(define-module (guix-cran packages b)
  #: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 gcc)
  #:use-module (gnu packages bioconductor)
  #:use-module (gnu packages haskell-xyz)
  #:use-module (gnu packages web)
  #:use-module (gnu packages multiprecision)
  #:use-module (gnu packages algebra)
  #:use-module (gnu packages compression)
  #:use-module (gnu packages julia)
  #:use-module (gnu packages pkg-config)
  #:use-module (gnu packages tls)
  #:use-module (gnu packages java)
  #:use-module (gnu packages python)
  #:use-module (gnu packages geo)
  #: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 g)
  #:use-module (guix-cran packages f)
  #:use-module (guix-cran packages e)
  #:use-module (guix-cran packages d)
  #:use-module (guix-cran packages c)
  #:use-module (guix-cran packages a))

(define-public r-bzinb
  (package
    (name "r-bzinb")
    (version "1.0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bzinb" version))
       (sha256
        (base32 "06sn7j0i6znwdka8rww2xc4q6r4a0pmm77vsd6bjdjqmkpai7kj1"))))
    (properties `((upstream-name . "bzinb")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-bh))
    (home-page "https://cran.r-project.org/package=bzinb")
    (synopsis "Bivariate Zero-Inflated Negative Binomial Model Estimator")
    (description
     "This package provides a maximum likelihood estimation of Bivariate Zero-Inflated
Negative Binomial (BZINB) model or the nested model parameters.  Also estimates
the underlying correlation of the a pair of count data.  See Cho, H., Liu, C.,
Preisser, J., and Wu, D. (In preparation) for details.")
    (license license:gpl2)))

(define-public r-bytescircle
  (package
    (name "r-bytescircle")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bytescircle" version))
       (sha256
        (base32 "0786dkdww0qyhqr1di7pdcwrnymdcqq7kwrjalagykc41aa4jgza"))))
    (properties `((upstream-name . "bytescircle")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bytescircle")
    (synopsis "Statistics About Bytes Contained in a File as a Circle Plot")
    (description
     "Shows statistics about bytes contained in a file as a circle graph of deviations
from mean in sigma increments.  The function can be useful for statistically
analyze the content of files in a glimpse: text files are shown as a green
centered crown, compressed and encrypted files should be shown as equally
distributed variations with a very low CV (sigma/mean), and other types of files
can be classified between these two categories depending on their text vs binary
content, which can be useful to quickly determine how information is stored
inside them (databases, multimedia files, etc).")
    (license license:gpl3)))

(define-public r-bysykkel
  (package
    (name "r-bysykkel")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bysykkel" version))
       (sha256
        (base32 "1lvqbax7dvli4265856f9667ckp9g5l4radz2cp0yk0slsjgsdpg"))))
    (properties `((upstream-name . "bysykkel")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-lubridate r-jsonlite r-httr r-glue))
    (home-page "http://github.com/imangR/bysykkel")
    (synopsis "Get City Bike Data from Norway")
    (description
     "This package provides functions to get and download city bike data from the
website and API service of each city bike service in Norway.  The package aims
to reduce time spent on getting Norwegian city bike data, and lower barriers to
start analyzing it.  The data is retrieved from Oslo City Bike, Bergen City
Bike, and Trondheim City Bike.  The data is made available under NLOD 2.0
<https://data.norge.no/nlod/en/2.0>.")
    (license license:expat)))

(define-public r-bwstools
  (package
    (name "r-bwstools")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bwsTools" version))
       (sha256
        (base32 "181pm9pr0ykyd3mikn6z4z5m7jkv72sisixmilijh6q3393g9pqw"))))
    (properties `((upstream-name . "bwsTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rlang
                             r-magrittr
                             r-igraph
                             r-dplyr
                             r-crossdes))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/markhwhiteii/bwsTools")
    (synopsis "Tools for Case 1 Best-Worst Scaling (MaxDiff) Designs")
    (description
     "This package provides tools to design best-worst scaling designs (i.e., balanced
incomplete block designs) and to analyze data from these designs, using
aggregate and individual methods such as: difference scores, Louviere, Lings,
Islam, Gudergan, & Flynn (2013) <doi:10.1016/j.ijresmar.2012.10.002>; analytical
estimation, Lipovetsky & Conklin (2014) <doi:10.1016/j.jocm.2014.02.001>;
empirical Bayes, Lipovetsky & Conklin (2015) <doi:10.1142/S1793536915500028>;
Elo, Hollis (2018) <doi:10.3758/s13428-017-0898-2>; and network-based measures.")
    (license license:expat)))

(define-public r-bws
  (package
    (name "r-bws")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bws" version))
       (sha256
        (base32 "11gci4z434n68agc71ab16br8yp8fd1lylgnaqz7p7w676yrygn8"))))
    (properties `((upstream-name . "bws")))
    (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=bws")
    (synopsis "Bayesian Weighted Sums")
    (description
     "An interface to the Bayesian Weighted Sums model implemented in RStan'.  It
estimates the summed effect of multiple, often moderately to highly correlated,
continuous predictors.  Its applications can be found in analysis of exposure
mixtures.  The model was proposed by Hamra, Maclehose, Croen, Kauffman, and
Newschaffer (2021) <doi:10.3390/ijerph18041373>.  This implementation includes
an extension to model binary outcome.")
    (license license:gpl2+)))

(define-public r-bwquant
  (package
    (name "r-bwquant")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BwQuant" version))
       (sha256
        (base32 "0pna0ppj9hkxpc2mbyy0ss7zrl5y736h5xsfkhmqr0389vmgibrj"))))
    (properties `((upstream-name . "BwQuant")))
    (build-system r-build-system)
    (propagated-inputs (list r-quantreg r-nleqslv r-kernsmooth))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=BwQuant")
    (synopsis "Bandwidth Selectors for Local Linear Quantile Regression")
    (description
     "Bandwidth selectors for local linear quantile regression, including
cross-validation and plug-in methods.  The local linear quantile regression
estimate is also implemented.")
    (license license:gpl2)))

(define-public r-bwimage
  (package
    (name "r-bwimage")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bwimage" version))
       (sha256
        (base32 "1r5l7z90q2qs92dzp78jj4gf1kg1gc85g6y5rn2z8vxjbvbc5vma"))))
    (properties `((upstream-name . "bwimage")))
    (build-system r-build-system)
    (propagated-inputs (list r-png r-jpeg))
    (home-page "https://cran.r-project.org/package=bwimage")
    (synopsis "Describe Image Patterns in Natural Structures")
    (description
     "This package provides a computational tool to describe patterns in black and
white images from natural structures.  bwimage implemented functions for
exceptionally broad subject.  For instance, bwimage provide examples that range
from calculation of canopy openness, description of patterns in vertical
vegetation structure, to patterns in bird nest structure.")
    (license license:gpl2+)))

(define-public r-bwgs
  (package
    (name "r-bwgs")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BWGS" version))
       (sha256
        (base32 "1473lvkmpf2dsgnccwl5a8wp9yvddciqdql4naba4lwghimxy39w"))))
    (properties `((upstream-name . "BWGS")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringi
                             r-rrblup
                             r-randomforest
                             r-glmnet
                             r-e1071
                             r-brnn
                             r-bglr))
    (home-page "https://github.com/byzheng/BWGS")
    (synopsis "BreedWheat Genomic Selection Pipeline")
    (description
     "Package for Breed Wheat Genomic Selection Pipeline.  The R package BWGS is
developed by Louis Gautier Tran <louis.gautier.tran@@gmail.com> and Gilles
Charmet <gilles.charmet@@inra.fr>.  This repository is forked from original
repository <https://forgemia.inra.fr/umr-gdec/bwgs> and modified as a R package.")
    (license license:gpl2+)))

(define-public r-bwgr
  (package
    (name "r-bwgr")
    (version "2.2.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bWGR" version))
       (sha256
        (base32 "0irr0imyni5m43mzlyd4fymj78nbwmmvbqrs8ksk5hyznjcfn9wp"))))
    (properties `((upstream-name . "bWGR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp r-matrix))
    (home-page "https://cran.r-project.org/package=bWGR")
    (synopsis "Bayesian Whole-Genome Regression")
    (description
     "Whole-genome regression methods on Bayesian framework fitted via EM or Gibbs
sampling, single step (<doi:10.1534/g3.119.400728>), univariate and multivariate
(<doi:10.1186/s12711-022-00730-w>), with optional kernel term and sampling
techniques (<doi:10.1186/s12859-017-1582-3>).")
    (license license:gpl3)))

(define-public r-bwd
  (package
    (name "r-bwd")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bwd" version))
       (sha256
        (base32 "1ryd5cqbpns9dsis2a9vjg6fcg23284c3dr3j3l18krdshdksfr0"))))
    (properties `((upstream-name . "bwd")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=bwd")
    (synopsis "Backward Procedure for Change-Point Detection")
    (description
     "This package implements a backward procedure for single and multiple change
point detection proposed by Shin et al. <@code{arXiv:1812.10107>}.  The backward
approach is particularly useful to detect short and sparse signals which is
common in copy number variation (CNV) detection.")
    (license license:gpl2)))

(define-public r-bvsnlp
  (package
    (name "r-bvsnlp")
    (version "1.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BVSNLP" version))
       (sha256
        (base32 "1k2sr9wq0q7lq2hmgp75hzxy6v688lw5npwvpma88975yqi6ghiw"))))
    (properties `((upstream-name . "BVSNLP")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppnumerical
                             r-rcppeigen
                             r-rcpparmadillo
                             r-rcpp
                             r-foreach
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=BVSNLP")
    (synopsis
     "Bayesian Variable Selection in High Dimensional Settings using Nonlocal Priors")
    (description
     "Variable/Feature selection in high or ultra-high dimensional settings has gained
a lot of attention recently specially in cancer genomic studies.  This package
provides a Bayesian approach to tackle this problem, where it exploits mixture
of point masses at zero and nonlocal priors to improve the performance of
variable selection and coefficient estimation.  product moment (@code{pMOM}) and
product inverse moment (@code{piMOM}) nonlocal priors are implemented and can be
used for the analyses.  This package performs variable selection for binary
response and survival time response datasets which are widely used in
biostatistic and bioinformatics community.  Benefiting from parallel computing
ability, it reports necessary outcomes of Bayesian variable selection such as
Highest Posterior Probability Model (HPPM), Median Probability Model (MPM) and
posterior inclusion probability for each of the covariates in the model.  The
option to use Bayesian Model Averaging (BMA) is also part of this package that
can be exploited for predictive power measurements in real datasets.")
    (license license:gpl2+)))

(define-public r-bvpa
  (package
    (name "r-bvpa")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bvpa" version))
       (sha256
        (base32 "01ymikjbk29vfqh31pd6b0rnh12l26b1mia1l34kh7v81z1b04ai"))))
    (properties `((upstream-name . "bvpa")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv))
    (home-page "https://cran.r-project.org/package=bvpa")
    (synopsis "Bivariate Pareto Distribution")
    (description
     "This package implements the EM algorithm with one-step Gradient Descent method
to estimate the parameters of the Block-Basu bivariate Pareto distribution with
location and scale.  We also found parametric bootstrap and asymptotic
confidence intervals based on the observed Fisher information of scale and shape
parameters, and exact confidence intervals for location parameters.  Details are
in Biplab Paul and Arabin Kumar Dey (2023)
<doi:10.48550/@code{arXiv.1608.02199>} \"An EM algorithm for absolutely
continuous Marshall-Olkin bivariate Pareto distribution with location and
scale\"; E L Lehmann and George Casella (1998) <doi:10.1007/b98854> \"Theory of
Point Estimation\"; Bradley Efron and R J Tibshirani (1994)
<doi:10.1201/9780429246593> \"An Introduction to the Bootstrap\"; A P Dempster, N
M Laird and D B Rubin (1977) <www.jstor.org/stable/2984875> \"Maximum Likelihood
from Incomplete Data via the EM Algorithm\".")
    (license license:gpl2+)))

(define-public r-bvls
  (package
    (name "r-bvls")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bvls" version))
       (sha256
        (base32 "18aaf7kk5mks3a59wwqhm1ckpn6s704l9m5nzy0x5iw0s98ijbm2"))))
    (properties `((upstream-name . "bvls")))
    (build-system r-build-system)
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=bvls")
    (synopsis "The Stark-Parker algorithm for bounded-variable least squares")
    (description
     "An R interface to the Stark-Parker implementation of an algorithm for
bounded-variable least squares.")
    (license license:gpl2+)))

(define-public r-bvhar
  (package
    (name "r-bvhar")
    (version "2.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bvhar" version))
       (sha256
        (base32 "171ngw5a8zbq2vaxs3ipr76jnan4614fxr8ka28fhlx6qh6fy703"))))
    (properties `((upstream-name . "bvhar")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rcppeigen
                             r-rcpp
                             r-purrr
                             r-posterior
                             r-optimparallel
                             r-magrittr
                             r-lifecycle
                             r-ggplot2
                             r-foreach
                             r-dplyr
                             r-bh
                             r-bayesplot))
    (native-inputs (list r-knitr))
    (home-page "https://ygeunkim.github.io/package/bvhar/")
    (synopsis "Bayesian Vector Heterogeneous Autoregressive Modeling")
    (description
     "This package provides tools to model and forecast multivariate time series
including Bayesian Vector heterogeneous autoregressive (VHAR) model by Kim &
Baek (2023) (<doi:10.1080/00949655.2023.2281644>).  bvhar can model Vector
Autoregressive (VAR), VHAR, Bayesian VAR (BVAR), and Bayesian VHAR (BVHAR)
models.")
    (license license:gpl3+)))

(define-public r-bvarverse
  (package
    (name "r-bvarverse")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BVARverse" version))
       (sha256
        (base32 "00nghvd5bavmn5hw89pcz20x5kbpxr707r5yvvsakn77rdis37ig"))))
    (properties `((upstream-name . "BVARverse")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-rlang r-ggplot2 r-generics r-bvar))
    (home-page "https://github.com/nk027/bvarverse")
    (synopsis "Tidy Bayesian Vector Autoregression")
    (description
     "This package provides functions to prepare tidy objects from estimated models
via BVAR (see Kuschnig & Vashold, 2019 <doi:10.13140/RG.2.2.25541.60643>) and
visualisation thereof.  Bridges the gap between estimating models with BVAR and
plotting the results in a more sophisticated way with ggplot2 as well as passing
them on in a tidy format.")
    (license license:gpl3)))

(define-public r-bvartools
  (package
    (name "r-bvartools")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bvartools" version))
       (sha256
        (base32 "0klbzxqypqm5v549k56fvl67yri7bwaazf02ypsg6xyriz2a9ck4"))))
    (properties `((upstream-name . "bvartools")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-matrix r-coda))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/franzmohr/bvartools")
    (synopsis
     "Bayesian Inference of Vector Autoregressive and Error Correction Models")
    (description
     "Assists in the set-up of algorithms for Bayesian inference of vector
autoregressive (VAR) and error correction (VEC) models.  Functions for posterior
simulation, forecasting, impulse response analysis and forecast error variance
decomposition are largely based on the introductory texts of Chan, Koop, Poirier
and Tobias (2019, ISBN: 9781108437493), Koop and Korobilis (2010)
<doi:10.1561/0800000013> and Luetkepohl (2006, ISBN: 9783540262398).")
    (license license:gpl2+)))

(define-public r-bvarsv
  (package
    (name "r-bvarsv")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bvarsv" version))
       (sha256
        (base32 "1bv4fbbi8bn7sqqpjlf8w5jpgydjr15wv5v9940wc42yk792yjrx"))))
    (properties `((upstream-name . "bvarsv")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://sites.google.com/site/fk83research/code")
    (synopsis
     "Bayesian Analysis of a Vector Autoregressive Model with Stochastic Volatility and Time-Varying Parameters")
    (description
     "R/C++ implementation of the model proposed by Primiceri (\"Time Varying
Structural Vector Autoregressions and Monetary Policy\", Review of Economic
Studies, 2005), with functionality for computing posterior predictive
distributions and impulse responses.")
    (license license:gpl2+)))

(define-public r-bvar
  (package
    (name "r-bvar")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BVAR" version))
       (sha256
        (base32 "05bf78pjf7bzhfnaq5pn8jfl25k4qdvhrl7m7fnfdmdj5bf46cml"))))
    (properties `((upstream-name . "BVAR")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://github.com/nk027/bvar")
    (synopsis "Hierarchical Bayesian Vector Autoregression")
    (description
     "Estimation of hierarchical Bayesian vector autoregressive models following
Kuschnig & Vashold (2021) <doi:10.18637/jss.v100.i14>.  Implements hierarchical
prior selection for conjugate priors in the fashion of Giannone, Lenza &
Primiceri (2015) <doi:10.1162/REST_a_00483>.  Functions to compute and identify
impulse responses, calculate forecasts, forecast error variance decompositions
and scenarios are available.  Several methods to print, plot and summarise
results facilitate analysis.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-bvalue
  (package
    (name "r-bvalue")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Bvalue" version))
       (sha256
        (base32 "17zlllzjrcq4grggal3hlg384990n62wnflr2g21fa6hcnglwy7d"))))
    (properties `((upstream-name . "Bvalue")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=Bvalue")
    (synopsis "B-Value and Empirical Equivalence Bound")
    (description
     "Calculates B-value and empirical equivalence bound.  B-value is defined as the
maximum magnitude of a confidence interval; and the empirical equivalence bound
is the minimum B-value at a certain level.  A new two-stage procedure for
hypothesis testing is proposed, where the first stage is conventional hypothesis
testing and the second is an equivalence testing procedure using the introduced
empirical equivalence bound.  See Zhao et al. (2019) \"B-Value and Empirical
Equivalence Bound: A New Procedure of Hypothesis Testing\"
<@code{arXiv:1912.13084>} for details.")
    (license license:gpl2+)))

(define-public r-buysetest
  (package
    (name "r-buysetest")
    (version "3.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BuyseTest" version))
       (sha256
        (base32 "0lv9yj7r9sj1i67zfca6wagjjvxcvf8vv2i7475aaf9w5rlxpd9r"))))
    (properties `((upstream-name . "BuyseTest")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-rlang
                             r-riskregression
                             r-rcpparmadillo
                             r-rcpp
                             r-prodlim
                             r-lava
                             r-ggplot2
                             r-foreach
                             r-dosnow
                             r-data-table))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/bozenne/BuyseTest")
    (synopsis "Generalized Pairwise Comparisons")
    (description
     "Implementation of the Generalized Pairwise Comparisons (GPC) as defined in Buyse
(2010) <doi:10.1002/sim.3923> for complete observations, and extended in Peron
(2018) <doi:10.1177/0962280216658320> to deal with right-censoring.  GPC compare
two groups of observations (intervention vs. control group) regarding several
prioritized endpoints to estimate the probability that a random observation
drawn from one group performs better/worse/equivalently than a random
observation drawn from the other group.  Summary statistics such as the net
treatment benefit, win ratio, or win odds are then deduced from these
probabilities.  Confidence intervals and p-values are obtained based on
asymptotic results (Ozenne 2021 <doi:10.1177/09622802211037067>), non-parametric
bootstrap, or permutations.  The software enables the use of thresholds of
minimal importance difference, stratification, non-prioritized endpoints (O
Brien test), and can handle right-censoring and competing-risks.")
    (license license:gpl3)))

(define-public r-butterflyoptions
  (package
    (name "r-butterflyoptions")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "butterflyOptions" version))
       (sha256
        (base32 "0kmzrk8gqyqrpp4wfsl04cff13m6aiag7d6fbq7kbb6ln8fa6as9"))))
    (properties `((upstream-name . "butterflyOptions")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-magrittr r-ggplot2 r-dplyr))
    (home-page "https://cran.r-project.org/package=butterflyOptions")
    (synopsis "Trading Butterfly Options Strategies")
    (description
     "Trading of Butterfly Options Strategies is represented here through their
Graphs.  The graphic indicators, strategies, calculations, functions and all the
discussions are for academic, research, and educational purposes only and should
not be construed as investment advice and come with absolutely no Liability.
Guy Cohen (âThe Bible of Options Strategies (2nd ed.)â, 2015, ISBN:
9780133964028).  Zura Kakushadze, Juan A. Serur (â151 Trading Strategiesâ,
2018, ISBN: 9783030027919).  John C. Hull (âOptions, Futures, and Other
Derivatives (11th ed.)â, 2022, ISBN: 9780136939979).")
    (license license:gpl3)))

(define-public r-businessplanr
  (package
    (name "r-businessplanr")
    (version "0.1-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "businessPlanR" version))
       (sha256
        (base32 "19fbyycydivkldj845jsg5xidysiddnxqd4by14v2yyripy8d44j"))))
    (properties `((upstream-name . "businessPlanR")))
    (build-system r-build-system)
    (propagated-inputs (list r-knitr r-kableextra))
    (native-inputs (list r-knitr))
    (home-page "https://www.c3s.cc")
    (synopsis "Simple Modelling Tools for Business Plans")
    (description
     "This package provides a collection of S4 classes, methods and functions to
create and visualize business plans.  Different types of cash flows can be
defined, which can then be used and tabulated to create profit and loss
statements, cash flow plans, investment and depreciation schedules, loan
amortization schedules, etc.  The methods are designed to produce handsome
tables in both PDF and HTML using RMarkdown or Shiny'.")
    (license license:gpl3+)))

(define-public r-businessduration
  (package
    (name "r-businessduration")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BusinessDuration" version))
       (sha256
        (base32 "17923n1r3n8kp7qqzr2dv8ffax355yyc3b9f6mv2hxy6k31hrrb8"))))
    (properties `((upstream-name . "BusinessDuration")))
    (build-system r-build-system)
    (propagated-inputs (list r-chron))
    (home-page "https://cran.r-project.org/package=BusinessDuration")
    (synopsis "Calculates Business Duration Between Two Dates")
    (description
     "Calculates business duration between two dates.  This excluding weekends, public
holidays and non-business hours.")
    (license license:agpl3)))

(define-public r-busdater
  (package
    (name "r-busdater")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "busdater" version))
       (sha256
        (base32 "0hib73zay9r7rv49zv1lx0l15jzjyli9f1vrk414l8apggvx4c6s"))))
    (properties `((upstream-name . "busdater")))
    (build-system r-build-system)
    (propagated-inputs (list r-lubridate))
    (native-inputs (list r-knitr))
    (home-page "https://mickmioduszewski.github.io/busdater/")
    (synopsis "Standard Date Calculations for Business")
    (description
     "Get a current financial year, start of current month, End of current month,
start of financial year and end of it.  Allow for offset from the date.")
    (license license:expat)))

(define-public r-bursts
  (package
    (name "r-bursts")
    (version "1.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bursts" version))
       (sha256
        (base32 "0cbksnxavkssp3mz80rz0sdvbg21m5yzjn009wclinz12mm1p9rq"))))
    (properties `((upstream-name . "bursts")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=bursts")
    (synopsis "Markov Model for Bursty Behavior in Streams")
    (description
     "An implementation of Jon Kleinberg's burst detection algorithm (Kleinberg (2003)
<doi:10.1023/A:1024940629314>).  Uses an infinite Markov model to detect periods
of increased activity in a series of discrete events with known times, and
provides a simple visualization of the results.")
    (license license:expat)))

(define-public r-burstmisc
  (package
    (name "r-burstmisc")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BurStMisc" version))
       (sha256
        (base32 "0cyi42zkn2dby162x9f95b3hpqxbzx25s7nahb4p86r60xj3a5c0"))))
    (properties `((upstream-name . "BurStMisc")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BurStMisc")
    (synopsis "Burns Statistics Miscellaneous")
    (description
     "Script search, corner, genetic optimization, permutation tests, write expect
test.")
    (license (license:fsdg-compatible "Unlimited"))))

(define-public r-burstfin
  (package
    (name "r-burstfin")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BurStFin" version))
       (sha256
        (base32 "082kbs4blc6ldgp18k34fxqilqmwgynrjj1mbji198v7pn4y8s0h"))))
    (properties `((upstream-name . "BurStFin")))
    (build-system r-build-system)
    (home-page "https://www.burns-stat.com/")
    (synopsis "Burns Statistics Financial")
    (description
     "This package provides a suite of functions for finance, including the estimation
of variance matrices via a statistical factor model or Ledoit-Wolf shrinkage.")
    (license (license:fsdg-compatible "Unlimited"))))

(define-public r-bursa
  (package
    (name "r-bursa")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bursa" version))
       (sha256
        (base32 "14pjl5l0rh7aap8d2zmsd2spg6521vkkpiigh0jh5ly2cc56zyvr"))))
    (properties `((upstream-name . "bursa")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rvest
                             r-readr
                             r-openxlsx
                             r-jsonlite
                             r-dplyr))
    (home-page "https://github.com/ozancanozdemir/bursa")
    (synopsis "R Wrapper for Bursa Municipality Open Data Portal")
    (description
     "Call the data wrappers for Bursa Metropolitan Municipality's Open Data Portal
<https://acikyesil.bursa.bel.tr/>.  This will return all datasets stored in
different formats.")
    (license license:expat)))

(define-public r-burnr
  (package
    (name "r-burnr")
    (version "0.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "burnr" version))
       (sha256
        (base32 "0pgki4inyql8h10i78yzqm73jy2h7rdp9ywsk65virxaadpngy9c"))))
    (properties `((upstream-name . "burnr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-rlang
                             r-reshape2
                             r-plyr
                             r-mass
                             r-ggplot2
                             r-forcats))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ltrr-arizona-edu/burnr/")
    (synopsis "Forest Fire History Analysis")
    (description
     "This package provides tools to read, write, parse, and analyze forest fire
history data (e.g. FHX).  Described in Malevich et al. (2018)
<doi:10.1016/j.dendro.2018.02.005>.")
    (license license:gpl3+)))

(define-public r-burgle
  (package
    (name "r-burgle")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "burgle" version))
       (sha256
        (base32 "0166pqcyhzlk8xv58hql5szanyh47blpwnawxmwxwqysfrm0w7xj"))))
    (properties `((upstream-name . "burgle")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-riskregression r-mass))
    (home-page "https://cran.r-project.org/package=burgle")
    (synopsis "'Burgle': Stealing the Necessary Parts of Model Objects")
    (description
     "This package provides a way to reduce model objects to necessary parts, making
them easier to work with, store, share and simulate multiple values for new
responses while allowing for parameter uncertainty.")
    (license license:expat)))

(define-public r-bupaverse
  (package
    (name "r-bupaverse")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bupaverse" version))
       (sha256
        (base32 "1h79flc3ks23n1ih8vf741akvii3y25hqdl8k479hkhk423pfma2"))))
    (properties `((upstream-name . "bupaverse")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-purrr
                             r-processmapr
                             r-processcheckr
                             r-magrittr
                             r-glue
                             r-eventdatar
                             r-edear
                             r-cli
                             r-bupar))
    (native-inputs (list r-knitr))
    (home-page "https://bupar.net/")
    (synopsis "Easily Install and Load the 'bupaverse'")
    (description
     "The bupaverse is an open-source, integrated suite of R-packages for handling and
analysing business process data, developed by the Business Informatics research
group at Hasselt University, Belgium.  Profoundly inspired by the tidyverse
package, the bupaverse package is designed to facilitate the installation and
loading of multiple bupaverse packages in a single step.  Learn more about
bupaverse at the <https://bupar.net> homepage.")
    (license license:expat)))

(define-public r-bupar
  (package
    (name "r-bupar")
    (version "0.5.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bupaR" version))
       (sha256
        (base32 "16m9n7h1nwfz564cxyhgkxdzszcr3nr7abz4d6mqx97kjv46k23n"))))
    (properties `((upstream-name . "bupaR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-stringi
                             r-shiny
                             r-rlang
                             r-purrr
                             r-pillar
                             r-miniui
                             r-magrittr
                             r-lubridate
                             r-lifecycle
                             r-glue
                             r-ggplot2
                             r-forcats
                             r-eventdatar
                             r-dplyr
                             r-data-table
                             r-cli))
    (home-page "https://bupar.net/")
    (synopsis "Business Process Analysis in R")
    (description
     "Comprehensive Business Process Analysis toolkit.  Creates S3-class for event log
objects, and related handler functions.  Imports related packages for filtering
event data, computation of descriptive statistics, handling of Petri Net objects
and visualization of process maps.  See also packages
@code{edeaR','processmapR}', @code{eventdataR} and @code{processmonitR}'.")
    (license license:expat)))

(define-public r-bundle
  (package
    (name "r-bundle")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bundle" version))
       (sha256
        (base32 "1b3l14khp6fblb47sy9p5wh78izsbj1xjjj2r55hp2hcx2f1isjz"))))
    (properties `((upstream-name . "bundle")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr r-rlang r-purrr r-glue))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rstudio/bundle")
    (synopsis "Serialize Model Objects with a Consistent Interface")
    (description
     "Typically, models in R exist in memory and can be saved via regular R
serialization.  However, some models store information in locations that cannot
be saved using R serialization alone.  The goal of bundle is to provide a common
interface to capture this information, situate it within a portable object, and
restore it for use in new settings.")
    (license license:expat)))

(define-public r-bundesbank
  (package
    (name "r-bundesbank")
    (version "0.1-12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bundesbank" version))
       (sha256
        (base32 "11jfw2blzsfs479jrz8h0nslk99g6g5ih0ahb0a2w9cad0hwhkdj"))))
    (properties `((upstream-name . "bundesbank")))
    (build-system r-build-system)
    (home-page "http://enricoschumann.net/R/packages/bundesbank/index.htm")
    (synopsis "Download Data from Bundesbank")
    (description
     "Download data from the time-series databases of the Bundesbank, the German
central bank.  See the overview at the Bundesbank website
(<https://www.bundesbank.de/en/statistics/time-series-databases>) for available
series.  The package provides only a single function, @code{getSeries}(), which
supports both traditional and real-time datasets; it will also download meta
data if available.  Downloaded data can automatically be arranged in various
formats, such as data frames or zoo series.  The data may optionally be cached,
so as to avoid repeated downloads of the same series.")
    (license license:gpl3)))

(define-public r-bunchr
  (package
    (name "r-bunchr")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bunchr" version))
       (sha256
        (base32 "1b8hdg2x26k0ahx4gxdpzr36hy056939r4496q3vbgyd4bbk2fbd"))))
    (properties `((upstream-name . "bunchr")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny))
    (native-inputs (list r-knitr))
    (home-page "http://github.com/trilnick/bunchr")
    (synopsis "Analyze Bunching in a Kink or Notch Setting")
    (description
     "View and analyze data where bunching is expected.  Estimate counter- factual
distributions.  For earnings data, estimate the compensated elasticity of
earnings w.r.t.  the net-of-tax rate.")
    (license license:expat)))

(define-public r-bunching
  (package
    (name "r-bunching")
    (version "0.8.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bunching" version))
       (sha256
        (base32 "08rsm2dgqgqr94sw7qijvsqcba4hqp43ci53plzifsa5rr2gwvfl"))))
    (properties `((upstream-name . "bunching")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-ggplot2 r-dplyr r-bb))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/mavpanos/bunching")
    (synopsis "Estimate Bunching")
    (description
     "Implementation of the bunching estimator for kinks and notches.  Allows for
flexible estimation of counterfactual (e.g. controlling for round number
bunching, accounting for other bunching masses within bunching window, fixing
bunching point to be minimum, maximum or median value in its bin, etc.).  It
produces publication-ready plots in the style followed since Chetty et al.
(2011) <doi:10.1093/qje/qjr013>, with lots of functionality to set plot options.")
    (license license:expat)))

(define-public r-bumblebee
  (package
    (name "r-bumblebee")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bumblebee" version))
       (sha256
        (base32 "055plkcswq7qsy3flkjjq5kqampj9s8dbc3ahzinps6s0jkcv2ad"))))
    (properties `((upstream-name . "bumblebee")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmarkdown r-magrittr r-hmisc r-gtools r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://magosil86.github.io/bumblebee/")
    (synopsis
     "Quantify Disease Transmission Within and Between Population Groups")
    (description
     "This package provides a simple tool to quantify the amount of transmission of an
infectious disease of interest occurring within and between population groups.
bumblebee uses counts of observed directed transmission pairs, identified
phylogenetically from deep-sequence data or from epidemiological contacts, to
quantify transmission flows within and between population groups accounting for
sampling heterogeneity.  Population groups might include: geographical areas
(e.g. communities, regions), demographic groups (e.g. age, gender) or arms of a
randomized clinical trial.  See the bumblebee website for statistical theory,
documentation and examples <https://magosil86.github.io/bumblebee/>.")
    (license license:expat)))

(define-public r-bumbl
  (package
    (name "r-bumbl")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bumbl" version))
       (sha256
        (base32 "16fpd62wkvjd25wv80rlp01q57sacx1jjww5j1v6hifc8097wj0i"))))
    (properties `((upstream-name . "bumbl")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rlang
                             r-purrr
                             r-mass
                             r-lifecycle
                             r-glue
                             r-ggplot2
                             r-dplyr
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Aariq/bumbl")
    (synopsis "Tools for Modeling Bumblebee Colony Growth and Decline")
    (description
     "Bumblebee colonies grow during worker production, then decline after switching
to production of reproductive individuals (drones and gynes).  This package
provides tools for modeling and visualizing this pattern by identifying a
switchpoint with a growth rate before and a decline rate after the switchpoint.
The mathematical models fit by bumbl are described in Crone and Williams (2016)
<doi:10.1111/ele.12581>.")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-bullwhipgame
  (package
    (name "r-bullwhipgame")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bullwhipgame" version))
       (sha256
        (base32 "03nwf2v4zhgkxvkghpkbkxz0cnkqcwwl51ykrk25qciakfqkgfws"))))
    (properties `((upstream-name . "bullwhipgame")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny))
    (home-page "https://cran.r-project.org/package=bullwhipgame")
    (synopsis "Bullwhip Effect Demo in Shiny")
    (description
     "The bullwhipgame is an educational game that has as purpose the illustration and
exploration of the bullwhip effect,i.e, the increase in demand variability along
the supply chain.  Marchena Marlene (2010) <@code{arXiv:1009.3977>}.")
    (license license:gpl3)))

(define-public r-bullseye
  (package
    (name "r-bullseye")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bullseye" version))
       (sha256
        (base32 "03lzd4xw9yh3kgp53w3cpli6n0m8z1ww5744pii35vyw9456bdik"))))
    (properties `((upstream-name . "bullseye")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rlang
                             r-polycor
                             r-labeling
                             r-ggplot2
                             r-ggiraph
                             r-dplyr
                             r-dendser
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://cbhurley.github.io/bullseye/")
    (synopsis
     "Visualising Multiple Pairwise Variable Correlations and Other Scores")
    (description
     "We provide a tidy data structure and visualisations for multiple or grouped
variable correlations, general association measures scagnostics and other
pairwise scores suitable for numerical, ordinal and nominal variables.
Supported measures include distance correlation, maximal information, ace
correlation, Kendall's tau, and polychoric correlation.")
    (license license:expat)))

(define-public r-bullishtrader
  (package
    (name "r-bullishtrader")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bullishTrader" version))
       (sha256
        (base32 "0mh6skgkayjsqww4q6rpzhj0n5l3lb9wwh29gy9y08cqd91i02rs"))))
    (properties `((upstream-name . "bullishTrader")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=bullishTrader")
    (synopsis "Bullish Trading Strategies Through Graphs")
    (description
     "Stock, Options and Futures Trading Strategies for Traders and Investors with
Bullish Outlook are represented here through their Graphs.  The graphic
indicators, strategies, calculations, functions and all the discussions are for
academic, research, and educational purposes only and should not be construed as
investment advice and come with absolutely no Liability.  Guy Cohen (âThe
Bible of Options Strategies (2nd ed.)â, 2015, ISBN: 9780133964028).  Zura
Kakushadze, Juan A. Serur (â151 Trading Strategiesâ, 2018, ISBN:
9783030027919).  John C. Hull (âOptions, Futures, and Other Derivatives (11th
ed.)â, 2022, ISBN: 9780136939979).")
    (license license:gpl3)))

(define-public r-bulletr
  (package
    (name "r-bulletr")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bulletr" version))
       (sha256
        (base32 "181rnrp62almf08gr41qnrnq8qnbqraqdvj4zixdh8fachsq2imp"))))
    (properties `((upstream-name . "bulletr")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xml2
                             r-smoother
                             r-robustbase
                             r-reshape2
                             r-plyr
                             r-plotly
                             r-ggplot2
                             r-dplyr))
    (home-page "https://github.com/erichare/bulletr")
    (synopsis "Algorithms for Matching Bullet Lands")
    (description
     "Analyze bullet lands using nonparametric methods.  We provide a reading routine
for x3p files (see <http://www.openfmc.org> for more information) and a host of
analysis functions designed to assess the probability that two bullets were
fired from the same gun barrel.")
    (license license:gpl3)))

(define-public r-bulletcp
  (package
    (name "r-bulletcp")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bulletcp" version))
       (sha256
        (base32 "1k7lw8gflih47ncz8hgsj0sxbajnkhz1k7yb7rwbdnzcki3n3jln"))))
    (properties `((upstream-name . "bulletcp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-mvtnorm r-dplyr r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bulletcp")
    (synopsis
     "Automatic Groove Identification via Bayesian Changepoint Detection")
    (description
     "This package provides functionality to automatically detect groove locations via
a Bayesian changepoint detection method to be used in the data preprocessing
step of forensic bullet matching algorithms.  The methods in this package are
based on those in Stephens (1994) <doi:10.2307/2986119>.  Bayesian changepoint
detection will simply be an option in the function from the package bulletxtrctr
which identifies the groove locations.")
    (license license:gpl3)))

(define-public r-bulkreadr
  (package
    (name "r-bulkreadr")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bulkreadr" version))
       (sha256
        (base32 "0hq4xaxqjp08qd7h54s6nbvz2q7xpzxg1n6s0w9flavhl5jfilzg"))))
    (properties `((upstream-name . "bulkreadr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-sjlabelled
                             r-rlang
                             r-readxl
                             r-readr
                             r-purrr
                             r-openxlsx
                             r-magrittr
                             r-lubridate
                             r-labelled
                             r-inspectdf
                             r-haven
                             r-googlesheets4
                             r-fs
                             r-dplyr
                             r-curl))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/gbganalyst/bulkreadr")
    (synopsis "The Ultimate Tool for Reading Data in Bulk")
    (description
     "Designed to simplify and streamline the process of reading and processing large
volumes of data in R, this package offers a collection of functions tailored for
bulk data operations.  It enables users to efficiently read multiple sheets from
Microsoft Excel and Google Sheets workbooks, as well as various CSV files from a
directory.  The data is returned as organized data frames, facilitating further
analysis and manipulation.  Ideal for handling extensive data sets or batch
processing tasks, bulkreadr empowers users to manage data in bulk effortlessly,
saving time and effort in data preparation workflows.  Additionally, the package
seamlessly works with labelled data from SPSS and Stata.")
    (license license:expat)))

(define-public r-bulkqc
  (package
    (name "r-bulkqc")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bulkQC" version))
       (sha256
        (base32 "0236knz4jh89nzwmdfxs0cinzj9ysnxvlcs6y87kjfpj7aywx6gb"))))
    (properties `((upstream-name . "bulkQC")))
    (build-system r-build-system)
    (propagated-inputs (list r-stddiff r-isotree))
    (home-page "https://cran.r-project.org/package=bulkQC")
    (synopsis
     "Quality Control and Outlier Identification in Bulk for Multicenter Trials")
    (description
     "Multicenter randomized trials involve the collection and analysis of data from
numerous study participants across multiple sites.  Outliers may be present.  To
identify outliers, this package examines data at the individual level
(univariate and multivariate) and site-level (with and without covariate
adjustment).  Methods are outlined in further detail in Rigdon et al (to
appear).")
    (license license:gpl3)))

(define-public r-bulkanalyser
  (package
    (name "r-bulkanalyser")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bulkAnalyseR" version))
       (sha256
        (base32 "008vnkcaa26d8620i1l49mbcr7px5x16s5x2wlq1vm0jkxya4c3z"))))
    (properties `((upstream-name . "bulkAnalyseR")))
    (build-system r-build-system)
    (propagated-inputs (list r-visnetwork
                             r-upsetr
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-shinywidgets
                             r-shinylp
                             r-shinyjs
                             r-shinyjqui
                             r-shiny
                             r-scales
                             r-rlang
                             r-rcolorbrewer
                             r-preprocesscore
                             r-noisyr
                             r-matrixstats
                             r-magrittr
                             r-gprofiler2
                             r-glue
                             r-ggvenndiagram
                             r-ggrepel
                             r-ggrastr
                             r-ggplot2
                             r-ggnewscale
                             r-ggforce
                             r-genie3
                             r-edger
                             r-dt
                             r-dplyr
                             r-deseq2
                             r-complexheatmap
                             r-circlize))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Core-Bioinformatics/bulkAnalyseR")
    (synopsis "Interactive Shiny App for Bulk Sequencing Data")
    (description
     "Given an expression matrix from a bulk sequencing experiment, pre-processes it
and creates a shiny app for interactive data analysis and visualisation.  The
app contains quality checks, differential expression analysis, volcano and cross
plots, enrichment analysis and gene regulatory network inference, and can be
customised to contain more panels by the user.")
    (license license:gpl2)))

(define-public r-bujar
  (package
    (name "r-bujar")
    (version "0.2-11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bujar" version))
       (sha256
        (base32 "11g25sa2adlrzvs10k907c1a4anyigqd7lkd337vf8dcwrajqxa9"))))
    (properties `((upstream-name . "bujar")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rms
                             r-mpath
                             r-modeltools
                             r-mda
                             r-mboost
                             r-gbm
                             r-elasticnet
                             r-earth
                             r-bst))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=bujar")
    (synopsis
     "Buckley-James Regression for Survival Data with High-Dimensional Covariates")
    (description
     "Buckley-James regression for right-censoring survival data with high-dimensional
covariates.  Implementations for survival data include boosting with
componentwise linear least squares, componentwise smoothing splines, regression
trees and MARS. Other high-dimensional tools include penalized regression for
survival data.  See Wang and Wang (2010) <doi:10.2202/1544-6115.1550>.")
    (license license:gpl2)))

(define-public r-buildsys
  (package
    (name "r-buildsys")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BuildSys" version))
       (sha256
        (base32 "1mqwnr8x883nhd7mpqzc351njdvdqps8w8b106l2b4l2620c546f"))))
    (properties `((upstream-name . "BuildSys")))
    (build-system r-build-system)
    (propagated-inputs (list r-digest))
    (home-page "https://github.com/pjumppanen/BuildSys")
    (synopsis "System for Building and Debugging C/C++ Dynamic Libraries")
    (description
     "This package provides a build system based on GNU make that creates and
maintains (simply) make files in an R session and provides GUI debugging support
through Microsoft Visual Code'.")
    (license license:gpl2)))

(define-public r-buildr
  (package
    (name "r-buildr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "buildr" version))
       (sha256
        (base32 "117j4z6c22d9c5aghsmqsvyjc94kr0vpandgbikl5cc4zphl7w03"))))
    (properties `((upstream-name . "buildr")))
    (build-system r-build-system)
    (propagated-inputs (list r-usethis
                             r-tibble
                             r-stringr
                             r-rstudioapi
                             r-readr
                             r-magrittr
                             r-glue))
    (native-inputs (list r-knitr))
    (home-page "https://netique.github.io/buildr/")
    (synopsis "Organize & Run Build Scripts Comfortably")
    (description
     "Working with reproducible reports or any other similar projects often require to
run the script that builds the output file in a specified way.  buildr can help
you organize, modify and comfortably run those scripts.  The package provides a
set of functions that interactively guides you through the process and that are
available as RStudio Addin, meaning you can set up the keyboard shortcuts,
enabling you to choose and run the desired build script with one keystroke
anywhere anytime.")
    (license license:gpl3+)))

(define-public r-buildmer
  (package
    (name "r-buildmer")
    (version "2.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "buildmer" version))
       (sha256
        (base32 "0lmgzvi2jz92nps1z7cxla28ymxp5vnagikzk6dma94hycb7x2b4"))))
    (properties `((upstream-name . "buildmer")))
    (build-system r-build-system)
    (propagated-inputs (list r-nlme r-mgcv r-lme4))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=buildmer")
    (synopsis
     "Stepwise Elimination and Term Reordering for Mixed-Effects Regression")
    (description
     "Finds the largest possible regression model that will still converge for various
types of regression analyses (including mixed models and generalized additive
models) and then optionally performs stepwise elimination similar to the forward
and backward effect-selection methods in SAS, based on the change in
log-likelihood or its significance, Akaike's Information Criterion, the Bayesian
Information Criterion, the explained deviance, or the F-test of the change in
RÂ².")
    (license (license:fsdg-compatible "FreeBSD"))))

(define-public r-buddle
  (package
    (name "r-buddle")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Buddle" version))
       (sha256
        (base32 "0hi516cbl93z00jpdgyp3j5hz0xf594vn65p6ahpffwzh1l8s1cf"))))
    (properties `((upstream-name . "Buddle")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-plyr))
    (home-page "https://cran.r-project.org/package=Buddle")
    (synopsis
     "Deep Learning for Statistical Classification and Regression Analysis with Random Effects")
    (description
     "Statistical classification and regression have been popular among various fields
and stayed in the limelight of scientists of those fields.  Examples of the
fields include clinical trials where the statistical classification of patients
is indispensable to predict the clinical courses of diseases.  Considering the
negative impact of diseases on performing daily tasks, correctly classifying
patients based on the clinical information is vital in that we need to identify
patients of the high-risk group to develop a severe state and arrange medical
treatment for them at an opportune moment.  Deep learning - a part of artificial
intelligence - has gained much attention, and research on it burgeons during
past decades: see, e.g, Kazemi and Mirroshandel (2018)
<DOI:10.1016/j.artmed.2017.12.001>.  It is a veritable technique which was
originally designed for the classification, and hence, the Buddle package can
provide sublime solutions to various challenging classification and regression
problems encountered in the clinical trials.  The Buddle package is based on the
back-propagation algorithm - together with various powerful techniques such as
batch normalization and dropout - which performs a multi-layer feed-forward
neural network: see Krizhevsky et.  al (2017) <DOI:10.1145/3065386>, Schmidhuber
(2015) <DOI:10.1016/j.neunet.2014.09.003> and @code{LeCun} et al. (1998)
<DOI:10.1109/5.726791> for more details.  This package contains two main
functions: @code{TrainBuddle}() and @code{FetchBuddle}(). @code{TrainBuddle}()
builds a feed-forward neural network model and trains the model.
@code{FetchBuddle}() recalls the trained model which is the output of
@code{TrainBuddle}(), classifies or regresses given data, and make a final
prediction for the data.")
    (license license:gpl2)))

(define-public r-bucss
  (package
    (name "r-bucss")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BUCSS" version))
       (sha256
        (base32 "1nf0xpy5r49yfd0xydzxxxrc6isj14s8lswrf5nhh7g60nkkwcxr"))))
    (properties `((upstream-name . "BUCSS")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BUCSS")
    (synopsis "Bias and Uncertainty Corrected Sample Size")
    (description
     "Bias- and Uncertainty-Corrected Sample Size.  BUCSS implements a method of
correcting for publication bias and uncertainty when planning sample sizes in a
future study from an original study.  See Anderson, Kelley, & Maxwell (2017;
Psychological Science, 28, 1547-1562).")
    (license license:gpl3+)))

(define-public r-bubblyr
  (package
    (name "r-bubblyr")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bubblyr" version))
       (sha256
        (base32 "04agfwk6rmbhlfblfw1irqnv3yrp00zfn4qwy2169b7ypg8r55v2"))))
    (properties `((upstream-name . "bubblyr")))
    (build-system r-build-system)
    (propagated-inputs (list r-htmltools r-glue))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/feddelegrand7/bubblyr")
    (synopsis "Beautiful Bubbles for 'shiny' and 'rmarkdown' Backgrounds")
    (description
     "This package creates bubbles within shiny and rmarkdown backgrounds using the
bubbly-bg @code{JavaScript} library.")
    (license license:expat)))

(define-public r-bubbleheatmap
  (package
    (name "r-bubbleheatmap")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bubbleHeatmap" version))
       (sha256
        (base32 "0b12bhk0c65m5izp5ayy6fl4iv0jhk7hhwbnavyggawrpz23ikxr"))))
    (properties `((upstream-name . "bubbleHeatmap")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bubbleHeatmap")
    (synopsis
     "Produces 'bubbleHeatmap' Plots for Visualising Metabolomics Data")
    (description
     "Plotting package based on the grid system, combining elements of a bubble plot
and heatmap to conveniently display two numerical variables, (represented by
color and size) grouped by categorical variables on the x and y axes.  This is a
useful alternative to a forest plot when the data can be grouped in two
dimensions, such as predictors x outcomes.  It has particular advantages for
visualising the metabolic measures produced by the Nightingale Health
metabolomics platform, and templates are included for automatically generating
figures from these datasets.")
    (license license:gpl3)))

(define-public r-btydplus
  (package
    (name "r-btydplus")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BTYDplus" version))
       (sha256
        (base32 "1shai7kij20g3isd6l29i696pkzymr0qq2i9gzfsxmvdb4g4pcm2"))))
    (properties `((upstream-name . "BTYDplus")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp
                             r-mvtnorm
                             r-data-table
                             r-coda
                             r-btyd
                             r-bayesm))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mplatzer/BTYDplus#readme")
    (synopsis
     "Probabilistic Models for Assessing and Predicting your Customer Base")
    (description
     "This package provides advanced statistical methods to describe and predict
customers purchase behavior in a non-contractual setting.  It uses historic
transaction records to fit a probabilistic model, which then allows to compute
quantities of managerial interest on a cohort- as well as on a customer level
(Customer Lifetime Value, Customer Equity, P(alive), etc.).  This package
complements the BTYD package by providing several additional buy-till-you-die
models, that have been published in the marketing literature, but whose
implementation are complex and non-trivial.  These models are: NBD [Ehrenberg
(1959) <doi:10.2307/2985810>], MBG/NBD [Batislam et al (2007)
<doi:10.1016/j.ijresmar.2006.12.005>], (M)BG/CNBD-k [Reutterer et al (2020)
<doi:10.1016/j.ijresmar.2020.09.002>], Pareto/NBD (HB) [Abe (2009)
<doi:10.1287/mksc.1090.0502>] and Pareto/GGG [Platzer and Reutterer (2016)
<doi:10.1287/mksc.2015.0963>].")
    (license license:gpl3)))

(define-public r-btyd
  (package
    (name "r-btyd")
    (version "2.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BTYD" version))
       (sha256
        (base32 "1szkpcmagpig32vbs7klx71avjlmbd0cn123ljpc3i02b55lma7z"))))
    (properties `((upstream-name . "BTYD")))
    (build-system r-build-system)
    (propagated-inputs (list r-optimx r-matrix r-hypergeo r-dplyr))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://cran.r-project.org/package=BTYD")
    (synopsis "Implementing BTYD Models with the Log Sum Exp Patch")
    (description
     "This package provides functions for data preparation, parameter estimation,
scoring, and plotting for the BG/BB (Fader, Hardie, and Shang 2010
<doi:10.1287/mksc.1100.0580>), BG/NBD (Fader, Hardie, and Lee 2005
<doi:10.1287/mksc.1040.0098>) and Pareto/NBD and Gamma/Gamma (Fader, Hardie, and
Lee 2005 <doi:10.1509/jmkr.2005.42.4.415>) models.")
    (license license:gpl3)))

