
    (description
     "Estimate non-regularized Gaussian graphical models, Ising models, and mixed
graphical models.  The current methods consist of multiple regression, a
non-parametric bootstrap <doi:10.1080/00273171.2019.1575716>, and Fisher z
transformed partial correlations <doi:10.1111/bmsp.12173>.  Parameter
uncertainty, predictability, and network replicability
<doi:10.31234/osf.io/fb4sa> are also implemented.")
    (license license:gpl2)))

(define-public r-ggmncv
  (package
    (name "r-ggmncv")
    (version "2.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GGMncv" version))
       (sha256
        (base32 "0lih9dyb5aqrawhiy2vhv63b132hhzq6zq0x6rbrhy8kbkldhigr"))))
    (properties `((upstream-name . "GGMncv")))
    (build-system r-build-system)
    (propagated-inputs (list r-sna
                             r-reshape
                             r-rdpack
                             r-rcpparmadillo
                             r-rcpp
                             r-pbapply
                             r-numderiv
                             r-network
                             r-mathjaxr
                             r-mass
                             r-glassofast
                             r-ggplot2
                             r-ggally))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GGMncv")
    (synopsis "Gaussian Graphical Models with Nonconvex Regularization")
    (description
     "Estimate Gaussian graphical models with nonconvex penalties
<doi:10.31234/osf.io/ad57p>, including the atan Wang and Zhu (2016)
<doi:10.1155/2016/6495417>, seamless L0 Dicker, Huang, and Lin (2013)
<doi:10.5705/ss.2011.074>, exponential Wang, Fan, and Zhu
<doi:10.1007/s10463-016-0588-3>, smooth integration of counting and absolute
deviation Lv and Fan (2009) <doi:10.1214/09-AOS683>, logarithm Mazumder,
Friedman, and Hastie (2011) <doi:10.1198/jasa.2011.tm09738>, Lq, smoothly
clipped absolute deviation Fan and Li (2001) <doi:10.1198/016214501753382273>,
and minimax concave penalty Zhang (2010) <doi:10.1214/09-AOS729>.  There are
also extensions for computing variable inclusion probabilities, multiple
regression coefficients, and statistical inference <doi:10.1214/15-EJS1031>.")
    (license license:gpl2)))

(define-public r-ggmix
  (package
    (name "r-ggmix")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggmix" version))
       (sha256
        (base32 "158pzmr4vmy2qxmp3nrabjgkg11mrdvl1skzxgwida2is900f7gf"))))
    (properties `((upstream-name . "ggmix")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-mass r-glmnet))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sahirbhatnagar/ggmix")
    (synopsis "Variable Selection in Linear Mixed Models for SNP Data")
    (description
     "Fit penalized multivariable linear mixed models with a single random effect to
control for population structure in genetic association studies.  The goal is to
simultaneously fit many genetic variants at the same time, in order to select
markers that are independently associated with the response.  Can also handle
prior annotation information, for example, rare variants, in the form of
variable weights.  For more information, see the website below and the
accompanying paper: Bhatnagar et al., \"Simultaneous SNP selection and adjustment
for population structure in high dimensional prediction models\", 2020,
<DOI:10.1371/journal.pgen.1008766>.")
    (license license:expat)))

(define-public r-ggmice
  (package
    (name "r-ggmice")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggmice" version))
       (sha256
        (base32 "14fb8889fqx3r0rky7rfnagfrrlhspj74zlp7wz7wj232y3j923h"))))
    (properties `((upstream-name . "ggmice")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-stringr
                             r-rlang
                             r-purrr
                             r-mice
                             r-magrittr
                             r-ggplot2
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/amices/ggmice")
    (synopsis "Visualizations for 'mice' with 'ggplot2'")
    (description
     "Enhance a mice imputation workflow with visualizations for incomplete and/or
imputed data.  The plotting functions produce ggplot objects which may be easily
manipulated or extended.  Use ggmice to inspect missing data, develop imputation
models, evaluate algorithmic convergence, or compare observed versus imputed
data.")
    (license license:gpl3+)))

(define-public r-ggmcmc
  (package
    (name "r-ggmcmc")
    (version "1.5.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggmcmc" version))
       (sha256
        (base32 "1y6ya2qihm8h6ds84akhmsxn9c8jriw03wakaqzsmi21agz87n21"))))
    (properties `((upstream-name . "ggmcmc")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-ggplot2 r-ggally r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "http://xavier-fim.net/packages/ggmcmc/")
    (synopsis "Tools for Analyzing MCMC Simulations from Bayesian Inference")
    (description
     "This package provides tools for assessing and diagnosing convergence of Markov
Chain Monte Carlo simulations, as well as for graphically display results from
full MCMC analysis.  The package also facilitates the graphical interpretation
of models by providing flexible functions to plot the results against observed
variables, and functions to work with hierarchical/multilevel batches of
parameters (FernÃ¡ndez-i-MarÃ­n, 2016 <doi:10.18637/jss.v070.i09>).")
    (license license:gpl2)))

(define-public r-ggmatplot
  (package
    (name "r-ggmatplot")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggmatplot" version))
       (sha256
        (base32 "0k062kqa9hrq1iyjwjj670sdk559zx1nxpbkk0sxkda2h235lxjr"))))
    (properties `((upstream-name . "ggmatplot")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/xuan-liang/ggmatplot")
    (synopsis
     "Plot Columns of Two Matrices Against Each Other Using 'ggplot2'")
    (description
     "This package provides a quick and easy way of plotting the columns of two
matrices or data frames against each other using ggplot2'.  Although ggmatplot
doesn't provide the same flexibility as ggplot2', it can be used as a workaround
for having to wrangle wide format data into long format for plotting with
ggplot2'.")
    (license license:gpl3)))

(define-public r-ggmapinset
  (package
    (name "r-ggmapinset")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggmapinset" version))
       (sha256
        (base32 "1cakn2iqjk117ngb0ssc68hxx8s4mgc8z8bl6mfvwffvjsyjyxyp"))))
    (properties `((upstream-name . "ggmapinset")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-rlang r-ggplot2 r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/cidm-ph/ggmapinset")
    (synopsis "Add Inset Panels to Maps")
    (description
     "Helper to add insets based on geom_sf() from ggplot2'.  This package gives you a
drop-in replacement for geom_sf() that supports adding a zoomed inset map
without having to create and embed a separate plot.")
    (license license:expat)))

(define-public r-gglorenz
  (package
    (name "r-gglorenz")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gglorenz" version))
       (sha256
        (base32 "1kag8p2chd0sbn8affpxzi4aawpm71ls97dwdgx7mxxfwkdb2q0y"))))
    (properties `((upstream-name . "gglorenz")))
    (build-system r-build-system)
    (propagated-inputs (list r-ineq r-ggplot2))
    (home-page "https://github.com/jjchern/gglorenz")
    (synopsis "Plotting Lorenz Curve with the Blessing of 'ggplot2'")
    (description
     "This package provides statistical transformations for plotting empirical
ordinary Lorenz curve (Lorenz 1905) <doi:10.2307/2276207> and generalized Lorenz
curve (Shorrocks 1983) <doi:10.2307/2554117>.")
    (license license:expat)))

(define-public r-gglm
  (package
    (name "r-gglm")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gglm" version))
       (sha256
        (base32 "1fybwxgsdwvl4d9ixicfgr1sgrzkwndmvp91zhhfhjlf0yy4qdaj"))))
    (properties `((upstream-name . "gglm")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-patchwork
                             r-nlme
                             r-metafor
                             r-ggplot2
                             r-dplyr
                             r-broom-mixed
                             r-broom))
    (home-page "https://github.com/graysonwhite/gglm")
    (synopsis "Grammar of Graphics for Linear Model Diagnostic Plots")
    (description
     "Allows for easy creation of diagnostic plots for a variety of model objects
using the Grammar of Graphics.  Provides functionality for both individual
diagnostic plots and an array of four standard diagnostic plots.")
    (license license:cc0)))

(define-public r-gglgbtq
  (package
    (name "r-gglgbtq")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gglgbtq" version))
       (sha256
        (base32 "1971cjn3xyn5wf2g9a8in05w3wjpvyql7928nkm3gxw23caczpd1"))))
    (properties `((upstream-name . "gglgbtq")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-rlang r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/turtletopia/gglgbtq")
    (synopsis "Show Pride on 'ggplot2' Plots")
    (description
     "This package provides multiple palettes based on pride flags with tailored
themes.")
    (license license:gpl3+)))

(define-public r-gglasso
  (package
    (name "r-gglasso")
    (version "1.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gglasso" version))
       (sha256
        (base32 "1gasfhwp74akmqk8c534iyag61r9jznalcjcpgvacnmbnf2irrif"))))
    (properties `((upstream-name . "gglasso")))
    (build-system r-build-system)
    (native-inputs (list r-knitr gfortran))
    (home-page "https://github.com/emeryyi/gglasso")
    (synopsis "Group Lasso Penalized Learning Using a Unified BMD Algorithm")
    (description
     "This package provides a unified algorithm, blockwise-majorization-descent (BMD),
for efficiently computing the solution paths of the group-lasso penalized least
squares, logistic regression, Huberized SVM and squared SVM. The package is an
implementation of Yang, Y. and Zou, H. (2015) DOI:
<doi:10.1007/s11222-014-9498-5>.")
    (license license:gpl2)))

(define-public r-ggisotonic
  (package
    (name "r-ggisotonic")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggisotonic" version))
       (sha256
        (base32 "1jz1qhx51gzq6lbk24pjxc55nhzc6alsy9h870jn88mn9fpx4lhp"))))
    (properties `((upstream-name . "ggisotonic")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-fdrtool r-dplyr))
    (home-page "https://github.com/talegari/ggisotonic")
    (synopsis "'ggplot2' Friendly Isotonic or Monotonic Regression Curves")
    (description
     "This package provides stat_isotonic() to add weighted univariate isotonic
regression curves.")
    (license license:gpl3)))

(define-public r-ggirread
  (package
    (name "r-ggirread")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GGIRread" version))
       (sha256
        (base32 "0kad21xqwb2c4phg4mq7480qvki5frn2c2slfbcfj9paxxl4ha16"))))
    (properties `((upstream-name . "GGIRread")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-matlab r-bitops))
    (home-page "https://github.com/wadpac/GGIRread/")
    (synopsis "Wearable Accelerometer Data File Readers")
    (description
     "Reads data collected from wearable acceleratometers as used in sleep and
physical activity research.  Currently supports file formats: binary data from
GENEActiv <https://activinsights.com/>, .bin-format from GENEA devices (not for
sale), and .cwa-format from Axivity <https://axivity.com>.  Primarily designed
to complement R package GGIR <https://CRAN.R-project.org/package=GGIR>.")
    (license license:asl2.0)))

(define-public r-ggiraphextra
  (package
    (name "r-ggiraphextra")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggiraphExtra" version))
       (sha256
        (base32 "0q8ppixw5i1hpphmswpw6lvq7gx8rx4gfs2b8d31wbn32ziiv2rk"))))
    (properties `((upstream-name . "ggiraphExtra")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-sjmisc
                             r-sjlabelled
                             r-scales
                             r-reshape2
                             r-rcolorbrewer
                             r-purrr
                             r-ppcor
                             r-plyr
                             r-mycor
                             r-mgcv
                             r-magrittr
                             r-ggplot2
                             r-ggiraph
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/cardiomoon/ggiraphExtra")
    (synopsis
     "Make Interactive 'ggplot2'. Extension to 'ggplot2' and 'ggiraph'")
    (description
     "Collection of functions to enhance ggplot2 and ggiraph'.  Provides functions for
exploratory plots.  All plot can be a static plot or an interactive plot using
ggiraph'.")
    (license license:gpl3)))

(define-public r-ggiraph
  (package
    (name "r-ggiraph")
    (version "0.8.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggiraph" version))
       (sha256
        (base32 "17vdw9xaizcdf6fcbyhz9l514kk4w4dz9yvikbz9bvfx93a9z5ps"))))
    (properties `((upstream-name . "ggiraph")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (inputs (list zlib libpng))
    (propagated-inputs (list r-vctrs
                             r-uuid
                             r-systemfonts
                             r-rlang
                             r-rcpp
                             r-purrr
                             r-htmlwidgets
                             r-htmltools
                             r-ggplot2
                             r-cli))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://davidgohel.github.io/ggiraph/")
    (synopsis "Make 'ggplot2' Graphics Interactive")
    (description "Create interactive ggplot2 graphics using htmlwidgets'.")
    (license license:gpl3)))

(define-public r-ggir
  (package
    (name "r-ggir")
    (version "3.1-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GGIR" version))
       (sha256
        (base32 "07d64kq788w4xsy51f0g5iw0cwknk106fs2qywfrq1bc29mm1lmj"))))
    (properties `((upstream-name . "GGIR")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-unisensr
                             r-signal
                             r-read-gt3x
                             r-psych
                             r-lubridate
                             r-irr
                             r-ineq
                             r-ggirread
                             r-foreach
                             r-doparallel
                             r-data-table
                             r-actcr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/wadpac/GGIR/")
    (synopsis "Raw Accelerometer Data Analysis")
    (description
     "This package provides a tool to process and analyse data collected with wearable
raw acceleration sensors as described in Migueles and colleagues (JMPB 2019),
and van Hees and colleagues (J@code{ApplPhysiol} 2014; P@code{LoSONE} 2015).
The package has been developed and tested for binary data from GENEActiv
<https://activinsights.com/>, binary (.gt3x) and .csv-export data from Actigraph
<https://theactigraph.com> devices, and binary (.cwa) and .csv-export data from
Axivity <https://axivity.com>.  These devices are currently widely used in
research on human daily physical activity.  Further, the package can handle
accelerometer data file from any other sensor brand providing that the data is
stored in csv format.  Also the package allows for external function embedding.")
    (license (list license:asl2.0
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-ggip
  (package
    (name "r-ggip")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggip" version))
       (sha256
        (base32 "0mg3p3lq3afw96ddk89v62jjd8ii7nhdcwlmdnmdxrw6awcmvyhk"))))
    (properties `((upstream-name . "ggip")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidyr
                             r-rlang
                             r-rcpp
                             r-ipaddress
                             r-ggplot2
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://davidchall.github.io/ggip/")
    (synopsis "Data Visualization for IP Addresses and Networks")
    (description
     "This package provides a ggplot2 extension that enables visualization of IP
(Internet Protocol) addresses and networks.  The address space is mapped onto
the Cartesian coordinate system using a space-filling curve.  Offers full
support for both IPv4 and IPv6 (Internet Protocol versions 4 and 6) address
spaces.")
    (license license:expat)))

(define-public r-gginnards
  (package
    (name "r-gginnards")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gginnards" version))
       (sha256
        (base32 "0xhnn0kcmlggyx50dv4avhs4ngfnya2m928dqpix8km4c3y140qg"))))
    (properties `((upstream-name . "gginnards")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-stringr r-rlang r-magrittr r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://www.r4photobiology.info")
    (synopsis "Explore the Innards of 'ggplot2' Objects")
    (description
     "Extensions to ggplot2 providing low-level debug tools: statistics and geometries
echoing their data argument.  Layer manipulation: deletion, insertion,
extraction and reordering of layers.  Deletion of unused variables from the data
object embedded in \"ggplot\" objects.")
    (license license:gpl2+)))

(define-public r-gginference
  (package
    (name "r-gginference")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gginference" version))
       (sha256
        (base32 "1r960cglnkii8xih4r1r7wd857jqn53f08zi9dnj54bpqrs374zx"))))
    (properties `((upstream-name . "gginference")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-ggplot2))
    (home-page "https://github.com/okgreece/gginference")
    (synopsis
     "Visualise the Results of Inferential Statistics using 'ggplot2'")
    (description
     "Visualise the results of F test to compare two variances, Student's t-test, test
of equal or given proportions, Pearson's chi-squared test for count data and
test for association/correlation between paired samples.")
    (license (list license:gpl2
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-ggimg
  (package
    (name "r-ggimg")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggimg" version))
       (sha256
        (base32 "1ynfq8lwlw851j00k7mm63w5yyqkd7v67fs8g2nwxk7ac0wb44ph"))))
    (properties `((upstream-name . "ggimg")))
    (build-system r-build-system)
    (propagated-inputs (list r-png r-jpeg r-ggplot2 r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/statsmaths/ggimg")
    (synopsis "Graphics Layers for Plotting Image Data with 'ggplot2'")
    (description
     "This package provides two new layer types for displaying image data as layers
within the Grammar of Graphics framework.  Displays images using either a
rectangle interface, with a fixed bounding box, or a point interface using a
central point and general size parameter.  Images can be given as local JPEG or
PNG files, external resources, or as a list column containing raster image data.")
    (license license:gpl2)))

(define-public r-ggimage
  (package
    (name "r-ggimage")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggimage" version))
       (sha256
        (base32 "0jm316dqnnnsifrwkh6q2xs07dqazaa77s713pr7h94q1r8ab1bs"))))
    (properties `((upstream-name . "ggimage")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-tibble
                             r-scales
                             r-magick
                             r-jsonlite
                             r-ggplotify
                             r-ggplot2
                             r-ggfun))
    (home-page "https://github.com/GuangchuangYu/ggimage")
    (synopsis "Use Image in 'ggplot2'")
    (description
     "Supports image files and graphic objects to be visualized in ggplot2 graphic
system.")
    (license license:artistic2.0)))

(define-public r-gghoriplot
  (package
    (name "r-gghoriplot")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggHoriPlot" version))
       (sha256
        (base32 "1sb567qrqawks9z4zhlfgyx6pcgsvhki3rafppj969xzjh59w6b7"))))
    (properties `((upstream-name . "ggHoriPlot")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-magrittr
                             r-glue
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://rivasiker.github.io/ggHoriPlot/")
    (synopsis "Horizon Plots for 'ggplot2'")
    (description
     "This package provides a user-friendly, highly customizable R package for
building horizon plots in the ggplot2 environment.")
    (license license:gpl3)))

(define-public r-gghist
  (package
    (name "r-gghist")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gghist" version))
       (sha256
        (base32 "0ncxx2ckc78gaa9dyddyli1wca7kkz33v13mplhnxd33gkk9pxmz"))))
    (properties `((upstream-name . "gghist")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://github.com/frederikziebell/gghist")
    (synopsis "Plot the Histogram of a Numeric Vector")
    (description
     "Wrapper around geom_histogram() of ggplot2 to plot the histogram of a numeric
vector.  This is especially useful, since qplot() was deprecated in ggplot2
3.4.0.")
    (license license:gpl3+)))

(define-public r-gghilbertstrings
  (package
    (name "r-gghilbertstrings")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gghilbertstrings" version))
       (sha256
        (base32 "0b3xp1w6fkkmjh7gihhfkp4npx553a75bvsal3w4j57xa34l8rap"))))
    (properties `((upstream-name . "gghilbertstrings")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-rlang
                             r-rcpp
                             r-magrittr
                             r-lifecycle
                             r-ggplot2
                             r-dplyr))
    (home-page "https://github.com/Sumidu/gghilbertstrings")
    (synopsis "Fast 'ggplot2'-Based Implementation of Hilbert Curves")
    (description
     "This package provides a set of functions that help to create plots based on
Hilbert curves.  Hilbert curves are used to map one dimensional data into the 2D
plane.  The package provides a function that generate a 2D coordinate from an
integer position.  As a specific use case the package provides a function that
allows mapping a character column in a data frame into 2D space using ggplot2'.
This allows visually comparing long lists of URLs, words, genes or other data
that has a fixed order and position.")
    (license license:expat)))

(define-public r-ggheatmap
  (package
    (name "r-ggheatmap")
    (version "2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggheatmap" version))
       (sha256
        (base32 "13cf3xk5drb6dfr3gcbgfi0hk6akk9b3g9f3c2jy6hz8rz5xnyn6"))))
    (properties `((upstream-name . "ggheatmap")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-patchwork
                             r-ggpubr
                             r-ggplot2
                             r-factoextra
                             r-aplot))
    (home-page "https://cran.r-project.org/package=ggheatmap")
    (synopsis "Plot Heatmap")
    (description
     "The flexibility and excellence of ggplot2 is unquestionable, so many drawing
tools basically need ggplot2 as the operating object.  In order to develop a
heatmap drawing system based on ggplot2, we developed this tool, mainly to solve
the heatmap puzzle problem and the flexible connection between the heatmap and
the ggplot2 object.  The advantages of this tool are as follows: 1.  More
flexible label settings; 2.  Realize the linkage of heatmap and ggplot2 drawing
system, which is helpful for operations such as puzzles; 3.  Simple and easy to
operate; 4.  Optimization of clustering tree visualization.")
    (license license:gpl3)))

(define-public r-gghdx
  (package
    (name "r-gghdx")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gghdx" version))
       (sha256
        (base32 "0lsa84afs31jch7wgamsg721wyac64qm5wxk12iiwkyxcmlv7234"))))
    (properties `((upstream-name . "gghdx")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-sysfonts
                             r-showtext
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-lifecycle
                             r-ggthemes
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/OCHA-DAP/gghdx")
    (synopsis "HDX Theme, Scales, and Other Conveniences for 'ggplot2'")
    (description
     "This package provides a Humanitarian Data Exchange (HDX) theme, color palettes,
and scales for ggplot2 to allow users to easily follow the HDX visual design
guide, including convenience functions for for loading and using the Source Sans
3 font.")
    (license license:gpl3+)))

(define-public r-gghalfnorm
  (package
    (name "r-gghalfnorm")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gghalfnorm" version))
       (sha256
        (base32 "1sy0m6pqmnjbqv60rljyblhis0dxwkhw751jhlad5arcgrcwf4k8"))))
    (properties `((upstream-name . "gghalfnorm")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggrepel r-ggplot2))
    (home-page "https://github.com/nathaneastwood/gghalfnorm")
    (synopsis "Create a Half Normal Plot Using 'ggplot2'")
    (description
     "Reproduce the halfnorm() function found in the faraway package using the ggplot2
API.")
    (license license:agpl3)))

(define-public r-gggrid
  (package
    (name "r-gggrid")
    (version "0.2-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gggrid" version))
       (sha256
        (base32 "0g8msi5qpnr2npcgjvdjncrq6p61ddpzasy5g0fxqbybbpfxsyi8"))))
    (properties `((upstream-name . "gggrid")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/pmur002/gggrid")
    (synopsis "Draw with 'grid' in 'ggplot2'")
    (description
     "An extension of ggplot2 that makes it easy to add raw grid output, such as
customised annotations, to a ggplot2 plot.")
    (license license:gpl3)))

(define-public r-gggibbous
  (package
    (name "r-gggibbous")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gggibbous" version))
       (sha256
        (base32 "1sy9d9hl04b13yz4563bzsdznjdh3xmikdrsqg8kdv8wn481lqbd"))))
    (properties `((upstream-name . "gggibbous")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mnbram/gggibbous")
    (synopsis "Moon Charts, a Pie Chart Alternative")
    (description
     "Moon charts are like pie charts except that the proportions are shown as
crescent or gibbous portions of a circle, like the lit and unlit portions of the
moon.  As such, they work best with only one or two groups.  gggibbous extends
ggplot2 to allow for plotting multiple moon charts in a single panel and does
not require a square coordinate system.")
    (license license:gpl3)))

(define-public r-gggenomes
  (package
    (name "r-gggenomes")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gggenomes" version))
       (sha256
        (base32 "1imahf69d17l7fk8j3jkiwlhbbc71yvdvp4q440w2mn4pnbzf6xx"))))
    (properties `((upstream-name . "gggenomes")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-snakecase
                             r-scales
                             r-rlang
                             r-readr
                             r-purrr
                             r-magrittr
                             r-jsonlite
                             r-ggplot2
                             r-ellipsis
                             r-dplyr
                             r-colorspace))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/thackl/gggenomes")
    (synopsis "Grammar of Graphics for Comparative Genomics")
    (description
     "An extension of ggplot2 for creating complex genomic maps.  It builds on the
power of ggplot2 and tidyverse adding new ggplot2'-style geoms & positions and
dplyr'-style verbs to manipulate the underlying data.  It implements a layout
concept inspired by ggraph and introduces tracks to bring tidiness to the mess
that is genomics data.")
    (license license:expat)))

(define-public r-gggenes
  (package
    (name "r-gggenes")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gggenes" version))
       (sha256
        (base32 "08s0fhk2kz3ks64nfmdbd8p3swkn62cbw7dpbqgk9ydpmh5gpjbj"))))
    (properties `((upstream-name . "gggenes")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-ggplot2 r-ggfittext))
    (native-inputs (list r-knitr))
    (home-page "https://wilkox.org/gggenes/")
    (synopsis "Draw Gene Arrow Maps in 'ggplot2'")
    (description
     "This package provides a ggplot2 extension for drawing gene arrow maps.")
    (license license:gpl2)))

(define-public r-gggap
  (package
    (name "r-gggap")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gggap" version))
       (sha256
        (base32 "1iidxm7qcrg0isw2q27cmjbfb3pkfj5jcg1nj8lgy6xmydw3vrw3"))))
    (properties `((upstream-name . "gggap")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-cowplot))
    (home-page "https://github.com/cmoralesmx/gggap")
    (synopsis
     "Streamlined Creation of Segments on the Y-Axis of 'ggplot2' Plots")
    (description
     "The function gggap() streamlines the creation of segments on the y-axis of
ggplot2 plots which is otherwise not a trivial task to accomplish.")
    (license license:gpl3)))

(define-public r-ggfx
  (package
    (name "r-ggfx")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggfx" version))
       (sha256
        (base32 "1lys5lzlilzvd6dm7rkxv7nvnp80fajl5yzhpwcbb5az5832ik27"))))
    (properties `((upstream-name . "ggfx")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-ragg r-magick r-gtable r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://ggfx.data-imaginist.com")
    (synopsis "Pixel Filters for 'ggplot2' and 'grid'")
    (description
     "This package provides a range of filters that can be applied to layers from the
ggplot2 package and its extensions, along with other graphic elements such as
guides and theme elements.  The filters are applied at render time and thus uses
the exact pixel dimensions needed.")
    (license license:expat)))

(define-public r-ggfoundry
  (package
    (name "r-ggfoundry")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggfoundry" version))
       (sha256
        (base32 "0m7r1845vx3snxadqbg3vxyxsh115h7bi5h3q3x1n4c1nd5y45r7"))))
    (properties `((upstream-name . "ggfoundry")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-lifecycle r-grimport2 r-ggplot2 r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/cgoo4/ggfoundry")
    (synopsis "Shape Foundry & Geom for 'ggplot2'")
    (description
     "This package provides a ggplot2 extension that supports arbitrary hand-crafted
colourable & fillable shapes.  New shapes may be feature requested via a Github
issue.")
    (license license:expat)))

(define-public r-ggfocus
  (package
    (name "r-ggfocus")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggfocus" version))
       (sha256
        (base32 "03q0rz7flm8sc8hrlkcm5fz66qiyxb8z8q4bh9dglhw0jc8q1fvb"))))
    (properties `((upstream-name . "ggfocus")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-rcolorbrewer r-magrittr r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Freguglia/ggfocus")
    (synopsis "Scales that Focus Specific Levels in your ggplot()")
    (description
     "This package provides a ggplot2 extension that provides tools for automatically
creating scales to focus on subgroups of the data plotted without losing other
information.")
    (license license:gpl3)))

(define-public r-ggflowchart
  (package
    (name "r-ggflowchart")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggflowchart" version))
       (sha256
        (base32 "0n93ydnj4q95a67ch2i3yjg7f05vi2xdb4jss620ccaj33chwqzv"))))
    (properties `((upstream-name . "ggflowchart")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rlang
                             r-igraph
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://nrennie.github.io/ggflowchart/")
    (synopsis "Flowcharts with 'ggplot2'")
    (description
     "Flowcharts can be a useful way to visualise complex processes.  This package
uses the layered grammar of graphics of ggplot2 to create simple flowcharts.")
    (license license:expat)))

(define-public r-ggfixest
  (package
    (name "r-ggfixest")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggfixest" version))
       (sha256
        (base32 "0i05lvm45kp8l2shxlznlk5yw96rzcilwmm9i9194x0zxz29q5nf"))))
    (properties `((upstream-name . "ggfixest")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-marginaleffects
                             r-ggplot2
                             r-ggh4x
                             r-fixest
                             r-dreamerr))
    (native-inputs (list r-knitr))
    (home-page "http://grantmcdermott.com/ggfixest/")
    (synopsis "Dedicated 'ggplot2' Methods for 'fixest' Objects")
    (description
     "This package provides ggplot2 equivalents of fixest::coefplot() and
fixest::iplot(), for producing nice coefficient plots and interaction plots.
Enables some additional functionality and convenience features, including
grouped multi-'fixest object faceting and programmatic updates to existing plots
(e.g., themes and aesthetics).")
    (license license:gpl3)))

(define-public r-ggfishplots
  (package
    (name "r-ggfishplots")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggFishPlots" version))
       (sha256
        (base32 "12p3ldbfxp51sqswms9zk6chmi8qy0ncv3l2bh9i6b1xhjcx86b2"))))
    (properties `((upstream-name . "ggFishPlots")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-scales
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-ggridges
                             r-ggrepel
                             r-ggplot2
                             r-fishmethods
                             r-dplyr
                             r-broom))
    (home-page "https://deepwaterimr.github.io/ggFishPlots/")
    (synopsis
     "Visualise and Calculate Life History Parameters for Fisheries Science using 'ggplot2'")
    (description
     "This package contains functions to create life history parameter plots from raw
data.  The plots are created using ggplot2', and calculations done using the
tidyverse collection of packages.  The package contains references to
@code{FishBase} (Froese R., Pauly D., 2023) <https://www.fishbase.se/>.")
    (license license:gpl3)))

(define-public r-ggfigdone
  (package
    (name "r-ggfigdone")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggfigdone" version))
       (sha256
        (base32 "1kd6k2lh6ah4faprg1vk2ala2iw136mqkw2f8gvpzgg3spc4s7q7"))))
    (properties `((upstream-name . "ggfigdone")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-uuid
                             r-readr
                             r-jsonlite
                             r-httr
                             r-httpuv
                             r-ggplot2
                             r-filelock
                             r-data-table))
    (native-inputs (list esbuild))
    (home-page "https://cran.r-project.org/package=ggfigdone")
    (synopsis "Manage & Modify 'ggplot' Figures using 'ggfigdone'")
    (description
     "When you prepare a presentation or a report, you often need to manage a large
number of ggplot figures.  You need to change the figure size, modify the title,
label, themes, etc.  It is inconvenient to go back to the original code to make
these changes.  This package provides a simple way to manage ggplot figures.
You can easily add the figure to the database and update them later using CLI
(command line interface) or GUI (graphical user interface).")
    (license license:gpl3)))

(define-public r-ggfields
  (package
    (name "r-ggfields")
    (version "0.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggfields" version))
       (sha256
        (base32 "1b23hxi9g5fcmrw63pmksayk9j7xsi0j23qkfkchlcx258yrf7la"))))
    (properties `((upstream-name . "ggfields")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-scales r-rlang r-ggplot2 r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://pepijn-devries.github.io/ggfields/")
    (synopsis "Add Vector Field Layers to Ggplots")
    (description
     "Add vector field layers to ggplots.  Ideal for visualising wind speeds, water
currents, electric/magnetic fields, etc.  Accepts data.frames, simple features
(sf), and spatiotemporal arrays (stars) objects as input.  Vector fields are
depicted as arrows starting at specified locations, and with specified angles
and radii.")
    (license license:gpl3+)))

(define-public r-ggfacto
  (package
    (name "r-ggfacto")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggfacto" version))
       (sha256
        (base32 "1cdj34y5saf2d5cwnigmxlfyny9alcl84cyhrybv5yqykc75sim8"))))
    (properties `((upstream-name . "ggfacto")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-vctrs
                             r-tidyselect
                             r-tidyr
                             r-tibble
                             r-tabxplor
                             r-stringr
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-gridextra
                             r-ggrepel
                             r-ggplot2
                             r-ggiraph
                             r-ggforce
                             r-forcats
                             r-factominer
                             r-dplyr
                             r-data-table))
    (home-page "https://github.com/BriceNocenti/ggfacto")
    (synopsis "Graphs for Correspondence Analysis")
    (description
     "Readable, complete and pretty graphs for correspondence analysis made with
@code{FactoMineR}'.  They can be rendered as interactive HTML plots, showing
useful informations at mouse hover.  The interest is not mainly visual but
statistical: it helps the reader to keep in mind the data contained in the
cross-table or Burt table while reading the correspondence analysis, thus
preventing over-interpretation.  Most graphs are made with ggplot2', which means
that you can use the + syntax to manually add as many graphical pieces you want,
or change theme elements.  3D graphs are made with plotly'.")
    (license license:gpl3+)))

(define-public r-ggetho
  (package
    (name "r-ggetho")
    (version "0.3.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggetho" version))
       (sha256
        (base32 "0bdqx3x42jp22bs297nhdgzvywhq4cy0lwmwprl7vmjjh8lwvkxk"))))
    (properties `((upstream-name . "ggetho")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-scales
                             r-rlang
                             r-labeling
                             r-ggplot2
                             r-data-table
                             r-cli
                             r-behavr))
    (home-page "https://github.com/rethomics/ggetho")
    (synopsis
     "Visualisation of High-Throughput Behavioural (i.e. Ethomics) Data")
    (description
     "Extension of ggplot2 providing layers, scales and preprocessing functions useful
to represent behavioural variables that are recorded over multiple animals and
days.  This package is part of the rethomics framework
<https://rethomics.github.io/>.")
    (license license:gpl3)))

(define-public r-ggesda
  (package
    (name "r-ggesda")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggESDA" version))
       (sha256
        (base32 "0abjjs1syk9w5fqs778njkz3f92kil1617239hzdcjy67hbbf3c0"))))
    (properties `((upstream-name . "ggESDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidyverse
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rsda
                             r-rlang
                             r-r6
                             r-prodlim
                             r-magrittr
                             r-gtools
                             r-gridextra
                             r-ggthemes
                             r-ggpubr
                             r-ggplot2
                             r-ggforce
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/kiangkiangkiang/ggESDA")
    (synopsis "Exploratory Symbolic Data Analysis with 'ggplot2'")
    (description
     "This package implements an extension of ggplot2 and visualizes the symbolic data
with multiple plot which can be adjusted by more general and flexible input
arguments.  It also provides a function to transform the classical data to
symbolic data by both clustering algorithm and customized method.")
    (license license:gpl2+)))

(define-public r-ggenealogy
  (package
    (name "r-ggenealogy")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggenealogy" version))
       (sha256
        (base32 "1dak8nwdjiv7947w4pf00vhh6bida7gahv4ijqa3m1k22j2mr2qa"))))
    (properties `((upstream-name . "ggenealogy")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-plyr r-plotly r-igraph r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ggenealogy")
    (synopsis "Visualization Tools for Genealogical Data")
    (description
     "This package provides methods for searching through genealogical data and
displaying the results.  Plotting algorithms assist with data exploration and
publication-quality image generation.  Includes interactive genealogy
visualization tools.  Provides parsing and calculation methods for variables in
descendant branches of interest.  Uses the Grammar of Graphics.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-ggedit
  (package
    (name "r-ggedit")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggedit" version))
       (sha256
        (base32 "1mnk2ygnflkp9bx8pbf41vb9fyhcnkqrw8j01vx7p56iga3lwi4g"))))
    (properties `((upstream-name . "ggedit")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-shinybs
                             r-shinyace
                             r-shiny
                             r-scales
                             r-rstudioapi
                             r-rlang
                             r-purrr
                             r-plyr
                             r-miniui
                             r-ggplot2
                             r-dplyr
                             r-colourpicker))
    (home-page "https://github.com/yonicd/ggedit")
    (synopsis "Interactive 'ggplot2' Layer and Theme Aesthetic Editor")
    (description
     "Interactively edit ggplot2 layer and theme aesthetics definitions.")
    (license license:expat)))

(define-public r-ggebiplots
  (package
    (name "r-ggebiplots")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GGEBiplots" version))
       (sha256
        (base32 "0h7irlmq283bx1hlqyv8bi228kd6xkc7sdw6nq18v3s7b0a9bhcm"))))
    (properties `((upstream-name . "GGEBiplots")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-ggplot2 r-ggforce))
    (home-page "https://cran.r-project.org/package=GGEBiplots")
    (synopsis "GGE Biplots with 'ggplot2'")
    (description
     "Genotype plus genotype-by-environment (GGE) biplots rendered using ggplot2'.
Provides a command line interface to all of the functionality contained within
the archived package GGE@code{BiplotGUI}'.")
    (license license:gpl3)))

(define-public r-ggeasy
  (package
    (name "r-ggeasy")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggeasy" version))
       (sha256
        (base32 "1dppcffkyr6ybpz17zkq97g0p2hs1f8iwg6vwrw124pbgr065dr0"))))
    (properties `((upstream-name . "ggeasy")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jonocarroll/ggeasy")
    (synopsis "Easy Access to 'ggplot2' Commands")
    (description
     "This package provides a series of aliases to commonly used but difficult to
remember ggplot2 sequences.")
    (license license:gpl2+)))

(define-public r-gge
  (package
    (name "r-gge")
    (version "1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gge" version))
       (sha256
        (base32 "1c8f8g4nw245cjxpx3fjw03cgwc1q13k9ly9jx1jcc6k6hsdkx9k"))))
    (properties `((upstream-name . "gge")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-nipals))
    (native-inputs (list r-knitr))
    (home-page "https://kwstat.github.io/gge/")
    (synopsis "Genotype Plus Genotype-by-Environment Biplots")
    (description
     "Create biplots for GGE (genotype plus genotype-by-environment) and GGB (genotype
plus genotype-by-block-of-environments) models.  See Laffont et al. (2013)
<doi:10.2135/cropsci2013.03.0178>.")
    (license license:expat)))

(define-public r-ggdoubleheat
  (package
    (name "r-ggdoubleheat")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggDoubleHeat" version))
       (sha256
        (base32 "05wlyiqjivw0am20vn0s6rm20xj1r3q75immlvf063li0pxqfp93"))))
    (properties `((upstream-name . "ggDoubleHeat")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-ggplot2 r-ggnewscale))
    (home-page "https://pursuitofdatascience.github.io/ggDoubleHeat/")
    (synopsis "Heatmap-Like Visualization Tool")
    (description
     "This package provides a data visualization design that provides comparison
between two (Double) data sources (usually on a par with each other) on one
reformed heatmap, while inheriting ggplot2 features.")
    (license license:gpl3+)))

(define-public r-ggdoe
  (package
    (name "r-ggdoe")
    (version "0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggDoE" version))
       (sha256
        (base32 "0r857z5iw30b8wq8ry0kgwgagb77jzk4n2qc8wy316gn7dq2y12c"))))
    (properties `((upstream-name . "ggDoE")))
    (build-system r-build-system)
    (propagated-inputs (list r-insight r-ggplot2))
    (home-page "https://ggdoe.netlify.app")
    (synopsis "Modern Graphs for Design of Experiments with 'ggplot2'")
    (description
     "Generate commonly used plots in the field of design of experiments using
ggplot2'. @code{ggDoE} currently supports the following plots: alias matrix, box
cox transformation, boxplots, lambda plot, regression diagnostic plots, half
normal plots, main and interaction effect plots for factorial designs, contour
plots for response surface methodology, Pareto plot, and two dimensional
projections of a latin hypercube design.")
    (license license:expat)))

(define-public r-ggdmc
  (package
    (name "r-ggdmc")
    (version "0.2.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggdmc" version))
       (sha256
        (base32 "1gncv7npl548k3zd41x2gwg5qywn5rp6zb4i7ga78sy77mvi3x92"))))
    (properties `((upstream-name . "ggdmc")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-matrixstats
                             r-ggplot2
                             r-data-table
                             r-coda))
    (home-page "https://github.com/yxlin/ggdmc")
    (synopsis "Cognitive Models")
    (description
     "Hierarchical Bayesian models.  The package provides tools to fit two response
time models, using the population-based Markov Chain Monte Carlo.")
    (license license:gpl2)))

(define-public r-ggdensity
  (package
    (name "r-ggdensity")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggdensity" version))
       (sha256
        (base32 "01ym1af6w39zg6xh5mls8kwl4mg0lpjd94j0hm2xrgl39llpwx6r"))))
    (properties `((upstream-name . "ggdensity")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tibble
                             r-scales
                             r-mass
                             r-isoband
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://jamesotto852.github.io/ggdensity/")
    (synopsis "Interpretable Bivariate Density Visualization with 'ggplot2'")
    (description
     "The ggplot2 package provides simple functions for visualizing contours of 2-d
kernel density estimates.  ggdensity implements several additional density
estimators as well as more interpretable visualizations based on highest density
regions instead of the traditional height of the estimated density surface.")
    (license license:expat)))

(define-public r-ggdemetra
  (package
    (name "r-ggdemetra")
    (version "0.2.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggdemetra" version))
       (sha256
        (base32 "18i96jkbc0zm9xgn5250v2316kccr5vcxag2gii7lag7bhj6fq51"))))
    (properties `((upstream-name . "ggdemetra")))
    (build-system r-build-system)
    (inputs (list openjdk))
    (propagated-inputs (list r-rjdemetra r-gridextra r-ggrepel r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://aqlt.github.io/ggdemetra/")
    (synopsis
     "'ggplot2' Extension for Seasonal and Trading Day Adjustment with 'RJDemetra'")
    (description
     "This package provides ggplot2 functions to return the results of seasonal and
trading day adjustment made by RJDemetra'.  RJDemetra is an R interface around
JDemetra+ (<https://github.com/jdemetra/jdemetra-app>), the seasonal adjustment
software officially recommended to the members of the European Statistical
System and the European System of Central Banks.")
    (license (license:fsdg-compatible "EUPL"))))

(define-public r-ggdaynight
  (package
    (name "r-ggdaynight")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggdaynight" version))
       (sha256
        (base32 "1wvpmpyfnq6j6wkk8ayaddb4yyk945w4isjn57dpkvabfk59cvfh"))))
    (properties `((upstream-name . "ggdaynight")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://github.com/GabrielSlPires/ggdaynight")
    (synopsis "Add Day/Night Patterns to 'ggplot2' Plots")
    (description
     "It provides a custom ggplot2 geom to add day/night patterns to plots.  It
visually distinguishes daytime and nighttime periods.  It is useful for
visualizing data that spans multiple days and for highlighting diurnal patterns.")
    (license license:expat)))

(define-public r-ggdark
  (package
    (name "r-ggdark")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggdark" version))
       (sha256
        (base32 "1w93g2j4g45x9s841v9zi18lxzda81ipa13fajqc6p9xk8frvgrf"))))
    (properties `((upstream-name . "ggdark")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://cran.r-project.org/package=ggdark")
    (synopsis "Dark Mode for 'ggplot2' Themes")
    (description
     "Activate dark mode on your favorite ggplot2 theme with dark_mode() or use the
dark versions of ggplot2 themes, including dark_theme_gray(),
dark_theme_minimal(), and others.  When a dark theme is applied, all geom color
and geom fill defaults are changed to make them visible against a dark
background.  To restore the defaults to their original values, use
invert_geom_defaults().")
    (license license:expat)))

(define-public r-ggdag
  (package
    (name "r-ggdag")
    (version "0.2.13")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggdag" version))
       (sha256
        (base32 "074n9w1vgzmx677nppjfp4k3wf7rp0yfh9fbyhb3man4gl2w17df"))))
    (properties `((upstream-name . "ggdag")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidygraph
                             r-tibble
                             r-stringr
                             r-rlang
                             r-purrr
                             r-pillar
                             r-magrittr
                             r-igraph
                             r-ggrepel
                             r-ggraph
                             r-ggplot2
                             r-forcats
                             r-dplyr
                             r-dagitty))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/r-causal/ggdag")
    (synopsis "Analyze and Create Elegant Directed Acyclic Graphs")
    (description
     "Tidy, analyze, and plot directed acyclic graphs (DAGs).  ggdag is built on top
of dagitty', an R package that uses the DAGitty web tool
(<https://dagitty.net/>) for creating and analyzing DAGs.  ggdag makes it easy
to tidy and plot dagitty objects using ggplot2 and ggraph', as well as common
analytic and graphical functions, such as determining adjustment sets and node
relationships.")
    (license license:expat)))

(define-public r-ggcorset
  (package
    (name "r-ggcorset")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggcorset" version))
       (sha256
        (base32 "1vswnr15xbsl3kbd7nbd33mc9931d4lk3mpx8ri0ph37h3b0sif9"))))
    (properties `((upstream-name . "ggcorset")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-gghalves))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ggcorset")
    (synopsis "The Corset Plot")
    (description
     "Corset plots are a visualization technique used strictly to visualize repeat
measures at 2 time points (such as pre- and post- data).  The distribution of
measurements are visualized at each time point, whilst the trajectories of
individual change are visualized by connecting the pre- and post- values
linearly.  These lines can be coloured to represent the magnitude of change, or
other user-defined value.  This method of visualization is ideal for showing the
heterogeneity of data, including differences by sub-groups.  The package relies
on ggplot2 allowing for easy integration so that users can customize their
visualizations as required.  Users can create corset plots using data in either
wide or long format using the functions gg_corset() or gg_corset_elongated(),
respectively.")
    (license license:expat)))

(define-public r-ggcompare
  (package
    (name "r-ggcompare")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggcompare" version))
       (sha256
        (base32 "0pdby6vjxg50zhxbi7vybfgf8i484bxgaj7y4cqv50gvywcpsns6"))))
    (properties `((upstream-name . "ggcompare")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://hmu-wh.github.io/ggcompare/")
    (synopsis "Mean Comparison in 'ggplot2'")
    (description
     "Add mean comparison annotations to a ggplot'.  This package provides an easy way
to indicate if two or more groups are significantly different in a ggplot'.
Usually you do not need to specify the test method, you only need to tell
stat_compare() whether you want to perform a parametric test or a nonparametric
test, and stat_compare() will automatically choose the appropriate test method
based on your data.  For comparisons between two groups, the p-value is
calculated by t-test (parametric) or Wilcoxon rank sum test (nonparametric).
For comparisons among more than two groups, the p-value is calculated by One-way
ANOVA (parametric) or Kruskal-Wallis test (nonparametric).")
    (license license:artistic2.0)))

(define-public r-ggcleveland
  (package
    (name "r-ggcleveland")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggcleveland" version))
       (sha256
        (base32 "1pl9qhj7p5kflx0ni59hq1k9afqchghg5wig9v1ml5pqrjz70pxa"))))
    (properties `((upstream-name . "ggcleveland")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-readr
                             r-magrittr
                             r-lattice
                             r-ggplot2
                             r-egg
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mpru/ggcleveland")
    (synopsis "Implementation of Plots from Cleveland's Visualizing Data Book")
    (description
     "William S. Cleveland's book Visualizing Data is a classic piece of literature on
Exploratory Data Analysis.  Although it was written several decades ago, its
content is still relevant as it proposes several tools which are useful to
discover patterns and relationships among the data under study, and also to
assess the goodness of fit o a model.  This package provides functions to
produce the ggplot2 versions of the visualization tools described in this book
and is thought to be used in the context of courses on Exploratory Data
Analysis.")
    (license license:gpl2)))

(define-public r-ggclassification
  (package
    (name "r-ggclassification")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GGClassification" version))
       (sha256
        (base32 "03wis5ng2mnfhp33xrag8xbmvjagzr1909a6x94wv7s2spji00rp"))))
    (properties `((upstream-name . "GGClassification")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp))
    (home-page "https://cran.r-project.org/package=GGClassification")
    (synopsis "Gabriel Graph Based Large-Margin Classifiers")
    (description
     "This package contains the implementation of a binary large margin classifier
based on Gabriel Graph.  References for this method can be found in L.C.B.
Torres et al. (2015) <doi:10.1049/el.2015.1644>.")
    (license license:gpl2+)))

(define-public r-ggchernoff
  (package
    (name "r-ggchernoff")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggChernoff" version))
       (sha256
        (base32 "0dkr3vl4a3css19j53x1c1gxxgjg57d444ahxrrrxsyva49lnq1y"))))
    (properties `((upstream-name . "ggChernoff")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-ggplot2))
    (home-page "https://github.com/Selbosh/ggChernoff")
    (synopsis "Chernoff Faces for 'ggplot2'")
    (description
     "This package provides a Chernoff face geom for ggplot2'.  Maps multivariate data
to human-like faces.  Inspired by Chernoff (1973)
<doi:10.1080/01621459.1973.10482434>.")
    (license license:expat)))

(define-public r-ggcharts
  (package
    (name "r-ggcharts")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggcharts" version))
       (sha256
        (base32 "1c0gdn8skkm82f3qxv9551l36zwqcga9b9sbc48q8hp71ay4ac6b"))))
    (properties `((upstream-name . "ggcharts")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-patchwork
                             r-magrittr
                             r-lifecycle
                             r-ggplot2
                             r-dplyr
                             r-colorspace))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/thomas-neitmann/ggcharts")
    (synopsis
     "Shorten the Distance from Data Visualization Idea to Actual Plot")
    (description
     "Streamline the creation of common charts by taking care of a lot of data
preprocessing and plot customization for the user.  Provides a high-level
interface to create plots using ggplot2'.")
    (license license:expat)))

(define-public r-ggchangepoint
  (package
    (name "r-ggchangepoint")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggchangepoint" version))
       (sha256
        (base32 "0brd9z8wj563j2kcx12bz4kyg2ynppxx5cz8k23xli1qijlib98p"))))
    (properties `((upstream-name . "ggchangepoint")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-rdpack
                             r-ggplot2
                             r-ecp
                             r-dplyr
                             r-changepoint-np
                             r-changepoint))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ggchangepoint")
    (synopsis "Combines Changepoint Analysis with 'ggplot2'")
    (description
     "R provides fantastic tools for changepoint analysis, but plots generated by the
tools do not have the ggplot2 style.  This tool, however, combines changepoint',
changepoint.np and ecp together, and uses ggplot2 to visualize changepoints.")
    (license license:gpl3+)))

(define-public r-ggbump
  (package
    (name "r-ggbump")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggbump" version))
       (sha256
        (base32 "15cnzv2q3gylkmd1zj044cp14ldnkc4fzwmxaqpc8cagwf9i10zk"))))
    (properties `((upstream-name . "ggbump")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-purrr r-ggplot2 r-dplyr))
    (home-page "https://cran.r-project.org/package=ggbump")
    (synopsis "Bump Chart and Sigmoid Curves")
    (description
     "This package provides a geom for ggplot to create bump plots.  Can be good to
use for showing rank over time.")
    (license license:expat)))

(define-public r-ggbuildr
  (package
    (name "r-ggbuildr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggbuildr" version))
       (sha256
        (base32 "0pld635v6fv46ky7s4icwxlcnr7z6bp5ikf20adpkljwhy0wwxak"))))
    (properties `((upstream-name . "ggbuildr")))
    (build-system r-build-system)
    (propagated-inputs (list r-readr r-purrr r-ggplot2))
    (home-page "https://cran.r-project.org/package=ggbuildr")
    (synopsis "Save Incremental Builds of Plots")
    (description
     "Saves a ggplot object into multiple files, each with a layer added
incrementally.  Generally to be used in presentation slides.  Flexible enough to
allow different file types for the final complete plot, and intermediate builds.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-ggbubbles
  (package
    (name "r-ggbubbles")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggBubbles" version))
       (sha256
        (base32 "116rl8j0s9xwhh9jl5yj9fspkhv2zrirbx9247hhns9ngrdn4i3x"))))
    (properties `((upstream-name . "ggBubbles")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ggBubbles")
    (synopsis
     "Mini Bubble Plots for Comparison of Discrete Data with 'ggplot2'")
    (description
     "When comparing discrete data mini bubble plots allow displaying more information
than traditional bubble plots via colour, shape or labels.  Exact overlapping
coordinates will be transformed so they surround the original point circularly
without overlapping.  This is implemented as a position_surround() function for
ggplot2'.")
    (license license:lgpl3+)))

(define-public r-ggbrick
  (package
    (name "r-ggbrick")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggbrick" version))
       (sha256
        (base32 "0w6cw5f8skx1diymsmpvkipk9n9c8yhi18b9mghynv3x0mn8qrwh"))))
    (properties `((upstream-name . "ggbrick")))
    (build-system r-build-system)
    (propagated-inputs (list r-purrr r-glue r-ggplot2 r-dplyr))
    (home-page "https://cran.r-project.org/package=ggbrick")
    (synopsis "Waffle Style Chart with a Brick Layout in 'ggplot2'")
    (description
     "This package provides a new take on the bar chart.  Similar to a waffle style
chart but instead of squares the layout resembles a brick wall.")
    (license license:expat)))

(define-public r-ggbrain
  (package
    (name "r-ggbrain")
    (version "0.8.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggbrain" version))
       (sha256
        (base32 "1nicqirp69k0ish7j3mr7zz82w2lj5jcvmn7cbx764r62s5pscvd"))))
    (properties `((upstream-name . "ggbrain")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-rnifti
                             r-rlang
                             r-rcpparmadillo
                             r-rcpp
                             r-patchwork
                             r-matrix
                             r-imager
                             r-glue
                             r-ggrepel
                             r-ggplot2
                             r-ggnewscale
                             r-dplyr
                             r-data-table
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://michaelhallquist.github.io/ggbrain/")
    (synopsis
     "Create Images of Volumetric Brain Data in NIfTI Format Using 'ggplot2' Syntax")
    (description
     "This package provides a ggplot2'-consistent approach to generating 2D displays
of volumetric brain imaging data.  Display data from multiple N@code{IfTI}
images using standard ggplot2 conventions such scales, limits, and themes to
control the appearance of displays.  The resulting plots are returned as
patchwork objects, inheriting from ggplot', allowing for any standard
modifications of display aesthetics supported by ggplot2'.")
    (license license:gpl3+)))

(define-public r-ggbrace
  (package
    (name "r-ggbrace")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggbrace" version))
       (sha256
        (base32 "0p5k9lp0c34ry3mf39w0j9pi8irych9p6lvh6zyk82my18b25yk0"))))
    (properties `((upstream-name . "ggbrace")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://cran.r-project.org/package=ggbrace")
    (synopsis "Curly Braces for 'ggplot2'")
    (description
     "This package provides curly braces in ggplot2 plus matching text.  stat_brace()
plots braces partially in the confines of data so that the brace is set apart
from it.  stat_bracetext() plots corresponding text, fitting to the braces from
stat_brace().")
    (license license:expat)))

(define-public r-ggborderline
  (package
    (name "r-ggborderline")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggborderline" version))
       (sha256
        (base32 "1acb3z2dwasmn4ngc7wqk3y4n34ah3fhdxsbdl3dmbc75nmdsn5a"))))
    (properties `((upstream-name . "ggborderline")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs r-rlang r-ggplot2 r-cli))
    (home-page "https://github.com/wurli/ggborderline")
    (synopsis "Line Plots that Pop")
    (description
     "This package provides a set of geometries to make line plots a little bit nicer.
 Use along with ggplot2 to: - Improve the clarity of line plots with many
overlapping lines - Draw more realistic worms.")
    (license license:expat)))

(define-public r-ggblend
  (package
    (name "r-ggblend")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggblend" version))
       (sha256
        (base32 "0wzy9lnvqxjl3511vk1y6nxpvhk9hvk1xq18bbwsv6886npls56z"))))
    (properties `((upstream-name . "ggblend")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-ggplot2))
    (home-page "https://mjskay.github.io/ggblend/")
    (synopsis "Blending and Compositing Algebra for 'ggplot2'")
    (description
     "Algebra of operations for blending, copying, adjusting, and compositing layers
in ggplot2'.  Supports copying and adjusting the aesthetics or parameters of an
existing layer, partitioning a layer into multiple pieces for re-composition,
applying affine transformations to layers, and combining layers (or partitions
of layers) using blend modes (including commutative blend modes, like multiply
and darken).  Blend mode support is particularly useful for creating plots with
overlapping groups where the layer drawing order does not change the output; see
Kindlmann and Scheidegger (2014) <doi:10.1109/TVCG.2014.2346325>.")
    (license license:expat)))