(define-public r-bttl
  (package
    (name "r-bttl")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BTTL" version))
       (sha256
        (base32 "0vkp85mmdzch26l1y28ngd1mq5d7disha5jp01r2ranpb5whqjmr"))))
    (properties `((upstream-name . "BTTL")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BTTL")
    (synopsis "Bradley-Terry Transfer Learning")
    (description
     "This package implements the methodological developments found in Hermes, van
Heerwaarden, and Behrouzi (2024) <doi:10.48550/@code{arXiv.2408.10558>}, and
allows for the statistical modeling of multi-attribute pairwise comparison data.")
    (license license:gpl3)))

(define-public r-bttest
  (package
    (name "r-bttest")
    (version "0.10.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BTtest" version))
       (sha256
        (base32 "1bp7x9j3ckndsga8q8j1k0vlz0pfsw6n6nmqwl2zzm9v2inc3jvi"))))
    (properties `((upstream-name . "BTtest")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://github.com/Paul-Haimerl/BTtest")
    (synopsis "Estimate the Number of Factors in Large Nonstationary Datasets")
    (description
     "Large panel data sets are often subject to common trends.  However, it can be
difficult to determine the exact number of these common factors and analyse
their properties.  The package implements the Barigozzi and Trapani (2022)
<doi:10.1080/07350015.2021.1901719> test, which not only provides an efficient
way of estimating the number of common factors in large nonstationary panel data
sets, but also gives further insights on factor classes.  The routine identifies
the existence of (i) a factor subject to a linear trend, (ii) the number of
zero-mean I(1) and (iii) zero-mean I(0) factors.  Furthermore, the package
includes the Integrated Panel Criteria by Bai (2004)
<doi:10.1016/j.jeconom.2003.10.022> that provide a complementary measure for the
number of factors.")
    (license license:gpl3+)))

(define-public r-btsr
  (package
    (name "r-btsr")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BTSR" version))
       (sha256
        (base32 "0kqgy61ijalpclrm20xwjbvbb5pkhkbgsbdbsz0mrb4v30a05dh4"))))
    (properties `((upstream-name . "BTSR")))
    (build-system r-build-system)
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=BTSR")
    (synopsis "Bounded Time Series Regression")
    (description
     "Simulate, estimate and forecast a wide range of regression based dynamic models
for bounded time series, covering the most commonly applied models in the
literature.  The main calculations are done in FORTRAN', which translates into
very fast algorithms.  The main references are Bayer et al. (2017)
<doi:10.1016/j.jhydrol.2017.10.006>, Pumi et al. (2019)
<doi:10.1016/j.jspi.2018.10.001>, Pumi et al. (2021) <doi:10.1111/sjos.12439>
and Pumi et al. (2022) <@code{arXiv:2211.02097>}.")
    (license license:gpl3+)))

(define-public r-btspas
  (package
    (name "r-btspas")
    (version "2024.5.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BTSPAS" version))
       (sha256
        (base32 "0gc0ik2257zk6bjhwc78z53jg9a34s32wbwdqk25a6m5wvjp2qlj"))))
    (properties `((upstream-name . "BTSPAS")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-scales
                             r-reshape2
                             r-r2jags
                             r-plyr
                             r-gridextra
                             r-ggplot2
                             r-ggforce
                             r-data-table
                             r-coda
                             r-actuar))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/cschwarz-stat-sfu-ca/BTSPAS")
    (synopsis "Bayesian Time-Stratified Population Analysis")
    (description
     "This package provides advanced Bayesian methods to estimate abundance and
run-timing from temporally-stratified Petersen mark-recapture experiments.
Methods include hierarchical modelling of the capture probabilities and spline
smoothing of the daily run size.  Theory described in Bonner and Schwarz (2011)
<doi:10.1111/j.1541-0420.2011.01599.x>.")
    (license license:gpl2+)))

(define-public r-btllasso
  (package
    (name "r-btllasso")
    (version "0.1-13")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BTLLasso" version))
       (sha256
        (base32 "19r45qm1iq3sjb6r5ficgv8q9zwpbilxwrf6rxvqdl333xcnnjy4"))))
    (properties `((upstream-name . "BTLLasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rcpparmadillo r-rcpp r-psychotools
                             r-matrix))
    (home-page "https://cran.r-project.org/package=BTLLasso")
    (synopsis "Modelling Heterogeneity in Paired Comparison Data")
    (description
     "This package performs BTLLasso as described by Schauberger and Tutz (2019)
<doi:10.18637/jss.v088.i09> and Schauberger and Tutz (2017)
<doi:10.1177/1471082X17693086>.  BTLLasso is a method to include different types
of variables in paired comparison models and, therefore, to allow for
heterogeneity between subjects.  Variables can be subject-specific,
object-specific and subject-object-specific and can have an influence on the
attractiveness/strength of the objects.  Suitable L1 penalty terms are used to
cluster certain effects and to reduce the complexity of the models.")
    (license license:gpl2+)))

(define-public r-btergm
  (package
    (name "r-btergm")
    (version "1.10.12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "btergm" version))
       (sha256
        (base32 "1xcn24m6ldmi5wh44169vg13f7a7apbk45iwsl88clbnf2l9wpb8"))))
    (properties `((upstream-name . "btergm")))
    (build-system r-build-system)
    (propagated-inputs (list r-statnet-common
                             r-sna
                             r-rocr
                             r-network
                             r-matrix
                             r-igraph
                             r-ergm
                             r-coda
                             r-boot))
    (home-page "https://github.com/leifeld/btergm")
    (synopsis
     "Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood")
    (description
     "Temporal Exponential Random Graph Models (TERGM) estimated by maximum
pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte
Carlo maximum likelihood.  Goodness of fit assessment for ERGMs, TERGMs, and
SAOMs. Micro-level interpretation of ERGMs and TERGMs. The methods are described
in Leifeld, Cranmer and Desmarais (2018), J@code{StatSoft}
<doi:10.18637/jss.v083.i06>.")
    (license license:gpl2+)))

(define-public r-btdecaylasso
  (package
    (name "r-btdecaylasso")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BTdecayLasso" version))
       (sha256
        (base32 "0ig1nbhjs1xb9743j3q7ggp01s780hw0b1xjn8spkfkjad1ijsbm"))))
    (properties `((upstream-name . "BTdecayLasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-optimx r-ggplot2))
    (home-page "https://cran.r-project.org/package=BTdecayLasso")
    (synopsis
     "Bradley-Terry Model with Exponential Time Decayed Log-Likelihood and Adaptive Lasso")
    (description
     "We utilize the Bradley-Terry Model to estimate the abilities of teams using
paired comparison data.  For dynamic approximation of current rankings, we
employ the Exponential Decayed Log-likelihood function, and we also apply the
Lasso penalty for variance reduction and grouping.  The main algorithm applies
the Augmented Lagrangian Method described by Masarotto and Varin (2012)
<doi:10.1214/12-AOAS581>.")
    (license license:gpl2+)))

(define-public r-btb
  (package
    (name "r-btb")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "btb" version))
       (sha256
        (base32 "1gm03md6ky134nzmnnp054mi2w8kl4bgcgg4k65zf2cyniz4arxq"))))
    (properties `((upstream-name . "btb")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf
                             r-rcppparallel
                             r-rcpparmadillo
                             r-rcpp
                             r-mapsf
                             r-magrittr
                             r-dplyr
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/InseeFr/btb")
    (synopsis
     "Beyond the Border - Kernel Density Estimation for Urban Geography")
    (description
     "The @code{kernelSmoothing}() function allows you to square and smooth geolocated
data.  It calculates a classical kernel smoothing (conservative) or a
geographically weighted median.  There are four major call modes of the
function.  The first call mode is @code{kernelSmoothing(obs}, epsg, cellsize,
bandwidth) for a classical kernel smoothing and automatic grid.  The second call
mode is @code{kernelSmoothing(obs}, epsg, cellsize, bandwidth, quantiles) for a
geographically weighted median and automatic grid.  The third call mode is
@code{kernelSmoothing(obs}, epsg, cellsize, bandwidth, centroids) for a
classical kernel smoothing and user grid.  The fourth call mode is
@code{kernelSmoothing(obs}, epsg, cellsize, bandwidth, quantiles, centroids) for
a geographically weighted median and user grid.  Geographically weighted summary
statistics : a framework for localised exploratory data analysis, C.Brunsdon &
al., in Computers, Environment and Urban Systems C.Brunsdon & al. (2002)
<doi:10.1016/S0198-9715(01)00009-6>, Statistical Analysis of Spatial and
Spatio-Temporal Point Patterns, Third Edition, Diggle, pp.  83-86, (2003)
<doi:10.1080/13658816.2014.937718>.")
    (license license:gpl2+)))

(define-public r-bt
  (package
    (name "r-bt")
    (version "0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BT" version))
       (sha256
        (base32 "1ifanv79rrbc9m6rxhnhhr5kss9idykpn672w3zxqi8fic9f0hsx"))))
    (properties `((upstream-name . "BT")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod r-rpart))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/GiregWillame/BT/")
    (synopsis "(Adaptive) Boosting Trees Algorithm")
    (description
     "This package performs (Adaptive) Boosting Trees for Poisson distributed response
variables, using log-link function.  The code approach is similar to the one
used in gbm'/'gbm3'.  Moreover, each tree in the expansion is built thanks to
the rpart package.  This package is based on following books and articles
Denuit, M., Hainaut, D., Trufin, J. (2019) <doi:10.1007/978-3-030-25820-7>
Denuit, M., Hainaut, D., Trufin, J. (2019) <doi:10.1007/978-3-030-57556-4>
Denuit, M., Hainaut, D., Trufin, J. (2019) <doi:10.1007/978-3-030-25827-6>
Denuit, M., Hainaut, D., Trufin, J. (2022) <doi:10.1080/03461238.2022.2037016>
Denuit, M., Huyghe, J., Trufin, J. (2022)
<https://dial.uclouvain.be/pr/boreal/fr/object/boreal%3A244325/datastream/PDF_01/view>
Denuit, M., Trufin, J., Verdebout, T. (2022)
<https://dial.uclouvain.be/pr/boreal/fr/object/boreal%3A268577>.")
    (license license:gpl3+)))

(define-public r-bsynth
  (package
    (name "r-bsynth")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bsynth" version))
       (sha256
        (base32 "1hgq999pkk54cnw21xgpmksr1x4lr2y23c3ma8fyn4637mqa0z7r"))))
    (properties `((upstream-name . "bsynth")))
    (build-system r-build-system)
    (propagated-inputs (list r-vizdraws
                             r-tidyr
                             r-tibble
                             r-stanheaders
                             r-scales
                             r-rstantools
                             r-rstan
                             r-rlang
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-r6
                             r-purrr
                             r-magrittr
                             r-glue
                             r-ggplot2
                             r-dplyr
                             r-cubelyr
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/google/bsynth")
    (synopsis "Bayesian Synthetic Control")
    (description
     "This package implements the Bayesian Synthetic Control method for causal
inference in comparative case studies.  This package provides tools for
estimating treatment effects in settings with a single treated unit and multiple
control units, allowing for uncertainty quantification and flexible modeling of
time-varying effects.  The methodology is based on the paper by Vives and
Martinez (2022) <doi:10.48550/@code{arXiv.2206.01779>}.")
    (license license:asl2.0)))

(define-public r-bsw
  (package
    (name "r-bsw")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BSW" version))
       (sha256
        (base32 "0l6sw8lyk9mxfdw4s72hz6a0b30gi2a2vlcndkgahjlym6i5bz83"))))
    (properties `((upstream-name . "BSW")))
    (build-system r-build-system)
    (propagated-inputs (list r-quadprog r-matrixstats r-matrix))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/adam-bec/BSW")
    (synopsis
     "Fitting a Log-Binomial Model using the Bekhit-SchÃ¶pe-Wagenpfeil (BSW) Algorithm")
    (description
     "This package implements a modified Newton-type algorithm (BSW algorithm) for
solving the maximum likelihood estimation problem in fitting a log-binomial
model under linear inequality constraints.")
    (license license:gpl3+)))

(define-public r-bsvarsigns
  (package
    (name "r-bsvarsigns")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bsvarSIGNs" version))
       (sha256
        (base32 "15kjmjzrn5vp9r35mjyh009v174jlz670kqchz6jhp7yqlxhnbqc"))))
    (properties `((upstream-name . "bsvarSIGNs")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppprogress r-rcpparmadillo r-rcpp r-r6
                             r-bsvars))
    (home-page "https://bsvars.github.io/bsvarSIGNs/")
    (synopsis "Bayesian SVARs with Sign, Zero, and Narrative Restrictions")
    (description
     "This package implements state-of-the-art algorithms for the Bayesian analysis of
Structural Vector Autoregressions (SVARs) identified by sign, zero, and
narrative restrictions.  The core model is based on a flexible Vector
Autoregression with estimated hyper-parameters of the Minnesota prior and the
dummy observation priors as in Giannone, Lenza, Primiceri (2015)
<doi:10.1162/REST_a_00483>.  The sign restrictions are implemented employing the
methods proposed by Rubio-RamÃ­rez, Waggoner & Zha (2010)
<doi:10.1111/j.1467-937X.2009.00578.x>, while identification through sign and
zero restrictions follows the approach developed by Arias, Rubio-RamÃ­rez, &
Waggoner (2018) <doi:10.3982/ECTA14468>.  Furthermore, our tool provides
algorithms for identification via sign and narrative restrictions, in line with
the methods introduced by AntolÃ­n-DÃ­az and Rubio-RamÃ­rez (2018)
<doi:10.1257/aer.20161852>.  Users can also estimate a model with sign, zero,
and narrative restrictions imposed at once.  The package facilitates predictive
and structural analyses using impulse responses, forecast error variance and
historical decompositions, forecasting and conditional forecasting, as well as
analyses of structural shocks and fitted values.  All this is complemented by
colourful plots, user-friendly summary functions, and comprehensive
documentation.  The @code{bsvarSIGNs} package is aligned regarding objects,
workflows, and code structure with the R package bsvars by WoÅºniak (2024)
<doi:10.32614/CRAN.package.bsvars>, and they constitute an integrated toolset.")
    (license license:gpl3+)))

(define-public r-bsvars
  (package
    (name "r-bsvars")
    (version "3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bsvars" version))
       (sha256
        (base32 "138810300y0lfmzbch5syp3sd2cfcvqvgnnvvy4xlbys9p6aif5a"))))
    (properties `((upstream-name . "bsvars")))
    (build-system r-build-system)
    (propagated-inputs (list r-stochvol
                             r-rcpptn
                             r-rcppprogress
                             r-rcpparmadillo
                             r-rcpp
                             r-r6
                             r-gigrvg))
    (home-page "https://bsvars.github.io/bsvars/")
    (synopsis "Bayesian Estimation of Structural Vector Autoregressive Models")
    (description
     "This package provides fast and efficient procedures for Bayesian analysis of
Structural Vector Autoregressions.  This package estimates a wide range of
models, including homo-, heteroskedastic, and non-normal specifications.
Structural models can be identified by adjustable exclusion restrictions,
time-varying volatility, or non-normality.  They all include a flexible
three-level equation-specific local-global hierarchical prior distribution for
the estimated level of shrinkage for autoregressive and structural parameters.
Additionally, the package facilitates predictive and structural analyses such as
impulse responses, forecast error variance and historical decompositions,
forecasting, verification of heteroskedasticity, non-normality, and hypotheses
on autoregressive parameters, as well as analyses of structural shocks,
volatilities, and fitted values.  Beautiful plots, informative summary
functions, and extensive documentation complement all this.  The implemented
techniques align closely with those presented in LÃ¼tkepohl, Shang, Uzeda, &
WoÅºniak (2024) <doi:10.48550/@code{arXiv.2404.11057>}, LÃ¼tkepohl & WoÅºniak
(2020) <doi:10.1016/j.jedc.2020.103862>, and Song & WoÅºniak (2021)
<doi:10.1093/acrefore/9780190625979.013.174>.")
    (license license:gpl3+)))

(define-public r-bsub
  (package
    (name "r-bsub")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bsub" version))
       (sha256
        (base32 "112hlvg15jlzg3drfzkslaly42vnvsycn9vif0w3iaw5fzsaa954"))))
    (properties `((upstream-name . "bsub")))
    (build-system r-build-system)
    (propagated-inputs (list r-globaloptions r-getoptlong r-digest r-crayon
                             r-clisymbols))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jokergoo/bsub")
    (synopsis "Submitter and Monitor of the 'LSF Cluster'")
    (description
     "It submits R code/R scripts/shell commands to LSF cluster
(<https://en.wikipedia.org/wiki/Platform_LSF>, the bsub system) without leaving
R. There is also an interactive shiny app for monitoring the job status.")
    (license license:expat)))

(define-public r-bsts
  (package
    (name "r-bsts")
    (version "0.9.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bsts" version))
       (sha256
        (base32 "1cbiha8pgb2xmpwqbgb16d1dpjj9x41wx3dzly1xxy2fs04bq0m3"))))
    (properties `((upstream-name . "bsts")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-xts r-boomspikeslab r-boom))
    (home-page "https://cran.r-project.org/package=bsts")
    (synopsis "Bayesian Structural Time Series")
    (description
     "Time series regression using dynamic linear models fit using MCMC. See Scott and
Varian (2014) <DOI:10.1504/IJMMNO.2014.059942>, among many other sources.")
    (license (list license:lgpl2.1 license:expat))))

(define-public r-bstrl
  (package
    (name "r-bstrl")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bstrl" version))
       (sha256
        (base32 "10ncaicda2rwwrl2ykbrdhh51fziy3ahwp8z859cr2k4v19lviv0"))))
    (properties `((upstream-name . "bstrl")))
    (build-system r-build-system)
    (propagated-inputs (list r-foreach r-extradistr r-doparallel r-brl))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bstrl")
    (synopsis "Bayesian Streaming Record Linkage")
    (description
     "Perform record linkage on streaming files using recursive Bayesian updating.")
    (license license:expat)))

(define-public r-bstools
  (package
    (name "r-bstools")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bsTools" version))
       (sha256
        (base32 "0akvk2i5gbrkxfwf69m1xjfn643d41rrdzp02gyhrxyhqwsx9p14"))))
    (properties `((upstream-name . "bsTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-toolbox r-html5))
    (home-page "https://cran.r-project.org/package=bsTools")
    (synopsis "Create HTML Content with Bootstrap 5 Classes and Layouts")
    (description
     "This package provides functions are pre-configured to utilize Bootstrap 5
classes and HTML structures to create Bootstrap-styled HTML quickly and easily.
Includes functions for creating common Bootstrap elements such as containers,
rows, cols, navbars, etc.  Intended to be used with the html5 package.  Learn
more at <https://getbootstrap.com/>.")
    (license license:gpl2+)))

(define-public r-bst
  (package
    (name "r-bst")
    (version "0.3-24")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bst" version))
       (sha256
        (base32 "1x297sv9px7ipd7a6xprlff8rgkdr2z379xsm8rfqd8xal9nxnb4"))))
    (properties `((upstream-name . "bst")))
    (build-system r-build-system)
    (propagated-inputs (list r-rpart r-gbm r-foreach r-doparallel))
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "https://cran.r-project.org/package=bst")
    (synopsis "Gradient Boosting")
    (description
     "Functional gradient descent algorithm for a variety of convex and non-convex
loss functions, for both classical and robust regression and classification
problems.  See Wang (2011) <doi:10.2202/1557-4679.1304>, Wang (2012)
<doi:10.3414/ME11-02-0020>, Wang (2018) <doi:10.1080/10618600.2018.1424635>,
Wang (2018) <doi:10.1214/18-EJS1404>.")
    (license license:gpl2+)))

(define-public r-bssprep
  (package
    (name "r-bssprep")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BSSprep" version))
       (sha256
        (base32 "0dkhx8vx9kg902i6nzkzbighf2szq12l0p8iwinlpb2qdf526lk6"))))
    (properties `((upstream-name . "BSSprep")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=BSSprep")
    (synopsis "Whitening Data as Preparation for Blind Source Separation")
    (description
     "Whitening is the first step of almost all blind source separation (BSS) methods.
 A fast implementation of whitening for BSS is implemented to serve as a
lightweight dependency for packages providing BSS methods.")
    (license license:gpl2+)))

(define-public r-bssoverspace
  (package
    (name "r-bssoverspace")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BSSoverSpace" version))
       (sha256
        (base32 "117av0y5bw44z6293fvbxmf1xqhar7c4lf5z09xrs78dn1xdsnbs"))))
    (properties `((upstream-name . "BSSoverSpace")))
    (build-system r-build-system)
    (propagated-inputs (list r-spatialbss r-rspde r-expm))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BSSoverSpace")
    (synopsis
     "Blind Source Separation for Multivariate Spatial Data using Eigen Analysis")
    (description
     "This package provides functions for blind source separation over multivariate
spatial data, and useful statistics for evaluating performance of estimation on
mixing matrix.  BS@code{SoverSpace} is based on an eigen analysis of a positive
definite matrix defined in terms of multiple normalized spatial local covariance
matrices, and thus can handle moderately high-dimensional random fields.  This
package is an implementation of the method described in Zhang, Hao and Yao
(2022)<@code{arXiv:2201.02023>}.")
    (license license:gpl3)))

(define-public r-bssn
  (package
    (name "r-bssn")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bssn" version))
       (sha256
        (base32 "1brxbvcvqy8n9xpvp5kz47x5ps8hdqgnh6s49051qaylp95qm432"))))
    (properties `((upstream-name . "bssn")))
    (build-system r-build-system)
    (propagated-inputs (list r-ssmn r-sn r-mvtnorm r-clusterr))
    (home-page "https://cran.r-project.org/package=bssn")
    (synopsis "Birnbaum-Saunders Model")
    (description
     "It provides the density, distribution function, quantile function, random number
generator, reliability function, failure rate, likelihood function, moments and
EM algorithm for Maximum Likelihood estimators, also empirical quantile and
generated envelope for a given sample, all this for the three parameter
Birnbaum-Saunders model based on Skew-Normal Distribution.  Also, it provides
the random number generator for the mixture of Birnbaum-Saunders model based on
Skew-Normal distribution.  Additionally, we incorporate the EM algorithm based
on the assumption that the error term follows a finite mixture of Sinh-normal
distributions.")
    (license license:gpl2+)))

(define-public r-bssm
  (package
    (name "r-bssm")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bssm" version))
       (sha256
        (base32 "121fb12n5z3w97xpj5ga1985drv3h4g7b0xy5yk9sfqfaqf92yyn"))))
    (properties `((upstream-name . "bssm")))
    (build-system r-build-system)
    (inputs (list pandoc))
    (propagated-inputs (list r-tidyr
                             r-sitmo
                             r-rlang
                             r-rcpparmadillo
                             r-rcpp
                             r-ramcmc
                             r-posterior
                             r-dplyr
                             r-diagis
                             r-coda
                             r-checkmate
                             r-bayesplot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/helske/bssm")
    (synopsis
     "Bayesian Inference of Non-Linear and Non-Gaussian State Space Models")
    (description
     "Efficient methods for Bayesian inference of state space models via Markov chain
Monte Carlo (MCMC) based on parallel importance sampling type weighted
estimators (Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>),
particle MCMC, and its delayed acceptance version.  Gaussian, Poisson, binomial,
negative binomial, and Gamma observation densities and basic stochastic
volatility models with linear-Gaussian state dynamics, as well as general
non-linear Gaussian models and discretised diffusion models are supported.  See
Helske and Vihola (2021, <doi:10.32614/RJ-2021-103>) for details.")
    (license license:gpl2+)))

(define-public r-bssasymp
  (package
    (name "r-bssasymp")
    (version "1.2-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BSSasymp" version))
       (sha256
        (base32 "1nqj8w8ilyhladjzhjp1rhi3ff7ik1m76pdhgs16ph7pzdw36w6i"))))
    (properties `((upstream-name . "BSSasymp")))
    (build-system r-build-system)
    (propagated-inputs (list r-jade r-fica))
    (home-page "https://cran.r-project.org/package=BSSasymp")
    (synopsis
     "Asymptotic Covariance Matrices of Some BSS Mixing and Unmixing Matrix Estimates")
    (description
     "This package provides functions to compute the asymptotic covariance matrices of
mixing and unmixing matrix estimates of the following blind source separation
(BSS) methods: symmetric and squared symmetric @code{FastICA}, regular and
adaptive deflation-based @code{FastICA}, FOBI, JADE, AMUSE and deflation-based
and symmetric SOBI. Also functions to estimate these covariances based on data
are available.")
    (license license:gpl2+)))

(define-public r-bss
  (package
    (name "r-bss")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BSS" version))
       (sha256
        (base32 "1lxmrdmr6c0niqz61kc9ybzszprn66jyinfy4dr9r6i161gvs4qg"))))
    (properties `((upstream-name . "BSS")))
    (build-system r-build-system)
    (propagated-inputs (list r-phangorn r-mass r-hypergeo))
    (home-page "https://cran.r-project.org/package=BSS")
    (synopsis "Brownian Semistationary Processes")
    (description
     "Efficient simulation of Brownian semistationary (BSS) processes using the hybrid
simulation scheme, as described in Bennedsen, Lunde, Pakkannen (2017)
<@code{arXiv:1507.03004v4>}, as well as functions to fit BSS processes to data,
and functions to estimate the stochastic volatility process of a BSS process.")
    (license license:expat)))

(define-public r-bsreg
  (package
    (name "r-bsreg")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bsreg" version))
       (sha256
        (base32 "13701llj5k7vssmgb4fvxswcf9bsc4pxjvh67znbsvrv5cmjh5x3"))))
    (properties `((upstream-name . "bsreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6 r-matrix))
    (home-page "https://cran.r-project.org/package=bsreg")
    (synopsis "Bayesian Spatial Regression Models")
    (description
     "Fit Bayesian models with a focus on the spatial econometric models.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-bspmma
  (package
    (name "r-bspmma")
    (version "0.1-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bspmma" version))
       (sha256
        (base32 "1a3p1h7bks5yrxv791wfa680v4a6q10p59iz6wvfifhf3ndlaw49"))))
    (properties `((upstream-name . "bspmma")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=bspmma")
    (synopsis "Bayesian Semiparametric Models for Meta-Analysis")
    (description
     "The main functions carry out Gibbs sampler routines for nonparametric and
semiparametric Bayesian models for random effects meta-analysis.")
    (license license:gpl2)))

(define-public r-bspm
  (package
    (name "r-bspm")
    (version "0.5.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bspm" version))
       (sha256
        (base32 "168mlimmab1i03fqvkgc449gcmdxr8dsy7b09pzvgswgxjgf7b2a"))))
    (properties `((upstream-name . "bspm")))
    (build-system r-build-system)
    (inputs (list))
    (home-page "https://cran4linux.github.io/bspm/")
    (synopsis "Bridge to System Package Manager")
    (description
     "Enables binary package installations on Linux distributions.  Provides functions
to manage packages via the distribution's package manager.  Also provides
transparent integration with R's install.packages() and a fallback mechanism.
When installed as a system package, interacts with the system's package manager
without requiring administrative privileges via an integrated D-Bus service;
otherwise, uses sudo.  Currently, the following backends are supported: DNF,
APT, ALPM.")
    (license license:expat)))

(define-public r-bsplus
  (package
    (name "r-bsplus")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bsplus" version))
       (sha256
        (base32 "0bf8qvj7pk3xn7g4p89as1xzx56c75n842g27rj6lcgsf9rdmv9g"))))
    (properties `((upstream-name . "bsplus")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rmarkdown
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-jsonlite
                             r-htmltools
                             r-glue))
    (home-page "https://github.com/ijlyttle/bsplus")
    (synopsis
     "Adds Functionality to the R Markdown + Shiny Bootstrap Framework")
    (description
     "The Bootstrap framework lets you add some @code{JavaScript} functionality to
your web site by adding attributes to your HTML tags - Bootstrap takes care of
the @code{JavaScript} <https://getbootstrap.com/docs/3.3/javascript/>.  If you
are using R Markdown or Shiny, you can use these functions to create collapsible
sections, accordion panels, modals, tooltips, popovers, and an accordion sidebar
framework (not described at Bootstrap site).  Please note this package was
designed for Bootstrap 3.3.")
    (license license:expat)))

(define-public r-bsplinepsd
  (package
    (name "r-bsplinepsd")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bsplinePsd" version))
       (sha256
        (base32 "0f785l02hiq3f7anxqhm09f7lrqgkkqhly7f1x78cxm22hvrqyhg"))))
    (properties `((upstream-name . "bsplinePsd")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=bsplinePsd")
    (synopsis
     "Bayesian Nonparametric Spectral Density Estimation Using B-Spline Priors")
    (description
     "Implementation of a Metropolis-within-Gibbs MCMC algorithm to flexibly estimate
the spectral density of a stationary time series.  The algorithm updates a
nonparametric B-spline prior using the Whittle likelihood to produce
pseudo-posterior samples and is based on the work presented in Edwards, M.C.,
Meyer, R. and Christensen, N., Statistics and Computing (2018).
<doi.org/10.1007/s11222-017-9796-9>.")
    (license license:gpl3+)))

(define-public r-bspline
  (package
    (name "r-bspline")
    (version "2.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bspline" version))
       (sha256
        (base32 "1m214gp6kjpl56vd7w8h1lwgwrh46pds5i77ilvgiv64nrccqih7"))))
    (properties `((upstream-name . "bspline")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-nlsic r-arrapply))
    (home-page "https://github.com/MathsCell/bspline")
    (synopsis "B-Spline Interpolation and Regression")
    (description
     "Build and use B-splines for interpolation and regression.  In case of
regression, equality constraints as well as monotonicity and/or positivity of
B-spline weights can be imposed.  Moreover, knot positions (not only spline
weights) can be part of optimized parameters too.  For this end, bspline is able
to calculate Jacobian of basis vectors as function of knot positions.  User is
provided with functions calculating spline values at arbitrary points.  These
functions can be differentiated and integrated to obtain B-splines calculating
derivatives/integrals at any point.  B-splines of this package can
simultaneously operate on a series of curves sharing the same set of knots.
bspline is written with concern about computing performance that's why the basis
and Jacobian calculation is implemented in C++.  The rest is implemented in R
but without notable impact on computing speed.")
    (license license:gpl2)))

(define-public r-bspec
  (package
    (name "r-bspec")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bspec" version))
       (sha256
        (base32 "19l1sagx6kn6i8mcs3i0h6rh7rwsyhphncv55jywpv4g6616kqcv"))))
    (properties `((upstream-name . "bspec")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=bspec")
    (synopsis "Bayesian Spectral Inference")
    (description
     "Bayesian inference on the (discrete) power spectrum of time series.")
    (license license:gpl2+)))

(define-public r-bspcov
  (package
    (name "r-bspcov")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bspcov" version))
       (sha256
        (base32 "1zxdcqydhzisfp2bkvw585z3qfcpy0yjra91qyxwdf0lvdvqwbqx"))))
    (properties `((upstream-name . "bspcov")))
    (build-system r-build-system)
    (propagated-inputs (list r-rspectra
                             r-purrr
                             r-progress
                             r-plyr
                             r-mvtnorm
                             r-mvnfast
                             r-matrixstats
                             r-matrixcalc
                             r-matrix
                             r-mass
                             r-magrittr
                             r-ks
                             r-gigrvg
                             r-ggplot2
                             r-ggmcmc
                             r-future
                             r-furrr
                             r-fincovregularization
                             r-dplyr
                             r-coda
                             r-cholwishart
                             r-caret
                             r-bayesfactor))
    (home-page "https://github.com/statjs/bspcov")
    (synopsis "Bayesian Sparse Estimation of a Covariance Matrix")
    (description
     "This package provides functions which perform Bayesian estimations of a
covariance matrix for multivariate normal data.  Assumes that the covariance
matrix is sparse or band matrix and positive-definite.  This software has been
developed using funding supported by Basic Science Research Program through the
National Research Foundation of Korea ('NRF') funded by the Ministry of
Education ('RS-2023-00211979', NRF-2022R1A5A7033499', NRF-2020R1A4A1018207 and
NRF-2020R1C1C1A01013338').")
    (license license:gpl2)))

(define-public r-bspbss
  (package
    (name "r-bspbss")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BSPBSS" version))
       (sha256
        (base32 "0ax92x657f8jm731wc1qry19mbi4d3xxcxh5zd07w3705va4k82y"))))
    (properties `((upstream-name . "BSPBSS")))
    (build-system r-build-system)
    (propagated-inputs (list r-svd
                             r-rstiefel
                             r-rcpparmadillo
                             r-rcpp
                             r-oro-nifti
                             r-neurobase
                             r-movmf
                             r-ica
                             r-gtools
                             r-gridextra
                             r-gplots
                             r-glmnet
                             r-ggplot2
                             r-bayesgpfit))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BSPBSS")
    (synopsis "Bayesian Spatial Blind Source Separation")
    (description
     "Gibbs sampling for Bayesian spatial blind source separation (BSP-BSS).  BSP-BSS
is designed for spatially dependent signals in high dimensional and large-scale
data, such as neuroimaging.  The method assumes the expectation of the observed
images as a linear mixture of multiple sparse and piece-wise smooth latent
source signals, and constructs a Bayesian nonparametric prior by thresholding
Gaussian processes.  Details can be found in our paper: Wu et al. (2022+)
\"Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process\"
<doi:10.1080/01621459.2022.2123336>.")
    (license license:gpl3+)))

(define-public r-bspadata
  (package
    (name "r-bspadata")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BSPADATA" version))
       (sha256
        (base32 "190zmjvw5q5gg47g21n2imclz1qg585cvjxl501mm910gx50rldb"))))
    (properties `((upstream-name . "BSPADATA")))
    (build-system r-build-system)
    (propagated-inputs (list r-spdep r-pscl r-pbapply r-mvtnorm r-coda))
    (home-page "https://cran.r-project.org/package=BSPADATA")
    (synopsis "Bayesian Proposal to Fit Spatial Econometric Models")
    (description
     "The purpose of this package is to fit the three Spatial Econometric Models
proposed in Anselin (1988, ISBN:9024737354) in the homoscedastic and the
heteroscedatic case.  The fit is made through MCMC algorithms and observational
working variables approach.")
    (license license:gpl2+)))

(define-public r-bsnsing
  (package
    (name "r-bsnsing")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bsnsing" version))
       (sha256
        (base32 "1a1vxh1fc5vz7wk73m093bvpz9qb52cfrxcj5yiqm56v309jckzk"))))
    (properties `((upstream-name . "bsnsing")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=bsnsing")
    (synopsis "Build Decision Trees with Optimal Multivariate Splits")
    (description
     "This package provides functions for training an optimal decision tree
classifier, making predictions and generating latex code for plotting.  Works
for two-class and multi-class classification problems.  The algorithm seeks the
optimal Boolean rule consisting of multiple variables to split a node, resulting
in shorter trees.  Use bsnsing() to build a tree, predict() to make predictions
and plot() to plot the tree into latex and PDF. See Yanchao Liu (2022)
<@code{arXiv:2205.15263>} for technical details.  Source code and more data sets
are at <https://github.com/profyliu/bsnsing/>.")
    (license license:gpl3)))

(define-public r-bsmd
  (package
    (name "r-bsmd")
    (version "2023.920")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BsMD" version))
       (sha256
        (base32 "13gwdgsq4c2g5y70adbz4hfgwygxna7k1q33q3sf0snz53y66gi9"))))
    (properties `((upstream-name . "BsMD")))
    (build-system r-build-system)
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=BsMD")
    (synopsis "Bayes Screening and Model Discrimination")
    (description "Bayes screening and model discrimination follow-up designs.")
    (license license:gpl3+)))

(define-public r-bsl
  (package
    (name "r-bsl")
    (version "3.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BSL" version))
       (sha256
        (base32 "12fksih33a4r6dd8flv7s1hidazhfqcbyvvnppp25rn6jb7p6344"))))
    (properties `((upstream-name . "BSL")))
    (build-system r-build-system)
    (propagated-inputs (list r-whitening
                             r-stringr
                             r-rdpack
                             r-rcpparmadillo
                             r-rcpp
                             r-mvtnorm
                             r-mass
                             r-gridextra
                             r-glasso
                             r-ggplot2
                             r-foreach
                             r-dorng
                             r-copula
                             r-coda))
    (home-page "https://cran.r-project.org/package=BSL")
    (synopsis "Bayesian Synthetic Likelihood")
    (description
     "Bayesian synthetic likelihood (BSL, Price et al. (2018)
<doi:10.1080/10618600.2017.1302882>) is an alternative to standard,
non-parametric approximate Bayesian computation (ABC).  BSL assumes a
multivariate normal distribution for the summary statistic likelihood and it is
suitable when the distribution of the model summary statistics is sufficiently
regular.  This package provides a Metropolis Hastings Markov chain Monte Carlo
implementation of four methods (BSL, @code{uBSL}, @code{semiBSL} and BSLmisspec)
and two shrinkage estimators (graphical lasso and Warton's estimator).
@code{uBSL} (Price et al. (2018) <doi:10.1080/10618600.2017.1302882>) uses an
unbiased estimator to the normal density.  A semi-parametric version of BSL
(@code{semiBSL}, An et al. (2018) <@code{arXiv:1809.05800>}) is more robust to
non-normal summary statistics.  BSLmisspec (Frazier et al.  2019
<@code{arXiv:1904.04551>}) estimates the Gaussian synthetic likelihood whilst
acknowledging that there may be incompatibility between the model and the
observed summary statistic.  Shrinkage estimation can help to decrease the
number of model simulations when the dimension of the summary statistic is high
(e.g., BSLasso, An et al. (2019) <doi:10.1080/10618600.2018.1537928>).
Extensions to this package are planned.  For a journal article describing how to
use this package, see An et al. (2022) <doi:10.18637/jss.v101.i11>.")
    (license license:gpl2+)))

(define-public r-bskyr
  (package
    (name "r-bskyr")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bskyr" version))
       (sha256
        (base32 "0qnw7mbsbg0w4afqnvrw69vzc7d1j5lpyk06zlpppkswp594bnbq"))))
    (properties `((upstream-name . "bskyr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-stringi
                             r-rlang
                             r-purrr
                             r-mime
                             r-lubridate
                             r-httr2
                             r-fs
                             r-dplyr
                             r-cli))
    (home-page "https://github.com/christopherkenny/bskyr")
    (synopsis "Interact with 'Bluesky' Social")
    (description
     "Collect data from and make posts on Bluesky Social via the Hypertext Transfer
Protocol (HTTP) Application Programming Interface (API), as documented at
<https://atproto.com/specs/xrpc>.  This further supports broader queries to the
Authenticated Transfer (AT) Protocol <https://atproto.com/> which Bluesky Social
relies on.  Data is returned in a tidy format and posts can be made using a
simple interface.")
    (license license:expat)))

(define-public r-bsitar
  (package
    (name "r-bsitar")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bsitar" version))
       (sha256
        (base32 "1h18hk5qr3lcxy0iqhz2wfxhvyjl1bs45y78skx070qbxrgwai5z"))))
    (properties `((upstream-name . "bsitar")))
    (build-system r-build-system)
    (propagated-inputs (list r-sitar
                             r-rstan
                             r-rlang
                             r-rdpack
                             r-marginaleffects
                             r-magrittr
                             r-loo
                             r-insight
                             r-dplyr
                             r-brms))
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "https://github.com/Sandhu-SS/bsitar")
    (synopsis
     "Bayesian Super Imposition by Translation and Rotation Growth Curve Analysis")
    (description
     "The Super Imposition by Translation and Rotation (SITAR) model is a
shape-invariant nonlinear mixed effect model that fits a natural cubic spline
mean curve to the growth data, and aligns individual-specific growth curves to
the underlying mean curve via a set of random effects (see Cole, 2010
<doi:10.1093/ije/dyq115> for details).  The non-Bayesian version of the SITAR
model can be fit by using an already available R package sitar'.  While sitar
package allows modelling of a single outcome only, the bsitar package offers a
great flexibility in fitting models of varying complexities that include joint
modelling of multiple outcomes such as height and weight (multivariate model).
Also, the bsitar package allows simultaneous analysis of a single outcome
separately for sub groups defined by a factor variable such as gender.  This is
achieved by fitting separate models for each sub group (such as males and
females for gender variable).  An advantage of such approach is that posterior
draws for each sub group are part of a single model object that makes it
possible to compare coefficients across groups and test hypotheses.  As bsitar
package is a front-end to the R package brms', it offers an excellent support
for post-processing of posterior draws via various functions that are directly
available from the brms package.  In addition, the bsitar package include
various customized functions that allow estimation and visualization growth
curves such as distance (increase in size with age) and velocity (change in
growth rate as a function of age).")
    (license license:gpl2)))

(define-public r-bsims
  (package
    (name "r-bsims")
    (version "0.3-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bSims" version))
       (sha256
        (base32 "1aflarz69rk4dlrp5gmgi6hs6mx0m8xhpp7ppv6m5gsjf8g18iaf"))))
    (properties `((upstream-name . "bSims")))
    (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-pbapply r-mefa4 r-mass r-intrval r-deldir))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://github.com/psolymos/bSims")
    (synopsis "Agent-Based Bird Point Count Simulator")
    (description
     "This package provides a highly scientific and utterly addictive bird point count
simulator to test statistical assumptions, aid survey design, and have fun while
doing it (Solymos 2024 <doi:10.1007/s42977-023-00183-2>).  The simulations
follow time-removal and distance sampling models based on Matsuoka et al. (2012)
<doi:10.1525/auk.2012.11190>, Solymos et al. (2013)
<doi:10.1111/2041-210X.12106>, and Solymos et al. (2018)
<doi:10.1650/CONDOR-18-32.1>, and sound attenuation experiments by Yip et al.
(2017) <doi:10.1650/CONDOR-16-93.1>.")
    (license license:gpl2)))

(define-public r-bsicons
  (package
    (name "r-bsicons")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bsicons" version))
       (sha256
        (base32 "1ifxb6qvm5m824n924pwa3f2ck33mzzbjq77jzdl36ikx4z0x47a"))))
    (properties `((upstream-name . "bsicons")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-htmltools r-cli))
    (home-page "https://github.com/rstudio/bsicons")
    (synopsis "Easily Work with 'Bootstrap' Icons")
    (description
     "Easily use Bootstrap icons inside Shiny apps and R Markdown documents.  More
generally, icons can be inserted in any htmltools document through inline SVG'.")
    (license license:expat)))

(define-public r-bsi
  (package
    (name "r-bsi")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bSi" version))
       (sha256
        (base32 "06ncvxf75khbwv5xg399717n3havj2ip0z07wzh9sjzx5irfw42v"))))
    (properties `((upstream-name . "bSi")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-ggpubr
                             r-ggplot2
                             r-dplyr
                             r-cowplot))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bSi")
    (synopsis
     "Modeling and Computing Biogenic Silica ('bSi') from Inland and Pelagic Sediments")
    (description
     "This package provides a collection of integrated tools designed to seamlessly
interact with each other for the analysis of biogenic silica @code{bSi} in
inland and marine sediments.  These tools share common data representations and
follow a consistent API design.  The primary goal of the @code{bSi} package is
to simplify the installation process, facilitate data loading, and enable the
analysis of multiple samples for biogenic silica fluxes.  This package is
designed to enhance the efficiency and coherence of the entire @code{bSi}
analytic workflow, from data loading to model construction and visualization
tailored towards reconstructing productivity in aquatic ecosystems.")
    (license license:gpl3)))

(define-public r-bshazard
  (package
    (name "r-bshazard")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bshazard" version))
       (sha256
        (base32 "1ghjb8rw6czrl1cy9c8wjap0vj4zvah2c1wdz2s7wff34gxynjmi"))))
    (properties `((upstream-name . "bshazard")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-epi))
    (home-page "https://cran.r-project.org/package=bshazard")
    (synopsis "Nonparametric Smoothing of the Hazard Function")
    (description
     "The function estimates the hazard function non parametrically from a survival
object (possibly adjusted for covariates).  The smoothed estimate is based on
B-splines from the perspective of generalized linear mixed models.  Left
truncated and right censoring data are allowed.  The package is based on the
work in Rebora P (2014) <doi:10.32614/RJ-2014-028>.")
    (license license:gpl2)))

(define-public r-bsgw
  (package
    (name "r-bsgw")
    (version "0.9.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BSGW" version))
       (sha256
        (base32 "05gr0p9fmj5560s083iss8j4gyh5rk0kdi3qhq5dzr9mfzy0n12q"))))
    (properties `((upstream-name . "BSGW")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mfusampler r-foreach r-doparallel))
    (home-page "https://cran.r-project.org/package=BSGW")
    (synopsis
     "Bayesian Survival Model with Lasso Shrinkage Using Generalized Weibull Regression")
    (description
     "Bayesian survival model using Weibull regression on both scale and shape
parameters.  Dependence of shape parameter on covariates permits deviation from
proportional-hazard assumption, leading to dynamic - i.e.  non-constant with
time - hazard ratios between subjects.  Bayesian Lasso shrinkage in the form of
two Laplace priors - one for scale and one for shape coefficients - allows for
many covariates to be included.  Cross-validation helper functions can be used
to tune the shrinkage parameters.  Monte Carlo Markov Chain (MCMC) sampling
using a Gibbs wrapper around Radford Neal's univariate slice sampler (R package
@code{MfUSampler}) is used for coefficient estimation.")
    (license license:gpl2+)))

(define-public r-bsgof
  (package
    (name "r-bsgof")
    (version "0.23.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bsgof" version))
       (sha256
        (base32 "0vgf61fglzlbgdk64z826nzb9rv37kw39rlk6m8rl84m3cjsw80a"))))
    (properties `((upstream-name . "bsgof")))
    (build-system r-build-system)
    (home-page "https://AppliedStat.GitHub.io/R/")
    (synopsis "Birnbaum-Saunders Goodness-of-Fit Test")
    (description
     "This package performs goodness of fit test for the Birnbaum-Saunders
distribution and provides the maximum likelihood estimate and the
method-of-moments estimate.  For more details, see Park and Wang (2013)
<@code{arXiv:2308.10150>}.  This work was supported by the National Research
Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No.
2022R1A2C1091319, RS-2023-00242528).")
    (license (list license:gpl2 license:gpl3))))

(define-public r-bsearchtools
  (package
    (name "r-bsearchtools")
    (version "0.0.61")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bsearchtools" version))
       (sha256
        (base32 "11fqz8kdhqpc3jq34vgv3frzawgsl5y0s83rb2p54yb5yhd3p68w"))))
    (properties `((upstream-name . "bsearchtools")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://github.com/digEmAll/bsearchtools")
    (synopsis "Binary Search Tools")
    (description
     "Exposes the binary search functions of the C++ standard library
(std::lower_bound, std::upper_bound) plus other convenience functions, allowing
faster lookups on sorted vectors.")
    (license license:gpl2+)))

(define-public r-bsda
  (package
    (name "r-bsda")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BSDA" version))
       (sha256
        (base32 "0i7jqx2a5nrabzzg27i3ag9w1dmasz2kxdbg5b469jcnnnk7kglb"))))
    (properties `((upstream-name . "BSDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-lattice r-e1071))
    (home-page "https://github.com/alanarnholt/BSDA")
    (synopsis "Basic Statistics and Data Analysis")
    (description
     "Data sets for book \"Basic Statistics and Data Analysis\" by Larry J. Kitchens.")
    (license license:gpl3)))

(define-public r-bscui
  (package
    (name "r-bscui")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bscui" version))
       (sha256
        (base32 "0qznfnqfji6vb0y2nawdi8xn6w7l1l8p1326ypjga1wla7ni5a52"))))
    (properties `((upstream-name . "bscui")))
    (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-webshot2 r-htmlwidgets))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://patzaw.github.io/bscui/")
    (synopsis "Build SVG Custom User Interface")
    (description
     "Render SVG as interactive figures to display contextual information, with
selectable and clickable user interface elements.  These figures can be
seamlessly integrated into rmarkdown and Quarto documents, as well as shiny
applications, allowing manipulation of elements and reporting actions performed
on them.  Additional features include pan, zoom in/out functionality, and the
ability to export the figures in SVG or PNG formats.")
    (license license:gpl3)))

(define-public r-bsamgp
  (package
    (name "r-bsamgp")
    (version "1.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bsamGP" version))
       (sha256
        (base32 "1792kksvpzinap83asyz6iz7mp7xyi0a7lppxx1m8as7jwnk2nsk"))))
    (properties `((upstream-name . "bsamGP")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-gridextra r-ggplot2))
    (native-inputs (list gfortran))
    (home-page "http://statlab2.korea.ac.kr/software/bsamgp")
    (synopsis
     "Bayesian Spectral Analysis Models using Gaussian Process Priors")
    (description
     "This package contains functions to perform Bayesian inference using a spectral
analysis of Gaussian process priors.  Gaussian processes are represented with a
Fourier series based on cosine basis functions.  Currently the package includes
parametric linear models, partial linear additive models with/without shape
restrictions, generalized linear additive models with/without shape
restrictions, and density estimation model.  To maximize computational
efficiency, the actual Markov chain Monte Carlo sampling for each model is done
using codes written in FORTRAN 90.  This software has been developed using
funding supported by Basic Science Research Program through the National
Research Foundation of Korea (NRF) funded by the Ministry of Education (no.
NRF-2016R1D1A1B03932178 and no.  NRF-2017R1D1A3B03035235).")
    (license license:gpl2+)))

(define-public r-bsam
  (package
    (name "r-bsam")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bsam" version))
       (sha256
        (base32 "1yiplh4wcc711w59ir5hxlp3mmgsnfz01d6203vfgclzsi7ix5m9"))))
    (properties `((upstream-name . "bsam")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-tibble
                             r-sp
                             r-rworldxtra
                             r-rjags
                             r-mvtnorm
                             r-msm
                             r-lubridate
                             r-gridextra
                             r-ggplot2
                             r-dplyr
                             r-coda))
    (home-page "<https://github.com/ianjonsen/bsam>")
    (synopsis "Bayesian State-Space Models for Animal Movement")
    (description
     "This package provides tools to fit Bayesian state-space models to animal
tracking data.  Models are provided for location filtering, location filtering
and behavioural state estimation, and their hierarchical versions.  The models
are primarily intended for fitting to ARGOS satellite tracking data but options
exist to fit to other tracking data types.  For Global Positioning System data,
consider the @code{moveHMM} package.  Simplified Markov Chain Monte Carlo
convergence diagnostic plotting is provided but users are encouraged to explore
tools available in packages such as coda and boa'.")
    (license license:gpl2)))

(define-public r-bsagri
  (package
    (name "r-bsagri")
    (version "0.1-10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BSagri" version))
       (sha256
        (base32 "096l2ilr4x2fbjxchkksxkbiyaf7wwdmzn4xyjx001w873x9dgf6"))))
    (properties `((upstream-name . "BSagri")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm
                             r-multcomp
                             r-mratios
                             r-mcpan
                             r-gamlss
                             r-boot))
    (home-page "https://cran.r-project.org/package=BSagri")
    (synopsis "Safety Assessment in Agricultural Field Trials")
    (description
     "Collection of functions, data sets and code examples for evaluations of field
trials with the objective of equivalence assessment.")
    (license license:gpl2)))

(define-public r-bs4dash
  (package
    (name "r-bs4dash")
    (version "2.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bs4Dash" version))
       (sha256
        (base32 "1swh87gx6qzkxqa9wf2x7f20pnpq94gm4dgvkhh3kwwz1gv9c2g2"))))
    (properties `((upstream-name . "bs4Dash")))
    (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-waiter
                             r-shiny
                             r-lifecycle
                             r-jsonlite
                             r-httr
                             r-httpuv
                             r-htmltools
                             r-fresh
                             r-bslib))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://github.com/RinteRface/bs4Dash")
    (synopsis "'Bootstrap 4' Version of 'shinydashboard'")
    (description
     "Make Bootstrap 4 Shiny dashboards.  Use the full power of @code{AdminLTE3}', a
dashboard template built on top of Bootstrap 4
<https://github.com/@code{ColorlibHQ/AdminLTE>}.")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-bs4cards
  (package
    (name "r-bs4cards")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bs4cards" version))
       (sha256
        (base32 "0fhjj3wjhxl263lhlbm5jw9l227a5k9cpannpankr5h8bkn1408h"))))
    (properties `((upstream-name . "bs4cards")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-magrittr r-htmltools))
    (home-page "https://github.com/djnavarro/bs4cards")
    (synopsis "Generate Bootstrap Cards")
    (description
     "Allows the user to generate bootstrap cards within R markdown documents.
Intended for use in conjunction with R markdown HTML outputs and other formats
that support the bootstrap 4 library.")
    (license license:expat)))

(define-public r-brxx
  (package
    (name "r-brxx")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brxx" version))
       (sha256
        (base32 "08qlfnf84k3yjcvr1wm6gd96mk26xf2gz59ychb3m981228hbbs6"))))
    (properties `((upstream-name . "brxx")))
    (build-system r-build-system)
    (propagated-inputs (list r-teachingdemos
                             r-rstan
                             r-mcmcpack
                             r-mass
                             r-gparotation
                             r-blme
                             r-blavaan))
    (home-page "https://cran.r-project.org/package=brxx")
    (synopsis "Bayesian Test Reliability Estimation")
    (description
     "When samples contain missing data, are small, or are suspected of bias,
estimation of scale reliability may not be trustworthy.  A recommended solution
for this common problem has been Bayesian model estimation.  Bayesian methods
rely on user specified information from historical data or researcher intuition
to more accurately estimate the parameters.  This package provides a user
friendly interface for estimating test reliability.  Here, reliability is
modeled as a beta distributed random variable with shape parameters alpha=true
score variance and beta=error variance (Tanzer & Harlow, 2020)
<doi:10.1080/00273171.2020.1854082>.")
    (license license:expat)))

(define-public r-brvm
  (package
    (name "r-brvm")
    (version "5.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BRVM" version))
       (sha256
        (base32 "0vkx0lbamfkmpcpd7kp85jqzf2fivp898yw0dvkgan8kkfqclra5"))))
    (properties `((upstream-name . "BRVM")))
    (build-system r-build-system)
    (propagated-inputs (list r-xts
                             r-xml2
                             r-tseries
                             r-timedate
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rvest
                             r-rlang
                             r-nortest
                             r-magrittr
                             r-lubridate
                             r-httr2
                             r-httr
                             r-highcharter
                             r-gsheet
                             r-goftest
                             r-formattable
                             r-fbasics
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://rpubs.com/Fredysessie/Readme_BRVM_Stock")
    (synopsis
     "Retrieve Historical Data of Companies Listed on the 'BRVM' Stock Exchange")
    (description
     "Provide real-time access to data from the Regional Securities Exchange
SA(<https://www.brvm.org/en>), commonly known as the BRVM stock exchange.  The
goal is to facilitate data access for users of the R programming language.  The
package includes a variety of data that can be accessed by calling functions.")
    (license license:expat)))

(define-public r-brunnermunzel
  (package
    (name "r-brunnermunzel")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brunnermunzel" version))
       (sha256
        (base32 "1sz1nx9lgl7pwgx9a5m59svajsk4qv0ajzzkw9123vrh0wydy6j3"))))
    (properties `((upstream-name . "brunnermunzel")))
    (build-system r-build-system)
    (native-inputs (list r-knitr gfortran))
    (home-page "https://github.com/toshi-ara/brunnermunzel")
    (synopsis "(Permuted) Brunner-Munzel Test")
    (description
     "This package provides the functions for Brunner-Munzel test and permuted
Brunner-Munzel test, which enable to use formula, matrix, and table as argument.
 These functions are based on Brunner and Munzel (2000)
<doi:10.1002/(SICI)1521-4036(200001)42:1%3C17::AID-BIMJ17%3E3.0.CO;2-U> and
Neubert and Brunner (2007) <doi:10.1016/j.csda.2006.05.024>, and are written
with FORTRAN.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-brulee
  (package
    (name "r-brulee")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brulee" version))
       (sha256
        (base32 "1lzn06q1i575n9b7axqxfp0ngycx2mdmh0cqw6zz80z3ikbi7hq9"))))
    (properties `((upstream-name . "brulee")))
    (build-system r-build-system)
    (propagated-inputs (list r-torch
                             r-tibble
                             r-rlang
                             r-hardhat
                             r-glue
                             r-ggplot2
                             r-generics
                             r-dplyr
                             r-coro
                             r-cli))
    (home-page "https://github.com/tidymodels/brulee")
    (synopsis "High-Level Modeling Functions with 'torch'")
    (description
     "This package provides high-level modeling functions to define and train models
using the torch R package.  Models include linear, logistic, and multinomial
regression as well as multilayer perceptrons.")
    (license license:expat)))

(define-public r-brugs
  (package
    (name "r-brugs")
    (version "0.9-2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BRugs" version))
       (sha256
        (base32 "002x1qliy2ziqgj56f10yw6n7n8rn60brzi1m0gayy3zb0jps9qm"))))
    (properties `((upstream-name . "BRugs")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-coda))
    (home-page "https://cran.r-project.org/package=BRugs")
    (synopsis "Interface to the 'OpenBUGS' MCMC Software")
    (description
     "Fully-interactive R interface to the @code{OpenBUGS} software for Bayesian
analysis using MCMC sampling.  Runs natively and stably in 32-bit R under
Windows.  Versions running on x86Linux and on 64-bit R under Windows are in
\"beta\" status and less efficient.")
    (license license:gpl2)))

(define-public r-brucer
  (package
    (name "r-brucer")
    (version "2024.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bruceR" version))
       (sha256
        (base32 "0ih2l2yf9vfmzx1l2npj8yp0brnqdiszq0ip26nw5vdgv9b7rm5l"))))
    (properties `((upstream-name . "bruceR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-texreg
                             r-stringr
                             r-rstudioapi
                             r-rio
                             r-psych
                             r-plyr
                             r-performance
                             r-mediation
                             r-lmertest
                             r-lavaan
                             r-jtools
                             r-interactions
                             r-ggplot2
                             r-emmeans
                             r-effectsize
                             r-dplyr
                             r-data-table
                             r-crayon
                             r-afex))
    (home-page "https://psychbruce.github.io/bruceR/")
    (synopsis "Broadly Useful Convenient and Efficient R Functions")
    (description
     "Broadly useful convenient and efficient R functions that bring users concise and
elegant R data analyses.  This package includes easy-to-use functions for (1)
basic R programming (e.g., set working directory to the path of currently opened
file; import/export data from/to files in any format; print tables to Microsoft
Word); (2) multivariate computation (e.g., compute scale sums/means/...  with
reverse scoring); (3) reliability analyses and factor analyses; (4) descriptive
statistics and correlation analyses; (5) t-test, multi-factor analysis of
variance (ANOVA), simple-effect analysis, and post-hoc multiple comparison; (6)
tidy report of statistical models (to R Console and Microsoft Word); (7)
mediation and moderation analyses (PROCESS); and (8) additional toolbox for
statistics and graphics.")
    (license license:gpl3)))

(define-public r-brsim
  (package
    (name "r-brsim")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brsim" version))
       (sha256
        (base32 "15clv6gfvvfclj9wmb6hr4ljfvs28spamq9rqj12nwzz5fihyhi9"))))
    (properties `((upstream-name . "brsim")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcmdrmisc r-corrplot r-cluster))
    (home-page "https://cran.r-project.org/package=brsim")
    (synopsis "Brainerd-Robinson Similarity Coefficient Matrix")
    (description
     "This package provides the facility to calculate the Brainerd-Robinson similarity
coefficient for the rows of an input table, and to calculate the significance of
each coefficient based on a permutation approach; a heatmap is produced to
visually represent the similarity matrix.  Optionally, hierarchical
agglomerative clustering can be performed and the silhouette method is used to
identify an optimal number of clusters; the results of the clustering can be
optionally used to sort the heatmap.")
    (license license:gpl2+)))

(define-public r-brq
  (package
    (name "r-brq")
    (version "3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Brq" version))
       (sha256
        (base32 "1gppp959i9kr8pdvn336v28gg9h0jl8qg6pqvzvp5wf257z8m4l8"))))
    (properties `((upstream-name . "Brq")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=Brq")
    (synopsis "Bayesian Analysis of Quantile Regression Models")
    (description
     "Bayesian estimation and variable selection for quantile regression models.")
    (license license:gpl3+)))

(define-public r-brpop
  (package
    (name "r-brpop")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brpop" version))
       (sha256
        (base32 "0zklcvzygps99xz9qk4k2jqnk4p5pxhrkf7pjh98b6d12mq84hcw"))))
    (properties `((upstream-name . "brpop")))
    (build-system r-build-system)
    (propagated-inputs (list r-zendown
                             r-tibble
                             r-rlang
                             r-magrittr
                             r-dtplyr
                             r-dplyr
                             r-data-table
                             r-checkmate))
    (home-page "https://rfsaldanha.github.io/brpop/")
    (synopsis "Brazilian Population Estimatives")
    (description
     "This package provides functions to handle and aggregate population estimates for
Brazilian municipalities by sex and age groups.")
    (license license:expat)))

(define-public r-browndog
  (package
    (name "r-browndog")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BrownDog" version))
       (sha256
        (base32 "0wnf1jcf4cakbvj9j1l7jk9mq7gd4j1il1ha2vglghqn04f27938"))))
    (properties `((upstream-name . "BrownDog")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcurl r-jsonlite r-httpuv))
    (home-page "https://cran.r-project.org/package=BrownDog")
    (synopsis "Brown Dog R Interface")
    (description
     "An R interface for the Brown Dog which allows researchers to leverage Brown Dog
Services that provides modules to identify the conversion options for a file, to
convert file to appropriate format, or to extract data from a file.  See
<http://browndog.ncsa.illinois.edu/> for more information.")
    (license (list (license:fsdg-compatible "FreeBSD")
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-brotli
  (package
    (name "r-brotli")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brotli" version))
       (sha256
        (base32 "0ihrf472ynjiby4k032cnj7383wvd5fi6rm19snjxmzxx7yc6xl7"))))
    (properties `((upstream-name . "brotli")))
    (build-system r-build-system)
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "https://jeroen.r-universe.dev/brotli")
    (synopsis "Compression Format Optimized for the Web")
    (description
     "This package provides a lossless compressed data format that uses a combination
of the LZ77 algorithm and Huffman coding
<https://www.rfc-editor.org/rfc/rfc7932>.  Brotli is similar in speed to deflate
(gzip) but offers more dense compression.")
    (license license:expat)))

(define-public r-broom-mixed
  (package
    (name "r-broom-mixed")
    (version "0.2.9.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "broom.mixed" version))
       (sha256
        (base32 "134qwdwhm0ih55n26hbyqb1hxj8d5k5jpc6gagpn9ny2vryzsszh"))))
    (properties `((upstream-name . "broom.mixed")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-purrr
                             r-nlme
                             r-furrr
                             r-forcats
                             r-dplyr
                             r-coda
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bbolker/broom.mixed")
    (synopsis "Tidying Methods for Mixed Models")
    (description
     "Convert fitted objects from various R mixed-model packages into tidy data frames
along the lines of the broom package.  The package provides three S3 generics
for each model: tidy(), which summarizes a model's statistical findings such as
coefficients of a regression; augment(), which adds columns to the original data
such as predictions, residuals and cluster assignments; and glance(), which
provides a one-row summary of model-level statistics.")
    (license license:gpl3)))

(define-public r-broman
  (package
    (name "r-broman")
    (version "0.84")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "broman" version))
       (sha256
        (base32 "01q05xvxlam6isdhaq48sfw5fh60pcqlhivs7c38lh7c8r3w2172"))))
    (properties `((upstream-name . "broman")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://github.com/kbroman/broman")
    (synopsis "Karl Broman's R Code")
    (description
     "Miscellaneous R functions, including functions related to graphics (mostly for
base graphics), permutation tests, running mean/median, and general utilities.")
    (license license:gpl3)))

(define-public r-brolgar
  (package
    (name "r-brolgar")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brolgar" version))
       (sha256
        (base32 "19ps8faycijfqlmzhmcbf03gpcfwdayica456zqyark4pl2n0gjq"))))
    (properties `((upstream-name . "brolgar")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tsibble
                             r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-glue
                             r-ggplot2
                             r-fabletools
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/njtierney/brolgar")
    (synopsis
     "Browse Over Longitudinal Data Graphically and Analytically in R")
    (description
     "This package provides a framework of tools to summarise, visualise, and explore
longitudinal data.  It builds upon the tidy time series data frames used in the
tsibble package, and is designed to integrate within the tidyverse', and
tidyverts (for time series) ecosystems.  The methods implemented include
calculating features for understanding longitudinal data, including calculating
summary statistics such as quantiles, medians, and numeric ranges, sampling
individual series, identifying individual series representative of a group, and
extending the facet system in ggplot2 to facilitate exploration of samples of
data.  These methods are fully described in the paper \"brolgar: An R package to
Browse Over Longitudinal Data Graphically and Analytically in R\", Nicholas
Tierney, Dianne Cook, Tania Prvan (2020) <doi:10.32614/RJ-2022-023>.")
    (license license:expat)))

(define-public r-brokenstick
  (package
    (name "r-brokenstick")
    (version "2.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brokenstick" version))
       (sha256
        (base32 "0bq470kvv9084qaaaz5yicmxpljp23gb3dqijrmdwk1agqxn09mc"))))
    (properties `((upstream-name . "brokenstick")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rlang
                             r-matrixsampling
                             r-lme4
                             r-dplyr
                             r-coda))
    (native-inputs (list r-knitr))
    (home-page "doi:10.18637/jss.v106.i07")
    (synopsis "Broken Stick Model for Irregular Longitudinal Data")
    (description
     "Data on multiple individuals through time are often sampled at times that differ
between persons.  Irregular observation times can severely complicate the
statistical analysis of the data.  The broken stick model approximates each
subjectâs trajectory by one or more connected line segments.  The times at
which segments connect (breakpoints) are identical for all subjects and under
control of the user.  A well-fitting broken stick model effectively transforms
individual measurements made at irregular times into regular trajectories with
common observation times.  Specification of the model requires three variables:
time, measurement and subject.  The model is a special case of the linear mixed
model, with time as a linear B-spline and subject as the grouping factor.  The
main assumptions are: subjects are exchangeable, trajectories between
consecutive breakpoints are straight, random effects follow a multivariate
normal distribution, and unobserved data are missing at random.  The package
contains functions for fitting the broken stick model to data, for predicting
curves in new data and for plotting broken stick estimates.  The package
supports two optimization methods, and includes options to structure the
variance-covariance matrix of the random effects.  The analyst may use the
software to smooth growth curves by a series of connected straight lines, to
align irregularly observed curves to a common time grid, to create synthetic
curves at a user-specified set of breakpoints, to estimate the time-to-time
correlation matrix and to predict future observations.  See
<doi:10.18637/jss.v106.i07> for additional documentation on background,
methodology and applications.")
    (license license:expat)))

(define-public r-brokenadaptiveridge
  (package
    (name "r-brokenadaptiveridge")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BrokenAdaptiveRidge" version))
       (sha256
        (base32 "0f46wwyfcqslk25cbm63pbnp8bwamqhr4g4wdlrqn666yiwn1sc2"))))
    (properties `((upstream-name . "BrokenAdaptiveRidge")))
    (build-system r-build-system)
    (propagated-inputs (list r-futile-logger r-cyclops r-bit64))
    (home-page "https://cran.r-project.org/package=BrokenAdaptiveRidge")
    (synopsis "Broken Adaptive Ridge Regression with Cyclops")
    (description
     "Approximates best-subset selection (L0) regression with an iteratively adaptive
Ridge (L2) penalty for large-scale models.  This package uses Cyclops for an
efficient implementation and the iterative method is described in Kawaguchi et
al (2020) <doi:10.1002/sim.8438> and Li et al (2021)
<doi:10.1016/j.jspi.2020.12.001>.")
    (license license:asl2.0)))

(define-public r-brnn
  (package
    (name "r-brnn")
    (version "0.9.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brnn" version))
       (sha256
        (base32 "14gqbsrmv9r5kbp4c5swrpclympchyg4fw5f0b6g767y6mm22h7m"))))
    (properties `((upstream-name . "brnn")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm r-formula))
    (home-page "https://cran.r-project.org/package=brnn")
    (synopsis "Bayesian Regularization for Feed-Forward Neural Networks")
    (description "Bayesian regularization for feed-forward neural networks.")
    (license license:gpl2)))

(define-public r-brmsmargins
  (package
    (name "r-brmsmargins")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brmsmargins" version))
       (sha256
        (base32 "1qdfig1cy5cf3csky995m0jybb2hyp7yq7ka67wircrln919c1nm"))))
    (properties `((upstream-name . "brmsmargins")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-posterior
                             r-extraoperators
                             r-data-table
                             r-brms
                             r-bayestestr))
    (native-inputs (list r-knitr))
    (home-page "https://joshuawiley.com/brmsmargins/")
    (synopsis "Bayesian Marginal Effects for 'brms' Models")
    (description
     "Calculate Bayesian marginal effects, average marginal effects, and marginal
coefficients (also called population averaged coefficients) for models fit using
the brms package including fixed effects, mixed effects, and location scale
models.  These are based on marginal predictions that integrate out random
effects if necessary (see for example <doi:10.1186/s12874-015-0046-6> and
<doi:10.1111/biom.12707>).")
    (license license:gpl3+)))

(define-public r-brms-mmrm
  (package
    (name "r-brms-mmrm")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brms.mmrm" version))
       (sha256
        (base32 "0y0bhdkpfs5xx9a5dcs00a69bzwiarf90ap34j07ym0f19fsnj0n"))))
    (properties `((upstream-name . "brms.mmrm")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-trialr
                             r-tidyselect
                             r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-posterior
                             r-mass
                             r-ggridges
                             r-ggplot2
                             r-dplyr
                             r-brms))
    (native-inputs (list r-knitr))
    (home-page "https://openpharma.github.io/brms.mmrm/")
    (synopsis "Bayesian MMRMs using 'brms'")
    (description
     "The mixed model for repeated measures (MMRM) is a popular model for longitudinal
clinical trial data with continuous endpoints, and brms is a powerful and
versatile package for fitting Bayesian regression models.  The brms.mmrm R
package leverages brms to run MMRMs, and it supports a simplified interfaced to
reduce difficulty and align with the best practices of the life sciences.
References: BÃ¼rkner (2017) <doi:10.18637/jss.v080.i01>, Mallinckrodt (2008)
<doi:10.1177/009286150804200402>.")
    (license license:expat)))

(define-public r-brm
  (package
    (name "r-brm")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brm" version))
       (sha256
        (base32 "0kyr8mxz5g3w41kp08d47f29cff8fqrfbm1ri7hgfg2lgr1151xy"))))
    (properties `((upstream-name . "brm")))
    (build-system r-build-system)
    (home-page "http://github.com/mclements/brm")
    (synopsis "Binary Regression Model")
    (description
     "Fits novel models for the conditional relative risk, risk difference and odds
ratio <doi:10.1080/01621459.2016.1192546>.")
    (license license:expat)))

(define-public r-brlrmr
  (package
    (name "r-brlrmr")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brlrmr" version))
       (sha256
        (base32 "0s47qz8y416x0gz91pzdpr97pmlvddwkvll454vnlafxxk9p5lds"))))
    (properties `((upstream-name . "brlrmr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-profilemodel r-mass r-brglm r-boot))
    (home-page "https://cran.r-project.org/package=brlrmr")
    (synopsis "Bias Reduction with Missing Binary Response")
    (description
     "This package provides two main functions, il() and fil().  The il() function
implements the EM algorithm developed by Ibrahim and Lipsitz (1996)
<DOI:10.2307/2533068> to estimate the parameters of a logistic regression model
with the missing response when the missing data mechanism is nonignorable.  The
fil() function implements the algorithm proposed by Maity et.  al. (2017+)
<https://github.com/arnabkrmaity/brlrmr> to reduce the bias produced by the
method of Ibrahim and Lipsitz (1996) <DOI:10.2307/2533068>.")
    (license license:gpl3)))

(define-public r-brl
  (package
    (name "r-brl")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BRL" version))
       (sha256
        (base32 "13qi78v3057qn4hfby14sp26hy3ibl50f06x8gpak6gi76g8bhwi"))))
    (properties `((upstream-name . "BRL")))
    (build-system r-build-system)
    (home-page "https://github.com/msadinle/BRL")
    (synopsis "Beta Record Linkage")
    (description
     "Implementation of the record linkage methodology proposed by Sadinle (2017)
<doi:10.1080/01621459.2016.1148612>.  It handles the bipartite record linkage
problem, where two duplicate-free datafiles are to be merged.")
    (license license:gpl3)))

(define-public r-brisk
  (package
    (name "r-brisk")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brisk" version))
       (sha256
        (base32 "1l6m47rj25klrjil0275bnv0m149d2kjh23r8cv7zw2vhmlza7z1"))))
    (properties `((upstream-name . "brisk")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rlang
                             r-purrr
                             r-hitandrun
                             r-ggplot2
                             r-ellipsis
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://rich-payne.github.io/brisk/")
    (synopsis "Bayesian Benefit Risk Analysis")
    (description
     "Quantitative methods for benefit-risk analysis help to condense complex
decisions into a univariate metric describing the overall benefit relative to
risk.  One approach is to use the multi-criteria decision analysis framework
(MCDA), as in Mussen, Salek, and Walker (2007) <doi:10.1002/pds.1435>.  Bayesian
benefit-risk analysis incorporates uncertainty through posterior distributions
which are inputs to the benefit-risk framework.  The brisk package provides
functions to assist with Bayesian benefit-risk analyses, such as MCDA. Users
input posterior samples, utility functions, weights, and the package outputs
quantitative benefit-risk scores.  The posterior of the benefit-risk scores for
each group can be compared.  Some plotting capabilities are also included.")
    (license license:expat)))

(define-public r-brisc
  (package
    (name "r-brisc")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BRISC" version))
       (sha256
        (base32 "1pmi34xfiij053pqr04r13679khrilqmhw7jj527hbqdwnk53gjr"))))
    (properties `((upstream-name . "BRISC")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdist r-rann r-pbapply r-matrixstats))
    (home-page "https://github.com/ArkajyotiSaha/BRISC")
    (synopsis "Fast Inference for Large Spatial Datasets using BRISC")
    (description
     "Fits bootstrap with univariate spatial regression models using Bootstrap for
Rapid Inference on Spatial Covariances (BRISC) for large datasets using nearest
neighbor Gaussian processes detailed in Saha and Datta (2018)
<doi:10.1002/sta4.184>.")
    (license license:gpl2+)))

(define-public r-briqr
  (package
    (name "r-briqr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "briqr" version))
       (sha256
        (base32 "073pdk1094ng4kggzdjvihl791r2qlja1j2w3pfmh2027kn520r8"))))
    (properties `((upstream-name . "briqr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-magrittr r-jsonlite r-httr r-dplyr))
    (home-page "https://cran.r-project.org/package=briqr")
    (synopsis "Interface to the 'Briq' API")
    (description
     "An interface to the Briq API <https://briq.github.io>.  Briq is a tool that aims
to promote employee engagement by helping employees recognize and reward each
other.  Employees can praise and thank one another (for achieving a company
goal, for example) by giving virtual credits (known as briqs or bqs') that can
be redeemed for various rewards.  The Briq API lets you create, read, update and
delete users, user groups, transactions and messages.  This package provides
functions that simplify getting the users, user groups and transactions of your
organization into R.")
    (license license:expat)))

(define-public r-brinton
  (package
    (name "r-brinton")
    (version "0.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brinton" version))
       (sha256
        (base32 "1z2d7a1wq5ms5j0mn2hgqvvp9x3mfvxw4llia8d0bs0mpm0jkjvn"))))
    (properties `((upstream-name . "brinton")))
    (build-system r-build-system)
    (inputs (list pandoc))
    (propagated-inputs (list r-tibble
                             r-sm
                             r-scales
                             r-rmarkdown
                             r-rcolorbrewer
                             r-patchwork
                             r-pander
                             r-lubridate
                             r-gridextra
                             r-glue
                             r-ggplot2
                             r-ggally
                             r-forcats))
    (native-inputs (list r-knitr))
    (home-page "https://sciencegraph.github.io/brinton/")
    (synopsis "Graphical EDA Tool")
    (description
     "An automated graphical exploratory data analysis (EDA) tool that introduces: a.)
wideplot graphics for exploring the structure of a dataset through a grid of
variables and graphic types.  b.) longplot graphics, which present the entire
catalog of available graphics for representing a particular variable using a
grid of graphic types and variations on these types.  c.) plotup function, which
presents a particular graphic for a specific variable of a dataset.  The
plotup() function also makes it possible to obtain the code used to generate the
graphic, meaning that the user can adjust its properties as needed.  d.)
matrixplot graphics that is a grid of a particular graphic showing bivariate
relationships between all pairs of variables of a certain(s) type(s) in a
multivariate data set.")
    (license license:gpl3)))

(define-public r-brinda
  (package
    (name "r-brinda")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BRINDA" version))
       (sha256
        (base32 "0jxw3jxxp5k3q80pyiwlg4s5z5vbaplvf5bqb092hppgvkk2q2r5"))))
    (properties `((upstream-name . "BRINDA")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-hmisc r-dplyr r-data-table
                             r-berryfunctions))
    (home-page "https://github.com/hanqiluo/BRINDA")
    (synopsis
     "Computation of BRINDA Adjusted Micronutrient Biomarkers for Inflammation")
    (description
     "Inflammation can affect many micronutrient biomarkers and can thus lead to
incorrect diagnosis of individuals and to over- or under-estimate the prevalence
of deficiency in a population.  Biomarkers Reflecting Inflammation and
Nutritional Determinants of Anemia (BRINDA) is a multi-agency and multi-country
partnership designed to improve the interpretation of nutrient biomarkers in
settings of inflammation and to generate context-specific estimates of risk
factors for anemia (Suchdev (2016) <doi:10.3945/an.115.010215>).  In the past
few years, BRINDA published a series of papers to provide guidance on how to
adjust micronutrient biomarkers, retinol binding protein, serum retinol, serum
ferritin by Namaste (2020), soluble transferrin receptor (@code{sTfR}), serum
zinc, serum and Red Blood Cell (RBC) folate, and serum B-12, using inflammation
markers, alpha-1-acid glycoprotein (AGP) and/or C-Reactive Protein (CRP) by
Namaste (2020) <doi:10.1093/ajcn/nqaa141>, Rohner (2017)
<doi:10.3945/ajcn.116.142232>, @code{McDonald} (2020) <doi:10.1093/ajcn/nqz304>,
and Young (2020) <doi:10.1093/ajcn/nqz303>.  The BRINDA inflammation adjustment
method mainly focuses on Women of Reproductive Age (WRA) and Preschool-age
Children (PSC); however, the general principle of the BRINDA method might apply
to other population groups.  The BRINDA R package is a user-friendly all-in-one
R package that uses a series of functions to implement BRINDA adjustment method,
as described above.  The BRINDA R package will first carry out rigorous checks
and provides users guidance to correct data or input errors (if they occur)
prior to inflammation adjustments.  After no errors are detected, the package
implements the BRINDA inflammation adjustment for up to five micronutrient
biomarkers, namely retinol-binding-protein, serum retinol, serum ferritin,
@code{sTfR}, and serum zinc (when appropriate), using inflammation indicators of
AGP and/or CRP for various population groups.  Of note, adjustment for serum and
RBC folate and serum B-12 is not included in the R package, since evidence shows
that no adjustment is needed for these micronutrient biomarkers in either WRA or
PSC groups (Young (2020) <doi:10.1093/ajcn/nqz303>).")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-brikmeans
  (package
    (name "r-brikmeans")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "briKmeans" version))
       (sha256
        (base32 "158q2yb4dj4vhw8warv005s3rr8svr2vpkp2insgqv8mbvz7kzwq"))))
    (properties `((upstream-name . "briKmeans")))
    (build-system r-build-system)
    (propagated-inputs (list r-splines2 r-depthtools r-cluster r-boot))
    (home-page "https://cran.r-project.org/package=briKmeans")
    (synopsis
     "Package for Brik, Fabrik and Fdebrik Algorithms to Initialise Kmeans")
    (description
     "Implementation of the BRIk, FABRIk and FDEBRIk algorithms to initialise k-means.
 These methods are intended for the clustering of multivariate and functional
data, respectively.  They make use of the Modified Band Depth and bootstrap to
identify appropriate initial seeds for k-means, which are proven to be better
options than many techniques in the literature.  Torrente and Romo (2021)
<doi:10.1007/s00357-020-09372-3> It makes use of the functions kma and
kma.similarity, from the archived package fdakma, by Alice Parodi et al.")
    (license license:gpl3+)))

(define-public r-brif
  (package
    (name "r-brif")
    (version "1.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brif" version))
       (sha256
        (base32 "0zavddjs6y37ll68s0c3ap9p79disv7v54505pv6nyxqh40s22m8"))))
    (properties `((upstream-name . "brif")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=brif")
    (synopsis "Tree and Forest Tool for Classification and Regression")
    (description
     "Build decision trees and random forests for classification and regression.  The
implementation strikes a balance between minimizing computing efforts and
maximizing the expected predictive accuracy, thus scales well to large data
sets.  Multi-threading is available through @code{OpenMP}
<https://gcc.gnu.org/wiki/openmp>.")
    (license license:gpl2+)))

(define-public r-bridger2
  (package
    (name "r-bridger2")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bridger2" version))
       (sha256
        (base32 "0clp42nnbvd01n65azdi2ghp0mfqwsipzdl2d30q04lcvfkdpxrf"))))
    (properties `((upstream-name . "bridger2")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinydashboard
                             r-shiny
                             r-plotly
                             r-outliers
                             r-ggplot2
                             r-data-table
                             r-bsda))
    (home-page "https://cran.r-project.org/package=bridger2")
    (synopsis "Genome-Wide RNA Degradation Analysis Using BRIC-Seq Data")
    (description
     "BRIC-seq is a genome-wide approach for determining RNA stability in mammalian
cells.  This package provides a series of functions for performing quality check
of your BRIC-seq data, calculation of RNA half-life for each transcript and
comparison of RNA half-lives between two conditions.")
    (license license:expat)))

(define-public r-bridger
  (package
    (name "r-bridger")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bridger" version))
       (sha256
        (base32 "1rx16zwa3zz4bs7h0b75vsx78dvkif8d9c7prb5pmwccizll7v53"))))
    (properties `((upstream-name . "bridger")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-scales
                             r-pdftools
                             r-patchwork
                             r-magrittr
                             r-kableextra
                             r-gridextra
                             r-glue
                             r-ggplot2
                             r-ggedit
                             r-dplyr
                             r-cowplot))
    (home-page "https://github.com/CommoditiesAI/bridger")
    (synopsis "Bridge Hand Generator with Criteria Selector")
    (description
     "Produce bridge hands, allowing parameters for hands to offer specific for
bidding sequences.")
    (license license:gpl3+)))

(define-public r-bridgedist
  (package
    (name "r-bridgedist")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bridgedist" version))
       (sha256
        (base32 "06y4c4ym27dh39ysr5sf4ymad0bk7wxyf8kmj1y560p7h97f5d32"))))
    (properties `((upstream-name . "bridgedist")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/swihart/bridgedist")
    (synopsis
     "An Implementation of the Bridge Distribution with Logit-Link as in Wang and Louis (2003)")
    (description
     "An implementation of the bridge distribution with logit-link in R. In Wang and
Louis (2003) <DOI:10.1093/biomet/90.4.765>, such a univariate bridge
distribution was derived as the distribution of the random intercept that
bridged a marginal logistic regression and a conditional logistic regression.
The conditional and marginal regression coefficients are a scalar multiple of
each other.  Such is not the case if the random intercept distribution was
Gaussian.")
    (license license:gpl2+)))

(define-public r-brickset
  (package
    (name "r-brickset")
    (version "2024.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brickset" version))
       (sha256
        (base32 "0jfjqrc2d75zhygfxpschn2mqgjc35wwgczqrh9d3f58amkg5m61"))))
    (properties `((upstream-name . "brickset")))
    (build-system r-build-system)
    (propagated-inputs (list r-piggyback r-jsonlite r-httr r-dplyr))
    (home-page "https://github.com/jbryer/brickset")
    (synopsis
     "Interface with the Brickset API for Getting Data About LEGO Sets")
    (description
     "Interface with the Brickset API
<https://brickset.com/article/52664/api-version-3-documentation> for getting
data about LEGO sets.  Data sets that can be used for teaching and learning
without the need of a Brickset account and API key are also included.  Includes
all LEGO since through the end of 2023.")
    (license license:gpl3+)))

(define-public r-brglm2
  (package
    (name "r-brglm2")
    (version "0.9.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brglm2" version))
       (sha256
        (base32 "0islmx4kdy22qn2cq7lpzqq6lc6mci5qly645nfdhad9001jmhsa"))))
    (properties `((upstream-name . "brglm2")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv r-nnet r-matrix r-mass r-enrichwith))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ikosmidis/brglm2")
    (synopsis "Bias Reduction in Generalized Linear Models")
    (description
     "Estimation and inference from generalized linear models based on various methods
for bias reduction and maximum penalized likelihood with powers of the Jeffreys
prior as penalty.  The @code{brglmFit} fitting method can achieve reduction of
estimation bias by solving either the mean bias-reducing adjusted score
equations in Firth (1993) <doi:10.1093/biomet/80.1.27> and Kosmidis and Firth
(2009) <doi:10.1093/biomet/asp055>, or the median bias-reduction adjusted score
equations in Kenne et al. (2017) <doi:10.1093/biomet/asx046>, or through the
direct subtraction of an estimate of the bias of the maximum likelihood
estimator from the maximum likelihood estimates as in Cordeiro and
@code{McCullagh} (1991) <https://www.jstor.org/stable/2345592>.  See Kosmidis et
al (2020) <doi:10.1007/s11222-019-09860-6> for more details.  Estimation in all
cases takes place via a quasi Fisher scoring algorithm, and S3 methods for the
construction of of confidence intervals for the reduced-bias estimates are
provided.  In the special case of generalized linear models for binomial and
multinomial responses (both ordinal and nominal), the adjusted score approaches
to mean and media bias reduction have been found to return estimates with
improved frequentist properties, that are also always finite, even in cases
where the maximum likelihood estimates are infinite (e.g. complete and
quasi-complete separation; see Kosmidis and Firth, 2020
<doi:10.1093/biomet/asaa052>, for a proof for mean bias reduction in logistic
regression).")
    (license license:gpl3)))

(define-public r-bretigea
  (package
    (name "r-bretigea")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BRETIGEA" version))
       (sha256
        (base32 "0q4phggz6smzd294znr1fp0gskpcwdbd6hnvvl3q3z1h7cpf40sn"))))
    (properties `((upstream-name . "BRETIGEA")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BRETIGEA")
    (synopsis "Brain Cell Type Specific Gene Expression Analysis")
    (description
     "Analysis of relative cell type proportions in bulk gene expression data.
Provides a well-validated set of brain cell type-specific marker genes derived
from multiple types of experiments, as described in @code{McKenzie} (2018)
<doi:10.1038/s41598-018-27293-5>.  For brain tissue data sets, there are marker
genes available for astrocytes, endothelial cells, microglia, neurons,
oligodendrocytes, and oligodendrocyte precursor cells, derived from each of
human, mice, and combination human/mouse data sets.  However, if you have access
to your own marker genes, the functions can be applied to bulk gene expression
data from any tissue.  Also implements multiple options for relative cell type
proportion estimation using these marker genes, adapting and expanding on
approaches from the @code{CellCODE} R package described in Chikina (2015)
<doi:10.1093/bioinformatics/btv015>.  The number of cell type marker genes used
in a given analysis can be increased or decreased based on your preferences and
the data set.  Finally, provides functions to use the estimates to adjust for
variability in the relative proportion of cell types across samples prior to
downstream analyses.")
    (license license:expat)))

(define-public r-breeze
  (package
    (name "r-breeze")
    (version "0.4-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bReeze" version))
       (sha256
        (base32 "1zx933iaxqp91s41dham2chzf0f4rxs0d1ww564gg352fs3gi1vn"))))
    (properties `((upstream-name . "bReeze")))
    (build-system r-build-system)
    (propagated-inputs (list r-lubridate))
    (home-page "https://github.com/chgrl/bReeze")
    (synopsis "Functions for Wind Resource Assessment")
    (description
     "This package provides a collection of functions to analyse, visualize and
interpret wind data and to calculate the potential energy production of wind
turbines.")
    (license license:expat)))

(define-public r-breathteststan
  (package
    (name "r-breathteststan")
    (version "0.8.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "breathteststan" version))
       (sha256
        (base32 "1b495qb0r8wdh4rnv3namnhrd1d6mba55ayj7h9s0076h8aw04hk"))))
    (properties `((upstream-name . "breathteststan")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppeigen
                             r-rcpp
                             r-purrr
                             r-dplyr
                             r-breathtestcore
                             r-bh))
    (home-page "https://github.com/dmenne/breathteststan")
    (synopsis "Stan-Based Fit to Gastric Emptying Curves")
    (description
     "Stan-based curve-fitting function for use with package breathtestcore by the
same author.  Stan functions are refactored here for easier testing.")
    (license license:gpl3+)))

(define-public r-breathtestcore
  (package
    (name "r-breathtestcore")
    (version "0.8.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "breathtestcore" version))
       (sha256
        (base32 "0i5mih2v6397g0glgdhn5lrz3j0l6yv3pmlvs9pkg2vbjppif2sc"))))
    (properties `((upstream-name . "breathtestcore")))
    (build-system r-build-system)
    (inputs (list pandoc))
    (propagated-inputs (list r-xml2
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-signal
                             r-readxl
                             r-readr
                             r-purrr
                             r-nlme
                             r-multcomp
                             r-mass
                             r-ggplot2
                             r-ggfittext
                             r-dplyr
                             r-broom
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/dmenne/breathtestcore")
    (synopsis
     "Core Functions to Read and Fit 13c Time Series from Breath Tests")
    (description
     "Reads several formats of 13C data (IRIS/Wagner, @code{BreathID}) and CSV.
Creates artificial sample data for testing.  Fits Maes/Ghoos, Bluck-Coward
self-correcting formula using nls', nlme'.  Methods to fit breath test curves
with Bayesian Stan methods are refactored to package breathteststan'.  For a
Shiny GUI, see package dmenne/breathtestshiny on github.")
    (license license:gpl3)))

(define-public r-breakpoints
  (package
    (name "r-breakpoints")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BreakPoints" version))
       (sha256
        (base32 "125fvzkmfnriyd4y4nywj5ihm766xlsyi7dpnq57fa9d38ffbyf2"))))
    (properties `((upstream-name . "BreakPoints")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-mass))
    (home-page "https://cran.r-project.org/package=BreakPoints")
    (synopsis "Identify Breakpoints in Series of Data")
    (description
     "Compute Buishand Range Test, Pettit Test, SNHT, Student t-test, and Mann-Whitney
Rank Test, to identify breakpoints in series.  For all functions NA is allowed.
Since all of the mention methods identify only one breakpoint in a series, a
general function to look for N breakpoint is given.  Also, the Yamamoto test for
climate jump is available.  Alexandersson, H. (1986)
<doi:10.1002/joc.3370060607>, Buishand, T. (1982)
<doi:10.1016/0022-1694(82)90066-X>, Hurtado, S. I., Zaninelli, P. G., & Agosta,
E. A. (2020) <doi:10.1016/j.atmosres.2020.104955>, Mann, H. B., Whitney, D. R.
(1947) <doi:10.1214/aoms/1177730491>, Pettitt, A. N. (1979)
<doi:10.2307/2346729>, Ruxton, G. D., jul (2006) <doi:10.1093/beheco/ark016>,
Yamamoto, R., Iwashima, T., Kazadi, S. N., & Hoshiai, M. (1985)
<doi:10.2151/jmsj1965.63.6_1157>.")
    (license license:gpl3)))

(define-public r-breakpoint
  (package
    (name "r-breakpoint")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "breakpoint" version))
       (sha256
        (base32 "004vi1qr7iib8ykg6sp7xzv0bb841h4vsz2x0cyrhkdp41frglx9"))))
    (properties `((upstream-name . "breakpoint")))
    (build-system r-build-system)
    (propagated-inputs (list r-msm r-mass r-ggplot2 r-foreach r-doparallel))
    (home-page "https://github.com/madawaweer/breakpoint")
    (synopsis
     "An R Package for Multiple Break-Point Detection via the Cross-Entropy Method")
    (description
     "This package implements the Cross-Entropy (CE) method, which is a model based
stochastic optimization technique to estimate both the number and their
corresponding locations of break-points in continuous and discrete measurements
(Priyadarshana and Sofronov (2015), Priyadarshana and Sofronov (2012a),
Priyadarshana and Sofronov (2012b)).")
    (license license:gpl2+)))

(define-public r-breakfast
  (package
    (name "r-breakfast")
    (version "2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "breakfast" version))
       (sha256
        (base32 "027q949qqyxlj58a4qpn6a2qcaqzawgzmck8ab1dnfgpyi0vkrpm"))))
    (properties `((upstream-name . "breakfast")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-plyr r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=breakfast")
    (synopsis
     "Methods for Fast Multiple Change-Point/Break-Point Detection and Estimation")
    (description
     "This package provides a developing software suite for multiple change-point and
change-point-type feature detection/estimation (data segmentation) in data
sequences.")
    (license license:gpl2)))

(define-public r-breakdown
  (package
    (name "r-breakdown")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "breakDown" version))
       (sha256
        (base32 "1p02qy5kpv98qxlawn6l2hq7q48kv97ks3s9jnxawhqz44c08lwv"))))
    (properties `((upstream-name . "breakDown")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://pbiecek.github.io/breakDown/")
    (synopsis "Model Agnostic Explainers for Individual Predictions")
    (description
     "Model agnostic tool for decomposition of predictions from black boxes.  Break
Down Table shows contributions of every variable to a final prediction.  Break
Down Plot presents variable contributions in a concise graphical way.  This
package work for binary classifiers and general regression models.")
    (license license:gpl2)))

(define-public r-breakaway
  (package
    (name "r-breakaway")
    (version "4.8.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "breakaway" version))
       (sha256
        (base32 "08mxp41d79iaqbz96bpzw7halasijghv0557ccz5p563y5lk3n3m"))))
    (properties `((upstream-name . "breakaway")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-phyloseq
                             r-mass
                             r-magrittr
                             r-lme4
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://adw96.github.io/breakaway/")
    (synopsis "Species Richness Estimation and Modeling")
    (description
     "Understanding the drivers of microbial diversity is an important frontier of
microbial ecology, and investigating the diversity of samples from microbial
ecosystems is a common step in any microbiome analysis.  breakaway is the
premier package for statistical analysis of microbial diversity.  breakaway
implements the latest and greatest estimates of species richness, described in
Willis and Bunge (2015) <doi:10.1111/biom.12332>, Willis et al. (2017)
<doi:10.1111/rssc.12206>, and Willis (2016) <@code{arXiv:1604.02598>}, as well
as the most commonly used estimates, including the objective Bayes approach
described in Barger and Bunge (2010) <doi:10.1214/10-BA527>.")
    (license license:gpl2)))

(define-public r-breadr
  (package
    (name "r-breadr")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BREADR" version))
       (sha256
        (base32 "1hbjwf86575hqxbz3lz89r0sbzfr5cyy8jmid2jx4yj9f9lqc8h5"))))
    (properties `((upstream-name . "BREADR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-readr
                             r-purrr
                             r-matrixstats
                             r-mass
                             r-magrittr
                             r-ggpubr
                             r-ggplot2
                             r-forcats
                             r-dplyr
                             r-data-table))
    (home-page "https://github.com/jonotuke/BREADR")
    (synopsis
     "Estimates Degrees of Relatedness (Up to the Second Degree) for Extreme Low-Coverage Data")
    (description
     "The goal of the package is to provide an easy-to-use method for estimating
degrees of relatedness (up to the second degree) for extreme low-coverage data.
The package also allows users to quantify and visualise the level of confidence
in the estimated degrees of relatedness.")
    (license license:expat)))

(define-public r-bread
  (package
    (name "r-bread")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bread" version))
       (sha256
        (base32 "0vsxmqpgi2yjcpdpgdz6gzqkpjsq5ax4qkpys60bwmxyp1ahv90r"))))
    (properties `((upstream-name . "bread")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table))
    (home-page "https://github.com/MagicHead99/bread/")
    (synopsis "Analyze Big Files Without Loading Them in Memory")
    (description
     "This package provides a simple set of wrapper functions for data.table::fread()
that allows subsetting or filtering rows and selecting columns of
table-formatted files too large for the available RAM. b stands for big files'.
bread makes heavy use of Unix commands like grep', sed', wc', awk and cut'.
They are available by default in all Unix environments.  For Windows, you need
to install those commands externally in order to simulate a Unix environment and
make sure that the executables are in the Windows PATH variable.  To my
knowledge, the simplest ways are to install RTools', Git or Cygwin'.  If they
have been correctly installed (with the expected registry entries), they should
be detected on loading the package and the correct directories will be added
automatically to the PATH.")
    (license license:gpl3+)))

(define-public r-brea
  (package
    (name "r-brea")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brea" version))
       (sha256
        (base32 "1sh0m4hfik0kzc19ygqcbrxn67n52zz9l90gs494iqqwj1fzs8f7"))))
    (properties `((upstream-name . "brea")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=brea")
    (synopsis "Bayesian Recurrent Events Analysis")
    (description
     "This package provides functions to produce MCMC samples for posterior inference
in semiparametric Bayesian discrete time competing risks recurrent events models
and multistate models.")
    (license license:gpl3)))

(define-public r-brdt
  (package
    (name "r-brdt")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BRDT" version))
       (sha256
        (base32 "1yv2fh4cc9h35l8cngpxnqa83a7bx79pr7c1gp9h84p1pigg8282"))))
    (properties `((upstream-name . "BRDT")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ericchen12377/BRDT")
    (synopsis "Binomial Reliability Demonstration Tests")
    (description
     "This is an implementation of design methods for binomial reliability
demonstration tests (BRDTs) with failure count data.  The acceptance decision
uncertainty of BRDT has been quantified and the impacts of the uncertainty on
related reliability assurance activities such as reliability growth (RG) and
warranty services (WS) are evaluated.  This package is associated with the work
from the published paper \"Optimal Binomial Reliability Demonstration Tests
Design under Acceptance Decision Uncertainty\" by Suiyao Chen et al. (2020)
<doi:10.1080/08982112.2020.1757703>.")
    (license license:gpl3)))

(define-public r-brclimr
  (package
    (name "r-brclimr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brclimr" version))
       (sha256
        (base32 "11870q60spq4pi7d6yvly48x8n9is15cj957aq8h7bw2lgcyzb1z"))))
    (properties `((upstream-name . "brclimr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-magrittr
                             r-lobstr
                             r-glue
                             r-duckdb
                             r-dbi
                             r-checkmate))
    (home-page "https://rfsaldanha.github.io/brclimr/")
    (synopsis
     "Fetch Zonal Statistics of Weather Indicators for Brazilian Municipalities")
    (description
     "Fetches zonal statistics from weather indicators that were calculated for each
municipality in Brazil using data from the BR-DWGD and @code{TerraClimate}
projects.  Zonal statistics such as mean, maximum, minimum, standard deviation,
and sum were computed by taking into account the data cells that intersect the
boundaries of each municipality and stored in Parquet files.  This procedure was
carried out for all Brazilian municipalities, and for all available dates, for
every indicator available in the weather products (BR-DWGD and
@code{TerraClimate} projects).  This package queries on-line the already
calculated statistics on the Parquet files and returns easy-to-use data.frames.")
    (license license:expat)))

(define-public r-brcal
  (package
    (name "r-brcal")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BRcal" version))
       (sha256
        (base32 "1z8mr76rj8xdqsgny6hskbs3czgy23xqrjrhw9006y4mdhd39hbr"))))
    (properties `((upstream-name . "BRcal")))
    (build-system r-build-system)
    (propagated-inputs (list r-nloptr r-lifecycle r-ggplot2 r-fields))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/apguthrie/BRcal")
    (synopsis "Boldness-Recalibration of Binary Events")
    (description
     "Boldness-recalibration maximally spreads out probability predictions while
maintaining a user specified level of calibration, facilitated the brcal()
function.  Supporting functions to assess calibration via Bayesian and
Frequentist approaches, Maximum Likelihood Estimator (MLE) recalibration, Linear
in Log Odds (LLO)-adjust via any specified parameters, and visualize results are
also provided.  Methodological details can be found in Guthrie & Franck (2024)
<doi:10.1080/00031305.2024.2339266>.")
    (license license:expat)))

(define-public r-brbvs
  (package
    (name "r-brbvs")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BRBVS" version))
       (sha256
        (base32 "0vhwwsdbslr6craaflvllvba7s1b1jzhjqpmmh70f7k0qi61x5qc"))))
    (properties `((upstream-name . "BRBVS")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-gjrm r-ggplot2 r-copent))
    (home-page "https://cran.r-project.org/package=BRBVS")
    (synopsis
     "Variable Selection and Ranking in Copula Survival Models Affected by General Censoring Scheme")
    (description
     "This package performs variable selection and ranking based on several measures
for the class of copula survival model(s) in high dimensional domain.  The
package is based on the class of copula survival model(s) implemented in the
GJRM package.")
    (license license:gpl3+)))