(define-public r-ggblanket
  (package
    (name "r-ggblanket")
    (version "10.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggblanket" version))
       (sha256
        (base32 "1ym4sv692ly1r16hf1cxkm6mjz6djik3hwsw2gj2ydchypkdsq3q"))))
    (properties `((upstream-name . "ggblanket")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridislite
                             r-tidyselect
                             r-tidyr
                             r-stringr
                             r-snakecase
                             r-scales
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-labelled
                             r-hms
                             r-ggplot2
                             r-forcats
                             r-farver
                             r-dplyr
                             r-colorspace))
    (native-inputs (list r-knitr))
    (home-page "https://davidhodge931.github.io/ggblanket/")
    (synopsis "Simplify 'ggplot2' Visualisation")
    (description
     "Simplify ggplot2 visualisation with ggblanket wrapper functions.")
    (license license:expat)))

(define-public r-ggbiplot
  (package
    (name "r-ggbiplot")
    (version "0.6.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggbiplot" version))
       (sha256
        (base32 "0ys4l5fhy1p1ys3q7012vxafmfrzaqlvnzmkmv32zar32j3hvmkc"))))
    (properties `((upstream-name . "ggbiplot")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-ggplot2))
    (home-page "https://github.com/friendly/ggbiplot")
    (synopsis "Grammar of Graphics Implementation of Biplots")
    (description
     "This package provides a ggplot2 based implementation of biplots, giving a
representation of a dataset in a two dimensional space accounting for the
greatest variance, together with variable vectors showing how the data variables
relate to this space.  It provides a replacement for stats::biplot(), but with
many enhancements to control the analysis and graphical display.  It implements
biplot and scree plot methods which can be used with the results of prcomp(),
princomp(), @code{FactoMineR::PCA}(), ade4::dudi.pca() or MASS::lda() and can be
customized using ggplot2 techniques.")
    (license license:gpl2)))

(define-public r-ggautomap
  (package
    (name "r-ggautomap")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggautomap" version))
       (sha256
        (base32 "0fy1saym15l3d76ix26kz9il3nzsypchh028vigcvan7g0yg7168"))))
    (properties `((upstream-name . "ggautomap")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidyr
                             r-sf
                             r-rlang
                             r-packcircles
                             r-ggplot2
                             r-ggmapinset
                             r-dplyr
                             r-cli
                             r-cartographer))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/cidm-ph/ggautomap")
    (synopsis "Create Maps from a Column of Place Names")
    (description
     "Mapping tools that convert place names to coordinates on the fly.  These ggplot2
extensions make maps from a data frame where one of the columns contains place
names, without having to directly work with the underlying geospatial data and
tools.  The corresponding map data must be registered with cartographer either
by the user or by another package.")
    (license license:expat)))

(define-public r-ggarrow
  (package
    (name "r-ggarrow")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggarrow" version))
       (sha256
        (base32 "14jvrjrzp7wk94c3s0ixdcbhfsx9nlr5rf85a5gmrhvq7qjaicxc"))))
    (properties `((upstream-name . "ggarrow")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-scales
                             r-rlang
                             r-polyclip
                             r-ggplot2
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://teunbrand.github.io/ggarrow/")
    (synopsis "Arrows for 'ggplot2'")
    (description
     "This package provides a ggplot2 extension that adds specialised arrow geometry
layers.  It offers more arrow options than the standard grid arrows that are
built-in many line-based geom layers.")
    (license license:expat)))

(define-public r-ggarchery
  (package
    (name "r-ggarchery")
    (version "0.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggarchery" version))
       (sha256
        (base32 "1fgcy26gq0cpk4vhc412yw5wj8j4afs21lp7fcqsdah1xmpnhym2"))))
    (properties `((upstream-name . "ggarchery")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-glue
                             r-ggplot2
                             r-dplyr))
    (home-page "https://github.com/mdhall272/ggarchery")
    (synopsis "Flexible Segment Geoms with Arrows for 'ggplot2'")
    (description
     "Geoms for placing arrowheads at multiple points along a segment, not just at the
end; position function to shift starts and ends of arrows to avoid exactly
intersecting points.")
    (license license:gpl3)))

(define-public r-ggamma
  (package
    (name "r-ggamma")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggamma" version))
       (sha256
        (base32 "1zc4p2bz4pxqycrzgb9wwl53lxqcikxsihna6ff8pmw1n623j85z"))))
    (properties `((upstream-name . "ggamma")))
    (build-system r-build-system)
    (home-page "https://mjsaldanha.com/posts/ggamma")
    (synopsis "Generalized Gamma Probability Distribution")
    (description
     "Density, distribution function, quantile function and random generation for the
Generalized Gamma proposed in Stacy, E. W. (1962) <doi:10.1214/aoms/1177704481>.")
    (license license:expat)))

(define-public r-ggallin
  (package
    (name "r-ggallin")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggallin" version))
       (sha256
        (base32 "0hrxa7m07ppvnndivxcgxksdyblb6fw17aw46maqavlvcrz3fjgm"))))
    (properties `((upstream-name . "ggallin")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-ggplot2))
    (home-page "https://github.com/shabbychef/ggallin")
    (synopsis "Grab Bag of 'ggplot2' Functions")
    (description
     "Extra geoms and scales for ggplot2', including geom_cloud(), a Normal density
cloud replacement for errorbars; transforms ssqrt_trans and pseudolog10_trans,
which are loglike but appropriate for negative data; interp_trans() and
warp_trans() which provide scale transforms based on interpolation; and an infix
compose operator for scale transforms.")
    (license license:lgpl3)))

(define-public r-ggalignment
  (package
    (name "r-ggalignment")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggalignment" version))
       (sha256
        (base32 "1rqndv1xqz350v6a23pfrl4zkwjyh4zg633kwcxnv57v0g01mmif"))))
    (properties `((upstream-name . "ggalignment")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-magrittr r-ggplot2 r-ggimage r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=ggalignment")
    (synopsis "Plots 'D&D'-Style Alignment Charts")
    (description
     "D&D alignment charts show 9 boxes with values for good through evil and values
for chaotic through lawful.  This package easily creates these alignment charts
from user-provided image paths and alignment values.")
    (license license:expat)))

(define-public r-ggaligner
  (package
    (name "r-ggaligner")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggaligner" version))
       (sha256
        (base32 "1b2fk8bxj4y8an7higv6251fvba748j8x6j7cqqdznmk9az86g01"))))
    (properties `((upstream-name . "ggaligner")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-ggplot2))
    (home-page "https://cran.r-project.org/package=ggaligner")
    (synopsis
     "Visualizing Sequence Alignment by Generating Publication-Ready Plots")
    (description
     "Providing publication-ready graphs for Multiple sequence alignment.  Moreover,
it provides a unique solution for visualizing the multiple sequence alignment
without the need to do the alignment in each run which is a big limitation in
other available packages.")
    (license license:gpl3)))

(define-public r-ggalign
  (package
    (name "r-ggalign")
    (version "0.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ggalign" version))
       (sha256
        (base32 "1yk36738ccnig6npq4z8v5liji2vpajigk7flrj6j0zpnaz883m0"))))
    (properties `((upstream-name . "ggalign")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-rlang
                             r-gtable
                             r-ggplot2
                             r-data-table
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Yunuuuu/ggalign")
    (synopsis "'ggplot2' Extension for Consistent Axis Alignment")
    (description
     "This package provides a ggplot2 extension offers various tools for organizing
and arranging plots.  It is designed to consistently align a specific axis
across multiple ggplot objects, making it especially useful for plots requiring
data order manipulation.  A typical use case includes organizing combinations
like a dendrogram and a heatmap.")
    (license license:expat)))

(define-public r-gfunctions
  (package
    (name "r-gfunctions")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gfunctions" version))
       (sha256
        (base32 "1rjrw4g0daw219spcz0pmy78m68gidk7vdbxd31wqgn1p7xwa4jb"))))
    (properties `((upstream-name . "gfunctions")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-sandwich))
    (home-page "https://cran.r-project.org/package=gfunctions")
    (synopsis "G-Functions")
    (description
     "Modified versions of the lag() and summary() functions: glag() and gsummary().
The prefix g is a reminder of who to blame if things do not work as they should.")
    (license license:gpl2+)))

(define-public r-gformulami
  (package
    (name "r-gformulami")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gFormulaMI" version))
       (sha256
        (base32 "1zp8gn83h7bnfygi46qqhda4z5n2axbvcmqvhz60ba0daknw0jx0"))))
    (properties `((upstream-name . "gFormulaMI")))
    (build-system r-build-system)
    (propagated-inputs (list r-mice))
    (native-inputs (list r-knitr))
    (home-page "https://jwb133.github.io/gFormulaMI/")
    (synopsis "G-Formula for Causal Inference via Multiple Imputation")
    (description
     "This package implements the G-Formula method for causal inference with
time-varying treatments and confounders using Bayesian multiple imputation
methods, as described by Bartlett, Olarte Parra and Daniel (2023)
<@code{arXiv:2301.12026>}.  It creates multiple synthetic imputed datasets under
treatment regimes of interest using the mice package.  These can then be
analysed using rules developed for analysing multiple synthetic datasets.")
    (license license:gpl3+)))

(define-public r-gformula
  (package
    (name "r-gformula")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gfoRmula" version))
       (sha256
        (base32 "09bjc5wp8rc7wmjcx8vkk7vg386j1wq5hkf6v63fpfpcsb887251"))))
    (properties `((upstream-name . "gfoRmula")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncreg
                             r-truncnorm
                             r-survival
                             r-stringr
                             r-progress
                             r-nnet
                             r-ggpubr
                             r-ggplot2
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/CausalInference/gfoRmula")
    (synopsis "Parametric G-Formula")
    (description
     "This package implements the non-iterative conditional expectation (NICE)
algorithm of the g-formula algorithm (Robins (1986)
<doi:10.1016/0270-0255(86)90088-6>, HernÃ¡n and Robins (2024,
ISBN:9781420076165)).  The g-formula can estimate an outcome's counterfactual
mean or risk under hypothetical treatment strategies (interventions) when there
is sufficient information on time-varying treatments and confounders.  This
package can be used for discrete or continuous time-varying treatments and for
failure time outcomes or continuous/binary end of follow-up outcomes.  The
package can handle a random measurement/visit process and a priori knowledge of
the data structure, as well as censoring (e.g., by loss to follow-up) and two
options for handling competing events for failure time outcomes.  Interventions
can be flexibly specified, both as interventions on a single treatment or as
joint interventions on multiple treatments.  See @code{McGrath} et al. (2020)
<doi:10.1016/j.patter.2020.100008> for a guide on how to use the package.")
    (license license:gpl3)))

(define-public r-gfm
  (package
    (name "r-gfm")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GFM" version))
       (sha256
        (base32 "13v8gsn1zsxcji52n3mv1bik5xjqzg8n01rak1bvffm9qwakg571"))))
    (properties `((upstream-name . "GFM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mass r-irlba r-dosnow))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/feiyoung/GFM")
    (synopsis "Generalized Factor Model")
    (description
     "Generalized factor model is implemented for ultra-high dimensional data with
mixed-type variables.  Two algorithms, variational EM and alternate
maximization, are designed to implement the generalized factor model,
respectively.  The factor matrix and loading matrix together with the number of
factors can be well estimated.  This model can be employed in social and
behavioral sciences, economy and finance, and genomics, to extract interpretable
nonlinear factors.  More details can be referred to Wei Liu, Huazhen Lin,
Shurong Zheng and Jin Liu. (2021) <doi:10.1080/01621459.2021.1999818>.")
    (license license:gpl3)))

(define-public r-gfiultra
  (package
    (name "r-gfiultra")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gfiUltra" version))
       (sha256
        (base32 "1n6sp7h0ys92mqixznrs2j8kmban3mx8swrwdpxgxc6vxycky8d8"))))
    (properties `((upstream-name . "gfiUltra")))
    (build-system r-build-system)
    (propagated-inputs (list r-sis r-mvtnorm r-lazyeval))
    (home-page "https://github.com/stla/gfiUltra")
    (synopsis
     "Generalized Fiducial Inference for Ultrahigh-Dimensional Regression")
    (description
     "Variable selection for ultrahigh-dimensional (\"large p small n\") linear Gaussian
models using a fiducial framework allowing to draw inference on the parameters.
Reference: Lai, Hannig & Lee (2015) <doi:10.1080/01621459.2014.931237>.")
    (license license:gpl3)))

(define-public r-gfisher
  (package
    (name "r-gfisher")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GFisher" version))
       (sha256
        (base32 "0ilvl8y118iicq5anzcsm94yz3bq890mb627dxi6z61cqazjbbai"))))
    (properties `((upstream-name . "GFisher")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-matrix))
    (home-page "https://cran.r-project.org/package=GFisher")
    (synopsis "Generalized Fisher's Combination Tests Under Dependence")
    (description
     "Accurate and computationally efficient p-value calculation methods for a general
family of Fisher type statistics (GFisher).  The GFisher covers Fisher's
combination, Good's statistic, Lancaster's statistic, weighted Z-score
combination, etc.  It allows a flexible weighting scheme, as well as an omnibus
procedure that automatically adapts proper weights and degrees of freedom to a
given data.  The new p-value calculation methods are based on novel ideas of
moment-ratio matching and joint-distribution approximation.  The technical
details can be found in Hong Zhang and Zheyang Wu (2020)
<@code{arXiv:2003.01286>}.")
    (license license:gpl2)))

(define-public r-gfiextremes
  (package
    (name "r-gfiextremes")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gfiExtremes" version))
       (sha256
        (base32 "0iljf63pwwxzj0syvj7p13m6q82y7nf1kq5xpgyczkbrrbb59p7n"))))
    (properties `((upstream-name . "gfiExtremes")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-foreach
                             r-doparallel
                             r-coda
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/stla/gfiExtremes")
    (synopsis "Generalized Fiducial Inference for Extremes")
    (description
     "Fiducial framework to perform inference on the quantiles for a generalized
Pareto distribution model and on the parameters of the Pareto exceedance
distribution, assuming the exceedance threshold is a known or unknown parameter.
 Reference: Damian V. Wandler & Jan Hannig (2012)
<doi:10.1007/s10687-011-0127-9>.")
    (license license:gpl2+)))

(define-public r-gfgm-copula
  (package
    (name "r-gfgm-copula")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GFGM.copula" version))
       (sha256
        (base32 "0cw8ih9dj86cvks0qcpffzjx8lv6qiwvk3f1wri8zashj8lk9am6"))))
    (properties `((upstream-name . "GFGM.copula")))
    (build-system r-build-system)
    (propagated-inputs (list r-joint-cox r-compound-cox r-cmprsk))
    (home-page "https://cran.r-project.org/package=GFGM.copula")
    (synopsis "Generalized Farlie-Gumbel-Morgenstern Copula")
    (description
     "Compute bivariate dependence measures and perform bivariate competing risks
analysis under the generalized Farlie-Gumbel-Morgenstern (FGM) copula.  See Shih
and Emura (2018) <doi:10.1007/s00180-018-0804-0> and Shih and Emura (2019)
<doi:10.1007/s00362-016-0865-5> for details.")
    (license license:gpl2)))

(define-public r-gfer
  (package
    (name "r-gfer")
    (version "0.1.12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gfer" version))
       (sha256
        (base32 "19iarppqaz05352f3ng01yi7na9p612nxwwwagm2nxy1gxrjz5rh"))))
    (properties `((upstream-name . "gfer")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-v8
                             r-tidyr
                             r-stringi
                             r-scatterpie
                             r-rvest
                             r-jsonlite
                             r-httr
                             r-gsheet
                             r-googlesheets4
                             r-ggrepel
                             r-ggplot2
                             r-data-table
                             r-circlize))
    (native-inputs (list r-knitr))
    (home-page "https://yuanchao-xu.github.io/gfer/")
    (synopsis "Green Finance and Environmental Risk")
    (description
     "Focuses on data collecting, analyzing and visualization in green finance and
environmental risk research and analysis.  Main function includes environmental
data collecting from official websites such as MEP (Ministry of Environmental
Protection of China, <https://www.mee.gov.cn>), water related projects
identification and environmental data visualization.")
    (license license:gpl2)))

(define-public r-gfe
  (package
    (name "r-gfe")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GFE" version))
       (sha256
        (base32 "1mhlvp7i8g4b3mj4qwfnk0vl7af9sflgi6zgsjjp16kdsv42zkwn"))))
    (properties `((upstream-name . "GFE")))
    (build-system r-build-system)
    (propagated-inputs (list r-teachingsampling r-dplyr r-data-table))
    (home-page "https://cran.r-project.org/package=GFE")
    (synopsis "Gross Flows Estimation under Complex Surveys")
    (description
     "The philosophy in the package is described in Stasny (1988)
<doi:10.2307/1391558> and Gutierrez, A., Trujillo, L. & Silva, N. (2014),
<ISSN:1492-0921> to estimate the gross flows under complex surveys using a
Markov chain approach with non response.")
    (license license:gpl2+)))

(define-public r-gfdsurv
  (package
    (name "r-gfdsurv")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GFDsurv" version))
       (sha256
        (base32 "0qq66f8ddw92m9k9raid89rggla9691xi2sn1w3r7lmwqf6ajsl4"))))
    (properties `((upstream-name . "GFDsurv")))
    (build-system r-build-system)
    (propagated-inputs (list r-tippy
                             r-survminer
                             r-survival
                             r-shinythemes
                             r-shinyjs
                             r-shiny
                             r-plyr
                             r-mass
                             r-magic
                             r-gridextra))
    (home-page "https://github.com/PhilippSteinhauer/GFDsurv")
    (synopsis "Tests for Survival Data in General Factorial Designs")
    (description
     "Implemented are three Wald-type statistic and respective permuted versions for
null hypotheses formulated in terms of cumulative hazard rate functions, medians
and the concordance measure, respectively, in the general framework of survival
factorial designs with possibly heterogeneous survival and/or censoring
distributions, for crossed designs with an arbitrary number of factors and
nested designs with up to three factors.  Ditzhaus, Dobler and Pauly (2020)
<doi:10.1177/0962280220980784> Ditzhaus, Janssen, Pauly (2020) <@code{arXiv}:
2004.10818v2> Dobler and Pauly (2019) <doi:10.1177/0962280219831316>.")
    (license license:gpl3+)))

(define-public r-gfdrmtl
  (package
    (name "r-gfdrmtl")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GFDrmtl" version))
       (sha256
        (base32 "1kdfgz8y0bfnq40ab083z4ra8f7sv2s2ng94mfa3fcjh6ixk2fvj"))))
    (properties `((upstream-name . "GFDrmtl")))
    (build-system r-build-system)
    (propagated-inputs (list r-tippy
                             r-shinywidgets
                             r-shinythemes
                             r-shinymatrix
                             r-shinyjs
                             r-shiny
                             r-plyr
                             r-mvtnorm
                             r-mass
                             r-lpsolve
                             r-gfdrmst
                             r-gfdmcv))
    (home-page "https://cran.r-project.org/package=GFDrmtl")
    (synopsis
     "Multiple RMTL-Based Tests for Competing Risks Data in General Factorial Designs")
    (description
     "We implemented multiple tests based on the restricted mean time lost (RMTL) for
general factorial designs as described in Munko et al. (2024)
<doi:10.48550/@code{arXiv.2409.07917>}.  Therefore, an asymptotic test and a
permutation test are incorporated with a Wald-type test statistic.  The
asymptotic test takes the asymptotic exact dependence structure of the test
statistics into account to gain more power.  Furthermore, confidence intervals
for RMTL contrasts can be calculated and plotted and a stepwise extension that
can improve the power of the multiple tests is available.")
    (license license:gpl3+)))

(define-public r-gfdrmst
  (package
    (name "r-gfdrmst")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GFDrmst" version))
       (sha256
        (base32 "0d1cfy97qclymycq03i8gqnivm1x2slqmbv4zlyxk8frg7j4wb57"))))
    (properties `((upstream-name . "GFDrmst")))
    (build-system r-build-system)
    (propagated-inputs (list r-tippy
                             r-shinywidgets
                             r-shinythemes
                             r-shinymatrix
                             r-shinyjs
                             r-shiny
                             r-plyr
                             r-mvtnorm
                             r-mass
                             r-lpsolve
                             r-gfdmcv))
    (home-page "https://cran.r-project.org/package=GFDrmst")
    (synopsis "Multiple RMST-Based Tests in General Factorial Designs")
    (description
     "We implemented multiple tests based on the restricted mean survival time (RMST)
for general factorial designs as described in Munko et al. (2024)
<doi:10.1002/sim.10017>.  Therefore, an asymptotic test, a groupwise bootstrap
test, and a permutation test are incorporated with a Wald-type test statistic.
The asymptotic and groupwise bootstrap test take the asymptotic exact dependence
structure of the test statistics into account to gain more power.  Furthermore,
confidence intervals for RMST contrasts can be calculated and plotted and a
stepwise extension that can improve the power of the multiple tests is
available.")
    (license license:gpl3+)))

(define-public r-gfdmcv
  (package
    (name "r-gfdmcv")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GFDmcv" version))
       (sha256
        (base32 "094b41sx3czz9sjqhvxggkmrdcwfwv8ww9k4286vzd055jbvk5hh"))))
    (properties `((upstream-name . "GFDmcv")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rcpparmadillo
                             r-rcpp
                             r-mvtnorm
                             r-matrix
                             r-mass
                             r-hsaur
                             r-foreach
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=GFDmcv")
    (synopsis
     "General Hypothesis Testing Problems for Multivariate Coefficients of Variation")
    (description
     "This package performs test procedures for general hypothesis testing problems
for four multivariate coefficients of variation (Ditzhaus and Smaga, 2023
<@code{arXiv:2301.12009>}).  We can verify the global hypothesis about equality
as well as the particular hypotheses defined by contrasts, e.g., we can conduct
post hoc tests.  We also provide the simultaneous confidence intervals for
contrasts.")
    (license (list license:lgpl2.0 license:lgpl3 license:gpl2 license:gpl3))))

(define-public r-gfd
  (package
    (name "r-gfd")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GFD" version))
       (sha256
        (base32 "1d40fwk367ww544z25jwlx2kdm1gv7y6ijl8vnpi198n7k1hdgpl"))))
    (properties `((upstream-name . "GFD")))
    (build-system r-build-system)
    (propagated-inputs (list r-tippy
                             r-shinywidgets
                             r-shinythemes
                             r-shinyjs
                             r-shiny
                             r-plyr
                             r-plotrix
                             r-matrix
                             r-mass
                             r-magic))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://cran.r-project.org/package=GFD")
    (synopsis "Tests for General Factorial Designs")
    (description
     "Implemented are the Wald-type statistic, a permuted version thereof as well as
the ANOVA-type statistic for general factorial designs, even with non-normal
error terms and/or heteroscedastic variances, for crossed designs with an
arbitrary number of factors and nested designs with up to three factors.
Friedrich et al. (2017) <doi:10.18637/jss.v079.c01>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-gfcanalysis
  (package
    (name "r-gfcanalysis")
    (version "1.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gfcanalysis" version))
       (sha256
        (base32 "0j13y90nyd3qczrkzxwi1wpf5xlh7c0nlsyn8p7z1b4y93v63byc"))))
    (properties `((upstream-name . "gfcanalysis")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-stringr
                             r-sf
                             r-rcurl
                             r-rastervis
                             r-raster
                             r-plyr
                             r-ggplot2
                             r-geosphere
                             r-animation))
    (home-page "https://github.com/azvoleff/gfcanalysis")
    (synopsis
     "Tools for Working with Hansen et al. Global Forest Change Dataset")
    (description
     "Supports analyses using the Global Forest Change dataset released by Hansen et
al.  gfcanalysis was originally written for the Tropical Ecology Assessment and
Monitoring (TEAM) Network.  For additional details on the Global Forest Change
dataset, see: Hansen, M. et al.  2013. \"High-Resolution Global Maps of
21st-Century Forest Cover Change.\" Science 342 (15 November): 850-53.  The
forest change data and more information on the product is available at
<http://earthenginepartners.appspot.com>.")
    (license license:gpl3+)))

(define-public r-gfboost
  (package
    (name "r-gfboost")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gfboost" version))
       (sha256
        (base32 "1flvvqm9rk156nqnd0qwpagjl7kp2r4mkxpmp1i4zdnnyp2cydwk"))))
    (properties `((upstream-name . "gfboost")))
    (build-system r-build-system)
    (propagated-inputs (list r-pcapp r-mvtnorm r-mboost))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gfboost")
    (synopsis "Gradient-Free Gradient Boosting")
    (description
     "Implementation of routines of the author's @code{PhD} thesis on gradient-free
Gradient Boosting (Werner, Tino (2020) \"Gradient-Free Gradient Boosting\", URL
<https://oops.uni-oldenburg.de/id/eprint/4290>').")
    (license license:gpl2+)))

(define-public r-gfa
  (package
    (name "r-gfa")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GFA" version))
       (sha256
        (base32 "1ldpa12awb5d8mn25wn34nclqc4fyq690siq0wdq4c8fzgpv2xad"))))
    (properties `((upstream-name . "GFA")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GFA")
    (synopsis "Group Factor Analysis")
    (description
     "Factor analysis implementation for multiple data sources, i.e., for groups of
variables.  The whole data analysis pipeline is provided, including functions
and recommendations for data normalization and model definition, as well as
missing value prediction and model visualization.  The model group factor
analysis (GFA) is inferred with Gibbs sampling, and it has been presented
originally by Virtanen et al. (2012), and extended in Klami et al. (2015)
<DOI:10.1109/TNNLS.2014.2376974> and Bunte et al. (2016)
<DOI:10.1093/bioinformatics/btw207>; for details, see the citation info.")
    (license license:expat)))

(define-public r-geysertimes
  (package
    (name "r-geysertimes")
    (version "0.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geysertimes" version))
       (sha256
        (base32 "08d04znd1h4s5vdp5981bzz4rb48lnx2nwwnvcqk84rl6zmc0l97"))))
    (properties `((upstream-name . "geysertimes")))
    (build-system r-build-system)
    (propagated-inputs (list r-readr r-lubridate r-jsonlite))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/geysertimes/geysertimes-r-package")
    (synopsis "Geyser Data from GeyserTimes.org")
    (description
     "Download geyser eruption and observation data from the @code{GeyserTimes} site
(<https://geysertimes.org>) and optionally store it locally.  The vignette shows
a simple analysis of downloading, accessing, and summarizing the data.")
    (license license:expat)))

(define-public r-gexp
  (package
    (name "r-gexp")
    (version "1.0-21")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gexp" version))
       (sha256
        (base32 "1x2lpz4mqk5khqjyfval59m6cc6bw0c9a8dgr29yn4l8r8vsm9ky"))))
    (properties `((upstream-name . "gexp")))
    (build-system r-build-system)
    (propagated-inputs (list r-png r-mvtnorm r-jpeg))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ivanalaman/gexp")
    (synopsis "Generator of Experiments")
    (description
     "Generates experiments - simulating structured or experimental data as:
completely randomized design, randomized block design, latin square design,
factorial and split-plot experiments (Ferreira, 2008, ISBN:8587692526; Naes et
al., 2007 <doi:10.1002/qre.841>; Rencher et al., 2007, ISBN:9780471754985;
Montgomery, 2001, ISBN:0471316490).")
    (license license:gpl2+)))

(define-public r-gevcdn
  (package
    (name "r-gevcdn")
    (version "1.1.6-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GEVcdn" version))
       (sha256
        (base32 "0vmm9fhyrk5m0zwy7fpq5j9zinws0gn2saf4j9plqc9gj1gphjn0"))))
    (properties `((upstream-name . "GEVcdn")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GEVcdn")
    (synopsis "GEV Conditional Density Estimation Network")
    (description
     "This package implements a flexible nonlinear modelling framework for
nonstationary generalized extreme value analysis in hydroclimatology following
Cannon (2010) <doi:10.1002/hyp.7506>.")
    (license license:gpl3)))

(define-public r-gevaco
  (package
    (name "r-gevaco")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GEVACO" version))
       (sha256
        (base32 "0zkan4w96g3qc1hgla90mh32b55bk58gax095p4nxrfi46f8w3rf"))))
    (properties `((upstream-name . "GEVACO")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlrsim r-nlme))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GEVACO")
    (synopsis
     "Joint Test of Gene and GxE Interactions via Varying Coefficients")
    (description
     "This package provides a novel statistical model to detect the joint genetic and
dynamic gene-environment (@code{GxE}) interaction with continuous traits in
genetic association studies.  It uses varying-coefficient models to account for
different @code{GxE} trajectories, regardless whether the relationship is linear
or not.  The package includes one function, @code{GxEtest}(), to test a single
genetic variant (e.g., a single nucleotide polymorphism or SNP), and another
function, @code{GxEscreen}(), to test for a set of genetic variants.  The method
involves a likelihood ratio test described in Crainiceanu, C. M., and Ruppert,
D. (2004) <doi:10.1111/j.1467-9868.2004.00438.x>.")
    (license license:gpl3)))

(define-public r-getwiki
  (package
    (name "r-getwiki")
    (version "0.9.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "getwiki" version))
       (sha256
        (base32 "1dj91hc65l84yhzdl7i05s7nw0ybgijjs2g2dxgfmsvxnj10fgby"))))
    (properties `((upstream-name . "getwiki")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=getwiki")
    (synopsis "R Wrapper for Wikipedia Data")
    (description
     "This package provides a simple wrapper for Wikipedia data.  Specifically, this
package looks to fill a gap in retrieving text data in a tidy format that can be
used for Natural Language Processing.")
    (license license:expat)))

(define-public r-gettz
  (package
    (name "r-gettz")
    (version "0.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gettz" version))
       (sha256
        (base32 "0dgzw5mji5983588vj6q2kyjv9dngmrd0ilrkhfc3b4ngvfq61mf"))))
    (properties `((upstream-name . "gettz")))
    (build-system r-build-system)
    (home-page "https://github.com/eddelbuettel/gettz/")
    (synopsis "Get the Timezone Information")
    (description
     "This package provides a function to retrieve the system timezone on Unix systems
which has been found to find an answer when Sys.timezone() has failed.  It is
based on an answer by Duane @code{McCully} posted on @code{StackOverflow}', and
adapted to be callable from R. The package also builds on Windows, but just
returns NULL.")
    (license license:gpl2+)))

(define-public r-gettddata
  (package
    (name "r-gettddata")
    (version "1.5.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GetTDData" version))
       (sha256
        (base32 "1l2kc85sxnwmccyyjf18d8npg40pa3kgsvd73lj6b8pxwrsg9wj7"))))
    (properties `((upstream-name . "GetTDData")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rvest
                             r-readxl
                             r-purrr
                             r-jsonlite
                             r-humanize
                             r-fs
                             r-dplyr
                             r-curl
                             r-cli
                             r-bizdays))
    (home-page "https://github.com/msperlin/GetTDData/")
    (synopsis "Get Data for Brazilian Bonds (Tesouro Direto)")
    (description
     "Downloads and aggregates data for Brazilian government issued bonds directly
from the website of Tesouro Direto <https://www.tesourodireto.com.br/>.")
    (license license:expat)))

(define-public r-getspres
  (package
    (name "r-getspres")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "getspres" version))
       (sha256
        (base32 "190y4qrm1wawp0787zhw3g8h9s2smacvvxbs70pxbhysq4bkzcdd"))))
    (properties `((upstream-name . "getspres")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer
                             r-plotrix
                             r-metafor
                             r-dplyr
                             r-colorspace
                             r-colorramps))
    (native-inputs (list r-knitr))
    (home-page "https://magosil86.github.io/getspres/")
    (synopsis "SPRE Statistics for Exploring Heterogeneity in Meta-Analysis")
    (description
     "An implementation of SPRE (standardised predicted random-effects) statistics in
R to explore heterogeneity in genetic association meta- analyses, as described
by Magosi et al. (2019) <doi:10.1093/bioinformatics/btz590>.  SPRE statistics
are precision weighted residuals that indicate the direction and extent with
which individual study-effects in a meta-analysis deviate from the average
genetic effect.  Overly influential positive outliers have the potential to
inflate average genetic effects in a meta-analysis whilst negative outliers
might lower or change the direction of effect.  See the getspres website for
documentation and examples <https://magosil86.github.io/getspres/>.")
    (license license:expat)))

(define-public r-getspanel
  (package
    (name "r-getspanel")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "getspanel" version))
       (sha256
        (base32 "0xccla4w7vc82gqxfm927xzynchn9zi37smnq03rkamn4pf7v4gz"))))
    (properties `((upstream-name . "getspanel")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-matrix r-ggplot2 r-gets r-fastdummies))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/moritzpschwarz/getspanel")
    (synopsis "General-to-Specific Modelling of Panel Data")
    (description
     "Uses several types of indicator saturation and automated General-to-Specific
(GETS) modelling from the gets package and applies it to panel data.  This
allows the detection of structural breaks in panel data, operationalising a
reverse causal approach of causal inference, see Pretis and Schwarz (2022)
<doi:10.2139/ssrn.4022745>.")
    (license license:expat)))

(define-public r-gets
  (package
    (name "r-gets")
    (version "0.38")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gets" version))
       (sha256
        (base32 "0jwjwlg8r4mgk12iszxyy2nfnmqy75ajal3nzdgb8rn2xa3q35vc"))))
    (properties `((upstream-name . "gets")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo))
    (home-page "https://CRAN.R-project.org/package=gets")
    (synopsis
     "General-to-Specific (GETS) Modelling and Indicator Saturation Methods")
    (description
     "Automated General-to-Specific (GETS) modelling of the mean and variance of a
regression, and indicator saturation methods for detecting and testing for
structural breaks in the mean, see Pretis, Reade and Sucarrat (2018)
<doi:10.18637/jss.v086.i03> for an overview of the package.  In advanced use,
the estimator and diagnostics tests can be fully user-specified, see Sucarrat
(2021) <doi:10.32614/RJ-2021-024>.")
    (license license:gpl2+)))

(define-public r-getquandldata
  (package
    (name "r-getquandldata")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GetQuandlData" version))
       (sha256
        (base32 "0j5n2xbjk64nyfdpzphsiwa9k08rvgdfkzbfqahjdy11rk4x1fgi"))))
    (properties `((upstream-name . "GetQuandlData")))
    (build-system r-build-system)
    (propagated-inputs (list r-readr
                             r-purrr
                             r-memoise
                             r-jsonlite
                             r-fs
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/msperlin/GetQuandlData/")
    (synopsis
     "Fast and Cached Import of Data from 'Quandl' Using the 'json API'")
    (description
     "Imports time series data from the Quandl database <https://data.nasdaq.com/>.
The package uses the json api at <https://data.nasdaq.com/search>, local caching
('memoise package) and the tidy format by default.  Also allows queries of
databases, allowing the user to see which time series are available for each
database id.  In short, it is an alternative to package Quandl', with faster
data importation in the tidy/long format.")
    (license license:gpl2)))

(define-public r-getproxy
  (package
    (name "r-getproxy")
    (version "1.13")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "getProxy" version))
       (sha256
        (base32 "0n8ja0i1v0cd2piqyx4hfc4qw18d535j3ffpz164c3r8cmppnqzd"))))
    (properties `((upstream-name . "getProxy")))
    (build-system r-build-system)
    (propagated-inputs (list r-httr r-cli))
    (home-page "https://selesnow.github.io/getProxy/")
    (synopsis "Get Free Proxy IP and Port")
    (description
     "Allows get address and port of the free proxy server, from one of two services
<http://gimmeproxy.com/> or <https://getproxylist.com/>.  And it's easy to
redirect your Internet connection through a proxy server.")
    (license license:gpl2)))

(define-public r-getmstatistic
  (package
    (name "r-getmstatistic")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "getmstatistic" version))
       (sha256
        (base32 "100m5cbwx9anlh1hzjv7d1h1viqsxb0bx0ksc1dsjx3gqrf77q1n"))))
    (properties `((upstream-name . "getmstatistic")))
    (build-system r-build-system)
    (propagated-inputs (list r-stargazer
                             r-psych
                             r-metafor
                             r-gtable
                             r-gridextra
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://magosil86.github.io/getmstatistic/")
    (synopsis "Quantifying Systematic Heterogeneity in Meta-Analysis")
    (description
     "Quantifying systematic heterogeneity in meta-analysis using R. The M statistic
aggregates heterogeneity information across multiple variants to, identify
systematic heterogeneity patterns and their direction of effect in
meta-analysis.  It's primary use is to identify outlier studies, which either
show \"null\" effects or consistently show stronger or weaker genetic effects than
average across, the panel of variants examined in a GWAS meta-analysis.  In
contrast to conventional heterogeneity metrics (Q-statistic, I-squared and
tau-squared) which measure random heterogeneity at individual variants, M
measures systematic (non-random) heterogeneity across multiple independently
associated variants.  Systematic heterogeneity can arise in a meta-analysis due
to differences in the study characteristics of participating studies.  Some of
the differences may include: ancestry, allele frequencies, phenotype definition,
age-of-disease onset, family-history, gender, linkage disequilibrium and quality
control thresholds.  See <https://magosil86.github.io/getmstatistic/> for
statistical statistical theory, documentation and examples.")
    (license license:expat)))

(define-public r-getlattesdata
  (package
    (name "r-getlattesdata")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GetLattesData" version))
       (sha256
        (base32 "0gfjqvzn03qxlyf4hj5kzfy8pqbphrw421bqbkb2pksa6gyvnfnq"))))
    (properties `((upstream-name . "GetLattesData")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml
                             r-stringr
                             r-stringdist
                             r-readr
                             r-lubridate
                             r-dplyr
                             r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/msperlin/GetLattesData/")
    (synopsis "Reading Bibliometric Data from Lattes Platform")
    (description
     "This package provides a simple API for downloading and reading xml data directly
from Lattes <http://lattes.cnpq.br/>.")
    (license license:gpl2)))

(define-public r-getlattes
  (package
    (name "r-getlattes")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "getLattes" version))
       (sha256
        (base32 "1rrd9x9jmnsy0770c86lqws4a6b2zxf3384h1dcvzrmlk20hhqxw"))))
    (properties `((upstream-name . "getLattes")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tibble
                             r-rlang
                             r-purrr
                             r-piper
                             r-janitor
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/roneyfraga/getLattes")
    (synopsis "Import and Process Data from the 'Lattes' Curriculum Platform")
    (description
     "Tool for import and process data from Lattes curriculum platform
(<http://lattes.cnpq.br/>).  The Brazilian government keeps an extensive base of
curricula for academics from all over the country, with over 5 million
registrations.  The academic life of the Brazilian researcher, or related to
Brazilian universities, is documented in Lattes'.  Some information that can be
obtained: professional formation, research area, publications, academics
advisories, projects, etc. @code{getLattes} package allows work with Lattes data
exported to XML format.")
    (license license:gpl3)))

(define-public r-getip
  (package
    (name "r-getip")
    (version "0.1-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "getip" version))
       (sha256
        (base32 "0qyld6sy1i7kmbvcyg3vdv31qdyj81n54v6gvknb7nrqwwl8bgv3"))))
    (properties `((upstream-name . "getip")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=getip")
    (synopsis "'IP' Address 'Lookup'")
    (description
     "This package provides a micro-package for getting your IP address, either the
local/internal or the public/external one.  Currently only IPv4 addresses are
supported.")
    (license (license:fsdg-compatible "BSD 2-clause License + file LICENSE"))))

(define-public r-getfredata
  (package
    (name "r-getfredata")
    (version "0.8.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GetFREData" version))
       (sha256
        (base32 "15rca6pfsx5hcl1lnz49904b2ps0312mwjv8d4f0g76jhkvqfvss"))))
    (properties `((upstream-name . "GetFREData")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-xml
                             r-stringr
                             r-rvest
                             r-readr
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-getdfpdata2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/msperlin/GetFREData/")
    (synopsis "Reading FRE Corporate Data of Public Traded Companies from B3")
    (description
     "Reads corporate data such as board composition and compensation for companies
traded at B3, the Brazilian exchange <https://www.b3.com.br/>.  All data is
downloaded and imported from the ftp site
<http://dados.cvm.gov.br/dados/CIA_ABERTA/DOC/FRE/>.")
    (license license:gpl2)))

(define-public r-getdteval
  (package
    (name "r-getdteval")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "getDTeval" version))
       (sha256
        (base32 "16y24jhy9k1adlmf6yqzc8kla2vw7r0c6zd52jpfkiljxwkm4634"))))
    (properties `((upstream-name . "getDTeval")))
    (build-system r-build-system)
    (propagated-inputs (list r-microbenchmark r-formulaic r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=getDTeval")
    (synopsis
     "Translating Coding Statements using get() and eval() for Improved Run-Time Coding Efficiency")
    (description
     "The @code{getDTeval}() function facilitates the translation of the original
coding statement to an optimized form for improved runtime efficiency without
compromising on the programmatic coding design.  The function can either provide
a translation of the coding statement, directly evaluate the translation to
return a coding result, or provide both of these outputs.")
    (license license:gpl3)))

(define-public r-getdfpdata2
  (package
    (name "r-getdfpdata2")
    (version "0.6.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GetDFPData2" version))
       (sha256
        (base32 "1dc1ks9mrwc8sjmp6fh47hlhhfwvfign6jmf4cxik28q3253al01"))))
    (properties `((upstream-name . "GetDFPData2")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-writexl
                             r-tidyr
                             r-stringr
                             r-shiny
                             r-rvest
                             r-readr
                             r-rcurl
                             r-purrr
                             r-memoise
                             r-magrittr
                             r-lubridate
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/msperlin/GetDFPData2/")
    (synopsis "Reading Annual and Quarterly Financial Reports from B3")
    (description
     "Reads annual and quarterly financial reports from companies traded at B3, the
Brazilian exchange <https://www.b3.com.br/>.  All data is downloaded and
imported from CVM's public ftp site
<https://dados.cvm.gov.br/dados/CIA_ABERTA/>.")
    (license license:gpl2)))

(define-public r-getdfpdata
  (package
    (name "r-getdfpdata")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GetDFPData" version))
       (sha256
        (base32 "1yxf1i5bipz16pbydl6qxlalh2zpdiigbxd1qczbjc1z78bl5727"))))
    (properties `((upstream-name . "GetDFPData")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml
                             r-xlsx
                             r-tibble
                             r-stringr
                             r-reshape2
                             r-readr
                             r-lubridate
                             r-dplyr
                             r-curl
                             r-crayon))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/msperlin/GetDFPData/")
    (synopsis
     "Reading Annual Financial Reports from Bovespa's DFP, FRE and FCA System")
    (description
     "Reads annual financial reports including assets, liabilities, dividends history,
stockholder composition and much more from Bovespa's DFP, FRE and FCA systems
<http://www.b3.com.br/pt_br/produtos-e-servicos/negociacao/renda-variavel/empresas-listadas.htm>.
 These are web based interfaces for all financial reports of companies traded at
Bovespa.  The package is specially designed for large scale data importation,
keeping a tabular (long) structure for easier processing.")
    (license license:gpl2)))

(define-public r-getdesigns
  (package
    (name "r-getdesigns")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GETdesigns" version))
       (sha256
        (base32 "1kd08vdra3akdkvk6n9i6w4m2qmlnkjqjnssdn8f317vwbhjcmf3"))))
    (properties `((upstream-name . "GETdesigns")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GETdesigns")
    (synopsis "Generalized Extended Triangular Designs ('GETdesigns')")
    (description
     "Since their introduction by Bose and Nair (1939)
<https://www.jstor.org/stable/40383923>, partially balanced incomplete block
(PBIB) designs remain an important class of incomplete block designs.  The
concept of association scheme was used by Bose and Shimamoto (1952)
<doi:10.1080/01621459.1952.10501161> for the classification of these designs.
The constraint of resources always motivates the experimenter to advance towards
PBIB designs, more specifically to higher associate class PBIB designs from
balanced incomplete block designs.  It is interesting to note that many times
higher associate PBIB designs perform better than their counterpart lower
associate PBIB designs for the same set of parameters v, b, r, k and lambda_i
(i=1,2...m).  This package contains functions named GETD() for generating
m-associate (m>=2) class PBIB designs along with parameters (v, b, r, k and
lambda_i, i = 1, 2,â¦,m) based on Generalized Triangular (GT) Association
Scheme.  It also calculates the Information matrix, Average variance factor and
canonical efficiency factor of the generated design.  These designs, besides
having good efficiency, require smaller number of replications and smallest
possible concurrence of treatment pairs.")
    (license license:gpl2+)))

(define-public r-getbcbdata
  (package
    (name "r-getbcbdata")
    (version "0.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GetBCBData" version))
       (sha256
        (base32 "0bc1ar1cx7vxvlld2dishi46a9bxs075vgby1xs8kbxlllknvcsi"))))
    (properties `((upstream-name . "GetBCBData")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-purrr
                             r-memoise
                             r-jsonlite
                             r-future
                             r-furrr
                             r-dplyr
                             r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/msperlin/GetBCBData/")
    (synopsis
     "Imports Datasets from BCB (Central Bank of Brazil) using Its Official API")
    (description
     "Downloads and organizes datasets using BCB's API <https://www.bcb.gov.br/>.
Offers options for caching with the memoise package and , multicore/multisession
with furrr and format of output data (long/wide).")
    (license license:gpl2)))

(define-public r-getable
  (package
    (name "r-getable")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "getable" version))
       (sha256
        (base32 "0v45nqpx55vbj68gj4wxr0bdlrl897ayac78rxjd2lkkjx49hlyh"))))
    (properties `((upstream-name . "getable")))
    (build-system r-build-system)
    (propagated-inputs (list r-knitr))
    (native-inputs (list r-knitr))
    (home-page "https://yongfu.name/getable/")
    (synopsis
     "Fetching Tabular Data \"Onload\" in Compiled R Markdown HTML Documents")
    (description
     "Dynamically retrieve data from the web to render HTML tables on inspection in R
Markdown HTML documents.")
    (license license:expat)))

(define-public r-get
  (package
    (name "r-get")
    (version "1.0-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GET" version))
       (sha256
        (base32 "0mg6q10022f35aqnvfrh2czvk2m31d5jsidhc85bjgnkhwg65pcy"))))
    (properties `((upstream-name . "GET")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridislite r-gridextra r-ggplot2 r-cluster))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/myllym/GET")
    (synopsis "Global Envelopes")
    (description
     "Implementation of global envelopes for a set of general d-dimensional vectors T
in various applications.  A 100(1-alpha)% global envelope is a band bounded by
two vectors such that the probability that T falls outside this envelope in any
of the d points is equal to alpha.  Global means that the probability is
controlled simultaneously for all the d elements of the vectors.  The global
envelopes can be used for graphical Monte Carlo and permutation tests where the
test statistic is a multivariate vector or function (e.g. goodness-of-fit
testing for point patterns and random sets, functional analysis of variance,
functional general linear model, n-sample test of correspondence of distribution
functions), for central regions of functional or multivariate data (e.g. outlier
detection, functional boxplot) and for global confidence and prediction bands
(e.g. confidence band in polynomial regression, Bayesian posterior prediction).
See MyllymÃ¤ki and MrkviÄka (2023) <doi:10.48550/@code{arXiv.1911.06583>},
MyllymÃ¤ki et al. (2017) <doi:10.1111/rssb.12172>, MrkviÄka and MyllymÃ¤ki
(2023) <doi:10.1007/s11222-023-10275-7>, MrkviÄka et al. (2016)
<doi:10.1016/j.spasta.2016.04.005>, MrkviÄka et al. (2017)
<doi:10.1007/s11222-016-9683-9>, MrkviÄka et al. (2020)
<doi:10.14736/kyb-2020-3-0432>, MrkviÄka et al. (2021)
<doi:10.1007/s11009-019-09756-y>, MyllymÃ¤ki et al. (2021)
<doi:10.1016/j.spasta.2020.100436>, MrkviÄka et al. (2022)
<doi:10.1002/sim.9236>, Dai et al. (2022) <doi:10.5772/intechopen.100124>,
DvoÅÃ¡k and MrkviÄka (2022) <doi:10.1007/s00180-021-01134-y>, MrkviÄka et al.
(2023) <doi:10.48550/@code{arXiv.2309.04746>}, and Konstantinou et al. (2024)
<doi: 10.48550/@code{arXiv.2403.01838>}.")
    (license license:gpl3)))

(define-public r-gesttools
  (package
    (name "r-gesttools")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gesttools" version))
       (sha256
        (base32 "1lrjr60vrl0j31hb12i8c78k5554vwpgj8pzzrhbdhmb6y0972fx"))))
    (properties `((upstream-name . "gesttools")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-testthat
                             r-rsample
                             r-nnet
                             r-magrittr
                             r-geem
                             r-datacombine))
    (home-page "https://github.com/danieltompsett/gesttools")
    (synopsis
     "General Purpose G-Estimation for End of Study or Time-Varying Outcomes")
    (description
     "This package provides a series of general purpose tools to perform g-estimation
using the methods described in Sjolander and Vansteelandt (2016)
<doi:10.1515/em-2015-0005> and Dukes and Vansteelandt <doi:10.1093/aje/kwx347>.
The package allows for g-estimation in a wide variety of circumstances,
including an end of study or time-varying outcome, and an exposure that is a
binary, continuous, or a categorical variable with three or more categories.
The package also supports g-estimation with time-varying causal effects and
effect modification by a confounding variable.")
    (license license:gpl3)))

(define-public r-gestate
  (package
    (name "r-gestate")
    (version "1.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gestate" version))
       (sha256
        (base32 "1b6zyrph3h7cgnl8rxj3yv14mgnvxvgk26m84k5v8cbnly9ky2k2"))))
    (properties `((upstream-name . "gestate")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-shinythemes r-shiny r-foreach
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gestate")
    (synopsis "Generalised Survival Trial Assessment Tool Environment")
    (description
     "This package provides tools to assist planning and monitoring of time-to-event
trials under complicated censoring assumptions and/or non-proportional hazards.
There are three main components: The first is analytic calculation of predicted
time-to-event trial properties, providing estimates of expected hazard ratio,
event numbers and power under different analysis methods.  The second is
simulation, allowing stochastic estimation of these same properties.  Thirdly,
it provides parametric event prediction using blinded trial data, including
creation of prediction intervals.  Methods are based upon numerical integration
and a flexible object-orientated structure for defining event, censoring and
recruitment distributions (Curves).")
    (license license:gpl3)))

(define-public r-gestalt
  (package
    (name "r-gestalt")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gestalt" version))
       (sha256
        (base32 "0p8xrlmc81irh9s2c57sxgmvdjmmgmya0h31904c4cwi9aa32f4z"))))
    (properties `((upstream-name . "gestalt")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/egnha/gestalt")
    (synopsis "Tools for Making and Combining Functions")
    (description
     "This package provides a suite of function-building tools centered around a
(forward) composition operator, %>>>%, which extends the semantics of the
magrittr %>% operator and supports Tidyverse quasiquotation.  It enables you to
construct composite functions that can be inspected and transformed as list-like
objects.  In conjunction with %>>>%, a compact function constructor, fn(), and a
partial-application constructor, partial(), are also provided; both support
quasiquotation.")
    (license license:expat)))

(define-public r-gesso
  (package
    (name "r-gesso")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gesso" version))
       (sha256
        (base32 "0i7a927mbll8mlisxb0ic62fvq1wp5v0m69n7zkhcjabkxr7gbn3"))))
    (properties `((upstream-name . "gesso")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppthread
                             r-rcppeigen
                             r-rcpp
                             r-matrix
                             r-dplyr
                             r-bigmemory
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gesso")
    (synopsis
     "Hierarchical GxE Interactions in a Regularized Regression Model")
    (description
     "The method focuses on a single environmental exposure and induces a
main-effect-before-interaction hierarchical structure for the joint selection of
interaction terms in a regularized regression model.  For details see
Zemlianskaia et al. (2021) <arxiv:2103.13510>.")
    (license license:expat)))

(define-public r-geslar
  (package
    (name "r-geslar")
    (version "1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geslaR" version))
       (sha256
        (base32 "05dswf8c10vwhswsgdpzkqlhbazrw8bd62ndk768lw31fj1n0cab"))))
    (properties `((upstream-name . "geslaR")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-dplyr r-cli r-arrow))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/EireExtremes/geslaR")
    (synopsis "Get and Manipulate the GESLA Dataset")
    (description
     "Promote access to the GESLA <https://gesla787883612.wordpress.com> (Global
Extreme Sea Level Analysis) dataset, a higher-frequency sea-level record data
from all over the world.  It provides functions to download it entirely, or
query subsets directly into R, without the need of downloading the full dataset.
 Also, it provides a built-in web-application, so that users can apply basic
filters to select the data of interest, generating informative plots, and
showing the selected sites.")
    (license license:gpl3+)))

(define-public r-gesisdata
  (package
    (name "r-gesisdata")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gesisdata" version))
       (sha256
        (base32 "1cabgmprwlwn09hd1jd9hvj50bqaarxyajz251l0vv2qb3d5l11a"))))
    (properties `((upstream-name . "gesisdata")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rselenium
                             r-rio
                             r-netstat
                             r-magrittr
                             r-foreign
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/fsolt/gesisdata")
    (synopsis "Reproducible Data Retrieval from the GESIS Data Archive")
    (description
     "Reproducible, programmatic retrieval of datasets from the GESIS Data Archive.
The GESIS Data Archive <https://search.gesis.org> makes available thousands of
invaluable datasets, but researchers using these datasets are caught in a bind.
The archive's terms and conditions bar dissemination of downloaded datasets to
third parties, but to ensure that one's work can be reproduced, assessed, and
built upon by others, one must provide access to the raw data one has employed.
The gesisdata package cuts this knot by providing registered users with
programmatic, reproducible access to GESIS datasets from within R'.")
    (license license:expat)))

(define-public r-gese
  (package
    (name "r-gese")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GESE" version))
       (sha256
        (base32 "0h3s2sbnv5wilr2mj6bnl2892p16pxrs341gg6jql1a540p669sk"))))
    (properties `((upstream-name . "GESE")))
    (build-system r-build-system)
    (propagated-inputs (list r-kinship2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GESE")
    (synopsis "Gene-Based Segregation Test")
    (description
     "This package implements the gene-based segregation test(GESE) and the weighted
GESE test for identifying genes with causal variants of large effects for
family-based sequencing data.  The methods are described in Qiao, D. Lange, C.,
Laird, N.M., Won, S., Hersh, C.P., et al. (2017). <DOI:10.1002/gepi.22037>.
Gene-based segregation method for identifying rare variants for family-based
sequencing studies.  Genet Epidemiol 41(4):309-319.  More details can be found
at <http://scholar.harvard.edu/dqiao/gese>.")
    (license license:gpl2)))

(define-public r-gernika
  (package
    (name "r-gernika")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeRnika" version))
       (sha256
        (base32 "0d61vx33rxlmjk5kazji0a6wli6cfcardn308wzbjqpvixsqy2vl"))))
    (properties `((upstream-name . "GeRnika")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-reshape2
                             r-purrr
                             r-magrittr
                             r-dplyr
                             r-diagrammer
                             r-data-tree
                             r-colorspace))
    (native-inputs (list r-rmarkdown r-knitr r-knitcitations r-ggpubr
                         r-ggplot2))
    (home-page "https://cran.r-project.org/package=GeRnika")
    (synopsis
     "Simulation, Visualization and Comparison of Tumor Evolution Data")
    (description
     "Simulating, visualizing and comparing tumor clonal data by using simple
commands.  This aims at providing a tool to help researchers to easily simulate
tumor data and analyze the results of their approaches for studying the
composition and the evolutionary history of tumors.")
    (license license:gpl3+)))