(define-public r-brazilmet
  (package
    (name "r-brazilmet")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BrazilMet" version))
       (sha256
        (base32 "0pcr665j4balmcm4sq6l19nmhpbs7ayw5dljsdxr91bn6srb33b3"))))
    (properties `((upstream-name . "BrazilMet")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-readxl r-dplyr))
    (home-page "https://cran.r-project.org/package=BrazilMet")
    (synopsis
     "Download and Processing of Automatic Weather Stations (AWS) Data of INMET-Brazil")
    (description
     "This package provides a compilation of functions to download and processing AWS
data of INMET-Brazil, with the purpose of reference evapotranspiration (ETo)
estimation.  The package aims to make meteorological and agricultural data
analysis more parsimonious.")
    (license license:gpl3)))

(define-public r-brazilcrime
  (package
    (name "r-brazilcrime")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BrazilCrime" version))
       (sha256
        (base32 "194ckp3b6yini9z6nh953n7x0yp6x1zwvxg42z6pq5wsd07g76n5"))))
    (properties `((upstream-name . "BrazilCrime")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-openxlsx r-janitor r-geobr r-dplyr))
    (home-page "https://cran.r-project.org/package=BrazilCrime")
    (synopsis "Accesses Brazilian Public Security Data from SINESP Since 2015")
    (description
     "Allows access to data from the Brazilian Public Security Information System
(SINESP) by state and municipality.
<https://www.gov.br/mj/pt-br/assuntos/sua-seguranca/seguranca-publica/sinesp-1>.")
    (license license:expat)))

(define-public r-bravo
  (package
    (name "r-bravo")
    (version "3.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bravo" version))
       (sha256
        (base32 "0q7qm5jsj92sras5z5xr4hkz73l202xl8f0izhg2hiv3kam0z8rf"))))
    (properties `((upstream-name . "bravo")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-matrix))
    (home-page "https://cran.r-project.org/package=bravo")
    (synopsis "Bayesian Screening and Variable Selection")
    (description
     "This package performs Bayesian variable screening and selection for ultra-high
dimensional linear regression models.")
    (license license:gpl3)))

(define-public r-bratteli
  (package
    (name "r-bratteli")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bratteli" version))
       (sha256
        (base32 "07hl2s2vcjqx4db0gksfnwdarl59a9441d0ff13zf0vj0pbaqam2"))))
    (properties `((upstream-name . "bratteli")))
    (build-system r-build-system)
    (propagated-inputs (list r-kantorovich r-gmp r-diagram r-data-table))
    (home-page "https://github.com/stla/bratteliR")
    (synopsis "Deal with Bratteli Graphs")
    (description
     "Utilities for Bratteli graphs.  A tree is an example of a Bratteli graph.  The
package provides a function which generates a @code{LaTeX} file that renders the
given Bratteli graph.  It also provides functions to compute the dimensions of
the vertices, the intrinsic kernels and the intrinsic distances.  Intrinsic
kernels and distances were introduced by Vershik (2014)
<doi:10.1007/s10958-014-1958-0>.")
    (license license:gpl3)))

(define-public r-brassica
  (package
    (name "r-brassica")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brassica" version))
       (sha256
        (base32 "1av52p39jsv54q02v8xa7xilwmz5vzj6zy68y9m5lgl6s86r509y"))))
    (properties `((upstream-name . "brassica")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=brassica")
    (synopsis "1970s BASIC Interpreter")
    (description
     "Executes BASIC programs from the 1970s, for historical and educational purposes.
 This enables famous examples of early machine learning, artificial
intelligence, natural language processing, cellular automata, and so on, to be
run in their original form.")
    (license license:gpl3)))

(define-public r-braqca
  (package
    (name "r-braqca")
    (version "1.2.1.29")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "braQCA" version))
       (sha256
        (base32 "0w1mg8lzl64mp5clinq3zai733iz3628zp4pw6vr51z24s56jg6i"))))
    (properties `((upstream-name . "braQCA")))
    (build-system r-build-system)
    (propagated-inputs (list r-qca r-dplyr r-bootstrap))
    (home-page "https://cran.r-project.org/package=braQCA")
    (synopsis
     "Bootstrapped Robustness Assessment for Qualitative Comparative Analysis")
    (description
     "Test the robustness of a user's Qualitative Comparative Analysis solutions to
randomness, using the bootstrapped assessment: @code{baQCA}().  This package
also includes a function that provides recommendations for improving solutions
to reach typical significance levels: @code{brQCA}().  Data included come from
@code{McVeigh} et al. (2014) <doi:10.1177/0003122414534065>.")
    (license license:gpl3)))

(define-public r-brant
  (package
    (name "r-brant")
    (version "0.3-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brant" version))
       (sha256
        (base32 "0rcp0am4wjlkinvmv0n5bky84v31ds28v163p5mlp343cgma2b62"))))
    (properties `((upstream-name . "brant")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-mass))
    (home-page "https://benjaminschlegel.ch/r/brant/")
    (synopsis "Test for Parallel Regression Assumption")
    (description
     "Tests the parallel regression assumption wit the brant test by Brant (1990)
<doi: 10.2307/2532457> for ordinal logit models generated with the function
polr() from the package MASS'.")
    (license license:gpl2+)))

(define-public r-brandwatchr
  (package
    (name "r-brandwatchr")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brandwatchR" version))
       (sha256
        (base32 "10bccsaswsq06wdrxqg71amzx5rabmgfi4n074y651r2c1pyy98w"))))
    (properties `((upstream-name . "brandwatchR")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-httr r-data-table))
    (home-page "https://github.com/Phippsy/brandwatchR")
    (synopsis "'Brandwatch' API to R")
    (description
     "Interact with the Brandwatch API <https://developers.brandwatch.com/docs>.
Allows you to authenticate to the API and obtain data for projects, queries,
query groups tags and categories.  Also allows you to directly obtain mentions
and aggregate data for a specified query or query group.")
    (license license:expat)))

(define-public r-branching
  (package
    (name "r-branching")
    (version "0.9.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Branching" version))
       (sha256
        (base32 "15znbxpv0kcl8qqihl9f5irvcsyanfhszcz0xq6r7g8gsay4s760"))))
    (properties `((upstream-name . "Branching")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=Branching")
    (synopsis "Simulation and Estimation for Branching Processes")
    (description
     "Simulation and parameter estimation of multitype Bienayme - Galton - Watson
processes.")
    (license license:gpl2+)))

(define-public r-branchglm
  (package
    (name "r-branchglm")
    (version "3.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BranchGLM" version))
       (sha256
        (base32 "1d0cqrfh4p5wfljys3xm8brpmvycl6ix28qwillq1zyvbik9r1js"))))
    (properties `((upstream-name . "BranchGLM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/JacobSeedorff21/BranchGLM")
    (synopsis
     "Efficient Best Subset Selection for GLMs via Branch and Bound Algorithms")
    (description
     "This package performs efficient and scalable glm best subset selection using a
novel implementation of a branch and bound algorithm.  To speed up the model
fitting process, a range of optimization methods are implemented in
@code{RcppArmadillo}'.  Parallel computation is available using @code{OpenMP}'.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-brainr
  (package
    (name "r-brainr")
    (version "1.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brainR" version))
       (sha256
        (base32 "1l5rm6grslc6mivwh2fv3pznhy2cw16fz5695pmyfamzvsjggir1"))))
    (properties `((upstream-name . "brainR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rgl r-oro-nifti r-misc3d))
    (home-page "https://cran.r-project.org/package=brainR")
    (synopsis
     "Helper Functions to 'misc3d' and 'rgl' Packages for Brain Imaging")
    (description
     "This includes functions for creating 3D and 4D images using @code{WebGL}', rgl',
and @code{JavaScript} commands.  This package relies on the X toolkit ('XTK',
<https://github.com/xtk/X#readme>).")
    (license license:gpl2)))

(define-public r-brainkcca
  (package
    (name "r-brainkcca")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brainKCCA" version))
       (sha256
        (base32 "1ag3qahlba4h4126873hg2bbi6sr729mhiw22gjgr4g4jg6lhh09"))))
    (properties `((upstream-name . "brainKCCA")))
    (build-system r-build-system)
    (propagated-inputs (list r-rgl
                             r-oro-nifti
                             r-misc3d
                             r-knitr
                             r-kernlab
                             r-elasticnet
                             r-cca
                             r-brainr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=brainKCCA")
    (synopsis
     "Region-Level Connectivity Network Construction via Kernel Canonical Correlation Analysis")
    (description
     "It is designed to calculate connection between (among) brain regions and plot
connection lines.  Also, the summary function is included to summarize
group-level connectivity network.  Kang, Jian (2016)
<doi:10.1016/j.neuroimage.2016.06.042>.")
    (license license:lgpl2.0+)))

(define-public r-braingraph
  (package
    (name "r-braingraph")
    (version "3.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brainGraph" version))
       (sha256
        (base32 "009m3297bn1jl1aacypajkdawgjp3pwx7krz3bxifb0bmrdi0n7b"))))
    (properties `((upstream-name . "brainGraph")))
    (build-system r-build-system)
    (propagated-inputs (list r-permute
                             r-matrix
                             r-mass
                             r-lattice
                             r-igraph
                             r-foreach
                             r-doparallel
                             r-data-table
                             r-abind))
    (home-page "https://github.com/cwatson/brainGraph")
    (synopsis "Graph Theory Analysis of Brain MRI Data")
    (description
     "This package provides a set of tools for performing graph theory analysis of
brain MRI data.  It works with data from a Freesurfer analysis (cortical
thickness, volumes, local gyrification index, surface area), diffusion tensor
tractography data (e.g., from FSL) and resting-state @code{fMRI} data (e.g.,
from DPABI).  It contains a graphical user interface for graph visualization and
data exploration, along with several functions for generating useful figures.")
    (license license:gpl3)))

(define-public r-braincon
  (package
    (name "r-braincon")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BrainCon" version))
       (sha256
        (base32 "137pagkjb4xzk3skmsf5vlp8q0k79zl9xjz8wwda1mdrknkdnbj6"))))
    (properties `((upstream-name . "BrainCon")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-glmnet))
    (home-page "https://cran.r-project.org/package=BrainCon")
    (synopsis "Inference the Partial Correlations Based on Time Series Data")
    (description
     "This package provides a statistical tool to inference the multi-level partial
correlations based on multi-subject time series data, especially for brain
functional connectivity.  It combines both individual and population level
inference by using the methods of Qiu and Zhou. (2021)<DOI:
10.1080/01621459.2021.1917417> and Genovese and Wasserman. (2006)<DOI:
10.1198/016214506000000339>.  It realizes two reliable estimation methods of
partial correlation coefficients, using scaled lasso and lasso.  It can be used
to estimate individual- or population-level partial correlations, identify
nonzero ones, and find out unequal partial correlation coefficients between two
populations.")
    (license license:gpl2+)))

(define-public r-brailler
  (package
    (name "r-brailler")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BrailleR" version))
       (sha256
        (base32 "1gh4n9zl7yczmhy85xmsx1mh9qmc99xvhmm5cdnjcb644i2n214k"))))
    (properties `((upstream-name . "BrailleR")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-xml
                             r-whisker
                             r-tidyr
                             r-rolocisccnbs
                             r-roloc
                             r-rmarkdown
                             r-rdpack
                             r-quarto
                             r-moments
                             r-mathjaxr
                             r-knitr
                             r-hunspell
                             r-gridsvg
                             r-gridgraphics
                             r-ggplot2
                             r-extrafont
                             r-dplyr
                             r-devtools))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ajrgodfrey/BrailleR")
    (synopsis "Improved Access for Blind Users")
    (description
     "Blind users do not have access to the graphical output from R without printing
the content of graphics windows to an embosser of some kind.  This is not as
immediate as is required for efficient access to statistical output.  The
functions here are created so that blind people can make even better use of R.
This includes the text descriptions of graphs, convenience functions to replace
the functionality offered in many GUI front ends, and experimental functionality
for optimising graphical content to prepare it for embossing as tactile images.")
    (license license:gpl2)))

(define-public r-braids
  (package
    (name "r-braids")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "braids" version))
       (sha256
        (base32 "1sbkdimqg4vxhw4djknjixzj37hp69nm7dcki2j6zfmrzhmvx1fl"))))
    (properties `((upstream-name . "braids")))
    (build-system r-build-system)
    (propagated-inputs (list r-maybe))
    (home-page "https://github.com/stla/braids")
    (synopsis "The Braid Groups")
    (description
     "Deals with the braid groups.  Includes creation of some specific braids, group
operations, free reduction, and Bronfman polynomials.  Braid theory has
applications in fluid mechanics and quantum physics.  The code is adapted from
the Haskell library combinat', and is based on Birman and Brendle (2005)
<doi:10.48550/@code{arXiv.math/0409205>}.")
    (license license:gpl3)))

(define-public r-braidrm
  (package
    (name "r-braidrm")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "braidrm" version))
       (sha256
        (base32 "16wj2bnjpr93vsiyngn4agyiizslzpglld5kr3xxhmn8n2dfmc3k"))))
    (properties `((upstream-name . "braidrm")))
    (build-system r-build-system)
    (propagated-inputs (list r-basicdrm))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=braidrm")
    (synopsis "Fitting Combined Action with the BRAID Response Surface Model")
    (description
     "This package contains functions for evaluating, analyzing, and fitting combined
action dose response surfaces with the Bivariate Response to Additive
Interacting Doses (BRAID) model of combined action, along with tools for
implementing other combination analysis methods, including Bliss independence,
combination index, and additional response surface methods.")
    (license license:gpl3+)))

(define-public r-braidreports
  (package
    (name "r-braidreports")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "braidReports" version))
       (sha256
        (base32 "1w6bxvpkh8wslnzickwlxk6sw793x142cinn6sn6836ldlhgzlg9"))))
    (properties `((upstream-name . "braidReports")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-gtable
                             r-ggplot2
                             r-cowplot
                             r-braidrm
                             r-basicdrm))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=braidReports")
    (synopsis
     "Visualize Combined Action Response Surfaces and Report BRAID Analyses")
    (description
     "This package provides functions to visualize combined action data in ggplot2'.
Also provides functions for producing full BRAID analysis reports with custom
layouts and aesthetics, using the BRAID method originally described in Twarog et
al. (2016) <doi:10.1038/srep25523>.")
    (license license:gpl3+)))

(define-public r-braggr
  (package
    (name "r-braggr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "braggR" version))
       (sha256
        (base32 "1sq7knrnz3nqprlvsj24f9gj1i0viawq3wcv2b97dmbwa7rd5qfz"))))
    (properties `((upstream-name . "braggR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=braggR")
    (synopsis "Calculate the Revealed Aggregator of Probability Predictions")
    (description
     "Forecasters predicting the chances of a future event may disagree due to
differing evidence or noise.  To harness the collective evidence of the crowd,
Ville SatopÃ¤Ã¤ (2021) \"Regularized Aggregation of One-off Probability
Predictions\" <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3769945>
proposes a Bayesian aggregator that is regularized by analyzing the forecasters
disagreement and ascribing over-dispersion to noise.  This aggregator requires
no user intervention and can be computed efficiently even for a large numbers of
predictions.  The author evaluates the aggregator on subjective probability
predictions collected during a four-year forecasting tournament sponsored by the
US intelligence community.  The aggregator improves the accuracy of simple
averaging by around 20% and other state-of-the-art aggregators by 10-25%.  The
advantage stems almost exclusively from improved calibration.  This aggregator
-- know as \"the revealed aggregator\" -- inputs a) forecasters probability
predictions (p) of a future binary event and b) the forecasters common prior
(p0) of the future event.  In this R-package, the function
sample_aggregator(p,p0,...) allows the user to calculate the revealed
aggregator.  Its use is illustrated with a simple example.")
    (license license:gpl2)))

(define-public r-bradleyterry2
  (package
    (name "r-bradleyterry2")
    (version "1.1-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BradleyTerry2" version))
       (sha256
        (base32 "0vss3ib74cz2zn1afmrvllawghj9z2ay4x1ysvk4rqvjmiia4a2i"))))
    (properties `((upstream-name . "BradleyTerry2")))
    (build-system r-build-system)
    (propagated-inputs (list r-qvcalc r-lme4 r-gtools r-brglm))
    (home-page "https://github.com/hturner/BradleyTerry2")
    (synopsis "Bradley-Terry Models")
    (description
     "Specify and fit the Bradley-Terry model, including structured versions in which
the parameters are related to explanatory variables through a linear predictor
and versions with contest-specific effects, such as a home advantage.")
    (license license:gpl2+)))

(define-public r-brada
  (package
    (name "r-brada")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "brada" version))
       (sha256
        (base32 "07m5xj4wjf3w85fhhh8qw8pl2gb94i50099ql8anmrm1ax5pj8sr"))))
    (properties `((upstream-name . "brada")))
    (build-system r-build-system)
    (propagated-inputs (list r-progress
                             r-foreach
                             r-fbst
                             r-extradistr
                             r-dosnow
                             r-doparallel
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=brada")
    (synopsis "Bayesian Response-Adaptive Design Analysis")
    (description
     "This package provides access to a range of functions for analyzing, applying and
visualizing Bayesian response-adaptive trial designs for a binary endpoint.
Includes the predictive probability approach and the predictive evidence value
designs for binary endpoints.")
    (license license:gpl3)))

(define-public r-bracod-r
  (package
    (name "r-bracod-r")
    (version "0.0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BRACoD.R" version))
       (sha256
        (base32 "1jxxrgq2s5fqflp9ac4fx51jhlx33pdwijsp4vq63y430k79vcpp"))))
    (properties `((upstream-name . "BRACoD.R")))
    (build-system r-build-system)
    (propagated-inputs (list r-reticulate))
    (home-page "https://cran.r-project.org/package=BRACoD.R")
    (synopsis "BRACoD: Bayesian Regression Analysis of Compositional Data")
    (description
     "The goal of this method is to identify associations between bacteria and an
environmental variable in 16S or other compositional data.  The environmental
variable is any variable which is measure for each microbiome sample, for
example, a butyrate measurement paired with every sample in the data.
Microbiome data is compositional, meaning that the total abundance of each
sample sums to 1, and this introduces severe statistical distortions.  This
method takes a Bayesian approach to correcting for these statistical
distortions, in which the total abundance is treated as an unknown variable.
This package runs the python implementation using reticulate.")
    (license license:expat)))

(define-public r-bracer
  (package
    (name "r-bracer")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bracer" version))
       (sha256
        (base32 "0kbx30icv966sjv93as4yq88vkavbw68a7v2fnrvblzx2dgcis1n"))))
    (properties `((upstream-name . "bracer")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://trevorldavis.com/R/bracer/")
    (synopsis "Brace Expansions")
    (description
     "This package performs brace expansions on strings.  Made popular by Unix shells,
brace expansion allows users to concisely generate certain character vectors by
taking a single string and (recursively) expanding the comma-separated lists and
double-period-separated integer and character sequences enclosed within braces
in that string.  The double-period-separated numeric integer expansion also
supports padding the resulting numbers with zeros.")
    (license license:expat)))

(define-public r-brace
  (package
    (name "r-brace")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BRACE" version))
       (sha256
        (base32 "19paaxk0szd5azcw95sd22fryb38lamd8r4y1f6zmcngbza470dk"))))
    (properties `((upstream-name . "BRACE")))
    (build-system r-build-system)
    (propagated-inputs (list r-survminer r-survival))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BRACE")
    (synopsis "Bias Reduction Through Analysis of Competing Events (BRACE)")
    (description
     "Adjusting the bias due to residual confounding (often called treatment selection
bias) in estimating the treatment effect in a proportional hazard model, as
described in Williamson et al. (2022) <doi:10.1158/1078-0432.ccr-21-2468>.")
    (license license:gpl3+)))

(define-public r-bracatus
  (package
    (name "r-bracatus")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bRacatus" version))
       (sha256
        (base32 "0jz5c1sbnnl5db0iiqh1paq55rwsbpz6srshcf4yf3ki58m3a9sm"))))
    (properties `((upstream-name . "bRacatus")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-sf
                             r-rnaturalearth
                             r-rgbif
                             r-raster
                             r-plotfunctions
                             r-jsonlite
                             r-geojsonio
                             r-data-table))
    (home-page "https://github.com/EduardoArle/bRacatus")
    (synopsis
     "Method to Estimate the Accuracy and Biogeographical Status of Georeferenced Biological Data")
    (description
     "Automated assessment of accuracy and geographical status of georeferenced
biological data.  The methods rely on reference regions, namely checklists and
range maps.  Includes functions to obtain data from the Global Biodiversity
Information Facility <https://www.gbif.org/> and from the Global Inventory of
Floras and Traits <https://gift.uni-goettingen.de/home>.  Alternatively, the
user can input their own data.  Furthermore, provides easy visualisation of the
data and the results through the plotting functions.  Especially suited for
large datasets.  The reference for the methodology is: ArlÃ© et al. (under
review).")
    (license license:gpl2+)))

(define-public r-bqror
  (package
    (name "r-bqror")
    (version "1.7.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bqror" version))
       (sha256
        (base32 "1pisp3p3w5r9bfv5m5yykk410z5qvwsqdblmv7vsj1sc1nwf5y2v"))))
    (properties `((upstream-name . "bqror")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm
                             r-progress
                             r-pracma
                             r-npflow
                             r-mass
                             r-invgamma
                             r-gigrvg))
    (home-page "https://github.com/prajual/bqror")
    (synopsis "Bayesian Quantile Regression for Ordinal Models")
    (description
     "Package provides functions for estimation and inference in Bayesian quantile
regression with ordinal outcomes.  An ordinal model with 3 or more outcomes
(labeled OR1 model) is estimated by a combination of Gibbs sampling and
Metropolis-Hastings (MH) algorithm.  Whereas an ordinal model with exactly 3
outcomes (labeled OR2 model) is estimated using a Gibbs sampling algorithm.  The
summary output presents the posterior mean, posterior standard deviation, 95%
credible intervals, and the inefficiency factors along with the two model
comparison measures â logarithm of marginal likelihood and the deviance
information criterion (DIC).  The package also provides functions for computing
the covariate effects and other functions that aids either the estimation or
inference in quantile ordinal models.  Rahman, M. A. (2016).âBayesian Quantile
Regression for Ordinal Models.â Bayesian Analysis, 11(1): 1-24 <doi:
10.1214/15-BA939>.  Yu, K., and Moyeed, R. A. (2001).  âBayesian Quantile
Regression.â Statistics and Probability Letters, 54(4): 437â447 <doi:
10.1016/S0167-7152(01)00124-9>.  Koenker, R., and Bassett, G.
(1978).âRegression Quantiles.â Econometrica, 46(1): 33-50 <doi:
10.2307/1913643>.  Chib, S. (1995).  âMarginal likelihood from the Gibbs
output.â Journal of the American Statistical Association, 90(432):1313â1321,
1995. <doi: 10.1080/01621459.1995.10476635>.  Chib, S., and Jeliazkov, I.
(2001).  âMarginal likelihood from the Metropolis-Hastings output.â Journal
of the American Statistical Association, 96(453):270â281, 2001. <doi:
10.1198/016214501750332848>.")
    (license license:gpl2+)))

(define-public r-bprinstrattte
  (package
    (name "r-bprinstrattte")
    (version "0.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BPrinStratTTE" version))
       (sha256
        (base32 "1iqy36am0isiyjjgrf2jr1ywxxl0n33da637r7j3c3n3zc6yyqyg"))))
    (properties `((upstream-name . "BPrinStratTTE")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-purrr
                             r-magrittr
                             r-furrr
                             r-dplyr
                             r-bh))
    (home-page "https://github.com/Boehringer-Ingelheim/BPrinStratTTE")
    (synopsis
     "Causal Effects in Principal Strata Defined by Antidrug Antibodies")
    (description
     "Bayesian models to estimate causal effects of biological treatments on
time-to-event endpoints in clinical trials with principal strata defined by the
occurrence of antidrug antibodies.  The methodology is based on Frangakis and
Rubin (2002) <doi:10.1111/j.0006-341x.2002.00021.x> and Imbens and Rubin (1997)
<doi:10.1214/aos/1034276631>, and here adapted to a specific time-to-event
setting.")
    (license license:gpl3+)))

(define-public r-bpr
  (package
    (name "r-bpr")
    (version "1.0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bpr" version))
       (sha256
        (base32 "1hiwz1s8gc6skl0zg7mjh29bxazka9wgrbbldqhy6f9vzrddl4pn"))))
    (properties `((upstream-name . "bpr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mass r-coda r-bh))
    (home-page "https://cran.r-project.org/package=bpr")
    (synopsis "Fitting Bayesian Poisson Regression")
    (description
     "Posterior sampling and inference for Bayesian Poisson regression models.  The
model specification makes use of Gaussian (or conditionally Gaussian) prior
distributions on the regression coefficients.  Details on the algorithm are
found in D'Angelo and Canale (2023) <doi:10.1080/10618600.2022.2123337>.")
    (license license:gpl2+)))

(define-public r-bpp
  (package
    (name "r-bpp")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bpp" version))
       (sha256
        (base32 "07vz00y9m71n3a847jpviwyy0zb59402yyklsfmyxmggjivpjshi"))))
    (properties `((upstream-name . "bpp")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bpp")
    (synopsis "Computations Around Bayesian Predictive Power")
    (description
     "This package implements functions to update Bayesian Predictive Power
Computations after not stopping a clinical trial at an interim analysis.  Such
an interim analysis can either be blinded or unblinded.  Code is provided for
Normally distributed endpoints with known variance, with a prominent example
being the hazard ratio.")
    (license license:gpl2+)))

(define-public r-bpnreg
  (package
    (name "r-bpnreg")
    (version "2.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bpnreg" version))
       (sha256
        (base32 "1yy1rbbnqjrz0dkpvwi49432ddpjbql0vrpwvhzv9qzpv8ydvlw4"))))
    (properties `((upstream-name . "bpnreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-haven r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/joliencremers/bpnreg")
    (synopsis "Bayesian Projected Normal Regression Models for Circular Data")
    (description
     "Fitting Bayesian multiple and mixed-effect regression models for circular data
based on the projected normal distribution.  Both continuous and categorical
predictors can be included.  Sampling from the posterior is performed via an
MCMC algorithm.  Posterior descriptives of all parameters, model fit statistics
and Bayes factors for hypothesis tests for inequality constrained hypotheses are
provided.  See Cremers, Mulder & Klugkist (2018) <doi:10.1111/bmsp.12108> and
NuÃ±ez-Antonio & GuttiÃ©rez-PeÃ±a (2014) <doi:10.1016/j.csda.2012.07.025>.")
    (license license:gpl3)))

(define-public r-bpmnvisualizationr
  (package
    (name "r-bpmnvisualizationr")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bpmnVisualizationR" version))
       (sha256
        (base32 "135afhq1x2aqdmn609vfk0248wsqbp66lkw7wqqzsb6czpap1q90"))))
    (properties `((upstream-name . "bpmnVisualizationR")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-xml2 r-rlang r-htmlwidgets))
    (native-inputs (list esbuild))
    (home-page "https://process-analytics.github.io/bpmn-visualization-R/")
    (synopsis "Visualize Process Execution Data on 'BPMN' Diagrams")
    (description
     "To visualize the execution data of the processes on BPMN (Business Process Model
and Notation) diagrams, using overlays, style customization and interactions,
with the bpmn-visualization @code{TypeScript} library.")
    (license (license:fsdg-compatible "Apache License (== 2)"))))

(define-public r-bpmnr
  (package
    (name "r-bpmnr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bpmnR" version))
       (sha256
        (base32 "0cskqfib5fw34zfqms4dp53cwb7z0cricr452a4cjfw563kjhmqf"))))
    (properties `((upstream-name . "bpmnR")))
    (build-system r-build-system)
    (arguments
     (list
      #:modules '((guix build r-build-system)
                  (guix build minify-build-system)
                  (guix build utils)
                  (ice-9 match))
      #:imported-modules `(,@%r-build-system-modules (guix build
                                                      minify-build-system))
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'process-javascript
                    (lambda* (#:key inputs #:allow-other-keys)
                      (with-directory-excursion "inst/"
                        (for-each (match-lambda
                                    ((source . target) (minify source
                                                               #:target target)))
                                  '())))))))
    (propagated-inputs (list r-xml2
                             r-uuid
                             r-tidyr
                             r-stringr
                             r-rvest
                             r-rlang
                             r-readr
                             r-purrr
                             r-knitr
                             r-huxtable
                             r-htmlwidgets
                             r-htmltools
                             r-glue
                             r-dt
                             r-dplyr
                             r-diagrammersvg
                             r-diagrammer))
    (native-inputs (list r-knitr esbuild))
    (home-page "https://cran.r-project.org/package=bpmnR")
    (synopsis "Support for BPMN (Business Process Management Notation) Models")
    (description
     "Creating, rendering and writing BPMN diagrams <https://www.bpmn.org/>.
Functionalities can be used to visualize and export BPMN diagrams created using
the pm4py and @code{bupaRminer} packages.  Part of the @code{bupaR} ecosystem.")
    (license license:expat)))

(define-public r-bpm
  (package
    (name "r-bpm")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BPM" version))
       (sha256
        (base32 "17k2d1xjfz85ygx0xnh6yqca9nyypd32d7sbna3k33ynsjqay3x8"))))
    (properties `((upstream-name . "BPM")))
    (build-system r-build-system)
    (propagated-inputs (list r-limma))
    (home-page "https://cran.r-project.org/package=BPM")
    (synopsis "Bayesian Purity Model to Estimate Tumor Purity")
    (description
     "Bayesian purity model to estimate tumor purity using methylation array data (DNA
methylation Infinium 450K array data) without reference samples.")
    (license license:gpl2+)))

(define-public r-bpgmm
  (package
    (name "r-bpgmm")
    (version "1.0.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bpgmm" version))
       (sha256
        (base32 "0wrdh4c8yws7z5zd1yl23wbja88x57x651kqbnqj70k9a1195rwc"))))
    (properties `((upstream-name . "bpgmm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-pgmm
                             r-mvtnorm
                             r-mcmcse
                             r-mclust
                             r-mass
                             r-label-switching
                             r-gtools
                             r-fabmix))
    (home-page "https://cran.r-project.org/package=bpgmm")
    (synopsis
     "Bayesian Model Selection Approach for Parsimonious Gaussian Mixture Models")
    (description
     "Model-based clustering using Bayesian parsimonious Gaussian mixture models.
MCMC (Markov chain Monte Carlo) are used for parameter estimation.  The RJMCMC
(Reversible-jump Markov chain Monte Carlo) is used for model selection.  GREEN
et al. (1995) <doi:10.1093/biomet/82.4.711>.")
    (license license:gpl3)))

(define-public r-bpdir
  (package
    (name "r-bpdir")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bpDir" version))
       (sha256
        (base32 "1wkj25srsbidpcvgm6c74584ahybjy39r86dxir1vd13kbi8l120"))))
    (properties `((upstream-name . "bpDir")))
    (build-system r-build-system)
    (propagated-inputs (list r-plotrix r-mass r-circular))
    (home-page "https://cran.r-project.org/package=bpDir")
    (synopsis "Boxplots for Directional Data")
    (description
     "This package provides functions for drawing boxplots for data on (the boundary
of) a unit circle (i.e., circular and axial data), from Buttarazzi D., Pandolfo
G., Porzio G.C. (2018) <doi:10.1111/biom.12889>.")
    (license license:gpl2)))

(define-public r-bpcp
  (package
    (name "r-bpcp")
    (version "1.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bpcp" version))
       (sha256
        (base32 "1xgspmknjs7cqh0iw3qgikbxpbl2qscjx9n07ib5s3z8hi282wrn"))))
    (properties `((upstream-name . "bpcp")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-ggplot2))
    (home-page "https://cran.r-project.org/package=bpcp")
    (synopsis "Beta Product Confidence Procedure for Right Censored Data")
    (description
     "Calculates nonparametric pointwise confidence intervals for the survival
distribution for right censored data, and for medians [Fay and Brittain
<DOI:10.1002/sim.6905>].  Has two-sample tests for dissimilarity (e.g.,
difference, ratio or odds ratio) in survival at a fixed time, and differences in
medians [Fay, Proschan, and Brittain <DOI:10.1111/biom.12231>].  Basically, the
package gives exact inference methods for one- and two-sample exact inferences
for Kaplan-Meier curves (e.g., generalizing Fisher's exact test to allow for
right censoring), which are especially important for latter parts of the
survival curve, small sample sizes or heavily censored data.  Includes mid-p
options.")
    (license license:gpl2+)))

(define-public r-bpca
  (package
    (name "r-bpca")
    (version "1.3-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bpca" version))
       (sha256
        (base32 "13rb6k50527aiaydapkvjyh3c2pk1anmz537y97mx35malfh1kmd"))))
    (properties `((upstream-name . "bpca")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable r-scatterplot3d r-rgl))
    (home-page "https://cran.r-project.org/package=bpca")
    (synopsis
     "Biplot of Multivariate Data Based on Principal Components Analysis")
    (description
     "This package implements biplot (2d and 3d) of multivariate data based on
principal components analysis and diagnostic tools of the quality of the
reduction.")
    (license license:gpl2+)))

(define-public r-bpbounds
  (package
    (name "r-bpbounds")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bpbounds" version))
       (sha256
        (base32 "0f8knwvwllzraxkhawkljdm7mj2i0crd6yj415dya7nvp01ilaql"))))
    (properties `((upstream-name . "bpbounds")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/remlapmot/bpbounds")
    (synopsis
     "Nonparametric Bounds for the Average Causal Effect Due to Balke and Pearl and Extensions")
    (description
     "Implementation of the nonparametric bounds for the average causal effect under
an instrumental variable model by Balke and Pearl (Bounds on Treatment Effects
from Studies with Imperfect Compliance, JASA, 1997, 92, 439, 1171-1176,
<doi:10.2307/2965583>).  The package can calculate bounds for a binary outcome,
a binary treatment/phenotype, and an instrument with either 2 or 3 categories.
The package implements bounds for situations where these 3 variables are
measured in the same dataset (trivariate data) or where the outcome and
instrument are measured in one study and the treatment/phenotype and instrument
are measured in another study (bivariate data).")
    (license license:gpl3)))

(define-public r-bpacc
  (package
    (name "r-bpacc")
    (version "0.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bpAcc" version))
       (sha256
        (base32 "0341mndgl9jybmkgqa3pfgk9ryn8738j04d1gy2yjacbs68xxhn8"))))
    (properties `((upstream-name . "bpAcc")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=bpAcc")
    (synopsis
     "Blood Pressure Device Accuracy Evaluation: Statistical Considerations")
    (description
     "This package provides a comprehensive statistical analysis of the accuracy of
blood pressure devices based on the method of AAMI/ANSI SP10 standards developed
by the AAMI Sphygmomanometer Committee for indirect measurement of blood
pressure, incorporated into IS0 81060-2.  The @code{bpAcc} package gives the
exact probability of accepting a device D derived from the join distribution of
the sample standard deviation and a non-linear transformation of the sample mean
for a specified sample size introduced by Chandel et al. (2023) and by the
Association for the Advancement of Medical Instrumentation (2003,
ISBN:1-57020-183-8).")
    (license license:gpl2)))

(define-public r-bpa
  (package
    (name "r-bpa")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bpa" version))
       (sha256
        (base32 "0np7q1nasrq4j7ssaipvbjgrsi9n612p9lp96frq1dgix95mcgf0"))))
    (properties `((upstream-name . "bpa")))
    (build-system r-build-system)
    (propagated-inputs (list r-plyr r-magrittr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bgreenwell/bpa")
    (synopsis "Basic Pattern Analysis")
    (description
     "Run basic pattern analyses on character sets, digits, or combined input
containing both characters and numeric digits.  Useful for data cleaning and for
identifying columns containing multiple or nonstandard formats.")
    (license license:gpl2+)))

(define-public r-bp
  (package
    (name "r-bp")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bp" version))
       (sha256
        (base32 "0qdpm5nfx2jhc0s77x839jdmk07c88xf739dwf2n8hqhk24y0cdr"))))
    (properties `((upstream-name . "bp")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-magrittr
                             r-lubridate
                             r-gtable
                             r-gridextra
                             r-ggplot2
                             r-dplyr
                             r-cowplot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/johnschwenck/bp")
    (synopsis "Blood Pressure Analysis in R")
    (description
     "This package provides a comprehensive package to aid in the analysis of blood
pressure data of all forms by providing both descriptive and visualization tools
for researchers.")
    (license license:gpl3)))

(define-public r-boxr
  (package
    (name "r-boxr")
    (version "0.3.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "boxr" version))
       (sha256
        (base32 "1hdm4p7zx2r566zaiyw06qg3c790gsvylrvav7g9fl4i1x6qgkyk"))))
    (properties `((upstream-name . "boxr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-rlang
                             r-rio
                             r-purrr
                             r-mime
                             r-magrittr
                             r-lifecycle
                             r-jsonlite
                             r-httr
                             r-httpuv
                             r-glue
                             r-fs
                             r-dplyr
                             r-digest
                             r-bit64
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/r-box/boxr/")
    (synopsis "Interface for the 'Box.com API'")
    (description
     "An R interface for the remote file hosting service Box (<https://www.box.com/>).
 In addition to uploading and downloading files, this package includes functions
which mirror base R operations for local files, (e.g. box_load(), box_save(),
box_read(), box_setwd(), etc.), as well as git style functions for entire
directories (e.g. box_fetch(), box_push()).")
    (license license:expat)))

(define-public r-boxplotdbl
  (package
    (name "r-boxplotdbl")
    (version "1.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "boxplotdbl" version))
       (sha256
        (base32 "0yfkkafrq329l6yxbnpwm8yqidixxh7k124xjy5xa92pbng2clbd"))))
    (properties `((upstream-name . "boxplotdbl")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=boxplotdbl")
    (synopsis "Double Box Plot for Two-Axes Correlation")
    (description
     "Correlation chart of two set (x and y) of data.  Using Quartiles with boxplot
style.  Visualize the effect of factor.")
    (license license:expat)))

(define-public r-boxplotcluster
  (package
    (name "r-boxplotcluster")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "boxplotcluster" version))
       (sha256
        (base32 "1c3hy8h780c11hl98vwdn614nprxd2vb3n3rv23qdcl8fld0r16h"))))
    (properties `((upstream-name . "boxplotcluster")))
    (build-system r-build-system)
    (propagated-inputs (list r-cluster))
    (home-page "https://cran.r-project.org/package=boxplotcluster")
    (synopsis "Clustering Method Based on Boxplot Statistics")
    (description
     "Following Arroyo-MatÃ©-Roque (2006), the function calculates the distance
between rows or columns of the dataset using the generalized Minkowski metric as
described by Ichino-Yaguchi (1994).  The distance measure gives more weight to
differences between quartiles than to differences between extremes, making it
less sensitive to outliers.  Further,the function calculates the silhouette
width (Rousseeuw 1987) for different numbers of clusters and selects the number
of clusters that maximizes the average silhouette width, unless a specific
number of clusters is provided by the user.  The approach implemented in this
package is based on the following publications: Rousseeuw (1987)
<doi:10.1016/0377-0427(87)90125-7>; Ichino-Yaguchi (1994)
<doi:10.1109/21.286391>; Arroyo-MatÃ©-Roque (2006)
<doi:10.1007/3-540-34416-0_7>.")
    (license license:gpl2+)))

(define-public r-boxly
  (package
    (name "r-boxly")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "boxly" version))
       (sha256
        (base32 "0fvk95f2nn06aaxn2qq7jxr2yvsnc40806xkamryhrnmrwiphia9"))))
    (properties `((upstream-name . "boxly")))
    (build-system r-build-system)
    (propagated-inputs (list r-uuid
                             r-rlang
                             r-plotly
                             r-metalite
                             r-htmlwidgets
                             r-htmltools
                             r-ggplot2
                             r-dt
                             r-crosstalk
                             r-brew))
    (native-inputs (list r-knitr))
    (home-page "https://merck.github.io/boxly/")
    (synopsis "Interactive Box Plot")
    (description
     "Interactive box plot using plotly for clinical trial analysis.")
    (license license:gpl3+)))

(define-public r-boxfilter
  (package
    (name "r-boxfilter")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "boxfilter" version))
       (sha256
        (base32 "03qx2bsqg7kkpf2q6xrj7ap0aczgascdrnkj5bcl3aq278fahnxi"))))
    (properties `((upstream-name . "boxfilter")))
    (build-system r-build-system)
    (propagated-inputs (list r-gridextra r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=boxfilter")
    (synopsis "Filter Noisy Data")
    (description
     "Noise filter based on determining the proportion of neighboring points.  A false
point will be rejected if it has only few neighbors, but accepted if the
proportion of neighbors in a rectangular frame is high.  The size of the
rectangular frame as well as the cut-off value, i.e.  of a minimum proportion of
neighbor-points, may be supplied or can be calculated automatically.  Originally
designed for the cleaning of heart rates, but suitable for filtering any
slowly-changing physiological variable.For more information see Signer
(2010)<doi:10.1111/j.2041-210X.2009.00010.x>.")
    (license license:gpl3+)))

(define-public r-boxcoxmix
  (package
    (name "r-boxcoxmix")
    (version "0.46")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "boxcoxmix" version))
       (sha256
        (base32 "0lalj4sd9ydk2k5r5jww60bm68fbwhin0p81wzraq025pil5cddm"))))
    (properties `((upstream-name . "boxcoxmix")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod r-qicharts r-npmlreg))
    (native-inputs (list))
    (home-page "https://gitlab.com/iagogv/boxcoxmix")
    (synopsis
     "Box-Cox-Type Transformations for Linear and Logistic Models with Random Effects")
    (description
     "Box-Cox-type transformations for linear and logistic models with random effects
using non-parametric profile maximum likelihood estimation, as introduced in
Almohaimeed (2018) <http://etheses.dur.ac.uk/12831/> and Almohaimeed and Einbeck
(2022) <doi:10.1177/1471082X20966919>.  The main functions are optim.boxcox()
for linear models with random effects and boxcoxtype() for logistic models with
random effects.")
    (license license:gpl3+)))

(define-public r-box-lsp
  (package
    (name "r-box-lsp")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "box.lsp" version))
       (sha256
        (base32 "0rayi6fsnk2cjzvx7i3bmih9d5x93ssm0kg3r6w1dqk0djj0xrsj"))))
    (properties `((upstream-name . "box.lsp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-fs r-cli r-box))
    (home-page "https://github.com/Appsilon/box.lsp")
    (synopsis "Provides 'box' Compatibility for 'languageserver'")
    (description
     "This package provides a box compatible custom language parser for the
languageserver package to provide completion and signature hints in code
editors.")
    (license license:lgpl3)))

(define-public r-box-linters
  (package
    (name "r-box-linters")
    (version "0.10.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "box.linters" version))
       (sha256
        (base32 "1j6zjs5cpafyk1f74p059mggr9kp7c92fv4msyycs0lwji01ar88"))))
    (properties `((upstream-name . "box.linters")))
    (build-system r-build-system)
    (propagated-inputs (list r-xmlparsedata
                             r-xml2
                             r-xfun
                             r-withr
                             r-stringr
                             r-rlang
                             r-purrr
                             r-lintr
                             r-glue
                             r-fs
                             r-cli))
    (home-page "https://appsilon.github.io/box.linters/")
    (synopsis "Linters for 'box' Modules")
    (description
     "Static code analysis of box modules.  The package enhances code quality by
providing linters that check for common issues, enforce best practices, and
ensure consistent coding standards.")
    (license license:lgpl3)))

(define-public r-boutroslab-plotting-general
  (package
    (name "r-boutroslab-plotting-general")
    (version "7.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BoutrosLab.plotting.general" version))
       (sha256
        (base32 "0q5l0w0nlclz3k13mxwqd2j4q8hqy9lhywz6d7jx5lk3jq72gf5i"))))
    (properties `((upstream-name . "BoutrosLab.plotting.general")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass
                             r-latticeextra
                             r-lattice
                             r-hexbin
                             r-gtable
                             r-gridextra
                             r-e1071
                             r-cluster))
    (native-inputs (list r-knitr))
    (home-page
     "https://github.com/uclahs-cds/package-BoutrosLab-plotting-general")
    (synopsis "Functions to Create Publication-Quality Plots")
    (description
     "This package contains several plotting functions such as barplots, scatterplots,
heatmaps, as well as functions to combine plots and assist in the creation of
these plots.  These functions will give users great ease of use and
customization options in broad use for biomedical applications, as well as
general purpose plotting.  Each of the functions also provides valid default
settings to make plotting data more efficient and producing high quality plots
with standard colour schemes simpler.  All functions within this package are
capable of producing plots that are of the quality to be presented in scientific
publications and journals.  P'ng et al.; BPG: Seamless, automated and
interactive visualization of scientific data; BMC Bioinformatics 2019
<doi:10.1186/s12859-019-2610-2>.")
    (license license:gpl2)))

(define-public r-boutliers
  (package
    (name "r-boutliers")
    (version "1.1-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "boutliers" version))
       (sha256
        (base32 "0j2y6ndj03shssa7pgvg9cwbm5mv7ahy9kypj9z1ahskdi4xg1m1"))))
    (properties `((upstream-name . "boutliers")))
    (build-system r-build-system)
    (propagated-inputs (list r-metafor r-mass))
    (home-page "https://cran.r-project.org/package=boutliers")
    (synopsis "Outlier Detection and Influence Diagnostics for Meta-Analysis")
    (description
     "Computational tools for outlier detection and influence diagnostics of
meta-analysis.  Bootstrap distributions of the influence statistics are
calculated, and the thresholds to determine outliers are explicitly provided.")
    (license license:gpl3)))

(define-public r-boussinesq
  (package
    (name "r-boussinesq")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "boussinesq" version))
       (sha256
        (base32 "11dwc0j88zqxqwnrinc4f990vjbyv4bs16z6id6xw7yp7i2n5nla"))))
    (properties `((upstream-name . "boussinesq")))
    (build-system r-build-system)
    (home-page "https://github.com/ecor/boussinesq")
    (synopsis "Analytic Solutions for (Ground-Water) Boussinesq Equation")
    (description
     "This package provides a collection of R functions were implemented from
published and available analytic solutions for the One-Dimensional Boussinesq
Equation (ground-water).  In particular, the function \"beq.lin()\" is the
analytic solution of the linearized form of Boussinesq Equation between two
different head-based boundary (Dirichlet) conditions; \"beq.song\" is the
non-linear power-series analytic solution of the motion of a wetting front over
a dry bedrock (Song at al, 2007, see complete reference on function
documentation).  Bugs/comments/questions/collaboration of any kind are warmly
welcomed.")
    (license license:gpl3+)))

(define-public r-boundingbox
  (package
    (name "r-boundingbox")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "boundingbox" version))
       (sha256
        (base32 "0kgpsvnq8f0qyw55rldwqapys36y6lb4w2bw2azfl4nmrrnayxbc"))))
    (properties `((upstream-name . "boundingbox")))
    (build-system r-build-system)
    (propagated-inputs (list r-imager r-gplots))
    (native-inputs (list r-knitr))
    (home-page "<https://github.com/stomperusa/boundingbox>")
    (synopsis "Create a Bounding Box in an Image")
    (description
     "Generate ground truth cases for object localization algorithms.  Cycle through a
list of images, select points around which to generate bounding boxes and assign
classifiers.  Output the coordinates, and images annotated with boxes and
labels.  For an example study that uses bounding boxes for image localization
and classification see Ibrahim, Badr, Abdallah, and Eissa (2012) \"Bounding Box
Object Localization Based on Image Superpixelization\"
<doi:10.1016/j.procs.2012.09.119>.")
    (license license:expat)))

(define-public r-boundedgeworth
  (package
    (name "r-boundedgeworth")
    (version "0.1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BoundEdgeworth" version))
       (sha256
        (base32 "03gqm9afqv7z176f9yv5vxf0r4v0bq1zs8rfwfld38mk66sfb2m1"))))
    (properties `((upstream-name . "BoundEdgeworth")))
    (build-system r-build-system)
    (propagated-inputs (list r-mathjaxr r-expint))
    (home-page "https://cran.r-project.org/package=BoundEdgeworth")
    (synopsis "Bound on the Error of the First-Order Edgeworth Expansion")
    (description
     "Computes uniform bounds on the distance between the cumulative distribution
function of a standardized sum of random variables and its first-order Edgeworth
expansion, following the article Derumigny, Girard, Guyonvarch (2021)
<@code{arXiv:2101.05780>}.")
    (license license:gpl3)))

(define-public r-boundarystats
  (package
    (name "r-boundarystats")
    (version "2.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BoundaryStats" version))
       (sha256
        (base32 "06kz1d5fwiilqidiz2iry0fg7c5265fc40yrdgddd0aia75cf9jv"))))
    (properties `((upstream-name . "BoundaryStats")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-terra
                             r-sf
                             r-scales
                             r-rgeoda
                             r-pdqr
                             r-magrittr
                             r-igraph
                             r-ggplot2
                             r-fields))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BoundaryStats")
    (synopsis "Boundary Overlap Statistics")
    (description
     "Analysis workflow for finding geographic boundaries of ecological or landscape
traits and comparing the placement of geographic boundaries of two traits.  If
data are trait values, trait data are transformed to boundary intensities based
on approximate first derivatives across latitude and longitude.  The package
includes functions to create custom null models based on the input data.  The
boundary statistics are described in: Fortin, Drapeau, and Jacquez (1996)
<doi:10.2307/3545584>.")
    (license license:gpl3+)))

(define-public r-botor
  (package
    (name "r-botor")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "botor" version))
       (sha256
        (base32 "0avw7h0m2dm9nn5ya1y6kcgdh438v63y42w3y2dag24iyligx0wq"))))
    (properties `((upstream-name . "botor")))
    (build-system r-build-system)
    (propagated-inputs (list r-reticulate r-logger r-jsonlite r-checkmate))
    (home-page "https://daroczig.github.io/botor/")
    (synopsis "'AWS Python SDK' ('boto3') for R")
    (description
     "Fork-safe, raw access to the Amazon Web Services ('AWS') SDK via the boto3
Python module, and convenient helper functions to query the Simple Storage
Service ('S3') and Key Management Service ('KMS'), partial support for IAM', the
Systems Manager Parameter Store and Secrets Manager'.")
    (license license:agpl3)))

(define-public r-bossreg
  (package
    (name "r-bossreg")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BOSSreg" version))
       (sha256
        (base32 "0ln4nx0jlvz5kqk9qkcl8a2cyhsdcqqfs5112qcaabjiwjnvla7w"))))
    (properties `((upstream-name . "BOSSreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-matrix r-glmnet))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/sentian/BOSSreg")
    (synopsis "Best Orthogonalized Subset Selection (BOSS)")
    (description
     "Best Orthogonalized Subset Selection (BOSS) is a least-squares (LS) based subset
selection method, that performs best subset selection upon an orthogonalized
basis of ordered predictors, with the computational effort of a single ordinary
LS fit.  This package provides a highly optimized implementation of BOSS and
estimates a heuristic degrees of freedom for BOSS, which can be plugged into an
information criterion (IC) such as AICc in order to select the subset from
candidates.  It provides various choices of IC, including AIC, BIC, AICc, Cp and
GCV. It also implements the forward stepwise selection (FS) with no additional
computational cost, where the subset of FS is selected via cross-validation
(CV).  CV is also an option for BOSS. For details see: Tian, Hurvich and
Simonoff (2021), \"On the Use of Information Criteria for Subset Selection in
Least Squares Regression\", <@code{arXiv:1911.10191>}.")
    (license license:gpl2+)))

(define-public r-bossr
  (package
    (name "r-bossr")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bossR" version))
       (sha256
        (base32 "04m0pbs5ywmq5yf1xq5skay7fsvvh7mlc9ks0z6wl7pg9ibwrxkf"))))
    (properties `((upstream-name . "bossR")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mvtnorm))
    (home-page "https://cran.r-project.org/package=bossR")
    (synopsis "Biomarker Optimal Segmentation System")
    (description
     "The Biomarker Optimal Segmentation System R package, @code{bossR}', is designed
for precision medicine, helping to identify individual traits using biomarkers.
It focuses on determining the most effective cutoff value for a continuous
biomarker, which is crucial for categorizing patients into two groups with
distinctly different clinical outcomes.  The package simultaneously finds the
optimal cutoff from given candidate values and tests its significance.
Simulation studies demonstrate that @code{bossR} offers statistical power and
false positive control non-inferior to the permutation approach (considered the
gold standard in this field), while being hundreds of times faster.")
    (license license:gpl3)))

(define-public r-bossa
  (package
    (name "r-bossa")
    (version "3.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BoSSA" version))
       (sha256
        (base32 "0qhcar3grp6l6islbjc27jqvgh37kkdi0nql6plsqwilldv3kis6"))))
    (properties `((upstream-name . "BoSSA")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsqlite r-plotrix r-phangorn r-jsonlite r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BoSSA")
    (synopsis "Bunch of Structure and Sequence Analysis")
    (description "Reads and plots phylogenetic placements.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-bosonsampling
  (package
    (name "r-bosonsampling")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BosonSampling" version))
       (sha256
        (base32 "059g4sl7c2cns729sssv1syj0zx8rs8wbjfsc761fxl435ca7hqk"))))
    (properties `((upstream-name . "BosonSampling")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=BosonSampling")
    (synopsis "Classical Boson Sampling")
    (description
     "Classical Boson Sampling using the algorithm of Clifford and Clifford (2017)
<@code{arXiv:1706.01260>}.  Also provides functions for generating random
unitary matrices, evaluation of matrix permanents (both real and complex) and
evaluation of complex permanent minors.")
    (license license:gpl2)))

(define-public r-borrowr
  (package
    (name "r-borrowr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "borrowr" version))
       (sha256
        (base32 "0hq9rplb22dm8nikapxnvzq10y4xzi1y7k61ghw9x1fq4fdfyy2f"))))
    (properties `((upstream-name . "borrowr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-mvtnorm r-bart))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=borrowr")
    (synopsis "Estimate Causal Effects with Borrowing Between Data Sources")
    (description
     "Estimate population average treatment effects from a primary data source with
borrowing from supplemental sources.  Causal estimation is done with either a
Bayesian linear model or with Bayesian additive regression trees (BART) to
adjust for confounding.  Borrowing is done with multisource exchangeability
models (MEMs).  For information on BART, see Chipman, George, & @code{McCulloch}
(2010) <doi:10.1214/09-AOAS285>.  For information on MEMs, see Kaizer,
Koopmeiners, & Hobbs (2018) <doi:10.1093/biostatistics/kxx031>.")
    (license license:gpl3+)))

(define-public r-boral
  (package
    (name "r-boral")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "boral" version))
       (sha256
        (base32 "1x53g3y283i92624rkp36lwfmz0p8rkkqsndn5h35sapjga6wmy3"))))
    (properties `((upstream-name . "boral")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2
                             r-r2jags
                             r-mvtnorm
                             r-mass
                             r-lifecycle
                             r-fishmod
                             r-corpcor
                             r-coda
                             r-abind))
    (home-page "https://cran.r-project.org/package=boral")
    (synopsis "Bayesian Ordination and Regression AnaLysis")
    (description
     "Bayesian approaches for analyzing multivariate data in ecology.  Estimation is
performed using Markov Chain Monte Carlo (MCMC) methods via Three.  JAGS types
of models may be fitted: 1) With explanatory variables only, boral fits
independent column Generalized Linear Models (GLMs) to each column of the
response matrix; 2) With latent variables only, boral fits a purely latent
variable model for model-based unconstrained ordination; 3) With explanatory and
latent variables, boral fits correlated column GLMs with latent variables to
account for any residual correlation between the columns of the response matrix.")
    (license license:gpl2)))

(define-public r-bor
  (package
    (name "r-bor")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bor" version))
       (sha256
        (base32 "1r5jacmin0cq9zipxa9nmp3jnh6wsddd4wnzw2n5sggnf24ryp8g"))))
    (properties `((upstream-name . "bor")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=bor")
    (synopsis "Transforming Behavioral Observation Records into Data Matrices")
    (description
     "Transforms focal observations data, where different types of social interactions
can be recorded by multiple observers, into asymmetric data matrices.  Each cell
in these matrices provides counts on the number of times a specific type of
social interaction was initiated by the row subject and directed to the column
subject.")
    (license license:gpl3)))

(define-public r-boptbd
  (package
    (name "r-boptbd")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Boptbd" version))
       (sha256
        (base32 "1p786avc37yq5g01zh6sqx7b1rvbxrh6gbxsad4bdvr12cjdsss6"))))
    (properties `((upstream-name . "Boptbd")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-mass r-igraph))
    (home-page "https://cran.r-project.org/package=Boptbd")
    (synopsis "Bayesian Optimal Block Designs")
    (description
     "Computes Bayesian A- and D-optimal block designs under the linear mixed effects
model settings using block/array exchange algorithm of Debusho, Gemechu and
Haines (2018) <doi:10.1080/03610918.2018.1429617> where the interest is in a
comparison of all possible elementary treatment contrasts.  The package also
provides an optional method of using the graphical user interface (GUI) R
package tcltk to ensure that it is user friendly.")
    (license license:gpl2)))

(define-public r-bootwptos
  (package
    (name "r-bootwptos")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BootWPTOS" version))
       (sha256
        (base32 "1s6qvc2nvkqfxl8d4cy7ckvn6l0svq85g274pvjjxg8f883hax0j"))))
    (properties `((upstream-name . "BootWPTOS")))
    (build-system r-build-system)
    (propagated-inputs (list r-wavethresh r-tseries))
    (home-page "https://cran.r-project.org/package=BootWPTOS")
    (synopsis "Test Stationarity using Bootstrap Wavelet Packet Tests")
    (description
     "This package provides significance tests for second-order stationarity for time
series using bootstrap wavelet packet tests.  Provides functionality to
visualize the time series with the results of the hypothesis tests superimposed.
 The methodology is described in Cardinali, A and Nason, G P (2016) \"Practical
powerful wavelet packet tests for second-order stationarity.\" Applied and
Computational Harmonic Analysis, 44, 558-585 <doi:10.1016/j.acha.2016.06.006>.")
    (license license:gpl2)))

(define-public r-bootwar
  (package
    (name "r-bootwar")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bootwar" version))
       (sha256
        (base32 "0cvwdi8qvm737qsnyr87wpsfmyzcdr7sga4zbkal8y5bj73h9y63"))))
    (properties `((upstream-name . "bootwar")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinythemes
                             r-shinyjs
                             r-shiny
                             r-npboottprm
                             r-mmcards
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mightymetrika/bootwar")
    (synopsis "Nonparametric Bootstrap Test with Pooled Resampling Card Game")
    (description
     "The card game War is simple in its rules but can be lengthy.  In another domain,
the nonparametric bootstrap test with pooled resampling (nbpr) methods, as
outlined in Dwivedi, Mallawaarachchi, and Alvarado (2017)
<doi:10.1002/sim.7263>, is optimal for comparing paired or unpaired means in
non-normal data, especially for small sample size studies.  However, many
researchers are unfamiliar with these methods.  The bootwar package bridges this
gap by enabling users to grasp the concepts of nbpr via Boot War, a variation of
the card game War designed for small samples.  The package provides functions
like score_keeper() and play_round() to streamline gameplay and scoring.  Once a
predetermined number of rounds concludes, users can employ the analyze_game()
function to derive game results.  This function leverages the npboottprm
package's nonparboot() to report nbpr results and, for comparative analysis,
also reports results from the stats package's t.test() function.  Additionally,
bootwar features an interactive shiny web application, bootwar().  This offers a
user-centric interface to experience Boot War, enhancing understanding of nbpr
methods across various distributions, sample sizes, number of bootstrap
resamples, and confidence intervals.")
    (license license:expat)))

(define-public r-bootur
  (package
    (name "r-bootur")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bootUR" version))
       (sha256
        (base32 "19sjh9hwx9hcqyrjx5kc2id1ml1sfd6c37g412vzfgk931bm0lbs"))))
    (properties `((upstream-name . "bootUR")))
    (build-system r-build-system)
    (propagated-inputs (list r-urca
                             r-rcppthread
                             r-rcppparallel
                             r-rcpparmadillo
                             r-rcpp
                             r-parallelly))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/smeekes/bootUR")
    (synopsis "Bootstrap Unit Root Tests")
    (description
     "Set of functions to perform various bootstrap unit root tests for both
individual time series (including augmented Dickey-Fuller test and union tests),
multiple time series and panel data; see Smeekes and Wilms (2023)
<doi:10.18637/jss.v106.i12>, Palm, Smeekes and Urbain (2008)
<doi:10.1111/j.1467-9892.2007.00565.x>, Palm, Smeekes and Urbain (2011)
<doi:10.1016/j.jeconom.2010.11.010>, Moon and Perron (2012)
<doi:10.1016/j.jeconom.2012.01.008>, Smeekes and Taylor (2012)
<doi:10.1017/S0266466611000387> and Smeekes (2015) <doi:10.1111/jtsa.12110> for
key references.")
    (license license:gpl2+)))

(define-public r-bootsvd
  (package
    (name "r-bootsvd")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bootSVD" version))
       (sha256
        (base32 "16d3qv8vzhfvl9vbfglkb3csmc4npb29jcq9apv89hj7bkcapvi9"))))
    (properties `((upstream-name . "bootSVD")))
    (build-system r-build-system)
    (propagated-inputs (list r-ff))
    (home-page "http://arxiv.org/abs/1405.0922")
    (synopsis
     "Fast, Exact Bootstrap Principal Component Analysis for High Dimensional Data")
    (description
     "This package implements fast, exact bootstrap Principal Component Analysis and
Singular Value Decompositions for high dimensional data, as described in
<doi:10.1080/01621459.2015.1062383> (see also <@code{arXiv:1405.0922>} ).  For
data matrices that are too large to operate on in memory, users can input
objects with class ff (see the ff package), where the actual data is stored on
disk.  In response, this package will implement a block matrix algebra procedure
for calculating the principal components (PCs) and bootstrap PCs.  Depending on
options set by the user, the parallel package can be used to parallelize the
calculation of the bootstrap PCs.")
    (license license:gpl2)))

(define-public r-bootsurv
  (package
    (name "r-bootsurv")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bootsurv" version))
       (sha256
        (base32 "0c60xyhq8x3k75dxi8lcsz7i5xnizdzrypcvy27w5mb5nx9wg58i"))))
    (properties `((upstream-name . "bootsurv")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=bootsurv")
    (synopsis "Bootstrap Methods for Complete Survey Data")
    (description
     "Bootstrap resampling methods have been widely studied in the context of survey
data.  This package implements various bootstrap resampling techniques tailored
for survey data, with a focus on stratified simple random sampling and
stratified two-stage cluster sampling.  It provides tools for precise and
consistent bootstrap variance estimation for population totals, means, and
quartiles.  Additionally, it enables easy generation of bootstrap samples for
in-depth analysis.")
    (license license:gpl3)))

(define-public r-bootstrapqtl
  (package
    (name "r-bootstrapqtl")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BootstrapQTL" version))
       (sha256
        (base32 "04lyr9x6lz4q5y70p92ffdil2i9f1fsykw5wh6wsv0qcqypdmawy"))))
    (properties `((upstream-name . "BootstrapQTL")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrixeqtl r-foreach r-data-table))
    (home-page "https://cran.r-project.org/package=BootstrapQTL")
    (synopsis "Bootstrap cis-QTL Method that Corrects for the Winner's Curse")
    (description
     "Identifies genome-related molecular traits with significant evidence of genetic
regulation and performs a bootstrap procedure to correct estimated effect sizes
for over-estimation present in cis-QTL mapping studies (The \"Winner's Curse\"),
described in Huang QQ *et al.* 2018 <doi: 10.1093/nar/gky780>.")
    (license license:gpl2)))

(define-public r-bootstrapfp
  (package
    (name "r-bootstrapfp")
    (version "0.4.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bootstrapFP" version))
       (sha256
        (base32 "104vl9a6qqk0xr0pqmnm9v0rdc0x1jizvshf494zqkg9l97nq3yb"))))
    (properties `((upstream-name . "bootstrapFP")))
    (build-system r-build-system)
    (propagated-inputs (list r-sampling))
    (home-page "https://cran.r-project.org/package=bootstrapFP")
    (synopsis "Bootstrap Algorithms for Finite Population Inference")
    (description
     "Finite Population bootstrap algorithms to estimate the variance of the
Horvitz-Thompson estimator for single-stage sampling.  For a survey of bootstrap
methods for finite populations, see Mashreghi et Al. (2016)
<doi:10.1214/16-SS113>.")
    (license license:gpl3)))

(define-public r-bootstepaic
  (package
    (name "r-bootstepaic")
    (version "1.3-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bootStepAIC" version))
       (sha256
        (base32 "1dmziq6yfycpap4hhlzl3xg2hsx4y135hy7r6qh9p98vxpa6mycl"))))
    (properties `((upstream-name . "bootStepAIC")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=bootStepAIC")
    (synopsis "Bootstrap stepAIC")
    (description
     "Model selection by bootstrapping the @code{stepAIC}() procedure.")
    (license license:gpl2+)))

(define-public r-bootruin
  (package
    (name "r-bootruin")
    (version "1.2-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bootruin" version))
       (sha256
        (base32 "1gbvh99snchipf13kjhymcx60s2kni23y7lv8lhzd3d402grp68h"))))
    (properties `((upstream-name . "bootruin")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=bootruin")
    (synopsis
     "Bootstrap Test for the Probability of Ruin in the Classical Risk Process")
    (description
     "We provide a framework for testing the probability of ruin in the classical
(compound Poisson) risk process.  It also includes some procedures for assessing
and comparing the performance between the bootstrap test and the test using
asymptotic normality.")
    (license license:agpl3)))

(define-public r-bootpr
  (package
    (name "r-bootpr")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BootPR" version))
       (sha256
        (base32 "11cpg3klm5qk7447wnwhfv1kg0r3kqr0m4vvwkv1jll5v9zkq7h7"))))
    (properties `((upstream-name . "BootPR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BootPR")
    (synopsis "Bootstrap Prediction Intervals and Bias-Corrected Forecasting")
    (description
     "This package contains functions for bias-Corrected Forecasting and Bootstrap
Prediction Intervals for Autoregressive Time Series.")
    (license license:gpl2)))

(define-public r-bootpls
  (package
    (name "r-bootpls")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bootPLS" version))
       (sha256
        (base32 "1sk7327fwqhy9arxn2shlxhflg4i99864wqs782jh325z18nqxwb"))))
    (properties `((upstream-name . "bootPLS")))
    (build-system r-build-system)
    (propagated-inputs (list r-spls
                             r-plsrglm
                             r-pls
                             r-mvtnorm
                             r-foreach
                             r-doparallel
                             r-boot
                             r-bipartite))
    (native-inputs (list r-knitr))
    (home-page "https://fbertran.github.io/bootPLS/")
    (synopsis
     "Bootstrap Hyperparameter Selection for PLS Models and Extensions")
    (description
     "Several implementations of non-parametric stable bootstrap-based techniques to
determine the numbers of components for Partial Least Squares linear or
generalized linear regression models as well as and sparse Partial Least Squares
linear or generalized linear regression models.  The package collects techniques
that were published in a book chapter (Magnanensi et al.  2016, The Multiple
Facets of Partial Least Squares and Related Methods',
<doi:10.1007/978-3-319-40643-5_18>) and two articles (Magnanensi et al.  2017,
Statistics and Computing', <doi:10.1007/s11222-016-9651-4>) and (Magnanensi et
al.  2021, Frontiers in Applied Mathematics and Statistics',
<doi:10.3389/fams.2021.693126>).")
    (license license:gpl3)))

(define-public r-bootnet
  (package
    (name "r-bootnet")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bootnet" version))
       (sha256
        (base32 "0r7ad6ybzr3jm5s2791sv402293wi4wy59n7dv5q5ss1vykpsk6h"))))
    (properties `((upstream-name . "bootnet")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-snow
                             r-rlang
                             r-qgraph
                             r-pbapply
                             r-networktools
                             r-networktoolbox
                             r-mvtnorm
                             r-mgm
                             r-matrix
                             r-isingsampler
                             r-isingfit
                             r-igraph
                             r-gtools
                             r-ggplot2
                             r-dplyr
                             r-corpcor
                             r-abind))
    (home-page "https://github.com/SachaEpskamp/bootnet")
    (synopsis "Bootstrap Methods for Various Network Estimation Routines")
    (description
     "Bootstrap methods to assess accuracy and stability of estimated network
structures and centrality indices <doi:10.3758/s13428-017-0862-1>.  Allows for
flexible specification of any undirected network estimation procedure in R, and
offers default sets for various estimation routines.")
    (license license:gpl2)))

(define-public r-bootmrmr
  (package
    (name "r-bootmrmr")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BootMRMR" version))
       (sha256
        (base32 "07am80603pv1dh5ma3zb0idnslkzm5m3ym8sd1nhkh8zbc6qqmzs"))))
    (properties `((upstream-name . "BootMRMR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BootMRMR")
    (synopsis "Bootstrap-MRMR Technique for Informative Gene Selection")
    (description
     "Selection of informative features like genes, transcripts, RNA seq, etc.  using
Bootstrap Maximum Relevance and Minimum Redundancy technique from a given high
dimensional genomic dataset.  Informative gene selection involves identification
of relevant genes and removal of redundant genes as much as possible from a
large gene space.  Main applications in high-dimensional expression data
analysis (e.g. microarray data, NGS expression data and other genomics and
proteomics applications).")
    (license license:gpl2+)))

(define-public r-bootlr
  (package
    (name "r-bootlr")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bootLR" version))
       (sha256
        (base32 "1aj5l42d5y7czxzlg6r9ykdxyjf8m8bahl41xk4k6xpxckdnka14"))))
    (properties `((upstream-name . "bootLR")))
    (build-system r-build-system)
    (propagated-inputs (list r-boot r-binom))
    (home-page "https://cran.r-project.org/package=bootLR")
    (synopsis
     "Bootstrapped Confidence Intervals for (Negative) Likelihood Ratio Tests")
    (description
     "Computes appropriate confidence intervals for the likelihood ratio tests
commonly used in medicine/epidemiology, using the method of Marill et al. (2015)
<doi:10.1177/0962280215592907>.  It is particularly useful when the sensitivity
or specificity in the sample is 100%.  Note that this does not perform the test
on nested models--for that, see epicalc::lrtest'.")
    (license license:lgpl2.1)))

(define-public r-bootimpute
  (package
    (name "r-bootimpute")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bootImpute" version))
       (sha256
        (base32 "1z7vhfn43rd91bnflygyx3f9bvlzz9djncxa8abqs8ip8rz1pqj0"))))
    (properties `((upstream-name . "bootImpute")))
    (build-system r-build-system)
    (propagated-inputs (list r-smcfcs r-mice))
    (home-page "https://cran.r-project.org/package=bootImpute")
    (synopsis "Bootstrap Inference for Multiple Imputation")
    (description
     "Bootstraps and imputes incomplete datasets.  Then performs inference on
estimates obtained from analysing the imputed datasets as proposed by von Hippel
and Bartlett (2021) <doi:10.1214/20-STS793>.")
    (license license:gpl3)))

(define-public r-bootgof
  (package
    (name "r-bootgof")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bootGOF" version))
       (sha256
        (base32 "11nn4lc9wdx1z35mfpc3zkk1xr0pvvjyvjzpjlq3z4gm0hmniifz"))))
    (properties `((upstream-name . "bootGOF")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6 r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/MarselScheer/bootGOF")
    (synopsis "Bootstrap Based Goodness-of-Fit Tests")
    (description
     "Bootstrap based goodness-of-fit tests.  It allows to perform rigorous
statistical tests to check if a chosen model family is correct based on the
marked empirical process.  The implemented algorithms are described in (Dikta
and Scheer (2021) <doi:10.1007/978-3-030-73480-0>) and can be applied to
generalized linear models without any further implementation effort.  As far as
certain linearity conditions are fulfilled the resampling scheme are also
applicable beyond generalized linear models.  This is reflected in the software
architecture which allows to reuse the resampling scheme by implementing only
certain interfaces for models that are not supported natively by the package.")
    (license license:gpl3)))

(define-public r-bootf2
  (package
    (name "r-bootf2")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bootf2" version))
       (sha256
        (base32 "17wrl678x9mb5pny2vwrr6sdl2hsqr1nbjzsizp2q6l3w5m6s152"))))
    (properties `((upstream-name . "bootf2")))
    (build-system r-build-system)
    (propagated-inputs (list r-readxl r-minpack-lm r-mass r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/zhengguoxu/bootf2")
    (synopsis "Simulation and Comparison of Dissolution Profiles")
    (description
     "Compare dissolution profiles with confidence interval of similarity factor f2
using bootstrap methodology as described in the literature, such as Efron and
Tibshirani (1993, ISBN:9780412042317), Davison and Hinkley (1997,
ISBN:9780521573917), and Shah et al. (1998) <doi:10.1023/A:1011976615750>.  The
package can also be used to simulate dissolution profiles based on mathematical
modelling and multivariate normal distribution.")
    (license license:gpl3+)))

(define-public r-bootes
  (package
    (name "r-bootes")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bootES" version))
       (sha256
        (base32 "05brahlh7jk9n1wv4l429vf048x51v1ivvchlxfcqvky06zjwk1z"))))
    (properties `((upstream-name . "bootES")))
    (build-system r-build-system)
    (propagated-inputs (list r-boot))
    (home-page "https://github.com/dgerlanc/bootES")
    (synopsis "Bootstrap Confidence Intervals on Effect Sizes")
    (description
     "Calculate robust measures of effect sizes using the bootstrap.")
    (license license:gpl2+)))

(define-public r-bootct
  (package
    (name "r-bootct")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bootCT" version))
       (sha256
        (base32 "1025n0rxvjsyqwba3y4frjhpfqila70p43pk4p4rf9fhxcs4xg6m"))))
    (properties `((upstream-name . "bootCT")))
    (build-system r-build-system)
    (propagated-inputs (list r-vars
                             r-usethis
                             r-urca
                             r-stringr
                             r-rcpparmadillo
                             r-rcpp
                             r-pracma
                             r-magrittr
                             r-gtools
                             r-dynamac
                             r-dplyr
                             r-ardl
                             r-aod))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bootCT")
    (synopsis "Bootstrapping the ARDL Tests for Cointegration")
    (description
     "The bootstrap ARDL tests for cointegration is the main functionality of this
package.  It also acts as a wrapper of the most commond ARDL testing procedures
for cointegration: the bound tests of Pesaran, Shin and Smith (PSS; 2001 -
<doi:10.1002/jae.616>) and the asymptotic test on the independent variables of
Sam, @code{McNown} and Goh (SMG: 2019 - <doi:10.1016/j.econmod.2018.11.001>).
Bootstrap and bound tests are performed under both the conditional and
unconditional ARDL models.")
    (license license:gpl2+)))

(define-public r-bootcomb
  (package
    (name "r-bootcomb")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bootComb" version))
       (sha256
        (base32 "0gg07ck2nf0ddqc5nflk5ms16m3c6dlf1a77m58q581mkavj6gyb"))))
    (properties `((upstream-name . "bootComb")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=bootComb")
    (synopsis "Combine Parameter Estimates via Parametric Bootstrap")
    (description
     "Propagate uncertainty from several estimates when combining these estimates via
a function.  This is done by using the parametric bootstrap to simulate values
from the distribution of each estimate to build up an empirical distribution of
the combined parameter.  Finally either the percentile method is used or the
highest density interval is chosen to derive a confidence interval for the
combined parameter with the desired coverage.  Gaussian copulas are used for
when parameters are assumed to be dependent / correlated.  References: Davison
and Hinkley (1997,ISBN:0-521-57471-4) for the parametric bootstrap and
percentile method, Gelman et al. (2014,ISBN:978-1-4398-4095-5) for the highest
density interval, Stockdale et al. (2020)<doi:10.1016/j.jhep.2020.04.008> for an
example of combining conditional prevalences.")
    (license license:gpl3)))

(define-public r-bootcluster
  (package
    (name "r-bootcluster")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bootcluster" version))
       (sha256
        (base32 "0wkc9li0hqgns7pz72fby187q81yzrgl62qs8w5llayghf8lhaml"))))
    (properties `((upstream-name . "bootcluster")))
    (build-system r-build-system)
    (propagated-inputs (list r-sna
                             r-plyr
                             r-network
                             r-mclust
                             r-intergraph
                             r-igraph
                             r-gridextra
                             r-ggplot2
                             r-ggally
                             r-fpc
                             r-foreach
                             r-flexclust
                             r-dplyr
                             r-doparallel
                             r-cluster))
    (home-page "https://cran.r-project.org/package=bootcluster")
    (synopsis "Bootstrapping Estimates of Clustering Stability")
    (description
     "Implementation of the bootstrapping approach for the estimation of clustering
stability and its application in estimating the number of clusters, as
introduced by Yu et al (2016)<doi:10.1142/9789814749411_0007>.  Implementation
of the non-parametric bootstrap approach to assessing the stability of module
detection in a graph, the extension for the selection of a parameter set that
defines a graph from data in a way that optimizes stability and the
corresponding visualization functions, as introduced by Tian et al (2021)
<doi:10.1002/sam.11495>.")
    (license license:gpl2)))

(define-public r-boot-pval
  (package
    (name "r-boot-pval")
    (version "0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "boot.pval" version))
       (sha256
        (base32 "0572n82402i609q0jl6p2ar1gq5jlqqssv28xs3rxsr3nza49dxv"))))
    (properties `((upstream-name . "boot.pval")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rms
                             r-rdpack
                             r-lme4
                             r-gt
                             r-flextable
                             r-car
                             r-boot))
    (home-page "https://cran.r-project.org/package=boot.pval")
    (synopsis "Bootstrap p-Values")
    (description
     "Computation of bootstrap p-values through inversion of confidence intervals,
including convenience functions for regression models.")
    (license license:expat)))

(define-public r-boot-heterogeneity
  (package
    (name "r-boot-heterogeneity")
    (version "1.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "boot.heterogeneity" version))
       (sha256
        (base32 "0g4zkvx8krqh54xvjh1a0ybwl3v5bg3l670ycgnps8kkw32w5p1f"))))
    (properties `((upstream-name . "boot.heterogeneity")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmarkdown r-pbmcapply r-metafor r-knitr
                             r-hsaur3))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/gabriellajg/boot.heterogeneity/")
    (synopsis "Bootstrap-Based Heterogeneity Test for Meta-Analysis")
    (description
     "This package implements a bootstrap-based heterogeneity test for standardized
mean differences (d), Fisher-transformed Pearson's correlations (r), and
natural-logarithm-transformed odds ratio (or) in meta-analysis studies.
Depending on the presence of moderators, this Monte Carlo based test can be
implemented in the random- or mixed-effects model.  This package uses rma()
function from the R package metafor to obtain parameter estimates and
likelihoods, so installation of R package metafor is required.  This approach
refers to the studies of Anscombe (1956) <doi:10.2307/2332926>, Haldane (1940)
<doi:10.2307/2332614>, Hedges (1981) <doi:10.3102/10769986006002107>, Hedges &
Olkin (1985, ISBN:978-0123363800), Silagy, Lancaster, Stead, Mant, & Fowler
(2004) <doi:10.1002/14651858.CD000146.pub2>, Viechtbauer (2010)
<doi:10.18637/jss.v036.i03>, and Zuckerman (1994, ISBN:978-0521432009).")
    (license license:gpl2+)))

(define-public r-boostrq
  (package
    (name "r-boostrq")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "boostrq" version))
       (sha256
        (base32 "1lf3gj1adnwxavlpmw9ygwz5pxz45bbdgz7cv46cp2jyh06y3sa3"))))
    (properties `((upstream-name . "boostrq")))
    (build-system r-build-system)
    (propagated-inputs (list r-stabs r-quantreg r-mboost r-checkmate))
    (home-page "https://github.com/stefanlinner/boostrq")
    (synopsis "Boosting Regression Quantiles")
    (description
     "Boosting Regression Quantiles is a component-wise boosting algorithm, that
embeds all boosting steps in the well-established framework of quantile
regression.  It is initialized with the corresponding quantile, uses a
quantile-specific learning rate, and uses quantile regression as its base
learner.  The package implements this algorithm and allows cross-validation and
stability selection.")
    (license license:gpl2+)))

(define-public r-boostmtree
  (package
    (name "r-boostmtree")
    (version "1.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "boostmtree" version))
       (sha256
        (base32 "17na38k052lzjrssvxssllm9fwpnzhr29g9wwcfkbja6rv2pw9p1"))))
    (properties `((upstream-name . "boostmtree")))
    (build-system r-build-system)
    (propagated-inputs (list r-randomforestsrc r-nlme))
    (home-page "https://ishwaran.org/ishwaran.html")
    (synopsis "Boosted Multivariate Trees for Longitudinal Data")
    (description
     "This package implements Friedman's gradient descent boosting algorithm for
modeling longitudinal response using multivariate tree base learners.
Longitudinal response could be continuous, binary, nominal or ordinal.  A
time-covariate interaction effect is modeled using penalized B-splines
(P-splines) with estimated adaptive smoothing parameter.  Although the package
is design for longitudinal data, it can handle cross-sectional data as well.
Implementation details are provided in Pande et al. (2017), Mach Learn
<DOI:10.1007/s10994-016-5597-1>.")
    (license license:gpl3+)))

(define-public r-boostmlr
  (package
    (name "r-boostmlr")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BoostMLR" version))
       (sha256
        (base32 "0gk8jf04qda1qsc6987d8nfa453v9cqkjsvvrrg68yx3fd96m8aj"))))
    (properties `((upstream-name . "BoostMLR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-nlme))
    (home-page "https://cran.r-project.org/package=BoostMLR")
    (synopsis "Boosting for Multivariate Longitudinal Responses")
    (description
     "Jointly models the multivariate longitudinal responses and multiple covariates
and time using gradient boosting approach.")
    (license license:gpl2+)))

(define-public r-boostingdea
  (package
    (name "r-boostingdea")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "boostingDEA" version))
       (sha256
        (base32 "0df19q44fsv0hvda4dwq302wsh6vlalwszyh0ir66r5ryrrmhas4"))))
    (properties `((upstream-name . "boostingDEA")))
    (build-system r-build-system)
    (propagated-inputs (list r-rglpk r-mlmetrics r-lpsolveapi r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/itsmeryguillen/boostingDEA")
    (synopsis "Boosting Approach to Data Envelopment Analysis")
    (description
     "Includes functions to estimate production frontiers and make ideal output
predictions in the Data Envelopment Analysis (DEA) context using both standard
models from DEA and Free Disposal Hull (FDH) and boosting techniques.  In
particular, EATBoosting (Guillen et al., 2023 <doi:10.1016/j.eswa.2022.119134>)
and MARSBoosting.  Moreover, the package includes code for estimating several
technical efficiency measures using different models such as the input and
output-oriented radial measures, the input and output-oriented Russell measures,
the Directional Distance Function (DDF), the Weighted Additive Measure (WAM) and
the Slacks-Based Measure (SBM).")
    (license license:agpl3+)))

(define-public r-boomspikeslab
  (package
    (name "r-boomspikeslab")
    (version "1.2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BoomSpikeSlab" version))
       (sha256
        (base32 "0gn6fb68hk0kzpa4mp4465rsbk37fkq7nnx6bvd5bhhk1zi25xl7"))))
    (properties `((upstream-name . "BoomSpikeSlab")))
    (build-system r-build-system)
    (propagated-inputs (list r-boom))
    (home-page "https://cran.r-project.org/package=BoomSpikeSlab")
    (synopsis "MCMC for Spike and Slab Regression")
    (description
     "Spike and slab regression with a variety of residual error distributions
corresponding to Gaussian, Student T, probit, logit, SVM, and a few others.
Spike and slab regression is Bayesian regression with prior distributions
containing a point mass at zero.  The posterior updates the amount of mass on
this point, leading to a posterior distribution that is actually sparse, in the
sense that if you sample from it many coefficients are actually zeros.  Sampling
from this posterior distribution is an elegant way to handle Bayesian variable
selection and model averaging.  See <DOI:10.1504/IJMMNO.2014.059942> for an
explanation of the Gaussian case.")
    (license (list license:lgpl2.1
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-boomer
  (package
    (name "r-boomer")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "boomer" version))
       (sha256
        (base32 "1v3fiif0xrrpvl9m7mbqn3c3nv04r0xxsa0fji6grzk6prqw9zqd"))))
    (properties `((upstream-name . "boomer")))
    (build-system r-build-system)
    (arguments
     (list
      #:phases '(modify-phases %standard-phases
                  (add-after 'unpack 'set-HOME
                    (lambda _
                      (setenv "HOME" "/tmp"))))))
    (propagated-inputs (list r-withr
                             r-styler
                             r-rstudioapi
                             r-rlang
                             r-pryr
                             r-crayon))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/moodymudskipper/boomer")
    (synopsis "Debugging Tools to Inspect the Intermediate Steps of a Call")
    (description
     "This package provides debugging tools that let you inspect the intermediate
results of a call.  The output looks as if we explode a call into its parts
hence the package name.")
    (license license:gpl3)))

(define-public r-boom
  (package
    (name "r-boom")
    (version "0.9.15")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Boom" version))
       (sha256
        (base32 "00fn41sk0fdv8xv98j3wzc30vkhrw48clkqwpymvrh57rjdh4q2k"))))
    (properties `((upstream-name . "Boom")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=Boom")
    (synopsis "Bayesian Object Oriented Modeling")
    (description
     "This package provides a C++ library for Bayesian modeling, with an emphasis on
Markov chain Monte Carlo.  Although boom contains a few R utilities (mainly
plotting functions), its primary purpose is to install the BOOM C++ library on
your system so that other packages can link against it.")
    (license (list license:lgpl2.1
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-boolnet
  (package
    (name "r-boolnet")
    (version "2.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BoolNet" version))
       (sha256
        (base32 "19xnmz5dq8lvk1va8zi931xkawkwfcfjf4vh0dsg5n8zhy4p321f"))))
    (properties `((upstream-name . "BoolNet")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml r-igraph))
    (home-page "https://cran.r-project.org/package=BoolNet")
    (synopsis "Construction, Simulation and Analysis of Boolean Networks")
    (description
     "This package provides functions to reconstruct, generate, and simulate
synchronous, asynchronous, probabilistic, and temporal Boolean networks.
Provides also functions to analyze and visualize attractors in Boolean networks
<doi:10.1093/bioinformatics/btq124>.")
    (license license:artistic2.0)))

(define-public r-boolfilter
  (package
    (name "r-boolfilter")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BoolFilter" version))
       (sha256
        (base32 "14z4fzf8p4wgi939qs6v39a3xizccjcx7j4szsydllpkvxbwggbw"))))
    (properties `((upstream-name . "BoolFilter")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlab r-boolnet))
    (home-page "https://cran.r-project.org/package=BoolFilter")
    (synopsis
     "Optimal Estimation of Partially Observed Boolean Dynamical Systems")
    (description
     "This package provides tools for optimal and approximate state estimation as well
as network inference of Partially-Observed Boolean Dynamical Systems.")
    (license license:artistic2.0)))

(define-public r-boodist
  (package
    (name "r-boodist")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "boodist" version))
       (sha256
        (base32 "1haplr64f5kai1y6kdxhxiayidy1mivh85dx140fya5r6fdk0shm"))))
    (properties `((upstream-name . "boodist")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppnumerical r-rcppeigen r-rcpp r-r6 r-bh))
    (home-page "https://github.com/stla/boodist")
    (synopsis "Some Distributions from the 'Boost' Library and More")
    (description
     "Make some distributions from the C++ library Boost available in R'.  In
addition, the normal-inverse Gaussian distribution and the generalized inverse
Gaussian distribution are provided.  The distributions are represented by R6
classes.  The method to sample from the generalized inverse Gaussian
distribution is the one given in \"Random variate generation for the generalized
inverse Gaussian distribution\" Luc Devroye (2012)
<doi:10.1007/s11222-012-9367-z>.")
    (license license:gpl3)))

(define-public r-bonsaiforest
  (package
    (name "r-bonsaiforest")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bonsaiforest" version))
       (sha256
        (base32 "1h16bwwl14ikyx489n8bx63qnp7k982lxm1xlrdr8jnaq6kc7fgp"))))
    (properties `((upstream-name . "bonsaiforest")))
    (build-system r-build-system)
    (propagated-inputs (list r-vdiffr
                             r-tidyselect
                             r-tidyr
                             r-tibble
                             r-survival
                             r-splines2
                             r-rcpparmadillo
                             r-rcpp
                             r-mass
                             r-glmnet
                             r-ggplot2
                             r-gbm
                             r-forcats
                             r-dplyr
                             r-checkmate
                             r-broom
                             r-brms))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/insightsengineering/bonsaiforest/")
    (synopsis "Shrinkage Based Forest Plots")
    (description
     "Subgroup analyses are routinely performed in clinical trial analyses.  From a
methodological perspective, two key issues of subgroup analyses are multiplicity
(even if only predefined subgroups are investigated) and the low sample sizes of
subgroups which lead to highly variable estimates, see e.g. Yusuf et al (1991)
<doi:10.1001/jama.1991.03470010097038>.  This package implements subgroup
estimates based on Bayesian shrinkage priors, see Carvalho et al (2019)
<https://proceedings.mlr.press/v5/carvalho09a.html>.  In addition, estimates
based on penalized likelihood inference are available, based on Simon et al
(2011) <doi:10.18637/jss.v039.i05>.  The corresponding shrinkage based forest
plots address the aforementioned issues and can complement standard forest plots
in practical clinical trial analyses.")
    (license license:asl2.0)))

(define-public r-bonsai
  (package
    (name "r-bonsai")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bonsai" version))
       (sha256
        (base32 "01k4b1d0rl6fry1y5594fca7rsp7i55d0wadm7ibydn9jf1hmsiv"))))
    (properties `((upstream-name . "bonsai")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-rlang
                             r-purrr
                             r-parsnip
                             r-glue
                             r-dplyr
                             r-dials
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://bonsai.tidymodels.org/")
    (synopsis "Model Wrappers for Tree-Based Models")
    (description
     "Bindings for additional tree-based model engines for use with the parsnip
package.  Models include gradient boosted decision trees with @code{LightGBM}
(Ke et al, 2017.), conditional inference trees and conditional random forests
with partykit (Hothorn and Zeileis, 2015.  and Hothorn et al, 2006.
<doi:10.1198/106186006X133933>), and accelerated oblique random forests with
aorsf (Jaeger et al, 2022 <doi:10.5281/zenodo.7116854>).")
    (license license:expat)))

(define-public r-bonn
  (package
    (name "r-bonn")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bonn" version))
       (sha256
        (base32 "1z22cdizz8sgw4fry98adcjvn23ag3qvvcinbg3kmafksdk1ivmb"))))
    (properties `((upstream-name . "bonn")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite r-httr))
    (home-page "https://github.com/sumtxt/bonn/")
    (synopsis "Access INKAR Database")
    (description
     "Retrieve and import data from the INKAR database (Indikatoren und Karten zur
Raum- und Stadtentwicklung Datenbank, <https://www.inkar.de>) of the Federal
Office for Building and Regional Planning (BBSR) in Bonn using their JSON API.")
    (license license:gpl3)))

(define-public r-bonev
  (package
    (name "r-bonev")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BonEV" version))
       (sha256
        (base32 "0lmgrg53b0abb5hidyjjmwn7lf2ani84k9fil7g6j6mdajjhh1b7"))))
    (properties `((upstream-name . "BonEV")))
    (build-system r-build-system)
    (propagated-inputs (list r-qvalue))
    (home-page "https://cran.r-project.org/package=BonEV")
    (synopsis
     "An Improved Multiple Testing Procedure for Controlling False Discovery Rates")
    (description
     "An improved multiple testing procedure for controlling false discovery rates
which is developed based on the Bonferroni procedure with integrated estimates
from the Benjamini-Hochberg procedure and the Storey's q-value procedure.  It
controls false discovery rates through controlling the expected number of false
discoveries.")
    (license license:gpl2+)))

(define-public r-boneprofiler
  (package
    (name "r-boneprofiler")
    (version "3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BoneProfileR" version))
       (sha256
        (base32 "0r9fmrb2v0jlbp9f2c644v17hbdfz91dl6j87mgxwkpc472iz9w8"))))
    (properties `((upstream-name . "BoneProfileR")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-rmarkdown r-knitr r-imager r-helpersmg))
    (home-page "https://cran.r-project.org/package=BoneProfileR")
    (synopsis "Tools to Study Bone Compactness")
    (description
     "Bone Profiler is a scientific method and a software used to model bone section
for paleontological and ecological studies.  See Girondot and Laurin (2003)
<https://www.researchgate.net/publication/280021178_Bone_profiler_A_tool_to_quantify_model_and_statistically_compare_bone-section_compactness_profiles>
and GÃ´net, Laurin and Girondot (2022)
<https://palaeo-electronica.org/content/2022/3590-bone-section-compactness-model>.")
    (license license:gpl2)))

(define-public r-bondvaluation
  (package
    (name "r-bondvaluation")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BondValuation" version))
       (sha256
        (base32 "00fdqsy7znlwszwjm7mzd5yb3bbx463iy4fbkrzc5nn08rfa395g"))))
    (properties `((upstream-name . "BondValuation")))
    (build-system r-build-system)
    (propagated-inputs (list r-timedate r-rcpp))
    (home-page "https://cran.r-project.org/package=BondValuation")
    (synopsis
     "Fixed Coupon Bond Valuation Allowing for Odd Coupon Periods and Various Day Count Conventions")
    (description
     "Analysis of large datasets of fixed coupon bonds, allowing for irregular first
and last coupon periods and various day count conventions.  With this package
you can compute the yield to maturity, the modified and @code{MacAulay}
durations and the convexity of fixed-rate bonds.  It provides the function
@code{AnnivDates}, which can be used to evaluate the quality of the data and
return time-invariant properties and temporal structure of a bond.")
    (license license:gpl3)))

(define-public r-bondanalyst
  (package
    (name "r-bondanalyst")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bondAnalyst" version))
       (sha256
        (base32 "0bygaldjgxbjqjnlgs64vdayr7kp6sjl6qffdywxpi7v0yfi072a"))))
    (properties `((upstream-name . "bondAnalyst")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack))
    (home-page "https://cran.r-project.org/package=bondAnalyst")
    (synopsis "Methods for Fixed-Income Valuation, Risk and Return")
    (description
     "Bond Pricing and Fixed-Income Valuation of Selected Securities included here
serve as a quick reference of Quantitative Methods for undergraduate courses on
Fixed-Income and CFA Level I Readings on Fixed-Income Valuation, Risk and
Return.  CFA Institute (\"CFA Program Curriculum 2020 Level I Volumes 1-6. (Vol.
5, pp.  107-151, pp.  237-299)\", 2019, ISBN: 9781119593577).  Barbara S. Petitt
(\"Fixed Income Analysis\", 2019, ISBN: 9781119628132).  Frank J. Fabozzi
(\"Handbook of Finance: Financial Markets and Instruments\", 2008, ISBN:
9780470078143).  Frank J. Fabozzi (\"Fixed Income Analysis\", 2007, ISBN:
9780470052211).")
    (license license:gpl3)))

(define-public r-boltzmm
  (package
    (name "r-boltzmm")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BoltzMM" version))
       (sha256
        (base32 "17nsjr2y8nniaxd6f13g85gb9rshar72qqp45yh2ygg4zrx1d226"))))
    (properties `((upstream-name . "BoltzMM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-bh))
    (home-page "https://cran.r-project.org/package=BoltzMM")
    (synopsis "Boltzmann Machines with MM Algorithms")
    (description
     "This package provides probability computation, data generation, and model
estimation for fully-visible Boltzmann machines.  It follows the methods
described in Nguyen and Wood (2016a) <doi:10.1162/NECO_a_00813> and Nguyen and
Wood (2016b) <doi:10.1109/TNNLS.2015.2425898>.")
    (license license:gpl3)))

(define-public r-bolstad2
  (package
    (name "r-bolstad2")
    (version "1.0-29")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Bolstad2" version))
       (sha256
        (base32 "1pxl5q2kb8k2dkq4n34y6fj5snqx77fl76dznx22l64x4dig2q6d"))))
    (properties `((upstream-name . "Bolstad2")))
    (build-system r-build-system)
    (home-page "https://github.com/jmcurran/Bolstad2")
    (synopsis "Bolstad Functions")
    (description
     "This package provides a set of R functions and data sets for the book
\"Understanding Computational Bayesian Statistics.\" This book was written by Bill
(WM) Bolstad and published in 2009 by John Wiley & Sons (ISBN 978-0470046098).")
    (license license:gpl2+)))

(define-public r-bolstad
  (package
    (name "r-bolstad")
    (version "0.2-41")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Bolstad" version))
       (sha256
        (base32 "0rrx254wmr4i57h097ahba151chm8l2287qfg01p1lg47znld21h"))))
    (properties `((upstream-name . "Bolstad")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://cran.r-project.org/package=Bolstad")
    (synopsis "Functions for Elementary Bayesian Inference")
    (description
     "This package provides a set of R functions and data sets for the book
Introduction to Bayesian Statistics, Bolstad, W.M. (2017), John Wiley & Sons
ISBN 978-1-118-09156-2.")
    (license license:gpl2+)))

(define-public r-bolsec
  (package
    (name "r-bolsec")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bolsec" version))
       (sha256
        (base32 "0ab5cydn4hbm1fbbd71xh5724wizrv9ckj3j6rklz46nhbwfjmr2"))))
    (properties `((upstream-name . "bolsec")))
    (build-system r-build-system)
    (propagated-inputs (list r-rvest r-formattable r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bolsec")
    (synopsis "Bolivian Securities")
    (description
     "This package provides data import and offers 3 daily snapshot functions from
securities of varying prices traded on the Bolivian Securities Exchange, website
<https://www.bbv.com.bo/>.  The snapshots include a detailed list, scatter plot
correlation, and descriptive statistics table for the securities.")
    (license license:gpl3)))

(define-public r-bolasso
  (package
    (name "r-bolasso")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bolasso" version))
       (sha256
        (base32 "1fbd39jyxq0zfbybrzzwnyql2w8hf2b9133jj0nl99d1326wr7dh"))))
    (properties `((upstream-name . "bolasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-rdpack
                             r-progressr
                             r-matrix
                             r-glmnet
                             r-ggplot2
                             r-gamlr
                             r-future-apply))
    (home-page "https://www.dmolitor.com/bolasso/")
    (synopsis "Model Consistent Lasso Estimation Through the Bootstrap")
    (description
     "This package implements the bolasso algorithm for consistent variable selection
and estimation accuracy.  Includes support for many parallel backends via the
future package.  For details see: Bach (2008), Bolasso: model consistent Lasso
estimation through the bootstrap', <@code{arXiv:0804.1302>}.")
    (license license:expat)))

(define-public r-boj
  (package
    (name "r-boj")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BOJ" version))
       (sha256
        (base32 "1x9lz01fjdk0aybs6zsbbq2xfjvh99qiimmnrkbcd6sj880vn3a3"))))
    (properties `((upstream-name . "BOJ")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tidyselect
                             r-tidyr
                             r-rvest
                             r-readr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BOJ")
    (synopsis "Interface to Bank of Japan Statistics")
    (description
     "This package provides an interface to Bank of Japan <https://www.boj.or.jp>
statistics.")
    (license license:expat)))

(define-public r-boiwsa
  (package
    (name "r-boiwsa")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "boiwsa" version))
       (sha256
        (base32 "1ys3s4f2ydf5m1ab7y9yf1lvbjlarffn5cjcycq0yfis45gs8xqj"))))
    (properties `((upstream-name . "boiwsa")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rlang
                             r-lubridate
                             r-hmisc
                             r-gridextra
                             r-ggplot2
                             r-dplyr))
    (home-page "https://github.com/timginker/boiwsa")
    (synopsis "Seasonal Adjustment of Weekly Data")
    (description
     "Perform seasonal adjustment of weekly data.  The package provides a
user-friendly interface for computing seasonally adjusted estimates of weekly
data and includes functions for the creation of country-specific prior
adjustment variables, as well as diagnostic tools to assess the quality of the
adjustments.  The method is described in more detail in Ginker (2023)
<doi:10.13140/RG.2.2.12221.44000>.")
    (license license:expat)))

(define-public r-boinet
  (package
    (name "r-boinet")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "boinet" version))
       (sha256
        (base32 "0jk1igg45zpds8qadzwaifmcj3b61ghn2rqx4x8b4pv3mi2sk2cn"))))
    (properties `((upstream-name . "boinet")))
    (build-system r-build-system)
    (propagated-inputs (list r-mfp r-iso r-copula))
    (home-page "https://cran.r-project.org/package=boinet")
    (synopsis
     "Conduct Simulation Study of Bayesian Optimal Interval Design with BOIN-ET Family")
    (description
     "Bayesian optimal interval based on both efficacy and toxicity outcomes (BOIN-ET)
design is a model-assisted oncology phase I/II trial design, aiming to establish
an optimal biological dose accounting for efficacy and toxicity in the framework
of dose-finding.  Some extensions of BOIN-ET design are also available to allow
for time-to-event efficacy and toxicity outcomes based on cumulative and pending
data (time-to-event BOIN-ET: TITE-BOIN-ET), ordinal graded efficacy and toxicity
outcomes (generalized BOIN-ET: @code{gBOIN-ET}), and their combination
(TITE-@code{gBOIN-ET}).  boinet is a package to implement the BOIN-ET design
family and supports the conduct of simulation studies to assess operating
characteristics of BOIN-ET, TITE-BOIN-ET, @code{gBOIN-ET}, and
TITE-@code{gBOIN-ET}, where users can choose design parameters in flexible and
straightforward ways depending on their own application.")
    (license license:expat)))