(define-public r-germinationmetrics
  (package
    (name "r-germinationmetrics")
    (version "0.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "germinationmetrics" version))
       (sha256
        (base32 "1lssjs9rp09l9draylrkslb74apizbanacgp00yyxhgxa39dk2g6"))))
    (properties `((upstream-name . "germinationmetrics")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack
                             r-plyr
                             r-mathjaxr
                             r-gslnls
                             r-ggrepel
                             r-ggplot2
                             r-data-table
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/aravind-j/germinationmetrics")
    (synopsis "Seed Germination Indices and Curve Fitting")
    (description
     "This package provides functions to compute various germination indices such as
germinability, median germination time, mean germination time, mean germination
rate, speed of germination, Timson's index, germination value, coefficient of
uniformity of germination, uncertainty of germination process, synchrony of
germination etc.  from germination count data.  Includes functions for fitting
cumulative seed germination curves using four-parameter hill function and
computation of associated parameters.  See the vignette for more, including full
list of citations for the methods implemented.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-germinar
  (package
    (name "r-germinar")
    (version "2.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GerminaR" version))
       (sha256
        (base32 "0f2dbq1adbd5p30kf9bi8qqk2s8da0p65i9a0igm16ckwpam7lkk"))))
    (properties `((upstream-name . "GerminaR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-shiny
                             r-purrr
                             r-ggplot2
                             r-dt
                             r-dplyr
                             r-agricolae))
    (native-inputs (list r-knitr))
    (home-page "https://germinar.inkaverse.com/")
    (synopsis "Indices and Graphics for Assess Seed Germination Process")
    (description
     "This package provides a collection of different indices and visualization
techniques for evaluate the seed germination process in ecophysiological studies
(Lozano-Isla et al.  2019) <doi:10.1111/1440-1703.1275>.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-gerefer
  (package
    (name "r-gerefer")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gerefer" version))
       (sha256
        (base32 "0jvy2m6bgdp7isgblj0vlrx0piyfzhib0wihwxg08v754x4xnwi3"))))
    (properties `((upstream-name . "gerefer")))
    (build-system r-build-system)
    (propagated-inputs (list r-bibliorefer))
    (home-page "https://cran.r-project.org/package=gerefer")
    (synopsis
     "Preparer of Main Scientific References for Automatic Insertion in Academic Papers")
    (description
     "Generates a file, containing the main scientific references, prepared to be
automatically inserted into an academic paper.  The articles present in the list
are chosen from the main references generated, by function principal_lister(),
of the package bibliorefer'.  The generated file contains the list of metadata
of the principal references in @code{BibTex} format.  Massimo Aria, Corrado
Cuccurullo. (2017) <doi:10.1016/j.joi.2017.08.007>.  Caibo Zhou, Wenyan Song.
(2021) <doi:10.1016/j.jclepro.2021.126943>.  Hamid DerviÅ. (2019)
<doi:10.5530/jscires.8.3.32>.")
    (license license:gpl3)))

(define-public r-gerbil
  (package
    (name "r-gerbil")
    (version "0.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gerbil" version))
       (sha256
        (base32 "1ra1piza922sigxk61v55dbpkmzha716drmb6m1qxn892vykmm8w"))))
    (properties `((upstream-name . "gerbil")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm
                             r-pbapply
                             r-openxlsx
                             r-mvtnorm
                             r-mass
                             r-lattice
                             r-desctools))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gerbil")
    (synopsis
     "Generalized Efficient Regression-Based Imputation with Latent Processes")
    (description
     "This package implements a new multiple imputation method that draws imputations
from a latent joint multivariate normal model which underpins generally
structured data.  This model is constructed using a sequence of flexible
conditional linear models that enables the resulting procedure to be efficiently
implemented on high dimensional datasets in practice.  See Robbins (2021)
<@code{arXiv:2008.02243>}.")
    (license license:gpl2)))

(define-public r-geppe
  (package
    (name "r-geppe")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geppe" version))
       (sha256
        (base32 "08d50sg80dj3jazznszs7b0jpb21jp9khjvplx1qgdlpnlljwq4h"))))
    (properties `((upstream-name . "geppe")))
    (build-system r-build-system)
    (propagated-inputs (list r-rfast2))
    (home-page "https://cran.r-project.org/package=geppe")
    (synopsis
     "Generalised Exponential Poisson and Poisson Exponential Distributions")
    (description
     "Maximum likelihood estimation, random values generation, density computation and
other functions for the exponential-Poisson generalised exponential-Poisson and
Poisson-exponential distributions.  References include: Rodrigues G. C., Louzada
F. and Ramos P. L. (2018). \"Poisson-exponential distribution: different methods
of estimation\".  Journal of Applied Statistics, 45(1): 128--144.
<doi:10.1080/02664763.2016.1268571>.  Louzada F., Ramos, P. L. and Ferreira, H.
P. (2020). \"Exponential-Poisson distribution: estimation and applications to
rainfall and aircraft data with zero occurrence\".  Communications in
Statistics--Simulation and Computation, 49(4): 1024--1043.
<doi:10.1080/03610918.2018.1491988>.  Barreto-Souza W. and Cribari-Neto F.
(2009). \"A generalization of the exponential-Poisson distribution\".  Statistics
and Probability Letters, 79(24): 2493--2500. <doi:10.1016/j.spl.2009.09.003>.")
    (license license:gpl2+)))

(define-public r-gephiforr
  (package
    (name "r-gephiforr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GephiForR" version))
       (sha256
        (base32 "0g1aq1k25pipnkm1xbsb7f9nx4j86ra2a39h9nrq9jzh40vd3ldr"))))
    (properties `((upstream-name . "GephiForR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-igraph))
    (home-page "https://cran.r-project.org/package=GephiForR")
    (synopsis "'Gephi' Network Visualization")
    (description
     "This package implements key features of Gephi for network visualization,
including @code{ForceAtlas2} (with @code{LinLog} mode), network scaling, and
network rotations.  It also includes easy network visualization tools such as
edge and node color assignment for recreating Gephi'-style graphs in R. The
package references layout algorithms developed by Jacomy, M., Venturini T.,
Heymann S., and Bastian M. (2014) <doi:10.1371/journal.pone.0098679> and Noack,
A. (2009) <doi:10.48550/@code{arXiv.0807.4052>}.")
    (license license:expat)))

(define-public r-gepaf
  (package
    (name "r-gepaf")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gepaf" version))
       (sha256
        (base32 "1fmxm8dzz8jx9qc7q9rwxh8zm8flwb9xkbpbv50526klsixp0qs3"))))
    (properties `((upstream-name . "gepaf")))
    (build-system r-build-system)
    (propagated-inputs (list r-bitops))
    (home-page "https://github.com/riatelab/gepaf")
    (synopsis "Google Encoded Polyline Algorithm Format")
    (description
     "Encode and decode the Google Encoded Polyline Algorithm Format.  See
<https://developers.google.com/maps/documentation/utilities/polylinealgorithm>
for more information.")
    (license license:gpl3)))

(define-public r-geozoo
  (package
    (name "r-geozoo")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geozoo" version))
       (sha256
        (base32 "0g91yhg7zw1bp0lxxblr2irckjg2rl4pg1vgglccnmxkzn0ji2qi"))))
    (properties `((upstream-name . "geozoo")))
    (build-system r-build-system)
    (propagated-inputs (list r-bitops))
    (home-page "http://schloerke.github.io/geozoo/")
    (synopsis "Zoo of Geometric Objects")
    (description
     "Geometric objects defined in geozoo can be simulated or displayed in the R
package tourr'.")
    (license license:gpl2)))

(define-public r-geoweightedmodel
  (package
    (name "r-geoweightedmodel")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeoWeightedModel" version))
       (sha256
        (base32 "1ylfggfqylqi76z3ac95cnxmmw3gzddq2r5qx5w0gjyjvrxr8wsv"))))
    (properties `((upstream-name . "GeoWeightedModel")))
    (build-system r-build-system)
    (propagated-inputs (list r-spdep
                             r-sp
                             r-shinywidgets
                             r-shinyjs
                             r-shinydashboard
                             r-shinybusy
                             r-shinybs
                             r-shinyalert
                             r-shiny
                             r-readxl
                             r-raster
                             r-gwmodel
                             r-dt
                             r-dplyr
                             r-cartography
                             r-beepr))
    (home-page "https://cran.r-project.org/package=GeoWeightedModel")
    (synopsis "User-Friendly Interface for Geographically-Weighted Models")
    (description
     "This package contains the development of a tool that provides a web-based
graphical user interface (GUI) to perform Techniques from a subset of spatial
statistics known as geographically weighted (GW) models.  Contains methods
described by Brunsdon et al., 1996 <doi:10.1111/j.1538-4632.1996.tb00936.x>,
Brunsdon et al., 2002 <doi:10.1016/s0198-9715(01)00009-6>, Harris et al., 2011
<doi:10.1080/13658816.2011.554838>, Brunsdon et al., 2007
<doi:10.1111/j.1538-4632.2007.00709.x>.")
    (license license:gpl3)))

(define-public r-geovol
  (package
    (name "r-geovol")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geovol" version))
       (sha256
        (base32 "13ll9jyh1fadm2rpfvja0fnqnpr6haivz1vmmpi6yc95rpl4234b"))))
    (properties `((upstream-name . "geovol")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo))
    (home-page "https://sites.google.com/site/susanacamposmartins/")
    (synopsis "Geopolitical Volatility (GEOVOL) Modelling")
    (description
     "Simulation, estimation and inference for the geopolitical volatility (GEOVOL)
model of Engle and Campos-Martins (2020) <doi:10.2139/ssrn.3685213>, where
GEOVOL is modelled as a latent multiplicative volatility factor with
heterogeneous factor loadings.  Estimation is carried out as a
maximization-maximization procedure, where GEOVOL and the GEOVOL loadings are
estimated iteratively until convergence.")
    (license license:gpl2+)))

(define-public r-geouy
  (package
    (name "r-geouy")
    (version "0.2.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geouy" version))
       (sha256
        (base32 "02x52fid3pmaaj22b729zvrh8giwb2a4az66xxwp0lw03pf188zi"))))
    (properties `((upstream-name . "geouy")))
    (build-system r-build-system)
    (inputs (list proj geos gdal))
    (propagated-inputs (list r-viridis
                             r-tidyselect
                             r-stringr
                             r-sp
                             r-sf
                             r-rlang
                             r-rjson
                             r-raster
                             r-magrittr
                             r-glue
                             r-ggthemes
                             r-ggspatial
                             r-ggplot2
                             r-fs
                             r-dplyr
                             r-curl
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=geouy")
    (synopsis "Geographic Information of Uruguay")
    (description
     "The toolbox have functions to load and process geographic information for
Uruguay.  And extra-function to get address coordinates and orthophotos through
the uruguayan IDE API
<https://www.gub.uy/infraestructura-datos-espaciales/tramites-y-servicios/servicios/sistema-unico-direcciones-geograficas>.")
    (license license:gpl3)))

(define-public r-geots
  (package
    (name "r-geots")
    (version "0.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geoTS" version))
       (sha256
        (base32 "0hjhc68pl1gq9wcgvvagn903vlhwsayf7kka88rdcb6y7rgdlp5v"))))
    (properties `((upstream-name . "geoTS")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-robustbase
                             r-raster
                             r-foreach
                             r-ff
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=geoTS")
    (synopsis
     "Methods for Handling and Analyzing Time Series of Satellite Images")
    (description
     "This package provides functions and methods for: splitting large raster objects
into smaller chunks, transferring images from a binary format into raster
layers, transferring raster layers into an RData file, calculating the maximum
gap (amount of consecutive missing values) of a numeric vector, and fitting
harmonic regression models to periodic time series.  The homoscedastic harmonic
regression model is based on G. Roerink, M. Menenti and W. Verhoef (2000)
<doi:10.1080/014311600209814>.")
    (license license:gpl2+)))

(define-public r-geotopbricks
  (package
    (name "r-geotopbricks")
    (version "1.5.8.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geotopbricks" version))
       (sha256
        (base32 "1wl0zggilgq2nrwdjvmba9k4avgqfv06ffsj2fmql2qmlzan744q"))))
    (properties `((upstream-name . "geotopbricks")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-terra r-stringr r-sf r-raster))
    (home-page "https://zenodo.org/record/8228413")
    (synopsis "An R Plug-in for the Distributed Hydrological Model GEOtop")
    (description
     "It analyzes raster maps and other information as input/output files from the
Hydrological Distributed Model GEOtop.  It contains functions and methods to
import maps and other keywords from geotop.inpts file.  Some examples with
simulation cases of GEOtop 2.x/3.x are presented in the package.  Any
information about the GEOtop Distributed Hydrological Model source code is
available on www.geotop.org.  Technical details about the model are available in
Endrizzi et al (2014)
<https://gmd.copernicus.org/articles/7/2831/2014/gmd-7-2831-2014.html>.")
    (license license:gpl3+)))

(define-public r-geotoolsr
  (package
    (name "r-geotoolsr")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geotoolsR" version))
       (sha256
        (base32 "0chiyvc3f3y45v4ja1jhj4pag54ir18d42qnfd3q8dnyisk8cags"))))
    (properties `((upstream-name . "geotoolsR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-ggplot2 r-geor r-dplyr))
    (home-page "https://cran.r-project.org/package=geotoolsR")
    (synopsis "Tools to Improve the Use of Geostatistic")
    (description
     "The basic idea of this package is provides some tools to help the researcher to
work with geostatistics.  Initially, we present a collection of functions that
allow the researchers to deal with spatial data using bootstrap procedure.
There are five methods available and two ways to display them: bootstrap
confidence interval - provides a two-sided bootstrap confidence interval;
bootstrap plot - a graphic with the original variogram and each of the B
bootstrap variograms.")
    (license license:gpl2+)))

(define-public r-geotools
  (package
    (name "r-geotools")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geotools" version))
       (sha256
        (base32 "0d0vf9dvrrv68ivssp58qzaj8vra26ms33my097jmzmgagwy1spd"))))
    (properties `((upstream-name . "geotools")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=geotools")
    (synopsis "Geo tools")
    (description "This package provides tools.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-geothinner
  (package
    (name "r-geothinner")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeoThinneR" version))
       (sha256
        (base32 "0fsiik19cck9pmfk4nwwfy3vyj19s1x0m2y9a14jlrqy40v98lza"))))
    (properties `((upstream-name . "GeoThinneR")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-rcpp
                             r-nabor
                             r-matrixstats
                             r-fields
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jmestret/GeoThinneR")
    (synopsis "Simple Spatial Thinning for Ecological and Spatial Analysis")
    (description
     "This package provides efficient geospatial thinning algorithms to reduce the
density of coordinate data while maintaining spatial relationships.  Implements
K-D Tree and brute-force distance-based thinning, as well as grid-based and
precision-based thinning methods.  For more information on the methods, see
Elseberg et al. (2012) <https://hdl.handle.net/10446/86202>.")
    (license license:expat)))

(define-public r-geostatsp
  (package
    (name "r-geostatsp")
    (version "2.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geostatsp" version))
       (sha256
        (base32 "10xmdgmmd4f67778g02hyqzkk9zj9c0cdfs9cw8py1b413vfclyv"))))
    (properties `((upstream-name . "geostatsp")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-numderiv r-matrix r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=geostatsp")
    (synopsis "Geostatistical Modelling with Likelihood and Bayes")
    (description
     "Geostatistical modelling facilities using @code{SpatRaster} and
@code{SpatVector} objects are provided.  Non-Gaussian models are fit using
INLA', and Gaussian geostatistical models use Maximum Likelihood Estimation.
For details see Brown (2015) <doi:10.18637/jss.v063.i12>.  The
@code{RandomFields} package is available at
<https://www.wim.uni-mannheim.de/schlather/publications/software>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-geostats
  (package
    (name "r-geostats")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geostats" version))
       (sha256
        (base32 "0fmp6883a3xqay3wcc3bz7czhyik1mki3zn5z195iag11yvc5wr6"))))
    (properties `((upstream-name . "geostats")))
    (build-system r-build-system)
    (home-page "https://github.com/pvermees/geostats/")
    (synopsis "An Introduction to Statistics for Geoscientists")
    (description
     "This package provides a collection of datasets and simplified functions for an
introductory (geo)statistics module at University College London.  Provides
functionality for compositional, directional and spatial data, including ternary
diagrams, Wulff and Schmidt stereonets, and ordinary kriging interpolation.
Implements logistic and (additive and centred) logratio transformations.
Computes vector averages and concentration parameters for the von-Mises
distribution.  Includes a collection of natural and synthetic fractals, and a
simulator for deterministic chaos using a magnetic pendulum example.  The main
purpose of these functions is pedagogical.  Researchers can find more complete
alternatives for these tools in other packages such as compositions',
@code{robCompositions}', sp', gstat and RFOC'.  All the functions are written in
plain R, with no compiled code and a minimal number of dependencies.
Theoretical background and worked examples are available at
<https://tinyurl.com/UCLgeostats/>.")
    (license license:gpl3)))

(define-public r-geostan
  (package
    (name "r-geostan")
    (version "0.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geostan" version))
       (sha256
        (base32 "0s35rcjcimc00fsaiy7dbpg2nncyy4y2h8bpqgdcy3933xsz3jqm"))))
    (properties `((upstream-name . "geostan")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm
                             r-stanheaders
                             r-spdep
                             r-signs
                             r-sf
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-matrix
                             r-mass
                             r-gridextra
                             r-ggplot2
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://connordonegan.github.io/geostan/")
    (synopsis "Bayesian Spatial Analysis")
    (description
     "For spatial data analysis; provides exploratory spatial analysis tools, spatial
regression models, disease mapping models, model diagnostics, and special
methods for inference with small area survey data (e.g., the America Community
Survey (ACS)) and censored population health surveillance data.  Models are
pre-specified using the Stan programming language, a platform for Bayesian
inference using Markov chain Monte Carlo (MCMC).  References: Carpenter et al.
(2017) <doi:10.18637/jss.v076.i01>; Donegan (2021) <doi:10.31219/osf.io/3ey65>;
Donegan (2022) <doi:10.21105/joss.04716>; Donegan, Chun and Hughes (2020)
<doi:10.1016/j.spasta.2020.100450>; Donegan, Chun and Griffith (2021)
<doi:10.3390/ijerph18136856>; Morris et al. (2019)
<doi:10.1016/j.sste.2019.100301>.")
    (license license:gpl3+)))

(define-public r-geosptdb
  (package
    (name "r-geosptdb")
    (version "1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geosptdb" version))
       (sha256
        (base32 "1n1jvigavcxlbc5wki74lnhax3060i44m1cvkcr664wsjqhx3kl2"))))
    (properties `((upstream-name . "geosptdb")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmatch
                             r-sp
                             r-minqa
                             r-limsolve
                             r-gsl
                             r-geospt
                             r-fields
                             r-fd))
    (home-page "https://cran.r-project.org/package=geosptdb")
    (synopsis
     "Spatio-Temporal Radial Basis Functions with Distance-Based Methods (Optimization, Prediction and Cross Validation)")
    (description
     "Spatio-temporal radial basis functions (optimization, prediction and
cross-validation), summary statistics from cross-validation, Adjusting
distance-based linear regression model and generation of the principal
coordinates of a new individual from Gower's distance.")
    (license license:gpl2+)))

(define-public r-geospt
  (package
    (name "r-geospt")
    (version "1.0-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geospt" version))
       (sha256
        (base32 "1fmvrsmh4nfc4g8z9ca5k4c4pm7wy218crhigp58n6i6avvf9scz"))))
    (properties `((upstream-name . "geospt")))
    (build-system r-build-system)
    (propagated-inputs (list r-teachingdemos
                             r-sp
                             r-sgeostat
                             r-plyr
                             r-minqa
                             r-mass
                             r-limsolve
                             r-gstat
                             r-gsl
                             r-genalg
                             r-fields))
    (home-page "https://github.com/amsantac/geospt")
    (synopsis
     "Geostatistical Analysis and Design of Optimal Spatial Sampling Networks")
    (description
     "Estimation of the variogram through trimmed mean, radial basis functions
(optimization, prediction and cross-validation), summary statistics from
cross-validation, pocket plot, and design of optimal sampling networks through
sequential and simultaneous points methods.")
    (license license:gpl2+)))

(define-public r-geospark
  (package
    (name "r-geospark")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geospark" version))
       (sha256
        (base32 "004axa0k9xg1hra8jihwgnc379k8pib1h0zkr82j0m9sii4wrk6r"))))
    (properties `((upstream-name . "geospark")))
    (build-system r-build-system)
    (propagated-inputs (list r-sparklyr r-dplyr r-dbplyr))
    (home-page "https://cran.r-project.org/package=geospark")
    (synopsis "Bring Local Sf to Spark")
    (description
     "R binds @code{GeoSpark} <http://geospark.datasyslab.org/> extending sparklyr
<https://spark.rstudio.com/> R package to make distributed geocomputing easier.
Sf is a package that provides [simple features]
<https://en.wikipedia.org/wiki/Simple_Features> access for R and which is a
leading geospatial data processing tool.  Geospark R package bring the same
simple features access like sf but running on Spark distributed system.")
    (license license:asl2.0)))

(define-public r-geosimilarity
  (package
    (name "r-geosimilarity")
    (version "3.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geosimilarity" version))
       (sha256
        (base32 "10hdll5niqd1k5kgqzp44dipqwp6mvbq00h0x9h16yswvxrx6ji9"))))
    (properties `((upstream-name . "geosimilarity")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-purrr
                             r-magrittr
                             r-ggrepel
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ausgis/geosimilarity")
    (synopsis "Geographically Optimal Similarity")
    (description
     "Understanding spatial association is essential for spatial statistical
inference, including factor exploration and spatial prediction.  Geographically
optimal similarity (GOS) model is an effective method for spatial prediction, as
described in Yongze Song (2022) <doi:10.1007/s11004-022-10036-8>.  GOS was
developed based on the geographical similarity principle, as described in Axing
Zhu (2018) <doi:10.1080/19475683.2018.1534890>.  GOS has advantages in more
accurate spatial prediction using fewer samples and critically reduced
prediction uncertainty.")
    (license license:gpl3)))

(define-public r-geosed
  (package
    (name "r-geosed")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geosed" version))
       (sha256
        (base32 "07j4q6sgrf83h2gi2j5qap5rifz8gimdacd3b7ld1p3b6ai6kv3r"))))
    (properties `((upstream-name . "geosed")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=geosed")
    (synopsis "Smallest Enclosing Disc for Latitude and Longitude Points")
    (description
     "Find the smallest circle that contains all longitude and latitude input points.
From the generated center and radius, variable side polygons can be created,
navigation based on bearing and distance can be applied, and more.  Based on a
modified version of Welzl's algorithm for smallest circle.  Distance
calculations are based on the haversine formula.  Calculations for distance,
midpoint, bearing and more are derived from <https://www.movable-type.co.uk>.")
    (license license:expat)))

(define-public r-geoscale
  (package
    (name "r-geoscale")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geoscale" version))
       (sha256
        (base32 "1ykzxbwjdy0xi5hj5r201j3wrgahcxvxrgp40mqlw7zj80wgdgif"))))
    (properties `((upstream-name . "geoscale")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=geoscale")
    (synopsis "Geological Time Scale Plotting")
    (description
     "Functionality for adding the geological timescale to bivariate plots.")
    (license license:gpl2+)))

(define-public r-geosapi
  (package
    (name "r-geosapi")
    (version "0.7-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geosapi" version))
       (sha256
        (base32 "0zq02xnzqszmkdj99686g2x91gyc8x16yhkixhpd7frap7zy6h23"))))
    (properties `((upstream-name . "geosapi")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-readr
                             r-r6
                             r-openssl
                             r-magrittr
                             r-keyring
                             r-httr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/eblondel/geosapi")
    (synopsis "GeoServer REST API R Interface")
    (description
     "This package provides an R interface to the @code{GeoServer} REST API, allowing
to upload and publish data in a @code{GeoServer} web-application and expose data
to OGC Web-Services.  The package currently supports all CRUD
(Create,Read,Update,Delete) operations on @code{GeoServer} workspaces,
namespaces, datastores (stores of vector data), featuretypes, layers, styles, as
well as vector data upload operations.  For more information about the
@code{GeoServer} REST API, see
<https://docs.geoserver.org/stable/en/user/rest/>.")
    (license license:expat)))

(define-public r-geosae
  (package
    (name "r-geosae")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geoSAE" version))
       (sha256
        (base32 "16fkai95cqwxkylpk1gddyzja80036ghfb4ps3rz1vbbqzhg01x8"))))
    (properties `((upstream-name . "geoSAE")))
    (build-system r-build-system)
    (propagated-inputs (list r-nlme r-mass))
    (home-page "https://github.com/ketutdika/geoSAE")
    (synopsis "Geoadditive Small Area Model")
    (description
     "This function is an extension of the Small Area Estimation (SAE) model.
Geoadditive Small Area Model is a combination of the geoadditive model with the
Small Area Estimation (SAE) model, by adding geospatial information to the SAE
model.  This package refers to J.N.K Rao and Isabel Molina (2015, ISBN:
978-1-118-73578-7), Bocci, C., & Petrucci, A.
(2016)<doi:10.1002/9781118814963.ch13>, and Ardiansyah, M., Djuraidah, A., &
Kurnia, A. (2018)<doi:10.21082/jpptp.v2n2.2018.p101-110>.")
    (license license:gpl3)))

(define-public r-geos
  (package
    (name "r-geos")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geos" version))
       (sha256
        (base32 "03cxm9rby7qmk9apqk3sh373rnzqcrj23cn6q153gq93lx372b8n"))))
    (properties `((upstream-name . "geos")))
    (build-system r-build-system)
    (propagated-inputs (list r-wk r-libgeos))
    (home-page "https://paleolimbot.github.io/geos/")
    (synopsis "Open Source Geometry Engine ('GEOS') R API")
    (description
     "This package provides an R API to the Open Source Geometry Engine ('GEOS')
library (<https://libgeos.org/>) and a vector format with which to efficiently
store GEOS geometries.  High-performance functions to extract information from,
calculate relationships between, and transform geometries are provided.
Finally, facilities to import and export geometry vectors to other spatial
formats are provided.")
    (license license:expat)))

(define-public r-georob
  (package
    (name "r-georob")
    (version "0.3-20")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "georob" version))
       (sha256
        (base32 "0z1g26z4dbayxbh506ynh03v51v0q9dgz2b98l3cfbb9g53q1d7q"))))
    (properties `((upstream-name . "georob")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-snowfall
                             r-robustbase
                             r-quantreg
                             r-nlme
                             r-nleqslv
                             r-lmtest
                             r-fields
                             r-constrainedkriging
                             r-abind))
    (home-page "https://cran.r-project.org/package=georob")
    (synopsis "Robust Geostatistical Analysis of Spatial Data")
    (description
     "This package provides functions for efficiently fitting linear models with
spatially correlated errors by robust (Kuensch et al. (2011)
<doi:10.3929/ethz-a-009900710>) and Gaussian (Harville (1977)
<doi:10.1080/01621459.1977.10480998>) (Restricted) Maximum Likelihood and for
computing robust and customary point and block external-drift Kriging
predictions (Cressie (1993) <doi:10.1002/9781119115151>), along with utility
functions for variogram modelling in ad hoc geostatistical analyses, model
building, model evaluation by cross-validation, (conditional) simulation of
Gaussian processes (Davies and Bryant (2013) <doi:10.18637/jss.v055.i09>),
unbiased back-transformation of Kriging predictions of log-transformed data
(Cressie (2006) <doi:10.1007/s11004-005-9022-8>).")
    (license (list license:gpl2+ license:lgpl2.0+))))

(define-public r-georefdatar
  (package
    (name "r-georefdatar")
    (version "0.6.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "georefdatar" version))
       (sha256
        (base32 "1cqdl4zskgxjb1s29ka6q6wk7kpvrapjl40q5pfjwxh16b3bfdbh"))))
    (properties `((upstream-name . "georefdatar")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack))
    (home-page "https://github.com/abuseki/georefdatar")
    (synopsis "Geosciences Reference Datasets")
    (description
     "Reference datasets commonly used in the geosciences.  These include standard
atomic weights of the elements, a periodic table, a list of minerals including
their abbreviations and chemistry, geochemical data of reservoirs (primitive
mantle, continental crust, mantle, basalts, etc.), decay constants and isotopic
ratios frequently used in geochronology, color codes of the chronostratigraphic
chart.  In addition, the package provides functions for basic queries of atomic
weights, the list of minerals, and chronostratigraphic chart colors.  All
datasets are fully referenced, and a @code{BibTeX} file containing the
references is included.")
    (license license:expat)))

(define-public r-georange
  (package
    (name "r-georange")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeoRange" version))
       (sha256
        (base32 "0krj9570wkhdvpaqkq3nf0maglqd44mpwn4v1bymvgpk1i1wf5p3"))))
    (properties `((upstream-name . "GeoRange")))
    (build-system r-build-system)
    (propagated-inputs (list r-velociraptr r-sp r-raster r-proj4 r-moments))
    (home-page "https://cran.r-project.org/package=GeoRange")
    (synopsis "Calculating Geographic Range from Occurrence Data")
    (description
     "Calculates and analyzes six measures of geographic range from a set of
longitudinal and latitudinal occurrence data.  Measures included are minimum
convex hull area, minimum spanning tree distance, longitudinal range,
latitudinal range, maximum pairwise great circle distance, and number of X by X
degree cells occupied.")
    (license license:gpl3)))

(define-public r-geor
  (package
    (name "r-geor")
    (version "1.9-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geoR" version))
       (sha256
        (base32 "1ggbswsw01r3cfx88khprx6rhrq40rnqsbsljcjkpyg0xdy9g7df"))))
    (properties `((upstream-name . "geoR")))
    (build-system r-build-system)
    (propagated-inputs (list r-splancs r-sp r-mass))
    (home-page "http://www.leg.ufpr.br/geoR/")
    (synopsis "Analysis of Geostatistical Data")
    (description
     "Geostatistical analysis including variogram-based, likelihood-based and Bayesian
methods.  Software companion for Diggle and Ribeiro (2007)
<doi:10.1007/978-0-387-48536-2>.")
    (license license:gpl2+)))

(define-public r-geoperu
  (package
    (name "r-geoperu")
    (version "0.0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geoperu" version))
       (sha256
        (base32 "1hzdbghskdp0gnngycjn1z6zxdzas71mg7pw2bh6xw7ll52agkp5"))))
    (properties `((upstream-name . "geoperu")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-httr r-data-table r-curl))
    (home-page "https://github.com/PaulESantos/geoperu")
    (synopsis "Download Spatial Datasets of Peru")
    (description
     "This package provides convenient access to the official spatial datasets of Peru
as sf objects in R. This package includes a wide range of geospatial data
covering various aspects of Peruvian geography, such as: administrative
divisions (Source: INEI <https://ide.inei.gob.pe/>), protected natural areas
(Source: GEO ANP - SERNANP <https://geo.sernanp.gob.pe/visorsernanp/>).  All
datasets are harmonized in terms of attributes, projection, and topology,
ensuring consistency and ease of use for spatial analysis and visualization.")
    (license license:expat)))

(define-public r-geonode4r
  (package
    (name "r-geonode4r")
    (version "0.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geonode4R" version))
       (sha256
        (base32 "1pyyknkg9dv58vq8w4pmxd36j8l352zb2jv99skvyg0vn07m6bax"))))
    (properties `((upstream-name . "geonode4R")))
    (build-system r-build-system)
    (propagated-inputs (list r-readr r-r6 r-openssl r-keyring r-httr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/eblondel/geonode4R")
    (synopsis "Interface to 'GeoNode' REST API")
    (description
     "This package provides an interface to the @code{GeoNode} API, allowing to upload
and publish metadata and data in @code{GeoNode}'.  For more information about
the @code{GeoNode} API, see <https://geonode.org/>.")
    (license license:expat)))

(define-public r-geonetwork
  (package
    (name "r-geonetwork")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geonetwork" version))
       (sha256
        (base32 "0yg6pp2ya62qws6jdjxmhqjh5gdn4cfm6zbljazc35xf7d97jjnx"))))
    (properties `((upstream-name . "geonetwork")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-igraph r-geosphere))
    (home-page "https://umr-astre.pages.mia.inra.fr/geonetwork")
    (synopsis "Geographic Networks")
    (description
     "This package provides classes and methods for handling networks or graphs whose
nodes are geographical (i.e.  locations in the globe).  The functionality
includes the creation of objects of class geonetwork as a graph with node
coordinates, the computation of network measures, the support of spatial
operations (projection to different Coordinate Reference Systems, handling of
bounding boxes, etc.) and the plotting of the geonetwork object combined with
supplementary cartography for spatial representation.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-geonapi
  (package
    (name "r-geonapi")
    (version "0.7-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geonapi" version))
       (sha256
        (base32 "0n6s9gvg9z8hn3sih0gj35x8zxspqa8h8bml50bhsl5y4gcfihgd"))))
    (properties `((upstream-name . "geonapi")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml
                             r-r6
                             r-plyr
                             r-openssl
                             r-keyring
                             r-httr
                             r-geometa))
    (home-page "https://github.com/eblondel/geonapi/wiki")
    (synopsis "'GeoNetwork' API R Interface")
    (description
     "This package provides an R interface to the @code{GeoNetwork} API
(<https://geonetwork-opensource.org/#api>) allowing to upload and publish
metadata in a @code{GeoNetwork} web-application and expose it to OGC CSW.")
    (license license:expat)))

(define-public r-geonames
  (package
    (name "r-geonames")
    (version "0.999")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geonames" version))
       (sha256
        (base32 "0yz1b7y3vwa4izrhpry4n3iwzb7y67s5p3kgnd7d29cl5pcbpmqx"))))
    (properties `((upstream-name . "geonames")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjson))
    (home-page "https://cran.r-project.org/package=geonames")
    (synopsis "Interface to the \"Geonames\" Spatial Query Web Service")
    (description
     "The web service at <https://www.geonames.org/> provides a number of spatial data
queries, including administrative area hierarchies, city locations and some
country postal code queries.  A (free) username is required and rate limits
exist.")
    (license license:gpl3)))

(define-public r-geomultistar
  (package
    (name "r-geomultistar")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geomultistar" version))
       (sha256
        (base32 "0vvk48na7130d8j3gm0c5vgi1riqwkjk314pd03i7b4mw00pcm92"))))
    (properties `((upstream-name . "geomultistar")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-sf
                             r-rsqlite
                             r-rlang
                             r-purrr
                             r-generics
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://josesamos.github.io/geomultistar/")
    (synopsis "Multidimensional Queries Enriched with Geographic Data")
    (description
     "Multidimensional systems allow complex queries to be carried out in an easy way.
 The geographical dimension, together with the temporal dimension, plays a
fundamental role in multidimensional systems.  Through this package, vector
geographic data layers can be associated to the attributes of geographic
dimensions, so that the results of multidimensional queries can be obtained
directly as vector layers.  The multidimensional structures on which we can
define the queries can be created from a flat table or imported directly using
functions from this package.")
    (license license:expat)))

(define-public r-geomtextpath
  (package
    (name "r-geomtextpath")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geomtextpath" version))
       (sha256
        (base32 "1a33rcks9hvg3jyiplzsm1na6n80n934zvbcm57n3jg6gxcv2v5s"))))
    (properties `((upstream-name . "geomtextpath")))
    (build-system r-build-system)
    (propagated-inputs (list r-textshaping r-systemfonts r-scales r-rlang
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://allancameron.github.io/geomtextpath/")
    (synopsis "Curved Text in 'ggplot2'")
    (description
     "This package provides a ggplot2 extension that allows text to follow curved
paths.  Curved text makes it easier to directly label paths or neatly annotate
in polar co-ordinates.")
    (license license:expat)))

(define-public r-geomorph
  (package
    (name "r-geomorph")
    (version "4.0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geomorph" version))
       (sha256
        (base32 "0m8c1y3mi794k7b8zrnkvs098gjmaq3lvp1ds3kxjn7v0ddavd0l"))))
    (properties `((upstream-name . "geomorph")))
    (build-system r-build-system)
    (propagated-inputs (list r-rrpp
                             r-rgl
                             r-matrix
                             r-jpeg
                             r-ggplot2
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/geomorphR/geomorph")
    (synopsis "Geometric Morphometric Analyses of 2D and 3D Landmark Data")
    (description
     "Read, manipulate, and digitize landmark data, generate shape variables via
Procrustes analysis for points, curves and surfaces, perform shape analyses, and
provide graphical depictions of shapes and patterns of shape variation.")
    (license license:gpl3+)))

(define-public r-geomongo
  (package
    (name "r-geomongo")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeoMongo" version))
       (sha256
        (base32 "0m5nz5q81jz2v2bl2dbgdbdprrkckxr2c67qn0cg9vms0pi3fawr"))))
    (properties `((upstream-name . "GeoMongo")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-reticulate r-r6 r-geojsonr r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mlampros/GeoMongo")
    (synopsis "Geospatial Queries Using 'PyMongo'")
    (description
     "Utilizes methods of the @code{PyMongo} Python library to initialize, insert and
query @code{GeoJson} data (see <https://github.com/mongodb/mongo-python-driver>
for more information on @code{PyMongo}').  Furthermore, it allows the user to
validate @code{GeoJson} objects and to use the console for @code{MongoDB} (bulk)
commands.  The reticulate package provides the R interface to Python modules,
classes and functions.")
    (license license:asl2.0)))

(define-public r-geomodels
  (package
    (name "r-geomodels")
    (version "2.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeoModels" version))
       (sha256
        (base32 "1r917r9khllm5qhjlqx0xn6j2j1lk8k9z5w5ww7pn6z348qksqs2"))))
    (properties `((upstream-name . "GeoModels")))
    (build-system r-build-system)
    (propagated-inputs (list r-zipfr
                             r-vgam
                             r-spam
                             r-sp
                             r-sn
                             r-shape
                             r-scatterplot3d
                             r-progressr
                             r-pracma
                             r-plotrix
                             r-pbivnorm
                             r-nabor
                             r-mapproj
                             r-lamw
                             r-hypergeo
                             r-future
                             r-foreach
                             r-fields
                             r-fastgp
                             r-dotcall64
                             r-dofuture
                             r-data-table
                             r-codetools))
    (native-inputs (list gfortran))
    (home-page "https://vmoprojs.github.io/GeoModels-page/")
    (synopsis
     "Procedures for Gaussian and Non Gaussian Geostatistical (Large) Data Analysis")
    (description
     "This package provides functions for Gaussian and Non Gaussian (bivariate)
spatial and spatio-temporal data analysis are provided for a) (fast) simulation
of random fields, b) inference for random fields using standard likelihood and a
likelihood approximation method called weighted composite likelihood based on
pairs and b) prediction using (local) best linear unbiased prediction.  Weighted
composite likelihood can be very efficient for estimating massive datasets.
Both regression and spatial (temporal) dependence analysis can be jointly
performed.  Flexible covariance models for spatial and spatial-temporal data on
Euclidean domains and spheres are provided.  There are also many useful
functions for plotting and performing diagnostic analysis.  Different non
Gaussian random fields can be considered in the analysis.  Among them, random
fields with marginal distributions such as Skew-Gaussian, Student-t, Tukey-h,
Sin-Arcsin, Two-piece, Weibull, Gamma, Log-Gaussian, Binomial, Negative Binomial
and Poisson.  See the URL for the papers associated with this package, as for
instance, Bevilacqua and Gaetan (2015) <doi:10.1007/s11222-014-9460-6>,
Bevilacqua et al. (2016) <doi:10.1007/s13253-016-0256-3>, Vallejos et al. (2020)
<doi:10.1007/978-3-030-56681-4>, Bevilacqua et.  al (2020)
<doi:10.1002/env.2632>, Bevilacqua et.  al (2021) <doi:10.1111/sjos.12447>,
Bevilacqua et al. (2022) <doi:10.1016/j.jmva.2022.104949>, Morales-Navarrete et
al. (2023) <doi:10.1080/01621459.2022.2140053>, and a large class of examples
and tutorials.")
    (license license:gpl3+)))

(define-public r-geomod
  (package
    (name "r-geomod")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geomod" version))
       (sha256
        (base32 "06a211cyhcicy5arpjxiglami56plkm6ybxbzbmjd0z6yn9yyd38"))))
    (properties `((upstream-name . "geomod")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-rpart
                             r-rastervis
                             r-raster
                             r-ranger
                             r-randomforest
                             r-quantregforest
                             r-qrnn
                             r-nnet
                             r-kernlab
                             r-e1071
                             r-cubist
                             r-caret
                             r-arm))
    (home-page "https://cran.r-project.org/package=geomod")
    (synopsis "Computer Program for Geotechnical Investigations")
    (description
     "The geomod does spatial prediction of the Geotechnical soil properties.  It
predicts the spatial distribution of Geotechnical properties of soil e.g. shear
strength, permeability, plasticity index, Standard Penetration Test (SPT)
counts, etc.  The output of the prediction takes the form of a map or a series
of maps.  It uses the interpolation technique where a single or statistically
âbestâ estimate of spatial occurrence soil property is determined.  The
interpolation is based on both the sampled data and a variogram model for the
spatial correlation of the sampled data.  The single estimate is produced by a
Kriging technique.")
    (license license:gpl3)))

(define-public r-geommc
  (package
    (name "r-geommc")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geommc" version))
       (sha256
        (base32 "172iv017p5lfsy0436sqmvf8r1grwyb146czp6x6cyzn2n9qxhqx"))))
    (properties `((upstream-name . "geommc")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp
                             r-mcmc
                             r-matrixcalc
                             r-matrix
                             r-magrittr
                             r-cubature))
    (home-page "https://github.com/vroys/geommc")
    (synopsis "Geometric Markov Chain Sampling")
    (description
     "Simulates from discrete and continuous target distributions using geometric
Metropolis-Hastings (MH) algorithms.  Users specify the target distribution by
an R function that evaluates the log un-normalized pdf or pmf.  The package also
contains a function implementing a specific geometric MH algorithm for
performing high dimensional Bayesian variable selection.")
    (license license:gpl3+)))

(define-public r-geometa
  (package
    (name "r-geometa")
    (version "0.8-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geometa" version))
       (sha256
        (base32 "14f1g5ijm2hv0cncrddfr8fca9517g0dyrkfgc0k1v301nhlvcn4"))))
    (properties `((upstream-name . "geometa")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml
                             r-readr
                             r-r6
                             r-keyring
                             r-jsonlite
                             r-httr
                             r-crayon))
    (home-page "https://github.com/eblondel/geometa/wiki")
    (synopsis "Tools for Reading and Writing ISO/OGC Geographic Metadata")
    (description
     "This package provides facilities to read, write and validate geographic metadata
defined with ISO TC211 / OGC ISO geographic information metadata standards, and
encoded using the ISO 19139 (XML) standard technical specification.  This
includes ISO 19110 (Feature cataloguing), 19115 (dataset metadata), 19119
(service metadata) and 19136 (GML).  Other interoperable schemas from the OGC
are progressively supported as well, such as the Sensor Web Enablement (SWE)
Common Data Model, the OGC GML Coverage Implementation Schema (GMLCOV), or the
OGC GML Referenceable Grid (GMLRGRID).")
    (license license:expat)))

(define-public r-geomerge
  (package
    (name "r-geomerge")
    (version "0.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geomerge" version))
       (sha256
        (base32 "1isap50gbkcrbpg0wmzvxxa45vaa9szsxf9k9z3j2jjmv2gbg5mk"))))
    (properties `((upstream-name . "geomerge")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-spdep
                             r-sp
                             r-sf
                             r-scales
                             r-raster
                             r-lubridate
                             r-gridextra
                             r-ggplot2
                             r-geosphere))
    (home-page "https://cran.r-project.org/package=geomerge")
    (synopsis "Geospatial Data Integration")
    (description
     "Geospatial data integration framework that merges raster, spatial polygon, and
(dynamic) spatial points data into a spatial (panel) data frame at any
geographical resolution.")
    (license license:lgpl3)))

(define-public r-geomcomb
  (package
    (name "r-geomcomb")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeomComb" version))
       (sha256
        (base32 "05xb6m2ciszxd13yhqdkildh9nsq19ss8885ngj6ynvbchqkii7r"))))
    (properties `((upstream-name . "GeomComb")))
    (build-system r-build-system)
    (propagated-inputs (list r-psych
                             r-mtsdi
                             r-matrix
                             r-ggplot2
                             r-forecastcombinations
                             r-forecast))
    (home-page "https://github.com/ceweiss/GeomComb")
    (synopsis "(Geometric) Forecast Combination Methods")
    (description
     "This package provides eigenvector-based (geometric) forecast combination
methods; also includes simple approaches (simple average, median, trimmed and
winsorized mean, inverse rank method) and regression-based combination.  Tools
for data pre-processing are available in order to deal with common problems in
forecast combination (missingness, collinearity).")
    (license license:gpl2+)))

(define-public r-geomaroc
  (package
    (name "r-geomaroc")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geomaroc" version))
       (sha256
        (base32 "09j48ms1j7sqakznd2bj6sks1b6ckhch7ydhbdky4jk2acvl7lqw"))))
    (properties `((upstream-name . "geomaroc")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-jsonlite))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/AmineAndam04/R-geomaroc")
    (synopsis "Easily Visualize Geographic Data of Morocco")
    (description
     "This package provides tools to easily visualize geographic data of Morocco.
This package interacts with data available through the geomarocdata package,
which is available in a drat repository.  The size of the geomarocdata package
is approximately 12 MB.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-geomarchetypal
  (package
    (name "r-geomarchetypal")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeomArchetypal" version))
       (sha256
        (base32 "072llxc2chj5acbpyg5h9hyi9mrwr0s7xg508bs2sxjx4s77kf5l"))))
    (properties `((upstream-name . "GeomArchetypal")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-rlang
                             r-plot3d
                             r-mirai
                             r-matrix
                             r-magrittr
                             r-geometry
                             r-dplyr
                             r-doparallel
                             r-distances
                             r-archetypal
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GeomArchetypal")
    (synopsis "Finds the Geometrical Archetypal Analysis of a Data Frame")
    (description
     "This package performs Geometrical Archetypal Analysis after creating Grid
Archetypes which are the Cartesian Product of all minimum, maximum variable
values.  Since the archetypes are fixed now, we have the ability to compute the
convex composition coefficients for all our available data points much faster by
using the half part of Principal Convex Hull Archetypal method.  Additionally we
can decide to keep as archetypes the closer to the Grid Archetypes ones.
Finally the number of archetypes is always 2 to the power of the dimension of
our data points if we consider them as a vector space.  Cutler, A., Breiman, L.
(1994) <doi:10.1080/00401706.1994.10485840>.  Morup, M., Hansen, LK. (2012)
<doi:10.1016/j.neucom.2011.06.033>.  Christopoulos, DT. (2024)
<doi:10.13140/RG.2.2.14030.88642>.")
    (license license:gpl2+)))

(define-public r-geomapdata
  (package
    (name "r-geomapdata")
    (version "2.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geomapdata" version))
       (sha256
        (base32 "1fp0qwsk4h7inp6jhz1b0zsdhv6frlxbbk8crv4xjzxzc72zhzdx"))))
    (properties `((upstream-name . "geomapdata")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=geomapdata")
    (synopsis "Data for Topographic and Geologic Mapping")
    (description
     "Data sets included here are for use with package GEOmap.  These include world
map, USA map, Coso map, Japan Map.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-geomap
  (package
    (name "r-geomap")
    (version "2.5-11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GEOmap" version))
       (sha256
        (base32 "1h6f8wyf3p5a0mr5mvm7cv9v67gjfyzip1fw4rsqx6zkc9nyaswp"))))
    (properties `((upstream-name . "GEOmap")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-rpmg r-mba r-fields))
    (home-page "https://cran.r-project.org/package=GEOmap")
    (synopsis "Topographic and Geologic Mapping")
    (description
     "Set of routines for making map projections (forward and inverse), topographic
maps, perspective plots, geological maps, geological map symbols, geological
databases, interactive plotting and selection of focus regions.")
    (license license:gpl2+)))

(define-public r-geomander
  (package
    (name "r-geomander")
    (version "2.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geomander" version))
       (sha256
        (base32 "1w47ln4vxxlp45rqmr6diglwmyadhrq7n6smbgipznwkfkr975jm"))))
    (properties `((upstream-name . "geomander")))
    (build-system r-build-system)
    (propagated-inputs (list r-tinytiger
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-sf
                             r-rlang
                             r-readr
                             r-rcpp
                             r-magrittr
                             r-ggplot2
                             r-geos
                             r-dplyr
                             r-dataverse
                             r-cli
                             r-censable))
    (native-inputs (list r-knitr))
    (home-page "https://christophertkenny.com/geomander/")
    (synopsis "Geographic Tools for Studying Gerrymandering")
    (description
     "This package provides a compilation of tools to complete common tasks for
studying gerrymandering.  This focuses on the geographic tool side of common
problems, such as linking different levels of spatial units or estimating how to
break up units.  Functions exist for creating redistricting-focused data for the
US.")
    (license (license:fsdg-compatible "MIT + file LICENCE"))))

(define-public r-geojsonr
  (package
    (name "r-geojsonr")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geojsonR" version))
       (sha256
        (base32 "0l4zwyf3f827jpwbsv097d0h8i7pjwpkvdr89hc6vv9pzavfymhp"))))
    (properties `((upstream-name . "geojsonR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-r6))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mlampros/geojsonR")
    (synopsis "GeoJson Processing Toolkit")
    (description
     "Includes functions for processing @code{GeoJson} objects
<https://en.wikipedia.org/wiki/@code{GeoJSON>} relying on RFC 7946
<https://datatracker.ietf.org/doc/pdf/rfc7946.pdf>.  The geojson encoding is
based on json11', a tiny JSON library for C++11
<https://github.com/dropbox/json11>.  Furthermore, the source code is exported
in R through the Rcpp and @code{RcppArmadillo} packages.")
    (license license:expat)))

(define-public r-geojsonio
  (package
    (name "r-geojsonio")
    (version "0.11.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geojsonio" version))
       (sha256
        (base32 "0as0lcypj05j1vxkf3f13z4x6crjvgwmy06xrgq7j124f4syns3i"))))
    (properties `((upstream-name . "geojsonio")))
    (build-system r-build-system)
    (propagated-inputs (list r-v8
                             r-sp
                             r-sf
                             r-readr
                             r-magrittr
                             r-jsonlite
                             r-jqr
                             r-geojsonsf
                             r-geojson
                             r-crul))
    (home-page "https://github.com/ropensci/geojsonio")
    (synopsis "Convert Data from and to 'GeoJSON' or 'TopoJSON'")
    (description
     "Convert data to @code{GeoJSON} or @code{TopoJSON} from various R classes,
including vectors, lists, data frames, shape files, and spatial classes.
geojsonio does not aim to replace packages like sp', rgdal', rgeos', but rather
aims to be a high level client to simplify conversions of data from and to
@code{GeoJSON} and @code{TopoJSON}'.")
    (license license:expat)))

(define-public r-geojson
  (package
    (name "r-geojson")
    (version "0.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geojson" version))
       (sha256
        (base32 "0aj00zy603w03y36r2x8gvnya9c2xl73pnvi3wy5xky9kmbwh9g4"))))
    (properties `((upstream-name . "geojson")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-protolite
                             r-magrittr
                             r-lazyeval
                             r-jsonlite
                             r-jqr))
    (native-inputs (list r-knitr))
    (home-page "https://docs.ropensci.org/geojson/")
    (synopsis "Classes for 'GeoJSON'")
    (description
     "This package provides classes for @code{GeoJSON} to make working with
@code{GeoJSON} easier.  Includes S3 classes for @code{GeoJSON} classes with
brief summary output, and a few methods such as extracting and adding bounding
boxes, properties, and coordinate reference systems; working with newline
delimited @code{GeoJSON}'; and serializing to/from Geobuf binary @code{GeoJSON}
format.")
    (license license:expat)))

(define-public r-geoidep
  (package
    (name "r-geoidep")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geoidep" version))
       (sha256
        (base32 "1cjga1s2ffxhk0ki7rfmacwr39qi738pxaphvfg6nfgxm09j42v8"))))
    (properties `((upstream-name . "geoidep")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-sf
                             r-jsonlite
                             r-httr
                             r-dplyr
                             r-cli
                             r-archive))
    (native-inputs (list r-knitr))
    (home-page "https://geografo.pe/geoidep/")
    (synopsis
     "Download Geographic Data on Various Topics Provided and Managed by the Spatial Data Infrastructure of Peru")
    (description
     "This package provides R users with easy access to official cartographic data
from Peru across a range of topics, including society, transport, environment,
agriculture, climate, and more.  It also includes data from regional government
entities and technical-scientific institutions, all managed by Peru's Spatial
Data Infrastructure.  For more information, please visit:
<https://www.geoidep.gob.pe/catalogos-de-metadatos>.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-geoheatmap
  (package
    (name "r-geoheatmap")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geoheatmap" version))
       (sha256
        (base32 "1jqbvzz3cjyrkxj7q50w1hl62q5pdw0qfi1l7ca0j3b0m4ndbhgi"))))
    (properties `((upstream-name . "geoheatmap")))
    (build-system r-build-system)
    (propagated-inputs (list r-statebins r-rlang r-plotly r-ggplot2 r-geofacet))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=geoheatmap")
    (synopsis "Create Geospatial Cartogram Heatmaps")
    (description
     "The functionality provided by this package is an expansion of the code of the
statebins package, created by B. Rudis (2022),
<doi:10.32614/CRAN.package.statebins>.  It allows for the creation of square
choropleths for the entire world, provided an appropriate specified grid is
supplied.")
    (license license:expat)))

(define-public r-geohashtools
  (package
    (name "r-geohashtools")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geohashTools" version))
       (sha256
        (base32 "04hvlxq5fm4mcrlbx09j079522qhlyg4jg6dymwkmqxrwdibcnqq"))))
    (properties `((upstream-name . "geohashTools")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MichaelChirico/geohashTools")
    (synopsis "Tools for Working with Geohashes")
    (description
     "This package provides tools for working with Gustavo Niemeyer's geohash
coordinate system, including API for interacting with other common R GIS
libraries.")
    (license (list (license:fsdg-compatible "MPL-2.0")
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-geogrid
  (package
    (name "r-geogrid")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geogrid" version))
       (sha256
        (base32 "0jqqv5agnw071ysrmwz5r2z76f80rzm5bfl5g23gq1mbw85x8z8m"))))
    (properties `((upstream-name . "geogrid")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp r-sf r-rcpparmadillo r-rcpp))
    (home-page "https://github.com/jbaileyh/geogrid")
    (synopsis "Turn Geospatial Polygons into Regular or Hexagonal Grids")
    (description
     "Turn irregular polygons (such as geographical regions) into regular or hexagonal
grids.  This package enables the generation of regular (square) and hexagonal
grids through the package sp and then assigns the content of the existing
polygons to the new grid using the Hungarian algorithm, Kuhn (1955)
(<doi:10.1007/978-3-540-68279-0_2>).  This prevents the need for manual
generation of hexagonal grids or regular grids that are supposed to reflect
existing geography.")
    (license license:expat)))

(define-public r-geogenr
  (package
    (name "r-geogenr")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geogenr" version))
       (sha256
        (base32 "17jg4mvwfaayda4igx4qigk83xbvn5s05y82393abwjiakp14lvi"))))
    (properties `((upstream-name . "geogenr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-sf
                             r-rolap
                             r-readr
                             r-httr
                             r-geomultistar
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://josesamos.github.io/geogenr/")
    (synopsis "Generator from American Community Survey Geodatabases")
    (description
     "The American Community Survey (ACS)
<https://www.census.gov/programs-surveys/acs> offers geodatabases with
geographic information and associated data of interest to researchers in the
area.  The goal of this package is to generate objects that allow us to access
and consult the information available in various formats, such as in
@code{GeoPackage} format or in multidimensional ROLAP (Relational On-Line
Analytical Processing) star format.")
    (license license:expat)))

(define-public r-geogam
  (package
    (name "r-geogam")
    (version "0.1-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geoGAM" version))
       (sha256
        (base32 "080rls4rbac809hd4ddjsj2ndb2hv6fvwdnh4yl7j1hnfm1xwv64"))))
    (properties `((upstream-name . "geoGAM")))
    (build-system r-build-system)
    (propagated-inputs (list r-mgcv r-mboost r-mass r-grpreg))
    (home-page "https://cran.r-project.org/package=geoGAM")
    (synopsis "Select Sparse Geoadditive Models for Spatial Prediction")
    (description
     "This package provides a model building procedure to build parsimonious
geoadditive model from a large number of covariates.  Continuous, binary and
ordered categorical responses are supported.  The model building is based on
component wise gradient boosting with linear effects, smoothing splines and a
smooth spatial surface to model spatial autocorrelation.  The resulting
covariate set after gradient boosting is further reduced through backward
elimination and aggregation of factor levels.  The package provides a model
based bootstrap method to simulate prediction intervals for point predictions.
A test data set of a soil mapping case study in Berne (Switzerland) is provided.
 Nussbaum, M., Walthert, L., Fraefel, M., Greiner, L., and Papritz, A. (2017)
<doi:10.5194/soil-3-191-2017>.")
    (license license:gpl2+)))

(define-public r-geofourierfda
  (package
    (name "r-geofourierfda")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geoFourierFDA" version))
       (sha256
        (base32 "1a5ggjyfj5ic9h7fqvy3r7s0i6bmbwk95bf7y3vrmmmj0sxdm1mf"))))
    (properties `((upstream-name . "geoFourierFDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-orthopolynom r-magrittr))
    (home-page "https://cran.r-project.org/package=geoFourierFDA")
    (synopsis
     "Ordinary Functional Kriging Using Fourier Smoothing and Gaussian Quadrature")
    (description
     "Implementation of the ordinary functional kriging method proposed by Giraldo
(2011) <doi:10.1007/s10651-010-0143-y>.  This implements an alternative method
to estimate the trace-variogram using Fourier Smoothing and Gaussian Quadrature.")
    (license license:expat)))

(define-public r-geofkf
  (package
    (name "r-geofkf")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geoFKF" version))
       (sha256
        (base32 "1fwzc32dq3ip0crxlmnqfxqky1jbadvdyfaprsbcxpmyrcs4f3qj"))))
    (properties `((upstream-name . "geoFKF")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-numderiv))
    (home-page "https://github.com/gilberto-sassi/geoFKF")
    (synopsis "Kriging Method for Spatial Functional Data")
    (description
     "This package provides a Kriging method for functional datasets with spatial
dependency.  This functional Kriging method avoids the need to estimate the
trace-variogram, and the curve is estimated by minimizing a quadratic form.  The
curves in the functional dataset are smoothed using Fourier series.  The
functional Kriging of this package is a modification of the method proposed by
Giraldo (2011) <doi:10.1007/s10651-010-0143-y>.")
    (license license:expat)))

(define-public r-geofis
  (package
    (name "r-geofis")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeoFIS" version))
       (sha256
        (base32 "0akcs04ip30jb37jh0nmc3vdmhlr8zmpspgnbsaz9w3qxklhlarw"))))
    (properties `((upstream-name . "GeoFIS")))
    (build-system r-build-system)
    (inputs (list mpfr gmp))
    (propagated-inputs (list r-sp
                             r-sf
                             r-rdpack
                             r-rcpp
                             r-r6
                             r-purrr
                             r-nnls
                             r-magrittr
                             r-itertools
                             r-foreach
                             r-fispro
                             r-data-tree
                             r-bh))
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "https://www.geofis.org")
    (synopsis "Spatial Data Processing for Decision Making")
    (description
     "This package provides methods for processing spatial data for decision-making.
This package is an R implementation of methods provided by the open source
software @code{GeoFIS} <https://www.geofis.org> (Leroux et al.  2018)
<doi:10.3390/agriculture8060073>.  The main functionalities are the management
zone delineation (Pedroso et al.  2010) <doi:10.1016/j.compag.2009.10.007> and
data aggregation (Mora-Herrera et al.  2020) <doi:10.1016/j.compag.2020.105624>.")
    (license license:cecill)))

(define-public r-geofi
  (package
    (name "r-geofi")
    (version "1.0.16")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geofi" version))
       (sha256
        (base32 "1hf8q6k41d2mj4k34yqy6a0mbhrjz9ci5iplfrcb3qy4nppns14w"))))
    (properties `((upstream-name . "geofi")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-xml2
                             r-sf
                             r-rlang
                             r-purrr
                             r-httr
                             r-httpcache
                             r-dplyr
                             r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://ropengov.github.io/geofi/")
    (synopsis "Access Finnish Geospatial Data")
    (description
     "Designed to simplify geospatial data access from the Statistics Finland Web
Feature Service API <https://geo.stat.fi/geoserver/index.html>, the geofi
package offers researchers and analysts a set of tools to obtain and harmonize
administrative spatial data for a wide range of applications, from urban
planning to environmental research.  The package contains annually updated time
series of municipality key datasets that can be used for data aggregation and
language translations.")
    (license license:bsd-2)))

(define-public r-geofacet
  (package
    (name "r-geofacet")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geofacet" version))
       (sha256
        (base32 "1bj201afh7df3smgmllglmgs7bkkc1k1arvjr7kwn1cnn67b3mzj"))))
    (properties `((upstream-name . "geofacet")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-rnaturalearth
                             r-rlang
                             r-imgur
                             r-gtable
                             r-gridextra
                             r-ggrepel
                             r-ggplot2
                             r-geogrid))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/hafen/geofacet")
    (synopsis "'ggplot2' Faceting Utilities for Geographical Data")
    (description
     "This package provides geographical faceting functionality for ggplot2'.
Geographical faceting arranges a sequence of plots of data for different
geographical entities into a grid that preserves some of the geographical
orientation.")
    (license license:expat)))

(define-public r-geoelectrics
  (package
    (name "r-geoelectrics")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geoelectrics" version))
       (sha256
        (base32 "1zxikh4jj750grpnaazqm51q7yp5df5a77kljvmim4nazzrsyrpg"))))
    (properties `((upstream-name . "geoelectrics")))
    (build-system r-build-system)
    (propagated-inputs (list r-rgl r-lattice r-fields))
    (home-page "https://github.com/kleebaum/geoelectrics")
    (synopsis
     "3D-Visualization of Geoelectric Resistivity Measurement Profiles")
    (description
     "Visualizes two-dimensional geoelectric resistivity measurement profiles in three
dimensions.")
    (license license:gpl2+)))

(define-public r-geodregr
  (package
    (name "r-geodregr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeodRegr" version))
       (sha256
        (base32 "029yclgi4np000yh42j68v32m32lq2hmkh94q33hh1qkx8rilfa7"))))
    (properties `((upstream-name . "GeodRegr")))
    (build-system r-build-system)
    (propagated-inputs (list r-zipfr r-mass))
    (home-page "https://github.com/hayoungshin1/GeodRegr")
    (synopsis "Geodesic Regression")
    (description
     "This package provides a gradient descent algorithm to find a geodesic
relationship between real-valued independent variables and a manifold-valued
dependent variable (i.e.  geodesic regression).  Available manifolds are
Euclidean space, the sphere, hyperbolic space, and Kendall's 2-dimensional shape
space.  Besides the standard least-squares loss, the least absolute deviations,
Huber, and Tukey biweight loss functions can also be used to perform robust
geodesic regression.  Functions to help choose appropriate cutoff parameters to
maintain high efficiency for the Huber and Tukey biweight estimators are
included, as are functions for generating random tangent vectors from the
Riemannian normal distributions on the sphere and hyperbolic space.  The
n-sphere is a n-dimensional manifold: we represent it as a sphere of radius 1
and center 0 embedded in (n+1)-dimensional space.  Using the hyperboloid model
of hyperbolic space, n-dimensional hyperbolic space is embedded in
(n+1)-dimensional Minkowski space as the upper sheet of a hyperboloid of two
sheets.  Kendall's 2D shape space with K landmarks is of real dimension 2K-4;
preshapes are represented as complex K-vectors with mean 0 and magnitude 1.
Details are described in Shin, H.-Y. and Oh, H.-S. (2020)
<@code{arXiv:2007.04518>}.  Also see Fletcher, P. T. (2013)
<doi:10.1007/s11263-012-0591-y>.")
    (license license:gpl3)))

(define-public r-geodrawr
  (package
    (name "r-geodrawr")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geodrawr" version))
       (sha256
        (base32 "05irfyfpk5lgsh0wax8dqdyfw4prynxz1bhs9k1nrz8yn2dd0imn"))))
    (properties `((upstream-name . "geodrawr")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinydashboard r-shiny r-sf r-leaflet))
    (home-page "https://github.com/Curycu/geodrawr")
    (synopsis "Making Geospatial Objects")
    (description
     "Draw geospatial objects by clicks on the map.  This packages can help data
analyst who want to check their own geospatial hypothesis but has no ready-made
geospatial objects.")
    (license license:gpl3)))

(define-public r-geodl
  (package
    (name "r-geodl")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geodl" version))
       (sha256
        (base32 "0fds29bsijihi47g3bh5796ni42shap8jyqrml385is6g50x9yy5"))))
    (properties `((upstream-name . "geodl")))
    (build-system r-build-system)
    (propagated-inputs (list r-torchvision
                             r-torch
                             r-terra
                             r-rlang
                             r-readr
                             r-r6
                             r-psych
                             r-multiscaledtm
                             r-luz
                             r-dplyr
                             r-coro))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/maxwell-geospatial/geodl")
    (synopsis "Geospatial Semantic Segmentation with Torch and Terra")
    (description
     "This package provides tools for semantic segmentation of geospatial data using
convolutional neural network-based deep learning.  Utility functions allow for
creating masks, image chips, data frames listing image chips in a directory, and
@code{DataSets} for use within @code{DataLoaders}.  Additional functions are
provided to serve as checks during the data preparation and training process.  A
UNet architecture can be defined with 4 blocks in the encoder, a bottleneck
block, and 4 blocks in the decoder.  The UNet can accept a variable number of
input channels, and the user can define the number of feature maps produced in
each encoder and decoder block and the bottleneck.  Users can also choose to (1)
replace all rectified linear unit (@code{ReLU}) activation functions with leaky
@code{ReLU} or swish, (2) implement attention gates along the skip connections,
(3) implement squeeze and excitation modules within the encoder blocks, (4) add
residual connections within all blocks, (5) replace the bottleneck with a
modified atrous spatial pyramid pooling (ASPP) module, and/or (6) implement deep
supervision using predictions generated at each stage in the decoder.  A unified
focal loss framework is implemented after Yeung et al. (2022)
<doi:10.1016/j.compmedimag.2021.102026>.  We have also implemented assessment
metrics using the luz package including F1-score, recall, and precision.
Trained models can be used to predict to spatial data without the need to
generate chips from larger spatial extents.  Functions are available for
performing accuracy assessment.  The package relies on torch for implementing
deep learning, which does not require the installation of a Python environment.
Raster geospatial data are handled with terra'.  Models can be trained using a
Compute Unified Device Architecture (CUDA)-enabled graphics processing unit
(GPU); however, multi-GPU training is not supported by torch in R'.")
    (license license:gpl3+)))

(define-public r-geodiv
  (package
    (name "r-geodiv")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geodiv" version))
       (sha256
        (base32 "0580q71m71xd0kn9z8l536ypl31gx4fyw6cn1nc03sazrdilkz8r"))))
    (properties `((upstream-name . "geodiv")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-terra
                             r-spatial
                             r-sf
                             r-rlang
                             r-rcpparmadillo
                             r-rcpp
                             r-pracma
                             r-e1071
                             r-dplyr))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/bioXgeo/geodiv")
    (synopsis "Methods for Calculating Gradient Surface Metrics")
    (description
     "This package provides methods for calculating gradient surface metrics for
continuous analysis of landscape features.")
    (license license:expat)))

(define-public r-geodist
  (package
    (name "r-geodist")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geodist" version))
       (sha256
        (base32 "16nldq2fph4nxy0rcy33z6vw9xar3ckvdpagbaqsa2w0023bzq7x"))))
    (properties `((upstream-name . "geodist")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/hypertidy/geodist")
    (synopsis "Fast, Dependency-Free Geodesic Distance Calculations")
    (description
     "Dependency-free, ultra fast calculation of geodesic distances.  Includes the
reference nanometre-accuracy geodesic distances of Karney (2013)
<doi:10.1007/s00190-012-0578-z>, as used by the sf package, as well as Haversine
and Vincenty distances.  Default distance measure is the \"Mapbox cheap ruler\"
which is generally more accurate than Haversine or Vincenty for distances out to
a few hundred kilometres, and is considerably faster.  The main function accepts
one or two inputs in almost any generic rectangular form, and returns either
matrices of pairwise distances, or vectors of sequential distances.")
    (license license:expat)))

(define-public r-geodimension
  (package
    (name "r-geodimension")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geodimension" version))
       (sha256
        (base32 "1bx948hwy1c2src03vwvb29plzihqvsq92mbgg396xda560kxpfd"))))
    (properties `((upstream-name . "geodimension")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect r-tibble r-snakecase r-sf r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://josesamos.github.io/geodimension/")
    (synopsis "Definition of Geographic Dimensions")
    (description
     "The geographic dimension plays a fundamental role in multidimensional systems.
To define a geographic dimension in a star schema, we need a table with
attributes corresponding to the levels of the dimension.  Additionally, we will
also need one or more geographic layers to represent the data using this
dimension.  The goal of this package is to support the definition of geographic
dimensions from layers of geographic information related to each other.  It
makes it easy to define relationships between layers and obtain the necessary
data from them.")
    (license license:expat)))

(define-public r-geodetector
  (package
    (name "r-geodetector")
    (version "1.0-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geodetector" version))
       (sha256
        (base32 "17651yppczr3rdnd1zb34pxllg42fs7j7hcg96vzljsaaaziv4bj"))))
    (properties `((upstream-name . "geodetector")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=geodetector")
    (synopsis
     "Stratified Heterogeneity Measure, Dominant Driving Force Detection, Interaction Relationship Investigation")
    (description
     "Spatial stratified heterogeneity (SSH), referring to the within strata are more
similar than the between strata, a model with global parameters would be
confounded if input data is SSH. Note that the \"spatial\" here can be either
geospatial or the space in mathematical meaning.  Geographical detector is a
novel tool to investigate SSH: (1) measure and find SSH of a variable Y; (2)
test the power of determinant X of a dependent variable Y according to the
consistency between their spatial distributions; and (3) investigate the
interaction between two explanatory variables X1 and X2 to a dependent variable
Y (Wang et al 2014 <doi:10.1080/13658810802443457>, Wang, Zhang, and Fu 2016
<doi:10.1016/j.ecolind.2016.02.052>).")
    (license license:gpl2+)))

(define-public r-geodata
  (package
    (name "r-geodata")
    (version "0.6-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geodata" version))
       (sha256
        (base32 "0yiir9dslk6l60zf40kbvycd2x755whpp07is5bah1wa1wwgxwn5"))))
    (properties `((upstream-name . "geodata")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra))
    (home-page "https://cran.r-project.org/package=geodata")
    (synopsis "Download Geographic Data")
    (description
     "This package provides functions for downloading of geographic data for use in
spatial analysis and mapping.  The package facilitates access to climate, crops,
elevation, land use, soil, species occurrence, accessibility, administrative
boundaries and other data.")
    (license license:gpl3+)))

(define-public r-geodadata
  (package
    (name "r-geodadata")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geodaData" version))
       (sha256
        (base32 "003m5wff0q4fdlg64qispmhj2aw83isxg3ijxwkwhvi33bpwpq4z"))))
    (properties `((upstream-name . "geodaData")))
    (build-system r-build-system)
    (home-page "https://github.com/spatialanalysis/geodaData")
    (synopsis "Spatial Analysis Datasets for Teaching")
    (description
     "Stores small spatial datasets used to teach basic spatial analysis concepts.
Datasets are based off of the @code{GeoDa} software workbook and data site
<https://geodacenter.github.io/data-and-lab/> developed by Luc Anselin and team
at the University of Chicago.  Datasets are stored as sf objects.")
    (license license:cc0)))

(define-public r-geocomplexity
  (package
    (name "r-geocomplexity")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geocomplexity" version))
       (sha256
        (base32 "1b5sbr973nqc6jkv6lmdlp3ry9gx52hsp77sg8pxy041plx0hk69"))))
    (properties `((upstream-name . "geocomplexity")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-terra
                             r-sf
                             r-sdsfun
                             r-rcpparmadillo
                             r-rcpp
                             r-purrr
                             r-magrittr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://ausgis.github.io/geocomplexity/")
    (synopsis "Mitigating Spatial Bias Through Geographical Complexity")
    (description
     "The geographical complexity of individual variables can be characterized by the
differences in local attribute variables, while the common geographical
complexity of multiple variables can be represented by fluctuations in the
similarity of vectors composed of multiple variables.  In spatial regression
tasks, the goodness of fit can be improved by incorporating a geographical
complexity representation vector during modeling, using a geographical
complexity-weighted spatial weight matrix, or employing local geographical
complexity kernel density.  Similarly, in spatial sampling tasks, samples can be
selected more effectively by using a method that weights based on geographical
complexity.  By optimizing performance in spatial regression and spatial
sampling tasks, the spatial bias of the model can be effectively reduced.")
    (license license:gpl3)))

(define-public r-geocmeans
  (package
    (name "r-geocmeans")
    (version "0.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geocmeans" version))
       (sha256
        (base32 "02h171flg295ixpgl2glav2ij0wbvy7kclazdlldw40461aar5nk"))))
    (properties `((upstream-name . "geocmeans")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmap
                             r-terra
                             r-spdep
                             r-shiny
                             r-sf
                             r-reshape2
                             r-reldist
                             r-rdpack
                             r-rcpparmadillo
                             r-rcpp
                             r-progressr
                             r-plotly
                             r-matrixstats
                             r-leaflet
                             r-ggplot2
                             r-future-apply
                             r-fmsb
                             r-fclust
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/JeremyGelb/geocmeans")
    (synopsis
     "Implementing Methods for Spatial Fuzzy Unsupervised Classification")
    (description
     "This package provides functions to apply spatial fuzzy unsupervised
classification, visualize and interpret results.  This method is well suited
when the user wants to analyze data with a fuzzy clustering algorithm and to
account for the spatial dimension of the dataset.  In addition, indexes for
estimating the spatial consistency and classification quality are proposed.  The
methods were originally proposed in the field of brain imagery (seed Cai and al.
 2007 <doi:10.1016/j.patcog.2006.07.011> and Zaho and al.  2013
<doi:10.1016/j.dsp.2012.09.016>) and recently applied in geography (see Gelb and
Apparicio <doi:10.4000/cybergeo.36414>).")
    (license license:gpl2)))

(define-public r-geocausal
  (package
    (name "r-geocausal")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geocausal" version))
       (sha256
        (base32 "0cz1pzfyygiriwagqlhcwibmr2zakkdcnz9jnkghyw7s9xqvnnd6"))))
    (properties `((upstream-name . "geocausal")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyterra
                             r-tidyselect
                             r-tidyr
                             r-terra
                             r-spatstat-univar
                             r-spatstat-model
                             r-spatstat-geom
                             r-spatstat-explore
                             r-sf
                             r-purrr
                             r-progressr
                             r-mclust
                             r-latex2exp
                             r-ggpubr
                             r-ggplot2
                             r-furrr
                             r-dplyr
                             r-data-table))
    (home-page "https://github.com/mmukaigawara/geocausal")
    (synopsis "Causal Inference with Spatio-Temporal Data")
    (description
     "Spatio-temporal causal inference based on point process data.  You provide the
raw data of locations and timings of treatment and outcome events, specify
counterfactual scenarios, and the package estimates causal effects over
specified spatial and temporal windows.  See Papadogeorgou, et al. (2022)
<doi:10.1111/rssb.12548>.")
    (license license:expat)))

(define-public r-geocacher
  (package
    (name "r-geocacher")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geocacheR" version))
       (sha256
        (base32 "04ckxmird6n179xvsx50cjdz046igjxxqdnz5pfxi8pdklnky20c"))))
    (properties `((upstream-name . "geocacheR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-threewords r-stringr r-magrittr
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=geocacheR")
    (synopsis "Tools for Geocaching")
    (description
     "This package provides tools for solving common geocaching puzzle types, and
other Geocaching-related tasks.")
    (license license:gpl3)))

(define-public r-geobr
  (package
    (name "r-geobr")
    (version "1.9.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geobr" version))
       (sha256
        (base32 "0mn6qrlxxhf444ngdvq5gwvi1kadpg57zgg4gzmmrv186n709s0j"))))
    (properties `((upstream-name . "geobr")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-fs r-dplyr r-data-table r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://ipeagit.github.io/geobr/")
    (synopsis "Download Official Spatial Data Sets of Brazil")
    (description
     "Easy access to official spatial data sets of Brazil as sf objects in R. The
package includes a wide range of geospatial data available at various geographic
scales and for various years with harmonized attributes, projection and fixed
topology.")
    (license license:expat)))

(define-public r-geobayes
  (package
    (name "r-geobayes")
    (version "0.7.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geoBayes" version))
       (sha256
        (base32 "1d32c9f9wk1x3s89v957f8pb98m6qq5j084p35skbppkmc60y5lp"))))
    (properties `((upstream-name . "geoBayes")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp r-optimx r-coda))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=geoBayes")
    (synopsis
     "Analysis of Geostatistical Data using Bayes and Empirical Bayes Methods")
    (description
     "This package provides functions to fit geostatistical data.  The data can be
continuous, binary or count data and the models implemented are flexible.
Conjugate priors are assumed on some parameters while inference on the other
parameters can be done through a full Bayesian analysis of by empirical Bayes
methods.")
    (license license:gpl2+)))

(define-public r-geoarrow
  (package
    (name "r-geoarrow")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geoarrow" version))
       (sha256
        (base32 "0sdwlx4v9024m7lz93r9sfcg8zyzs3pvc7hm419y18vapqlvpm24"))))
    (properties `((upstream-name . "geoarrow")))
    (build-system r-build-system)
    (propagated-inputs (list r-wk r-nanoarrow))
    (home-page "https://github.com/geoarrow/geoarrow-r")
    (synopsis "Extension Types for Spatial Data for Use with 'Arrow'")
    (description
     "This package provides extension types and conversions to between R-native object
types and Arrow columnar types.  This includes integration among the arrow',
nanoarrow', sf', and wk packages such that spatial metadata is preserved
wherever possible.  Extension type implementations ensure first-class geometry
data type support in the arrow and nanoarrow packages.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-geoar
  (package
    (name "r-geoar")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geoAr" version))
       (sha256
        (base32 "06vj4r764cwnc131vlfvd3qhy959z49mw24sg3r1dq20frs195di"))))
    (properties `((upstream-name . "geoAr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-sf
                             r-purrr
                             r-magrittr
                             r-leaflet
                             r-jsonlite
                             r-httr
                             r-glue
                             r-dplyr
                             r-curl
                             r-attempt
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/PoliticaArgentina/geoAr")
    (synopsis "Argentina's Spatial Data Toolbox")
    (description
     "Collection of tools that facilitates data access and workflow for spatial
analysis of Argentina.  Includes historical information from censuses,
administrative limits at different levels of aggregation, location of human
settlements, among others.  Since it is expected that the majority of users will
be Spanish-speaking, the documentation of the package prioritizes this language,
although an effort is made to also offer annotations in English.")
    (license license:expat)))

(define-public r-geoadjust
  (package
    (name "r-geoadjust")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeoAdjust" version))
       (sha256
        (base32 "0251672j2vl9jck31zfcqzsyw8pphzs934j8ljjmnw9qqd3gzd2p"))))
    (properties `((upstream-name . "GeoAdjust")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmb
                             r-terra
                             r-summer
                             r-sf
                             r-rcppeigen
                             r-matrix
                             r-ggplot2
                             r-fmesher
                             r-fields))
    (home-page "https://cran.r-project.org/package=GeoAdjust")
    (synopsis
     "Accounting for Random Displacements of True GPS Coordinates of Data")
    (description
     "The purpose is to account for the random displacements (jittering) of true
survey household cluster center coordinates in geostatistical analyses of
Demographic and Health Surveys program (DHS) data.  Adjustment for jittering can
be implemented either in the spatial random effect, or in the raster/distance
based covariates, or in both.  Detailed information about the methods behind the
package functionality can be found in two preprints.  Umut Altay, John Paige,
Andrea Riebler, Geir-Arne Fuglstad (2022) <@code{arXiv:2202.11035v2>}.  Umut
Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad (2022)
<@code{arXiv:2211.07442v1>}.")
    (license license:gpl2+)))

(define-public r-genwin
  (package
    (name "r-genwin")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GenWin" version))
       (sha256
        (base32 "0fy5ws481xxk16x6wj7z8mwdzy044lqkqkw36xf1yxq18yxcg9xj"))))
    (properties `((upstream-name . "GenWin")))
    (build-system r-build-system)
    (propagated-inputs (list r-pspline))
    (home-page "https://cran.r-project.org/package=GenWin")
    (synopsis "Spline Based Window Boundaries for Genomic Analyses")
    (description
     "Defines window or bin boundaries for the analysis of genomic data.  Boundaries
are based on the inflection points of a cubic smoothing spline fitted to the raw
data.  Along with defining boundaries, a technique to evaluate results obtained
from unequally-sized windows is provided.  Applications are particularly
pertinent for, though not limited to, genome scans for selection based on
variability between populations (e.g. using Wright's fixations index, Fst, which
measures variability in subpopulations relative to the total population).")
    (license license:expat)))

(define-public r-genular
  (package
    (name "r-genular")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genular" version))
       (sha256
        (base32 "0i77sxynwanm2rwc0dw3yq1j0vmha6mbjjcvmv1vkgdf8xq1aqmz"))))
    (properties `((upstream-name . "genular")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-jsonlite r-httr r-dplyr))
    (home-page "https://github.com/atomiclaboratory/genular-database")
    (synopsis "'Genular' Database API")
    (description
     "This package provides an interface to the Genular database API
(<https://genular.atomic-lab.org>), allowing efficient retrieval and integration
of genomic, proteomic, and single-cell data.  It supports operations like
fetching gene annotations, cell expression profiles, and other information as
defined in the Genular database, enabling seamless incorporation of biological
data into R workflows.  With functions tailored for bioinformatics and machine
learning, the package facilitates exploration of cellular heterogeneity,
gene-disease relationships, and pathway analysis, streamlining multi-omics data
analysis.")
    (license license:gpl3)))

(define-public r-gentwoarmstrialsize
  (package
    (name "r-gentwoarmstrialsize")
    (version "0.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GenTwoArmsTrialSize" version))
       (sha256
        (base32 "0qf3a19hv860ll28pmx6skphwbn65ia8k0pvb3gzkdz3wlzmd1zw"))))
    (properties `((upstream-name . "GenTwoArmsTrialSize")))
    (build-system r-build-system)
    (propagated-inputs (list r-trialsize r-hmisc r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GenTwoArmsTrialSize")
    (synopsis "Generalized Two Arms Clinical Trial Sample Size Calculation")
    (description
     "Two arms clinical trials required sample size is calculated in the comprehensive
parametric context.  The calculation is based on the type of
endpoints(continuous/binary/time-to-event/ordinal), design (parallel/crossover),
hypothesis tests (equality/noninferiority/superiority/equivalence), trial arms
noncompliance rates and expected loss of follow-up.  Methods are described in:
Chow SC, Shao J, Wang H, Lokhnygina Y (2017) <doi:10.1201/9781315183084>,
Wittes, J (2002) <doi:10.1093/epirev/24.1.39>, Sato, T (2000)
<doi:10.1002/1097-0258(20001015)19:19%3C2689::aid-sim555%3E3.0.co;2-0>, Lachin J
M, Foulkes, M A (1986) <doi:10.2307/2531201>, Whitehead J(1993)
<doi:10.1002/sim.4780122404>, Julious SA (2023) <doi:10.1201/9780429503658>.")
    (license license:gpl3)))

(define-public r-gents
  (package
    (name "r-gents")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genTS" version))
       (sha256
        (base32 "172xpx8g6048z5hgy9h6j3k6j975hw1q7pmcn5k2s5iaj9x5kjzv"))))
    (properties `((upstream-name . "genTS")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny))
    (home-page "https://cran.r-project.org/package=genTS")
    (synopsis "R Shiny App for Creating Simplified Trial Summary (TS) Domain")
    (description
     "Make it easy to create simplified trial summary (TS) domain based on FDA FDA
guide
<https://github.com/@code{TuCai/phuse/blob/master/inst/examples/07_genTS/www/Simplified_TS_Creation_Guide_v2.pdf>}.")
    (license license:expat)))

(define-public r-gentag
  (package
    (name "r-gentag")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GenTag" version))
       (sha256
        (base32 "0yirl7n7p7w1dfbbr48v1151qyqm7p9m00vc6jq0s2d6bjdspin9"))))
    (properties `((upstream-name . "GenTag")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GenTag")
    (synopsis "Generate Color Tag Sequences")
    (description
     "Implement a coherent and flexible protocol for animal color tagging.
@code{GenTag} provides a simple computational routine with low CPU usage to
create color sequences for animal tag.  First, a single-color tag sequence is
created from an algorithm selected by the user, followed by verification of the
combination uniqueness.  Three methods to produce color tag sequences are
provided.  Users can modify the main function core to allow a wide range of
applications.")
    (license license:gpl2+)))

(define-public r-gensvm
  (package
    (name "r-gensvm")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gensvm" version))
       (sha256
        (base32 "0r1n5icxs313aipl63hdbvki37h2hv1r8ii7m8pyf9h36jxk4lfg"))))
    (properties `((upstream-name . "gensvm")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gensvm")
    (synopsis "Generalized Multiclass Support Vector Machine")
    (description
     "The @code{GenSVM} classifier is a generalized multiclass support vector machine
(SVM).  This classifier aims to find decision boundaries that separate the
classes with as wide a margin as possible.  In @code{GenSVM}, the loss function
is very flexible in the way that misclassifications are penalized.  This allows
the user to tune the classifier to the dataset at hand and potentially obtain
higher classification accuracy than alternative multiclass SVMs. Moreover, this
flexibility means that @code{GenSVM} has a number of other multiclass SVMs as
special cases.  One of the other advantages of @code{GenSVM} is that it is
trained in the primal space, allowing the use of warm starts during
optimization.  This means that for common tasks such as cross validation or
repeated model fitting, @code{GenSVM} can be trained very quickly.  Based on:
G.J.J. van den Burg and P.J.F. Groenen (2018)
<https://www.jmlr.org/papers/v17/14-526.html>.")
    (license license:gpl2+)))

(define-public r-gensurv
  (package
    (name "r-gensurv")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genSurv" version))
       (sha256
        (base32 "07f6prqpc1mafzb8dyp8xgycrrd548rj9cpn3qz64lh1qjymns3m"))))
    (properties `((upstream-name . "genSurv")))
    (build-system r-build-system)
    (home-page "https://github.com/arturstat/genSurv")
    (synopsis "Generating Multi-State Survival Data")
    (description
     "Generation of survival data with one (binary) time-dependent covariate.
Generation of survival data arising from a progressive illness-death model.")
    (license license:gpl3)))

(define-public r-genstab
  (package
    (name "r-genstab")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genstab" version))
       (sha256
        (base32 "09kg70v8qj8rbqwk0zm3xg54pmrr26l8n1s9x26s8qrsns0dgz0x"))))
    (properties `((upstream-name . "genstab")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=genstab")
    (synopsis "Resampling Based Yield Stability Analyses")
    (description
     "Several yield stability analyses are mentioned in this package: variation and
regression based yield stability analyses.  Resampling techniques are integrated
with these stability analyses.  The function stab.mean() provides the genotypic
means and ranks including their corresponding confidence intervals.  The
function stab.var() provides the genotypic variances over environments including
their corresponding confidence intervals.  The function stab.fw() is an extended
method from the Finlay-Wilkinson method (1963).  This method can include several
other factors that might impact yield stability.  Resampling technique is
integrated into this method.  A few missing data points or unbalanced data are
allowed too.  The function stab.fw.check() is an extended method from the
Finlay-Wilkinson method (1963).  The yield stability is evaluated via common
check line(s).  Resampling technique is integrated.")
    (license license:gpl2)))

(define-public r-gensphere
  (package
    (name "r-gensphere")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gensphere" version))
       (sha256
        (base32 "0cf1xmwi32x3ga9m5k7a93vdwix84ny7wxb74mddmfvb3m1v4c7g"))))
    (properties `((upstream-name . "gensphere")))
    (build-system r-build-system)
    (propagated-inputs (list r-sphericalcubature r-simplicialcubature r-rgl
                             r-mvmesh r-geometry))
    (home-page "https://cran.r-project.org/package=gensphere")
    (synopsis "Generalized Spherical Distributions")
    (description
     "Define and compute with generalized spherical distributions - multivariate
probability laws that are specified by a star shaped contour (directional
behavior) and a radial component.  The methods are described in Nolan (2016)
<doi:10.1186/s40488-016-0053-0>.")
    (license license:gpl3+)))

(define-public r-genset
  (package
    (name "r-genset")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genset" version))
       (sha256
        (base32 "02zldi136sfnq1i8pwb3b97zd44pjdgnfadk4bqp36wm8x95phwi"))))
    (properties `((upstream-name . "genset")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=genset")
    (synopsis "Generates Data Sets for Class Demonstrations")
    (description
     "For educational purposes to demonstrate the importance of multiple regression.
The genset function generates a data set from an initial data set to have the
same summary statistics (mean, median, and standard deviation) but opposing
regression results.")
    (license license:gpl2)))

(define-public r-genseir
  (package
    (name "r-genseir")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genSEIR" version))
       (sha256
        (base32 "02lprxxpf7ll23habq05b6slgbh978k09sqr6myr2jhlax2snxvk"))))
    (properties `((upstream-name . "genSEIR")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma r-nlsr r-minpack-lm r-ggplot2))
    (home-page "https://cran.r-project.org/package=genSEIR")
    (synopsis "Predict Epidemic Curves with Generalized SEIR Modeling")
    (description
     "This package performs generalized Susceptible-Exposed-Infected-Recovered (SEIR)
modeling to predict epidemic curves.  The method is described in Peng et al.
(2020) <doi:10.1101/2020.02.16.20023465>.")
    (license license:gpl2+)))

(define-public r-genscore
  (package
    (name "r-genscore")
    (version "1.0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genscore" version))
       (sha256
        (base32 "1ffwnxh1c2wlad4wh9lkvixly0a19l8fcgpp0vxxby6iwsd2vshf"))))
    (properties `((upstream-name . "genscore")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmvtnorm r-stringr r-rdpack r-mvtnorm))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sqyu/genscore")
    (synopsis "Generalized Score Matching Estimators")
    (description
     "Implementation of the Generalized Score Matching estimator in Yu et al. (2019)
<https://jmlr.org/papers/v20/18-278.html> for non-negative graphical models
(truncated Gaussian, exponential square-root, gamma, a-b models) and univariate
truncated Gaussian distributions.  Also includes the original estimator for
untruncated Gaussian graphical models from Lin et al. (2016)
<doi:10.1214/16-EJS1126>, with the addition of a diagonal multiplier.")
    (license license:gpl3)))

(define-public r-genridge
  (package
    (name "r-genridge")
    (version "0.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genridge" version))
       (sha256
        (base32 "1f7yvq3g1bgxkkbklklfirk4nqiyh6csvygfbp3z57zywrbp1019"))))
    (properties `((upstream-name . "genridge")))
    (build-system r-build-system)
    (propagated-inputs (list r-rgl r-car))
    (home-page "https://friendly.github.io/genridge/")
    (synopsis "Generalized Ridge Trace Plots for Ridge Regression")
    (description
     "The genridge package introduces generalizations of the standard univariate ridge
trace plot used in ridge regression and related methods.  These graphical
methods show both bias (actually, shrinkage) and precision, by plotting the
covariance ellipsoids of the estimated coefficients, rather than just the
estimates themselves.  2D and 3D plotting methods are provided, both in the
space of the predictor variables and in the transformed space of the PCA/SVD of
the predictors.")
    (license license:gpl2+)))

(define-public r-genpwr
  (package
    (name "r-genpwr")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genpwr" version))
       (sha256
        (base32 "0cys4zmfq07g99xywqampvmn8g0a6bjjj368d62q5xki2mi9z649"))))
    (properties `((upstream-name . "genpwr")))
    (build-system r-build-system)
    (propagated-inputs (list r-nleqslv r-mass r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=genpwr")
    (synopsis "Power Calculations Under Genetic Model Misspecification")
    (description
     "Power and sample size calculations for genetic association studies allowing for
misspecification of the model of genetic susceptibility. \"Hum Hered.
2019;84(6):256-271.<doi:10.1159/000508558>.  Epub 2020 Jul 28.\" Power and/or
sample size can be calculated for logistic (case/control study design) and
linear (continuous phenotype) regression models, using additive, dominant,
recessive or degree of freedom coding of the genetic covariate while assuming a
true dominant, recessive or additive genetic effect.  In addition, power and
sample size calculations can be performed for gene by environment interactions.
These methods are extensions of Gauderman (2002) <doi:10.1093/aje/155.5.478> and
Gauderman (2002) <doi:10.1002/sim.973> and are described in: Moore CM, Jacobson
S, Fingerlin TE. Power and Sample Size Calculations for Genetic Association
Studies in the Presence of Genetic Model Misspecification.  American Society of
Human Genetics.  October 2018, San Diego.")
    (license license:gpl3)))

(define-public r-genpathmox
  (package
    (name "r-genpathmox")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genpathmox" version))
       (sha256
        (base32 "1pjg3irjlw9gbp2cwqrl5d7nvfhncds3bmxp0878y5yf35sl4smk"))))
    (properties `((upstream-name . "genpathmox")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrixcalc r-diagram r-csem))
    (home-page "https://cran.r-project.org/package=genpathmox")
    (synopsis "Pathmox Approach Segmentation Tree Analysis")
    (description
     "It provides an interesting solution for handling a high number of segmentation
variables in partial least squares structural equation modeling.  The package
implements the \"Pathmox\" algorithm (Lamberti, Sanchez, and
Aluja,(2016)<doi:10.1002/asmb.2168>) including the F-coefficient test (Lamberti,
Sanchez, and Aluja,(2017)<doi:10.1002/asmb.2270>) to detect the path
coefficients responsible for the identified differences).  The package also
allows running the hybrid multi-group approach (Lamberti (2021)
<doi:10.1007/s11135-021-01096-9>).")
    (license license:gpl3)))

(define-public r-genotriplo
  (package
    (name "r-genotriplo")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GenoTriplo" version))
       (sha256
        (base32 "04v1llw3ppzx9s9cm8wp78n5hpm65bpmkmsliczlp33l91cbspp8"))))
    (properties `((upstream-name . "GenoTriplo")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-shinythemes
                             r-shinybs
                             r-shiny
                             r-rmixmod
                             r-rlang
                             r-processx
                             r-htmltools
                             r-ggplot2
                             r-foreach
                             r-dt
                             r-dplyr
                             r-doparallel
                             r-cowplot))
    (home-page "https://cran.r-project.org/package=GenoTriplo")
    (synopsis "Genotyping Triploids (or Diploids) from Luminescence Data")
    (description
     "Genotyping of triploid individuals from luminescence data (marker probeset A and
B).  Works also for diploids.  Two main functions: Run_Clustering() that
regroups individuals with a same genotype based on proximity and
Run_Genotyping() that assigns a genotype to each cluster.  For Shiny interface
use: launch_@code{GenoShiny}().")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-genoscan
  (package
    (name "r-genoscan")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GenoScan" version))
       (sha256
        (base32 "0p98imfvvz7l94kim3pxypyjs2pbxng4a2nagbafbady82ylr2vi"))))
    (properties `((upstream-name . "GenoScan")))
    (build-system r-build-system)
    (propagated-inputs (list r-skat r-seqminer r-matrix r-mass r-data-table))
    (home-page "https://cran.r-project.org/package=GenoScan")
    (synopsis
     "Genome-Wide Scan Statistic Framework for Whole-Genome Sequence Data Analysis")
    (description
     "This package provides functions for whole-genome sequencing studies, including
genome-wide scan, candidate region scan and single window test.")
    (license license:gpl3)))

(define-public r-genord
  (package
    (name "r-genord")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GenOrd" version))
       (sha256
        (base32 "17mfrj1fwj8mri1w0bl2pw1rqriidmd67i7gpn9v56g9dzw5rzms"))))
    (properties `((upstream-name . "GenOrd")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-matrix r-mass))
    (home-page "https://cran.r-project.org/package=GenOrd")
    (synopsis
     "Simulation of Discrete Random Variables with Given Correlation Matrix and Marginal Distributions")
    (description
     "This package provides a gaussian copula based procedure for generating samples
from discrete random variables with prescribed correlation matrix and marginal
distributions.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-genopop
  (package
    (name "r-genopop")
    (version "0.9.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GenoPop" version))
       (sha256
        (base32 "1vcd17x7bj0br31s8khiv6np2xmr5c1382lmlbyydfhj7407qzw4"))))
    (properties `((upstream-name . "GenoPop")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsamtools
                             r-missforest
                             r-iranges
                             r-genomicranges
                             r-foreach
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GenoPop")
    (synopsis
     "Genotype Imputation and Population Genomics Efficiently from Variant Call Formatted (VCF) Files")
    (description
     "This package provides tools for efficient processing of large, whole genome
genotype data sets in variant call format (VCF).  It includes several functions
to calculate commonly used population genomic metrics and a method for reference
panel free genotype imputation, which is described in the preprint Gurke & Mayer
(2024) <doi:10.22541/au.172515591.10119928/v1>.")
    (license license:gpl3+)))

(define-public r-genoplotr
  (package
    (name "r-genoplotr")
    (version "0.8.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genoPlotR" version))
       (sha256
        (base32 "01qn81nsdk3s150gqwisb7xnm4hq5sfzd2zrzpn9kj0rigzgf9zi"))))
    (properties `((upstream-name . "genoPlotR")))
    (build-system r-build-system)
    (propagated-inputs (list r-ade4))
    (home-page "http://genoplotr.r-forge.r-project.org/")
    (synopsis "Plot Publication-Grade Gene and Genome Maps")
    (description
     "Draws gene or genome maps and comparisons between these, in a publication-grade
manner.  Starting from simple, common files, it will draw postscript or PDF
files that can be sent as such to journals.")
    (license license:gpl2+)))

(define-public r-genomictools-filehandler
  (package
    (name "r-genomictools-filehandler")
    (version "0.1.5.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GenomicTools.fileHandler" version))
       (sha256
        (base32 "09l87wf78bj49cdh7r3x81hn15c4pihxih8l59xxrpgdhp69268c"))))
    (properties `((upstream-name . "GenomicTools.fileHandler")))
    (build-system r-build-system)
    (propagated-inputs (list r-snpstats r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GenomicTools.fileHandler")
    (synopsis "File Handlers for Genomic Data Analysis")
    (description
     "This package provides a collection of I/O tools for handling the most commonly
used genomic datafiles, like fasta/-q, bed, gff, gtf, ped/map and vcf.")
    (license license:gpl2+)))

(define-public r-genomicsig
  (package
    (name "r-genomicsig")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GenomicSig" version))
       (sha256
        (base32 "0l4sl2f90c4vgz24fn805jcqk1llzhqlf5qj4dpdls3zfdmcd9qq"))))
    (properties `((upstream-name . "GenomicSig")))
    (build-system r-build-system)
    (propagated-inputs (list r-seqinr r-kaos r-entropy r-biostrings))
    (home-page "https://cran.r-project.org/package=GenomicSig")
    (synopsis "Computation of Genomic Signatures")
    (description
     "Genomic signatures represent unique features within a species DNA, enabling the
differentiation of species and offering broad applications across various
fields.  This package provides essential tools for calculating these specific
signatures, streamlining the process for researchers and offering a
comprehensive and time-saving solution for genomic analysis.The amino acid
contents are identified based on the work published by Sandberg et al. (2003)
<doi:10.1016/s0378-1119(03)00581-x> and Xiao et al. (2015)
<doi:10.1093/bioinformatics/btv042>.  The Average Mutual Information Profiles
(AMIP) values are calculated based on the work of Bauer et al. (2008)
<doi:10.1186/1471-2105-9-48>.  The Chaos Game Representation (CGR) plot
visualization was done based on the work of Deschavanne et al. (1999)
<doi:10.1093/oxfordjournals.molbev.a026048> and Jeffrey et al. (1990)
<doi:10.1093/nar/18.8.2163>.  The GC content is calculated based on the work
published by Nakabachi et al. (2006) <doi:10.1126/science.1134196> and Barbu et
al. (1956) <https://pubmed.ncbi.nlm.nih.gov/13363015>.  The Oligonucleotide
Frequency Derived Error Gradient (OFDEG) values are computed based on the work
published by Saeed et al. (2009) <doi:10.1186/1471-2164-10-S3-S10>.  The
Relative Synonymous Codon Usage (RSCU) values are calculated based on the work
published by Elek (2018) <https://urn.nsk.hr/urn:nbn:hr:217:686131>.")
    (license license:gpl3)))

(define-public r-genomicper
  (package
    (name "r-genomicper")
    (version "1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genomicper" version))
       (sha256
        (base32 "1sran9gjjkrq9yxv4630cyhjhm9w9cggyx3amhcncmyiknz6pa76"))))
    (properties `((upstream-name . "genomicper")))
    (build-system r-build-system)
    (propagated-inputs (list r-reactome-db r-dbi r-annotationdbi))
    (home-page "https://cran.r-project.org/package=genomicper")
    (synopsis
     "Circular Genomic Permutation using Genome Wide Association p-Values")
    (description
     "Circular genomic permutation approach uses genome wide association studies
(GWAS) results to establish the significance of pathway/gene-set associations
whilst accounting for genomic structure(Cabrera et al (2012)
<doi:10.1534/g3.112.002618>).  All single nucleotide polymorphisms (SNPs) in the
GWAS are placed in a circular genome according to their location.  Then the
complete set of SNP association p-values are permuted by rotation with respect
to the SNPs genomic locations.  Two testing frameworks are available:
permutations at the gene level, and permutations at the SNP level.  The
permutation at the gene level uses Fisher's combination test to calculate a
single gene p-value, followed by the hypergeometric test.  The SNP count
methodology maps each SNP to pathways/gene-sets and calculates the proportion of
SNPs for the real and the permutated datasets above a pre-defined threshold.
Genomicper requires a matrix of GWAS association p-values and SNPs annotation to
genes.  Pathways can be obtained from within the package or can be provided by
the user.")
    (license license:gpl2)))

(define-public r-genomic-autocorr
  (package
    (name "r-genomic-autocorr")
    (version "1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genomic.autocorr" version))
       (sha256
        (base32 "1lidrjz1flxw4jvhqdi8y813m7ss4kkvm7bxsdpz60dxxw3204gm"))))
    (properties `((upstream-name . "genomic.autocorr")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape r-magrittr r-data-table))
    (home-page "https://github.com/chr1swallace/genomic.autocorr")
    (synopsis "Models Dealing with Spatial Dependency in Genomic Data")
    (description
     "Local structure in genomic data often induces dependence between observations
taken at different genomic locations.  Ignoring this dependence leads to
underestimation of the standard error of parameter estimates.  This package uses
block bootstrapping to estimate asymptotically correct standard errors of
parameters from any standard generalised linear model that may be fit by the
glm() function.")
    (license license:gpl2)))

(define-public r-genomeadmixr
  (package
    (name "r-genomeadmixr")
    (version "2.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GenomeAdmixR" version))
       (sha256
        (base32 "14qnabg1pj8sg2bvd3k4m467bh1q2zfjr0ljxn1ny031sqwis4cy"))))
    (properties `((upstream-name . "GenomeAdmixR")))
    (build-system r-build-system)
    (propagated-inputs (list r-vcfr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-rcppparallel
                             r-rcpparmadillo
                             r-rcpp
                             r-hierfstat
                             r-ggridges
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/thijsjanzen/GenomeAdmixR")
    (synopsis "Simulate Admixture of Genomes")
    (description
     "Individual-based simulations forward in time, simulating how patterns in
ancestry along the genome change after admixture.  Full description can be found
in Janzen (2021) <doi:10.1111/2041-210X.13612>.")
    (license license:gpl2+)))

(define-public r-genogeographer
  (package
    (name "r-genogeographer")
    (version "0.1.19")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genogeographer" version))
       (sha256
        (base32 "0q56ysv5333i27iv1gmd9ilbgn3rw8sb9yq7z76gmn1fak5aqci8"))))
    (properties `((upstream-name . "genogeographer")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-shinywidgets
                             r-shinyjs
                             r-shinycssloaders
                             r-shiny
                             r-rmarkdown
                             r-rlang
                             r-rio
                             r-readr
                             r-purrr
                             r-maps
                             r-magrittr
                             r-leaflet
                             r-knitr
                             r-ggplot2
                             r-forcats
                             r-dt
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=genogeographer")
    (synopsis "Methods for Analysing Forensic Ancestry Informative Markers")
    (description
     "Evaluates likelihood ratio tests for alleged ancestry.  Implements the methods
of Tvedebrink et al (2018) <doi:10.1016/j.tpb.2017.12.004>.")
    (license license:gpl2+)))

(define-public r-genodds
  (package
    (name "r-genodds")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genodds" version))
       (sha256
        (base32 "1dn39h5yxm3mjvm1xciyz127zrd79kw7fi2dlmxwmqk17ssmhrwq"))))
    (properties `((upstream-name . "genodds")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=genodds")
    (synopsis "Generalised Odds Ratios")
    (description
     "Calculates Agresti's generalized odds ratios.  For a randomly selected pair of
observations from two groups, calculates the odds that the second group will
have a higher scoring outcome than that of the first group.  Package provides
hypothesis testing for if this odds ratio is significantly different to 1 (equal
chance).")
    (license license:gpl2+)))

(define-public r-geno2proteo
  (package
    (name "r-geno2proteo")
    (version "0.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geno2proteo" version))
       (sha256
        (base32 "1gq8lqwwqvmxqqxwf4kms7dhd1zzz33z37m67q53p4a1j5z5n12s"))))
    (properties `((upstream-name . "geno2proteo")))
    (build-system r-build-system)
    (inputs (list perl))
    (propagated-inputs (list r-s4vectors
                             r-runit
                             r-r-utils
                             r-iranges
                             r-genomicranges
                             r-biocgenerics))
    (home-page "https://cran.r-project.org/package=geno2proteo")
    (synopsis
     "Finding the DNA and Protein Sequences of Any Genomic or Proteomic Loci")
    (description
     "Using the DNA sequence and gene annotation files provided in ENSEMBL
<https://www.ensembl.org/index.html>, the functions implemented in the package
try to find the DNA sequences and protein sequences of any given genomic loci,
and to find the genomic coordinates and protein sequences of any given protein
locations, which are the frequent tasks in the analysis of genomic and proteomic
data.")
    (license license:artistic2.0)))

(define-public r-genmeta
  (package
    (name "r-genmeta")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GENMETA" version))
       (sha256
        (base32 "11wlcnj919jws62ngf29gr2p0shdlm917hkz5iwfss1m8gcbfjnl"))))
    (properties `((upstream-name . "GENMETA")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma r-matrix r-mass r-magic))
    (home-page "https://cran.r-project.org/package=GENMETA")
    (synopsis
     "Implements Generalized Meta-Analysis Using Iterated Reweighted Least Squares Algorithm")
    (description
     "Generalized meta-analysis is a technique for estimating parameters associated
with a multiple regression model through meta-analysis of studies which may have
information only on partial sets of the regressors.  It estimates the effects of
each variable while fully adjusting for all other variables that are measured in
at least one of the studies.  Using algebraic relationships between regression
parameters in different dimensions, a set of moment equations is specified for
estimating the parameters of a maximal model through information available on
sets of parameter estimates from a series of reduced models available from the
different studies.  The specification of the equations requires a reference
dataset to estimate the joint distribution of the covariates.  These equations
are solved using the generalized method of moments approach, with the optimal
weighting of the equations taking into account uncertainty associated with
estimates of the parameters of the reduced models.  The proposed framework is
implemented using iterated reweighted least squares algorithm for fitting
generalized linear regression models.  For more details about the method, please
see pre-print version of the manuscript on generalized meta-analysis by
Prosenjit Kundu, Runlong Tang and Nilanjan Chatterjee (2018)
<doi:10.1093/biomet/asz030>.The current version (0.2.0) is updated to address
some of the stability issues in the previous version (0.1).")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-genmcmcdiag
  (package
    (name "r-genmcmcdiag")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genMCMCDiag" version))
       (sha256
        (base32 "0vf9r9qc477mdjvi54sh0m67lscb8j6lfc7ikqpd4xnzsd4aklb6"))))
    (properties `((upstream-name . "genMCMCDiag")))
    (build-system r-build-system)
    (propagated-inputs (list r-mcmcse r-lifecycle r-knitr r-ggplot2 r-coda))
    (home-page "https://github.com/LukeDuttweiler/genMCMCDiag")
    (synopsis
     "Generalized Convergence Diagnostics for Difficult MCMC Algorithms")
    (description
     "Trace plots and convergence diagnostics for Markov Chain Monte Carlo (MCMC)
algorithms on highly multivariate or unordered spaces.  Methods outlined in a
forthcoming paper.")
    (license license:expat)))

(define-public r-genmarkov
  (package
    (name "r-genmarkov")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GenMarkov" version))
       (sha256
        (base32 "1dqs8wapdpmgqnrf4xgvdmaz74gck23sbnpjxzj2g03gl8k9lq5w"))))
    (properties `((upstream-name . "GenMarkov")))
    (build-system r-build-system)
    (propagated-inputs (list r-nnet
                             r-maxlik
                             r-matrixcalc
                             r-hmisc
                             r-fastdummies
                             r-alabama))
    (home-page "https://cran.r-project.org/package=GenMarkov")
    (synopsis "Multivariate Markov Chains")
    (description
     "This package provides routines to estimate the Mixture Transition Distribution
Model based on Raftery (1985) <http://www.jstor.org/stable/2345788> and Nicolau
(2014) <doi:10.1111/sjos.12087> specifications, for multivariate data.
Additionally, provides a function for the estimation of a new model for
multivariate non-homogeneous Markov chains.  This new specification, Generalized
Multivariate Markov Chains (GMMC) was proposed by Carolina Vasconcelos and Bruno
Damasio and considers (continuous or discrete) covariates exogenous to the
Markov chain.")
    (license license:gpl2+)))

(define-public r-genlogis
  (package
    (name "r-genlogis")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genlogis" version))
       (sha256
        (base32 "1d6ni84rdbqyi2052kzfm8v2kvmki84vp8lfjip8c9bp0crig52i"))))
    (properties `((upstream-name . "genlogis")))
    (build-system r-build-system)
    (propagated-inputs (list r-manipulate r-ggplot2 r-foreach r-doparallel
                             r-distr))
    (home-page "https://pinduzera.github.io/genlogis/")
    (synopsis "Generalized Logistic Distribution")
    (description
     "This package provides basic distribution functions for a generalized logistic
distribution proposed by Rathie and Swamee (2006)
<https://www.rroij.com/open-access/on-new-generalized-logistic-distributions-and-applicationsbarreto-fhs-mota-jma-and-rathie-pn-.pdf>.
 It also has an interactive RStudio plot for better guessing dynamically of
initial values for ease of included optimization and simulating.")
    (license license:gpl3)))

(define-public r-genlib
  (package
    (name "r-genlib")
    (version "1.1.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GENLIB" version))
       (sha256
        (base32 "1h0digaa1szgwq0qzf3114id7bx7q6zmgar591jlxgyihihk4x9j"))))
    (properties `((upstream-name . "GENLIB")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp
                             r-quadprog
                             r-matrix
                             r-lattice
                             r-kinship2
                             r-foreach
                             r-doparallel
                             r-bootstrap
                             r-bh))
    (home-page "https://cran.r-project.org/package=GENLIB")
    (synopsis "Genealogical Data Analysis")
    (description
     "Genealogical data analysis including descriptive statistics (e.g., kinship and
inbreeding coefficients) and gene-dropping simulations.  See: \"GENLIB: an R
package for the analysis of genealogical data\" Gauvin et al. (2015)
<doi:10.1186/s12859-015-0581-5>.")
    (license license:gpl2+)))