(define-public r-boin
  (package
    (name "r-boin")
    (version "2.7.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BOIN" version))
       (sha256
        (base32 "03h6s1nn87xgkk830wls06mgflg0arq084k74gfp1k4wccwkjr5y"))))
    (properties `((upstream-name . "BOIN")))
    (build-system r-build-system)
    (propagated-inputs (list r-iso))
    (home-page "https://cran.r-project.org/package=BOIN")
    (synopsis
     "Bayesian Optimal INterval (BOIN) Design for Single-Agent and Drug- Combination Phase I Clinical Trials")
    (description
     "The Bayesian optimal interval (BOIN) design is a novel phase I clinical trial
design for finding the maximum tolerated dose (MTD).  It can be used to design
both single-agent and drug-combination trials.  The BOIN design is motivated by
the top priority and concern of clinicians when testing a new drug, which is to
effectively treat patients and minimize the chance of exposing them to
subtherapeutic or overly toxic doses.  The prominent advantage of the BOIN
design is that it achieves simplicity and superior performance at the same time.
 The BOIN design is algorithm-based and can be implemented in a simple way
similar to the traditional 3+3 design.  The BOIN design yields an average
performance that is comparable to that of the continual reassessment method
(CRM, one of the best model-based designs) in terms of selecting the MTD, but
has a substantially lower risk of assigning patients to subtherapeutic or overly
toxic doses.  For tutorial, please check Yan et al. (2020)
<doi:10.18637/jss.v094.i13>.")
    (license license:gpl2)))

(define-public r-boilerpiper
  (package
    (name "r-boilerpiper")
    (version "1.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "boilerpipeR" version))
       (sha256
        (base32 "1dws84ghc5r6zpnr23qd4l9bkazds8ar723wkrkikjni58vshjf1"))))
    (properties `((upstream-name . "boilerpipeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjava))
    (home-page "https://github.com/mannau/boilerpipeR")
    (synopsis "Interface to the Boilerpipe Java Library")
    (description
     "Generic Extraction of main text content from HTML files; removal of ads,
sidebars and headers using the boilerpipe
<https://github.com/kohlschutter/boilerpipe> Java library.  The extraction
heuristics from boilerpipe show a robust performance for a wide range of web
site templates.")
    (license license:asl2.0)))

(define-public r-boggy
  (package
    (name "r-boggy")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "boggy" version))
       (sha256
        (base32 "0vxzwhmahfd4dx7x28n0c7l4gmz1921y60mfpcwaz2qjpl9d27m7"))))
    (properties `((upstream-name . "boggy")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble))
    (home-page "https://rmagno.eu/boggy/")
    (synopsis "Real-Time PCR Data Sets by Boggy et al. (2010)")
    (description
     "Real-time quantitative polymerase chain reaction (@code{qPCR}) data sets by
Boggy et al. (2008) <doi:10.1371/journal.pone.0012355>.  This package provides a
dilution series for one PCR target: a random sequence that minimizes secondary
structure and off-target primer binding.  The data set is a six-point, ten-fold
dilution series.  For each concentration there are two replicates.  Each
amplification curve is 40 cycles long.  Original raw data file:
<https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0012355.s004>.")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-bodycompref
  (package
    (name "r-bodycompref")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bodycompref" version))
       (sha256
        (base32 "1nsgn3x2w8ri059iwpj9rhg4abxrk6ryq1lbbrwihrxdq9xqvg4v"))))
    (properties `((upstream-name . "bodycompref")))
    (build-system r-build-system)
    (propagated-inputs (list r-sae r-gamlss r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://bodycomp-metrics.mgh.harvard.edu")
    (synopsis "Reference Values for CT-Assessed Body Composition")
    (description
     "Get z-scores, percentiles, absolute values, and percent of predicted of a
reference cohort.  Functionality requires installing the data packages
adiposerefdata and musclerefdata'.  For more information on the underlying
research, please visit our website which also includes a graphical interface.
The models and underlying data are described in Marquardt JP et al.(planned
publication 2025; reserved doi 10.1097/RLI.0000000000001104), \"Subcutaneous and
Visceral adipose tissue Reference Values from Framingham Heart Study Thoracic
and Abdominal CT\", *Investigative Radiology* and Tonnesen PE et al. (2023),
\"Muscle Reference Values from Thoracic and Abdominal CT for Sarcopenia
Assessment [column] The Framingham Heart Study\", *Investigative Radiology*,
<doi:10.1097/RLI.0000000000001012>.")
    (license license:gpl3+)))

(define-public r-bodycomp
  (package
    (name "r-bodycomp")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bodycomp" version))
       (sha256
        (base32 "1hi3x2chs87ba5nh7ka6xjrl5yvwqqcdyj1dg87rpaiipyl8396n"))))
    (properties `((upstream-name . "bodycomp")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=bodycomp")
    (synopsis
     "Percent Body Fat Values Using Anthropometric Prediction Equations")
    (description
     "Skinfold measurements is one of the most popular and practical methods for
estimating percent body fat.  Body composition is a term that describes the
relative proportions of fat, bone, and muscle mass in the human body.  Following
the collection of skinfold measurements, regression analysis (a statistical
procedure used to predict a dependent variable based on one or more independent
or predictor variables) is used to estimate total percent body fat in humans.
<doi:10.4324/9780203868744>.")
    (license license:gpl2+)))

(define-public r-bodsr
  (package
    (name "r-bodsr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bodsr" version))
       (sha256
        (base32 "13d3fhdq1cyq7zhr6sj9nmw1zdfibq7mv6cjx8zlb1dmqi1m5d07"))))
    (properties `((upstream-name . "bodsr")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tibble
                             r-rlang
                             r-purrr
                             r-jsonlite
                             r-httr
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=bodsr")
    (synopsis "Call the Bus Open Data Service ('BODS') API Through R")
    (description
     "This package provides a wrapper to allow users to download Bus Open Data Service
BODS transport information from the API (<https://www.bus-data.dft.gov.uk/>).
This includes timetable and fare metadata (including links for full datasets),
timetable data at line level, and real-time location data.")
    (license license:expat)))

(define-public r-bodi
  (package
    (name "r-bodi")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Bodi" version))
       (sha256
        (base32 "1z3xamj4qh3g5asrl3kbvcnx3r66mch34d0hlz9vg0498s9fwn6s"))))
    (properties `((upstream-name . "Bodi")))
    (build-system r-build-system)
    (propagated-inputs (list r-rpart r-ranger r-opera r-mgcv r-gbm))
    (home-page "https://cran.r-project.org/package=Bodi")
    (synopsis "Boosting Diversity in Regression Ensembles")
    (description
     "This package provides a gradient boosting-based algorithm by incorporating a
diversity term to guide the gradient boosting iterations, see Bourel, Cugliari,
Goude, Poggi (2021) <https://hal.archives-ouvertes.fr/hal-03041309/>.")
    (license license:expat)))

(define-public r-bodenmiller
  (package
    (name "r-bodenmiller")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bodenmiller" version))
       (sha256
        (base32 "0rdbk3f95iw4352fl9d81ir503q072chbb4hn1k12sp1wjc3apid"))))
    (properties `((upstream-name . "bodenmiller")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/yannabraham/bodenmiller")
    (synopsis "Profiling of Peripheral Blood Mononuclear Cells using CyTOF")
    (description
     "This data package contains a subset of the Bodenmiller et al, Nat Biotech 2012
dataset for testing single cell, high dimensional analysis and visualization
methods.")
    (license license:cc0)))

(define-public r-boardgames
  (package
    (name "r-boardgames")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BoardGames" version))
       (sha256
        (base32 "1w3ghs29qlnjrd46lvv055snclwwy6a22fgdqszqm377w4favnhm"))))
    (properties `((upstream-name . "BoardGames")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BoardGames")
    (synopsis "Board Games and Tools for Building Board Games")
    (description
     "This package provides tools for constructing board/grid based games, as well as
readily available game(s) for your entertainment.")
    (license license:gpl2+)))

(define-public r-bnviewer
  (package
    (name "r-bnviewer")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bnviewer" version))
       (sha256
        (base32 "036jmypxg2ciiz388f90vm73l9djhpi6gzp1ibn9l0x3d072r7zz"))))
    (properties `((upstream-name . "bnviewer")))
    (build-system r-build-system)
    (propagated-inputs (list r-visnetwork
                             r-shiny
                             r-igraph
                             r-e1071
                             r-caret
                             r-bnlearn
                             r-assertthat))
    (home-page "http://robsonfernandes.net/bnviewer/")
    (synopsis
     "Bayesian Networks Interactive Visualization and Explainable Artificial Intelligence")
    (description
     "Bayesian networks provide an intuitive framework for probabilistic reasoning and
its graphical nature can be interpreted quite clearly.  Graph based methods of
machine learning are becoming more popular because they offer a richer model of
knowledge that can be understood by a human in a graphical format.  The bnviewer
is an R Package that allows the interactive visualization of Bayesian Networks.
The aim of this package is to improve the Bayesian Networks visualization over
the basic and static views offered by existing packages.")
    (license license:expat)))

(define-public r-bnstruct
  (package
    (name "r-bnstruct")
    (version "1.0.15")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bnstruct" version))
       (sha256
        (base32 "0gy3msq6i20xbf6vwrxj5k4b6bav9pbdjpci47b7g01npbhbdzsz"))))
    (properties `((upstream-name . "bnstruct")))
    (build-system r-build-system)
    (propagated-inputs (list r-igraph r-bitops))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bnstruct")
    (synopsis
     "Bayesian Network Structure Learning from Data with Missing Values")
    (description
     "Bayesian Network Structure Learning from Data with Missing Values.  The package
implements the Silander-Myllymaki complete search, the Max-Min
Parents-and-Children, the Hill-Climbing, the Max-Min Hill-climbing heuristic
searches, and the Structural Expectation-Maximization algorithm.  Available
scoring functions are BDeu, AIC, BIC. The package also implements methods for
generating and using bootstrap samples, imputed data, inference.")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-bnsp
  (package
    (name "r-bnsp")
    (version "2.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BNSP" version))
       (sha256
        (base32 "01rrqvgz9655psm7xcysf50y94lkzl2q3davgjzymg73kzbv3s41"))))
    (properties `((upstream-name . "BNSP")))
    (build-system r-build-system)
    (propagated-inputs (list r-threejs
                             r-plyr
                             r-plot3d
                             r-mgcv
                             r-label-switching
                             r-gridextra
                             r-ggplot2
                             r-formula
                             r-cubature
                             r-corrplot
                             r-coda))
    (home-page "https://cran.r-project.org/package=BNSP")
    (synopsis "Bayesian Non- And Semi-Parametric Model Fitting")
    (description
     "MCMC algorithms & processing functions for: 1.  single response multiple
regression, see Papageorgiou, G. (2018) <doi: 10.32614/RJ-2018-069>, 2.
multivariate response multiple regression, with nonparametric models for the
means, the variances and the correlation matrix, with variable selection, see
Papageorgiou, G. and Marshall, B. C. (2020) <doi:
10.1080/10618600.2020.1739534>, 3.  joint mean-covariance models for
multivariate responses, see Papageorgiou, G. (2022) <doi: 10.1002/sim.9376>, and
4.Dirichlet process mixtures, see Papageorgiou, G. (2019) <doi:
10.1111/anzs.12273>.")
    (license license:gpl2+)))

(define-public r-bnsl
  (package
    (name "r-bnsl")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BNSL" version))
       (sha256
        (base32 "0nrlx55364kgvkdpyaimgjxwnzmmdl1gmvyv3wkm8n2jyki92xxp"))))
    (properties `((upstream-name . "BNSL")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-igraph r-bnlearn))
    (home-page "https://cran.r-project.org/package=BNSL")
    (synopsis "Bayesian Network Structure Learning")
    (description
     "From a given data frame, this package learns its Bayesian network structure
based on a selected score.")
    (license license:gpl2+)))

(define-public r-bnrich
  (package
    (name "r-bnrich")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BNrich" version))
       (sha256
        (base32 "0m3cz7npc7mwq2vj68hllbd2vf5xqw00jaxv1pm3c28mq7pb459j"))))
    (properties `((upstream-name . "BNrich")))
    (build-system r-build-system)
    (propagated-inputs (list r-graph r-glmnet r-corpcor r-bnlearn))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Samaneh-Bioinformatics/BNrich")
    (synopsis "Pathway Enrichment Analysis Based on Bayesian Network")
    (description
     "Maleknia et al. (2020) <doi:10.1101/2020.01.13.905448>.  A novel pathway
enrichment analysis package based on Bayesian network to investigate the
topology features of the pathways.  firstly, 187 kyoto encyclopedia of genes and
genomes (KEGG) human non-metabolic pathways which their cycles were eliminated
by biological approach, enter in analysis as Bayesian network structures.  The
constructed Bayesian network were optimized by the Least Absolute Shrinkage
Selector Operator (lasso) and the parameters were learned based on gene
expression data.  Finally, the impacted pathways were enriched by Fisherâs
Exact Test on significant parameters.")
    (license license:gpl2+)))

(define-public r-bnrep
  (package
    (name "r-bnrep")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bnRep" version))
       (sha256
        (base32 "09ri3ms49w6qxck78cj5qw48jqd8vs544yw46fmz4vhq9ng08m7w"))))
    (properties `((upstream-name . "bnRep")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinythemes
                             r-shinyjs
                             r-shiny
                             r-rgraphviz
                             r-qgraph
                             r-dt
                             r-dplyr
                             r-bnlearn))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/manueleleonelli/bnRep")
    (synopsis "Repository of Bayesian Networks from the Academic Literature")
    (description
     "This package provides a collection of Bayesian networks (discrete, Gaussian, and
conditional linear Gaussian) collated from recent academic literature.  The
@code{bnRep_summary} object provides an overview of the Bayesian networks in the
repository and the package documentation includes details about the variables in
each network.  A Shiny app to explore the repository can be launched with
@code{bnRep_app}() and is available online at
<https://manueleleonelli.shinyapps.io/@code{bnRep>}.  Reference: M. Leonelli
(2024) <doi:10.48550/@code{arXiv.2409.19158>}.")
    (license license:expat)))

(define-public r-bnptsclust
  (package
    (name "r-bnptsclust")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BNPTSclust" version))
       (sha256
        (base32 "1ymvklv6d9684xzwdkvaxdjd1q20xmsf6kkfibn2wphlaqs2rh2n"))))
    (properties `((upstream-name . "BNPTSclust")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-mass))
    (home-page "https://cran.r-project.org/package=BNPTSclust")
    (synopsis "Bayesian Nonparametric Algorithm for Time Series Clustering")
    (description
     "This package performs the algorithm for time series clustering described in
Nieto-Barajas and Contreras-Cristan (2014).")
    (license license:gpl2+)))

(define-public r-bnpsd
  (package
    (name "r-bnpsd")
    (version "1.3.13")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bnpsd" version))
       (sha256
        (base32 "0wnbsz5c52pglycykzkd5lsnxfkjqva0xhwnb4kz7x1zd8vbd1sl"))))
    (properties `((upstream-name . "bnpsd")))
    (build-system r-build-system)
    (propagated-inputs (list r-nnls r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/StoreyLab/bnpsd/")
    (synopsis "Simulate Genotypes from the BN-PSD Admixture Model")
    (description
     "The Pritchard-Stephens-Donnelly (PSD) admixture model has k intermediate
subpopulations from which n individuals draw their alleles dictated by their
individual-specific admixture proportions.  The BN-PSD model additionally
imposes the Balding-Nichols (BN) allele frequency model to the intermediate
populations, which therefore evolved independently from a common ancestral
population T with subpopulation-specific FST (Wright's fixation index)
parameters.  The BN-PSD model can be used to yield complex population
structures.  This simulation approach is now extended to subpopulations related
by a tree.  Method described in Ochoa and Storey (2021)
<doi:10.1371/journal.pgen.1009241>.")
    (license license:gpl3+)))

(define-public r-bnpmix
  (package
    (name "r-bnpmix")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BNPmix" version))
       (sha256
        (base32 "114gn80b7yyl6hi5cdnc0rp2hzy7pp8z6q4is1h29yxlqa6acbq4"))))
    (properties `((upstream-name . "BNPmix")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppdist
                             r-rcpparmadillo
                             r-rcpp
                             r-ggpubr
                             r-ggplot2
                             r-coda))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=BNPmix")
    (synopsis "Bayesian Nonparametric Mixture Models")
    (description
     "This package provides functions to perform Bayesian nonparametric univariate and
multivariate density estimation and clustering, by means of Pitman-Yor mixtures,
and dependent Dirichlet process mixtures for partially exchangeable data.  See
Corradin et al. (2021) <doi:10.18637/jss.v100.i15> for more details.")
    (license (list license:lgpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-bnpdensity
  (package
    (name "r-bnpdensity")
    (version "2023.3.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BNPdensity" version))
       (sha256
        (base32 "0b0hv634k5vnc74iw2c0arx9rl3bh5hahhrhp14gx324jv279fs5"))))
    (properties `((upstream-name . "BNPdensity")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-tidyr
                             r-survival
                             r-gridextra
                             r-ggplot2
                             r-dplyr
                             r-coda))
    (home-page "https://cran.r-project.org/package=BNPdensity")
    (synopsis
     "Ferguson-Klass Type Algorithm for Posterior Normalized Random Measures")
    (description
     "Bayesian nonparametric density estimation modeling mixtures by a Ferguson-Klass
type algorithm for posterior normalized random measures.")
    (license license:gpl2+)))

(define-public r-bnpa
  (package
    (name "r-bnpa")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bnpa" version))
       (sha256
        (base32 "0qsxk47i7q4d8hbs2xj5k5sf8bqyzhq7rbj4y7w8ljprznlydgl9"))))
    (properties `((upstream-name . "bnpa")))
    (build-system r-build-system)
    (propagated-inputs (list r-xlsx
                             r-semplot
                             r-rgraphviz
                             r-lavaan
                             r-fastdummies
                             r-bnlearn))
    (home-page "https://sites.google.com/site/bnparp/.")
    (synopsis "Bayesian Networks & Path Analysis")
    (description
     "This project aims to enable the method of Path Analysis to infer causalities
from data.  For this we propose a hybrid approach, which uses Bayesian network
structure learning algorithms from data to create the input file for creation of
a PA model.  The process is performed in a semi-automatic way by our
intermediate algorithm, allowing novice researchers to create and evaluate their
own PA models from a data set.  The references used for this project are:
Koller, D., & Friedman, N. (2009).  Probabilistic graphical models: principles
and techniques.  MIT press. <doi:10.1017/S0269888910000275>.  Nagarajan, R.,
Scutari, M., & LÃ¨bre, S. (2013).  Bayesian networks in r.  Springer, 122,
125-127.  Scutari, M., & Denis, J. B. <doi:10.1007/978-1-4614-6446-4>.  Scutari
M (2010).  Bayesian networks: with examples in R. Chapman and Hall/CRC.
<doi:10.1201/b17065>.  Rosseel, Y. (2012).  lavaan: An R Package for Structural
Equation Modeling.  Journal of Statistical Software, 48(2), 1 - 36.
<doi:10.18637/jss.v048.i02>.")
    (license license:gpl3)))

(define-public r-bnnsurvival
  (package
    (name "r-bnnsurvival")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bnnSurvival" version))
       (sha256
        (base32 "1d9jmfx0id4lmw122zga7hb52vlfdfqn4amhzpsmhyck99rv92j4"))))
    (properties `((upstream-name . "bnnSurvival")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-prodlim r-pec))
    (home-page "https://cran.r-project.org/package=bnnSurvival")
    (synopsis "Bagged k-Nearest Neighbors Survival Prediction")
    (description
     "This package implements a bootstrap aggregated (bagged) version of the k-nearest
neighbors survival probability prediction method (Lowsky et al.  2013).  In
addition to the bootstrapping of training samples, the features can be
subsampled in each baselearner to break the correlation between them.  The Rcpp
package is used to speed up the computation.")
    (license license:gpl3)))

(define-public r-bnmonitor
  (package
    (name "r-bnmonitor")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bnmonitor" version))
       (sha256
        (base32 "081j10b6akgya5f1hpfg2ccnlc26igf6crmhv96asachs1c5662d"))))
    (properties `((upstream-name . "bnmonitor")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rlang
                             r-reshape2
                             r-rcolorbrewer
                             r-qgraph
                             r-purrr
                             r-igraph
                             r-grbase
                             r-grain
                             r-ggplot2
                             r-dplyr
                             r-bnlearn))
    (home-page "https://manueleleonelli.github.io/bnmonitor/")
    (synopsis "An Implementation of Sensitivity Analysis in Bayesian Networks")
    (description
     "An implementation of sensitivity and robustness methods in Bayesian networks in
R. It includes methods to perform parameter variations via a variety of
co-variation schemes, to compute sensitivity functions and to quantify the
dissimilarity of two Bayesian networks via distances and divergences.  It
further includes diagnostic methods to assess the goodness of fit of a Bayesian
networks to data, including global, node and parent-child monitors.  Reference:
M. Leonelli, R. Ramanathan, R.L. Wilkerson (2022)
<doi:10.1016/j.knosys.2023.110882>.")
    (license license:gpl3)))

(define-public r-bnma
  (package
    (name "r-bnma")
    (version "1.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bnma" version))
       (sha256
        (base32 "0z2kd9x5hi192dlr36r4vh9c66dzprm63y1l9v8av6dfnz63sj5b"))))
    (properties `((upstream-name . "bnma")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjags r-igraph r-ggplot2 r-coda))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bnma")
    (synopsis "Bayesian Network Meta-Analysis using 'JAGS'")
    (description
     "Network meta-analyses using Bayesian framework following Dias et al. (2013)
<DOI:10.1177/0272989X12458724>.  Based on the data input, creates prior, model
file, and initial values needed to run models in rjags'.  Able to handle
binomial, normal and multinomial arm-level data.  Can handle multi-arm trials
and includes methods to incorporate covariate and baseline risk effects.
Includes standard diagnostics and visualization tools to evaluate the results.")
    (license license:gpl3)))

(define-public r-bnlearn
  (package
    (name "r-bnlearn")
    (version "5.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bnlearn" version))
       (sha256
        (base32 "17pr1b3dg576fsrzgvhdh0c4vfi3cl4ngkak8njddd6v4favsli4"))))
    (properties `((upstream-name . "bnlearn")))
    (build-system r-build-system)
    (home-page "https://www.bnlearn.com/")
    (synopsis
     "Bayesian Network Structure Learning, Parameter Learning and Inference")
    (description
     "Bayesian network structure learning, parameter learning and inference.  This
package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC,
Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and
Tabu Search) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for
discrete, Gaussian and conditional Gaussian networks, along with many score
functions and conditional independence tests.  The Naive Bayes and the
Tree-Augmented Naive Bayes (TAN) classifiers are also implemented.  Some utility
functions (model comparison and manipulation, random data generation, arc
orientation testing, simple and advanced plots) are included, as well as support
for parameter estimation (maximum likelihood and Bayesian) and inference,
conditional probability queries, cross-validation, bootstrap and model
averaging.  Development snapshots with the latest bugfixes are available from
<https://www.bnlearn.com/>.")
    (license license:gpl2+)))

(define-public r-bndovb
  (package
    (name "r-bndovb")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bndovb" version))
       (sha256
        (base32 "16mvkvwnx61jrvraagvxf56pvmf5klm8lnkqq4s5y2z92kd0clgp"))))
    (properties `((upstream-name . "bndovb")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma
                             r-np
                             r-nnet
                             r-mass
                             r-factormodel
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bndovb")
    (synopsis "Bounding Omitted Variable Bias Using Auxiliary Data")
    (description
     "This package provides functions to implement a Hwang(2021)
<doi:10.2139/ssrn.3866876> estimator, which bounds an omitted variable bias
using auxiliary data.")
    (license license:gpl3)))

(define-public r-bndesr
  (package
    (name "r-bndesr")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bndesr" version))
       (sha256
        (base32 "0yx5grnaginmdxdi9philz24qvncr6gva01zrasl098p49dx0zpv"))))
    (properties `((upstream-name . "bndesr")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-readxl
                             r-readr
                             r-rcurl
                             r-lubridate
                             r-janitor
                             r-dplyr
                             r-curl))
    (home-page "https://cran.r-project.org/package=bndesr")
    (synopsis "Access Data from the Brazilian Development Bank (BNDES)")
    (description
     "Allows access to data on BNDES disbursements and contracts since 1995.  The
package makes it easy to import data from the bank into
R.<https://www.bndes.gov.br/@code{SiteBNDES/bndes/bndes_en>}.")
    (license license:expat)))

(define-public r-bnclassify
  (package
    (name "r-bnclassify")
    (version "0.4.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bnclassify" version))
       (sha256
        (base32 "0sqyqw3jlzjhsmcblzxcc3nak4z63pvg3paniysnjzyi139fjyam"))))
    (properties `((upstream-name . "bnclassify")))
    (build-system r-build-system)
    (propagated-inputs (list r-rpart
                             r-rcpp
                             r-matrixstats
                             r-entropy
                             r-bh
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bmihaljevic/bnclassify")
    (synopsis "Learning Discrete Bayesian Network Classifiers from Data")
    (description
     "State-of-the art algorithms for learning discrete Bayesian network classifiers
from data, including a number of those described in Bielza & Larranaga (2014)
<doi:10.1145/2576868>, with functions for prediction, model evaluation and
inspection.")
    (license license:gpl2+)))

(define-public r-bmtar
  (package
    (name "r-bmtar")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BMTAR" version))
       (sha256
        (base32 "0c70vkgv2sdci33yrk2n297k105wnlxj1qchskdparkiipf47xsh"))))
    (properties `((upstream-name . "BMTAR")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm
                             r-mcmcpack
                             r-mass
                             r-ks
                             r-ggplot2
                             r-expm
                             r-doparallel
                             r-brobdingnag))
    (home-page "https://cran.r-project.org/package=BMTAR")
    (synopsis "Bayesian Approach for MTAR Models with Missing Data")
    (description
     "This package implements parameter estimation using a Bayesian approach for
Multivariate Threshold Autoregressive (MTAR) models with missing data using
Markov Chain Monte Carlo methods.  Performs the simulation of MTAR processes
(mtarsim()), estimation of matrix parameters and the threshold values
(mtarns()), identification of the autoregressive orders using Bayesian variable
selection (mtarstr()), identification of the number of regimes using
Metropolised Carlin and Chib (mtarnumreg()) and estimate missing data,
coefficients and covariance matrices conditional on the autoregressive orders,
the threshold values and the number of regimes (mtarmissing()).  Calderon and
Nieto (2017) <doi:10.1080/03610926.2014.990758>.")
    (license license:gpl2+)))

(define-public r-bmt
  (package
    (name "r-bmt")
    (version "0.1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BMT" version))
       (sha256
        (base32 "1vdah5bsn41s2qriq15xi7dw9qzngaacmn5gk6yc7hi89dwyy3yj"))))
    (properties `((upstream-name . "BMT")))
    (build-system r-build-system)
    (propagated-inputs (list r-partitions r-fitdistrplus))
    (home-page "https://cran.r-project.org/package=BMT")
    (synopsis "The BMT Distribution")
    (description
     "Density, distribution, quantile function, random number generation for the BMT
(Bezier-Montenegro-Torres) distribution.  Torres-Jimenez C.J. and
Montenegro-Diaz A.M. (2017) <@code{arXiv:1709.05534>}.  Moments, descriptive
measures and parameter conversion for different parameterizations of the BMT
distribution.  Fit of the BMT distribution to non-censored data by maximum
likelihood, moment matching, quantile matching, maximum goodness-of-fit, also
known as minimum distance, maximum product of spacing, also called maximum
spacing, and minimum quantile distance, which can also be called maximum
quantile goodness-of-fit.  Fit of univariate distributions for non-censored data
using maximum product of spacing estimation and minimum quantile distance
estimation is also included.")
    (license license:gpl2+)))

(define-public r-bmstdr
  (package
    (name "r-bmstdr")
    (version "0.7.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bmstdr" version))
       (sha256
        (base32 "0myv2mj5kp97dzvbf4sw63fzjxzpv2d82vxwiqxaqd1pllcgrzz1"))))
    (properties `((upstream-name . "bmstdr")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-sptimer
                             r-sptdyn
                             r-spbayes
                             r-rstantools
                             r-rstan
                             r-rdpack
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-mnormt
                             r-mcmcpack
                             r-inlabru
                             r-ggpubr
                             r-ggplot2
                             r-carbayesst
                             r-carbayes
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://www.sujitsahu.com")
    (synopsis "Bayesian Modeling of Spatio-Temporal Data with R")
    (description
     "Fits, validates and compares a number of Bayesian models for spatial and space
time point referenced and areal unit data.  Model fitting is done using several
packages: rstan', INLA', @code{spBayes}', @code{spTimer}', @code{spTDyn}',
CARBayes and CAR@code{BayesST}'.  Model comparison is performed using the DIC
and WAIC, and K-fold cross-validation where the user is free to select their own
subset of data rows for validation.  Sahu (2022) <doi:10.1201/9780429318443>
describes the methods in detail.")
    (license license:gpl2)))

(define-public r-bmscstan
  (package
    (name "r-bmscstan")
    (version "1.2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bmscstan" version))
       (sha256
        (base32 "05w6pz0mwqighhdl36f2ya1b4dw0xdhpfcbq6saw73ag5hvl57i9"))))
    (properties `((upstream-name . "bmscstan")))
    (build-system r-build-system)
    (propagated-inputs (list r-rstan
                             r-loo
                             r-logspline
                             r-laplacesdemon
                             r-ggplot2
                             r-bayesplot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/michelescandola/bmscstan")
    (synopsis "Bayesian Multilevel Single Case Models using 'Stan'")
    (description
     "Analyse single case analyses against a control group.  Its purpose is to provide
a flexible, with good power and low first type error approach that can manage at
the same time controls and patient's data.  The use of Bayesian statistics
allows to test both the alternative and null hypothesis.  Scandola, M., &
Romano, D. (2020, August 3). <doi:10.31234/osf.io/sajdq> Scandola, M., & Romano,
D. (2021). <doi:10.1016/j.neuropsychologia.2021.107834>.")
    (license license:gpl2+)))

(define-public r-bms
  (package
    (name "r-bms")
    (version "0.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BMS" version))
       (sha256
        (base32 "0mivdvazhmvfg4in56hlw41amf470a20qczdi4if9r1f9kdcji19"))))
    (properties `((upstream-name . "BMS")))
    (build-system r-build-system)
    (home-page "http://bms.zeugner.eu")
    (synopsis "Bayesian Model Averaging Library")
    (description
     "Bayesian Model Averaging for linear models with a wide choice of (customizable)
priors.  Built-in priors include coefficient priors (fixed, hyper-g and
empirical priors), 5 kinds of model priors, moreover model sampling by
enumeration or various MCMC approaches.  Post-processing functions allow for
inferring posterior inclusion and model probabilities, various moments,
coefficient and predictive densities.  Plotting functions available for
posterior model size, MCMC convergence, predictive and coefficient densities,
best models representation, BMA comparison.  Also includes Bayesian
normal-conjugate linear model with Zellner's g prior, and assorted methods.")
    (license license:bsd-3)))

(define-public r-bmrmm
  (package
    (name "r-bmrmm")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BMRMM" version))
       (sha256
        (base32 "1hkk1wwxl4hn9nj43akx6d01pivg3cmkbdngip5f7g2qxd483111"))))
    (properties `((upstream-name . "BMRMM")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma r-multicool r-mcmcpack r-logofgamma
                             r-fields))
    (home-page "https://cran.r-project.org/package=BMRMM")
    (synopsis
     "An Implementation of the Bayesian Markov (Renewal) Mixed Models")
    (description
     "The Bayesian Markov renewal mixed models take sequentially observed categorical
data with continuous duration times, being either state duration or inter-state
duration.  These models comprehensively analyze the stochastic dynamics of both
state transitions and duration times under the influence of multiple exogenous
factors and random individual effect.  The default setting flexibly models the
transition probabilities using Dirichlet mixtures and the duration times using
gamma mixtures.  It also provides the flexibility of modeling the categorical
sequences using Bayesian Markov mixed models alone, either ignoring the duration
times altogether or dividing duration time into multiples of an additional
category in the sequence by a user-specific unit.  The package allows extensive
inference of the state transition probabilities and the duration times as well
as relevant plots and graphs.  It also includes a synthetic data set to
demonstrate the desired format of input data set and the utility of various
functions.  Methods for Bayesian Markov renewal mixed models are as described
in: Abhra Sarkar et al., (2018) <doi:10.1080/01621459.2018.1423986> and Yutong
Wu et al., (2022) <doi:10.1093/biostatistics/kxac050>.")
    (license license:expat)))

(define-public r-bmrbr
  (package
    (name "r-bmrbr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BMRBr" version))
       (sha256
        (base32 "0v2ysap5jfkq9fc5blqlfhsv2bs8rqybmp5z4q3g6y3y7qzhypjw"))))
    (properties `((upstream-name . "BMRBr")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-rvest))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/billchenxi/BMRBr")
    (synopsis "'BMRB' File Downloader")
    (description
     "Nuclear magnetic resonance (NMR) is a highly versatile analytical technique for
studying molecular configuration, conformation, and dynamics, especially those
of biomacromolecules such as proteins.  Biological Magnetic Resonance Data Bank
('BMRB') is a repository for Data from NMR Spectroscopy on Proteins, Peptides,
Nucleic Acids, and other Biomolecules.  Currently, BMRB offers an R package
RBMRB to fetch data, however, it doesn't easily offer individual data file
downloading and storing in a local directory.  When using RBMRB', the data will
stored as an R object, which fundamentally hinders the NMR researches to access
the rich information from raw data, for example, the metadata.  Here, BMRBr File
Downloader ('BMRBr') offers a more fundamental, low level downloader, which will
download original deposited .str format file.  This type of file contains
information such as entry title, authors, citation, protein sequences, and so
on.  Many factors affect NMR experiment outputs, such as temperature, resonance
sensitivity and etc., approximately 40% of the entries in the BMRB have chemical
shift accuracy problems [1,2] Unfortunately, current reference correction
methods are heavily dependent on the availability of assigned protein chemical
shifts or protein structure.  This is my current research project is going to
solve, which will be included in the future release of the package.  The current
version of the package is sufficient and robust enough for downloading
individual BMRB data file from the BMRB database <http://www.bmrb.wisc.edu>.
The functionalities of this package includes but not limited: * To simplifies
NMR researches by combine data downloading and results analysis together. * To
allows NMR data reaches a broader audience that could utilize more than just
chemical shifts but also metadata. * To offer reference corrected data for
entries without assignment or structure information (future release).
Reference: [1] E.L. Ulrich, H. Akutsu, J.F. Doreleijers, Y. Harano, Y.E.
Ioannidis, J. Lin, et al., @code{BioMagResBank}, Nucl.  Acids Res.  36 (2008)
D402â8. <doi:10.1093/nar/gkm957>. [2] L. Wang, H.R. Eghbalnia, A. Bahrami,
J.L. Markley, Linear analysis of carbon-13 chemical shift differences and its
application to the detection and correction of errors in referencing and spin
system identifications, J. Biomol.  NMR. 32 (2005) 13â22.
<doi:10.1007/s10858-005-1717-0>.")
    (license license:gpl3)))

(define-public r-bmm
  (package
    (name "r-bmm")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bmm" version))
       (sha256
        (base32 "11fl9a0350602ipjzcwqh2fcbh1zhbachxb37wd4n25qaraqds6l"))))
    (properties `((upstream-name . "bmm")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-tidyr
                             r-matrixstats
                             r-magrittr
                             r-glue
                             r-fs
                             r-dplyr
                             r-crayon
                             r-brms))
    (home-page "https://github.com/venpopov/bmm")
    (synopsis "Easy and Accessible Bayesian Measurement Models Using 'brms'")
    (description
     "Fit computational and measurement models using full Bayesian inference.  The
package provides a simple and accessible interface by translating complex
domain-specific models into brms syntax, a powerful and flexible framework for
fitting Bayesian regression models using Stan'.  The package is designed so that
users can easily apply state-of-the-art models in various research fields, and
so that researchers can use it as a new model development framework.
References: Frischkorn and Popov (2023) <doi:10.31234/osf.io/umt57>.")
    (license license:gpl2)))

(define-public r-bmlm
  (package
    (name "r-bmlm")
    (version "1.3.15")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bmlm" version))
       (sha256
        (base32 "1iig406z8whdjpbvajks2vzp1s1ay1f99r1n6qhs9xyi1z6yff2l"))))
    (properties `((upstream-name . "bmlm")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-ggplot2
                             r-bh))
    (home-page "https://github.com/mvuorre/bmlm/")
    (synopsis "Bayesian Multilevel Mediation")
    (description
     "Easy estimation of Bayesian multilevel mediation models with Stan.")
    (license license:gpl3+)))

(define-public r-bmixture
  (package
    (name "r-bmixture")
    (version "1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bmixture" version))
       (sha256
        (base32 "09ahs2dwrbqhk4aki9291v5lr5fa9mvi087zrqjmv2057ypyv2n5"))))
    (properties `((upstream-name . "bmixture")))
    (build-system r-build-system)
    (propagated-inputs (list r-bdgraph))
    (home-page "https://www.uva.nl/profile/a.mohammadi")
    (synopsis "Bayesian Estimation for Finite Mixture of Distributions")
    (description
     "This package provides statistical tools for Bayesian estimation of mixture
distributions, mainly a mixture of Gamma, Normal, and t-distributions.  The
package is implemented based on the Bayesian literature for the finite mixture
of distributions, including Mohammadi and et al. (2013)
<doi:10.1007/s00180-012-0323-3> and Mohammadi and Salehi-Rad (2012)
<doi:10.1080/03610918.2011.588358>.")
    (license license:gpl2+)))

(define-public r-bmisc
  (package
    (name "r-bmisc")
    (version "1.4.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BMisc" version))
       (sha256
        (base32 "1j1qxa5hgc9zdmk1y4xa87jcxi8m75hms88ishfvbqg8n2k9ah1h"))))
    (properties `((upstream-name . "BMisc")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-dplyr r-data-table))
    (home-page "https://bcallaway11.github.io/BMisc/")
    (synopsis
     "Miscellaneous Functions for Panel Data, Quantiles, and Printing Results")
    (description
     "These are miscellaneous functions for working with panel data, quantiles, and
printing results.  For panel data, the package includes functions for making a
panel data balanced (that is, dropping missing individuals that have missing
observations in any time period), converting id numbers to row numbers, and to
treat repeated cross sections as panel data under the assumption of rank
invariance.  For quantiles, there are functions to make distribution functions
from a set of data points (this is particularly useful when a distribution
function is created in several steps), to combine distribution functions based
on some external weights, and to invert distribution functions.  Finally, there
are several other miscellaneous functions for obtaining weighted means, weighted
distribution functions, and weighted quantiles; to generate summary statistics
and their differences for two groups; and to add or drop covariates from
formulas.")
    (license license:gpl2)))

(define-public r-bmggum
  (package
    (name "r-bmggum")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bmggum" version))
       (sha256
        (base32 "1r0j5gklmknyan68nc3vfn96ck414v75547486ic70v2pdr3xnpf"))))
    (properties `((upstream-name . "bmggum")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-loo
                             r-ggum
                             r-ggplot2
                             r-edstan
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Naidantu/bmggum")
    (synopsis "Bayesian Multidimensional Generalized Graded Unfolding Model")
    (description
     "Full Bayesian estimation of Multidimensional Generalized Graded Unfolding Model
(MGGUM) using rstan (See Stan Development Team (2020) <https://mc-stan.org/>).
Functions are provided for estimation, result extraction, model fit statistics,
and plottings.")
    (license license:gpl3+)))

(define-public r-bmgarch
  (package
    (name "r-bmgarch")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bmgarch" version))
       (sha256
        (base32 "0a0svnb7wxm9v8mzk0ld28scp51sp4rsgspxccqpkbmj31njc4d0"))))
    (properties `((upstream-name . "bmgarch")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rdpack
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-mass
                             r-loo
                             r-ggplot2
                             r-forecast
                             r-bh))
    (home-page "https://cran.r-project.org/package=bmgarch")
    (synopsis "Bayesian Multivariate GARCH Models")
    (description
     "Fit Bayesian multivariate GARCH models using Stan for full Bayesian inference.
Generate (weighted) forecasts for means, variances (volatility) and
correlations.  Currently DCC(P,Q), CCC(P,Q), @code{pdBEKK(P,Q}), and BEKK(P,Q)
parameterizations are implemented, based either on a multivariate gaussian
normal or student-t distribution.  DCC and CCC models are based on Engle (2002)
<doi:10.1198/073500102288618487> and Bollerslev (1990).  The BEKK
parameterization follows Engle and Kroner (1995) <doi:10.1017/S0266466600009063>
while the @code{pdBEKK} as well as the estimation approach for this package is
described in Rast et al. (2020) <doi:10.31234/osf.io/j57pk>.  The fitted models
contain rstan objects and can be examined with rstan functions.")
    (license license:gpl3+)))

(define-public r-bmet
  (package
    (name "r-bmet")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bmet" version))
       (sha256
        (base32 "128mnyq4jkigirf9xkpfrk4l4d8pivrj43wbaj5hizhfd84q1qzj"))))
    (properties `((upstream-name . "bmet")))
    (build-system r-build-system)
    (propagated-inputs (list r-mcmcpack r-mass))
    (home-page "https://cran.r-project.org/package=bmet")
    (synopsis "Bayesian Multigroup Equivalence Testing")
    (description
     "Calculates the necessary quantities to perform Bayesian multigroup equivalence
testing.  Currently the package includes the Bayesian models and equivalence
criteria outlined in Pourmohamad and Lee (2023) <doi:10.1002/sta4.645>, but more
models and equivalence testing features may be added over time.")
    (license license:expat)))

(define-public r-bmemlavaan
  (package
    (name "r-bmemlavaan")
    (version "0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bmemLavaan" version))
       (sha256
        (base32 "0axwc7y7ncvmpnmdlxjdaw2kfhqklxd0zb7n22m6aw67hkqqqcg5"))))
    (properties `((upstream-name . "bmemLavaan")))
    (build-system r-build-system)
    (propagated-inputs (list r-snowfall
                             r-sem
                             r-rsem
                             r-mass
                             r-lavaan
                             r-amelia))
    (native-inputs (list r-r-rsp))
    (home-page "https://bigdatalab.nd.edu")
    (synopsis "Mediation Analysis with Missing Data and Non-Normal Data")
    (description
     "This package provides methods for mediation analysis with missing data and
non-normal data are implemented.  For missing data, four methods are available:
Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maximum
Likelihood algorithm.  For MI and TS-ML, auxiliary variables can be included to
handle missing data.  For handling non-normal data, bootstrap and two-stage
robust methods can be used.  Technical details of the methods can be found in
Zhang and Wang (2013, <doi:10.1007/s11336-012-9301-5>), Zhang (2014,
<doi:10.3758/s13428-013-0424-0>), and Yuan and Zhang (2012,
<doi:10.1007/s11336-012-9282-4>).")
    (license license:gpl2)))

(define-public r-bmem
  (package
    (name "r-bmem")
    (version "2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bmem" version))
       (sha256
        (base32 "0acw1xwswf5yi1a0j8pv996qcma3br0xw7a9w9mh7bx5xavlivvl"))))
    (properties `((upstream-name . "bmem")))
    (build-system r-build-system)
    (propagated-inputs (list r-snowfall r-sem r-mass r-lavaan r-amelia))
    (home-page "https://bigdatalab.nd.edu")
    (synopsis "Mediation Analysis with Missing Data Using Bootstrap")
    (description
     "Four methods for mediation analysis with missing data: Listwise deletion,
Pairwise deletion, Multiple imputation, and Two Stage Maximum Likelihood
algorithm.  For MI and TS-ML, auxiliary variables can be included.  Bootstrap
confidence intervals for mediation effects are obtained.  The robust method is
also implemented for TS-ML. Since version 1.4, bmem adds the capability to
conduct power analysis for mediation models.  Details about the methods used can
be found in these articles.  Zhang and Wang (2003)
<doi:10.1007/s11336-012-9301-5>.  Zhang (2014) <doi:10.3758/s13428-013-0424-0>.")
    (license license:gpl2)))

(define-public r-bmconcor
  (package
    (name "r-bmconcor")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BMconcor" version))
       (sha256
        (base32 "0sm18dy9d4byzhjymcl9i30660i7x4zd53mbgyns5ffaww4h7fcc"))))
    (properties `((upstream-name . "BMconcor")))
    (build-system r-build-system)
    (home-page "https://fatelarico.github.io/BMconcor/")
    (synopsis "CONCOR for Structural- And Regular-Equivalence Blockmodeling")
    (description
     "The four functions svdcp() ('cp for column partitioned), svdbip() or svdbip2()
('bip for bipartitioned), and svdbips() ('s for a simultaneous optimization of a
set of r solutions), correspond to a singular value decomposition (SVD) by
blocks notion, by supposing each block depending on relative subspaces, rather
than on two whole spaces as usual SVD does.  The other functions, based on this
notion, are relative to two column partitioned data matrices x and y defining
two sets of subsets x_i and y_j of variables and amount to estimate a link
between x_i and y_j for the pair (x_i, y_j) relatively to the links associated
to all the other pairs.  These methods were first presented in: Lafosse R. &
Hanafi M.,(1997) <https://eudml.org/doc/106424> and Hanafi M. & Lafosse, R.
(2001) <https://eudml.org/doc/106494>.")
    (license license:gpl3+)))

(define-public r-bmass
  (package
    (name "r-bmass")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bmass" version))
       (sha256
        (base32 "0dafixd996lamn5kfbqpiawblalhyw1gi394j46n2sij2r209d76"))))
    (properties `((upstream-name . "bmass")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mturchin20/bmass")
    (synopsis "Bayesian Multivariate Analysis of Summary Statistics")
    (description
     "Multivariate tool for analyzing genome-wide association study results in the
form of univariate summary statistics.  The goal of bmass is to comprehensively
test all possible multivariate models given the phenotypes and datasets
provided.  Multivariate models are determined by assigning each phenotype to
being either Unassociated (U), Directly associated (D) or Indirectly associated
(I) with the genetic variant of interest.  Test results for each model are
presented in the form of Bayes factors, thereby allowing direct comparisons
between models.  The underlying framework implemented here is based on the
modeling developed in \"A Unified Framework for Association Analysis with
Multiple Related Phenotypes\", M. Stephens (2013)
<doi:10.1371/journal.pone.0065245>.")
    (license license:gpl3+)))

(define-public r-bmamevt
  (package
    (name "r-bmamevt")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BMAmevt" version))
       (sha256
        (base32 "19mmrz54n8ai9sfjpb3815p4r2pzy0v18hk020vqkjxakh9zgpab"))))
    (properties `((upstream-name . "BMAmevt")))
    (build-system r-build-system)
    (propagated-inputs (list r-coda))
    (home-page "https://cran.r-project.org/package=BMAmevt")
    (synopsis
     "Multivariate Extremes: Bayesian Estimation of the Spectral Measure")
    (description
     "Toolkit for Bayesian estimation of the dependence structure in multivariate
extreme value parametric models, following Sabourin and Naveau (2014)
<doi:10.1016/j.csda.2013.04.021> and Sabourin, Naveau and Fougeres (2013)
<doi:10.1007/s10687-012-0163-0>.")
    (license license:gpl2+)))

(define-public r-bmabasket
  (package
    (name "r-bmabasket")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bmabasket" version))
       (sha256
        (base32 "0w8y992ylyg0cyz1lfqll96fivng13lvxp0mhyb48flfnkg8rcdd"))))
    (properties `((upstream-name . "bmabasket")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-partitions))
    (home-page "https://github.com/ethan-alt/bmabasket")
    (synopsis "Bayesian Model Averaging for Basket Trials")
    (description
     "An implementation of the Bayesian model averaging method of Psioda and others
(2019) <doi:10.1093/biostatistics/kxz014> for basket trials.  Contains a
user-friendly wrapper for simulating basket trials under conditions and
analyzing them with a Bayesian model averaging approach.")
    (license license:gpl3+)))

(define-public r-bma
  (package
    (name "r-bma")
    (version "3.18.19")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BMA" version))
       (sha256
        (base32 "0hldrbl7bcf5rchc5vl8v1y679yjbxq8pca79pgc3g607pnddsh9"))))
    (properties `((upstream-name . "BMA")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rrcov r-robustbase r-leaps r-inline))
    (native-inputs (list gfortran))
    (home-page "https://github.com/hanase/BMA")
    (synopsis "Bayesian Model Averaging")
    (description
     "Package for Bayesian model averaging and variable selection for linear models,
generalized linear models and survival models (cox regression).")
    (license license:gpl2+)))

(define-public r-blythstillcasellaci
  (package
    (name "r-blythstillcasellaci")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BlythStillCasellaCI" version))
       (sha256
        (base32 "1pvbnhm6y1canq67vh5c8qm6942vhglwh31w5gnrmrydyrvay7vj"))))
    (properties `((upstream-name . "BlythStillCasellaCI")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BlythStillCasellaCI")
    (synopsis "Blyth-Still-Casella Exact Binomial Confidence Intervals")
    (description
     "Computes Blyth-Still-Casella exact binomial confidence intervals based on a
refining procedure proposed by George Casella (1986) <doi:10.2307/3314658>.")
    (license license:gpl3)))

(define-public r-bluebike
  (package
    (name "r-bluebike")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bluebike" version))
       (sha256
        (base32 "1nlxgq145igvf22ar7fpi1a7caw64jhi44fjvqsimpcqxpjycy38"))))
    (properties `((upstream-name . "bluebike")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-stringr
                             r-sf
                             r-readr
                             r-magrittr
                             r-lubridate
                             r-leaflet
                             r-janitor
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bluebike")
    (synopsis "Blue Bike Comprehensive Data")
    (description
     "Facilitates the importation of the Boston Blue Bike trip data since 2015.
Functions include the computation of trip distances of given trip data.  It can
also map the location of stations within a given radius and calculate the
distance to nearby stations.  Data is from
<https://www.bluebikes.com/system-data>.")
    (license license:expat)))

(define-public r-bltm
  (package
    (name "r-bltm")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bltm" version))
       (sha256
        (base32 "1sx4ii65ddvdw45za8j320v1s9d1pz5k2gx979yymhyxi8fmq69z"))))
    (properties `((upstream-name . "bltm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rfast r-mvnfast))
    (home-page "https://github.com/curso-r/bltm")
    (synopsis "Bayesian Latent Threshold Modeling")
    (description
     "Fits latent threshold model for simulated data and describes how to adjust model
using real data.  Implements algorithm proposed by Nakajima and West (2013)
<doi:10.1080/07350015.2012.747847>.  This package has a function to generate
data, a function to configure priors and a function to fit the model.  Examples
may be checked inside the demonstration files.")
    (license license:expat)))

(define-public r-blsr
  (package
    (name "r-blsr")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blsR" version))
       (sha256
        (base32 "0par5yqfn9f63di1dqjncfvvn7q3ikvc1g91gbk7svi074v9l6wx"))))
    (properties `((upstream-name . "blsR")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rlang
                             r-readr
                             r-purrr
                             r-httr
                             r-dplyr))
    (home-page "https://github.com/groditi/blsR")
    (synopsis "Make Requests from the Bureau of Labor Statistics API")
    (description
     "This package implements v2 of the B.L.S. API for requests of survey information
and time series data through 3-tiered API that allows users to interact with the
raw API directly, create queries through a functional interface, and re-shape
the data structures returned to fit common uses.  The API definition is located
at: <https://www.bls.gov/developers/api_signature_v2.htm>.")
    (license license:expat)))