(define-public r-genlasso
  (package
    (name "r-genlasso")
    (version "1.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genlasso" version))
       (sha256
        (base32 "0bhpsz4qcvnmf09l1j6r94ljrdfxidjrsmggb3q4xvyf2xxnnbfx"))))
    (properties `((upstream-name . "genlasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-igraph))
    (home-page "https://github.com/glmgen/genlasso")
    (synopsis "Path Algorithm for Generalized Lasso Problems")
    (description
     "Computes the solution path for generalized lasso problems.  Important use cases
are the fused lasso over an arbitrary graph, and trend fitting of any given
polynomial order.  Specialized implementations for the latter two subproblems
are given to improve stability and speed.  See Taylor Arnold and Ryan Tibshirani
(2016) <doi:10.1080/10618600.2015.1008638>.")
    (license license:gpl2+)))

(define-public r-geniusr
  (package
    (name "r-geniusr")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geniusr" version))
       (sha256
        (base32 "14flyjrnw3l4j02dnr8495r5xdsr83firz0irpqbxw0jrajbj5dq"))))
    (properties `((upstream-name . "geniusr")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tibble
                             r-stringr
                             r-rvest
                             r-purrr
                             r-httr
                             r-dplyr
                             r-curl))
    (home-page "https://ewenme.github.io/geniusr/")
    (synopsis "Tools for Working with the 'Genius' API")
    (description
     "This package provides tools to interact nicely with the Genius API
<https://docs.genius.com/>.  Search hosted content, extract associated metadata
and retrieve lyrics with ease.")
    (license license:expat)))

(define-public r-genio
  (package
    (name "r-genio")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genio" version))
       (sha256
        (base32 "0izx8yv8mvnfxdqnqpnp2ldw1hzs6ggxi7jgmjlgxkgmm4vngbgl"))))
    (properties `((upstream-name . "genio")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-readr r-rcpp r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/OchoaLab/genio")
    (synopsis "Genetics Input/Output Functions")
    (description
     "This package implements readers and writers for file formats associated with
genetics data.  Reading and writing Plink BED/BIM/FAM and GCTA binary GRM
formats is fully supported, including a lightning-fast BED reader and writer
implementations.  Other functions are readr wrappers that are more constrained,
user-friendly, and efficient for these particular applications; handles Plink
and Eigenstrat tables (FAM, BIM, IND, and SNP files).  There are also make
functions for FAM and BIM tables with default values to go with simulated
genotype data.")
    (license license:gpl3)))

(define-public r-genieclust
  (package
    (name "r-genieclust")
    (version "1.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genieclust" version))
       (sha256
        (base32 "131nax76dsg3b6700qwgq3gpin4pl152rjsfqgh41m89ffchk0m4"))))
    (properties `((upstream-name . "genieclust")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-rcpp))
    (home-page "https://genieclust.gagolewski.com/")
    (synopsis
     "Fast and Robust Hierarchical Clustering with Noise Points Detection")
    (description
     "This package provides a retake on the Genie algorithm (Gagolewski, 2021
<DOI:10.1016/j.softx.2021.100722>) - a robust hierarchical clustering method
(Gagolewski, Bartoszuk, Cena, 2016 <DOI:10.1016/j.ins.2016.05.003>).  Now faster
and more memory efficient; determining the whole hierarchy for datasets of 10M
points in low dimensional Euclidean spaces or 100K points in high-dimensional
ones takes only 1-2 minutes.  Allows clustering with respect to mutual
reachability distances so that it can act as a noise point detector or a
robustified version of HDBSCAN* (that is able to detect a predefined number of
clusters and hence it does not dependent on the somewhat fragile eps parameter).
 The package also features an implementation of inequality indices (the Gini,
Bonferroni index), external cluster validity measures (e.g., the normalised
clustering accuracy and partition similarity scores such as the adjusted Rand,
Fowlkes-Mallows, adjusted mutual information, and the pair sets index), and
internal cluster validity indices (e.g., the Calinski-Harabasz, Davies-Bouldin,
Ball-Hall, Silhouette, and generalised Dunn indices).  See also the Python
version of genieclust available on @code{PyPI}', which supports sparse data,
more metrics, and even larger datasets.")
    (license license:agpl3)))

(define-public r-geniebpc
  (package
    (name "r-geniebpc")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genieBPC" version))
       (sha256
        (base32 "02jfa24qjpdn6z25312hs9vgasr6pkfab5apmmsh3yqhq6l4g7jg"))))
    (properties `((upstream-name . "genieBPC")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-sunburstr
                             r-stringr
                             r-rlang
                             r-purrr
                             r-jsonlite
                             r-httr
                             r-dtplyr
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://genie-bpc.github.io/genieBPC/")
    (synopsis "Project GENIE BioPharma Collaborative Data Processing Pipeline")
    (description
     "The American Association Research (AACR) Project Genomics Evidence Neoplasia
Information Exchange (GENIE) @code{BioPharma} Collaborative represents a
multi-year, multi-institution effort to build a pan-cancer repository of linked
clinico-genomic data.  The genomic and clinical data are provided in multiple
releases (separate releases for each cancer cohort with updates following data
corrections), which are stored on the data sharing platform Synapse
<https://www.synapse.org/>.  The @code{genieBPC} package provides a seamless way
to obtain the data corresponding to each release from Synapse and to prepare
datasets for analysis.")
    (license license:expat)))

(define-public r-genie
  (package
    (name "r-genie")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genie" version))
       (sha256
        (base32 "07w6rf4a3d34j7dwz9zcxshg2sk1cd5v59xs3jxph17c9gga0nlq"))))
    (properties `((upstream-name . "genie")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-rcpp r-genieclust))
    (home-page "http://genieclust.gagolewski.com/")
    (synopsis "Fast, Robust, and Outlier Resistant Hierarchical Clustering")
    (description
     "Includes the reference implementation of Genie - a hierarchical clustering
algorithm that links two point groups in such a way that an inequity measure
(namely, the Gini index) of the cluster sizes does not significantly increase
above a given threshold.  This method most often outperforms many other data
segmentation approaches in terms of clustering quality as tested on a wide range
of benchmark datasets.  At the same time, Genie retains the high speed of the
single linkage approach, therefore it is also suitable for analysing larger data
sets.  For more details see (Gagolewski et al.  2016
<DOI:10.1016/j.ins.2016.05.003>).  For an even faster and more feature-rich
implementation, including, amongst others, noise point detection, see the
genieclust package.")
    (license license:gpl3+)))

(define-public r-genfrn
  (package
    (name "r-genfrn")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genfrn" version))
       (sha256
        (base32 "1y2ggdsbv7dbmi7y32nnhg76s6raj29j7riqlwbq49zg414p926a"))))
    (properties `((upstream-name . "genfrn")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=genfrn")
    (synopsis
     "Generating Triangular and Trapezoidal Fuzzy Random Numbers via Uniform Distribution")
    (description
     "Triangular and trapezoidal fuzzy numbers are used to study fuzzy logic, fuzzy
reasoning and approximating, fuzzy regression models, etc.  This package builds
the generating function for triangular and trapezoidal fuzzy numbers based on
Souliotis et al. (2022)<doi:10.3390/math10183350>.  They proposed a method for
the construction of fuzzy numbers via a cumulative distribution function based
on the possibility theory.")
    (license license:gpl3)))

(define-public r-genetit
  (package
    (name "r-genetit")
    (version "0.1-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeNetIt" version))
       (sha256
        (base32 "1ay40ym6khs016bs7ljcn7krqn9bmj2csd7p4nhzapbhp4g45wsc"))))
    (properties `((upstream-name . "GeNetIt")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidygraph
                             r-terra
                             r-spdep
                             r-sfnetworks
                             r-sf
                             r-nlme
                             r-igraph
                             r-exactextractr
                             r-dplyr))
    (home-page "https://github.com/jeffreyevans/GeNetIt")
    (synopsis "Spatial Graph-Theoretic Genetic Gravity Modelling")
    (description
     "Implementation of spatial graph-theoretic genetic gravity models.  The model
framework is applicable for other types of spatial flow questions.  Includes
functions for constructing spatial graphs, sampling and summarizing associated
raster variables and building unconstrained and singly constrained gravity
models.")
    (license license:gpl3)))

(define-public r-geneticsubsetter
  (package
    (name "r-geneticsubsetter")
    (version "0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeneticSubsetter" version))
       (sha256
        (base32 "0bd4snv3dwabc7mknmd2rjmffj67xq535x0bycajhd83d1jhjars"))))
    (properties `((upstream-name . "GeneticSubsetter")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GeneticSubsetter")
    (synopsis "Identify Favorable Subsets of Germplasm Collections")
    (description
     "Finds subsets of sets of genotypes with a high Heterozygosity, and Mean of
Transformed Kinships (MTK), measures that can indicate a subset would be
beneficial for rare-trait discovery and genome-wide association scanning,
respectively.")
    (license license:gpl2+)))

(define-public r-geneticae
  (package
    (name "r-geneticae")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geneticae" version))
       (sha256
        (base32 "0ka0m4dq8a9bzdk3gi7gj6dnc2a7c4chrzixw2g4qd7md31z33vx"))))
    (properties `((upstream-name . "geneticae")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-scales
                             r-rrcov
                             r-rlang
                             r-reshape2
                             r-prettydoc
                             r-pcamethods
                             r-missmda
                             r-matrixstats
                             r-mass
                             r-ggplot2
                             r-ggforce
                             r-ggebiplots
                             r-dplyr
                             r-calibrate))
    (native-inputs (list r-knitr))
    (home-page "https://jangelini.github.io/geneticae/")
    (synopsis
     "Statistical Tools for the Analysis of Multi Environment Agronomic Trials")
    (description
     "Data from multi environment agronomic trials, which are often carried out by
plant breeders, can be analyzed with the tools offered by this package such as
the Additive Main effects and Multiplicative Interaction model or AMMI ('Gauch
1992, ISBN:9780444892409) and the Site Regression model or SREG ('Cornelius
1996, <doi:10.1201/9780367802226>).  Since these methods present a poor
performance under the presence of outliers and missing values, this package
includes robust versions of the AMMI model ('Rodrigues 2016,
<doi:10.1093/bioinformatics/btv533>), and also imputation techniques
specifically developed for this kind of data ('Arciniegas-AlarcÃ³n 2014,
<doi:10.2478/bile-2014-0006>).")
    (license license:gpl2)))

(define-public r-genetic-algo-optimizer
  (package
    (name "r-genetic-algo-optimizer")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genetic.algo.optimizeR" version))
       (sha256
        (base32 "14s8cwx7bx6a206cxm2i7rp8kisqz620qrgzj3cn1qg0pxd49ixj"))))
    (properties `((upstream-name . "genetic.algo.optimizeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tinytex
                             r-stringr
                             r-rsconnect
                             r-magrittr
                             r-ggplot2
                             r-dplyr
                             r-diagrammer
                             r-biocviews))
    (native-inputs (list r-knitr))
    (home-page "https://danymukesha.github.io/genetic.algo.optimizeR/")
    (synopsis "Genetic Algorithm Optimization")
    (description
     "Genetic algorithm are a class of optimization algorithms inspired by the process
of natural selection and genetics.  This package is for learning purposes and
allows users to optimize various functions or parameters by mimicking biological
evolution processes such as selection, crossover, and mutation.  Ideal for tasks
like machine learning parameter tuning, mathematical function optimization, and
solving an optimization problem that involves finding the best solution in a
discrete space.")
    (license license:expat)))

(define-public r-genesysr
  (package
    (name "r-genesysr")
    (version "2.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genesysr" version))
       (sha256
        (base32 "0gf63gmhxvyxms2xhbgwmg8c5xpc4bswbpvfv4nyx25zr97ba7vi"))))
    (properties `((upstream-name . "genesysr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-readr
                             r-magrittr
                             r-jsonlite
                             r-httr2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://gitlab.croptrust.org/genesys-pgr/genesysr")
    (synopsis "Genesys PGR Client")
    (description
     "Access data on plant genetic resources from genebanks around the world published
on Genesys (<https://www.genesys-pgr.org>).  Your use of data is subject to
terms and conditions available at
<https://www.genesys-pgr.org/content/legal/terms>.")
    (license license:asl2.0)))

(define-public r-genest
  (package
    (name "r-genest")
    (version "1.4.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GenEst" version))
       (sha256
        (base32 "0ifqzpqrc9bgg6sp8zx30lvcz9kcbzivw5sf8f3axnwzfia9x376"))))
    (properties `((upstream-name . "GenEst")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-shinyjs
                             r-shiny
                             r-rcpp
                             r-mvtnorm
                             r-matrixstats
                             r-mass
                             r-lubridate
                             r-htmltools
                             r-hellno
                             r-gtools
                             r-gsl
                             r-dt))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GenEst")
    (synopsis "Generalized Mortality Estimator")
    (description
     "Command-line and shiny GUI implementation of the @code{GenEst} models for
estimating bird and bat mortality at wind and solar power facilities, following
Dalthorp, et al. (2018) <doi:10.3133/tm7A2>.")
    (license license:cc0)))

(define-public r-geneslope
  (package
    (name "r-geneslope")
    (version "0.38.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geneSLOPE" version))
       (sha256
        (base32 "08fbrssj03ak6xqm9fmb9v6ir7229qds891088wznvg58k81yslv"))))
    (properties `((upstream-name . "geneSLOPE")))
    (build-system r-build-system)
    (propagated-inputs (list r-slope r-ggplot2 r-bigmemory))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/psobczyk/geneSLOPE")
    (synopsis "Genome-Wide Association Study with SLOPE")
    (description
     "Genome-wide association study (GWAS) performed with SLOPE, short for Sorted
L-One Penalized Estimation, a method for estimating the vector of coefficients
in a linear model.  In the first step of GWAS, single nucleotide polymorphisms
(SNPs) are clumped according to their correlations and distances.  Then, SLOPE
is performed on the data where each clump has one representative.  Malgorzata
Bogdan, Ewout van den Berg, Chiara Sabatti, Weijie Su and Emmanuel Candes (2014)
\"SLOPE - Adaptive Variable Selection via Convex Optimization\"
<@code{arXiv:1407.3824>}.")
    (license license:gpl3)))

(define-public r-geneset
  (package
    (name "r-geneset")
    (version "0.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geneset" version))
       (sha256
        (base32 "1fiyrlsy5zqmxdj71w5m0h70ln5b84y7mlhv2j8lad623gpng3l6"))))
    (properties `((upstream-name . "geneset")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-stringi r-rcurl r-fst r-dplyr))
    (home-page "https://github.com/GangLiLab/geneset")
    (synopsis "Get Gene Sets for Gene Enrichment Analysis")
    (description
     "Gene sets are fundamental for gene enrichment analysis.  The package geneset
enables querying gene sets from public databases including GO (Gene Ontology
Consortium. (2004) <doi:10.1093/nar/gkh036>), KEGG (Minoru et al. (2000)
<doi:10.1093/nar/28.1.27>), @code{WikiPathway} (Marvin et al. (2020)
<doi:10.1093/nar/gkaa1024>), @code{MsigDb} (Arthur et al. (2015)
<doi:10.1016/j.cels.2015.12.004>), Reactome (David et al. (2011)
<doi:10.1093/nar/gkq1018>), @code{MeSH} (Ish et al. (2014)
<doi:10.4103/0019-5413.139827>), @code{DisGeNET} (Janet et al. (2017)
<doi:10.1093/nar/gkw943>), Disease Ontology (Lynn et al. (2011)
<doi:10.1093/nar/gkr972>), Network of Cancer Genes (Dimitra et al. (2019)
<doi:10.1186/s13059-018-1612-0>) and COVID-19 (Maxim et al. (2020)
<doi:10.21203/rs.3.rs-28582/v1>).  Gene sets are stored in the list object which
provides data frame of geneset and geneset_name'.  The geneset has two columns
of term ID and gene ID. The geneset_name has two columns of terms ID and term
description.")
    (license license:gpl3)))

(define-public r-geneselectr
  (package
    (name "r-geneselectr")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeneSelectR" version))
       (sha256
        (base32 "0hxdnfhah3a29f00ycanf7ssxm2z7inp9sf72l61ilcrg9bx22hq"))))
    (properties `((upstream-name . "GeneSelectR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmod
                             r-tidyr
                             r-tibble
                             r-testthat
                             r-rlang
                             r-reticulate
                             r-reshape2
                             r-rcolorbrewer
                             r-magrittr
                             r-glue
                             r-ggplot2
                             r-dplyr
                             r-cowplot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/dzhakparov/GeneSelectR")
    (synopsis
     "'GeneSelectR' - Comprehensive Feature Selection Workflow for Bulk RNAseq Datasets")
    (description
     "The workflow is a versatile R package designed for comprehensive feature
selection in bulk RNAseq datasets.  Its key innovation lies in the seamless
integration of the Python scikit-learn
(<https://scikit-learn.org/stable/index.html>) machine learning framework with
R-based bioinformatics tools. @code{GeneSelectR} performs robust Machine
Learning-driven (ML) feature selection while leveraging Gene Ontology (GO)
enrichment analysis as described by Thomas PD et al. (2022)
<doi:10.1002/pro.4218>, using @code{clusterProfiler} (Wu et al., 2021)
<doi:10.1016/j.xinn.2021.100141> and semantic similarity analysis powered by
@code{simplifyEnrichment} (Gu, Huebschmann, 2021)
<doi:10.1016/j.gpb.2022.04.008>.  This combination of methodologies optimizes
computational and biological insights for analyzing complex RNAseq datasets.")
    (license license:expat)))

(define-public r-genescape
  (package
    (name "r-genescape")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeneScape" version))
       (sha256
        (base32 "1yjz9bc5qy46z6ha9c7khx6mgzyqqbx3aq5w7nqw5zfym1zkfiaq"))))
    (properties `((upstream-name . "GeneScape")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-corpcor))
    (home-page "https://cran.r-project.org/package=GeneScape")
    (synopsis "Simulation of Single Cell RNA-Seq Data with Complex Structure")
    (description
     "Simulating single cell RNA-seq data with complicated structure.  This package is
developed based on the Splat method (Zappia, Phipson and Oshlack (2017)
<doi:10.1186/s13059-017-1305-0>). @code{GeneScape} incorporates additional
features to simulate single cell RNA-seq data with complicated differential
expression and correlation structures, such as sub-cell-types, correlated genes
(pathway genes) and hub genes.")
    (license license:gpl3+)))

(define-public r-genero
  (package
    (name "r-genero")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genero" version))
       (sha256
        (base32 "0akwwy5xbh7s40v8dr1zr7jfglv0iyh4zn0sb0rnz4r57qr6nh8w"))))
    (properties `((upstream-name . "genero")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/datasketch/genero")
    (synopsis "Estimate Gender from Names in Spanish and Portuguese")
    (description
     "Estimate gender from names in Spanish and Portuguese.  Works with vectors and
dataframes.  The estimation works not only for first names but also full names.
The package relies on a compilation of common names with it's most frequent
associated gender in both languages which are used as look up tables for gender
inference.")
    (license license:expat)))

(define-public r-genericml
  (package
    (name "r-genericml")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GenericML" version))
       (sha256
        (base32 "1r2jl3say11by21ja7m6m55yzmgrdmp4nlanvh8h4cm8v37bzql7"))))
    (properties `((upstream-name . "GenericML")))
    (build-system r-build-system)
    (propagated-inputs (list r-splitstackshape
                             r-sandwich
                             r-mlr3learners
                             r-mlr3
                             r-lmtest
                             r-ggplot2
                             r-abind))
    (home-page "https://github.com/mwelz/GenericML/")
    (synopsis "Generic Machine Learning Inference")
    (description
     "Generic Machine Learning Inference on heterogeneous treatment effects in
randomized experiments as proposed in Chernozhukov, Demirer, Duflo and
FernÃ¡ndez-Val (2020) <@code{arXiv:1712.04802>}.  This package's workhorse is
the mlr3 framework of Lang et al. (2019) <doi:10.21105/joss.01903>, which
enables the specification of a wide variety of machine learners.  The main
functionality, @code{GenericML}(), runs Algorithm 1 in Chernozhukov, Demirer,
Duflo and FernÃ¡ndez-Val (2020) <@code{arXiv:1712.04802>} for a suite of
user-specified machine learners.  All steps in the algorithm are customizable
via setup functions.  Methods for printing and plotting are available for
objects returned by @code{GenericML}().  Parallel computing is supported.")
    (license license:gpl3+)))

(define-public r-generator
  (package
    (name "r-generator")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "generator" version))
       (sha256
        (base32 "0xjvnmnpdms8rrxxcz6pd8w4rnbv3ghzqv4m63zxia2l98x7z4rf"))))
    (properties `((upstream-name . "generator")))
    (build-system r-build-system)
    (home-page "https://github.com/paulhendricks/generator")
    (synopsis
     "Generate Data Containing Fake Personally Identifiable Information")
    (description
     "Allows users to quickly and easily generate fake data containing Personally
Identifiable Information (PII) through convenience functions.")
    (license license:expat)))

(define-public r-generaloaxaca
  (package
    (name "r-generaloaxaca")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeneralOaxaca" version))
       (sha256
        (base32 "19j5c5xr6mdb6pmih94wbjas4yh0dmsqfggg8clvdxkpwk0h338v"))))
    (properties `((upstream-name . "GeneralOaxaca")))
    (build-system r-build-system)
    (propagated-inputs (list r-boot))
    (home-page "https://cran.r-project.org/package=GeneralOaxaca")
    (synopsis "Blinder-Oaxaca Decomposition for Generalized Linear Model")
    (description
     "Perform the Blinder-Oaxaca decomposition for generalized linear model with
bootstrapped standard errors.  The twofold and threefold decomposition are
given, even the generalized linear model output in each group.")
    (license license:gpl2+)))

(define-public r-generalizedwendland
  (package
    (name "r-generalizedwendland")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeneralizedWendland" version))
       (sha256
        (base32 "1mwinkjnf7wvsa077nzq641fkrj9y44gzmihhkkfgkjddcjnk4s4"))))
    (properties `((upstream-name . "GeneralizedWendland")))
    (build-system r-build-system)
    (inputs (list gsl))
    (propagated-inputs (list r-spam64
                             r-spam
                             r-rcppeigen
                             r-rcpp
                             r-optimparallel
                             r-matrix
                             r-fields
                             r-bh))
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "https://cran.r-project.org/package=GeneralizedWendland")
    (synopsis "Fully Parameterized Generalized Wendland Covariance Function")
    (description
     "This package provides a fully parameterized Generalized Wendland covariance
function for use in Gaussian process models, as well as multiple methods for
approximating it via covariance interpolation.  The available methods are linear
interpolation, polynomial interpolation, and cubic spline interpolation.  Moreno
Bevilacqua and Reinhard Furrer and Tarik Faouzi and Emilio Porcu (2019)
<url:<https://projecteuclid.org/@code{journalArticle/Download?urlId=10.1214%2F17-AOS1652}
>>.  Moreno Bevilacqua and Christian CaamaÃ±o-Carrillo and Emilio Porcu (2022)
<@code{arXiv:2008.02904>}.  Reinhard Furrer and Roman Flury and Florian Gerber
(2022) <url:<https://CRAN.R-project.org/package=spam >>.")
    (license license:gpl2+)))

(define-public r-generalizedumatrix
  (package
    (name "r-generalizedumatrix")
    (version "1.2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeneralizedUmatrix" version))
       (sha256
        (base32 "1mzxszsk2mzswndrq0j125l1b5qdmjryg2swb7f2mnipa5k3mqbm"))))
    (properties `((upstream-name . "GeneralizedUmatrix")))
    (build-system r-build-system)
    (inputs (list pandoc))
    (propagated-inputs (list r-rcppparallel r-rcpparmadillo r-rcpp r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://www.deepbionics.org")
    (synopsis "Credible Visualization for Two-Dimensional Projections of Data")
    (description
     "Projections are common dimensionality reduction methods, which represent
high-dimensional data in a two-dimensional space.  However, when restricting the
output space to two dimensions, which results in a two dimensional scatter plot
(projection) of the data, low dimensional similarities do not represent high
dimensional distances coercively [Thrun, 2018] <DOI: 10.1007/978-3-658-20540-9>.
 This could lead to a misleading interpretation of the underlying structures
[Thrun, 2018].  By means of the 3D topographic map the generalized Umatrix is
able to depict errors of these two-dimensional scatter plots.  The package is
derived from the book of Thrun, M.C.: \"Projection Based Clustering through
Self-Organization and Swarm Intelligence\" (2018) <DOI:10.1007/978-3-658-20540-9>
and the main algorithm called simplified self-organizing map for dimensionality
reduction methods is published in <DOI: 10.1016/j.mex.2020.101093>.")
    (license license:gpl3)))

(define-public r-generalizedhyperbolic
  (package
    (name "r-generalizedhyperbolic")
    (version "0.8-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeneralizedHyperbolic" version))
       (sha256
        (base32 "1a9n9hb6cxp9silcpskhp67l7p1azm72y4dngj48nngcavrm8i4r"))))
    (properties `((upstream-name . "GeneralizedHyperbolic")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-distributionutils))
    (home-page "https://r-forge.r-project.org/projects/rmetrics/")
    (synopsis "The Generalized Hyperbolic Distribution")
    (description
     "This package provides functions for the hyperbolic and related distributions.
Density, distribution and quantile functions and random number generation are
provided for the hyperbolic distribution, the generalized hyperbolic
distribution, the generalized inverse Gaussian distribution and the skew-Laplace
distribution.  Additional functionality is provided for the hyperbolic
distribution, normal inverse Gaussian distribution and generalized inverse
Gaussian distribution, including fitting of these distributions to data.  Linear
models with hyperbolic errors may be fitted using @code{hyperblmFit}.")
    (license license:gpl2+)))

(define-public r-generalisedcovariancemeasure
  (package
    (name "r-generalisedcovariancemeasure")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeneralisedCovarianceMeasure" version))
       (sha256
        (base32 "0xgxmfcpx3mnmp5hxdy5wiihj693qlksv6jxfb9ba4dy2glzdacf"))))
    (properties `((upstream-name . "GeneralisedCovarianceMeasure")))
    (build-system r-build-system)
    (propagated-inputs (list r-xgboost r-mgcv r-kernlab r-cvst))
    (home-page
     "https://cran.r-project.org/package=GeneralisedCovarianceMeasure")
    (synopsis
     "Test for Conditional Independence Based on the Generalized Covariance Measure (GCM)")
    (description
     "This package provides a statistical hypothesis test for conditional
independence.  It performs nonlinear regressions on the conditioning variable
and then tests for a vanishing covariance between the resulting residuals.  It
can be applied to both univariate random variables and multivariate random
vectors.  Details of the method can be found in Rajen D. Shah and Jonas Peters:
The Hardness of Conditional Independence Testing and the Generalised Covariance
Measure, Annals of Statistics 48(3), 1514--1538, 2020.")
    (license license:gpl2)))

(define-public r-generalhoslem
  (package
    (name "r-generalhoslem")
    (version "1.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "generalhoslem" version))
       (sha256
        (base32 "0fmfhmdb16gf3v9h50vif4f3f6bm03qq7wplnmzyxa80jb60fcc0"))))
    (properties `((upstream-name . "generalhoslem")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape r-mass))
    (home-page "https://cran.r-project.org/package=generalhoslem")
    (synopsis "Goodness of Fit Tests for Logistic Regression Models")
    (description
     "This package provides functions to assess the goodness of fit of binary,
multinomial and ordinal logistic models.  Included are the Hosmer-Lemeshow tests
(binary, multinomial and ordinal) and the Lipsitz and Pulkstenis-Robinson tests
(ordinal).")
    (license license:gpl2)))

(define-public r-generalcorr
  (package
    (name "r-generalcorr")
    (version "1.2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "generalCorr" version))
       (sha256
        (base32 "19yqhzz3xpgb7aixpj214mfkviz0iwap8hh758xyx25jqf4w4h5a"))))
    (properties `((upstream-name . "generalCorr")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable r-psych r-np r-meboot r-lattice))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=generalCorr")
    (synopsis "Generalized Correlations, Causal Paths and Portfolio Selection")
    (description
     "Function gmcmtx0() computes a more reliable (general) correlation matrix.  Since
causal paths from data are important for all sciences, the package provides many
sophisticated functions. @code{causeSummBlk}() and @code{causeSum2Blk}() give
easy-to-interpret causal paths.  Let Z denote control variables and compare two
flipped kernel regressions: X=f(Y, Z)+e1 and Y=g(X, Z)+e2.  Our criterion Cr1
says that if |e1*Y|>|e2*X| then variation in X is more \"exogenous or
independent\" than in Y, and the causal path is X to Y. Criterion Cr2 requires
|e2|<|e1|.  These inequalities between many absolute values are quantified by
four orders of stochastic dominance.  Our third criterion Cr3, for the causal
path X to Y, requires new generalized partial correlations to satisfy
|r*(x|y,z)|< |r*(y|x,z)|.  The function @code{parcorVec}() reports generalized
partials between the first variable and all others.  The package provides
several R functions including get0outliers() for outlier detection, bigfp() for
numerical integration by the trapezoidal rule, stochdom2() for stochastic
dominance, pillar3D() for 3D charts, @code{canonRho}() for generalized canonical
correlations, @code{depMeas}() measures nonlinear dependence, and
@code{causeSummary(mtx}) reports summary of causal paths among matrix columns.
Portfolio selection: @code{decileVote}(), @code{momentVote}(), dif4mtx(),
@code{exactSdMtx}() can rank several stocks.  Functions whose names begin with
boot provide bootstrap statistical inference, including a new @code{bootGcRsq}()
test for \"Granger-causality\" allowing nonlinear relations.  A new tool for
evaluation of out-of-sample portfolio performance is @code{outOFsamp}().  Panel
data implementation is now included.  See eight vignettes of the package for
theory, examples, and usage tips.  See Vinod (2019)
\\doi{10.1080/03610918.2015.1122048}.")
    (license license:gpl2+)))

(define-public r-genepop
  (package
    (name "r-genepop")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genepop" version))
       (sha256
        (base32 "13h3azszr42bzm5af8rkxw1lnd1x5c9lpawfcg3m5xgwvwzlzl3l"))))
    (properties `((upstream-name . "genepop")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-stringr r-rcppprogress r-rcpp))
    (native-inputs (list esbuild))
    (home-page "https://www.r-project.org")
    (synopsis "Population Genetic Data Analysis Using Genepop")
    (description
     "Makes the Genepop software available in R. This software implements a mixture of
traditional population genetic methods and some more focused developments: it
computes exact tests for Hardy-Weinberg equilibrium, for population
differentiation and for genotypic disequilibrium among pairs of loci; it
computes estimates of F-statistics, null allele frequencies, allele size-based
statistics for microsatellites, etc.; and it performs analyses of isolation by
distance from pairwise comparisons of individuals or population samples.")
    (license (license:fsdg-compatible "CeCILL-2"))))

(define-public r-genepi
  (package
    (name "r-genepi")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genepi" version))
       (sha256
        (base32 "0ff5cv8sxda55xjj1pdnaljsg840vmvaz51mgajczv9c0wgj70yj"))))
    (properties `((upstream-name . "genepi")))
    (build-system r-build-system)
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=genepi")
    (synopsis "Genetic Epidemiology Design and Inference")
    (description
     "Package for Genetic Epidemiologic Methods Developed at MSKCC. It contains
functions to calculate haplotype specific odds ratio and the power of two stage
design for GWAS studies.")
    (license license:gpl2+)))

(define-public r-genenmf
  (package
    (name "r-genenmf")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeneNMF" version))
       (sha256
        (base32 "1szmkfijy84dl0zbr08cw7likqmziwm85dhvhzqvhybd17g9ahlj"))))
    (properties `((upstream-name . "GeneNMF")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-seurat
                             r-rcppml
                             r-pheatmap
                             r-matrix
                             r-lsa
                             r-irlba
                             r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/carmonalab/GeneNMF")
    (synopsis "Non-Negative Matrix Factorization for Single-Cell Omics")
    (description
     "This package provides a collection of methods to extract gene programs from
single-cell gene expression data using non-negative matrix factorization (NMF).
@code{GeneNMF} contains functions to directly interact with the Seurat toolkit
and derive interpretable gene program signatures.")
    (license license:gpl3)))

(define-public r-genemodel
  (package
    (name "r-genemodel")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genemodel" version))
       (sha256
        (base32 "1x6n6k9ifv2swhyrghvm6fsz5vh85cdik8225175i9msvmkh928n"))))
    (properties `((upstream-name . "genemodel")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/greymonroe/genemodel")
    (synopsis "Gene Model Plotting in R")
    (description
     "Using simple input, this package creates plots of gene models.  Users can create
plots of alternatively spliced gene variants and the positions of mutations and
other gene features.")
    (license license:gpl2)))

(define-public r-genekitr
  (package
    (name "r-genekitr")
    (version "1.2.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genekitr" version))
       (sha256
        (base32 "04hjz6g4jzbghv28z1nfz7adx9lk93v0h1m17vpf548z0mkv6zyh"))))
    (properties `((upstream-name . "genekitr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-stringi
                             r-rlang
                             r-openxlsx
                             r-magrittr
                             r-igraph
                             r-ggvenn
                             r-ggraph
                             r-ggplot2
                             r-geneset
                             r-fst
                             r-europepmc
                             r-dplyr
                             r-clusterprofiler))
    (home-page "https://www.genekitr.fun/")
    (synopsis "Gene Analysis Toolkit")
    (description
     "This package provides features for searching, converting, analyzing, plotting,
and exporting data effortlessly by inputting feature IDs.  Enables easy
retrieval of feature information, conversion of ID types, gene enrichment
analysis, publication-level figures, group interaction plotting, and result
export in one Excel file for seamless sharing and communication.")
    (license license:gpl3)))

(define-public r-genehummus
  (package
    (name "r-genehummus")
    (version "1.0.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geneHummus" version))
       (sha256
        (base32 "1m6yyjsvmn3w87cw4da8js5vi74k5y739gxdx1yscx1n50bc3m34"))))
    (properties `((upstream-name . "geneHummus")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rentrez r-httr r-dplyr r-curl))
    (home-page "https://github.com/NCBI-Hackathons/GeneHummus")
    (synopsis "Pipeline to Define Gene Families in Legumes and Beyond")
    (description
     "This package provides a pipeline with high specificity and sensitivity in
extracting proteins from the @code{RefSeq} database (National Center for
Biotechnology Information).  Manual identification of gene families is highly
time-consuming and laborious, requiring an iterative process of manual and
computational analysis to identify members of a given family.  The pipelines
implements an automatic approach for the identification of gene families based
on the conserved domains that specifically define that family.  See Die et al.
(2018) <doi:10.1101/436659> for more information and examples.")
    (license license:expat)))

(define-public r-genehapr
  (package
    (name "r-genehapr")
    (version "1.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geneHapR" version))
       (sha256
        (base32 "1yairrndrggvkiwwamqv3qwjs8sci4jc6k53rqs766n752vsbisz"))))
    (properties `((upstream-name . "geneHapR")))
    (build-system r-build-system)
    (propagated-inputs (list r-vcfr
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-stringdist
                             r-shinyjs
                             r-shiny
                             r-sf
                             r-rtracklayer
                             r-rlang
                             r-reshape2
                             r-pegas
                             r-maps
                             r-lolliplot
                             r-iranges
                             r-ggpubr
                             r-ggplot2
                             r-genomicranges
                             r-genetics
                             r-biostrings
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=geneHapR")
    (synopsis
     "Gene Haplotype Statistics, Phenotype Association and Visualization")
    (description
     "Import genome variants data and perform gene haplotype Statistics, visualization
and phenotype association with R'.")
    (license license:gpl3)))

(define-public r-genef
  (package
    (name "r-genef")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeneF" version))
       (sha256
        (base32 "04fmnlj42m9ahq1504pymhq930azr8a5lyggc3bl5awqv1agpzf6"))))
    (properties `((upstream-name . "GeneF")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GeneF")
    (synopsis "Package for Generalized F-Statistics")
    (description
     "Implementation of several generalized F-statistics.  The current version
includes a generalized F-statistic based on the flexible isotonic/monotonic
regression or order restricted hypothesis testing.  Based on: Y. Lai (2011)
<doi:10.1371/journal.pone.0019754>.")
    (license license:gpl2+)))

(define-public r-geneexpressionfromgeo
  (package
    (name "r-geneexpressionfromgeo")
    (version "0.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geneExpressionFromGEO" version))
       (sha256
        (base32 "15dvx2f4nsmw1mgjfpcdcfnx8335fd043xb4hdnqcbjzf9h228bp"))))
    (properties `((upstream-name . "geneExpressionFromGEO")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-r-utils
                             r-markdown
                             r-geoquery
                             r-biocmanager
                             r-biobase
                             r-annotate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/davidechicco/geneExpressionFromGEO")
    (synopsis
     "Easily Downloads a Gene Expression Dataset from a GEO Code and Retrieves the Gene Symbols of Its Probesets")
    (description
     "This package provides a function that reads in the GEO code of a gene expression
dataset, retrieves its data from GEO, (optionally) retrieves the gene symbols of
the dataset, and returns a simple dataframe table containing all the data.
Platforms available: GPL11532, GPL23126, GPL6244, GPL8300, GPL80, GPL96, GPL570,
GPL571, GPL20115, GPL1293, GPL6102, GPL6104, GPL6883, GPL6884, GPL13497,
GPL14550, GPL17077, GPL6480.  GEO: Gene Expression Omnibus.  ID: identifier
code.  The GEO datasets are downloaded from the URL
<https://ftp.ncbi.nlm.nih.gov/geo/series/>.  More information can be found in
the following manuscript: Davide Chicco, \"@code{geneExpressionFromGEO}: an R
package to facilitate data reading from Gene Expression Omnibus (GEO)\".
Microarray Data Analysis, Methods in Molecular Biology, volume 2401, chapter 12,
pages 187-194, Springer Protocols, 2021, <doi:10.1007/978-1-0716-1839-4_12>.")
    (license license:gpl3)))

(define-public r-genecycle
  (package
    (name "r-genecycle")
    (version "1.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeneCycle" version))
       (sha256
        (base32 "08pr8xgcznm04c4lhvzv2qnzij5vj7pjzyj9pr1pqpmc977h8dvg"))))
    (properties `((upstream-name . "GeneCycle")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-longitudinal r-fdrtool))
    (home-page "https://cran.r-project.org/package=GeneCycle")
    (synopsis "Identification of Periodically Expressed Genes")
    (description
     "The @code{GeneCycle} package implements the approaches of Wichert et al. (2004)
<doi:10.1093/bioinformatics/btg364>, Ahdesmaki et al. (2005)
<doi:10.1186/1471-2105-6-117> and Ahdesmaki et al. (2007)
<DOI:10.1186/1471-2105-8-233> for detecting periodically expressed genes from
gene expression time series data.")
    (license license:gpl3+)))

(define-public r-genearead
  (package
    (name "r-genearead")
    (version "2.0.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GENEAread" version))
       (sha256
        (base32 "1yk66mzzk5xxkrg81cywq94b84y2l0m542g03xcmh31gvskccl1h"))))
    (properties `((upstream-name . "GENEAread")))
    (build-system r-build-system)
    (propagated-inputs (list r-mmap r-bitops))
    (home-page "https://cran.r-project.org/package=GENEAread")
    (synopsis "Package for Reading Binary Files")
    (description
     "This package provides functions and analytics for GENEA-compatible accelerometer
data into R objects.  See topic GENEAread for an introduction to the package.
See <https://activinsights.com/technology/geneactiv/> for more details on the
GENEActiv device.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-geneaclassify
  (package
    (name "r-geneaclassify")
    (version "1.5.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GENEAclassify" version))
       (sha256
        (base32 "0iy75m31i0wb27kvknx3ln9crbllpgdc6j2qsx6hwn4gxddl9lv0"))))
    (properties `((upstream-name . "GENEAclassify")))
    (build-system r-build-system)
    (propagated-inputs (list r-signal r-rpart r-mass r-genearead r-changepoint))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GENEAclassify")
    (synopsis "Segmentation and Classification of Accelerometer Data")
    (description
     "Segmentation and classification procedures for data from the Activinsights
GENEActiv <https://activinsights.com/technology/geneactiv/> accelerometer that
provides the user with a model to guess behaviour from test data where behaviour
is missing.  Includes a step counting algorithm, a function to create segmented
data with custom features and a function to use recursive partitioning provided
in the function rpart() of the rpart package to create classification models.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-gendist
  (package
    (name "r-gendist")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gendist" version))
       (sha256
        (base32 "0rs0sn1sb6j3pk2xncix04a093awlm2nw70g0rjhr7dlzmigspgb"))))
    (properties `((upstream-name . "gendist")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gendist")
    (synopsis "Generated Probability Distribution Models")
    (description
     "Computes the probability density function (pdf), cumulative distribution
function (cdf), quantile function (qf) and generates random values (rg) for the
following general models : mixture models, composite models, folded models,
skewed symmetric models and arc tan models.")
    (license license:gpl2+)))

(define-public r-genderstat
  (package
    (name "r-genderstat")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genderstat" version))
       (sha256
        (base32 "05i89xn1r94q53rnw5njj19nmix2ldywkadvinfc47nn60h6zfgx"))))
    (properties `((upstream-name . "genderstat")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=genderstat")
    (synopsis "Quantitative Analysis Tools for Gender Studies")
    (description
     "This package provides tools for quantitative analysis in gender studies,
including functions to calculate various gender inequality metrics such as the
Gender Pay Gap, Gender Inequality Index (GII), Gender Development Index (GDI),
and Gender Empowerment Measure (GEM).  Also includes extracted secondary example
datasets for practice and learning purposes, which were obtained from the UNDP
Human Development Reports Data Center and the World Bank Gender Data Portal by
the author the dataset is available on <doi:10.34740/kaggle/dsv/6359326>.
References: Miller, Kevin; Vagins, Deborah J. (2021)
<https://eric.ed.gov/?id=ED596219>.  Jacques Charmes & Saskia Wieringa (2003)
<doi:10.1080/1464988032000125773>.  GaÃ«lle Ferrant (2010)
<https://shs.hal.science/halshs-00462463/>.")
    (license license:expat)))

(define-public r-genderinfer
  (package
    (name "r-genderinfer")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GenderInfer" version))
       (sha256
        (base32 "1bdihxxwmp8ffb5blc8hkxnxh2z72vkq1sjak25njq8b1kwnazvn"))))
    (properties `((upstream-name . "GenderInfer")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-binom))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GenderInfer")
    (synopsis
     "This is a Collection of Functions to Analyse Gender Differences")
    (description
     "Implementation of functions, which combines binomial calculation and data
visualisation, to analyse the differences in publishing authorship by gender
described in Day et al. (2020) <doi:10.1039/C9SC04090K>.  It should only be used
when self-reported gender is unavailable.")
    (license license:expat)))

(define-public r-genderbr
  (package
    (name "r-genderbr")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genderBR" version))
       (sha256
        (base32 "19n3d6ps0wswq1bxgfqifq26svqf8q696im14wfglrl731mr0qbm"))))
    (properties `((upstream-name . "genderBR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-purrr
                             r-magrittr
                             r-jsonlite
                             r-httr
                             r-dplyr))
    (home-page "https://github.com/meirelesff/genderBR")
    (synopsis "Predict Gender from Brazilian First Names")
    (description
     "This package provides a method to predict and report gender from Brazilian first
names using the Brazilian Institute of Geography and Statistics Census data
(<https://censo2010.ibge.gov.br/nomes/>).")
    (license license:gpl2+)))

(define-public r-gender
  (package
    (name "r-gender")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gender" version))
       (sha256
        (base32 "18xiaif6iiwjvnjk611sfz7qi5r7mbi73dcrgrkzkjly8zygp42k"))))
    (properties `((upstream-name . "gender")))
    (build-system r-build-system)
    (propagated-inputs (list r-remotes r-jsonlite r-httr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/lmullen/gender")
    (synopsis "Predict Gender from Names Using Historical Data")
    (description
     "This package infers state-recorded gender categories from first names and dates
of birth using historical datasets.  By using these datasets instead of lists of
male and female names, this package is able to more accurately infer the gender
of a name, and it is able to report the probability that a name was male or
female.  GUIDELINES: This method must be used cautiously and responsibly.
Please be sure to see the guidelines and warnings about usage in the README or
the package documentation.  See Blevins and Mullen (2015)
<http://www.digitalhumanities.org/dhq/vol/9/3/000223/000223.html>.")
    (license license:expat)))

(define-public r-gendata
  (package
    (name "r-gendata")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gendata" version))
       (sha256
        (base32 "1jcyv7qsmms73s4n4833djr20wxaf8n6y0hlx86yjys7nmx2xiqn"))))
    (properties `((upstream-name . "gendata")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gendata")
    (synopsis "Generate and Modify Synthetic Datasets")
    (description
     "Set of functions to create datasets using a correlation matrix.")
    (license license:gpl3)))

(define-public r-gencountr
  (package
    (name "r-gencountr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genCountR" version))
       (sha256
        (base32 "0s5hbvg71vsg02c3z8g2vim431jrc2jfksh28si2ycqi6bz2aj0y"))))
    (properties `((upstream-name . "genCountR")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://gencounter.app.damoncroberts.com")
    (synopsis
     "Interacting with Roberts and Utych's (2019) Gendered Language Dictionary")
    (description
     "Allows users to generate a gendered language score according to the gendered
language dictionary in Roberts and Utych (2019) <doi:10.1177/1065912919874883>.")
    (license license:expat)))

(define-public r-gencor
  (package
    (name "r-gencor")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gencor" version))
       (sha256
        (base32 "11dai3d40mkayhwra57ywagfklm5kayyhklhbsb7l3r53pmmfym9"))))
    (properties `((upstream-name . "gencor")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gencor")
    (synopsis "Generate Customized Correlation Matrices")
    (description
     "This package provides a function that generates a customized correlation matrix
based on limit values and proportions for intervals composed by its limits.  It
can also generate random matrices with low, medium, and high correlations, in
which low, medium, and high thresholds are user-defined.")
    (license license:gpl3)))

(define-public r-genbinomapps
  (package
    (name "r-genbinomapps")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GenBinomApps" version))
       (sha256
        (base32 "1bcgfp7ilzqirb6adwf9wjlcch4xx3zwsd8n2n7xkpjfkdm3rx62"))))
    (properties `((upstream-name . "GenBinomApps")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GenBinomApps")
    (synopsis
     "Clopper-Pearson Confidence Interval and Generalized Binomial Distribution")
    (description
     "Density, distribution function, quantile function and random generation for the
Generalized Binomial Distribution.  Functions to compute the Clopper-Pearson
Confidence Interval and the required sample size.  Enhanced model for burn-in
studies, where failures are tackled by countermeasures.")
    (license license:gpl3)))

(define-public r-genbarcode
  (package
    (name "r-genbarcode")
    (version "1.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "genBaRcode" version))
       (sha256
        (base32 "1rgx0b3bpqfc75y5x4i714m8xbrgckvg4m9gkqxq0464386s53dd"))))
    (properties `((upstream-name . "genBaRcode")))
    (build-system r-build-system)
    (propagated-inputs (list r-visnetwork
                             r-venndiagram
                             r-stringdist
                             r-shortread
                             r-shiny
                             r-s4vectors
                             r-reshape2
                             r-rcolorbrewer
                             r-phangorn
                             r-network
                             r-igraph
                             r-ggtree
                             r-ggraph
                             r-ggplot2
                             r-ggnetwork
                             r-future-apply
                             r-future
                             r-futile-logger
                             r-dplyr
                             r-biostrings
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=genBaRcode")
    (synopsis "Analysis and Visualization Tools for Genetic Barcode Data")
    (description
     "This package provides the necessary functions to identify and extract a
selection of already available barcode constructs (Cornils, K. et al. (2014)
<doi:10.1093/nar/gku081>) and freely choosable barcode designs from next
generation sequence (NGS) data.  Furthermore, it offers the possibility to
account for sequence errors, the calculation of barcode similarities and
provides a variety of visualisation tools (Thielecke, L. et al. (2017)
<doi:10.1038/srep43249>).")
    (license license:lgpl2.0+)))

(define-public r-genalgo
  (package
    (name "r-genalgo")
    (version "2.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GenAlgo" version))
       (sha256
        (base32 "1pihqxif2v62hwf5761fshyhigcjmlgbvp2j7immxlsysjfw5xgq"))))
    (properties `((upstream-name . "GenAlgo")))
    (build-system r-build-system)
    (propagated-inputs (list r-oompabase r-mass r-classdiscovery))
    (home-page "http://oompa.r-forge.r-project.org/")
    (synopsis
     "Classes and Methods to Use Genetic Algorithms for Feature Selection")
    (description
     "Defines classes and methods that can be used to implement genetic algorithms for
feature selection.  The idea is that we want to select a fixed number of
features to combine into a linear classifier that can predict a binary outcome,
and can use a genetic algorithm heuristically to select an optimal set of
features.")
    (license license:asl2.0)))

(define-public r-genai
  (package
    (name "r-genai")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GenAI" version))
       (sha256
        (base32 "0qwls17p5nv24wx17yyq42sg903119s5z4hi91czas0l4wh9lnif"))))
    (properties `((upstream-name . "GenAI")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6
                             r-magrittr
                             r-magick
                             r-listenv
                             r-jsonlite
                             r-httr
                             r-ggplotify
                             r-base64enc))
    (home-page "https://genai.gd.edu.kg/")
    (synopsis "Generative Artificial Intelligence")
    (description
     "Utilizing Generative Artificial Intelligence models like GPT-4 and Gemini Pro as
coding and writing assistants for R users.  Through these models, @code{GenAI}
offers a variety of functions, encompassing text generation, code optimization,
natural language processing, chat, and image interpretation.  The goal is to aid
R users in streamlining laborious coding and language processing tasks.")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-gena
  (package
    (name "r-gena")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gena" version))
       (sha256
        (base32 "0hwfjzsz0hrca5vdlkhggzdpv9r8hybqwd5c8mlj5ja896vkrc0w"))))
    (properties `((upstream-name . "gena")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=gena")
    (synopsis "Genetic Algorithm and Particle Swarm Optimization")
    (description
     "This package implements genetic algorithm and particle swarm algorithm for
real-valued functions.  Various modifications (including hybridization and
elitism) of these algorithms are provided.  Implemented functions are based on
ideas described in S. Katoch, S. Chauhan, V. Kumar (2020)
<doi:10.1007/s11042-020-10139-6> and M. Clerc (2012)
<https://hal.archives-ouvertes.fr/hal-00764996>.")
    (license license:gpl2+)))

(define-public r-gen5helper
  (package
    (name "r-gen5helper")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gen5helper" version))
       (sha256
        (base32 "0z7hisn57md18vcfb6f44hy6zzp9dirrjyxi4x1id3mqa3mligb6"))))
    (properties `((upstream-name . "gen5helper")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rlang
                             r-pracma
                             r-plyr
                             r-naturalsort
                             r-minpack-lm
                             r-ggplot2
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=gen5helper")
    (synopsis "Processing 'Gen5' 2.06 Exported Data")
    (description
     "This package provides a collection of functions for processing Gen5 2.06
exported data.  Gen5 is an essential data analysis software for @code{BioTek}
plate readers
<https://www.biotek.com/products/software-robotics-software/gen5-microplate-reader-and-imager-software/>.
 This package contains functions for data cleaning, modeling and plotting using
exported data from Gen5 version 2.06.  It exports technically correct data
defined in (Edwin de Jonge and Mark van der Loo (2013)
<https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf>)
for customized analysis.  It contains Boltzmann fitting for general kinetic
analysis.  See <https://www.github.com/@code{yanxianUCSB/gen5helper>} for more
information, documentation and examples.")
    (license license:expat)))

(define-public r-gen3sis
  (package
    (name "r-gen3sis")
    (version "1.5.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gen3sis" version))
       (sha256
        (base32 "07r2x0mwrs1ghsfd0a2zj7m2khwq4pg64xiv3n89zy9w9b6vcyw9"))))
    (properties `((upstream-name . "gen3sis")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rcpp
                             r-raster
                             r-matrix
                             r-gdistance
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/project-Gen3sis/R-package")
    (synopsis "General Engine for Eco-Evolutionary Simulations")
    (description
     "This package contains an engine for spatially-explicit eco-evolutionary
mechanistic models with a modular implementation and several support functions.
It allows exploring the consequences of ecological and macroevolutionary
processes across realistic or theoretical spatio-temporal landscapes on
biodiversity patterns as a general term.  Reference: Oskar Hagen, Benjamin
Flueck, Fabian Fopp, Juliano S. Cabral, Florian Hartig, Mikael Pontarp, Thiago
F. Rangel, Loic Pellissier (2021) \"gen3sis: A general engine for
eco-evolutionary simulations of the processes that shape Earth's biodiversity\"
<doi:10.1371/journal.pbio.3001340>.")
    (license license:gpl3)))

(define-public r-gen2stage
  (package
    (name "r-gen2stage")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gen2stage" version))
       (sha256
        (base32 "16xbzgkjskzm1wik3dznvwhqddrpmcgsgd372n4a67rbb46jgx6r"))))
    (properties `((upstream-name . "gen2stage")))
    (build-system r-build-system)
    (propagated-inputs (list r-clinfun))
    (home-page "https://cran.r-project.org/package=gen2stage")
    (synopsis "Generalized Two-Stage Designs for Phase II Single-Arm Studies")
    (description
     "One can find single-stage and two-stage designs for a phase II single-arm study
with either efficacy or safety/toxicity endpoints as described in Kim and Wong
(2019) <doi:10.29220/CSAM.2019.26.2.163>.")
    (license license:gpl2+)))

(define-public r-gemtc
  (package
    (name "r-gemtc")
    (version "1.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gemtc" version))
       (sha256
        (base32 "01sas647d3s5adkqg96z0hhr9pig275fz896ghnp313p5k3fz90l"))))
    (properties `((upstream-name . "gemtc")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm
                             r-rjags
                             r-rglpk
                             r-plyr
                             r-meta
                             r-igraph
                             r-forcats
                             r-coda))
    (home-page "https://github.com/gertvv/gemtc")
    (synopsis "Network Meta-Analysis Using Bayesian Methods")
    (description
     "Network meta-analyses (mixed treatment comparisons) in the Bayesian framework
using JAGS. Includes methods to assess heterogeneity and inconsistency, and a
number of standard visualizations.  van Valkenhoef et al. (2012)
<doi:10.1002/jrsm.1054>; van Valkenhoef et al. (2015) <doi:10.1002/jrsm.1167>.")
    (license license:gpl3)))

(define-public r-gems
  (package
    (name "r-gems")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gems" version))
       (sha256
        (base32 "174mjnpldb0rwjvwdgpcg1acm526gs0pjf9gi5mn9d3x9yz4r5jx"))))
    (properties `((upstream-name . "gems")))
    (build-system r-build-system)
    (propagated-inputs (list r-plyr r-msm r-mass r-data-table))
    (home-page "https://cran.r-project.org/package=gems")
    (synopsis "Generalized Multistate Simulation Model")
    (description
     "Simulate and analyze multistate models with general hazard functions.  gems
provides functionality for the preparation of hazard functions and parameters,
simulation from a general multistate model and predicting future events.  The
multistate model is not required to be a Markov model and may take the history
of previous events into account.  In the basic version, it allows to simulate
from transition-specific hazard function, whose parameters are multivariable
normally distributed.")
    (license license:gpl2)))

(define-public r-gemma2
  (package
    (name "r-gemma2")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gemma2" version))
       (sha256
        (base32 "1l5axji33abrkm2b32ziilhfqssbq4ypv7kimd16fak6xjz50m0k"))))
    (properties `((upstream-name . "gemma2")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/fboehm/gemma2")
    (synopsis "GEMMA Multivariate Linear Mixed Model")
    (description
     "Fits a multivariate linear mixed effects model that uses a polygenic term, after
Zhou & Stephens (2014) (<https://www.nature.com/articles/nmeth.2848>).  Of
particular interest is the estimation of variance components with restricted
maximum likelihood (REML) methods.  Genome-wide efficient mixed-model
association (GEMMA), as implemented in the package gemma2', uses an
expectation-maximization algorithm for variance components inference for use in
quantitative trait locus studies.")
    (license license:expat)))

(define-public r-gemini-r
  (package
    (name "r-gemini-r")
    (version "0.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gemini.R" version))
       (sha256
        (base32 "1xppmjpsm670nak4c8bc7ssasrzjh56144xwsrc0sfnb28lp3n2l"))))
    (properties `((upstream-name . "gemini.R")))
    (build-system r-build-system)
    (propagated-inputs (list r-httr2 r-cli r-base64enc))
    (home-page "https://github.com/jhk0530/gemini.R")
    (synopsis "Interface for 'Google Gemini' API")
    (description
     "This package provides a comprehensive interface for Google Gemini API, enabling
users to access and utilize Gemini Large Language Model (LLM) functionalities
directly from R. This package facilitates seamless integration with Google
Gemini, allowing for advanced language processing, text generation, and other
AI-driven capabilities within the R environment.  For more information, please
visit <https://ai.google.dev/docs/gemini_api_overview>.")
    (license license:expat)))

(define-public r-gemetrics
  (package
    (name "r-gemetrics")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GEmetrics" version))
       (sha256
        (base32 "1is78g8jm551bc7qb61jm6c9qcqygbh4nxj89sfccbz08k1xljyl"))))
    (properties `((upstream-name . "GEmetrics")))
    (build-system r-build-system)
    (propagated-inputs (list r-bglr))
    (home-page "https://cran.r-project.org/package=GEmetrics")
    (synopsis
     "Best Linear Unbiased Prediction of Genotype-by-Environment Metrics")
    (description
     "This package provides functions to calculate the best linear unbiased prediction
of genotype-by-environment metrics: ecovalence, environmental variance, Finlay
and Wilkinson regression and Lin and Binns superiority measure, based on a
multi-environment genomic prediction model.")
    (license license:gpl3+)))

(define-public r-gelnet
  (package
    (name "r-gelnet")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gelnet" version))
       (sha256
        (base32 "10ygdfz9f5xhahlqb2divwvaljhiz8jhsd12wvq0qalx0v1h5j0p"))))
    (properties `((upstream-name . "gelnet")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gelnet")
    (synopsis "Generalized Elastic Nets")
    (description
     "This package implements several extensions of the elastic net regularization
scheme.  These extensions include individual feature penalties for the L1 term,
feature-feature penalties for the L2 term, as well as translation coefficients
for the latter.")
    (license license:gpl3+)))

(define-public r-gellipsoid
  (package
    (name "r-gellipsoid")
    (version "0.7.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gellipsoid" version))
       (sha256
        (base32 "16dh4gs44vnc9v1hj9xjmsq88ygk9vyfp0asqgql1ar61wgmlsav"))))
    (properties `((upstream-name . "gellipsoid")))
    (build-system r-build-system)
    (propagated-inputs (list r-rgl))
    (home-page "https://github.com/friendly/gellipsoid")
    (synopsis "Generalized Ellipsoids")
    (description
     "Represents generalized geometric ellipsoids with the \"(U,D)\" representation.  It
allows degenerate and/or unbounded ellipsoids, together with methods for linear
and duality transformations, and for plotting.  Thus ellipsoids are naturally
extended to include lines, hyperplanes, points, cylinders, etc.  This permits
exploration of a variety to statistical issues that can be visualized using
ellipsoids as discussed by Friendly, Fox & Monette (2013), Elliptical Insights:
Understanding Statistical Methods Through Elliptical Geometry
<doi:10.1214/12-STS402>.")
    (license license:gpl2+)))

(define-public r-geinter
  (package
    (name "r-geinter")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GEInter" version))
       (sha256
        (base32 "15nrnidbih5fw7i7g6d8rjllw2jbmligz2kzj2fqg30llck9ivl5"))))
    (properties `((upstream-name . "GEInter")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-reshape2
                             r-quantreg
                             r-pcapp
                             r-mass
                             r-hmisc
                             r-ggplot2))
    (home-page "https://cran.r-project.org/package=GEInter")
    (synopsis "Robust Gene-Environment Interaction Analysis")
    (description
     "Description: For the risk, progression, and response to treatment of many
complex diseases, it has been increasingly recognized that gene-environment
interactions play important roles beyond the main genetic and environmental
effects.  In practical interaction analyses, outliers in response variables and
covariates are not uncommon.  In addition, missingness in environmental factors
is routinely encountered in epidemiological studies.  The developed package
consists of five robust approaches to address the outliers problems, among which
two approaches can also accommodate missingness in environmental factors.  Both
continuous and right censored responses are considered.  The proposed approaches
are based on penalization and sparse boosting techniques for identifying
important interactions, which are realized using efficient algorithms.  Beyond
the gene-environment analysis, the developed package can also be adopted to
conduct analysis on interactions between other types of low-dimensional and
high-dimensional data. (Mengyun Wu et al (2017),
<doi:10.1080/00949655.2018.1523411>; Mengyun Wu et al (2017),
<doi:10.1002/gepi.22055>; Yaqing Xu et al (2018),
<doi:10.1080/00949655.2018.1523411>; Yaqing Xu et al (2019),
<doi:10.1016/j.ygeno.2018.07.006>; Mengyun Wu et al (2021),
<doi:10.1093/bioinformatics/btab318>).")
    (license license:gpl2)))

(define-public r-geinfo
  (package
    (name "r-geinfo")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GEInfo" version))
       (sha256
        (base32 "08ha9w3c64x9ib2fbh4djbhg7y6lykibx93mbm7i3gdvpajqk0jx"))))
    (properties `((upstream-name . "GEInfo")))
    (build-system r-build-system)
    (propagated-inputs (list r-rvest r-pheatmap r-mass r-glmnet r-dplyr))
    (home-page "https://cran.r-project.org/package=GEInfo")
    (synopsis
     "Gene-Environment Interaction Analysis Incorporating Prior Information")
    (description
     "Realize three approaches for Gene-Environment interaction analysis.  All of them
adopt Sparse Group Minimax Concave Penalty to identify important G variables and
G-E interactions, and simultaneously respect the hierarchy between main G and
G-E interaction effects.  All the three approaches are available for Linear,
Logistic, and Poisson regression.  Also realize to mine and construct prior
information for G variables and G-E interactions.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-geiger
  (package
    (name "r-geiger")
    (version "2.0.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geiger" version))
       (sha256
        (base32 "0s9zvqgn30spwggwpr02gzdjg3h4kgqamq37i03i14a3i2ls1ifw"))))
    (properties `((upstream-name . "geiger")))
    (build-system r-build-system)
    (propagated-inputs (list r-subplex
                             r-rcpp
                             r-phytools
                             r-ncbit
                             r-mvtnorm
                             r-mass
                             r-digest
                             r-desolve
                             r-colorspace
                             r-coda
                             r-ape))
    (home-page "https://cran.r-project.org/package=geiger")
    (synopsis "Analysis of Evolutionary Diversification")
    (description
     "This package provides methods for fitting macroevolutionary models to
phylogenetic trees Pennell (2014) <doi:10.1093/bioinformatics/btu181>.")
    (license license:gpl2+)))

(define-public r-geigen
  (package
    (name "r-geigen")
    (version "2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geigen" version))
       (sha256
        (base32 "0blg4w9wjprbax8z6md3285v12ajcjmcrpca52l3kd5qmxlvizy7"))))
    (properties `((upstream-name . "geigen")))
    (build-system r-build-system)
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=geigen")
    (synopsis
     "Calculate Generalized Eigenvalues, the Generalized Schur Decomposition and the Generalized Singular Value Decomposition of a Matrix Pair with Lapack")
    (description
     "This package provides functions to compute generalized eigenvalues and
eigenvectors, the generalized Schur decomposition and the generalized Singular
Value Decomposition of a matrix pair, using Lapack routines.")
    (license license:gpl2+)))

(define-public r-geex
  (package
    (name "r-geex")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geex" version))
       (sha256
        (base32 "0hs17vmwgpvv85za32rmnlylp0fbbirb1irxxqkgx3dsffgvpbm1"))))
    (properties `((upstream-name . "geex")))
    (build-system r-build-system)
    (propagated-inputs (list r-rootsolve r-numderiv r-matrix r-lme4))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bsaul/geex")
    (synopsis "An API for M-Estimation")
    (description
     "This package provides a general, flexible framework for estimating parameters
and empirical sandwich variance estimator from a set of unbiased estimating
equations (i.e., M-estimation in the vein of Stefanski & Boos (2002)
<doi:10.1198/000313002753631330>).  All examples from Stefanski & Boos (2002)
are published in the corresponding Journal of Statistical Software paper \"The
Calculus of M-Estimation in R with geex\" by Saul & Hudgens (2020)
<doi:10.18637/jss.v092.i02>.  Also provides an API to compute finite-sample
variance corrections.")
    (license license:expat)))

(define-public r-geeverse
  (package
    (name "r-geeverse")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geeVerse" version))
       (sha256
        (base32 "04hfw7y3bfgngiv5cahx3hckfl1i83x2vjc5vabqqk2nq400c4x5"))))
    (properties `((upstream-name . "geeVerse")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen
                             r-rcpp
                             r-quantreg
                             r-mvtnorm
                             r-foreach
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=geeVerse")
    (synopsis "Comprehensive Analysis of High Dimensional Longitudinal Data")
    (description
     "To provide a comprehensive analysis of high dimensional longitudinal data,this
package provides analysis for any combination of 1) simultaneous variable
selection and estimation, 2) mean regression or quantile regression for
heterogeneous data, 3) cross-sectional or longitudinal data, 4) balanced or
imbalanced data, 5) moderate, high or even ultra-high dimensional data, via
computationally efficient implementations of penalized generalized estimating
equations.")
    (license license:gpl3)))

(define-public r-geessbin
  (package
    (name "r-geessbin")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geessbin" version))
       (sha256
        (base32 "0vj1z29hccvwqsqj8gclmwzrqsqimzsr0sy2hh81jaznw9hghw5x"))))
    (properties `((upstream-name . "geessbin")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://github.com/rtishii/geessbin")
    (synopsis "Modified Generalized Estimating Equations for Binary Outcome")
    (description
     "Analyze small-sample clustered or longitudinal data with binary outcome using
modified generalized estimating equations (GEE) with bias-adjusted covariance
estimator.  The package provides any combination of three GEE methods and 12
covariance estimators.")
    (license license:gpl2+)))

(define-public r-geesmv
  (package
    (name "r-geesmv")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geesmv" version))
       (sha256
        (base32 "0gm953z8q5cc1adl3d6vj5djg2inc880zfcdl5gd56fnb5gl6h1w"))))
    (properties `((upstream-name . "geesmv")))
    (build-system r-build-system)
    (propagated-inputs (list r-nlme r-matrixcalc r-mass r-gee))
    (home-page "https://cran.r-project.org/package=geesmv")
    (synopsis
     "Modified Variance Estimators for Generalized Estimating Equations")
    (description
     "Generalized estimating equations with the original sandwich variance estimator
proposed by Liang and Zeger (1986), and eight types of more recent modified
variance estimators for improving the finite small-sample performance.")
    (license license:gpl3+)))

(define-public r-geepack
  (package
    (name "r-geepack")
    (version "1.3.12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geepack" version))
       (sha256
        (base32 "0k856hw7zh553yrqp9g3s19hjviadh36drg2vvaddv2ma5ayj6z0"))))
    (properties `((upstream-name . "geepack")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-magrittr r-broom))
    (home-page "https://cran.r-project.org/package=geepack")
    (synopsis "Generalized Estimating Equation Package")
    (description
     "Generalized estimating equations solver for parameters in mean, scale, and
correlation structures, through mean link, scale link, and correlation link.
Can also handle clustered categorical responses.  See e.g. Halekoh and
HÃ¸jsgaard, (2005, <doi:10.18637/jss.v015.i02>), for details.")
    (license license:gpl3+)))

(define-public r-geemediate
  (package
    (name "r-geemediate")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GEEmediate" version))
       (sha256
        (base32 "1x0r2qcyd9hrhkws5zi0pjdc40w8wal3jpvvajj3cr8c1r8dpfzz"))))
    (properties `((upstream-name . "GEEmediate")))
    (build-system r-build-system)
    (propagated-inputs (list r-gee))
    (home-page "https://cran.r-project.org/package=GEEmediate")
    (synopsis
     "Mediation Analysis for Generalized Linear Models Using the Difference Method")
    (description
     "Causal mediation analysis for a single exposure/treatment and a single mediator,
both allowed to be either continuous or binary.  The package implements the
difference method and provides point and interval estimates as well as testing
for the natural direct and indirect effects and the mediation proportion.  Nevo,
Xiao and Spiegelman (2017) <doi:10.1515/ijb-2017-0006>.")
    (license license:gpl3+)))

(define-public r-geem
  (package
    (name "r-geem")
    (version "0.10.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geeM" version))
       (sha256
        (base32 "1r9zyjv7r7r7avdqi54dvczf971frz8pgrs1a04kan5mh4ls6xpy"))))
    (properties `((upstream-name . "geeM")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (home-page "https://cran.r-project.org/package=geeM")
    (synopsis "Solve Generalized Estimating Equations")
    (description
     "GEE estimation of the parameters in mean structures with possible correlation
between the outcomes.  User-specified mean link and variance functions are
allowed, along with observation weighting.  The M in the name @code{geeM} is
meant to emphasize the use of the Matrix package, which allows for an
implementation based fully in R.")
    (license license:gpl3)))

(define-public r-geecure
  (package
    (name "r-geecure")
    (version "1.0-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geecure" version))
       (sha256
        (base32 "0nmnrkfgblv38qvk66bdk8933icr59mayqwljkvmpy59sm6br2k5"))))
    (properties `((upstream-name . "geecure")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-matrix r-mass r-geepack))
    (home-page "https://cran.r-project.org/package=geecure")
    (synopsis
     "Marginal Proportional Hazards Mixture Cure Models with Generalized Estimating Equations")
    (description
     "Features the marginal parametric and semi-parametric proportional hazards
mixture cure models for analyzing clustered survival data with a possible cure
fraction.  A reference is Yi Niu and Yingwei Peng (2014)
<doi:10.1016/j.jmva.2013.09.003>.")
    (license license:gpl2+)))

(define-public r-geecrt
  (package
    (name "r-geecrt")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geeCRT" version))
       (sha256
        (base32 "08gcifq3gv8b6j7mdgf2y2s9gdcza96681wkzmq7skcj9430m7dm"))))
    (properties `((upstream-name . "geeCRT")))
    (build-system r-build-system)
    (propagated-inputs (list r-rootsolve r-mvtnorm r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=geeCRT")
    (synopsis "Bias-Corrected GEE for Cluster Randomized Trials")
    (description
     "Population-averaged models have been increasingly used in the design and
analysis of cluster randomized trials (CRTs).  To facilitate the applications of
population-averaged models in CRTs, the package implements the generalized
estimating equations (GEE) and matrix-adjusted estimating equations (MAEE)
approaches to jointly estimate the marginal mean models correlation models both
for general CRTs and stepped wedge CRTs.  Despite the general GEE/MAEE approach,
the package also implements a fast cluster-period GEE method by Li et al. (2022)
<doi:10.1093/biostatistics/kxaa056> specifically for stepped wedge CRTs with
large and variable cluster-period sizes and gives a simple and efficient
estimating equations approach based on the cluster-period means to estimate the
intervention effects as well as correlation parameters.  In addition, the
package also provides functions for generating correlated binary data with
specific mean vector and correlation matrix based on the multivariate probit
method in Emrich and Piedmonte (1991) <doi:10.1080/00031305.1991.10475828> or
the conditional linear family method in Qaqish (2003)
<doi:10.1093/biomet/90.2.455>.")
    (license license:gpl2+)))

(define-public r-geeasy
  (package
    (name "r-geeasy")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geeasy" version))
       (sha256
        (base32 "0hfxryqn5dxpyl0s16ibbg1n8w19wl5ldxa8g437akb760pmsmvl"))))
    (properties `((upstream-name . "geeasy")))
    (build-system r-build-system)
    (propagated-inputs (list r-mess
                             r-matrix
                             r-lme4
                             r-ggplot2
                             r-geepack
                             r-geem))
    (home-page "https://cran.r-project.org/package=geeasy")
    (synopsis "Solve Generalized Estimating Equations for Clustered Data")
    (description
     "Estimation of generalized linear models with correlated/clustered observations
by use of generalized estimating equations (GEE).  See e.g. Halekoh and
HÃ¸jsgaard, (2005, <doi:10.18637/jss.v015.i02>), for details.  Several types of
clustering are supported, including exchangeable variance structures, AR1
structures, M-dependent, user-specified variance structures and more.  The model
fitting computations are performed using modified code from the @code{geeM}
package, while the interface and output objects have been written to resemble
the geepack package.  The package also contains additional tools for working
with and inspecting results from the geepack package, e.g. a confint method for
geeglm objects from geepack'.")
    (license license:gpl3)))

(define-public r-geeaspu
  (package
    (name "r-geeaspu")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GEEaSPU" version))
       (sha256
        (base32 "02pwjqd94kranc1f69bx9rzk27kchavhvhl9fygjhrr40nwq3pbg"))))
    (properties `((upstream-name . "GEEaSPU")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-gee))
    (home-page "https://cran.r-project.org/package=GEEaSPU")
    (synopsis
     "Adaptive Association Tests for Multiple Phenotypes using Generalized Estimating Equations (GEE)")
    (description
     "This package provides adaptive association tests for SNP level, gene level and
pathway level analyses.")
    (license license:gpl3+)))

(define-public r-geds
  (package
    (name "r-geds")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GeDS" version))
       (sha256
        (base32 "02c3hrpgp2y2il1db1da73zi3jikzihaaafp8snsdfw516q0a1pc"))))
    (properties `((upstream-name . "GeDS")))
    (build-system r-build-system)
    (propagated-inputs (list r-th-data
                             r-rmpfr
                             r-rcpp
                             r-plot3d
                             r-mi
                             r-mboost
                             r-matrix
                             r-mass
                             r-future
                             r-foreach
                             r-dorng
                             r-doparallel
                             r-dofuture))
    (home-page "https://github.com/emilioluissaenzguillen/GeDS")
    (synopsis "Geometrically Designed Spline Regression")
    (description
     "Spline Regression, Generalized Additive Models, and Component-wise Gradient
Boosting, utilizing Geometrically Designed (@code{GeD}) Splines. @code{GeDS}
regression is a non-parametric method inspired by geometric principles, for
fitting spline regression models with variable knots in one or two independent
variables.  It efficiently estimates the number of knots and their positions, as
well as the spline order, assuming the response variable follows a distribution
from the exponential family. @code{GeDS} models integrate the broader category
of Generalized (Non-)Linear Models, offering a flexible approach to modeling
complex relationships.  A description of the method can be found in Kaishev et
al. (2016) <doi:10.1007/s00180-015-0621-7> and Dimitrova et al. (2023)
<doi:10.1016/j.amc.2022.127493>.  Further extending its capabilities,
@code{GeDS's} implementation includes Generalized Additive Models (GAM) and
Functional Gradient Boosting (FGB), enabling versatile multivariate predictor
modeling, as discussed in the forthcoming work of Dimitrova et al. (2024).")
    (license license:gpl3)))

(define-public r-gecko
  (package
    (name "r-gecko")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gecko" version))
       (sha256
        (base32 "1q0wzg3mkb6z7zs81zynj6x159w99h5an20a6m8wdqbday5sccyc"))))
    (properties `((upstream-name . "gecko")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-sp
                             r-red
                             r-kernlab
                             r-geosphere
                             r-biomod2))
    (home-page "https://cran.r-project.org/package=gecko")
    (synopsis "Geographical Ecology and Conservation Knowledge Online")
    (description
     "Includes a collection of geographical analysis functions aimed primarily at
ecology and conservation science studies, allowing processing of both point and
raster data.  Future versions will integrate species threat datasets developed
by the authors.")
    (license license:gpl2)))

(define-public r-gecal
  (package
    (name "r-gecal")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GECal" version))
       (sha256
        (base32 "0i5l15v3g0mpr3byhg55pib5x84fjhp7yxwzm3hb04dfn1dx07lj"))))
    (properties `((upstream-name . "GECal")))
    (build-system r-build-system)
    (propagated-inputs (list r-nleqslv))
    (home-page "https://github.com/yonghyun-K/GECal")
    (synopsis "Generalized Entropy Calibration")
    (description
     "Generalized Entropy Calibration produces calibration weights using generalized
entropy as the objective function for optimization.  This approach, as
implemented in the GECal package, is based on Kwon, Kim, and Qiu (2024)
<doi:10.48550/@code{arXiv.2404.01076>}.  Unlike traditional methods, GECal
incorporates design weights into the constraints to maintain design consistency,
rather than including them in the objective function itself.")
    (license license:expat)))

(define-public r-gec
  (package
    (name "r-gec")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GEC" version))
       (sha256
        (base32 "1kcczyih3lg1cc6nppxbpz1zkq9priy8xwapd6kvkzmyw8npback"))))
    (properties `((upstream-name . "GEC")))
    (build-system r-build-system)
    (propagated-inputs (list r-mistr))
    (home-page "https://cran.r-project.org/package=GEC")
    (synopsis "Generalized Exponentiated Composite Distributions")
    (description
     "This package contains the framework of the estimation, sampling, and hypotheses
testing for two special distributions (Exponentiated Exponential-Pareto and
Exponentiated Inverse Gamma-Pareto) within the family of Generalized
Exponentiated Composite distributions.  The detailed explanation and the
applications of these two distributions were introduced in Bowen Liu, Malwane
M.A. Ananda (2022) <doi:10.1080/03610926.2022.2050399>, Bowen Liu, Malwane M.A.
Ananda (2022) <doi:10.3390/math10111895>, and Bowen Liu, Malwane M.A. Ananda
(2022) <doi:10.3390/app13010645>.")
    (license license:gpl3)))

(define-public r-geboes-score
  (package
    (name "r-geboes-score")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "geboes.score" version))
       (sha256
        (base32 "1i7ivcbrk1kyvh98xjs2v4zph85qrfsila50wx4vjxclaqp7afzl"))))
    (properties `((upstream-name . "geboes.score")))
    (build-system r-build-system)
    (propagated-inputs (list r-checkmate))
    (home-page "https://billdenney.github.io/geboes.score/")
    (synopsis "Evaluate the Geboes Score for Histology in Ulcerative Colitis")
    (description
     "Evaluate and validate the Geboes score for histological assessment of
inflammation in ulcerative colitis.  The original Geboes score from Geboes, et
al. (2000) <doi:10.1136/gut.47.3.404>, binary version from Li, et al. (2019)
<doi:10.1093/ecco-jcc/jjz022>, and continuous version from Magro, et al. (2020)
<doi:10.1093/ecco-jcc/jjz123> are all described and implemented.")
    (license license:gpl3+)))

(define-public r-gear
  (package
    (name "r-gear")
    (version "0.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gear" version))
       (sha256
        (base32 "0ibn2kr0rjj546cbcqhr6x69s25xh7si5fri59055j42g62mdnxy"))))
    (properties `((upstream-name . "gear")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-optimx r-autoimage))
    (home-page "https://cran.r-project.org/package=gear")
    (synopsis "Geostatistical Analysis in R")
    (description
     "This package implements common geostatistical methods in a clean,
straightforward, efficient manner.  The methods are discussed in Schabenberger
and Gotway (2004, <ISBN:9781584883227>) and Waller and Gotway (2004,
<ISBN:9780471387718>).")
    (license license:gpl2+)))

(define-public r-ge
  (package
    (name "r-ge")
    (version "0.4.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GE" version))
       (sha256
        (base32 "0fnf6z08989g132p0prif49zqa3a399wq0wzz7n964cqvz5g3j5f"))))
    (properties `((upstream-name . "GE")))
    (build-system r-build-system)
    (propagated-inputs (list r-diagrammer r-data-tree r-cge))
    (home-page "https://cran.r-project.org/package=GE")
    (synopsis "General Equilibrium Modeling")
    (description
     "Some tools for developing general equilibrium models and some general
equilibrium models.  These models can be used for teaching economic theory and
are built by the methods of new structural economics (see
<https://www.nse.pku.edu.cn/> and LI Wu, 2019, ISBN: 9787521804225, General
Equilibrium and Structural Dynamics: Perspectives of New Structural Economics.
Beijing: Economic Science Press).  The model form and mathematical methods can
be traced back to J. von Neumann (1945, A Model of General Economic Equilibrium.
 The Review of Economic Studies, 13.  pp.  1-9), J. G. Kemeny, O. Morgenstern
and G. L. Thompson (1956, A Generalization of the von Neumann Model of an
Expanding Economy, Econometrica, 24, pp.  115-135) et al.  By the way, J. G.
Kemeny is a co-inventor of the computer language BASIC.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-gdxdt
  (package
    (name "r-gdxdt")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gdxdt" version))
       (sha256
        (base32 "1yrjixq4srmaiy5iaprix4096yhf3ldyklgjag4kxd3l5acwap4h"))))
    (properties `((upstream-name . "gdxdt")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table))
    (home-page "https://cran.r-project.org/package=gdxdt")
    (synopsis "IO for GAMS GDX Files using 'data.table'")
    (description
     "Interfaces GAMS data (*.gdx) files with data.table's using the GAMS R package
gdxrrw'.  The gdxrrw package is available on the GAMS wiki:
<https://support.gams.com/doku.php?id=gdxrrw:interfacing_gams_and_r>.")
    (license (license:fsdg-compatible "MIT + file LICENCE"))))

(define-public r-gdverse
  (package
    (name "r-gdverse")
    (version "1.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gdverse" version))
       (sha256
        (base32 "0a8biv930198037a2qnkx4x0nxh1lp5jrcxazy4x93shrwxjrcg0"))))
    (properties `((upstream-name . "gdverse")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-sf
                             r-sdsfun
                             r-scatterpie
                             r-rpart
                             r-reticulate
                             r-rcpp
                             r-purrr
                             r-patchwork
                             r-magrittr
                             r-ggplot2
                             r-geosphere
                             r-forcats
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://stscl.github.io/gdverse/")
    (synopsis "Analysis of Spatial Stratified Heterogeneity")
    (description
     "Analyzing spatial factors and exploring spatial associations based on the
concept of spatial stratified heterogeneity, and also takes into account local
spatial dependencies, spatial interpretability, potential spatial interactions,
and robust spatial stratification.  Additionally, it supports geographical
detector models established in academic literature.")
    (license license:gpl3)))

(define-public r-gdsarm
  (package
    (name "r-gdsarm")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GDSARM" version))
       (sha256
        (base32 "0id3z450prnrd6rdcv4xaizmjkdhjv8vdbpbnpniaml66kqsadvh"))))
    (properties `((upstream-name . "GDSARM")))
    (build-system r-build-system)
    (propagated-inputs (list r-lpsolve))
    (home-page "https://github.com/agrakhi/GDSARM")
    (synopsis "Gauss - Dantzig Selector: Aggregation over Random Models")
    (description
     "The method aims to identify important factors in screening experiments by
aggregation over random models as studied in Singh and Stufken (2022)
<doi:10.48550/@code{arXiv.2205.13497>}.  This package provides functions to run
the Gauss-Dantzig selector on screening experiments when interactions may be
affecting the response.  Currently, all functions require each factor to be at
two levels coded as +1 and -1.")
    (license license:gpl3+)))

(define-public r-gds
  (package
    (name "r-gds")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gds" version))
       (sha256
        (base32 "0gwq0k80rnbg4ibz6dz8sk6hqaklrdbv4vzv8zyk74am3aqipn20"))))
    (properties `((upstream-name . "gds")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gds")
    (synopsis "Descriptive Statistics of Grouped Data")
    (description
     "This package contains a function called gds() which accepts three input
parameters like lower limits, upper limits and the frequencies of the
corresponding classes.  The gds() function calculate and return the values of
mean ('gmean'), median ('gmedian'), mode ('gmode'), variance ('gvar'), standard
deviation ('gstdev'), coefficient of variance ('gcv'), quartiles ('gq1', gq2',
gq3'), inter-quartile range ('@code{gIQR}'), skewness ('g1'), and kurtosis
('g2') which facilitate effective data analysis.  For skewness and kurtosis
calculations we use moments.")
    (license license:gpl2+)))

(define-public r-gdpuc
  (package
    (name "r-gdpuc")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GDPuc" version))
       (sha256
        (base32 "0g3s6jhn8ndx7jy97kmfzmxcc2b40k9j47rx703g61la1mm0a4fg"))))
    (properties `((upstream-name . "GDPuc")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-tidyselect
                             r-tidyr
                             r-tibble
                             r-rlang
                             r-magrittr
                             r-lifecycle
                             r-glue
                             r-dplyr
                             r-crayon
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/pik-piam/GDPuc")
    (synopsis "Easily Convert GDP Data")
    (description
     "Convert GDP time series data from one unit to another.  All common GDP units are
included, i.e.  current and constant local currency units, US$ via market
exchange rates and international dollars via purchasing power parities.")
    (license license:gpl3+)))

(define-public r-gdpc
  (package
    (name "r-gdpc")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gdpc" version))
       (sha256
        (base32 "1m54nm5ikarrmlpjnvl70v2xirk5xaqnv3i3pcwy96796ymk5ars"))))
    (properties `((upstream-name . "gdpc")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xts
                             r-rcpparmadillo
                             r-rcpp
                             r-foreach
                             r-doparallel))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=gdpc")
    (synopsis "Generalized Dynamic Principal Components")
    (description
     "This package provides functions to compute the Generalized Dynamic Principal
Components introduced in PeÃ±a and Yohai (2016)
<DOI:10.1080/01621459.2015.1072542>.  The implementation includes an automatic
procedure proposed in PeÃ±a, Smucler and Yohai (2020)
<DOI:10.18637/jss.v092.c02> for the identification of both the number of lags to
be used in the generalized dynamic principal components as well as the number of
components required for a given reconstruction accuracy.")
    (license license:gpl2+)))

(define-public r-gdm
  (package
    (name "r-gdm")
    (version "1.6.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gdm" version))
       (sha256
        (base32 "0m8jr7l5vif1p119pqb9kj101gi44pjl64gsz0qshrwhgn55fk2w"))))
    (properties `((upstream-name . "gdm")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-reshape2
                             r-rcpp
                             r-pbapply
                             r-foreach
                             r-doparallel))
    (home-page "https://mfitzpatrick.al.umces.edu/gdm/")
    (synopsis "Generalized Dissimilarity Modeling")
    (description
     "This package provides a toolkit with functions to fit, plot, summarize, and
apply Generalized Dissimilarity Models.  Mokany K, Ware C, Woolley SNC, Ferrier
S, Fitzpatrick MC (2022) <doi:10.1111/geb.13459> Ferrier S, Manion G, Elith J,
Richardson K (2007) <doi:10.1111/j.1472-4642.2007.00341.x>.")
    (license license:gpl3+)))

(define-public r-gdldata
  (package
    (name "r-gdldata")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gdldata" version))
       (sha256
        (base32 "19zvchn55d70miqgxnbz3y1svj6gn6236l897qc0cbqmv48mjxh4"))))
    (properties `((upstream-name . "gdldata")))
    (build-system r-build-system)
    (propagated-inputs (list r-httr2))
    (home-page "https://docs.globaldatalab.org/gdldata/")
    (synopsis "'Global Data Lab' R API")
    (description
     "Retrieve datasets from the Global Data Lab website <https://globaldatalab.org>
directly into R data frames.  Functions are provided to reference available
options (indicators, levels, countries, regions) as well.")
    (license license:expat)))

(define-public r-gdistance
  (package
    (name "r-gdistance")
    (version "1.6.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gdistance" version))
       (sha256
        (base32 "0awrv10adi31inrgxbb83p73fq05i2xifj2xf16z6mp2lwzgvxba"))))
    (properties `((upstream-name . "gdistance")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-sp r-raster r-matrix r-igraph))
    (native-inputs (list r-knitr))
    (home-page "https://AgrDataSci.github.io/gdistance/")
    (synopsis "Distances and Routes on Geographical Grids")
    (description
     "This package provides classes and functions to calculate various distance
measures and routes in heterogeneous geographic spaces represented as grids.
The package implements measures to model dispersal histories first presented by
van Etten and Hijmans (2010) <doi:10.1371/journal.pone.0012060>.  Least-cost
distances as well as more complex distances based on (constrained) random walks
can be calculated.  The distances implemented in the package are used in
geographical genetics, accessibility indicators, and may also have applications
in other fields of geospatial analysis.")
    (license license:gpl2+)))

(define-public r-gdim
  (package
    (name "r-gdim")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gdim" version))
       (sha256
        (base32 "02q5ynd22yisl3as9hyrg18p9j74lw6p1z3szm1k2ax0r3k04r27"))))
    (properties `((upstream-name . "gdim")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-rlang
                             r-progress
                             r-matrix
                             r-magrittr
                             r-irlba
                             r-ggplot2
                             r-dplyr))
    (home-page "https://github.com/RoheLab/gdim")
    (synopsis "Estimate Graph Dimension using Cross-Validated Eigenvalues")
    (description
     "Cross-validated eigenvalues are estimated by splitting a graph into two parts,
the training and the test graph.  The training graph is used to estimate
eigenvectors, and the test graph is used to evaluate the correlation between the
training eigenvectors and the eigenvectors of the test graph.  The correlations
follow a simple central limit theorem that can be used to estimate graph
dimension via hypothesis testing, see Chen et al. (2021)
<@code{arXiv:2108.03336>} for details.")
    (license license:gpl3+)))

(define-public r-gdilm-sir
  (package
    (name "r-gdilm-sir")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GDILM.SIR" version))
       (sha256
        (base32 "08bcxnv5vwshj5hsxs6z8sny8hkb65aihcijlns0zinsdf685rgl"))))
    (properties `((upstream-name . "GDILM.SIR")))
    (build-system r-build-system)
    (propagated-inputs (list r-psych r-numderiv r-mvtnorm r-matrix r-mass))
    (home-page "https://cran.r-project.org/package=GDILM.SIR")
    (synopsis "Inference for Infectious Disease Transmission in SIR Framework")
    (description
     "Model and estimate the model parameters for the spatial model of
individual-level infectious disease transmission in
Susceptible-Infected-Recovered (SIR) framework.")
    (license license:expat)))

(define-public r-gdilm-me
  (package
    (name "r-gdilm-me")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GDILM.ME" version))
       (sha256
        (base32 "1gq2snfbmzfddh3bnga26p37ywahqxq5mnnmr3in8an3x5fdpw2v"))))
    (properties `((upstream-name . "GDILM.ME")))
    (build-system r-build-system)
    (propagated-inputs (list r-psych
                             r-numderiv
                             r-ngspatial
                             r-mvtnorm
                             r-mass
                             r-corpcor))
    (home-page "https://cran.r-project.org/package=GDILM.ME")
    (synopsis "Spatial Modeling of Infectious Diseases with Co-Variate Error")
    (description
     "This package provides tools for simulating from spatial modeling of individual
level of infectious disease transmission when co-variates measured with error,
and carrying out infectious disease data analyses with the same models.  The
epidemic models considered are distance-based model within
Susceptible-Infectious-Removed (SIR) compartmental frameworks.")
    (license license:expat)))

(define-public r-gdiff
  (package
    (name "r-gdiff")
    (version "0.2-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gdiff" version))
       (sha256
        (base32 "1j3xmvpqs5vb9iq460dishi8bsb0kwfz6742bpvnhpk8z3mhf9af"))))
    (properties `((upstream-name . "gdiff")))
    (build-system r-build-system)
    (propagated-inputs (list r-pdftools r-magick))
    (home-page "https://github.com/pmur002/")
    (synopsis "Graphical Difference Testing")
    (description
     "This package provides functions for performing graphical difference testing.
Differences are generated between raster images.  Comparisons can be performed
between different package versions and between different R versions.")
    (license license:gpl2+)))

(define-public r-gdi
  (package
    (name "r-gdi")
    (version "1.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gdi" version))
       (sha256
        (base32 "0arqx98k9dkxr8fhp0x2zxmx97kk666ffhbaqh9mrzrmwkapa3vb"))))
    (properties `((upstream-name . "gdi")))
    (build-system r-build-system)
    (propagated-inputs (list r-png r-jpeg))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gdi")
    (synopsis "Volumetric Analysis using Graphic Double Integration")
    (description
     "This package provides tools implementing an automated version of the graphic
double integration technique (GDI) for volume implementation, and some other
related utilities for paleontological image-analysis.  GDI was first employed by
Jerison (1973) <ISBN:9780323141086> and Hurlburt (1999)
<doi:10.1080/02724634.1999.10011145> and is primarily used for volume or mass
estimation of (extinct) animals.  The package gdi aims to make this technique as
convenient and versatile as possible.  The core functions of gdi provide
utilities for automatically measuring diameters from digital silhouettes
provided as image files and calculating volume via graphic double integration
with simple elliptical, superelliptical (following Motani 2001
<doi:10.1666/0094-8373(2001)027%3C0735:EBMFST%3E2.0.CO;2>) or complex
cross-sectional models.  Additionally, the package provides functions for
estimating the center of mass position (COM), the moment of inertia (I) for 3D
shapes and the second moment of area (Ix, Iy, Iz) of 2D cross-sections, as well
as for visualization of results.")
    (license license:gpl3+)))

(define-public r-gdelttools
  (package
    (name "r-gdelttools")
    (version "1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GDELTtools" version))
       (sha256
        (base32 "0v368chcgqnrfy2isy8z7gl0xizafhxlcd29gr89iblhrzwmssid"))))
    (properties `((upstream-name . "GDELTtools")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-plyr r-dplyr r-datetimeutils))
    (home-page "https://cran.r-project.org/package=GDELTtools")
    (synopsis
     "Download, Slice, and Normalize GDELT V1 Event and Sentiment API Data")
    (description
     "The GDELT V1 Event data set is over 41 GB now and growing 250 MB a month.  The
number of source articles has increased over time and unevenly across countries.
 This package makes it easy to download a subset of that data, then normalize
that data to facilitate valid time series analysis.")
    (license license:expat)))

(define-public r-gde
  (package
    (name "r-gde")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gde" version))
       (sha256
        (base32 "05paf15dn2l7mfzhy3p3k7a2831fj9fwvwgljhcv7wpdj7djdccs"))))
    (properties `((upstream-name . "gde")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml
                             r-stringr
                             r-shinywidgets
                             r-shinycssloaders
                             r-shiny
                             r-rsqlite
                             r-readr
                             r-r-utils
                             r-progress
                             r-leaflet
                             r-jsonlite
                             r-httr
                             r-ggplot2
                             r-dt
                             r-dplyr
                             r-data-table))
    (home-page "https://github.com/Smithsonian/GBIF-Dataset-Explorer")
    (synopsis "GBIF Dataset Explorer")
    (description
     "This package provides functions to explore datasets from the Global Biodiversity
Information Facility (GBIF - <https://www.gbif.org/>) using a Shiny interface.")
    (license license:asl2.0)))

(define-public r-gdatools
  (package
    (name "r-gdatools")
    (version "2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GDAtools" version))
       (sha256
        (base32 "1561zzw177kc7x3hiqzi7qm1yvczx8lzjb2j7d45zgwgz96v9vf8"))))
    (properties `((upstream-name . "GDAtools")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-ggrepel r-ggplot2 r-factominer
                             r-descriptio))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/nicolas-robette/GDAtools")
    (synopsis "Geometric Data Analysis")
    (description
     "Many tools for Geometric Data Analysis (Le Roux & Rouanet (2005)
<doi:10.1007/1-4020-2236-0>), such as MCA variants (Specific Multiple
Correspondence Analysis, Class Specific Analysis), many graphical and
statistical aids to interpretation (structuring factors, concentration ellipses,
inductive tests, bootstrap validation, etc.) and multiple-table analysis
(Multiple Factor Analysis, between- and inter-class analysis, Principal
Component Analysis and Correspondence Analysis with Instrumental Variables,
etc.).")
    (license license:gpl2+)))

(define-public r-gdalutilities
  (package
    (name "r-gdalutilities")
    (version "1.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gdalUtilities" version))
       (sha256
        (base32 "19vi6mr5jwlb00r33hkz7d0n9dar95hwdvbqbhh2dmha128fjwia"))))
    (properties `((upstream-name . "gdalUtilities")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf))
    (home-page "https://github.com/JoshOBrien/gdalUtilities/")
    (synopsis "Wrappers for 'GDAL' Utilities Executables")
    (description
     "R's sf package ships with self-contained GDAL executables, including a bare
bones interface to several GDAL'-related utility programs collectively known as
the GDAL utilities'.  For each of those utilities, this package provides an R
wrapper whose formal arguments closely mirror those of the GDAL command line
interface.  The utilities operate on data stored in files and typically write
their output to other files.  Therefore, to process data stored in any of R's
more common spatial formats (i.e.  those supported by the sf and terra
packages), first write them to disk, then process them with the package's
wrapper functions before reading the outputted results back into R. GDAL
function arguments introduced in GDAL version 3.5.2 or earlier are supported.")
    (license license:gpl2+)))

(define-public r-gdalraster
  (package
    (name "r-gdalraster")
    (version "1.11.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gdalraster" version))
       (sha256
        (base32 "0vf28id5yq4f491b25fbmap1yzpzxxkkqmzfln1qc4hxfhbirv1p"))))
    (properties `((upstream-name . "gdalraster")))
    (build-system r-build-system)
    (inputs (list zlib
                  proj
                  pcre2
                  openssl
                  openssh
                  libxml2
                  gdal
                  curl))
    (propagated-inputs (list r-xml2 r-rcppint64 r-rcpp r-bit64))
    (native-inputs (list pkg-config r-knitr))
    (home-page "https://usdaforestservice.github.io/gdalraster/")
    (synopsis
     "Bindings to the 'Geospatial Data Abstraction Library' Raster API")
    (description
     "Interface to the Raster API of the Geospatial Data Abstraction Library ('GDAL',
<https://gdal.org>).  Bindings are implemented in an exposed C++ class
encapsulating a GDALDataset and its raster band objects, along with several
stand-alone functions.  These support manual creation of uninitialized datasets,
creation from existing raster as template, read/set dataset parameters, low
level I/O, color tables, raster attribute tables, virtual raster (VRT), and
gdalwarp wrapper for reprojection and mosaicing.  Includes GDAL algorithms
('dem_proc()', polygonize()', rasterize()', etc.), and functions for coordinate
transformation and spatial reference systems.  Calling signatures resemble the
native C, C++ and Python APIs provided by the GDAL project.  Includes raster
calc() to evaluate a given R expression on a layer or stack of layers, with
pixel x/y available as variables in the expression; and raster combine() to
identify and count unique pixel combinations across multiple input layers, with
optional output of the pixel-level combination IDs.  Provides raster display
using base graphics'.  Bindings to a subset of the OGR API are also included for
managing vector data sources.  Bindings to a subset of the Virtual Systems
Interface ('VSI') are also included to support operations on GDAL virtual file
systems.  These are general utility functions that abstract file system
operations on URLs, cloud storage services, Zip'/'GZip'/'7z'/'RAR archives, and
in-memory files.  gdalraster may be useful in applications that need scalable,
low-level I/O, or prefer a direct GDAL API.")
    (license license:expat)))

(define-public r-gdalcubes
  (package
    (name "r-gdalcubes")
    (version "0.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gdalcubes" version))
       (sha256
        (base32 "1bpskiv7pc09pwmh3ghf5l6wswbwlbmwx6sjqlqx7vs8d7gk6972"))))
    (properties `((upstream-name . "gdalcubes")))
    (build-system r-build-system)
    (inputs (list zlib
                  sqlite
                  proj
                  pcre2
                  openssl
                  openssh
                  netcdf
                  gdal
                  curl))
    (propagated-inputs (list r-rcpp r-ncdf4 r-jsonlite r-bh))
    (native-inputs (list pkg-config r-knitr))
    (home-page "https://github.com/appelmar/gdalcubes")
    (synopsis "Earth Observation Data Cubes from Satellite Image Collections")
    (description
     "Processing collections of Earth observation images as on-demand multispectral,
multitemporal raster data cubes.  Users define cubes by spatiotemporal extent,
resolution, and spatial reference system and let gdalcubes automatically apply
cropping, reprojection, and resampling using the Geospatial Data Abstraction
Library ('GDAL').  Implemented functions on data cubes include reduction over
space and time, applying arithmetic expressions on pixel band values, moving
window aggregates over time, filtering by space, time, bands, and predicates on
pixel values, exporting data cubes as @code{netCDF} or @code{GeoTIFF} files,
plotting, and extraction from spatial and or spatiotemporal features.  All
computational parts are implemented in C++, linking to the GDAL',
@code{netCDF}', CURL', and SQLite libraries.  See Appel and Pebesma (2019)
<doi:10.3390/data4030092> for further details.")
    (license license:expat)))

(define-public r-gdadata
  (package
    (name "r-gdadata")
    (version "0.93")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GDAdata" version))
       (sha256
        (base32 "13ks97i289rc4i7gpqrifwbj0m9rx8csjhnfg8mad10qmjwz7p8b"))))
    (properties `((upstream-name . "GDAdata")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GDAdata")
    (synopsis "Datasets for the Book Graphical Data Analysis with R")
    (description
     "Datasets used in the book Graphical Data Analysis with R (Antony Unwin, CRC
Press 2015).")
    (license license:gpl2+)))

(define-public r-gd
  (package
    (name "r-gd")
    (version "10.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GD" version))
       (sha256
        (base32 "0gyl2ss1kqkh1g1sbjhp5j67wsidlhwzyvvdls7aids2r57vrs4r"))))
    (properties `((upstream-name . "GD")))
    (build-system r-build-system)
    (propagated-inputs (list r-bammtools))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ausgis/GD")
    (synopsis "Geographical Detectors for Assessing Spatial Factors")
    (description
     "Geographical detectors for measuring spatial stratified heterogeneity, as
described in Jinfeng Wang (2010) <doi:10.1080/13658810802443457> and Jinfeng
Wang (2016) <doi:10.1016/j.ecolind.2016.02.052>.  Includes the optimal
discretization of continuous data, four primary functions of geographical
detectors, comparison of size effects of spatial unit and the visualizations of
results.  To use the package and to refer the descriptions of the package,
methods and case datasets, please cite Yongze Song (2020)
<doi:10.1080/15481603.2020.1760434>.  The model has been applied in factor
exploration of road performance and multi-scale spatial segmentation for network
data, as described in Yongze Song (2018) <doi:10.3390/rs10111696> and Yongze
Song (2020) <doi:10.1109/TITS.2020.3001193>, respectively.")
    (license license:gpl3)))

(define-public r-gcxgclab
  (package
    (name "r-gcxgclab")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gcxgclab" version))
       (sha256
        (base32 "0w6l9d23wbcvj5qzxc1yz20ic58hjkswk7scyjldiv6cbzm89wr1"))))
    (properties `((upstream-name . "gcxgclab")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-rdpack
                             r-ptw
                             r-nls-multstart
                             r-nilde
                             r-ncdf4
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gcxgclab")
    (synopsis "GCxGC Preprocessing and Analysis")
    (description
     "This package provides complete detailed preprocessing of two-dimensional gas
chromatogram (G@code{CxGC}) samples.  Baseline correction, smoothing, peak
detection, and peak alignment.  Also provided are some analysis functions, such
as finding extracted ion chromatograms, finding mass spectral data, targeted
analysis, and nontargeted analysis with either the National Institute of
Standards and Technology Mass Spectral Library or with the mass data.  There are
also several visualization methods provided for each step of the preprocessing
and analysis.")
    (license license:gpl3+)))

(define-public r-gctensor
  (package
    (name "r-gctensor")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gcTensor" version))
       (sha256
        (base32 "10iljda6ms5g1wm3xhznwgnfnjh0nbwi6szykqdnb15gdhhsx9j9"))))
    (properties `((upstream-name . "gcTensor")))
    (build-system r-build-system)
    (propagated-inputs (list r-rtensor r-einsum))
    (home-page "https://github.com/rikenbit/gcTensor")
    (synopsis "Generalized Coupled Tensor Factorization")
    (description
     "Multiple matrices/tensors can be specified and decomposed simultaneously by
Probabilistic Latent Tensor Factorisation (PLTF).  See the reference section of
@code{GitHub} README.md <https://github.com/rikenbit/@code{gcTensor>}, for
details of the method.")
    (license license:expat)))

(define-public r-gcsm
  (package
    (name "r-gcsm")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GCSM" version))
       (sha256
        (base32 "0apz1wh6nfpg2gyx12xnbdb3hjyrrpwlcrh5khn6cxhxm06q9l95"))))
    (properties `((upstream-name . "GCSM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://github.com/liuyadong/GCSM")
    (synopsis "Implements Generic Composite Similarity Measure")
    (description
     "This package provides implementation of the generic composite similarity measure
(GCSM) described in Liu et al. (2020) <doi:10.1016/j.ecoinf.2020.101169>.  The
implementation is in C++ and uses @code{RcppArmadillo}'.  Additionally,
implementations of the structural similarity (SSIM) and the composite similarity
measure based on means, standard deviations, and correlation coefficient (CMSC),
are included.")
    (license license:expat)))

(define-public r-gcpm
  (package
    (name "r-gcpm")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GCPM" version))
       (sha256
        (base32 "0k2ng78bk3bxpj6nz80j5cvjd56zjz328ga68vyyc3hvdjgpspqj"))))
    (properties `((upstream-name . "GCPM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppprogress r-rcpp))
    (home-page "https://cran.r-project.org/package=GCPM")
    (synopsis "Generalized Credit Portfolio Model")
    (description
     "Analyze the default risk of credit portfolios.  Commonly known models, like
@code{CreditRisk+} or the @code{CreditMetrics} model are implemented in their
very basic settings.  The portfolio loss distribution can be achieved either by
simulation or analytically in case of the classic @code{CreditRisk+} model.
Models are only implemented to respect losses caused by defaults, i.e.
migration risk is not included.  The package structure is kept flexible
especially with respect to distributional assumptions in order to quantify the
sensitivity of risk figures with respect to several assumptions.  Therefore the
package can be used to determine the credit risk of a given portfolio as well as
to quantify model sensitivities.")
    (license license:gpl2)))

(define-public r-gcplyr
  (package
    (name "r-gcplyr")
    (version "1.10.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gcplyr" version))
       (sha256
        (base32 "04g8p92aw8ihndlrlxlhvhrqgqv9x6izykpfs8rfn1jc3bisbyyd"))))
    (properties `((upstream-name . "gcplyr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-rlang r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://mikeblazanin.github.io/gcplyr/")
    (synopsis "Wrangle and Analyze Growth Curve Data")
    (description
     "Easy wrangling and model-free analysis of microbial growth curve data, as
commonly output by plate readers.  Tools for reshaping common plate reader
outputs into tidy formats and merging them with design information, making data
easy to work with using gcplyr and other packages.  Also streamlines common
growth curve processing steps, like smoothing and calculating derivatives, and
facilitates model-free characterization and analysis of growth data.  See
methods at <https://mikeblazanin.github.io/gcplyr/>.")
    (license license:expat)))

(define-public r-gcpbayes
  (package
    (name "r-gcpbayes")
    (version "4.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GCPBayes" version))
       (sha256
        (base32 "134rqclfix1p91nb1sn3js3l8pbgsl0i22ygz2gl73jjhckk0j75"))))
    (properties `((upstream-name . "GCPBayes")))
    (build-system r-build-system)
    (propagated-inputs (list r-wiqid
                             r-truncnorm
                             r-rcpparmadillo
                             r-rcpp
                             r-postpack
                             r-mvtnorm
                             r-mass
                             r-invgamma
                             r-gdata))
    (home-page "https://github.com/tbaghfalaki/GCPBayes")
    (synopsis
     "Bayesian Meta-Analysis of Pleiotropic Effects Using Group Structure")
    (description
     "Run a Gibbs sampler for a multivariate Bayesian sparse group selection model
with Dirac, continuous and hierarchical spike prior for detecting pleiotropy on
the traits.  This package is designed for summary statistics containing
estimated regression coefficients and its estimated covariance matrix.  The
methodology is available from: Baghfalaki, T., Sugier, P. E., Truong, T.,
Pettitt, A. N., Mengersen, K., & Liquet, B. (2021) <doi:10.1002/sim.8855>.")
    (license license:gpl2+)))

(define-public r-gcookbook
  (package
    (name "r-gcookbook")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gcookbook" version))
       (sha256
        (base32 "11g1q187l4j31b6cdzdx5z3s14z3s09l7ynl36pzzn9j19l8cmrc"))))
    (properties `((upstream-name . "gcookbook")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gcookbook")
    (synopsis "Data for \"R Graphics Cookbook\"")
    (description
     "Data sets used in the book \"R Graphics Cookbook\" by Winston Chang, published by
O'Reilly Media.")
    (license license:gpl2)))

(define-public r-gconsensus
  (package
    (name "r-gconsensus")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gconsensus" version))
       (sha256
        (base32 "065i587611nh3fiaxlp88qzd12cjxpbdvxn8pvd99fmk88d809zs"))))
    (properties `((upstream-name . "gconsensus")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjags r-mass r-coda))
    (home-page "https://cran.r-project.org/package=gconsensus")
    (synopsis "Consensus Value Constructor")
    (description
     "An implementation of the International Bureau of Weights and Measures (BIPM)
generalized consensus estimators used to assign the reference value in a key
comparison exercise.  This can also be applied to any interlaboratory study.
Given a set of different sources, primary laboratories or measurement methods
this package provides an evaluation of the variance components according to the
selected statistical method for consensus building.  It also implements the
comparison among different consensus builders and evaluates the participating
method or sources against the consensus reference value.  Based on a diverse set
of references, @code{DerSimonian-Laird} (1986)
<doi:10.1016/0197-2456(86)90046-2>, for a complete list of references look at
the reference section in the package documentation.")
    (license license:gpl3+)))

(define-public r-gcmr
  (package
    (name "r-gcmr")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gcmr" version))
       (sha256
        (base32 "0m4px1w41226clczzmnzal3hs0qwrvlhhdnkcxj1i7qp358ksxvm"))))
    (properties `((upstream-name . "gcmr")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-sandwich
                             r-nlme
                             r-lmtest
                             r-formula
                             r-car
                             r-betareg))
    (home-page "https://cran.r-project.org/package=gcmr")
    (synopsis "Gaussian Copula Marginal Regression")
    (description
     "Likelihood inference in Gaussian copula marginal regression models.")
    (license license:gpl2+)))

(define-public r-gclm
  (package
    (name "r-gclm")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gclm" version))
       (sha256
        (base32 "0b1nr8k8m48x78m6fkga11ikkcxyjsjpvqcd1lp6n9n4hv7akmfp"))))
    (properties `((upstream-name . "gclm")))
    (build-system r-build-system)
    (native-inputs (list gfortran))
    (home-page "https://github.com/gherardovarando/gclm")
    (synopsis "Graphical Continuous Lyapunov Models")
    (description
     "Estimation of covariance matrices as solutions of continuous time Lyapunov
equations.  Sparse coefficient matrix and diagonal noise are estimated with a
proximal gradient method for an l1-penalized loss minimization problem.  Varando
G, Hansen NR (2020) <@code{arXiv:2005.10483>}.")
    (license license:expat)))

(define-public r-gckrig
  (package
    (name "r-gckrig")
    (version "1.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gcKrig" version))
       (sha256
        (base32 "13zhl23j0rihg40b49s0my8sypxhqjd5h08sch3nsim9dsi766pl"))))
    (properties `((upstream-name . "gcKrig")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=gcKrig")
    (synopsis "Analysis of Geostatistical Count Data using Gaussian Copulas")
    (description
     "This package provides a variety of functions to analyze and model geostatistical
count data with Gaussian copulas, including 1) data simulation and
visualization; 2) correlation structure assessment (here also known as the
Normal To Anything); 3) calculate multivariate normal rectangle probabilities;
4) likelihood inference and parallel prediction at predictive locations.
Description of the method is available from: Han and @code{DeOliveira} (2018)
<doi:10.18637/jss.v087.i13>.")
    (license license:gpl2+)))