(define-public r-blsm
  (package
    (name "r-blsm")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BLSM" version))
       (sha256
        (base32 "02xidwp91rh34rcqd0q552wzm2j80axs1nnwwv2aj04g96lf7myj"))))
    (properties `((upstream-name . "BLSM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen r-rcpp))
    (home-page "https://cran.r-project.org/package=BLSM")
    (synopsis "Bayesian Latent Space Model")
    (description
     "This package provides a Bayesian latent space model for complex networks, either
weighted or unweighted.  Given an observed input graph, the estimates for the
latent coordinates of the nodes are obtained through a Bayesian MCMC algorithm.
The overall likelihood of the graph depends on a fundamental probability
equation, which is defined so that ties are more likely to exist between nodes
whose latent space coordinates are close.  The package is mainly based on the
model by Hoff, Raftery and Handcock (2002) <doi:10.1198/016214502388618906> and
contains some extra features (e.g., removal of the Procrustean step, weights
implemented as coefficients of the latent distances, 3D plots).  The original
code related to the above model was retrieved from
<https://www.stat.washington.edu/people/pdhoff/Code/hoff_raftery_handcock_2002_jasa/>.
 Users can inspect the MCMC simulation, create and customize insightful
graphical representations or apply clustering techniques.")
    (license license:gpl2+)))

(define-public r-blscraper
  (package
    (name "r-blscraper")
    (version "4.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blscrapeR" version))
       (sha256
        (base32 "0bz1bqma9r9qdd10k8yzm9bf0pxcw8dpsb44ch5hd57p9p1p9q4y"))))
    (properties `((upstream-name . "blscrapeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-purrr
                             r-magrittr
                             r-jsonlite
                             r-httr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/keberwein/blscrapeR")
    (synopsis
     "An API Wrapper for the United States Bureau of Labor Statistics")
    (description
     "Scrapes various data from <https://www.bls.gov/>.  The Bureau of Labor
Statistics is the statistical branch of the United States Department of Labor.
The package has additional functions to help parse, analyze and visualize the
data.")
    (license license:expat)))

(define-public r-blsbandit
  (package
    (name "r-blsbandit")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blsBandit" version))
       (sha256
        (base32 "08dppd0qym4lac5glifwaraddics8np522sfw4x8xg00vm335r25"))))
    (properties `((upstream-name . "blsBandit")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-shiny
                             r-rsqlite
                             r-plotly
                             r-jsonlite
                             r-dbi))
    (home-page "https://cran.r-project.org/package=blsBandit")
    (synopsis "Data Viewer for Bureau of Labor Statistics Data")
    (description
     "Allows users to easily visualize data from the BLS (United States of America
Bureau of Labor Statistics) <https://www.bls.gov>.  Currently unemployment data
series U1-U6 are available.  Not affiliated with the Bureau of Labor Statistics
or United States Government.")
    (license license:expat)))

(define-public r-blrshiny2
  (package
    (name "r-blrshiny2")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BLRShiny2" version))
       (sha256
        (base32 "0cl959hlchba4ikzdvxv2jz30sswh5hc0d8hwx8ckzz52kkj4csa"))))
    (properties `((upstream-name . "BLRShiny2")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny
                             r-rmarkdown
                             r-rhandsontable
                             r-ggplot2
                             r-e1071
                             r-dplyr
                             r-caret))
    (home-page "https://cran.r-project.org/package=BLRShiny2")
    (synopsis
     "Interactive Document for Working with Binary Logistic Regression Analysis")
    (description
     "An interactive document on the topic of binary logistic regression analysis
using rmarkdown and shiny packages.  Runtime examples are provided in the
package function as well as at
<https://analyticmodels.shinyapps.io/@code{BinaryLogisticRegressionModelling/>}.")
    (license license:gpl2)))

(define-public r-blrshiny
  (package
    (name "r-blrshiny")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BLRShiny" version))
       (sha256
        (base32 "0z3gr0bvjs7y0jkygh7h9habnk2807cwziajpgfmb7dxdrhccj68"))))
    (properties `((upstream-name . "BLRShiny")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny
                             r-rmarkdown
                             r-rhandsontable
                             r-ggplot2
                             r-e1071
                             r-dplyr
                             r-caret))
    (home-page "https://cran.r-project.org/package=BLRShiny")
    (synopsis
     "Interactive Document for Working with Binary Logistic Regression Analysis")
    (description
     "An interactive document on the topic of binary logistic regression analysis
using rmarkdown and shiny packages.  Runtime examples are provided in the
package function as well as at
<https://analyticmodels.shinyapps.io/@code{BinaryLogisticRegressionModelling/>}.")
    (license license:gpl2)))

(define-public r-blrpm
  (package
    (name "r-blrpm")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BLRPM" version))
       (sha256
        (base32 "15q0jkw4swz35c58nv23k8g1dy1kkbqh5aydjck7pd0fmppqrhpx"))))
    (properties `((upstream-name . "BLRPM")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6))
    (home-page "https://cran.r-project.org/package=BLRPM")
    (synopsis
     "Stochastic Rainfall Generator Bartlett-Lewis Rectangular Pulse Model")
    (description
     "Due to a limited availability of observed high-resolution precipitation records
with adequate length, simulations with stochastic precipitation models are used
to generate series for subsequent studies [e.g. Khaliq and Cunmae, 1996,
<doi:10.1016/0022-1694(95)02894-3>, Vandenberghe et al., 2011,
<doi:10.1029/2009WR008388>].  This package contains an R implementation of the
original Bartlett-Lewis rectangular pulse model (BLRPM), developed by
Rodriguez-Iturbe et al. (1987) <doi:10.1098/rspa.1987.0039>.  It contains a
function for simulating a precipitation time series based on storms and cells
generated by the model with given or estimated model parameters.  Additionally
BLRPM parameters can be estimated from a given or simulated precipitation time
series.  The model simulations can be plotted in a three-layer plot including an
overview of generated storms and cells by the model (which can also be plotted
individually), a continuous step-function and a discrete precipitation time
series at a chosen aggregation level.")
    (license license:gpl2+)))

(define-public r-blrm
  (package
    (name "r-blrm")
    (version "1.0-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blrm" version))
       (sha256
        (base32 "1lk7s7myxflr9as88944kvg8vn7m2ansg2caizr6h4d3690vr4m7"))))
    (properties `((upstream-name . "blrm")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny
                             r-rjags
                             r-reshape2
                             r-openxlsx
                             r-mvtnorm
                             r-boot))
    (home-page "https://cran.r-project.org/package=blrm")
    (synopsis
     "Dose Escalation Design in Phase I Oncology Trial Using Bayesian Logistic Regression Modeling")
    (description
     "Design dose escalation using Bayesian logistic regression modeling in Phase I
oncology trial.")
    (license license:lgpl2.0+)))

(define-public r-blr
  (package
    (name "r-blr")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BLR" version))
       (sha256
        (base32 "1wivqh19kxgr7nwa09s5ay6x1yidjk4hqanv0mzb1nbik98w9024"))))
    (properties `((upstream-name . "BLR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BLR")
    (synopsis "Bayesian Linear Regression")
    (description "Bayesian Linear Regression.")
    (license license:gpl2)))

(define-public r-blpestimator
  (package
    (name "r-blpestimator")
    (version "0.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BLPestimatoR" version))
       (sha256
        (base32 "07q5ifsvw1ccxys6y01ldh2n9v7chyv88rda1jbznb3nnwka2wh0"))))
    (properties `((upstream-name . "BLPestimatoR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-randtoolbox
                             r-numderiv
                             r-mvquad
                             r-matrix
                             r-formula))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BLPestimatoR")
    (synopsis "Performs a BLP Demand Estimation")
    (description
     "This package provides the estimation algorithm to perform the demand estimation
described in Berry, Levinsohn and Pakes (1995) <DOI:10.2307/2171802> .  The
routine uses analytic gradients and offers a large number of implemented
integration methods and optimization routines.")
    (license license:gpl3)))

(define-public r-blorr
  (package
    (name "r-blorr")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blorr" version))
       (sha256
        (base32 "1ynvg6ar7c7p01225q9czy4gxks0p1vw3sx207l7vzqxw20wb1bn"))))
    (properties `((upstream-name . "blorr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp
                             r-lest
                             r-gridextra
                             r-ggplot2
                             r-data-table
                             r-car))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rsquaredacademy/blorr")
    (synopsis "Tools for Developing Binary Logistic Regression Models")
    (description
     "This package provides tools designed to make it easier for beginner and
intermediate users to build and validate binary logistic regression models.
Includes bivariate analysis, comprehensive regression output, model fit
statistics, variable selection procedures, model validation techniques and a
shiny app for interactive model building.")
    (license license:expat)))

(define-public r-bloq
  (package
    (name "r-bloq")
    (version "0.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BLOQ" version))
       (sha256
        (base32 "0qs4pnzpfwkii6wpd8my34zw9k003b3is0y0aj1lhcm0qnn1p8h4"))))
    (properties `((upstream-name . "BLOQ")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-maxlik))
    (home-page "https://cran.r-project.org/package=BLOQ")
    (synopsis "Impute and Analyze Data with BLOQ Observations")
    (description
     "It includes estimating the area under the concentrations versus time curve (AUC)
and its standard error for data with Below the Limit of Quantification (BLOQ)
observations.  Two approaches are implemented: direct estimation using censored
maximum likelihood, also by first imputing the BLOQ's using various methods,
then compute AUC and its standard error using imputed data.  Technical details
can found in Barnett, Helen Yvette, Helena Geys, Tom Jacobs, and Thomas Jaki.
\"Methods for Non-Compartmental Pharmacokinetic Analysis With Observations Below
the Limit of Quantification.\" Statistics in Biopharmaceutical Research (2020):
1-12. (available online:
<https://www.tandfonline.com/doi/full/10.1080/19466315.2019.1701546>).")
    (license license:gpl2+)))

(define-public r-blogdown
  (package
    (name "r-blogdown")
    (version "1.19")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blogdown" version))
       (sha256
        (base32 "0glxgks9aql3k5hxjr9yfinvp4n69fdh99v7q33mgjk7n2ja88q5"))))
    (properties `((upstream-name . "blogdown")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-xfun
                             r-servr
                             r-rmarkdown
                             r-later
                             r-knitr
                             r-jsonlite
                             r-httpuv
                             r-htmltools
                             r-bookdown))
    (home-page "https://github.com/rstudio/blogdown")
    (synopsis "Create Blogs and Websites with R Markdown")
    (description
     "Write blog posts and web pages in R Markdown.  This package supports the static
site generator Hugo (<https://gohugo.io>) best, and it also supports Jekyll
(<https://jekyllrb.com>) and Hexo (<https://hexo.io>).")
    (license license:gpl3)))

(define-public r-blocs
  (package
    (name "r-blocs")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blocs" version))
       (sha256
        (base32 "084449bigcqc6jfarpb5cvvrhh7a4qghgz3ym9db64zhjrha384x"))))
    (properties `((upstream-name . "blocs")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-rlang
                             r-mgcv
                             r-ks
                             r-ggplot2
                             r-dplyr
                             r-collapse))
    (home-page "https://cran.r-project.org/package=blocs")
    (synopsis "Estimate and Visualize Voting Blocs' Partisan Contributions")
    (description
     "This package provides functions to combine data on voting blocs size, turnout,
and vote choice to estimate each bloc's vote contributions to the Democratic and
Republican parties.  The package also includes functions for uncertainty
estimation and plotting.  Users may define voting blocs along a discrete or
continuous variable.  The package implements methods described in Grimmer,
Marble, and Tanigawa-Lau (2023) <doi:10.31235/osf.io/c9fkg>.")
    (license license:gpl3+)))

(define-public r-blocktools
  (package
    (name "r-blocktools")
    (version "0.6.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blockTools" version))
       (sha256
        (base32 "0c62s4p477d9lxxninn18hrf348rph7w8w1jsqzqpm333dcamvhi"))))
    (properties `((upstream-name . "blockTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-mass r-dplyr))
    (home-page "https://www.ryantmoore.org/html/software.blockTools.html")
    (synopsis
     "Block, Assign, and Diagnose Potential Interference in Randomized Experiments")
    (description
     "Blocks units into experimental blocks, with one unit per treatment condition, by
creating a measure of multivariate distance between all possible pairs of units.
 Maximum, minimum, or an allowable range of differences between units on one
variable can be set.  Randomly assign units to treatment conditions.  Diagnose
potential interference between units assigned to different treatment conditions.
 Write outputs to .tex and .csv files.  For more information on the methods
implemented, see Moore (2012) <doi:10.1093/pan/mps025>.")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-blocksdesign
  (package
    (name "r-blocksdesign")
    (version "4.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blocksdesign" version))
       (sha256
        (base32 "1kr3fxs8vgc96clhx5ml11jkwll1rg9gf16xg93q14jpg4hg0j9h"))))
    (properties `((upstream-name . "blocksdesign")))
    (build-system r-build-system)
    (propagated-inputs (list r-polynomf r-plyr))
    (native-inputs (list r-r-rsp))
    (home-page "<doi:10.1007/s13253-020-00416-0>")
    (synopsis
     "Nested and Crossed Block Designs for Factorial and Unstructured Treatment Sets")
    (description
     "Constructs treatment and block designs for linear treatment models with crossed
or nested block factors.  The treatment design can be any feasible linear model
and the block design can be any feasible combination of crossed or nested block
factors.  The block design is a sum of one or more block factors and the block
design is optimized sequentially with the levels of each successive block factor
optimized conditional on all previously optimized block factors.  D-optimality
is used throughout except for square or rectangular lattice block designs which
are constructed algebraically using mutually orthogonal Latin squares.  Crossed
block designs with interaction effects are optimized using a weighting scheme
which allows for differential weighting of first and second-order block effects.
 Outputs include a table showing the allocation of treatments to blocks and
tables showing the achieved D-efficiency factors for each block and treatment
design.  Edmondson, R.N. Multi-level Block Designs for Comparative Experiments.
JABES 25, 500â522 (2020) <doi:10.1007/s13253-020-00416-0>.")
    (license license:gpl2+)))

(define-public r-blockrand
  (package
    (name "r-blockrand")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blockrand" version))
       (sha256
        (base32 "0bic3b8l7mp2hpwd8k1diwy6a4lj92i7s5k20i2wy8kw0c74jwfh"))))
    (properties `((upstream-name . "blockrand")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=blockrand")
    (synopsis "Randomization for Block Random Clinical Trials")
    (description
     "Create randomizations for block random clinical trials.  Can also produce a pdf
file of randomization cards.")
    (license license:gpl2)))

(define-public r-blockmodels
  (package
    (name "r-blockmodels")
    (version "1.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blockmodels" version))
       (sha256
        (base32 "1h2g13yp8xhkw9icl5bi1nkc0qfqkqhlhdqbcsp0246g9amssci9"))))
    (properties `((upstream-name . "blockmodels")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-digest))
    (home-page "https://cran.r-project.org/package=blockmodels")
    (synopsis
     "Latent and Stochastic Block Model Estimation by a 'V-EM' Algorithm")
    (description
     "Latent and Stochastic Block Model estimation by a Variational EM algorithm.
Various probability distribution are provided (Bernoulli, Poisson...), with or
without covariates.")
    (license license:lgpl2.1)))

(define-public r-blockmodelinggui
  (package
    (name "r-blockmodelinggui")
    (version "1.8.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BlockmodelingGUI" version))
       (sha256
        (base32 "09sbxwzvnnzjv5gy0zyd1lvm1z44ywqvanj99i15l0lp66l54572"))))
    (properties `((upstream-name . "BlockmodelingGUI")))
    (build-system r-build-system)
    (propagated-inputs (list r-visnetwork
                             r-shinythemes
                             r-shinyjs
                             r-shinybusy
                             r-shiny
                             r-network
                             r-intergraph
                             r-igraph
                             r-htmlwidgets
                             r-dt
                             r-blockmodeling))
    (home-page "https://cran.r-project.org/package=BlockmodelingGUI")
    (synopsis "GUI for the Generalised Blockmodeling of Valued Networks")
    (description
     "This app provides some useful tools for Offering an accessible GUI for
generalised blockmodeling of single-relation, one-mode networks.  The user can
execute blockmodeling without having to write a line code by using the app's
visual helps.  Moreover, there are several ways to visualisations networks and
their partitions.  Finally, the results can be exported as if they were produced
by writing code.  The development of this package is financially supported by
the Slovenian Research Agency (www.arrs.gov.si) within the research project
J5-2557 (Comparison and evaluation of different approaches to blockmodeling
dynamic networks by simulations with application to Slovenian co-authorship
networks).")
    (license license:gpl3+)))

(define-public r-blockmissingdata
  (package
    (name "r-blockmissingdata")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BlockMissingData" version))
       (sha256
        (base32 "12jgf58k3dgbyb7x4skcdc10m8pvyx8kzqq6fi4wn5x1g5595nxy"))))
    (properties `((upstream-name . "BlockMissingData")))
    (build-system r-build-system)
    (propagated-inputs (list r-pryr
                             r-matrix
                             r-mass
                             r-glmnetcr
                             r-glmnet
                             r-foreach
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=BlockMissingData")
    (synopsis
     "Integrating Multi-Source Block-Wise Missing Data in Model Selection")
    (description
     "Model selection method with multiple block-wise imputation for block-wise
missing data; see Xue, F., and Qu, A. (2021)
<doi:10.1080/01621459.2020.1751176>.")
    (license license:expat)))

(define-public r-blockmatrix
  (package
    (name "r-blockmatrix")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blockmatrix" version))
       (sha256
        (base32 "14k69ly4i8pb8z59005kaf5rpv611kk1mk96q6piyn1gz1s6sk6r"))))
    (properties `((upstream-name . "blockmatrix")))
    (build-system r-build-system)
    (home-page
     "http://cri.gmpf.eu/Research/Sustainable-Agro-Ecosystems-and-Bioresources/Dynamics-in-the-agro-ecosystems/people/Emanuele-Cordano")
    (synopsis
     "blockmatrix: Tools to solve algebraic systems with partitioned matrices")
    (description
     "Some elementary matrix algebra tools are implemented to manage block matrices or
partitioned matrix, i.e. \"matrix of matrices\"
(http://en.wikipedia.org/wiki/Block_matrix).  The block matrix is here defined
as a new S3 object.  In this package, some methods for \"matrix\" object are
rewritten for \"blockmatrix\" object.  New methods are implemented.  This package
was created to solve equation systems with block matrices for the analysis of
environmental vector time series .  Bugs/comments/questions/collaboration of any
kind are warmly welcomed.")
    (license license:gpl2+)))

(define-public r-blocklength
  (package
    (name "r-blocklength")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blocklength" version))
       (sha256
        (base32 "06d62hgkf2pphr6h3nba1b5r0izr45bvmrymrzq0lyc7xiwrh6na"))))
    (properties `((upstream-name . "blocklength")))
    (build-system r-build-system)
    (propagated-inputs (list r-tseries))
    (native-inputs (list r-knitr))
    (home-page "https://alecstashevsky.com/r/blocklength")
    (synopsis
     "Select an Optimal Block-Length to Bootstrap Dependent Data (Block Bootstrap)")
    (description
     "This package provides a set of functions to select the optimal block-length for
a dependent bootstrap (block-bootstrap).  Includes the Hall, Horowitz, and Jing
(1995) <doi:10.1093/biomet/82.3.561> cross-validation method and the Politis and
White (2004) <doi:10.1081/ETC-120028836> Spectral Density Plug-in method,
including the Patton, Politis, and White (2009) <doi:10.1080/07474930802459016>
correction with a corresponding set of S3 plot methods.")
    (license license:gpl2+)))

(define-public r-blockforest
  (package
    (name "r-blockforest")
    (version "0.2.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blockForest" version))
       (sha256
        (base32 "1i5vqs0hh3yc82hl8bsa647plcv0c4c95b1wzdzyslxz2m5khw0l"))))
    (properties `((upstream-name . "blockForest")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rcppeigen r-rcpp r-matrix))
    (home-page "https://github.com/bips-hb/blockForest")
    (synopsis
     "Block Forests: Random Forests for Blocks of Clinical and Omics Covariate Data")
    (description
     "This package provides a random forest variant block forest
('@code{BlockForest}') tailored to the prediction of binary, survival and
continuous outcomes using block-structured covariate data, for example, clinical
covariates plus measurements of a certain omics data type or multi-omics data,
that is, data for which measurements of different types of omics data and/or
clinical data for each patient exist.  Examples of different omics data types
include gene expression measurements, mutation data and copy number variation
measurements.  Block forest are presented in Hornung & Wright (2019).  The
package includes four other random forest variants for multi-omics data:
@code{RandomBlock}', @code{BlockVarSel}', @code{VarProb}', and
@code{SplitWeights}'.  These were also considered in Hornung & Wright (2019),
but performed worse than block forest in their comparison study based on 20 real
multi-omics data sets.  Therefore, we recommend to use block forest
('@code{BlockForest}') in applications.  The other random forest variants can,
however, be consulted for academic purposes, for example, in the context of
further methodological developments.  Reference: Hornung, R. & Wright, M. N.
(2019) Block Forests: random forests for blocks of clinical and omics covariate
data.  BMC Bioinformatics 20:358. <doi:10.1186/s12859-019-2942-y>.")
    (license license:gpl3)))

(define-public r-blockcv
  (package
    (name "r-blockcv")
    (version "3.1-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blockCV" version))
       (sha256
        (base32 "00fhsiqwf6d7i48b59rgg0iw3bslg19aglzpic80gp8dgdrsrvmf"))))
    (properties `((upstream-name . "blockCV")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rvalavi/blockCV")
    (synopsis
     "Spatial and Environmental Blocking for K-Fold and LOO Cross-Validation")
    (description
     "Creating spatially or environmentally separated folds for cross-validation to
provide a robust error estimation in spatially structured environments;
Investigating and visualising the effective range of spatial autocorrelation in
continuous raster covariates and point samples to find an initial realistic
distance band to separate training and testing datasets spatially described in
Valavi, R. et al. (2019) <doi:10.1111/2041-210X.13107>.")
    (license license:gpl3+)))

(define-public r-blockcov
  (package
    (name "r-blockcov")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BlockCov" version))
       (sha256
        (base32 "1ilqd8kvhlq0nc1n4y7g0aiwran3kyqs8i143j6dv2wy3myn5x0l"))))
    (properties `((upstream-name . "BlockCov")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-rlang
                             r-rdpack
                             r-matrix
                             r-magrittr
                             r-dplyr
                             r-bbmisc))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BlockCov")
    (synopsis "Estimation of Large Block Covariance Matrices")
    (description
     "Computation of large covariance matrices having a block structure up to a
permutation of their columns and rows from a small number of samples with
respect to the dimension of the matrix.  The method is described in the paper
Perrot-DockÃ¨s et al. (2019) <@code{arXiv:1806.10093>}.")
    (license license:gpl2+)))

(define-public r-blockcluster
  (package
    (name "r-blockcluster")
    (version "4.5.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blockcluster" version))
       (sha256
        (base32 "03149z73d19fg5cgsgvkj2m5vb0jssxwxp83bc76q7nq8mm4rwnr"))))
    (properties `((upstream-name . "blockcluster")))
    (build-system r-build-system)
    (propagated-inputs (list r-rtkore r-rcpp))
    (home-page "https://gitlab.inria.fr/iovleff/blockcluster")
    (synopsis
     "Co-Clustering Package for Binary, Categorical, Contingency and Continuous Data-Sets")
    (description
     "Simultaneous clustering of rows and columns, usually designated by biclustering,
co-clustering or block clustering, is an important technique in two way data
analysis.  It consists of estimating a mixture model which takes into account
the block clustering problem on both the individual and variables sets.  The
blockcluster package provides a bridge between the C++ core library build on top
of the STK++ library, and the R statistical computing environment.  This package
allows to co-cluster binary <doi:10.1016/j.csda.2007.09.007>, contingency
<doi:10.1080/03610920903140197>, continuous <doi:10.1007/s11634-013-0161-3> and
categorical data-sets <doi:10.1007/s11222-014-9472-2>.  It also provides utility
functions to visualize the results.  This package may be useful for various
applications in fields of Data mining, Information retrieval, Biology, computer
vision and many more.  More information about the project and comprehensive
tutorial can be found on the link mentioned in URL.")
    (license license:gpl3+)))

(define-public r-blmodel
  (package
    (name "r-blmodel")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BLModel" version))
       (sha256
        (base32 "0l5f2v5ggadmrci3qhhmxm3j9i6jii0anq5rv5vk50y2qzii2q15"))))
    (properties `((upstream-name . "BLModel")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BLModel")
    (synopsis "Black-Litterman Posterior Distribution")
    (description
     "Posterior distribution in the Black-Litterman model is computed from a prior
distribution given in the form of a time series of asset returns and a
continuous distribution of views provided by the user as an external function.")
    (license license:gpl3)))

(define-public r-blmeco
  (package
    (name "r-blmeco")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blmeco" version))
       (sha256
        (base32 "1370djpy87816bfr0g8730x7q4d4xx9aa41iqm622q5b5zm2jzmp"))))
    (properties `((upstream-name . "blmeco")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-lme4 r-arm))
    (home-page "https://cran.r-project.org/package=blmeco")
    (synopsis
     "Data Files and Functions Accompanying the Book \"Bayesian Data Analysis in Ecology using R, BUGS and Stan\"")
    (description
     "Data files and functions accompanying the book Korner-Nievergelt, Roth, von
Felten, Guelat, Almasi, Korner-Nievergelt (2015) \"Bayesian Data Analysis in
Ecology using R, BUGS and Stan\", Elsevier, New York.")
    (license license:gpl2)))

(define-public r-blm
  (package
    (name "r-blm")
    (version "2022.0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blm" version))
       (sha256
        (base32 "0pmh3fw5iv9xhr08gji1q5vcaaai5s2qq40wisqdrqn5xhk9rh2h"))))
    (properties `((upstream-name . "blm")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=blm")
    (synopsis "Binomial Linear Regression")
    (description
     "This package implements regression models for binary data on the absolute risk
scale.  These models are applicable to cohort and population-based case-control
data.")
    (license license:gpl2+)))

(define-public r-bliss
  (package
    (name "r-bliss")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bliss" version))
       (sha256
        (base32 "0ayqzkwq897nx02clw1qb413m1a6bzfdgxw8vmmpbqld8y6ajyk9"))))
    (properties `((upstream-name . "bliss")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppprogress r-rcpparmadillo r-rcpp r-mass
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/pmgrollemund/bliss")
    (synopsis
     "Bayesian Functional Linear Regression with Sparse Step Functions")
    (description
     "This package provides a method for the Bayesian functional linear regression
model (scalar-on-function), including two estimators of the coefficient function
and an estimator of its support.  A representation of the posterior distribution
is also available.  Grollemund P-M., Abraham C., Baragatti M., Pudlo P. (2019)
<doi:10.1214/18-BA1095>.")
    (license license:gpl3)))

(define-public r-blink
  (package
    (name "r-blink")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blink" version))
       (sha256
        (base32 "053nw592sglzh6fgz7hj8ra6q7lqs83hhv14kcgs67b56q8a3crb"))))
    (properties `((upstream-name . "blink")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringdist r-plyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=blink")
    (synopsis "Record Linkage for Empirically Motivated Priors")
    (description
     "An implementation of the model in Steorts (2015) <DOI:10.1214/15-BA965SI>, which
performs Bayesian entity resolution for categorical and text data, for any
distance function defined by the user.  In addition, the precision and recall
are in the package to allow one to compare to any other comparable method such
as logistic regression, Bayesian additive regression trees (BART), or random
forests.  The experiments are reproducible and illustrated using a simple
vignette.  LICENSE: GPL-3 + file license.")
    (license license:gpl3)))

(define-public r-blindreview
  (package
    (name "r-blindreview")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blindreview" version))
       (sha256
        (base32 "0hfqcykk5yg3sccqf73lxikb39ryir8fsd7a0qshpdxvbdjm6l1p"))))
    (properties `((upstream-name . "blindreview")))
    (build-system r-build-system)
    (inputs (list gmp))
    (propagated-inputs (list r-hmisc))
    (home-page "https://cran.r-project.org/package=blindreview")
    (synopsis "Enables Blind Review of Database")
    (description
     "Randomly reassigns the group identifications to one of the variables of the
database, say Treatment, and randomly reassigns the observation numbers of the
dataset.  Reorders the observations according to these new numbers.  Centers
each group of Treatment at the grand mean in order to further mask the
treatment.  An unmasking function is provided so that the user can identify the
potential outliers in terms of their original values when blinding is no longer
needed.  It is suggested that a forward search procedure be performed on the
masked data.  Details of some forward search functions may be found in
<https://CRAN.R-project.org/package=forsearch>.")
    (license license:gpl3+)))

(define-public r-blindrecalc
  (package
    (name "r-blindrecalc")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blindrecalc" version))
       (sha256
        (base32 "126mh5p1js4mm9sq3w6hs2vlx4baa6gzm9b9jybllfi751jsgjw5"))))
    (properties `((upstream-name . "blindrecalc")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://github.com/imbi-heidelberg/blindrecalc")
    (synopsis "Blinded Sample Size Recalculation")
    (description
     "Computation of key characteristics and plots for blinded sample size
recalculation.  Continuous as well as binary endpoints are supported in
superiority and non-inferiority trials.  See Baumann, Pilz, Kieser (2022)
<doi:10.32614/RJ-2022-001> for a detailed description.  The implemented methods
include the approaches by Lu, K. (2019) <doi:10.1002/pst.1737>, Kieser, M. and
Friede, T. (2000)
<doi:10.1002/(SICI)1097-0258(20000415)19:7%3C901::AID-SIM405%3E3.0.CO;2-L>,
Friede, T. and Kieser, M. (2004) <doi:10.1002/pst.140>, Friede, T., Mitchell,
C., Mueller-Veltern, G. (2007) <doi:10.1002/bimj.200610373>, and Friede, T. and
Kieser, M. (2011) <doi:10.3414/ME09-01-0063>.")
    (license license:expat)))

(define-public r-blendstat
  (package
    (name "r-blendstat")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Blendstat" version))
       (sha256
        (base32 "1nz5gwsjlvk6lajiyf0j2max6q5ifsj1arsbn1l4x6z8m7psm9wp"))))
    (properties `((upstream-name . "Blendstat")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-lattice))
    (home-page "https://cran.r-project.org/package=Blendstat")
    (synopsis "Joint Analysis of Experiments with Mixtures and Random Effects")
    (description
     "This package performs a joint analysis of experiments with mixtures and random
effects, taking on a process variable represented by a covariable.")
    (license license:gpl3)))

(define-public r-blender
  (package
    (name "r-blender")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blender" version))
       (sha256
        (base32 "1qqkfgf7fzwcz88a43cqr8bw86qda33f18dg3rv1k77gpjqr999c"))))
    (properties `((upstream-name . "blender")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan))
    (home-page "https://cran.r-project.org/package=blender")
    (synopsis "Analyze biotic homogenization of landscapes")
    (description
     "This package provides tools for assessing exotic species contributions to
landscape homogeneity using average pairwise Jaccard similarity and an
analytical approximation derived in Harris et al. (2011, \"Occupancy is
nine-tenths of the law,\" The American Naturalist).  Also includes a
randomization method for assessing sources of model error.")
    (license (list license:gpl2 license:artistic2.0))))

(define-public r-blcop
  (package
    (name "r-blcop")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BLCOP" version))
       (sha256
        (base32 "08y9vmx3cknjvk6ngfwxlfnc03cqpj97i1ijrs1cw5sahjc0vmhc"))))
    (properties `((upstream-name . "BLCOP")))
    (build-system r-build-system)
    (propagated-inputs (list r-timeseries
                             r-runit
                             r-rmarkdown
                             r-quadprog
                             r-mass
                             r-knitr
                             r-fportfolio
                             r-fmultivar
                             r-fbasics))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mangothecat/BLCOP")
    (synopsis "Black-Litterman and Copula Opinion Pooling Frameworks")
    (description
     "An implementation of the Black-Litterman Model and Attilio Meucci's copula
opinion pooling framework as described in Meucci, Attilio (2005)
<doi:10.2139/ssrn.848407>, Meucci, Attilio (2006) <doi:10.2139/ssrn.872577> and
Meucci, Attilio (2008) <doi:10.2139/ssrn.1117574>.")
    (license license:expat)))

(define-public r-blavaan
  (package
    (name "r-blavaan")
    (version "0.5-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blavaan" version))
       (sha256
        (base32 "0lbvsga6ba4n05xnzahmj2dw3jjpjhkg93p9kadb656wxffkq7qy"))))
    (properties `((upstream-name . "blavaan")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmvnsim
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-nonnest2
                             r-mnormt
                             r-matrix
                             r-loo
                             r-lavaan
                             r-future-apply
                             r-coda
                             r-bh
                             r-bayesplot))
    (home-page "https://ecmerkle.github.io/blavaan/")
    (synopsis "Bayesian Latent Variable Analysis")
    (description
     "Fit a variety of Bayesian latent variable models, including confirmatory factor
analysis, structural equation models, and latent growth curve models.
References: Merkle & Rosseel (2018) <doi:10.18637/jss.v085.i04>; Merkle et al.
(2021) <doi:10.18637/jss.v100.i06>.")
    (license license:gpl3+)))

(define-public r-blatr
  (package
    (name "r-blatr")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blatr" version))
       (sha256
        (base32 "1pn9qi3nlk82ajck3ikcxaxrpy8plnddrp9b5w4sfhw378n0xqkh"))))
    (properties `((upstream-name . "blatr")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=blatr")
    (synopsis "Send Emails Using 'Blat' for Windows")
    (description
     "This package provides a wrapper around the Blat command line SMTP mailer for
Windows.  Blat is public domain software, but be sure to read the license before
use.  It can be found at the Blat website http://www.blat.net.")
    (license license:expat)))

(define-public r-blatent
  (package
    (name "r-blatent")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blatent" version))
       (sha256
        (base32 "0vmybbb5xwmlmzgzyaxiiargi4fd2f661sap2wfmjw9rav89zhmv"))))
    (properties `((upstream-name . "blatent")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm
                             r-rcpparmadillo
                             r-rcpp
                             r-r6
                             r-mnormt
                             r-matrix
                             r-coda))
    (home-page "https://cran.r-project.org/package=blatent")
    (synopsis "Bayesian Latent Variable Models")
    (description
     "Estimation of latent variable models using Bayesian methods.  Currently
estimates the loglinear cognitive diagnosis model of Henson, Templin, and Willse
(2009) <doi:10.1007/s11336-008-9089-5>.")
    (license license:gpl2+)))

(define-public r-blastula
  (package
    (name "r-blastula")
    (version "0.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blastula" version))
       (sha256
        (base32 "1ppn3lyzdrarcq1laz0r8b018p0yxzs2ji1q82lg9nnmghqzs24b"))))
    (properties `((upstream-name . "blastula")))
    (build-system r-build-system)
    (propagated-inputs (list r-uuid
                             r-stringr
                             r-rmarkdown
                             r-rlang
                             r-mime
                             r-magrittr
                             r-jsonlite
                             r-httr
                             r-htmltools
                             r-here
                             r-getpass
                             r-fs
                             r-dplyr
                             r-digest
                             r-curl
                             r-commonmark
                             r-base64enc))
    (home-page "https://github.com/rstudio/blastula")
    (synopsis "Easily Send HTML Email Messages")
    (description
     "Compose and send out responsive HTML email messages that render perfectly across
a range of email clients and device sizes.  Helper functions let the user insert
embedded images, web link buttons, and ggplot2 plot objects into the message
body.  Messages can be sent through an SMTP server, through the RStudio Connect
service, or through the Mailgun API service <https://www.mailgun.com/>.")
    (license license:expat)))

(define-public r-blaster
  (package
    (name "r-blaster")
    (version "1.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blaster" version))
       (sha256
        (base32 "1p0d76dzn3kkdk6r4lp3kjh06iy3r2l4zhqwr3p888gxibzfwyb0"))))
    (properties `((upstream-name . "blaster")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://github.com/tamminenlab/blaster")
    (synopsis "Native R Implementation of an Efficient BLAST-Like Algorithm")
    (description
     "Implementation of an efficient BLAST-like sequence comparison algorithm, written
in C++11 and using native R datatypes.  Blaster is based on nsearch - Schmid et
al (2018) <doi:10.1101/399782>.")
    (license license:bsd-3)))

(define-public r-blapsr
  (package
    (name "r-blapsr")
    (version "0.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blapsr" version))
       (sha256
        (base32 "1vycwglrpb0d041xj3dza25am16ac80r93si8x45b5ry0aq38c46"))))
    (properties `((upstream-name . "blapsr")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-sn
                             r-rspectra
                             r-matrix
                             r-mass
                             r-coda))
    (native-inputs (list r-knitr))
    (home-page "<https://www.blapsr-project.org/>")
    (synopsis "Bayesian Inference with Laplace Approximations and P-Splines")
    (description
     "Laplace approximations and penalized B-splines are combined for fast Bayesian
inference in latent Gaussian models.  The routines can be used to fit survival
models, especially proportional hazards and promotion time cure models
(Gressani, O. and Lambert, P. (2018) <doi:10.1016/j.csda.2018.02.007>).  The
Laplace-P-spline methodology can also be implemented for inference in
(generalized) additive models (Gressani, O. and Lambert, P. (2021)
<doi:10.1016/j.csda.2020.107088>).  See the associated website for more
information and examples.")
    (license license:gpl3)))

(define-public r-blandr
  (package
    (name "r-blandr")
    (version "0.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blandr" version))
       (sha256
        (base32 "1ckbdgyqxgycd99r0bbyw38cw6k4sq1nhi5xcc0rblck5h8hzn2i"))))
    (properties `((upstream-name . "blandr")))
    (build-system r-build-system)
    (inputs (list pandoc))
    (propagated-inputs (list r-stringr
                             r-rmarkdown
                             r-markdown
                             r-knitr
                             r-jmvcore
                             r-glue
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/deepankardatta/blandr/")
    (synopsis "Bland-Altman Method Comparison")
    (description
     "Carries out Bland Altman analyses (also known as a Tukey mean-difference plot)
as described by JM Bland and DG Altman in 1986
<doi:10.1016/S0140-6736(86)90837-8>.  This package was created in 2015 as
existing Bland-Altman analysis functions did not calculate confidence intervals.
 This package was created to rectify this, and create reproducible plots.  This
package is also available as a module for the jamovi statistical spreadsheet
(see <https://www.jamovi.org> for more information).")
    (license license:gpl3)))

(define-public r-blandaltmanleh
  (package
    (name "r-blandaltmanleh")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BlandAltmanLeh" version))
       (sha256
        (base32 "11p30zqb3f9ifk3v18dspg18sclz5zxjygy7hw8ccb4bcqhx68lm"))))
    (properties `((upstream-name . "BlandAltmanLeh")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BlandAltmanLeh")
    (synopsis "Plots (Slightly Extended) Bland-Altman Plots")
    (description
     "Bland-Altman Plots using either base graphics or ggplot2, augmented with
confidence intervals, with detailed return values and a sunflowerplot option for
data with ties.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-blakerci
  (package
    (name "r-blakerci")
    (version "1.0-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BlakerCI" version))
       (sha256
        (base32 "0dcmnaf0k195caajy8h4qymyfj45cjwmkcgsbwv0z7x3h9sx5k6v"))))
    (properties `((upstream-name . "BlakerCI")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BlakerCI")
    (synopsis "Blaker's Binomial and Poisson Confidence Limits")
    (description
     "Fast and accurate calculation of Blaker's binomial and Poisson confidence limits
(and some related stuff).")
    (license license:gpl3)))

(define-public r-blaise
  (package
    (name "r-blaise")
    (version "1.3.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blaise" version))
       (sha256
        (base32 "1ll9yqfinc2x5bb90cvgj4mc5bl4gfsmdshi5pimnjdhzwmwa9jc"))))
    (properties `((upstream-name . "blaise")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-stringr r-readr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=blaise")
    (synopsis "Read and Write FWF Files in the 'Blaise' Format")
    (description
     "Can be used to read and write a fwf with an accompanying Blaise datamodel.
Blaise is the software suite built by Statistics Netherlands (CBS).  It is
essentially a way to write and collect surveys and perform statistical analysis
on the data.  It stores its data in fixed width format with an accompanying
metadata file, this is the Blaise format.  The package automatically interprets
this metadata and reads the file into an R dataframe.  When supplying a
datamodel for writing, the dataframe will be automatically converted to that
format and checked for compatibility.  Supports dataframes, tibbles and
@code{LaF} objects.  For more information about Blaise', see
<https://blaise.com/products/general-information>.")
    (license license:gpl3)))

(define-public r-blackmarbler
  (package
    (name "r-blackmarbler")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blackmarbler" version))
       (sha256
        (base32 "09h2vjbqnb3kfmnmsah9p00w1sff15csaw382lmzy2klxw8k5zgn"))))
    (properties `((upstream-name . "blackmarbler")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-terra
                             r-stringr
                             r-sf
                             r-readr
                             r-purrr
                             r-lubridate
                             r-httr
                             r-hdf5r
                             r-exactextractr
                             r-dplyr))
    (home-page "https://worldbank.github.io/blackmarbler/")
    (synopsis "Black Marble Data and Statistics")
    (description
     "Geographically referenced data and statistics of nighttime lights from NASA
Black Marble <https://blackmarble.gsfc.nasa.gov/>.")
    (license license:expat)))

(define-public r-blackbox
  (package
    (name "r-blackbox")
    (version "1.1.46")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "blackbox" version))
       (sha256
        (base32 "03m93v5nk5zg039axkf02drdj2q165k73xk6waaiyi0b8w0sbl8l"))))
    (properties `((upstream-name . "blackbox")))
    (build-system r-build-system)
    (propagated-inputs (list r-spamm
                             r-rcppeigen
                             r-rcpp
                             r-rcdd
                             r-proxy
                             r-pbapply
                             r-numderiv
                             r-nloptr
                             r-matrixstats
                             r-mass
                             r-lattice
                             r-geometry
                             r-foreach))
    (home-page "https://kimura.univ-montp2.fr/~rousset/Migraine.htm")
    (synopsis "Black Box Optimization and Exploration of Parameter Space")
    (description
     "This package performs prediction of a response function from simulated response
values, allowing black-box optimization of functions estimated with some error.
Includes a simple user interface for such applications, as well as more
specialized functions designed to be called by the Migraine software (Rousset
and Leblois, 2012 <doi:10.1093/molbev/MSR262>; Leblois et al., 2014
<doi:10.1093/molbev/msu212>; and see URL).  The latter functions are used for
prediction of likelihood surfaces and implied likelihood ratio confidence
intervals, and for exploration of predictor space of the surface.  Prediction of
the response is based on ordinary Kriging (with residual error) of the input.
Estimation of smoothing parameters is performed by generalized cross-validation.")
    (license (license:fsdg-compatible "CeCILL-2"))))

(define-public r-bla
  (package
    (name "r-bla")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BLA" version))
       (sha256
        (base32 "06nfsn67vgmkm0rrbs2q56gswx4hj7swysaq6vizx6zsl0xp7rvr"))))
    (properties `((upstream-name . "BLA")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv r-mvtnorm r-mass r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://chawezimiti.github.io/BLA/")
    (synopsis "Boundary Line Analysis")
    (description
     "Fits boundary line models to datasets as proposed by Webb (1972)
<doi:10.1080/00221589.1972.11514472> and makes statistical inferences about
their parameters.  Provides additional tools for testing datasets for evidence
of boundary presence and selecting initial starting values for model
optimization prior to fitting the boundary line models.  It also includes tools
for conducting post-hoc analyses such as predicting boundary values and
identifying the most limiting factor (Miti, Milne, Giller, Lark (2024)
<doi:10.1016/j.fcr.2024.109365>).  This ensures a comprehensive analysis for
datasets that exhibit upper boundary structures.")
    (license license:gpl3+)))

(define-public r-bktr
  (package
    (name "r-bktr")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BKTR" version))
       (sha256
        (base32 "17nibqn0bwbzdrxqyq10m7ll958cp5v431zda3ja16az2mss38cy"))))
    (properties `((upstream-name . "BKTR")))
    (build-system r-build-system)
    (propagated-inputs (list r-torch
                             r-r6p
                             r-r6
                             r-ggplot2
                             r-ggmap
                             r-data-table))
    (native-inputs (list r-rmarkdown r-r-rsp r-knitr))
    (home-page "https://cran.r-project.org/package=BKTR")
    (synopsis "Bayesian Kernelized Tensor Regression")
    (description
     "Facilitates scalable spatiotemporally varying coefficient modelling with
Bayesian kernelized tensor regression.  The important features of this package
are: (a) Enabling local temporal and spatial modeling of the relationship
between the response variable and covariates. (b) Implementing the model
described by Lei et al. (2023) <doi:10.48550/@code{arXiv.2109.00046>}. (c) Using
a Bayesian Markov Chain Monte Carlo (MCMC) algorithm to sample from the
posterior distribution of the model parameters. (d) Employing a tensor
decomposition to reduce the number of estimated parameters. (e) Accelerating
tensor operations and enabling graphics processing unit (GPU) acceleration with
the torch package.")
    (license license:expat)))

(define-public r-bkmrhat
  (package
    (name "r-bkmrhat")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bkmrhat" version))
       (sha256
        (base32 "0l575dav5wyia37vm5j1dyljgk0k6l1hwfwsqr2gq3sb0iim4my4"))))
    (properties `((upstream-name . "bkmrhat")))
    (build-system r-build-system)
    (propagated-inputs (list r-rstan r-future r-data-table r-coda r-bkmr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bkmrhat")
    (synopsis "Parallel Chain Tools for Bayesian Kernel Machine Regression")
    (description
     "Bayesian kernel machine regression (from the bkmr package) is a Bayesian
semi-parametric generalized linear model approach under identity and probit
links.  There are a number of functions in this package that extend Bayesian
kernel machine regression fits to allow multiple-chain inference and
diagnostics, which leverage functions from the future', rstan', and coda
packages.  Reference: Bobb, J. F., Henn, B. C., Valeri, L., & Coull, B. A.
(2018).  Statistical software for analyzing the health effects of multiple
concurrent exposures via Bayesian kernel machine regression. ;
<doi:10.1186/s12940-018-0413-y>.")
    (license license:gpl3+)))

(define-public r-bkmr
  (package
    (name "r-bkmr")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bkmr" version))
       (sha256
        (base32 "1bzr39a53p1xkq2fi2fphv2rpsml2z25xb1ycs63p3y7qis9zlky"))))
    (properties `((upstream-name . "bkmr")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm
                             r-tmvtnorm
                             r-tidyr
                             r-tibble
                             r-nlme
                             r-mass
                             r-magrittr
                             r-fields
                             r-dplyr))
    (home-page "https://github.com/jenfb/bkmr")
    (synopsis "Bayesian Kernel Machine Regression")
    (description
     "Implementation of a statistical approach for estimating the joint health effects
of multiple concurrent exposures, as described in Bobb et al (2015)
<doi:10.1093/biostatistics/kxu058>.")
    (license license:gpl2)))

(define-public r-bizicount
  (package
    (name "r-bizicount")
    (version "1.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bizicount" version))
       (sha256
        (base32 "0f6qjyc81mh3f8amqm6wipr41zglv8y3khs5g8rnsx247xpshqdv"))))
    (properties `((upstream-name . "bizicount")))
    (build-system r-build-system)
    (propagated-inputs (list r-texreg
                             r-rlang
                             r-pbivnorm
                             r-numderiv
                             r-mass
                             r-formula
                             r-dharma))
    (home-page "https://github.com/jmniehaus/bizicount")
    (synopsis "Bivariate Zero-Inflated Count Models Using Copulas")
    (description
     "Maximum likelihood estimation of copula-based zero-inflated (and non-inflated)
Poisson and negative binomial count models, based on the article
<doi:10.18637/jss.v109.i01>.  Supports Frank and Gaussian copulas.  Allows for
mixed margins (e.g., one margin Poisson, the other zero-inflated negative
binomial), and several marginal link functions.  Built-in methods for
publication-quality tables using texreg', post-estimation diagnostics using
DHARMa', and testing for marginal zero-modification via
<doi:10.1177/0962280217749991>.  For information on copula regression for count
data, see Genest and NeÅ¡lehovÃ¡ (2007) <doi:10.1017/S0515036100014963> as well
as Nikoloulopoulos (2013) <doi:10.1007/978-3-642-35407-6_11>.  For information
on zero-inflated count regression generally, see Lambert (1992)
<https://www.jstor.org/stable/1269547?origin=crossref>.  The author acknowledges
support by NSF DMS-1925119 and DMS-212324.")
    (license license:gpl3+)))

(define-public r-bizdays
  (package
    (name "r-bizdays")
    (version "1.0.16")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bizdays" version))
       (sha256
        (base32 "0drdmry39k6rxyy4x7d667znsv8r8ias66z0bvxkfk5vamsv744i"))))
    (properties `((upstream-name . "bizdays")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/wilsonfreitas/R-bizdays")
    (synopsis "Business Days Calculations and Utilities")
    (description
     "Business days calculations based on a list of holidays and nonworking weekdays.
Quite useful for fixed income and derivatives pricing.")
    (license license:expat)))

(define-public r-biwt
  (package
    (name "r-biwt")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biwt" version))
       (sha256
        (base32 "0gg381jhwdc0kc8v0yn7wcgjkpn8dfnnn20m5ycjv66jlk2jhdcw"))))
    (properties `((upstream-name . "biwt")))
    (build-system r-build-system)
    (propagated-inputs (list r-robustbase r-mass))
    (home-page "https://cran.r-project.org/package=biwt")
    (synopsis
     "Compute the Biweight Mean Vector and Covariance & Correlation Matrice")
    (description
     "Compute multivariate location, scale, and correlation estimates based on Tukey's
biweight M-estimator.")
    (license license:gpl2)))

(define-public r-biwavelet
  (package
    (name "r-biwavelet")
    (version "0.20.22")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biwavelet" version))
       (sha256
        (base32 "0j9y81rg9rmri39fjvy9s0wckxcdylbwm1ak62rxy65p397ppgcr"))))
    (properties `((upstream-name . "biwavelet")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-foreach r-fields))
    (home-page "https://github.com/tgouhier/biwavelet")
    (synopsis "Conduct Univariate and Bivariate Wavelet Analyses")
    (description
     "This is a port of the WTC MATLAB package written by Aslak Grinsted and the
wavelet program written by Christopher Torrence and Gibert P. Compo.  This
package can be used to perform univariate and bivariate (cross-wavelet, wavelet
coherence, wavelet clustering) analyses.")
    (license license:gpl2+)))