(define-public r-gcite
  (package
    (name "r-gcite")
    (version "0.10.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gcite" version))
       (sha256
        (base32 "0ljgzzl4yy1in5iz42iqkrsq6da4qx838anwdlvbdy96dyafl4ad"))))
    (properties `((upstream-name . "gcite")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-wordcloud
                             r-tm
                             r-rvest
                             r-pbapply
                             r-httr
                             r-data-table))
    (home-page "https://cran.r-project.org/package=gcite")
    (synopsis "Google Citation Parser")
    (description
     "Scrapes Google Citation pages and creates data frames of citations over time.")
    (license license:gpl3)))

(define-public r-gcerisk
  (package
    (name "r-gcerisk")
    (version "19.05.24")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gcerisk" version))
       (sha256
        (base32 "1dn3x9afb6lwys5adi621s6357s66lsif5zv64ilxmckmy2j71zl"))))
    (properties `((upstream-name . "gcerisk")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-ggplot2 r-cmprsk))
    (home-page "https://cran.r-project.org/package=gcerisk")
    (synopsis "Generalized Competing Event Model")
    (description
     "Generalized competing event model based on Cox PH model and Fine-Gray model.
This function is designed to develop optimized risk-stratification methods for
competing risks data, such as described in: 1.  Carmona R, Gulaya S, Murphy JD,
Rose BS, Wu J, Noticewala S,@code{McHale} MT, Yashar CM, Vaida F, and Mell LK
(2014) <DOI:10.1016/j.ijrobp.2014.03.047>.  2.  Carmona R, Zakeri K, Green G,
Hwang L, Gulaya S, Xu B, Verma R, Williamson CW, Triplett DP, Rose BS, Shen H,
Vaida F, Murphy JD, and Mell LK (2016) <DOI:10.1200/JCO.2015.65.0739>.  3.
Lunn, Mary, and Don @code{McNeil} (1995) <DOI:10.2307/2532940>.")
    (license license:gpl2+)))

(define-public r-gcdnet
  (package
    (name "r-gcdnet")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gcdnet" version))
       (sha256
        (base32 "1fkg80d6pq784cwaf28vgf8qpi4krhlhlhllvjdyv50v14i34svq"))))
    (properties `((upstream-name . "gcdnet")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (native-inputs (list gfortran))
    (home-page "https://github.com/emeryyi/gcdnet")
    (synopsis
     "The (Adaptive) LASSO and Elastic Net Penalized Least Squares, Logistic Regression, Hybrid Huberized Support Vector Machines, Squared Hinge Loss Support Vector Machines and Expectile Regression using a Fast Generalized Coordinate Descent Algorithm")
    (description
     "This package implements a generalized coordinate descent (GCD) algorithm for
computing the solution paths of the hybrid Huberized support vector machine
(HHSVM) and its generalizations.  Supported models include the (adaptive) LASSO
and elastic net penalized least squares, logistic regression, HHSVM, squared
hinge loss SVM and expectile regression.")
    (license license:gpl2+)))

(define-public r-gcd
  (package
    (name "r-gcd")
    (version "4.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GCD" version))
       (sha256
        (base32 "1259z76hajapzzq75fas2sq19r0wapn1ybghdaqdc6dksifdz6fv"))))
    (properties `((upstream-name . "GCD")))
    (build-system r-build-system)
    (propagated-inputs (list r-raster))
    (home-page "http://paleofire.org")
    (synopsis "Global Charcoal Database")
    (description
     "This package contains the Global Charcoal database data.  Data include charcoal
series (age, depth, charcoal quantity, associated units and methods) and
information on sedimentary sites (localisation, depositional environment, biome,
etc.) as well as publications informations.  Since 4.0.0 the GCD mirrors the
online SQL database at <http://paleofire.org>.")
    (license license:gpl2+)))

(define-public r-gccfactor
  (package
    (name "r-gccfactor")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GCCfactor" version))
       (sha256
        (base32 "1rxn0v4msfr5rwqc91ypafnvwa23n5574s8lhqmdqq53rs32ff89"))))
    (properties `((upstream-name . "GCCfactor")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-sandwich))
    (home-page "https://cran.r-project.org/package=GCCfactor")
    (synopsis "GCC Estimation of the Multilevel Factor Model")
    (description
     "This package provides methods for model selection, estimation, bootstrap
inference, and simulation for the multilevel factor model, based on the
principal component estimation and generalised canonical correlation approach.
Details can be found in \"Generalised Canonical Correlation Estimation of the
Multilevel Factor Model.\" Lin and Shin (2023) <doi:10.2139/ssrn.4295429>.")
    (license license:gpl3+)))

(define-public r-gcbd
  (package
    (name "r-gcbd")
    (version "0.2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gcbd" version))
       (sha256
        (base32 "1dvnly0aljv2c4x7sn3q55ncvfdfjfgp7w0k2900p77h4w8nixia"))))
    (properties `((upstream-name . "gcbd")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsqlite
                             r-reshape
                             r-plyr
                             r-matrix
                             r-lattice
                             r-dbi))
    (home-page "https://cran.r-project.org/package=gcbd")
    (synopsis "'GPU'/CPU Benchmarking in Debian-Based Systems")
    (description
     "GPU'/CPU Benchmarking on Debian-package based systems This package benchmarks
performance of a few standard linear algebra operations (such as a matrix
product and QR, SVD and LU decompositions) across a number of different BLAS
libraries as well as a GPU implementation.  To do so, it takes advantage of the
ability to plug and play different BLAS implementations easily on a Debian
and/or Ubuntu system.  The current version supports - Reference BLAS ('refblas')
which are un-accelerated as a baseline - Atlas which are tuned but typically
configure single-threaded - Atlas39 which are tuned and configured for
multi-threaded mode - Goto Blas which are accelerated and multi-threaded - Intel
MKL which is a commercial accelerated and multithreaded version.  As for GPU
computing, we use the CRAN package - gputools For Goto Blas', the
gotoblas2-helper script from the ISM in Tokyo can be used.  For Intel MKL we use
the Revolution R packages from Ubuntu 9.10.")
    (license license:gpl2+)))

(define-public r-gcat
  (package
    (name "r-gcat")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gCat" version))
       (sha256
        (base32 "0n1x039a0pm4sz2rwsgq82n423zgp4xni69129dh3nvxfrc0hzc5"))))
    (properties `((upstream-name . "gCat")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gCat")
    (synopsis "Graph-Based Two-Sample Tests for Categorical Data")
    (description
     "These are two-sample tests for categorical data utilizing similarity information
among the categories.  They are useful when there is underlying structure on the
categories.")
    (license license:gpl2+)))

(define-public r-gcalignr
  (package
    (name "r-gcalignr")
    (version "1.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GCalignR" version))
       (sha256
        (base32 "1c8vsklvkggwarh38rd1k88n0jaqcy7r23gyy5khsfkglzgkg2ns"))))
    (properties `((upstream-name . "GCalignR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-reshape2
                             r-readr
                             r-pbapply
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mottensmann/GCalignR")
    (synopsis "Simple Peak Alignment for Gas-Chromatography Data")
    (description
     "Aligns peak based on peak retention times and matches homologous peaks across
samples.  The underlying alignment procedure comprises three sequential steps.
(1) Full alignment of samples by linear transformation of retention times to
maximise similarity among homologous peaks (2) Partial alignment of peaks within
a user-defined retention time window to cluster homologous peaks (3) Merging
rows that are likely representing homologous substances (i.e.  no sample shows
peaks in both rows and the rows have similar retention time means).  The
algorithm is described in detail in Ottensmann et al., 2018
<doi:10.1371/journal.pone.0198311>.")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-gcalcium
  (package
    (name "r-gcalcium")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GCalcium" version))
       (sha256
        (base32 "0r84qmdsqsgpaqr08imh48mdvy26s2b62hi0hxxwhr9kg7qvj0rd"))))
    (properties `((upstream-name . "GCalcium")))
    (build-system r-build-system)
    (propagated-inputs (list r-dplyr r-catools))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GCalcium")
    (synopsis
     "Data Manipulation and Analysis Package for Calcium Indicator Data")
    (description
     "This package provides shortcuts in extracting useful data points and summarizing
waveform data.  It is optimized for speed to work efficiently with large data
sets so you can get to the analysis phase more quickly.  It also utilizes a
user-friendly format for use by both beginners and seasoned R users.")
    (license license:gpl3)))

(define-public r-gbutils
  (package
    (name "r-gbutils")
    (version "0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gbutils" version))
       (sha256
        (base32 "1gn6cmif5ixhgjcp29psfjsm3mk1y38mvv36vmxr77839rmkalxf"))))
    (properties `((upstream-name . "gbutils")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack))
    (home-page "https://github.com/GeoBosh/gbutils")
    (synopsis
     "Utilities for Simulation, Plots, Quantile Functions and Programming")
    (description
     "Plot density and distribution functions with automatic selection of suitable
regions.  Numerically invert (compute quantiles) distribution functions.
Simulate real and complex numbers from distributions of their magnitude and
arguments.  Optionally, the magnitudes and/or arguments may be fixed in almost
arbitrary ways.  Create polynomials from roots given in Cartesian or polar form.
 Small programming utilities: check if an object is identical to NA, count
positional arguments in a call, set intersection of more than two sets, check if
an argument is unnamed, compute the graph of S4 classes in packages.")
    (license license:gpl2+)))

(define-public r-gbts
  (package
    (name "r-gbts")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gbts" version))
       (sha256
        (base32 "1v8x4dnja8va28rjznyqa6js9wazndijasb95lfj2qwhxhgf5h65"))))
    (properties `((upstream-name . "gbts")))
    (build-system r-build-system)
    (propagated-inputs (list r-gbm r-foreach r-earth r-dorng r-doparallel))
    (home-page "https://cran.r-project.org/package=gbts")
    (synopsis "Hyperparameter Search for Gradient Boosted Trees")
    (description
     "An implementation of hyperparameter optimization for Gradient Boosted Trees on
binary classification and regression problems.  The current version provides two
optimization methods: Bayesian optimization and random search.  Instead of
giving the single best model, the final output is an ensemble of Gradient
Boosted Trees constructed via the method of ensemble selection.")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-gbs2ploidy
  (package
    (name "r-gbs2ploidy")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gbs2ploidy" version))
       (sha256
        (base32 "0gdjfqs9ccyaw7vi22wbyc742n1badr5ypr08g6rvi8ka5lmx836"))))
    (properties `((upstream-name . "gbs2ploidy")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjags r-mass))
    (home-page "https://cran.r-project.org/package=gbs2ploidy")
    (synopsis "Inference of Ploidy from (Genotyping-by-Sequencing) GBS Data")
    (description
     "This package provides functions for inference of ploidy from
(Genotyping-by-sequencing) GBS data, including a function to infer allelic
ratios and allelic proportions in a Bayesian framework.")
    (license license:gpl3)))

(define-public r-gbp
  (package
    (name "r-gbp")
    (version "0.1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gbp" version))
       (sha256
        (base32 "0awg724gsfwlb0fjcvw0450qdsk4m8x8is16pj5c8fx6nc8rn8bv"))))
    (properties `((upstream-name . "gbp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rgl r-rcpparmadillo r-rcpp r-magrittr
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/gyang274/gbp")
    (synopsis "Bin Packing Problem Solver")
    (description
     "Basic infrastructure and several algorithms for 1d-4d bin packing problem.  This
package provides a set of c-level classes and solvers for 1d-4d bin packing
problem, and an r-level solver for 4d bin packing problem, which is a wrapper
over the c-level 4d bin packing problem solver.  The 4d bin packing problem
solver aims to solve bin packing problem, a.k.a container loading problem, with
an additional constraint on weight.  Given a set of rectangular-shaped items,
and a set of rectangular-shaped bins with weight limit, the solver looks for an
orthogonal packing solution such that minimizes the number of bins and maximize
volume utilization.  Each rectangular-shaped item i = 1, .. , n is characterized
by length l_i, depth d_i, height h_i, and weight w_i, and each
rectangular-shaped bin j = 1, .. , m is specified similarly by length l_j, depth
d_j, height h_j, and weight limit w_j.  The item can be rotated into any
orthogonal direction, and no further restrictions implied.")
    (license license:expat)))

(define-public r-gbmt
  (package
    (name "r-gbmt")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gbmt" version))
       (sha256
        (base32 "10dyghrxjd9ddgk7gp2af8a46skzb2m9vazr9s216r20257ijd13"))))
    (properties `((upstream-name . "gbmt")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (home-page "https://cran.r-project.org/package=gbmt")
    (synopsis "Group-Based Multivariate Trajectory Modeling")
    (description
     "Estimation and analysis of group-based multivariate trajectory models (Nagin,
2018 <doi:10.1177/0962280216673085>; Magrini, 2022
<doi:10.1007/s10182-022-00437-9>).  The package implements an
Expectation-Maximization (EM) algorithm allowing unbalanced panel and missing
values, and provides several functionalities for prediction and graphical
representation.")
    (license license:gpl2)))

(define-public r-gbm2sas
  (package
    (name "r-gbm2sas")
    (version "3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gbm2sas" version))
       (sha256
        (base32 "1dzqmbfjasydghlb44nl1lsvr53w4gaiyh6852a9lnd62chapd45"))))
    (properties `((upstream-name . "gbm2sas")))
    (build-system r-build-system)
    (propagated-inputs (list r-gbm))
    (home-page "https://cran.r-project.org/package=gbm2sas")
    (synopsis "Convert GBM Object Trees to SAS Code")
    (description
     "Writes SAS code to get predicted values from every tree of a gbm.object.")
    (license license:gpl3)))

(define-public r-gbm-auto
  (package
    (name "r-gbm-auto")
    (version "2024.10.01")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gbm.auto" version))
       (sha256
        (base32 "1dyx937ajm461kpigbfgasb7ay96abw7s3lda8g54nfn3mxl82sq"))))
    (properties `((upstream-name . "gbm.auto")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-tidyselect
                             r-stringi
                             r-starsextra
                             r-stars
                             r-sf
                             r-readr
                             r-metrics
                             r-mapplots
                             r-lubridate
                             r-lifecycle
                             r-ggspatial
                             r-ggplot2
                             r-ggmap
                             r-gbm
                             r-dplyr
                             r-dismo
                             r-beepr))
    (home-page "https://cran.r-project.org/package=gbm.auto")
    (synopsis "Automated Boosted Regression Tree Modelling and Mapping Suite")
    (description
     "Automates delta log-normal boosted regression tree abundance prediction.  Loops
through parameters provided (LR (learning rate), TC (tree complexity), BF (bag
fraction)), chooses best, simplifies, & generates line, dot & bar plots, &
outputs these & predictions & a report, makes predicted abundance maps, and
Unrepresentativeness surfaces.  Package core built around gbm (gradient boosting
machine) functions in dismo (Hijmans, Phillips, Leathwick & Jane Elith, 2020 &
ongoing), itself built around gbm (Greenwell, Boehmke, Cunningham & Metcalfe,
2020 & ongoing, originally by Ridgeway).  Indebted to Elith/Leathwick/Hastie
2008 Working Guide <doi:10.1111/j.1365-2656.2008.01390.x>; workflow follows
Appendix S3.  See <https://www.simondedman.com/> for published guides and papers
using this package.")
    (license license:expat)))

(define-public r-gbj
  (package
    (name "r-gbj")
    (version "0.5.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GBJ" version))
       (sha256
        (base32 "0vi4zv59g4fjq28gq6b8mn5gmddwvq9sijjilcd6wr7jl67c0fbs"))))
    (properties `((upstream-name . "GBJ")))
    (build-system r-build-system)
    (propagated-inputs (list r-skat r-rcpp r-mvtnorm r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GBJ")
    (synopsis
     "Generalized Berk-Jones Test for Set-Based Inference in Genetic Association Studies")
    (description
     "Offers the Generalized Berk-Jones (GBJ) test for set-based inference in genetic
association studies.  The GBJ is designed as an alternative to tests such as
Berk-Jones (BJ), Higher Criticism (HC), Generalized Higher Criticism (GHC),
Minimum p-value (@code{minP}), and Sequence Kernel Association Test (SKAT).  All
of these other methods (except for SKAT) are also implemented in this package,
and we additionally provide an omnibus test (OMNI) which integrates information
from each of the tests.  The GBJ has been shown to outperform other tests in
genetic association studies when signals are correlated and moderately sparse.
Please see the vignette for a quickstart guide or Sun and Lin (2017)
<@code{arXiv:1710.02469>} for more details.")
    (license license:gpl3)))

(define-public r-gbifdb
  (package
    (name "r-gbifdb")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gbifdb" version))
       (sha256
        (base32 "0lcwi7bx2giwcgr0dw6y1isj9bszs9d5yv4lby6gn0z4cmv6qzcb"))))
    (properties `((upstream-name . "gbifdb")))
    (build-system r-build-system)
    (propagated-inputs (list r-duckdbfs r-dplyr r-arrow))
    (native-inputs (list r-knitr))
    (home-page "https://docs.ropensci.org/gbifdb/")
    (synopsis "High Performance Interface to 'GBIF'")
    (description
     "This package provides a high performance interface to the Global Biodiversity
Information Facility, GBIF'.  In contrast to rgbif', which can access small
subsets of GBIF data through web-based queries to a central server, gbifdb
provides enhanced performance for R users performing large-scale analyses on
servers and cloud computing providers, providing full support for arbitrary SQL
or dplyr operations on the complete GBIF data tables (now over 1 billion
records, and over a terabyte in size).  gbifdb accesses a copy of the GBIF data
in parquet format, which is already readily available in commercial computing
clouds such as the Amazon Open Data portal and the Microsoft Planetary Computer,
or can be accessed directly without downloading, or downloaded to any server
with suitable bandwidth and storage space.  The high-performance techniques for
local and remote access are described in <https://duckdb.org/why_duckdb> and
<https://arrow.apache.org/docs/r/articles/fs.html> respectively.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-gbfs
  (package
    (name "r-gbfs")
    (version "1.3.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gbfs" version))
       (sha256
        (base32 "0748gicg4mf3csgf75rcpkcsf0c8md4clyavh3kxs77jhq8h6j48"))))
    (properties `((upstream-name . "gbfs")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-readr
                             r-purrr
                             r-lubridate
                             r-jsonlite
                             r-httr
                             r-dplyr
                             r-curl))
    (home-page "https://github.com/simonpcouch/gbfs")
    (synopsis "Interface with Live Bikeshare Data")
    (description
     "Supplies a set of functions to interface with bikeshare data following the
General Bikeshare Feed Specification, allowing users to query and accumulate
tidy datasets for specified cities/bikeshare programs.")
    (license license:cc0)))

(define-public r-gbeta
  (package
    (name "r-gbeta")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gbeta" version))
       (sha256
        (base32 "0611rynyybmiq6pj5jvwijdgx3hnr6mscv20lrqzlfplbmvmz0bj"))))
    (properties `((upstream-name . "gbeta")))
    (build-system r-build-system)
    (propagated-inputs (list r-runuran r-rcppnumerical r-rcppeigen r-rcpp
                             r-gsl))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/stla/gbeta")
    (synopsis "Generalized Beta and Beta Prime Distributions")
    (description
     "Density, distribution function, quantile function, and random generation for the
generalized Beta and Beta prime distributions.  The family of generalized Beta
distributions is conjugate for the Bayesian binomial model, and the generalized
Beta prime distribution is the posterior distribution of the relative risk in
the Bayesian two Poisson samples model when a Gamma prior is assigned to the
Poisson rate of the reference group and a Beta prime prior is assigned to the
relative risk.  References: Laurent (2012) <doi:10.1214/11-BJPS139>, Hamza &
Vallois (2016) <doi:10.1016/j.spl.2016.03.014>, Chen & Novick (1984)
<doi:10.3102/10769986009002163>.")
    (license license:gpl2+)))

(define-public r-gb5mcpred
  (package
    (name "r-gb5mcpred")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GB5mcPred" version))
       (sha256
        (base32 "009ijrc0f04n74bxyrqnw553rf299m2gxbqrpkf3hrd78yfd3idy"))))
    (properties `((upstream-name . "GB5mcPred")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyverse
                             r-tibble
                             r-stringr
                             r-splitstackshape
                             r-seqinr
                             r-randomforest
                             r-party
                             r-iterators
                             r-gbm
                             r-ftrcool
                             r-foreach
                             r-entropy
                             r-e1071
                             r-doparallel
                             r-devtools
                             r-caret
                             r-biostrings))
    (home-page "https://cran.r-project.org/package=GB5mcPred")
    (synopsis "Gradient Boosting Algorithm for Predicting Methylation States")
    (description
     "DNA methylation of 5-methylcytosine (5@code{mC}) is the result of a multi-step,
enzyme-dependent process.  Predicting these sites in-vitro is laborious, time
consuming as well as costly.  This  Gb5@code{mC-Pred}  package is an in-silico
pipeline for predicting DNA sequences containing the 5@code{mC} sites.  It uses
a machine learning approach which uses Stochastic Gradient Boosting approach for
prediction of the sequences with 5@code{mC} sites.  This package has been
developed by using the concept of Navarez and Roxas (2022)
<doi:10.1109/TCBB.2021.3082184>.")
    (license license:gpl3)))

(define-public r-gb2group
  (package
    (name "r-gb2group")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GB2group" version))
       (sha256
        (base32 "1dbyc7izyggfrsq2gj7rwff5qjyy6bx6ip09xvbj73xqczv8fkcd"))))
    (properties `((upstream-name . "GB2group")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv r-minpack-lm r-ineq r-gb2))
    (home-page "https://cran.r-project.org/package=GB2group")
    (synopsis
     "Estimation of the Generalised Beta Distribution of the Second Kind from Grouped Data")
    (description
     "Estimation of the generalized beta distribution of the second kind (GB2) and
related models using grouped data in form of income shares.  The GB2 family is a
general class of distributions that provides an accurate fit to income data.
GB2group includes functions to estimate the GB2, the Singh-Maddala, the Dagum,
the Beta 2, the Lognormal and the Fisk distributions.  GB2group deploys two
different econometric strategies to estimate these parametric distributions, the
equally weighted minimum distance (EWMD) estimator and the optimally weighted
minimum distance (OMD) estimator.  Asymptotic standard errors are reported for
the OMD estimates.  Standard errors of the EWMD estimates are obtained by Monte
Carlo simulation.  See Jorda et al. (2018) <@code{arXiv:1808.09831>} for a
detailed description of the estimation procedure.")
    (license license:gpl2+)))

(define-public r-gb2
  (package
    (name "r-gb2")
    (version "2.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GB2" version))
       (sha256
        (base32 "05hvyyjlxckhgqipssb7qkfqv3dq202jpjyp9y7hgmka0vsix2yr"))))
    (properties `((upstream-name . "GB2")))
    (build-system r-build-system)
    (propagated-inputs (list r-survey r-numderiv r-laeken r-hypergeo
                             r-cubature))
    (home-page "https://cran.r-project.org/package=GB2")
    (synopsis
     "Generalized Beta Distribution of the Second Kind: Properties, Likelihood, Estimation")
    (description
     "Package GB2 explores the Generalized Beta distribution of the second kind.
Density, cumulative distribution function, quantiles and moments of the
distributions are given.  Functions for the full log-likelihood, the profile
log-likelihood and the scores are provided.  Formulas for various indicators of
inequality and poverty under the GB2 are implemented.  The GB2 is fitted by the
methods of maximum pseudo-likelihood estimation using the full and profile
log-likelihood, and non-linear least squares estimation of the model parameters.
 Various plots for the visualization and analysis of the results are provided.
Variance estimation of the parameters is provided for the method of maximum
pseudo-likelihood estimation.  A mixture distribution based on the compounding
property of the GB2 is presented (denoted as \"compound\" in the documentation).
This mixture distribution is based on the discretization of the distribution of
the underlying random scale parameter.  The discretization can be left or right
tail.  Density, cumulative distribution function, moments and quantiles for the
mixture distribution are provided.  The compound mixture distribution is fitted
using the method of maximum pseudo-likelihood estimation.  The fit can also
incorporate the use of auxiliary information.  In this new version of the
package, the mixture case is complemented with new functions for variance
estimation by linearization and comparative density plots.")
    (license license:gpl2+)))

(define-public r-gb
  (package
    (name "r-gb")
    (version "2.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gb" version))
       (sha256
        (base32 "0gkdkbwr168vi7lgccla49l43rkmjcfrwlqdr65mg5syzcxrh7nh"))))
    (properties `((upstream-name . "gb")))
    (build-system r-build-system)
    (propagated-inputs (list r-kernsmooth r-boot))
    (home-page "https://cran.r-project.org/package=gb")
    (synopsis "Generalize Lambda Distribution and Generalized Bootstrapping")
    (description
     "This package provides a collection of algorithms and functions for fitting data
to a generalized lambda distribution via moment matching methods, and
generalized bootstrapping.")
    (license (license:fsdg-compatible "Unlimited"))))

(define-public r-gawdis
  (package
    (name "r-gawdis")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gawdis" version))
       (sha256
        (base32 "13f7fx5j9r5wky2zriyqlr7vjda1y9c7n3iz0ajw6fxfr1c670dx"))))
    (properties `((upstream-name . "gawdis")))
    (build-system r-build-system)
    (propagated-inputs (list r-ga r-fd))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/pavel-fibich/gawdis/")
    (synopsis "Multi-Trait Dissimilarity with more Uniform Contributions")
    (description
     "R function gawdis() produces multi-trait dissimilarity with more uniform
contributions of different traits.  de Bello et al. (2021)
<doi:10.1111/2041-210X.13537> presented the approach based on minimizing the
differences in the correlation between the dissimilarity of each trait, or
groups of traits, and the multi-trait dissimilarity.  This is done using either
an analytic or a numerical solution, both available in the function.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-gausssuppression
  (package
    (name "r-gausssuppression")
    (version "0.9.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GaussSuppression" version))
       (sha256
        (base32 "0hygyj4l1ah31h7dlh54z5zni08cgzaxsn6zzh8xvcm2hnlvs54r"))))
    (properties `((upstream-name . "GaussSuppression")))
    (build-system r-build-system)
    (propagated-inputs (list r-ssbtools r-regsdc r-matrix))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/statisticsnorway/ssb-gausssuppression")
    (synopsis "Tabular Data Suppression using Gaussian Elimination")
    (description
     "This package provides a statistical disclosure control tool to protect tables by
suppression using the Gaussian elimination secondary suppression algorithm
(Langsrud, 2024) <doi:10.1007/978-3-031-69651-0_6>.  A suggestion is to start by
working with functions @code{SuppressSmallCounts}() and
@code{SuppressDominantCells}().  These functions use primary suppression
functions for the minimum frequency rule and the dominance rule, respectively.
Novel functionality for suppression of disclosive cells is also included.
General primary suppression functions can be supplied as input to the general
working horse function, @code{GaussSuppressionFromData}().  Suppressed
frequencies can be replaced by synthetic decimal numbers as described in
Langsrud (2019) <doi:10.1007/s11222-018-9848-9>.")
    (license license:expat)))

(define-public r-gaussquad
  (package
    (name "r-gaussquad")
    (version "1.0-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gaussquad" version))
       (sha256
        (base32 "04689l076agncsxqcbkx76z89cvjigs8p8v58p5kad655gjpqcx3"))))
    (properties `((upstream-name . "gaussquad")))
    (build-system r-build-system)
    (propagated-inputs (list r-polynom r-orthopolynom))
    (home-page "https://cran.r-project.org/package=gaussquad")
    (synopsis "Collection of Functions for Gaussian Quadrature")
    (description
     "This package provides a collection of functions to perform Gaussian quadrature
with different weight functions corresponding to the orthogonal polynomials in
package orthopolynom.  Examples verify the orthogonality and inner products of
the polynomials.")
    (license license:gpl2+)))

(define-public r-gaussplotr
  (package
    (name "r-gaussplotr")
    (version "0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gaussplotR" version))
       (sha256
        (base32 "0amkgzgh6764ri30210nhbi3wcll3248fmkpclx010dxc3hllfdx"))))
    (properties `((upstream-name . "gaussplotR")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridislite r-rgl r-metr r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/vbaliga/gaussplotR")
    (synopsis "Fit, Predict and Plot 2D Gaussians")
    (description
     "This package provides functions to fit two-dimensional Gaussian functions,
predict values from fits, and produce plots of predicted data via either ggplot2
or base R plotting.")
    (license license:gpl3)))

(define-public r-gaussianhmm1d
  (package
    (name "r-gaussianhmm1d")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GaussianHMM1d" version))
       (sha256
        (base32 "0c9khbg44yf04wp80bizd7j86gif1apqqafxyvi8692rrhf1r3ax"))))
    (properties `((upstream-name . "GaussianHMM1d")))
    (build-system r-build-system)
    (propagated-inputs (list r-foreach r-doparallel))
    (home-page "https://cran.r-project.org/package=GaussianHMM1d")
    (synopsis
     "Inference, Goodness-of-Fit and Forecast for Univariate Gaussian Hidden Markov Models")
    (description
     "Inference, goodness-of-fit test, and prediction densities and intervals for
univariate Gaussian Hidden Markov Models (HMM).  The goodness-of-fit is based on
a Cramer-von Mises statistic and uses parametric bootstrap to estimate the
p-value.  The description of the methodology is taken from Chapter 10.2 of
Remillard (2013) <doi:10.1201/b14285>.")
    (license license:gpl2+)))

(define-public r-gaussfacts
  (package
    (name "r-gaussfacts")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gaussfacts" version))
       (sha256
        (base32 "095x7k7m7sy1gqkbm6wiqn26252dclz62sbnkv6cjvmbdxajl7m9"))))
    (properties `((upstream-name . "gaussfacts")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gaussfacts")
    (synopsis "The Greatest Mathematician Since Antiquity")
    (description
     "Display a random fact about Carl Friedrich Gauss based the on collection curated
by Mike Cavers via the <http://gaussfacts.com> site.")
    (license license:gpl2+)))

(define-public r-gaussdiff
  (package
    (name "r-gaussdiff")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gaussDiff" version))
       (sha256
        (base32 "0fqjdxp2ibbami75ba16d02dz4rz5sk8mni45di9anydx44g9d45"))))
    (properties `((upstream-name . "gaussDiff")))
    (build-system r-build-system)
    (home-page "www.geo.fu-berlin.de/met/ag/clidia/Mitarbeiter/HenningRust/")
    (synopsis
     "Difference measures for multivariate Gaussian probability density functions")
    (description
     "This package provides a collection difference measures for multivariate Gaussian
probability density functions, such as the Euclidea mean, the Mahalanobis
distance, the Kullback-Leibler divergence, the J-Coefficient, the Minkowski
L2-distance, the Chi-square divergence and the Hellinger Coefficient.")
    (license license:gpl2+)))

(define-public r-gausscov
  (package
    (name "r-gausscov")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gausscov" version))
       (sha256
        (base32 "0dv3ifhi72kij820bny5jikps8zywyinddbc78q4bdbska820rvn"))))
    (properties `((upstream-name . "gausscov")))
    (build-system r-build-system)
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=gausscov")
    (synopsis "The Gaussian Covariate Method for Variable Selection")
    (description
     "The standard linear regression theory whether frequentist or Bayesian is based
on an assumed (revealed?) truth (John Tukey) attitude to models.  This is
reflected in the language of statistical inference which involves a concept of
truth, for example confidence intervals, hypothesis testing and consistency.
The motivation behind this package was to remove the word true from the theory
and practice of linear regression and to replace it by approximation.  The
approximations considered are the least squares approximations.  An
approximation is called valid if it contains no irrelevant covariates.  This is
operationalized using the concept of a Gaussian P-value which is the probability
that pure Gaussian noise is better in term of least squares than the covariate.
The precise definition given in the paper, it is intuitive and requires only
four simple equations.  Its overwhelming advantage compared with a standard F
P-value is that is is exact and valid whatever the data.  In contrast F P-values
are only valid for specially designed simulations.  Given this a valid
approximation is one where all the Gaussian P-values are less than a threshold
p0 specified by the statistician, in this package with the default value 0.01.
This approximations approach is not only much simpler it is overwhelmingly
better than the standard model based approach.  The will be demonstrated using
six real data sets, four from high dimensional regression and two from vector
autoregression.  The simplicity and superiority of Gaussian P-values derive from
their universal exactness and validity.  This is in complete contrast to
standard F P-values which are valid only for carefully designed simulations.
The function f1st is the most important function.  It is a greedy forward
selection procedure which results in either just one or no approximations which
may however not be valid.  If the size is less than than a threshold with
default value 21 then an all subset procedure is called which returns the best
valid subset.  A good default start is f1st(y,x,kmn=15) The best function for
returning multiple approximations is f3st which repeatedly calls f1st.  For more
information see the web site below and the accompanying papers: L. Davies and L.
Duembgen, \"Covariate Selection Based on a Model-free Approach to Linear
Regression with Exact Probabilities\", 2202,
<doi:10.48550/@code{arXiv.2202.01553>}.  L. Davies, \"An Approximation Based
Theory of Linear Regression\", 2402, <doi:10.48550/@code{arXiv.2402.09858>}.")
    (license license:gpl3)))

(define-public r-gauser
  (package
    (name "r-gauser")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gauseR" version))
       (sha256
        (base32 "1rvw7dbrsxqqch2dgslabc47llb64ljakc60m47gql5n9br7akbd"))))
    (properties `((upstream-name . "gauseR")))
    (build-system r-build-system)
    (propagated-inputs (list r-desolve))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gauseR")
    (synopsis "Lotka-Volterra Models for Gause's 'Struggle for Existence'")
    (description
     "This package provides a collection of tools and data for analyzing the Gause
microcosm experiments, and for fitting Lotka-Volterra models to time series
data.  Includes methods for fitting single-species logistic growth, and
multi-species interaction models, e.g. of competition, predator/prey
relationships, or mutualism.  See documentation for individual functions for
examples.  In general, see the lv_optim() function for examples of how to fit
parameter values in multi-species systems.  Note that the general methods
applied here, as well as the form of the differential equations that we use, are
described in detail in the Quantitative Ecology textbook by Lehman et al.,
available at <http://hdl.handle.net/11299/204551>, and in Lina K. MÃ¼hlbauer,
Maximilienne Schulze, W. Stanley Harpole, and Adam T. Clark. @code{gauseR}':
Simple methods for fitting Lotka-Volterra models describing Gause's Struggle for
Existence in the journal Ecology and Evolution.")
    (license license:gpl3)))

(define-public r-gaupro
  (package
    (name "r-gaupro")
    (version "0.2.13")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GauPro" version))
       (sha256
        (base32 "0l1sizyzrmxl9svdx1hhfg4aq00g1ss9ply3i2ll344nd9j54fvz"))))
    (properties `((upstream-name . "GauPro")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rmarkdown
                             r-rcpparmadillo
                             r-rcpp
                             r-r6
                             r-numderiv
                             r-mixopt
                             r-lbfgs
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/CollinErickson/GauPro")
    (synopsis "Gaussian Process Fitting")
    (description
     "Fits a Gaussian process model to data.  Gaussian processes are commonly used in
computer experiments to fit an interpolating model.  The model is stored as an
R6 object and can be easily updated with new data.  There are options to run in
parallel, and Rcpp has been used to speed up calculations.  For more info about
Gaussian process software, see Erickson et al. (2018)
<doi:10.1016/j.ejor.2017.10.002>.")
    (license license:gpl3)))

(define-public r-gators
  (package
    (name "r-gators")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gatoRs" version))
       (sha256
        (base32 "0a1z3rjgkz5wxccb5lzmwmskvjlgab3zmznwvjgk2bslda6yzqil"))))
    (properties `((upstream-name . "gatoRs")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-stringr
                             r-spthin
                             r-spatstat-geom
                             r-ridigbio
                             r-rgbif
                             r-parsedate
                             r-magrittr
                             r-leaflet
                             r-dplyr
                             r-coordinatecleaner))
    (native-inputs (list r-knitr))
    (home-page "https://nataliepatten.github.io/gatoRs/")
    (synopsis "Geographic and Taxonomic Occurrence R-Based Scrubbing")
    (description
     "Streamlines downloading and cleaning biodiversity data from Integrated Digitized
Biocollections (@code{iDigBio}) and the Global Biodiversity Information Facility
(GBIF).")
    (license license:gpl3)))

(define-public r-gater
  (package
    (name "r-gater")
    (version "0.1.15")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gateR" version))
       (sha256
        (base32 "1a818bypdiingv6kmrfmx7ivxybs8k7261vfqlmb4gdl87n71rb2"))))
    (properties `((upstream-name . "gateR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-terra
                             r-spatstat-geom
                             r-spatialpack
                             r-sparr
                             r-rlang
                             r-lifecycle
                             r-fields))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/lance-waller-lab/gateR")
    (synopsis
     "Flow/Mass Cytometry Gating via Spatial Kernel Density Estimation")
    (description
     "Estimates statistically significant marker combination values within which one
immunologically distinctive group (i.e., disease case) is more associated than
another group (i.e., healthy control), successively, using various combinations
(i.e., \"gates\") of markers to examine features of cells that may be different
between groups.  For a two-group comparison, the @code{gateR} package uses the
spatial relative risk function estimated using the sparr package.  Details about
the sparr package methods can be found in the tutorial: Davies et al. (2018)
<doi:10.1002/sim.7577>.  Details about kernel density estimation can be found in
J. F. Bithell (1990) <doi:10.1002/sim.4780090616>.  More information about
relative risk functions using kernel density estimation can be found in J. F.
Bithell (1991) <doi:10.1002/sim.4780101112>.")
    (license license:asl2.0)))

(define-public r-gatepoints
  (package
    (name "r-gatepoints")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gatepoints" version))
       (sha256
        (base32 "0f9n32s1ssp4nmjarls4llmi91jxh4w4qwxf7awxaalqz4lrzbg2"))))
    (properties `((upstream-name . "gatepoints")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/wjawaid/gatepoints")
    (synopsis "Easily Gate or Select Points on a Scatter Plot")
    (description
     "Allows user to choose/gate a region on the plot and returns points within it.")
    (license license:gpl2+)))

(define-public r-gastempt
  (package
    (name "r-gastempt")
    (version "0.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gastempt" version))
       (sha256
        (base32 "1fy6scbgi9qq78rfwq58p7f9x6mgam6lvrlmz224mz99qf3lz6s1"))))
    (properties `((upstream-name . "gastempt")))
    (build-system r-build-system)
    (propagated-inputs (list r-utf8
                             r-tibble
                             r-stringr
                             r-stanheaders
                             r-shiny
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-nlme
                             r-ggplot2
                             r-dplyr
                             r-bh
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/dmenne/gastempt")
    (synopsis "Analyzing Gastric Emptying from MRI or Scintigraphy")
    (description
     "Fits gastric emptying time series from MRI or scintigraphic measurements using
nonlinear mixed-model population fits with nlme and Bayesian methods with Stan;
computes derived parameters such as t50 and AUC.")
    (license license:gpl3+)))

(define-public r-gasper
  (package
    (name "r-gasper")
    (version "1.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gasper" version))
       (sha256
        (base32 "1ksvgccl4kqnsgrmaxj8daww5f2r9a4p6p2hsnsg3c5iqwnj6i35"))))
    (properties `((upstream-name . "gasper")))
    (build-system r-build-system)
    (propagated-inputs (list r-rspectra
                             r-rcpparmadillo
                             r-rcpp
                             r-matrix
                             r-httr
                             r-ggplot2
                             r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/fabnavarro/gasper")
    (synopsis "Graph Signal Processing")
    (description
     "This package provides the standard operations for signal processing on graphs:
graph Fourier transform, spectral graph wavelet transform, visualization tools.
It also implements a data driven method for graph signal denoising/regression,
for details see De Loynes, Navarro, Olivier (2019) <arxiv:1906.01882>.  The
package also provides an interface to the @code{SuiteSparse} Matrix Collection,
<https://sparse.tamu.edu/>, a large and widely used set of sparse matrix
benchmarks collected from a wide range of applications.")
    (license license:lgpl2.0+)))

(define-public r-gasp
  (package
    (name "r-gasp")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GaSP" version))
       (sha256
        (base32 "0hyalvx7j434g4z7imkwn6anl7sp8pp076v6a2bbsy054vh5l59z"))))
    (properties `((upstream-name . "GaSP")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GaSP")
    (synopsis "Train and Apply a Gaussian Stochastic Process Model")
    (description
     "Train a Gaussian stochastic process model of an unknown function, possibly
observed with error, via maximum likelihood or maximum a posteriori (MAP)
estimation, run model diagnostics, and make predictions, following Sacks, J.,
Welch, W.J., Mitchell, T.J., and Wynn, H.P. (1989) \"Design and Analysis of
Computer Experiments\", Statistical Science, <doi:10.1214/ss/1177012413>.
Perform sensitivity analysis and visualize low-order effects, following
Schonlau, M. and Welch, W.J. (2006), \"Screening the Input Variables to a
Computer Model Via Analysis of Variance and Visualization\",
<doi:10.1007/0-387-28014-6_14>.")
    (license license:gpl3)))

(define-public r-gasmodel
  (package
    (name "r-gasmodel")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gasmodel" version))
       (sha256
        (base32 "16wxv0fkaayxvd5h436wyp9is8y47k7ivim5f9md4g4acy3ca3cb"))))
    (properties `((upstream-name . "gasmodel")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-pracma
                             r-numderiv
                             r-nloptr
                             r-mvnfast
                             r-matrix
                             r-ggplot2
                             r-dplyr
                             r-copula
                             r-circstats
                             r-arrangements
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/vladimirholy/gasmodel")
    (synopsis "Generalized Autoregressive Score Models")
    (description
     "Estimation, forecasting, and simulation of generalized autoregressive score
(GAS) models of Creal, Koopman, and Lucas (2013) <doi:10.1002/jae.1279> and
Harvey (2013) <doi:10.1017/cbo9781139540933>.  Model specification allows for
various data types and distributions, different parametrizations, exogenous
variables, joint and separate modeling of exogenous variables and dynamics,
higher score and autoregressive orders, custom and unconditional initial values
of time-varying parameters, fixed and bounded values of coefficients, and
missing values.  Model estimation is performed by the maximum likelihood method.")
    (license license:gpl3)))

(define-public r-gasfluxes
  (package
    (name "r-gasfluxes")
    (version "0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gasfluxes" version))
       (sha256
        (base32 "1n2cfm3fa22nbirgkv91qapw5mipsw59m8kwvmb4ljgcslpzjrzp"))))
    (properties `((upstream-name . "gasfluxes")))
    (build-system r-build-system)
    (propagated-inputs (list r-sfsmisc r-mass r-data-table))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://git-dmz.thuenen.de/fuss/gasfluxes")
    (synopsis "Greenhouse Gas Flux Calculation from Chamber Measurements")
    (description
     "This package provides functions for greenhouse gas flux calculation from chamber
measurements.")
    (license license:gpl2+)))

(define-public r-gaselect
  (package
    (name "r-gaselect")
    (version "1.0.22")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gaselect" version))
       (sha256
        (base32 "0hk18dl851z9dnw9vd9836lr7j3wlg3kk31k185p2yyk7l821mqp"))))
    (properties `((upstream-name . "gaselect")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://github.com/dakep/gaselect")
    (synopsis
     "Genetic Algorithm (GA) for Variable Selection from High-Dimensional Data")
    (description
     "This package provides a genetic algorithm for finding variable subsets in high
dimensional data with high prediction performance.  The genetic algorithm can
use ordinary least squares (OLS) regression models or partial least squares
(PLS) regression models to evaluate the prediction power of variable subsets.
By supporting different cross-validation schemes, the user can fine-tune the
tradeoff between speed and quality of the solution.")
    (license license:gpl2+)))

(define-public r-gasanalyzer
  (package
    (name "r-gasanalyzer")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gasanalyzer" version))
       (sha256
        (base32 "0cb6xkzfrnxd28dm17r6b57i8l48psqcgx1yaw37csjll35ngxap"))))
    (properties `((upstream-name . "gasanalyzer")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-vctrs
                             r-units
                             r-tidyxl
                             r-tibble
                             r-stringi
                             r-jsonify))
    (native-inputs (list r-knitr))
    (home-page "https://gitlab.com/plantphys/gasanalyzer")
    (synopsis "Import, Recompute and Analyze Data from Portable Gas Analyzers")
    (description
     "The gasanalyzer R package offers methods for importing, preprocessing, and
analyzing data related to photosynthetic characteristics (gas exchange,
chlorophyll fluorescence and isotope ratios).  It translates variable names into
a standard format, and can recalculate derived, physiological quantities using
imported or predefined equations.  The package also allows users to assess the
sensitivity of their results to different assumptions used in the calculations.
See also Tholen (2024) <doi:10.1093/aobpla/plae035>.")
    (license license:gpl3)))

(define-public r-gas
  (package
    (name "r-gas")
    (version "0.3.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GAS" version))
       (sha256
        (base32 "16348wz41silrrp7vb5qscydf9di8zrbydyvhpkv7dl003c9x0rp"))))
    (properties `((upstream-name . "GAS")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xts
                             r-rsolnp
                             r-rcpparmadillo
                             r-rcpp
                             r-numderiv
                             r-mass
                             r-cubature))
    (home-page "https://github.com/LeopoldoCatania/GAS")
    (synopsis "Generalized Autoregressive Score Models")
    (description
     "Simulate, estimate and forecast using univariate and multivariate GAS models as
described in Ardia et al. (2019) <doi:10.18637/jss.v088.i06>.")
    (license license:gpl3)))

(define-public r-garray
  (package
    (name "r-garray")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "garray" version))
       (sha256
        (base32 "0s2dgi556x9jnhafvzz4qkxdbq9dm12gsvsqd2g9iy3468fx9flq"))))
    (properties `((upstream-name . "garray")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=garray")
    (synopsis
     "Generalized Array Arithmetic for Ragged Arrays with Named Margins")
    (description
     "Organize a so-called ragged array as generalized arrays, which is simply an
array with sub-dimensions denoting the subdivision of dimensions (grouping of
members within dimensions).  By the margins (names of dimensions and
sub-dimensions) in generalized arrays, operators and utility functions provided
in this package automatically match the margins, doing map-reduce style parallel
computation along margins.  Generalized arrays are also cooperative to R's
native functions that work on simple arrays.")
    (license license:gpl3)))

(define-public r-garma
  (package
    (name "r-garma")
    (version "0.9.23")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "garma" version))
       (sha256
        (base32 "0rm77ig8payprd715z635h35338mc71pd0k7fk46s4jqlwaqkybd"))))
    (properties `((upstream-name . "garma")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-signal
                             r-rsolnp
                             r-rlang
                             r-pracma
                             r-nloptr
                             r-lubridate
                             r-ggplot2
                             r-forecast
                             r-crayon))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rlph50/garma")
    (synopsis "Fitting and Forecasting Gegenbauer ARMA Time Series Models")
    (description
     "This package provides methods for estimating univariate long
memory-seasonal/cyclical Gegenbauer time series processes.  See for example
(2022) <doi:10.1007/s00362-022-01290-3>.  Refer to the vignette for details of
fitting these processes.")
    (license license:gpl3)))

(define-public r-gargoyle
  (package
    (name "r-gargoyle")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gargoyle" version))
       (sha256
        (base32 "0lsjplfrc047lb3zjfsc8wl47kll7d85v824k7zlr0n34xbbypq7"))))
    (properties `((upstream-name . "gargoyle")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-attempt))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gargoyle")
    (synopsis "An Event-Based Mechanism for 'Shiny'")
    (description
     "An event-Based framework for building Shiny apps.  Instead of relying on
standard Shiny reactive objects, this package allow to relying on a lighter set
of triggers, so that reactive contexts can be invalidated with more control.")
    (license license:expat)))

(define-public r-garcom
  (package
    (name "r-garcom")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GARCOM" version))
       (sha256
        (base32 "0fz8i4zidw47p6zpb83988k6rwb12scv90n1lvy2jb1fq29zp63q"))))
    (properties `((upstream-name . "GARCOM")))
    (build-system r-build-system)
    (propagated-inputs (list r-vcfr r-data-table))
    (home-page "https://cran.r-project.org/package=GARCOM")
    (synopsis "Gene and Region Counting of Mutations (\"GARCOM\")")
    (description
     "Gene and Region Counting of Mutations (GARCOM) package computes mutation (or
alleles) counts per gene per individuals based on gene annotation or genomic
base pair boundaries.  It comes with features to accept data formats in
plink(.raw) and VCF. It provides users flexibility to extract and filter
individuals, mutations and genes of interest.")
    (license license:expat)))

(define-public r-garchx
  (package
    (name "r-garchx")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "garchx" version))
       (sha256
        (base32 "0znb5drsbd6vfr6yp020r3w3k3jmk6p3xcnkx3n2sc7fm2qg765b"))))
    (properties `((upstream-name . "garchx")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo))
    (home-page "https://www.sucarrat.net/")
    (synopsis "Flexible and Robust GARCH-X Modelling")
    (description
     "Flexible and robust estimation and inference of generalised autoregressive
conditional heteroscedasticity (GARCH) models with covariates ('X') based on the
results by Francq and Thieu (2018) <doi:10.1017/S0266466617000512>.
Coefficients can straightforwardly be set to zero by omission, and quasi maximum
likelihood methods ensure estimates are generally consistent and inference
valid, even when the standardised innovations are non-normal and/or dependent
over time, see
<https://journal.r-project.org/archive/2021/RJ-2021-057/RJ-2021-057.pdf> for an
overview of the package.")
    (license license:gpl2+)))

(define-public r-garchsk
  (package
    (name "r-garchsk")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GARCHSK" version))
       (sha256
        (base32 "0fwzv8js3d9klbzs58m946k7q50yxpf0cxia3r2z2gy85xphrsm0"))))
    (properties `((upstream-name . "GARCHSK")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsolnp))
    (home-page "https://cran.r-project.org/package=GARCHSK")
    (synopsis "Estimating a GARCHSK Model and GJRSK Model")
    (description
     "This package provides functions for estimating a GARCHSK model and GJRSK model
based on a publication by Leon et,al (2005)<doi:10.1016/j.qref.2004.12.020> and
Nakagawa and Uchiyama (2020)<doi:10.3390/math8111990>.  These are a GARCH-type
model allowing for time-varying volatility, skewness and kurtosis.")
    (license license:gpl2+)))

(define-public r-garchito
  (package
    (name "r-garchito")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GARCHIto" version))
       (sha256
        (base32 "1rh9da9gb1krxasx1apldns5k3wl2xjjqc8bidsd5c0h5jlqdvr6"))))
    (properties `((upstream-name . "GARCHIto")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsolnp))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=GARCHIto")
    (synopsis "Class of GARCH-Ito Models")
    (description
     "This package provides functions to estimate model parameters and forecast future
volatilities using the Unified GARCH-Ito [Kim and Wang (2016)
<doi:10.1016/j.jeconom.2016.05.003>] and Realized GARCH-Ito [Song et.  al.
(2020) <doi:10.1016/j.jeconom.2020.07.007>] models.  Optimization is done using
augmented Lagrange multiplier method.")
    (license license:gpl3)))

(define-public r-gapminder
  (package
    (name "r-gapminder")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gapminder" version))
       (sha256
        (base32 "1hlpddji4z7j71fhpc6nkwl6fmwxfkdj9v053c9fhnvr1ywjzs4h"))))
    (properties `((upstream-name . "gapminder")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jennybc/gapminder")
    (synopsis "Data from Gapminder")
    (description
     "An excerpt of the data available at Gapminder.org.  For each of 142 countries,
the package provides values for life expectancy, GDP per capita, and population,
every five years, from 1952 to 2007.")
    (license license:cc0)))

(define-public r-gapmap
  (package
    (name "r-gapmap")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gapmap" version))
       (sha256
        (base32 "04hjla4vyi524dwk5y69f1mfk3l9kyb5p82a8pzrcwgfjbqx15gv"))))
    (properties `((upstream-name . "gapmap")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/evanbiederstedt/gapmap")
    (synopsis "Drawing Gapped Cluster Heatmaps with 'ggplot2'")
    (description
     "The gap encodes the distance between clusters and improves interpretation of
cluster heatmaps.  The gaps can be of the same distance based on a height
threshold to cut the dendrogram.  Another option is to vary the size of gaps
based on the distance between clusters.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-gapfill
  (package
    (name "r-gapfill")
    (version "0.9.6-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gapfill" version))
       (sha256
        (base32 "15mrn44v5l4sgxwlyp1rik5mdg7m2s6xxcdigc3sjd1yhxalgw12"))))
    (properties `((upstream-name . "gapfill")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-quantreg r-ggplot2 r-foreach r-fields))
    (home-page "https://github.com/florafauna/gapfill")
    (synopsis "Fill Missing Values in Satellite Data")
    (description
     "This package provides tools to fill missing values in satellite data and to
develop new gap-fill algorithms.  The methods are tailored to data (images)
observed at equally-spaced points in time.  The package is illustrated with
MODIS NDVI data.")
    (license license:gpl2+)))

(define-public r-gapclosing
  (package
    (name "r-gapclosing")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gapclosing" version))
       (sha256
        (base32 "1nq7wbl3236rg27hpfrl1f20gg0yrjmddcghmnh0yv5nzl52l798"))))
    (properties `((upstream-name . "gapclosing")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-ranger
                             r-mgcv
                             r-magrittr
                             r-glmnet
                             r-ggplot2
                             r-foreach
                             r-forcats
                             r-dplyr
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://ilundberg.github.io/gapclosing/")
    (synopsis "Estimate Gaps Under an Intervention")
    (description
     "This package provides functions to estimate the disparities across categories
(e.g. Black and white) that persists if a treatment variable (e.g. college) is
equalized.  Makes estimates by treatment modeling, outcome modeling, and
doubly-robust augmented inverse probability weighting estimation, with standard
errors calculated by a nonparametric bootstrap.  Cross-fitting is supported.
Survey weights are supported for point estimation but not for standard error
estimation; those applying this package with complex survey samples should
consult the data distributor to select an appropriate approach for standard
error construction, which may involve calling the functions repeatedly for many
sets of replicate weights provided by the data distributor.  The methods in this
package are described in Lundberg (2021) <doi:10.31235/osf.io/gx4y3>.")
    (license license:expat)))

(define-public r-gaparsimony
  (package
    (name "r-gaparsimony")
    (version "0.9.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GAparsimony" version))
       (sha256
        (base32 "1l3x6m7phbin6ba6rclljzym9zgx9nskz65ggkg0ij24pz5h7y51"))))
    (properties `((upstream-name . "GAparsimony")))
    (build-system r-build-system)
    (propagated-inputs (list r-iterators r-foreach))
    (home-page "https://github.com/jpison/GAparsimony")
    (synopsis "Searching Parsimony Models with Genetic Algorithms")
    (description
     "Methodology that combines feature selection, model tuning, and parsimonious
model selection with Genetic Algorithms (GA) proposed in {Martinez-de-Pison}
(2015) <DOI:10.1016/j.asoc.2015.06.012>.  To this objective, a novel GA
selection procedure is introduced based on separate cost and complexity
evaluations.")
    (license license:gpl2+)))

(define-public r-gap-datasets
  (package
    (name "r-gap-datasets")
    (version "0.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gap.datasets" version))
       (sha256
        (base32 "1jwpsb66sgj2zzgrpgwxc22df66gbhi278ywbmaicc10mipv050y"))))
    (properties `((upstream-name . "gap.datasets")))
    (build-system r-build-system)
    (home-page "https://jinghuazhao.github.io/R/")
    (synopsis "Datasets for 'gap'")
    (description
     "Datasets associated with the gap package.  Currently, it includes an example
data for regional association plot (CDKN), an example data for a genomewide
association meta-analysis (OPG), data in studies of Parkinson's diease (PD),
ALHD2 markers and alcoholism (aldh2), APOE/APOC1 markers and Schizophrenia
(apoeapoc), cystic fibrosis (cf), a Olink/INF panel (inf1), Manhattan plots with
(hr1420, mhtdata) and without (w4) gene annotations.")
    (license license:gpl2+)))

(define-public r-gap
  (package
    (name "r-gap")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gap" version))
       (sha256
        (base32 "1pz0j3vglccrwm3p9lj6wbq58varb8b0xg5r97gmz8c3biprpzww"))))
    (properties `((upstream-name . "gap")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-plotly r-ggplot2 r-gap-datasets
                             r-dplyr))
    (native-inputs (list r-knitr gfortran))
    (home-page "https://github.com/jinghuazhao/R")
    (synopsis "Genetic Analysis Package")
    (description
     "As first reported [Zhao, J. H. 2007. \"gap: Genetic Analysis Package\".  J Stat
Soft 23(8):1-18. <doi:10.18637/jss.v023.i08>], it is designed as an integrated
package for genetic data analysis of both population and family data.
Currently, it contains functions for sample size calculations of both
population-based and family-based designs, probability of familial disease
aggregation, kinship calculation, statistics in linkage analysis, and
association analysis involving genetic markers including haplotype analysis with
or without environmental covariates.  Over years, the package has been developed
in-between many projects hence also in line with the name (gap).")
    (license license:gpl2+)))

(define-public r-ganpadata
  (package
    (name "r-ganpadata")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GANPAdata" version))
       (sha256
        (base32 "0mhdadl7zgsacn59ym42magg3214k1xhabwn78fv7kgccszcgc86"))))
    (properties `((upstream-name . "GANPAdata")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GANPAdata")
    (synopsis "The GANPA Datasets Package")
    (description
     "This is a dataset package for GANPA, which implements a network-based gene
weighting approach to pathway analysis.  This package includes data useful for
GANPA, such as a functional association network, pathways, an expression dataset
and multi-subunit proteins.")
    (license license:gpl2)))

(define-public r-ganpa
  (package
    (name "r-ganpa")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GANPA" version))
       (sha256
        (base32 "1c2yvh6sqx75hg0lsmflgd9mdic8imysv8dn46g4abvfwjhxsafp"))))
    (properties `((upstream-name . "GANPA")))
    (build-system r-build-system)
    (propagated-inputs (list r-ganpadata))
    (home-page "https://cran.r-project.org/package=GANPA")
    (synopsis "Gene Association Network-Based Pathway Analysis (GANPA)")
    (description
     "This package provides a network-based gene weighting algorithm for pathway
enrichment analysis, using either RNA-seq or microarray data.  Zhaoyuan Fang,
Weidong Tian and Hongbin Ji (2012) <doi:10.1038/cr.2011.149>.")
    (license license:gpl2)))

(define-public r-gangenerativedata
  (package
    (name "r-gangenerativedata")
    (version "2.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ganGenerativeData" version))
       (sha256
        (base32 "04h07fnvhvrg7c0ycv2pzlvd902cxi42fcvlpqqffx25krnrakb0"))))
    (properties `((upstream-name . "ganGenerativeData")))
    (build-system r-build-system)
    (inputs (list tensorflow))
    (propagated-inputs (list r-tensorflow r-rcpp r-httr))
    (home-page "https://cran.r-project.org/package=ganGenerativeData")
    (synopsis "Generate Generative Data for a Data Source")
    (description
     "Generative Adversarial Networks are applied to generate generative data for a
data source.  A generative model consisting of a generator and a discriminator
network is trained.  During iterative training the distribution of generated
data is converging to that of the data source.  Direct applications of
generative data are the created functions for data evaluation and missing data
completion.  A software service for accelerated training of generative models on
graphics processing units is available.  Reference: Goodfellow et al. (2014)
<doi:10.48550/@code{arXiv.1406.2661>}.")
    (license license:gpl2+)))

(define-public r-gandatamodel
  (package
    (name "r-gandatamodel")
    (version "1.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "ganDataModel" version))
       (sha256
        (base32 "0zp6nx43qza572nl7wibxamw5ca56b3955z6zfjxq77kwlc8jcl7"))))
    (properties `((upstream-name . "ganDataModel")))
    (build-system r-build-system)
    (inputs (list tensorflow))
    (propagated-inputs (list r-tensorflow r-rcpp))
    (home-page "https://cran.r-project.org/package=ganDataModel")
    (synopsis "Build a Metric Subspaces Data Model for a Data Source")
    (description
     "Neural networks are applied to create a density value function which
approximates density values for a data source.  The trained neural network is
analyzed for different levels.  For each level metric subspaces with density
values above a level are determined.  The obtained set of metric subspaces and
the trained neural network are assembled into a data model.  A prerequisite is
the definition of a data source, the generation of generative data and the
calculation of density values.  These tasks are executed using package
@code{ganGenerativeData}
<https://cran.r-project.org/package=@code{ganGenerativeData>}.")
    (license license:gpl2+)))

(define-public r-gamstransfer
  (package
    (name "r-gamstransfer")
    (version "3.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamstransfer" version))
       (sha256
        (base32 "1xfrykgfd0xw1z2fs1ay1d7wq87ak78qrfgw8vsxf0lg8lvmjqpp"))))
    (properties `((upstream-name . "gamstransfer")))
    (build-system r-build-system)
    (inputs (list zlib))
    (propagated-inputs (list r-rcpp r-r6 r-r-utils r-collections))
    (home-page "https://github.com/GAMS-dev/transfer-r/tree/main/gamstransfer")
    (synopsis "Data Interface Between 'GAMS' and R")
    (description
     "Read, analyze, modify, and write GAMS (General Algebraic Modeling System) data.
The main focus of gamstransfer is the highly efficient transfer of data with
GAMS <https://www.gams.com/>, while keeping these operations as simple as
possible for the user.  The transfer of data usually takes place via an
intermediate GDX (GAMS Data Exchange) file.  Additionally, gamstransfer provides
utility functions to get an overview of GAMS data and to check its validity.")
    (license license:expat)))

(define-public r-gamselbayes
  (package
    (name "r-gamselbayes")
    (version "2.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamselBayes" version))
       (sha256
        (base32 "1947lpxhiyf2kf22miliikx8plzhb1l97w440fb626vvgwfa340j"))))
    (properties `((upstream-name . "gamselBayes")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=gamselBayes")
    (synopsis "Bayesian Generalized Additive Model Selection")
    (description
     "Generalized additive model selection via approximate Bayesian inference is
provided.  Bayesian mixed model-based penalized splines with spike-and-slab-type
coefficient prior distributions are used to facilitate fitting and selection.
The approximate Bayesian inference engine options are: (1) Markov chain Monte
Carlo and (2) mean field variational Bayes.  Markov chain Monte Carlo has better
Bayesian inferential accuracy, but requires a longer run-time.  Mean field
variational Bayes is faster, but less accurate.  The methodology is described in
He and Wand (2023) <@code{arXiv:2201.00412>}.")
    (license license:gpl2+)))

(define-public r-gamsel
  (package
    (name "r-gamsel")
    (version "1.8-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamsel" version))
       (sha256
        (base32 "17c3k72absrfzj8qqjgf9rni7p3s0d1jdw5hf94d03jds00n4w21"))))
    (properties `((upstream-name . "gamsel")))
    (build-system r-build-system)
    (propagated-inputs (list r-mda r-foreach))
    (native-inputs (list r-knitr))
    (home-page "https://arxiv.org/abs/1506.03850")
    (synopsis "Fit Regularization Path for Generalized Additive Models")
    (description
     "Using overlap grouped-lasso penalties, gamsel selects whether a term in a gam is
nonzero, linear, or a non-linear spline (up to a specified max df per variable).
 It fits the entire regularization path on a grid of values for the overall
penalty lambda, both for gaussian and binomial families.  See
<doi:10.48550/@code{arXiv.1506.03850>} for more details.")
    (license license:gpl2)))

(define-public r-gamrr
  (package
    (name "r-gamrr")
    (version "0.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamRR" version))
       (sha256
        (base32 "0zn8921a4i1lz5wpdfbd72pqb25r12abyqw7y2w53kfhdzs57f3l"))))
    (properties `((upstream-name . "gamRR")))
    (build-system r-build-system)
    (propagated-inputs (list r-mgcv r-boot))
    (home-page "https://cran.r-project.org/package=gamRR")
    (synopsis "Calculate the RR for the GAM")
    (description
     "To calculate the relative risk (RR) for the generalized additive model.")
    (license license:gpl3)))

(define-public r-gamreg
  (package
    (name "r-gamreg")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamreg" version))
       (sha256
        (base32 "1svrgbb8qdy2hzpq1g38v2lzmlbrn7qljix827biqzwgiajy69gg"))))
    (properties `((upstream-name . "gamreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-robusthd
                             r-rcpparmadillo
                             r-rcpp
                             r-glmnet
                             r-foreach
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=gamreg")
    (synopsis "Robust and Sparse Regression via Gamma-Divergence")
    (description
     "Robust regression via gamma-divergence with L1, elastic net and ridge.")
    (license license:gpl2+)))

(define-public r-gammslice
  (package
    (name "r-gammslice")
    (version "2.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gammSlice" version))
       (sha256
        (base32 "1klxg1yhmfaz1zjnw1kxl1lm5plkakqh014rpkak2lj8zgf8qnyy"))))
    (properties `((upstream-name . "gammSlice")))
    (build-system r-build-system)
    (propagated-inputs (list r-mgcv r-lattice r-kernsmooth))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=gammSlice")
    (synopsis "Generalized Additive Mixed Model Analysis via Slice Sampling")
    (description
     "Uses a slice sampling-based Markov chain Monte Carlo to conduct Bayesian fitting
and inference for generalized additive mixed models.  Generalized linear mixed
models and generalized additive models are also handled as special cases of
generalized additive mixed models.  The methodology and software is described in
Pham, T.H. and Wand, M.P. (2018).  Australian and New Zealand Journal of
Statistics, 60, 279-330 <DOI:10.1111/ANZS.12241>.")
    (license license:gpl2+)))

(define-public r-gammi
  (package
    (name "r-gammi")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gammi" version))
       (sha256
        (base32 "1zvmfkbjj70h7aq0yx3gchq17klgfwbjqd468pnq7wfrpvg04dgy"))))
    (properties `((upstream-name . "gammi")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-lme4))
    (home-page "https://cran.r-project.org/package=gammi")
    (synopsis "Generalized Additive Mixed Model Interface")
    (description
     "An interface for fitting generalized additive models (GAMs) and generalized
additive mixed models (GAMMs) using the lme4 package as the computational
engine, as described in Helwig (2024) <doi:10.3390/stats7010003>.  Supports
default and formula methods for model specification, additive and tensor product
splines for capturing nonlinear effects, and automatic determination of spline
type based on the class of each predictor.  Includes an S3 plot method for
visualizing the (nonlinear) model terms, an S3 predict method for forming
predictions from a fit model, and an S3 summary method for conducting
significance testing using the Bayesian interpretation of a smoothing spline.")
    (license license:gpl2+)))

(define-public r-gammareg
  (package
    (name "r-gammareg")
    (version "3.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Gammareg" version))
       (sha256
        (base32 "0y3iqhrnppznqmx7sfibmwq3yq1l0jd8cfnpv0hch9byllh0syad"))))
    (properties `((upstream-name . "Gammareg")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=Gammareg")
    (synopsis
     "Classic Gamma Regression: Joint Modeling of Mean and Shape Parameters")
    (description
     "This package performs Gamma regression, where both mean and shape parameters
follows lineal regression structures.")
    (license license:gpl2+)))

(define-public r-gamma
  (package
    (name "r-gamma")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamma" version))
       (sha256
        (base32 "0wsxy97kdfmlvrflp9l3fqn5ckdraxb7lbjpq1znqgj304g0j45n"))))
    (properties `((upstream-name . "gamma")))
    (build-system r-build-system)
    (propagated-inputs (list r-rxylib r-rlang r-isoplotr r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://crp2a.github.io/gamma/")
    (synopsis
     "Dose Rate Estimation from in-Situ Gamma-Ray Spectrometry Measurements")
    (description
     "Process in-situ Gamma-Ray Spectrometry for Luminescence Dating.  This package
allows to import, inspect and correct the energy shifts of gamma-ray spectra.
It provides methods for estimating the gamma dose rate by the use of a
calibration curve as described in Mercier and FalguÃ¨res (2007).  The package
only supports Canberra CNF and TKA and Kromek SPE files.")
    (license license:gpl3)))

(define-public r-gamlssx
  (package
    (name "r-gamlssx")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamlssx" version))
       (sha256
        (base32 "0lhn0rif1k6978j50cvb3krknn09dzvdln1dh4amz3h2jqsh6434"))))
    (properties `((upstream-name . "gamlssx")))
    (build-system r-build-system)
    (propagated-inputs (list r-nieve r-gamlss-dist r-gamlss))
    (home-page "https://paulnorthrop.github.io/gamlssx/")
    (synopsis
     "Generalized Additive Extreme Value Models for Location, Scale and Shape")
    (description
     "Fits generalized additive models for the location, scale and shape parameters of
a generalized extreme value response distribution.  The methodology is based on
Rigby, R.A. and Stasinopoulos, D.M. (2005),
<doi:10.1111/j.1467-9876.2005.00510.x> and implemented using functions from the
gamlss package <doi:10.32614/CRAN.package.gamlss>.")
    (license license:gpl3+)))

(define-public r-gamlssbssn
  (package
    (name "r-gamlssbssn")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamlssbssn" version))
       (sha256
        (base32 "1l8d4qwmq9dklm9imb3cvlncwa6jygf8kg2j1599h1nfhyyhj2vv"))))
    (properties `((upstream-name . "gamlssbssn")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-gamlss-dist r-gamlss))
    (home-page "https://cran.r-project.org/package=gamlssbssn")
    (synopsis "Bimodal Skew Symmetric Normal Distribution")
    (description
     "Density, distribution function, quantile function and random generation for the
bimodal skew symmetric normal distribution of Hassan and El-Bassiouni (2016)
<doi:10.1080/03610926.2014.882950>.")
    (license license:gpl2)))

(define-public r-gamlss-tr
  (package
    (name "r-gamlss-tr")
    (version "5.1-9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamlss.tr" version))
       (sha256
        (base32 "1r5pd19r4nbxqx7jbi14iyyhn220dz914fa6v000rl0bwqdya379"))))
    (properties `((upstream-name . "gamlss.tr")))
    (build-system r-build-system)
    (propagated-inputs (list r-gamlss-dist r-gamlss))
    (home-page "https://www.gamlss.com/")
    (synopsis "Generating and Fitting Truncated `gamlss.family' Distributions")
    (description
     "This is an add on package to GAMLSS. The purpose of this package is to allow
users to defined truncated distributions in GAMLSS models.  The main function
gen.trun() generates truncated version of an existing GAMLSS family
distribution.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-gamlss-spatial
  (package
    (name "r-gamlss-spatial")
    (version "3.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamlss.spatial" version))
       (sha256
        (base32 "1v1mwi0k38b1b6bwf9gw46fkx83gzfxfqcyykx6kzpc7shkvy8f8"))))
    (properties `((upstream-name . "gamlss.spatial")))
    (build-system r-build-system)
    (propagated-inputs (list r-spam r-mgcv r-gamlss-dist r-gamlss-add r-gamlss))
    (home-page "https://www.gamlss.com/")
    (synopsis
     "Spatial Terms in Generalized Additive Models for Location Scale and Shape Models")
    (description
     "It allows us to fit Gaussian Markov Random Field within the Generalized Additive
Models for Location Scale and Shape algorithms.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-gamlss-mx
  (package
    (name "r-gamlss-mx")
    (version "6.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamlss.mx" version))
       (sha256
        (base32 "0r19705wxc9wj4ji2plws9q7xn2nm53sb3x4z9c0ycymv9fkblb6"))))
    (properties `((upstream-name . "gamlss.mx")))
    (build-system r-build-system)
    (propagated-inputs (list r-nnet r-gamlss-dist r-gamlss))
    (home-page "https://www.gamlss.com/")
    (synopsis "Fitting Mixture Distributions with GAMLSS")
    (description
     "The main purpose of this package is to allow fitting of mixture distributions
with generalised additive models for location scale and shape models see Chapter
7 of Stasinopoulos et al. (2017) <doi:10.1201/b21973-4>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-gamlss-lasso
  (package
    (name "r-gamlss-lasso")
    (version "1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamlss.lasso" version))
       (sha256
        (base32 "03c8k6hl23ag98dihznfz1h0qc3pwxbnpv58pvmx3xbb3lipfjfb"))))
    (properties `((upstream-name . "gamlss.lasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-lars r-glmnet r-gamlss))
    (home-page "https://www.gamlss.com/")
    (synopsis "Extra Lasso-Type Additive Terms for GAMLSS")
    (description
     "Interface for extra high-dimensional smooth functions for Generalized Additive
Models for Location Scale and Shape (GAMLSS) including (adaptive) lasso, ridge,
elastic net and least angle regression.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-gamlss-inf
  (package
    (name "r-gamlss-inf")
    (version "1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamlss.inf" version))
       (sha256
        (base32 "0m091zxjm5wvhm6mzbpzkr275n3qnlb3v4099k0a1jymahk4w7vr"))))
    (properties `((upstream-name . "gamlss.inf")))
    (build-system r-build-system)
    (propagated-inputs (list r-gamlss-dist r-gamlss))
    (home-page "http://www.gamlss.com/")
    (synopsis "Fitting Mixed (Inflated and Adjusted) Distributions")
    (description
     "This is an add-on package to gamlss'.  The purpose of this package is to allow
users to fit GAMLSS (Generalised Additive Models for Location Scale and Shape)
models when the response variable is defined either in the intervals [0,1),
(0,1] and [0,1] (inflated at zero and/or one distributions), or in the positive
real line including zero (zero-adjusted distributions).  The mass points at zero
and/or one are treated as extra parameters with the possibility to include a
linear predictor for both.  The package also allows transformed or truncated
distributions from the GAMLSS family to be used for the continuous part of the
distribution.  Standard methods and GAMLSS diagnostics can be used with the
resulting fitted object.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-gamlss-ggplots
  (package
    (name "r-gamlss-ggplots")
    (version "2.1-12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamlss.ggplots" version))
       (sha256
        (base32 "0lm0nlc2rb4yl6fliv0017qpgyaaky7hws6arv36dgqqih9dmcii"))))
    (properties `((upstream-name . "gamlss.ggplots")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaimpute
                             r-mgcv
                             r-ggridges
                             r-ggplot2
                             r-gamlss-inf
                             r-gamlss-foreach
                             r-gamlss-dist
                             r-gamlss
                             r-foreach
                             r-ellipse))
    (home-page "https://www.gamlss.com/")
    (synopsis
     "Plotting Functions for Generalized Additive Model for Location Scale and Shape")
    (description
     "This package provides functions for plotting Generalized Additive Models for
Location Scale and Shape from the gamlss package, Stasinopoulos and Rigby (2007)
<doi:10.18637/jss.v023.i07>, using the graphical methods from ggplot2'.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-gamlss-foreach
  (package
    (name "r-gamlss-foreach")
    (version "1.1-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamlss.foreach" version))
       (sha256
        (base32 "1bld98lfbgj1v6l4vq3yysg1lnvpgj2r9vykkfy0q1yipv2xbnqb"))))
    (properties `((upstream-name . "gamlss.foreach")))
    (build-system r-build-system)
    (propagated-inputs (list r-glmnet
                             r-gamlss-dist
                             r-gamlss-data
                             r-gamlss
                             r-foreach
                             r-doparallel))
    (home-page "https://www.gamlss.com/")
    (synopsis "Parallel Computations for Distributional Regression")
    (description
     "Computational intensive calculations for Generalized Additive Models for
Location Scale and Shape, <doi:10.1111/j.1467-9876.2005.00510.x>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-gamlss-demo
  (package
    (name "r-gamlss-demo")
    (version "4.3-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamlss.demo" version))
       (sha256
        (base32 "01p6abppwbnh2a2ks1g08z4iwq2fxf125y9s4qzssybsn76a3gf3"))))
    (properties `((upstream-name . "gamlss.demo")))
    (build-system r-build-system)
    (propagated-inputs (list r-rpanel r-gamlss-tr r-gamlss-dist))
    (home-page "http://www.gamlss.org/")
    (synopsis "Demos for GAMLSS")
    (description "Demos for smoothing and gamlss.family distributions.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-gamlss-countkinf
  (package
    (name "r-gamlss-countkinf")
    (version "3.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamlss.countKinf" version))
       (sha256
        (base32 "1xalp909gxxhyhh4chlr1ssyfhydhw1w3szzbynajji98576zaqv"))))
    (properties `((upstream-name . "gamlss.countKinf")))
    (build-system r-build-system)
    (propagated-inputs (list r-gamlss-dist r-gamlss))
    (home-page "http://www.gamlss.org/")
    (synopsis
     "Generating and Fitting K-Inflated 'discrete gamlss.family' Distributions")
    (description
     "This is an add on package to GAMLSS'.  The main purpose of this package is
generating and fitting inflated distributions at any desired point (0, 1, 2,
...).  The function gen.Kinf() generates K-inflated version of an existing
discrete GAMLSS family distribution.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-gamlss-cens
  (package
    (name "r-gamlss-cens")
    (version "5.0-7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamlss.cens" version))
       (sha256
        (base32 "1l2g0gn794jyyjxvphfk1l8h4qwf6sk1akcf0c08xqx3kgillnjl"))))
    (properties `((upstream-name . "gamlss.cens")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-gamlss-dist r-gamlss))
    (home-page "https://www.gamlss.com/")
    (synopsis
     "Fitting an Interval Response Variable Using `gamlss.family' Distributions")
    (description
     "This is an add-on package to GAMLSS. The purpose of this package is to allow
users to fit interval response variables in GAMLSS models.  The main function
gen.cens() generates a censored version of an existing GAMLSS family
distribution.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-gamlss-add
  (package
    (name "r-gamlss-add")
    (version "5.1-13")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamlss.add" version))
       (sha256
        (base32 "1dhiq3x0dascjsq3c80j6zgx4xdcjljd4ishzpp8j9m5bk55z72z"))))
    (properties `((upstream-name . "gamlss.add")))
    (build-system r-build-system)
    (propagated-inputs (list r-rpart r-nnet r-mgcv r-gamlss-dist r-gamlss))
    (home-page "https://www.gamlss.com/")
    (synopsis
     "Extra Additive Terms for Generalized Additive Models for Location Scale and Shape")
    (description
     "Interface for extra smooth functions including tensor products, neural networks
and decision trees.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-gamlr
  (package
    (name "r-gamlr")
    (version "1.13-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamlr" version))
       (sha256
        (base32 "1psvxx7ymkgr4acis85842xgafc5azdl7v1cmgvgbhsdwksm95q7"))))
    (properties `((upstream-name . "gamlr")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix))
    (home-page "https://github.com/TaddyLab/gamlr")
    (synopsis "Gamma Lasso Regression")
    (description
     "The gamma lasso algorithm provides regularization paths corresponding to a range
of non-convex cost functions between L0 and L1 norms.  As much as possible,
usage for this package is analogous to that for the glmnet package (which does
the same thing for penalization between L1 and L2 norms).  For details see:
Taddy (2017 JCGS), One-Step Estimator Paths for Concave Regularization',
<@code{arXiv:1308.5623>}.")
    (license license:gpl3)))

(define-public r-gametheoryallocation
  (package
    (name "r-gametheoryallocation")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GameTheoryAllocation" version))
       (sha256
        (base32 "0733vmyr0d9scjd5ixpnggr548snd7nj70knf5hbzc59nmbc5y11"))))
    (properties `((upstream-name . "GameTheoryAllocation")))
    (build-system r-build-system)
    (propagated-inputs (list r-lpsolveapi r-e1071))
    (home-page "https://cran.r-project.org/package=GameTheoryAllocation")
    (synopsis "Tools for Calculating Allocations in Game Theory")
    (description
     "Many situations can be modeled as game theoretic situations.  Some procedures
are included in this package to calculate the most important allocations rules
in Game Theory: Shapley value, Owen value or nucleolus, among other.  First, we
must define as an argument the value of the unions of the envolved agents with
the characteristic function.")
    (license license:gpl2)))

(define-public r-gametheory
  (package
    (name "r-gametheory")
    (version "2.7.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GameTheory" version))
       (sha256
        (base32 "1mq8406zgh4yww2jb7xvvmizyis9cb2hwdliacr4hxpjg38c3jdn"))))
    (properties `((upstream-name . "GameTheory")))
    (build-system r-build-system)
    (propagated-inputs (list r-lpsolveapi r-kappalab r-ineq r-gtools
                             r-combinat))
    (home-page "https://cran.r-project.org/package=GameTheory")
    (synopsis "Cooperative Game Theory")
    (description
     "Implementation of a common set of punctual solutions for Cooperative Game
Theory.")
    (license license:gpl2+)))

(define-public r-gamesga
  (package
    (name "r-gamesga")
    (version "1.1.3.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamesGA" version))
       (sha256
        (base32 "0km0z2haqgykl01xkzsbkpwibavgqfjygx7cg08ni68gw501689l"))))
    (properties `((upstream-name . "gamesGA")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny))
    (home-page "https://bradduthie.github.io/gamesGA/")
    (synopsis "Genetic Algorithm for Sequential Symmetric Games")
    (description
     "Finds adaptive strategies for sequential symmetric games using a genetic
algorithm.  Currently, any symmetric two by two matrix is allowed, and
strategies can remember the history of an opponent's play from the previous
three rounds of moves in iterated interactions between players.  The genetic
algorithm returns a list of adaptive strategies given payoffs, and the mean
fitness of strategies in each generation.")
    (license license:expat)))

(define-public r-gamer
  (package
    (name "r-gamer")
    (version "0.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gameR" version))
       (sha256
        (base32 "05bn53ryl48s2r212wqaxljimkgskyd4qxzc7algzbyvmalcg6rx"))))
    (properties `((upstream-name . "gameR")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://www.constantine-cooke.com/gameR/")
    (synopsis "Color Palettes Inspired by Video Games")
    (description "Palettes based on video games.")
    (license license:gpl3+)))

(define-public r-gamens
  (package
    (name "r-gamens")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GAMens" version))
       (sha256
        (base32 "1z10wxcg277fra2lch464l0kb02lspw9qr1i2wmq11wcz0k9qnl8"))))
    (properties `((upstream-name . "GAMens")))
    (build-system r-build-system)
    (propagated-inputs (list r-mlbench r-gam r-catools))
    (home-page "https://cran.r-project.org/package=GAMens")
    (synopsis
     "Applies GAMbag, GAMrsm and GAMens Ensemble Classifiers for Binary Classification")
    (description
     "This package implements the GAMbag, GAMrsm and GAMens ensemble classifiers for
binary classification (De Bock et al., 2010) <doi:10.1016/j.csda.2009.12.013>.
The ensembles implement Bagging (Breiman, 1996) <doi:10.1023/A:1010933404324>,
the Random Subspace Method (Ho, 1998) <doi:10.1109/34.709601> , or both, and use
Hastie and Tibshirani's (1990, ISBN:978-0412343902) generalized additive models
(GAMs) as base classifiers.  Once an ensemble classifier has been trained, it
can be used for predictions on new data.  A function for cross validation is
also included.")
    (license license:gpl2+)))

(define-public r-gamcopula
  (package
    (name "r-gamcopula")
    (version "0.0-7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamCopula" version))
       (sha256
        (base32 "0zi9g2vxkn1hxc2qyyr1x8p1f9w7fc1j5dmm2mmm333gixkkz9nb"))))
    (properties `((upstream-name . "gamCopula")))
    (build-system r-build-system)
    (propagated-inputs (list r-vinecopula
                             r-numderiv
                             r-mgcv
                             r-mass
                             r-igraph
                             r-gsl
                             r-foreach
                             r-doparallel
                             r-copula))
    (home-page "https://github.com/tvatter/gamCopula")
    (synopsis
     "Generalized Additive Models for Bivariate Conditional Dependence Structures and Vine Copulas")
    (description
     "Implementation of various inference and simulation tools to apply generalized
additive models to bivariate dependence structures and non-simplified vine
copulas.")
    (license license:gpl3)))

(define-public r-gamclass
  (package
    (name "r-gamclass")
    (version "0.62.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamclass" version))
       (sha256
        (base32 "0y34970qwgssdnwnhb1hnkyav8j7pq3hkdskw63qs8cin4cknq7j"))))
    (properties `((upstream-name . "gamclass")))
    (build-system r-build-system)
    (propagated-inputs (list r-rpart r-randomforest r-latticeextra r-lattice))
    (native-inputs (list r-rmarkdown r-knitr r-bookdown))
    (home-page "https://cran.r-project.org/package=gamclass")
    (synopsis
     "Functions and Data for a Course on Modern Regression and Classification")
    (description
     "This package provides functions and data are provided that support a course that
emphasizes statistical issues of inference and generalizability.  The functions
are designed to make it straightforward to illustrate the use of
cross-validation, the training/test approach, simulation, and model-based
estimates of accuracy.  Methods considered are Generalized Additive Modeling,
Linear and Quadratic Discriminant Analysis, Tree-based methods, and Random
Forests.")
    (license license:gpl2+)))

(define-public r-gamboostmsm
  (package
    (name "r-gamboostmsm")
    (version "1.1.88")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamboostMSM" version))
       (sha256
        (base32 "16khm2fpsb3ivfszrilxbrzc8a281vmd1q36dcn50xbkvn19syxg"))))
    (properties `((upstream-name . "gamboostMSM")))
    (build-system r-build-system)
    (propagated-inputs (list r-mboost))
    (home-page "https://cran.r-project.org/package=gamboostMSM")
    (synopsis "Boosting Multistate Models")
    (description
     "This package contains infrastructure for using mboost::gamboost() in order to
estimate multistate models.")
    (license license:gpl2+)))

(define-public r-gamboostlss
  (package
    (name "r-gamboostlss")
    (version "2.0-7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamboostLSS" version))
       (sha256
        (base32 "1mx2aixdswmb8dgg1iawqzak2w7a1w4x6igk8wdwi05gifrgrs3i"))))
    (properties `((upstream-name . "gamboostLSS")))
    (build-system r-build-system)
    (propagated-inputs (list r-stabs r-mboost))
    (home-page "https://cran.r-project.org/package=gamboostLSS")
    (synopsis "Boosting Methods for 'GAMLSS'")
    (description
     "Boosting models for fitting generalized additive models for location, shape and
scale ('GAMLSS') to potentially high dimensional data.")
    (license license:gpl2)))

(define-public r-gamblers-ruin-gameplay
  (package
    (name "r-gamblers-ruin-gameplay")
    (version "4.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamblers.ruin.gameplay" version))
       (sha256
        (base32 "0h5sfjs2lzs3cfx7q0agq68x4hhqmkiqga04c1gvnzwn8k7i8svc"))))
    (properties `((upstream-name . "gamblers.ruin.gameplay")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis r-hrbrthemes r-ggplot2 r-gganimate))
    (home-page "https://cran.r-project.org/package=gamblers.ruin.gameplay")
    (synopsis
     "One-Dimensional Random Walks Through Simulation of the Gambler's Ruin Problem")
    (description
     "Simulates a gambling game under the gambler's ruin setup, after asking for the
money you have and the money you want to win, along with your win probability in
each round of the game.")
    (license license:expat)))

(define-public r-gambin
  (package
    (name "r-gambin")
    (version "2.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gambin" version))
       (sha256
        (base32 "137z6dxrvf05ss9bqh96bsl8rm6gzpk7kdgnfqh355mn0m5h2zx6"))))
    (properties `((upstream-name . "gambin")))
    (build-system r-build-system)
    (propagated-inputs (list r-gtools r-foreach r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/txm676/gambin/")
    (synopsis "Fit the Gambin Model to Species Abundance Distributions")
    (description
     "Fits unimodal and multimodal gambin distributions to species-abundance
distributions from ecological data, as in in Matthews et al. (2014)
<DOI:10.1111/ecog.00861>.  gambin is short for gamma-binomial'.  The main
function is fit_abundances(), which estimates the alpha parameter(s) of the
gambin distribution using maximum likelihood.  Functions are also provided to
generate the gambin distribution and for calculating likelihood statistics.")
    (license license:gpl3)))

(define-public r-gamair
  (package
    (name "r-gamair")
    (version "1.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gamair" version))
       (sha256
        (base32 "1cjrd576l9md1jb1fc1y6iay5y49i0d8by024qsc7yik6f6mdl13"))))
    (properties `((upstream-name . "gamair")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gamair")
    (synopsis "Data for 'GAMs: An Introduction with R'")
    (description
     "Data sets and scripts used in the book Generalized Additive Models: An
Introduction with R', Wood (2006,2017) CRC.")
    (license license:gpl2+)))

(define-public r-gam-hp
  (package
    (name "r-gam-hp")
    (version "0.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gam.hp" version))
       (sha256
        (base32 "0zhnwl533vb094vbn5bxggav3fqh0ahrdrhjliwrgwh2h3bwqpp7"))))
    (properties `((upstream-name . "gam.hp")))
    (build-system r-build-system)
    (propagated-inputs (list r-mgcv r-ggplot2))
    (home-page "https://github.com/laijiangshan/gam.hp")
    (synopsis
     "Hierarchical Partitioning of Adjusted R2 and Explained Deviance for Generalized Additive Models")
    (description
     "Conducts hierarchical partitioning to calculate individual contributions of each
predictor towards adjusted R2 and explained deviance for generalized additive
models based on output of gam()in mgcv package, applying the algorithm in this
paper: Lai(2024) <doi:10.1016/j.pld.2024.06.002>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-galvanizer
  (package
    (name "r-galvanizer")
    (version "0.5.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "galvanizer" version))
       (sha256
        (base32 "1b9431mmgpqyd49b8nlsrlxjq9j9fyj73226bi9jic1p867533kk"))))
    (properties `((upstream-name . "galvanizer")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tidyjson
                             r-tibble
                             r-rlang
                             r-magrittr
                             r-lubridate
                             r-jsonlite
                             r-httr
                             r-dplyr))
    (home-page "https://jonlinca.github.io/galvanizer/")
    (synopsis "Interface to Galvanize 'Highbond' Internal Audit Software")
    (description
     "An R interface to the Galvanize Highbond API <https://docs-apis.highbond.com>.")
    (license license:gpl3)))

(define-public r-galts
  (package
    (name "r-galts")
    (version "1.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "galts" version))
       (sha256
        (base32 "0a7yhdxja4jq2rn6p8imfg7ih3i4njqsz8zmkgwckj4q2x042lj5"))))
    (properties `((upstream-name . "galts")))
    (build-system r-build-system)
    (propagated-inputs (list r-genalg r-deoptim))
    (home-page "https://cran.r-project.org/package=galts")
    (synopsis
     "Genetic Algorithms and C-Steps Based LTS (Least Trimmed Squares) Estimation")
    (description
     "Includes the ga.lts() function that estimates LTS (Least Trimmed Squares)
parameters using genetic algorithms and C-steps.  ga.lts() constructs a genetic
algorithm to form a basic subset and iterates C-steps as defined in Rousseeuw
and van-Driessen (2006) to calculate the cost value of the LTS criterion.  OLS
(Ordinary Least Squares) regression is known to be sensitive to outliers.  A
single outlying observation can change the values of estimated parameters.  LTS
is a resistant estimator even the number of outliers is up to half of the data.
This package is for estimating the LTS parameters with lower bias and variance
in a reasonable time.  Version >=1.3 includes the function medmad for fast
outlier detection in linear regression.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-gallo
  (package
    (name "r-gallo")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GALLO" version))
       (sha256
        (base32 "0idzzkgxfr5im9p7xk7f4iph445rp8y2dzdzddm4x9i4fnh5hkwn"))))
    (properties `((upstream-name . "GALLO")))
    (build-system r-build-system)
    (propagated-inputs (list r-webshot
                             r-visnetwork
                             r-unbalhaar
                             r-stringr
                             r-rtracklayer
                             r-rcolorbrewer
                             r-matrix
                             r-lattice
                             r-igraph
                             r-ggplot2
                             r-foreach
                             r-dt
                             r-dplyr
                             r-doparallel
                             r-data-table
                             r-compquadform
                             r-circlize))
    (native-inputs (list r-knitr))
    (home-page "<https://github.com/pablobio/GALLO>")
    (synopsis "Genomic Annotation in Livestock for Positional Candidate LOci")
    (description
     "The accurate annotation of genes and Quantitative Trait Loci (QTLs) located
within candidate markers and/or regions (haplotypes, windows, CNVs, etc) is a
crucial step the most common genomic analyses performed in livestock, such as
Genome-Wide Association Studies or transcriptomics.  The Genomic Annotation in
Livestock for positional candidate LOci (GALLO) is an R package designed to
provide an intuitive and straightforward environment to annotate positional
candidate genes and QTLs from high-throughput genetic studies in livestock.
Moreover, GALLO allows the graphical visualization of gene and QTL annotation
results, data comparison among different grouping factors (e.g., methods,
breeds, tissues, statistical models, studies, etc.), and QTL enrichment in
different livestock species including cattle, pigs, sheep, and chicken, among
others.")
    (license license:gpl3)))

(define-public r-gallery
  (package
    (name "r-gallery")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gallery" version))
       (sha256
        (base32 "1mp247z0vh4n2xh3nwkgkxq7rrc6vcfkcx39c4z80vvvdx3rvw1m"))))
    (properties `((upstream-name . "gallery")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma r-matrix))
    (home-page "https://cran.r-project.org/package=gallery")
    (synopsis "Generate Test Matrices for Numerical Experiments")
    (description
     "Generates a variety of structured test matrices commonly used in numerical
linear algebra and computational experiments.  Includes well-known matrices for
benchmarking and testing the performance, stability, and accuracy of linear
algebra algorithms.  Inspired by MATLAB gallery functions.")
    (license license:expat)))

(define-public r-galigor
  (package
    (name "r-galigor")
    (version "0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "galigor" version))
       (sha256
        (base32 "1lfw1kikf90nv9g0xrb656fbilmxdk64zrzi43wrz7y2y55sd5xv"))))
    (properties `((upstream-name . "galigor")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rym
                             r-ryandexdirect
                             r-rvkstat
                             r-rstudioapi
                             r-rmytarget
                             r-rgoogleads
                             r-rfacebookstat
                             r-rappsflyer
                             r-purrr
                             r-magrittr
                             r-getproxy
                             r-gargle
                             r-dplyr
                             r-crayon
                             r-cli))
    (home-page "https://selesnow.github.io")
    (synopsis "Collection of Packages for Internet Marketing")
    (description
     "Collection of packages for work with API Google Ads
<https://developers.google.com/google-ads/api/docs/start>, Yandex Direct
<https://yandex.ru/dev/direct/>, Yandex Metrica
<https://yandex.ru/dev/metrika/>, @code{MyTarget}
<https://target.my.com/help/advertisers/api_arrangement/ru>, Vkontakte
<https://vk.com/dev/methods>, Facebook
<https://developers.facebook.com/docs/marketing-apis/> and @code{AppsFlyer}
<https://support.appsflyer.com/hc/en-us/articles/207034346-Using-Pull-API-aggregate-data>.
 This packages allows you loading data from ads account and manage your ads
materials.")
    (license license:expat)))

(define-public r-galamm
  (package
    (name "r-galamm")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "galamm" version))
       (sha256
        (base32 "1gjz1hmfimld6drildhmlf8xgh6s3p7l6064bsjvp58ggvlmwawk"))))
    (properties `((upstream-name . "galamm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack
                             r-rcppeigen
                             r-rcpp
                             r-nlme
                             r-mgcv
                             r-memoise
                             r-matrix
                             r-lme4))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/LCBC-UiO/galamm")
    (synopsis "Generalized Additive Latent and Mixed Models")
    (description
     "Estimates generalized additive latent and mixed models using maximum marginal
likelihood, as defined in Sorensen et al. (2023)
<doi:10.1007/s11336-023-09910-z>, which is an extension of Rabe-Hesketh and
Skrondal (2004)'s unifying framework for multilevel latent variable modeling
<doi:10.1007/BF02295939>.  Efficient computation is done using sparse matrix
methods, Laplace approximation, and automatic differentiation.  The framework
includes generalized multilevel models with heteroscedastic residuals, mixed
response types, factor loadings, smoothing splines, crossed random effects, and
combinations thereof.  Syntax for model formulation is close to lme4 (Bates et
al. (2015) <doi:10.18637/jss.v067.i01>) and PLmixed (Rockwood and Jeon (2019)
<doi:10.1080/00273171.2018.1516541>).")
    (license license:gpl3+)))

(define-public r-galah
  (package
    (name "r-galah")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "galah" version))
       (sha256
        (base32 "1z4z403b3jf0rj3gjrmsgjc4niab6jxdwb01rw0pzdblc8a2y4f1"))))
    (properties `((upstream-name . "galah")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-sf
                             r-rlang
                             r-readr
                             r-purrr
                             r-potions
                             r-lifecycle
                             r-jsonlite
                             r-httr2
                             r-glue
                             r-dplyr
                             r-crayon
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://galah.ala.org.au/R/")
    (synopsis "Biodiversity Data from the GBIF Node Network")
    (description
     "The Global Biodiversity Information Facility ('GBIF', <https://www.gbif.org>)
sources data from an international network of data providers, known as nodes'.
Several of these nodes - the \"living atlases\"
(<https://living-atlases.gbif.org>) - maintain their own web services using
software originally developed by the Atlas of Living Australia ('ALA',
<https://www.ala.org.au>).  galah enables the R community to directly access
data and resources hosted by GBIF and its partner nodes.")
    (license (license:fsdg-compatible "MPL-2.0"))))

(define-public r-gaipe
  (package
    (name "r-gaipe")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GAIPE" version))
       (sha256
        (base32 "035x5x8ig85q51bf4qgj5n5qq6clwjp4xk5bf35gswn7qxd13yls"))))
    (properties `((upstream-name . "GAIPE")))
    (build-system r-build-system)
    (home-page "https://www.r-project.org")
    (synopsis
     "Graphical Extension with Accuracy in Parameter Estimation (GAIPE)")
    (description
     "This package implements graphical extension with accuracy in parameter
estimation (AIPE) on RMSEA for sample size planning in structural equation
modeling based on Lin, T.-Z. & Weng, L.-J. (2014) <doi:
10.1080/10705511.2014.915380>.  And, it can also implement AIPE on RMSEA and
power analysis on RMSEA.")
    (license license:gpl2+)))

(define-public r-gains
  (package
    (name "r-gains")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gains" version))
       (sha256
        (base32 "1nmrhc7qjlyzn04r1qyjdws5fin3idmi789kbxzilax12ya8xr7k"))))
    (properties `((upstream-name . "gains")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gains")
    (synopsis "Lift (Gains) Tables and Charts")
    (description
     "Constructs gains tables and lift charts for prediction algorithms.  Gains tables
and lift charts are commonly used in direct marketing applications.  The method
is described in Drozdenko and Drake (2002), \"Optimal Database Marketing\",
Chapter 11.")
    (license license:gpl3)))

(define-public r-gainml
  (package
    (name "r-gainml")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gainML" version))
       (sha256
        (base32 "1mqhrdjdmc2n7dxilxkqn6xq7z0qnxpigw5bbffnjvvsmdamah0n"))))
    (properties `((upstream-name . "gainML")))
    (build-system r-build-system)
    (propagated-inputs (list r-fnn r-fields))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=gainML")
    (synopsis
     "Machine Learning-Based Analysis of Potential Power Gain from Passive Device Installation on Wind Turbine Generators")
    (description
     "This package provides an effective machine learning-based tool that quantifies
the gain of passive device installation on wind turbine generators.  H. Hwangbo,
Y. Ding, and D. Cabezon (2019) <@code{arXiv:1906.05776>}.")
    (license license:gpl3)))

(define-public r-gaiah
  (package
    (name "r-gaiah")
    (version "0.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gaiah" version))
       (sha256
        (base32 "1s2chr3zjgvmmic178c1fixbvf501q7jvxdg8204jalh9iii14vb"))))
    (properties `((upstream-name . "gaiah")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-rlang
                             r-raster
                             r-magrittr
                             r-geosphere
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=gaiah")
    (synopsis
     "Genetic and Isotopic Assignment Accounting for Habitat Suitability")
    (description
     "This package provides tools for using genetic markers, stable isotope data, and
habitat suitability data to calculate posterior probabilities of breeding origin
of migrating birds.")
    (license license:cc0)))

(define-public r-gagblup
  (package
    (name "r-gagblup")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GAGBLUP" version))
       (sha256
        (base32 "192r4fr3idl78g9vllb7hdvlrfkghh1airblb94c1mjyrjkj2hxa"))))
    (properties `((upstream-name . "GAGBLUP")))
    (build-system r-build-system)
    (propagated-inputs (list r-ga r-foreach r-doparallel))
    (home-page "https://cran.r-project.org/package=GAGBLUP")
    (synopsis
     "Genetic Algorithm Assisted Genomic Best Liner Unbiased Prediction")
    (description
     "This package performs genetic algorithm (Scrucca, L (2013)
<doi:10.18637/jss.v053.i04>) assisted genomic best liner unbiased prediction for
genomic selection.  It also provides a binning method in natural population for
genomic selection under the principle of linkage disequilibrium for dimensional
reduction.")
    (license license:gpl3)))

(define-public r-gagas
  (package
    (name "r-gagas")
    (version "0.6.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GAGAs" version))
       (sha256
        (base32 "04gfxhvbb9mm2rl6n2kfch8d9ggzblr5ch2p561x39dgwmwbpc2r"))))
    (properties `((upstream-name . "GAGAs")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rcppeigen r-rcpp))
    (home-page "https://arxiv.org/abs/1911.00658")
    (synopsis
     "Global Adaptive Generative Adjustment Algorithm for Generalized Linear Models")
    (description
     "Fits linear regression, logistic and multinomial regression models, Poisson
regression, Cox model via Global Adaptive Generative Adjustment Algorithm.  For
more detailed information, see Bin Wang, Xiaofei Wang and Jianhua Guo (2022)
<@code{arXiv:1911.00658>}.  This paper provides the theoretical properties of
Gaga linear model when the load matrix is orthogonal.  Further study is going on
for the nonorthogonal cases and generalized linear models.  These works are in
part supported by the National Natural Foundation of China (No.12171076).")
    (license license:gpl2)))

(define-public r-gafit
  (package
    (name "r-gafit")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gafit" version))
       (sha256
        (base32 "160z3cv22prf7118447dla9g1gimwkfad8zdil3fg8n4l22rk51a"))))
    (properties `((upstream-name . "gafit")))
    (build-system r-build-system)
    (home-page "http://lnx-bsp.net/")
    (synopsis "Genetic Algorithm for Curve Fitting")
    (description
     "This package provides a group of sample points are evaluated against a
user-defined expression, the sample points are lists of parameters with values
that may be substituted into that expression.  The genetic algorithm attempts to
make the result of the expression as low as possible (usually this would be the
sum of residuals squared).")
    (license license:gpl2)))

(define-public r-gadget3
  (package
    (name "r-gadget3")
    (version "0.12-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gadget3" version))
       (sha256
        (base32 "1jw834v98mwlx7pf78xcqvpqnlrdwl03qm1m4fd5x7yxkn5nhk92"))))
    (properties `((upstream-name . "gadget3")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmb r-rlang r-digest))
    (native-inputs (list r-knitr))
    (home-page "https://gadget-framework.github.io/gadget3/")
    (synopsis
     "Globally-Applicable Area Disaggregated General Ecosystem Toolbox V3")
    (description
     "This package provides a framework to assist creation of marine ecosystem models,
generating either R or C++ code which can then be optimised using the TMB
package and standard R tools.  Principally designed to reproduce gadget2 models
in TMB', but can be extended beyond gadget2's capabilities.  Kasper Kristensen,
Anders Nielsen, Casper W. Berg, Hans Skaug, Bradley M. Bell (2016)
<doi:10.18637/jss.v070.i05> \"TMB: Automatic Differentiation and Laplace
Approximation.\".  Begley, J., & Howell, D. (2004)
<https://core.ac.uk/download/pdf/225936648.pdf> \"An overview of Gadget, the
globally applicable area-disaggregated general ecosystem toolbox.  ICES.\".")
    (license license:gpl2)))

(define-public r-gadget2
  (package
    (name "r-gadget2")
    (version "2.3.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "gadget2" version))
       (sha256
        (base32 "0ka5mbr9nppgsr95l33k510h278z49j6chbbqvbba0gan9842kwg"))))
    (properties `((upstream-name . "gadget2")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=gadget2")
    (synopsis
     "Gadget is the Globally-Applicable Area Disaggregated General Ecosystem Toolbox")
    (description
     "This package provides a statistical ecosystem modelling package, taking many
features of the ecosystem into account.  Gadget works by running an internal
model based on many parameters, and then comparing the data from the output of
this model to real data to get a goodness-of-fit likelihood score.  These
parameters can then be adjusted, and the model re-run, until an optimum is
found, which corresponds to the model with the lowest likelihood score.  Gadget
allows the user to include a number of features into an ecosystem model: One or
more species, each of which may be split into multiple stocks; multiple areas
with migration between areas; predation between and within species; maturation;
reproduction and recruitment; multiple commercial and survey fleets taking
catches from the populations.  For more details see
<https://gadget-framework.github.io/gadget2/>.  This is the C++ Gadget2 runtime,
making it available for R.")
    (license license:gpl2)))

(define-public r-gadag
  (package
    (name "r-gadag")
    (version "0.99.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GADAG" version))
       (sha256
        (base32 "1ycg48idrq2yrk1fxs16yl5wwg3kbfki8xppmh311rrzf75c6z4g"))))
    (properties `((upstream-name . "GADAG")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mass r-igraph))
    (home-page "https://cran.r-project.org/package=GADAG")
    (synopsis "Genetic Algorithm for Learning Directed Acyclic Graphs")
    (description
     "Sparse large Directed Acyclic Graphs learning with a combination of a convex
program and a tailored genetic algorithm (see Champion et al. (2017)
<https://hal.archives-ouvertes.fr/hal-01172745v2/document>).")
    (license license:gpl2)))

(define-public r-gad
  (package
    (name "r-gad")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GAD" version))
       (sha256
        (base32 "1qb735hbqv00yz0xaw0k3vpg7j9lqbbkzkqdys25n64ji59qr33x"))))
    (properties `((upstream-name . "GAD")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrixstats))
    (home-page "https://cran.r-project.org/package=GAD")
    (synopsis "Analysis of Variance from General Principles")
    (description
     "Analysis of complex ANOVA models with any combination of orthogonal/nested and
fixed/random factors, as described by Underwood (1997).  There are two
restrictions: (i) data must be balanced; (ii) fixed nested factors are not
allowed.  Homogeneity of variances is checked using Cochran's C test and a
posteriori comparisons of means are done using Student-Newman-Keuls (SNK)
procedure.  For those terms with no denominator in the F-ratio calculation,
pooled mean squares and quasi F-ratios are provided.  Magnitute of effects are
assessed by components of variation.")
    (license license:gpl3+)))

(define-public r-gacff
  (package
    (name "r-gacff")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GACFF" version))
       (sha256
        (base32 "0lhjzb6cj8838dh4423929z7bwpa6cv3dn84rccwi3382vsq9g2c"))))
    (properties `((upstream-name . "GACFF")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=GACFF")
    (synopsis "Genetic Similarity in User-Based Collaborative Filtering")
    (description
     "The genetic algorithm can be used directly to find the similarity of users and
more effectively to increase the efficiency of the collaborative filtering
method.  By identifying the nearest neighbors to the active user, before the
genetic algorithm, and by identifying suitable starting points, an effective
method for user-based collaborative filtering method has been developed.  This
package uses an optimization algorithm (continuous genetic algorithm) to
directly find the optimal similarities between active users (users for whom
current recommendations are made) and others.  First, by determining the nearest
neighbor and their number, the number of genes in a chromosome is determined.
Each gene represents the neighbor's similarity to the active user.  By
estimating the starting points of the genetic algorithm, it quickly converges to
the optimal solutions.  The positive point is the independence of the genetic
algorithm on the number of data that for big data is an effective help in
solving the problem.")
    (license license:gpl2+)))

(define-public r-gabb
  (package
    (name "r-gabb")
    (version "0.3.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "GABB" version))
       (sha256
        (base32 "16gdg5yvzlvic3qc9ya68xkbx55jg7lvlnqjry9x4c5hvggywzvl"))))
    (properties `((upstream-name . "GABB")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-tidyr
                             r-pheatmap
                             r-hotelling
                             r-ggrepel
                             r-ggpubr
                             r-ggplotify
                             r-ggplot2
                             r-ggforce
                             r-egg
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=GABB")
    (synopsis
     "Facilitation of Data Preparation and Plotting Procedures for RDA and PCA Analyses")
    (description
     "Help to the occasional R user for synthesis and enhanced graphical visualization
of redundancy analysis (RDA) and principal component analysis (PCA) methods and
objects.  Inputs are : data frame, RDA (package vegan') and PCA (package
@code{FactoMineR}') objects.  Outputs are : synthesized results of RDA,
displayed in console and saved in tables ; displayed and saved objects of PCA
graphic visualization of individuals and variables projections with multiple
graphic parameters.")
    (license license:expat)))

(define-public r-g3viz
  (package
    (name "r-g3viz")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "g3viz" version))
       (sha256
        (base32 "1lkasna05pp5sc9jqgqmias8kl7h1fb64jfb9rpld575lmy2n2gv"))))
    (properties `((upstream-name . "g3viz")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-stringr
                             r-org-hs-eg-db
                             r-jsonlite
                             r-httr2
                             r-htmlwidgets
                             r-annotationdbi))
    (native-inputs (list esbuild))
    (home-page "https://github.com/G3viz/g3viz")
    (synopsis
     "Interactively Visualize Genetic Mutation Data using a Lollipop-Diagram")
    (description
     "Interface for g3-lollipop @code{JavaScript} library.  Visualize genetic mutation
data using an interactive lollipop diagram in RStudio or your web browser.")
    (license license:expat)))

(define-public r-g2sd
  (package
    (name "r-g2sd")
    (version "2.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "G2Sd" version))
       (sha256
        (base32 "165i6x2k56vwhk5p2p5m9vjmp9flblsapjdlz7nw9b719l6xz5zk"))))
    (properties `((upstream-name . "G2Sd")))
    (build-system r-build-system)
    (propagated-inputs (list r-xlsxjars
                             r-xlsx
                             r-shiny
                             r-rjava
                             r-reshape2
                             r-ggplot2))
    (home-page "https://cran.r-project.org/package=G2Sd")
    (synopsis "Grain-Size Statistics and Description of Sediment")
    (description
     "Full descriptive statistics, physical description of sediment, metric or phi
sieves.")
    (license license:gpl3)))

(define-public r-g-ridge
  (package
    (name "r-g-ridge")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "g.ridge" version))
       (sha256
        (base32 "1llks14akpdpa3v80kpr8sgmdlmjsvk7hcbilnk0zqlh1xi066km"))))
    (properties `((upstream-name . "g.ridge")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=g.ridge")
    (synopsis "Generalized Ridge Regression for Linear Models")
    (description
     "Ridge regression due to Hoerl and Kennard
(1970)<DOI:10.1080/00401706.1970.10488634> and generalized ridge regression due
to Yang and Emura (2017)<DOI:10.1080/03610918.2016.1193195> with optimized
tuning parameters.  These ridge regression estimators (the HK estimator and the
YE estimator) are computed by minimizing the cross-validated mean squared
errors.  Both the ridge and generalized ridge estimators are applicable for
high-dimensional regressors (p>n), where p is the number of regressors, and n is
the sample size.")
    (license license:gpl2)))

(define-public r-g-data
  (package
    (name "r-g-data")
    (version "2.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "g.data" version))
       (sha256
        (base32 "1v2sn117hmawgsa6f7nrpv2bpg6zzjd1ss356qjqp7kdwmifqlrq"))))
    (properties `((upstream-name . "g.data")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=g.data")
    (synopsis "Delayed-Data Packages")
    (description
     "Create and maintain delayed-data packages (ddp's).  Data stored in a ddp are
available on demand, but do not take up memory until requested.  You attach a
ddp with g.data.attach(), then read from it and assign to it in a manner similar
to S-PLUS, except that you must run g.data.save() to actually commit to disk.")
    (license (list license:gpl2+ license:gpl3+))))