(define-public r-bivunifbin
  (package
    (name "r-bivunifbin")
    (version "1.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BivUnifBin" version))
       (sha256
        (base32 "0l5s07mvip1k667rvdpkh3k6xkzns8cy50bcs2xlmxd9xp7c6j2w"))))
    (properties `((upstream-name . "BivUnifBin")))
    (build-system r-build-system)
    (propagated-inputs (list r-rootsolve r-binordnonnor))
    (home-page "https://cran.r-project.org/package=BivUnifBin")
    (synopsis
     "Generation of Bivariate Uniform Data and Its Relation to Bivariate Binary Data")
    (description
     "Simulation of bivariate uniform data with a full range of correlations based on
two beta densities and computation of the tetrachoric correlation (correlation
of bivariate uniform data) from the phi coefficient (correlation of bivariate
binary data) and vice versa.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-bivrp
  (package
    (name "r-bivrp")
    (version "1.2-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bivrp" version))
       (sha256
        (base32 "0b38nh38vgqjllb1z9627dm86yp0s06ky1g829g8kkz7rffdp2za"))))
    (properties `((upstream-name . "bivrp")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=bivrp")
    (synopsis "Bivariate Residual Plots with Simulation Polygons")
    (description
     "Generates bivariate residual plots with simulation polygons for any diagnostics
and bivariate model from which functions to extract the desired diagnostics,
simulate new data and refit the models are available.")
    (license license:gpl2+)))

(define-public r-bivregbls
  (package
    (name "r-bivregbls")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BivRegBLS" version))
       (sha256
        (base32 "1kpl0pwm2g40pxrfbz2d95vcrm51flg6bgc83661349cmc9a2511"))))
    (properties `((upstream-name . "BivRegBLS")))
    (build-system r-build-system)
    (propagated-inputs (list r-ellipse))
    (home-page "https://cran.r-project.org/package=BivRegBLS")
    (synopsis
     "Tolerance Interval and EIV Regression - Method Comparison Studies")
    (description
     "Assess the agreement in method comparison studies by tolerance intervals and
errors-in-variables (EIV) regressions.  The Ordinary Least Square regressions
(OLSv and OLSh), the Deming Regression (DR), and the (Correlated)-Bivariate
Least Square regressions (BLS and CBLS) can be used with unreplicated or
replicated data.  The BLS() and CBLS() are the two main functions to estimate a
regression line, while XY.plot() and MD.plot() are the two main graphical
functions to display, respectively an (X,Y) plot or (M,D) plot with the BLS or
CBLS results.  Four hyperbolic statistical intervals are provided: the
Confidence Interval (CI), the Confidence Bands (CB), the Prediction Interval and
the Generalized prediction Interval.  Assuming no proportional bias, the (M,D)
plot (Band-Altman plot) may be simplified by calculating univariate tolerance
intervals (beta-expectation (type I) or beta-gamma content (type II)).  Major
updates from last version 1.0.0 are: title shortened, include the new functions
BLS.fit() and CBLS.fit() as shortcut of the, respectively, functions BLS() and
CBLS().  References: B.G. Francq, B. Govaerts (2016) <doi:10.1002/sim.6872>,
B.G. Francq, B. Govaerts (2014) <doi:10.1016/j.chemolab.2014.03.006>, B.G.
Francq, B. Govaerts (2014)
<http://publications-sfds.fr/index.php/J-S@code{FdS/article/view/262>}, B.G.
Francq (2013), @code{PhD} Thesis, UCLouvain, Errors-in-variables regressions to
assess equivalence in method comparison studies,
<https://dial.uclouvain.be/pr/boreal/object/boreal%3A135862/datastream/PDF_01/view>.")
    (license license:agpl3)))

(define-public r-bivrec
  (package
    (name "r-bivrec")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BivRec" version))
       (sha256
        (base32 "0qqjp9m360db3vz15zpbpqbm8v112n9mchcvsc3iiv60ayfimbg4"))))
    (properties `((upstream-name . "BivRec")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-stringr r-rcpp r-mass r-dplyr))
    (native-inputs (list gfortran))
    (home-page "https://github.com/SandraCastroPearson/BivRec")
    (synopsis "Bivariate Alternating Recurrent Event Data Analysis")
    (description
     "This package provides a collection of models for bivariate alternating recurrent
event data analysis.  Includes non-parametric and semi-parametric methods.")
    (license license:gpl3)))

(define-public r-bivpois
  (package
    (name "r-bivpois")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bivpois" version))
       (sha256
        (base32 "1pajy04wy11nxzy9dvpg6zpp99fb6splfv0nn8hg76kk8j3zn844"))))
    (properties `((upstream-name . "bivpois")))
    (build-system r-build-system)
    (propagated-inputs (list r-rfast))
    (home-page "https://cran.r-project.org/package=bivpois")
    (synopsis "Bivariate Poisson Distribution")
    (description
     "Maximum likelihood estimation, random values generation, density computation and
other functions for the bivariate Poisson distribution.  References include:
Kawamura K. (1984). \"Direct calculation of maximum likelihood estimator for the
bivariate Poisson distribution\".  Kodai Mathematical Journal, 7(2): 211--221.
<doi:10.2996/kmj/1138036908>.  Kocherlakota S. and Kocherlakota K. (1992).
\"Bivariate discrete distributions\".  CRC Press. <doi:10.1201/9781315138480>.
Karlis D. and Ntzoufras I. (2003). \"Analysis of sports data by using bivariate
Poisson models\".  Journal of the Royal Statistical Society: Series D (The
Statistician), 52(3): 381--393. <doi:10.1111/1467-9884.00366>.")
    (license license:gpl2+)))

(define-public r-bivgeom
  (package
    (name "r-bivgeom")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bivgeom" version))
       (sha256
        (base32 "1cn8k7q011whr4f7zig85dsrkz04614z9ns2ky6k6i16s5n3glxy"))))
    (properties `((upstream-name . "bivgeom")))
    (build-system r-build-system)
    (propagated-inputs (list r-copula r-bbmle))
    (home-page "https://cran.r-project.org/package=bivgeom")
    (synopsis "Roy's Bivariate Geometric Distribution")
    (description
     "This package implements Roy's bivariate geometric model (Roy (1993)
<doi:10.1006/jmva.1993.1065>): joint probability mass function, distribution
function, survival function, random generation, parameter estimation, and more.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-bivgeo
  (package
    (name "r-bivgeo")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BivGeo" version))
       (sha256
        (base32 "1sj52v1zmk425jw9acq9y0jdp3agx103yzf1bjbc5vrmk1x0i0qr"))))
    (properties `((upstream-name . "BivGeo")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BivGeo")
    (synopsis "Basu-Dhar Bivariate Geometric Distribution")
    (description
     "Computes the joint probability mass function (pmf), the joint cumulative
function (cdf), the joint survival function (sf), the correlation coefficient,
the covariance, the cross-factorial moment and generate random deviates for the
Basu-Dhar bivariate geometric distribution as well the joint probability mass,
cumulative and survival function assuming the presence of a cure fraction given
by the standard bivariate mixture cure fraction model.  The package also
computes the estimators based on the method of moments.")
    (license license:gpl2+)))

(define-public r-bivariatemaps
  (package
    (name "r-bivariatemaps")
    (version "1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bivariatemaps" version))
       (sha256
        (base32 "0glhpdn1v1sgm83gyga63b9yx5a1gflg7rqj9awm0nwr2c11ak63"))))
    (properties `((upstream-name . "bivariatemaps")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-picante
                             r-commecol
                             r-classint
                             r-caper
                             r-betapart
                             r-ape))
    (home-page "https://cran.r-project.org/package=bivariatemaps")
    (synopsis "Creates Bivariate Maps")
    (description
     "This package contains functions mainly focused to plotting bivariate maps.")
    (license license:gpl3)))

(define-public r-bivariate-pareto
  (package
    (name "r-bivariate-pareto")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Bivariate.Pareto" version))
       (sha256
        (base32 "11knwynqfxdz6zl7vzr48jpygcilsxarpmwphaajh62asdlg5w6p"))))
    (properties `((upstream-name . "Bivariate.Pareto")))
    (build-system r-build-system)
    (propagated-inputs (list r-compound-cox))
    (home-page "https://cran.r-project.org/package=Bivariate.Pareto")
    (synopsis "Bivariate Pareto Models")
    (description
     "Perform competing risks analysis under bivariate Pareto models.  See Shih et al.
(2019) <doi:10.1080/03610926.2018.1425450> for details.")
    (license license:gpl2)))

(define-public r-bittermelon
  (package
    (name "r-bittermelon")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bittermelon" version))
       (sha256
        (base32 "0nd0jn28rfkqzvji54i4b53qjzl4xjbwnhsw55vyb3209hk84q1w"))))
    (properties `((upstream-name . "bittermelon")))
    (build-system r-build-system)
    (propagated-inputs (list r-unicode r-png r-cli))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://trevorldavis.com/R/bittermelon/")
    (synopsis "Bitmap Tools")
    (description
     "This package provides functions for creating, modifying, and displaying bitmaps
including printing them in the terminal.  There is a special emphasis on
monochrome bitmap fonts and their glyphs as well as colored pixel art/sprites.
Provides native read/write support for the hex and yaff bitmap font formats and
if monobit <https://github.com/robhagemans/monobit> is installed can also
read/write several additional bitmap font formats.")
    (license license:expat)))

(define-public r-bitrina
  (package
    (name "r-bitrina")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BiTrinA" version))
       (sha256
        (base32 "09x3w3njdm7pgjjcqc1g3lp8nj31qkq3hmn21dqgvq7acyblan08"))))
    (properties `((upstream-name . "BiTrinA")))
    (build-system r-build-system)
    (propagated-inputs (list r-diptest))
    (home-page "https://cran.r-project.org/package=BiTrinA")
    (synopsis "Binarization and Trinarization of One-Dimensional Data")
    (description
     "This package provides methods for the binarization and trinarization of
one-dimensional data and some visualization functions.")
    (license license:artistic2.0)))

(define-public r-bitmexr
  (package
    (name "r-bitmexr")
    (version "0.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bitmexr" version))
       (sha256
        (base32 "0zxym4cyxk2p9cgm8lmqfxmgb01jcyww0mm3zmhqprgpk5r0faks"))))
    (properties `((upstream-name . "bitmexr")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-rlang
                             r-purrr
                             r-progress
                             r-magrittr
                             r-lubridate
                             r-jsonlite
                             r-httr
                             r-dplyr
                             r-digest
                             r-curl
                             r-attempt))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/hfshr/bitmexr/")
    (synopsis "R Client for BitMEX")
    (description
     "This package provides a client for cryptocurrency exchange @code{BitMEX}
<https://www.bitmex.com/> including the ability to obtain historic trade data
and place, edit and cancel orders. @code{BitMEX's} Testnet and live API are both
supported.")
    (license license:expat)))

(define-public r-bistablehistory
  (package
    (name "r-bistablehistory")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bistablehistory" version))
       (sha256
        (base32 "114m7banr0h4l7lzq2bsjz9l3qb7xgg297ix9aj62rmz02p7x5hf"))))
    (properties `((upstream-name . "bistablehistory")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rlang
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-purrr
                             r-loo
                             r-glue
                             r-future
                             r-dplyr
                             r-boot
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/alexander-pastukhov/bistablehistory/")
    (synopsis
     "Cumulative History Analysis for Bistable Perception Time Series")
    (description
     "Estimates cumulative history for time-series for continuously viewed bistable
perceptual rivalry displays.  Computes cumulative history via a homogeneous
first order differential process.  I.e., it assumes exponential growth/decay of
the history as a function time and perceptually dominant state, Pastukhov &
Braun (2011) <doi:10.1167/11.10.12>.  Supports Gamma, log normal, and normal
distribution families.  Provides a method to compute history directly and
example of using the computation on a custom Stan code.")
    (license license:gpl3+)))

(define-public r-bisrna
  (package
    (name "r-bisrna")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BisRNA" version))
       (sha256
        (base32 "11jl5109nlm272vncprwplkz4kpvg7yx6yfnbjg47lfrsxcj8n0g"))))
    (properties `((upstream-name . "BisRNA")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=BisRNA")
    (synopsis "Analysis of RNA Cytosine-5 Methylation")
    (description
     "Bisulfite-treated RNA non-conversion in a set of samples is analysed as follows
: each sample's non-conversion distribution is identified to a Poisson
distribution.  P-values adjusted for multiple testing are calculated in each
sample.  Combined non-conversion P-values and standard errors are calculated on
the intersection of the set of samples.  For further details, see C Legrand, F
Tuorto, M Hartmann, R Liebers, D Jakob, M Helm and F Lyko (2017)
<doi:10.1101/gr.210666.116>.")
    (license license:gpl2+)))

(define-public r-bisque
  (package
    (name "r-bisque")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bisque" version))
       (sha256
        (base32 "0zp2r1vg29q2ds34hhyjv9pz761mi0qncx9p4ilwnfd5yj9pmzwq"))))
    (properties `((upstream-name . "bisque")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen
                             r-rcpparmadillo
                             r-rcpp
                             r-mvquad
                             r-itertools
                             r-foreach))
    (home-page "https://cran.r-project.org/package=bisque")
    (synopsis
     "Approximate Bayesian Inference via Sparse Grid Quadrature Evaluation (BISQuE) for Hierarchical Models")
    (description
     "Implementation of the bisque strategy for approximate Bayesian posterior
inference.  See Hewitt and Hoeting (2019) <@code{arXiv:1904.07270>} for complete
details.  bisque combines conditioning with sparse grid quadrature rules to
approximate marginal posterior quantities of hierarchical Bayesian models.  The
resulting approximations are computationally efficient for many hierarchical
Bayesian models.  The bisque package allows approximate posterior inference for
custom models; users only need to specify the conditional densities required for
the approximation.")
    (license license:gpl3)))

(define-public r-bispdep
  (package
    (name "r-bispdep")
    (version "1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bispdep" version))
       (sha256
        (base32 "1bcqj50snjzvggii3kd69wc0kqfldb6fc3w8nxwb1v828hml6b83"))))
    (properties `((upstream-name . "bispdep")))
    (build-system r-build-system)
    (propagated-inputs (list r-spdep
                             r-spdata
                             r-spatialreg
                             r-sp
                             r-sf
                             r-rcolorbrewer
                             r-ggplot2
                             r-combinat
                             r-boot))
    (home-page "https://github.com/carlosm77/bispdep")
    (synopsis "Statistical Tools for Bivariate Spatial Dependence Analysis")
    (description
     "This package provides a collection of functions to test spatial autocorrelation
between variables, including Moran I, Geary C and Getis G together with scatter
plots, functions for mapping and identifying clusters and outliers, functions
associated with the moments of the previous statistics that will allow testing
whether there is bivariate spatial autocorrelation, and a function that allows
identifying (visualizing neighbours) on the map, the neighbors of any region
once the scheme of the spatial weights matrix has been established.")
    (license license:gpl2+)))

(define-public r-bisep
  (package
    (name "r-bisep")
    (version "2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BiSEp" version))
       (sha256
        (base32 "1xg2m8rvxwpj47nb20kyd5hpa1lshg58bi0alf1v476yhcwqgrhx"))))
    (properties `((upstream-name . "BiSEp")))
    (build-system r-build-system)
    (propagated-inputs (list r-org-hs-eg-db r-mclust r-gosemsim r-go-db
                             r-annotationdbi))
    (home-page "https://cran.r-project.org/package=BiSEp")
    (synopsis "Toolkit to Identify Candidate Synthetic Lethality")
    (description
     "Enables the user to infer potential synthetic lethal relationships by analysing
relationships between bimodally distributed gene pairs in big gene expression
datasets.  Enables the user to visualise these candidate synthetic lethal
relationships.")
    (license license:artistic2.0)))

(define-public r-bisectr
  (package
    (name "r-bisectr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bisectr" version))
       (sha256
        (base32 "1vjsjshvzj66qqzg32rviklqswrb00jyq6vwrywg1hpqhf4kisv7"))))
    (properties `((upstream-name . "bisectr")))
    (build-system r-build-system)
    (propagated-inputs (list r-devtools))
    (home-page "https://github.com/wch/bisectr")
    (synopsis "Tools to find bad commits with git bisect")
    (description
     "This package provides tools to find bad commits with git bisect.  See
https://github.com/wch/bisectr for examples and test script templates.")
    (license license:gpl2)))

(define-public r-bisdata
  (package
    (name "r-bisdata")
    (version "0.2-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BISdata" version))
       (sha256
        (base32 "1bhlnwb88diyw2f9h9gyk73m4s9s30f51imclif8rwa45dpylmd3"))))
    (properties `((upstream-name . "BISdata")))
    (build-system r-build-system)
    (home-page "https://enricoschumann.net/R/packages/BISdata/")
    (synopsis
     "Download Data from the Bank for International Settlements (BIS)")
    (description
     "This package provides functions for downloading data from the Bank for
International Settlements (BIS; <https://www.bis.org/>) in Basel.  Supported are
only full datasets in (typically) CSV format.  The package is lightweight and
without dependencies; suggested packages are used only if data is to be
transformed into particular data structures, for instance into zoo objects.
Downloaded data can optionally be cached, to avoid repeated downloads of the
same files.")
    (license license:gpl3)))

(define-public r-biscale
  (package
    (name "r-biscale")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biscale" version))
       (sha256
        (base32 "1bjlw2vlgb9c50iah1w38b1g8bgdys86vr7bnbv0fapzp584d36g"))))
    (properties `((upstream-name . "biscale")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-classint))
    (native-inputs (list r-knitr))
    (home-page "https://chris-prener.github.io/biscale/")
    (synopsis "Tools and Palettes for Bivariate Thematic Mapping")
    (description
     "This package provides a ggplot2 centric approach to bivariate mapping.  This is
a technique that maps two quantities simultaneously rather than the single value
that most thematic maps display.  The package provides a suite of tools for
calculating breaks using multiple different approaches, a selection of palettes
appropriate for bivariate mapping and scale functions for ggplot2 calls that
adds those palettes to maps.  Tools for creating bivariate legends are also
included.")
    (license license:gpl3)))

(define-public r-birtr
  (package
    (name "r-birtr")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "birtr" version))
       (sha256
        (base32 "0bcxvj30wzwclw1lcyhvwj3k7awd39lrvzia1nrg53hw90bfs811"))))
    (properties `((upstream-name . "birtr")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=birtr")
    (synopsis
     "The R Package for \"The Basics of Item Response Theory Using R\"")
    (description
     "R functions for \"The Basics of Item Response Theory Using R\" by Frank B. Baker
and Seock-Ho Kim (Springer, 2017, ISBN-13: 978-3-319-54204-1) including
iccplot(), icccal(), icc(), iccfit(), groupinv(), tcc(), ability(), tif(), and
rasch().  For example, iccplot() plots an item characteristic curve under the
two-parameter logistic model.")
    (license license:gpl2+)))

(define-public r-birk
  (package
    (name "r-birk")
    (version "2.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "birk" version))
       (sha256
        (base32 "07ck59m5mw897a60vmdn1b3qrmqj20qpfsn3093haqbpn8z13dmn"))))
    (properties `((upstream-name . "birk")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=birk")
    (synopsis "MA Birk's Functions")
    (description
     "Collection of tools to make R more convenient.  Includes tools to summarize data
using statistics not available with base R and manipulate objects for analyses.")
    (license license:gpl3)))

(define-public r-birdscanr
  (package
    (name "r-birdscanr")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "birdscanR" version))
       (sha256
        (base32 "1brgz6x8ljgy9y87znx93d6d68vsr7a75i1p86rxnxvka0s91r15"))))
    (properties `((upstream-name . "birdscanR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-suntools
                             r-sp
                             r-rstudioapi
                             r-rpostgresql
                             r-rodbc
                             r-rlang
                             r-reshape2
                             r-modi
                             r-magrittr
                             r-ggplot2
                             r-dplyr
                             r-dbi))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/BirdScanCommunity/birdscanR")
    (synopsis
     "Migration Traffic Rate Calculation Package for 'Birdscan MR1' Radars")
    (description
     "Extract data from Birdscan MR1 SQL vertical-looking radar databases, filter, and
process them to Migration Traffic Rates (#objects per hour and km) or density
(#objects per km3) of, for example birds, and insects.  Object classifications
in the Birdscan MR1 databases are based on the dataset of Haest et al. (2021)
<doi:10.5281/zenodo.5734960>).  Migration Traffic Rates and densities can be
calculated separately for different height bins (with a height resolution of
choice) as well as over time periods of choice (e.g., 1/2 hour, 1 hour, 1 day,
day/night, the full time period of observation, and anything in between).  Two
plotting functions are also included to explore the data in the SQL databases
and the resulting Migration Traffic Rate results.  For details on the Migration
Traffic Rate calculation procedures, see Schmid et al. (2019)
<doi:10.1111/ecog.04025>.")
    (license license:gpl3)))

(define-public r-birdring
  (package
    (name "r-birdring")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "birdring" version))
       (sha256
        (base32 "17f6hlvz6k4ya82nai7j474k55w3is8if4vckbwqq1c4lj9zfm6r"))))
    (properties `((upstream-name . "birdring")))
    (build-system r-build-system)
    (propagated-inputs (list r-raster r-lazydata r-ks r-geosphere))
    (home-page "https://cran.r-project.org/package=birdring")
    (synopsis "Methods to Analyse Ring Re-Encounter Data")
    (description
     "R functions to read EURING data and analyse re-encounter data of birds marked by
metal rings.  For a tutorial, go to <doi:10.1080/03078698.2014.933053>.")
    (license license:gpl2)))

(define-public r-birdie
  (package
    (name "r-birdie")
    (version "0.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "birdie" version))
       (sha256
        (base32 "1xh3ga5f16p46jrw7xj2mwv41mnib0mijin2005q9wrfr5pr54yc"))))
    (properties `((upstream-name . "birdie")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-stringr
                             r-stringi
                             r-stanheaders
                             r-squarem
                             r-rlang
                             r-rcppthread
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-generics
                             r-dplyr
                             r-cli
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/CoryMcCartan/birdie")
    (synopsis "Bayesian Instrumental Regression for Disparity Estimation")
    (description
     "Bayesian models for accurately estimating conditional distributions by race,
using Bayesian Improved Surname Geocoding (BISG) probability estimates of
individual race.  Implements the methods described in @code{McCartan}, Fisher,
Goldin, Ho and Imai (2024) <doi:10.3386/w32373>.")
    (license license:gpl3+)))

(define-public r-birankr
  (package
    (name "r-birankr")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "birankr" version))
       (sha256
        (base32 "1azgrzgw5bnfac8p287ir5zl6kxc6jij20p6cfc3lbbl03f8sa1k"))))
    (properties `((upstream-name . "birankr")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-data-table))
    (home-page "https://cran.r-project.org/package=birankr")
    (synopsis "Ranking Nodes in Bipartite and Weighted Networks")
    (description
     "Highly efficient functions for estimating various rank (centrality) measures of
nodes in bipartite graphs (two-mode networks).  Includes methods for estimating
HITS, @code{CoHITS}, BGRM, and @code{BiRank} with implementation primarily
inspired by He et al. (2016) <doi:10.1109/TKDE.2016.2611584>.  Also provides
easy-to-use tools for efficiently estimating @code{PageRank} in one-mode graphs,
incorporating or removing edge-weights during rank estimation, projecting
two-mode graphs to one-mode, and for converting edgelists and matrices to
@code{sparseMatrix} format.  Best of all, the package's rank estimators can work
directly with common formats of network data including edgelists (class
data.frame, data.table, or tbl_df) and adjacency matrices (class matrix or
@code{dgCMatrix}).")
    (license license:expat)))

(define-public r-biplotgui
  (package
    (name "r-biplotgui")
    (version "0.0-7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BiplotGUI" version))
       (sha256
        (base32 "0vfig7hp6k0b117w764jzj8z2b279sr0k5vyz3v6kh7bsdfxx74a"))))
    (properties `((upstream-name . "BiplotGUI")))
    (build-system r-build-system)
    (propagated-inputs (list r-tkrplot
                             r-tcltk2
                             r-rgl
                             r-mass
                             r-kernsmooth
                             r-deldir
                             r-colorspace))
    (native-inputs (list gfortran))
    (home-page "http://biplotgui.r-forge.r-project.org/")
    (synopsis "Interactive Biplots in R")
    (description
     "This package provides a GUI with which users can construct and interact with
biplots.")
    (license license:gpl3+)))

(define-public r-biplotez
  (package
    (name "r-biplotez")
    (version "2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biplotEZ" version))
       (sha256
        (base32 "195hccdark20b0gmddm7nnx63nk4bpdvds2vjv3g44pxq5nhfna4"))))
    (properties `((upstream-name . "biplotEZ")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr r-plotrix))
    (native-inputs (list r-knitr gfortran))
    (home-page "https://cran.r-project.org/package=biplotEZ")
    (synopsis "EZ-to-Use Biplots")
    (description
     "This package provides users with an EZ-to-use platform for representing data
with biplots.  Currently principal component analysis (PCA), canonical variate
analysis (CVA) and simple correspondence analysis (CA) biplots are included.
This is accompanied by various formatting options for the samples and axes.
Alpha-bags and concentration ellipses are included for visual enhancements and
interpretation.  For an extensive discussion on the topic, see Gower, J.C.,
Lubbe, S. and le Roux, N.J. (2011, ISBN: 978-0-470-01255-0) Understanding
Biplots.  Wiley: Chichester.")
    (license license:expat)))

(define-public r-biplotbootgui
  (package
    (name "r-biplotbootgui")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biplotbootGUI" version))
       (sha256
        (base32 "1z2mlfya6c4lh970p6hpf8sqhxz7wjl16d1lx2djv4cr8sj7nhdh"))))
    (properties `((upstream-name . "biplotbootGUI")))
    (build-system r-build-system)
    (propagated-inputs (list r-tkrplot
                             r-tcltk2
                             r-shapes
                             r-rgl
                             r-matlib
                             r-mass
                             r-dendroextras
                             r-cluster))
    (home-page "https://cran.r-project.org/package=biplotbootGUI")
    (synopsis "Bootstrap on Classical Biplots and Clustering Disjoint Biplot")
    (description
     "This package provides a GUI with which the user can construct and interact with
Bootstrap methods on Classical Biplots and with Clustering and/or Disjoint
Biplot.  This GUI is also aimed for estimate any numerical data matrix using the
Clustering and Disjoint Principal component (CDPCA) methodology.")
    (license license:gpl2+)))

(define-public r-bipl5
  (package
    (name "r-bipl5")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bipl5" version))
       (sha256
        (base32 "0anir5fgvac9ka5y9wfar7pbwdqd0qwx6hynlmr6jwhna57linly"))))
    (properties `((upstream-name . "bipl5")))
    (build-system r-build-system)
    (propagated-inputs (list r-plotly r-knitr r-htmlwidgets r-crayon r-cluster))
    (home-page "https://cran.r-project.org/package=bipl5")
    (synopsis "Construct Reactive Calibrated Axes Biplots")
    (description
     "This package provides a modern view on the principal component analysis biplot
with calibrated axes.  Create principal component analysis biplots rendered in
HTML with significant reactivity embedded within the plot.  Furthermore, the
traditional biplot view is enhanced by translated axes with inter-class kernel
densities superimposed.  For more information on biplots, see Gower, J.C.,
Lubbe, S. and le Roux, N.J. (2011, ISBN: 978-0-470-01255-0).")
    (license license:expat)))

(define-public r-bipd
  (package
    (name "r-bipd")
    (version "0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bipd" version))
       (sha256
        (base32 "1d8mcanmkjki3md047widfgf2hi905w24hibqsg1r7nr8d0g03yg"))))
    (properties `((upstream-name . "bipd")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjags r-mvtnorm r-dplyr r-coda))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bipd")
    (synopsis "Bayesian Individual Patient Data Meta-Analysis using 'JAGS'")
    (description
     "We use a Bayesian approach to run individual patient data meta-analysis and
network meta-analysis using JAGS'.  The methods incorporate shrinkage methods
and calculate patient-specific treatment effects as described in Seo et al.
(2021) <DOI:10.1002/sim.8859>.  This package also includes user-friendly
functions that impute missing data in an individual patient data using
mice-related packages.")
    (license license:gpl3)))

(define-public r-bipartitemodularitymaximization
  (package
    (name "r-bipartitemodularitymaximization")
    (version "1.23.120.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BipartiteModularityMaximization" version))
       (sha256
        (base32 "0pz9cyg3avngxgd7ys5a1pfw4jw4cxbq36nh9hjarisl5phk761b"))))
    (properties `((upstream-name . "BipartiteModularityMaximization")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page
     "https://cran.r-project.org/package=BipartiteModularityMaximization")
    (synopsis
     "Partition Bipartite Network into Non-Overlapping Biclusters by Optimizing Bipartite Modularity")
    (description
     "Function bipmod() that partitions a bipartite network into non-overlapping
biclusters by maximizing bipartite modularity defined in Barber (2007)
<doi:10.1103/@code{PhysRevE.76.066102>} using the bipartite version of the
algorithm described in TreviÃ±o (2015) <doi:10.1088/1742-5468/2015/02/P02003>.")
    (license license:expat)))

(define-public r-bipartited3
  (package
    (name "r-bipartited3")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bipartiteD3" version))
       (sha256
        (base32 "0njqj4rz054vjp9chm14i20rl9hssvbf3hmrp1w1a4rnjabcijmd"))))
    (properties `((upstream-name . "bipartiteD3")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rcolorbrewer
                             r-r2d3
                             r-purrr
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bipartiteD3")
    (synopsis "Interactive Bipartite Graphs")
    (description
     "Generates interactive bipartite graphs using the D3 library.  Designed for use
with the bipartite analysis package.  Includes open source viz-js library
Adapted from examples at <https://bl.ocks.org/N@code{PashaP>} (released under
GPL-3).")
    (license license:gpl3)))

(define-public r-bipartite
  (package
    (name "r-bipartite")
    (version "2.20")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bipartite" version))
       (sha256
        (base32 "0wgx695r5knlrzg0wv5h2sivh3xzjsb50lafmb08j1fvg3hnrxl6"))))
    (properties `((upstream-name . "bipartite")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-sna
                             r-permute
                             r-mass
                             r-igraph
                             r-fields))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/biometry/bipartite")
    (synopsis
     "Visualising Bipartite Networks and Calculating Some (Ecological) Indices")
    (description
     "This package provides functions to visualise webs and calculate a series of
indices commonly used to describe pattern in (ecological) webs.  It focuses on
webs consisting of only two levels (bipartite), e.g. pollination webs or
predator-prey-webs.  Visualisation is important to get an idea of what we are
actually looking at, while the indices summarise different aspects of the web's
topology.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-biovenn
  (package
    (name "r-biovenn")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BioVenn" version))
       (sha256
        (base32 "0nwqfb65vhr2246w17nv27v186gzl5n3jxnqx7r9rq525qvyx0rg"))))
    (properties `((upstream-name . "BioVenn")))
    (build-system r-build-system)
    (propagated-inputs (list r-svglite r-plotrix r-biomart))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BioVenn")
    (synopsis "Create Area-Proportional Venn Diagrams from Biological Lists")
    (description
     "This package creates an area-proportional Venn diagram of 2 or 3 circles.
@code{BioVenn} is the only R package that can automatically generate an accurate
area-proportional Venn diagram by having only lists of (biological) identifiers
as input.  Also offers the option to map Entrez and/or Affymetrix IDs to Ensembl
IDs.  In SVG mode, text and numbers can be dragged and dropped.  Based on the
@code{BioVenn} web interface available at <https://www.biovenn.nl>.  Hulsen
(2021) <doi:10.3233/DS-210032>.")
    (license license:gpl3)))

(define-public r-biotools
  (package
    (name "r-biotools")
    (version "4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biotools" version))
       (sha256
        (base32 "1dyy9mk2afjbc8g1qygy6h1aksbhndm53c4sb5mfzriydl03ibxv"))))
    (properties `((upstream-name . "biotools")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://arsilva87.github.io/biotools/")
    (synopsis
     "Tools for Biometry and Applied Statistics in Agricultural Science")
    (description
     "This package provides tools designed to perform and evaluate cluster analysis
(including Tocher's algorithm), discriminant analysis and path analysis
(standard and under collinearity), as well as some useful miscellaneous tools
for dealing with sample size and optimum plot size calculations.  A test for
seed sample heterogeneity is now available.  Mantel's permutation test can be
found in this package.  A new approach for calculating its power is implemented.
 biotools also contains tests for genetic covariance components.  Heuristic
approaches for performing non-parametric spatial predictions of generic response
variables and spatial gene diversity are implemented.")
    (license license:gpl2+)))

(define-public r-biotimer
  (package
    (name "r-biotimer")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BioTIMEr" version))
       (sha256
        (base32 "027kx7244j575wn3yxhsmhbg2yaf1w611jvjnzaxivpns56rvg33"))))
    (properties `((upstream-name . "BioTIMEr")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-tidyr
                             r-ggplot2
                             r-dplyr
                             r-dggridr
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bioTIMEHub/BioTIMEr")
    (synopsis "Tools to Use and Explore the 'BioTIME' Database")
    (description
     "The @code{BioTIME} database was first published in 2018 and inspired ideas,
questions, project and research article.  To make it even more accessible, an R
package was created.  The @code{BioTIMEr} package provides tools designed to
interact with the @code{BioTIME} database.  The functions provided include the
@code{BioTIME} recommended methods for preparing (gridding and rarefaction) time
series data, a selection of standard biodiversity metrics (including species
richness, numerical abundance and exponential Shannon) alongside examples on how
to display change over time.  It also includes a sample subset of both the query
and meta data, the full versions of which are freely available on the
@code{BioTIME} website <https://biotime.st-andrews.ac.uk/home.php>.")
    (license license:expat)))

(define-public r-biotic
  (package
    (name "r-biotic")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biotic" version))
       (sha256
        (base32 "1p18jkdd263dgk90626n9awkj6m4zn1n7897596fzrzrahksa978"))))
    (properties `((upstream-name . "biotic")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/robbriers/biotic")
    (synopsis "Calculation of Freshwater Biotic Indices")
    (description
     "Calculates a range of UK freshwater invertebrate biotic indices including BMWP,
Whalley, WHPT, Habitat-specific BMWP, AWIC, LIFE and PSI.")
    (license license:gpl3)))

(define-public r-biostatsuhnplus
  (package
    (name "r-biostatsuhnplus")
    (version "0.0.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BiostatsUHNplus" version))
       (sha256
        (base32 "0wrppamh4m47zczci3csqfijf2hyz4lb5ks2z8h2ybn8lcnbjxgp"))))
    (properties `((upstream-name . "BiostatsUHNplus")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-stringi
                             r-rstatix
                             r-rlang
                             r-reportrmd
                             r-purrr
                             r-plyr
                             r-openxlsx
                             r-modeest
                             r-mcmcglmm
                             r-lifecycle
                             r-ggplot2
                             r-ggh4x
                             r-forcats
                             r-dplyr
                             r-cowplot
                             r-coda
                             r-afex))
    (home-page "https://cran.r-project.org/package=BiostatsUHNplus")
    (synopsis "Nested Data Summary, Adverse Events and REDCap")
    (description
     "This package provides tools and code snippets for summarizing nested data,
adverse events and REDCap study information.")
    (license license:expat)))

(define-public r-biostats101
  (package
    (name "r-biostats101")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biostats101" version))
       (sha256
        (base32 "035qk9n4drdhv7g37gq26z5idfxkg8h8b3shagjcsxyar5s5hcfh"))))
    (properties `((upstream-name . "biostats101")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-ggplot2 r-dplyr))
    (home-page "https://cran.r-project.org/package=biostats101")
    (synopsis "Practical Functions for Biostatistics Beginners")
    (description
     "This package provides a set of user-friendly functions designed to fill gaps in
existing introductory biostatistics R tools, making it easier for newcomers to
perform basic biostatistical analyses without needing advanced programming
skills.  The methods implemented in this package are based on the works: Connor
(1987) <doi:10.2307/2531961> Fleiss, Levin, & Paik (2013,
ISBN:978-1-118-62561-3) Levin & Chen (1999) <doi:10.1080/00031305.1999.10474431>
@code{McNemar} (1947) <doi:10.1007/BF02295996>.")
    (license license:expat)))

(define-public r-biostatr
  (package
    (name "r-biostatr")
    (version "4.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BioStatR" version))
       (sha256
        (base32 "0w8ly4pk0zv6rnmbzfq3i8sd4h4gjsxlnhj5nlyfr72687jjx5cm"))))
    (properties `((upstream-name . "BioStatR")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://fbertran.github.io/BioStatR/")
    (synopsis "Initiation Ã  La Statistique Avec R")
    (description
     "Datasets and functions for the book \"Initiation Ã  la Statistique avec R\", F.
Bertrand and M. Maumy-Bertrand (2022, ISBN:978-2100782826 Dunod, fourth
edition).")
    (license license:gpl3)))

(define-public r-biostatistics
  (package
    (name "r-biostatistics")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Biostatistics" version))
       (sha256
        (base32 "1fcrj53rqg7mdhrrsl95l0n6hycb0863isch2f6zzsqdqbxhq7dh"))))
    (properties `((upstream-name . "Biostatistics")))
    (build-system r-build-system)
    (propagated-inputs (list r-learnr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=Biostatistics")
    (synopsis "Statistics Tutorials for Biologists")
    (description
     "Tutorials for statistics, aimed at biological scientists.  Subjects range from
basic descriptive statistics through to complex linear modelling.  The tutorials
include text, videos, interactive coding exercises and multiple choice quizzes.
The package also includes 19 datasets which are used in the tutorials.")
    (license license:gpl3)))

(define-public r-biostat3
  (package
    (name "r-biostat3")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biostat3" version))
       (sha256
        (base32 "0g3phkzkidxrf0ajxywvw77x0cpcyhmgp9wqw13p2nnp38lak9l5"))))
    (properties `((upstream-name . "biostat3")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mass))
    (home-page "https://cran.r-project.org/package=biostat3")
    (synopsis
     "Utility Functions, Datasets and Extended Examples for Survival Analysis")
    (description
     "Utility functions, datasets and extended examples for survival analysis.  This
extends a range of other packages, some simple wrappers for time-to-event
analyses, datasets, and extensive examples in HTML with R scripts.  The package
also supports the course Biostatistics III entitled \"Survival analysis for
epidemiologists in R\".")
    (license license:gpl2+)))

(define-public r-biospear
  (package
    (name "r-biospear")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biospear" version))
       (sha256
        (base32 "10y4hbncwiqpxzqvzjrmfgp804zczdgvwpjcj0dzaa0sx1njdzja"))))
    (properties `((upstream-name . "biospear")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-survauc
                             r-rcurl
                             r-prroc
                             r-proc
                             r-plsrcox
                             r-pkgconfig
                             r-mboost
                             r-matrix
                             r-mass
                             r-grplasso
                             r-glmnet
                             r-devtools
                             r-corpcor
                             r-cobs))
    (home-page "https://cran.r-project.org/package=biospear")
    (synopsis "Biomarker Selection in Penalized Regression Models")
    (description
     "This package provides some tools for developing and validating prediction
models, estimate expected survival of patients and visualize them graphically.
Most of the implemented methods are based on penalized regressions such as: the
lasso (Tibshirani R (1996)), the elastic net (Zou H et al. (2005)
<doi:10.1111/j.1467-9868.2005.00503.x>), the adaptive lasso (Zou H (2006)
<doi:10.1198/016214506000000735>), the stability selection (Meinshausen N et al.
(2010) <doi:10.1111/j.1467-9868.2010.00740.x>), some extensions of the lasso
(Ternes et al. (2016) <doi:10.1002/sim.6927>), some methods for the interaction
setting (Ternes N et al. (2016) <doi:10.1002/bimj.201500234>), or others.  A
function generating simulated survival data set is also provided.")
    (license license:gpl2)))

(define-public r-biosnr
  (package
    (name "r-biosnr")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bioSNR" version))
       (sha256
        (base32 "19blfbw9d0did76llgffs8dyld8yr6wf4rmxmr2fb4vp1s8m2izc"))))
    (properties `((upstream-name . "bioSNR")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-pracma r-ggplot2 r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bioSNR")
    (synopsis
     "Bioacoustic Basic Operations with Decibels and the Passive Sonar Equation")
    (description
     "This package provides a beginners toolbox to help those in ecology who want to
deepen their understanding or utilize Bioacoustics in their work.  The package
has a number of utilizations from calculating frequency from waveform,
performing operations in @code{dB}, and determining acoustic range of recorders.
 The majority of this package is based on key concepts learned from the K. Lisa
Yang Center for Conservation Bioacoustics at Cornell University and their
associated course: Introduction to Bioacoustics course.  More information can be
found within the walk through vignettes at
<https://github.com/@code{MattyD797/bioSNR/tree/main/vignettes>}.")
    (license license:gpl3+)))

(define-public r-biosignalemg
  (package
    (name "r-biosignalemg")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biosignalEMG" version))
       (sha256
        (base32 "0bbkrfqkmks4z8c7rh0k43lvm71vn3fzrw3kpj012ym77whwkvay"))))
    (properties `((upstream-name . "biosignalEMG")))
    (build-system r-build-system)
    (propagated-inputs (list r-signal))
    (home-page "https://cran.r-project.org/package=biosignalEMG")
    (synopsis "Tools for Electromyogram Signals (EMG) Analysis")
    (description
     "Data processing tools to compute the rectified, integrated and the averaged EMG.
Routines for automatic detection of activation phases.  A routine to compute and
plot the ensemble average of the EMG. An EMG signal simulator for general
purposes.")
    (license license:gpl3+)))

(define-public r-bioseq
  (package
    (name "r-bioseq")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bioseq" version))
       (sha256
        (base32 "0m93s7vrpd392w2mcv3sdf5kcmpn1671ksplx1fiz7wdakck9fxq"))))
    (properties `((upstream-name . "bioseq")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tibble
                             r-stringr
                             r-stringi
                             r-stringdist
                             r-rlang
                             r-readr
                             r-pillar
                             r-dplyr
                             r-crayon
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://fkeck.github.io/bioseq/")
    (synopsis "Toolbox for Manipulating Biological Sequences")
    (description
     "This package provides classes and functions to work with biological sequences
(DNA, RNA and amino acid sequences).  Implements S3 infrastructure to work with
biological sequences as described in Keck (2020) <doi:10.1111/2041-210X.13490>.
Provides a collection of functions to perform biological conversion among
classes (transcription, translation) and basic operations on sequences
(detection, selection and replacement based on positions or patterns).  The
package also provides functions to import and export sequences from and to other
package formats.")
    (license license:gpl3)))

(define-public r-biosensors-usc
  (package
    (name "r-biosensors-usc")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biosensors.usc" version))
       (sha256
        (base32 "076isyk7dfjpi5db7ghk0hmz2kkdgmrnj3g9ffdnim5gxq3zsf1h"))))
    (properties `((upstream-name . "biosensors.usc")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm
                             r-rcpparmadillo
                             r-rcpp
                             r-paralleldist
                             r-osqp
                             r-fda-usc
                             r-energy))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=biosensors.usc")
    (synopsis "Distributional Data Analysis Techniques for Biosensor Data")
    (description
     "Unified and user-friendly framework for using new distributional representations
of biosensors data in different statistical modeling tasks: regression models,
hypothesis testing, cluster analysis, visualization, and descriptive analysis.
Distributional representations are a functional extension of compositional
time-range metrics and we have used them successfully so far in modeling glucose
profiles and accelerometer data.  However, these functional representations can
be used to represent any biosensor data such as ECG or medical imaging such as
@code{fMRI}.  Matabuena M, Petersen A, Vidal JC, Gude F. \"Glucodensities: A new
representation of glucose profiles using distributional data analysis\" (2021)
<doi:10.1177/0962280221998064>.")
    (license license:gpl2)))

(define-public r-biosampler
  (package
    (name "r-biosampler")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biosampleR" version))
       (sha256
        (base32 "1cx6as9nvivx5laz029wrbd7jrhl3fhqj0lm1sb65ckx3arm8dwj"))))
    (properties `((upstream-name . "biosampleR")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/csim063/biosampleR")
    (synopsis
     "Biodiversity Index Calculation and Bootstrap Confidence Interval Estimation")
    (description
     "This package provides tools for the calculation of common biodiversity indices
from count data.  Additionally, it incorporates bootstrapping techniques to
generate multiple samples, facilitating the estimation of confidence intervals
around these indices.  Furthermore, the package allows for the exploration of
how variation in these indices changes with differing numbers of sites, making
it a useful tool with which to begin an ecological analysis.  Methods are based
on the following references: Chao et al. (2014) <doi:10.1890/13-0133.1>, Chao
and Colwell (2022) <doi:10.1002/9781119902911.ch2>, Hsieh, Ma,` and Chao (2016)
<doi:10.1111/2041-210X.12613>.")
    (license license:expat)))

(define-public r-bios2mds
  (package
    (name "r-bios2mds")
    (version "1.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bios2mds" version))
       (sha256
        (base32 "1k0r3fnkzfczx3wp3m8zpndk6gxca7idsp0z13rw84zjrgqaw6jm"))))
    (properties `((upstream-name . "bios2mds")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-rgl r-e1071 r-cluster r-amap))
    (home-page "https://cran.r-project.org/package=bios2mds")
    (synopsis "From Biological Sequences to Multidimensional Scaling")
    (description
     "Utilities dedicated to the analysis of biological sequences by metric
@code{MultiDimensional} Scaling with projection of supplementary data.  It
contains functions for reading multiple sequence alignment files, calculating
distance matrices, performing metric multidimensional scaling and visualizing
results.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-biorssay
  (package
    (name "r-biorssay")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BioRssay" version))
       (sha256
        (base32 "0bs05vkidf7rfhb9s9vby7k4q9w7vwanlvk034kspqp4ydkhpm5n"))))
    (properties `((upstream-name . "BioRssay")))
    (build-system r-build-system)
    (propagated-inputs (list r-colorspace))
    (native-inputs (list r-knitr))
    (home-page "https://milesilab.github.io/BioRssay/")
    (synopsis "Analyze Bioassays and Probit Graphs")
    (description
     "This package provides a robust framework for analyzing mortality data from
bioassays for one or several strains/lines/populations.")
    (license license:agpl3+)))

(define-public r-bioregion
  (package
    (name "r-bioregion")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bioregion" version))
       (sha256
        (base32 "03qjyaw1kiqn9q4wc49rzy6vp83pf5567y07ih4rgp1r3pamnif0"))))
    (properties `((upstream-name . "bioregion")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-sf
                             r-segmented
                             r-rmarkdown
                             r-rlang
                             r-rdpack
                             r-rcpp
                             r-matrix
                             r-mathjaxr
                             r-igraph
                             r-ggplot2
                             r-fastkmedoids
                             r-fastcluster
                             r-dynamictreecut
                             r-dbscan
                             r-data-table
                             r-cluster
                             r-bipartite
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bioRgeo/bioregion")
    (synopsis "Comparison of Bioregionalisation Methods")
    (description
     "The main purpose of this package is to propose a transparent methodological
framework to compare bioregionalisation methods based on hierarchical and
non-hierarchical clustering algorithms (Kreft & Jetz (2010)
<doi:10.1111/j.1365-2699.2010.02375.x>) and network algorithms (Lenormand et al.
(2019) <doi:10.1002/ece3.4718> and Leroy et al. (2019) <doi:10.1111/jbi.13674>).")
    (license license:gpl3)))

(define-public r-biorad
  (package
    (name "r-biorad")
    (version "0.8.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bioRad" version))
       (sha256
        (base32 "1c7wl1wk1lh31dr7a7lwbjib4v1mfvvyi8iwfc9svj07y4wpzj58"))))
    (properties `((upstream-name . "bioRad")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridislite
                             r-viridis
                             r-tidyselect
                             r-tidyr
                             r-suntools
                             r-stringr
                             r-sp
                             r-sf
                             r-rlang
                             r-rhdf5
                             r-readr
                             r-raster
                             r-lutz
                             r-lubridate
                             r-jsonlite
                             r-glue
                             r-ggplot2
                             r-fields
                             r-dplyr
                             r-curl
                             r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/adokter/bioRad/")
    (synopsis "Biological Analysis and Visualization of Weather Radar Data")
    (description
     "Extract, visualize and summarize aerial movements of birds and insects from
weather radar data.  See Dokter, A. M. et al. (2018) \"@code{bioRad}: biological
analysis and visualization of weather radar data\" <doi:10.1111/ecog.04028> for a
software paper describing package and methodologies.")
    (license license:expat)))

(define-public r-bioprobability
  (package
    (name "r-bioprobability")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BioProbability" version))
       (sha256
        (base32 "051m761zf079s0p4q0ambwqc22qjz2yajxrsmnjnxvqiaadaj1q7"))))
    (properties `((upstream-name . "BioProbability")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BioProbability")
    (synopsis "Probability in Biostatistics")
    (description
     "Several tools for analyzing diagnostic tests and 2x2 contingency tables are
provided.  In particular, positive and negative predictive values for a
diagnostic tests can be calculated from prevalence, sensitivity and specificity
values.  For contingency tables, relative risk and odds ratio measures are
estimated.  Furthermore, confidence intervals are provided.")
    (license license:gpl2)))

(define-public r-biopred
  (package
    (name "r-biopred")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BioPred" version))
       (sha256
        (base32 "04v38zsg5n5pzg817pkz2jg8sgg1ckd909ckrapbdncyh3wsawan"))))
    (properties `((upstream-name . "BioPred")))
    (build-system r-build-system)
    (propagated-inputs (list r-xgboost
                             r-survminer
                             r-survival
                             r-propcis
                             r-proc
                             r-onewaytests
                             r-mgcv
                             r-ggplot2
                             r-car))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BioPred")
    (synopsis "An R Package for Biomarkers Analysis in Precision Medicine")
    (description
     "This package provides functions for training extreme gradient boosting model
using propensity score A-learning and weight-learning methods.  For details on
the A-learning and weight-learning methods, see Chen et al. (2017)
<doi:10.1111/biom.12676>.")
    (license license:gpl3)))

(define-public r-biopixr
  (package
    (name "r-biopixr")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biopixR" version))
       (sha256
        (base32 "0avdpg73s9hqkyfgrfmprhcc5hpxf4in6s3221n5gf3pcii8jw0c"))))
    (properties `((upstream-name . "biopixR")))
    (build-system r-build-system)
    (propagated-inputs (list r-magick r-imager r-data-table r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Brauckhoff/biopixR")
    (synopsis "Extracting Insights from Biological Images")
    (description
     "Combines the magick and imager packages to streamline image analysis, focusing
on feature extraction and quantification from biological images, especially
microparticles.  By providing high throughput pipelines and clustering
capabilities, @code{biopixR} facilitates efficient insight generation for
researchers (Schneider J. et al. (2019) <doi:10.21037/jlpm.2019.04.05>).")
    (license license:lgpl3+)))

(define-public r-biopetsurv
  (package
    (name "r-biopetsurv")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BioPETsurv" version))
       (sha256
        (base32 "0svwf8pzvlmlp8aacf70hzlg2h81m6lyhh4p73ixqw5v9bnyq6m0"))))
    (properties `((upstream-name . "BioPETsurv")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-gridextra r-ggplot2))
    (home-page "https://cran.r-project.org/package=BioPETsurv")
    (synopsis "Biomarker Prognostic Enrichment Tool for Time-to-Event Trial")
    (description
     "Prognostic Enrichment is a strategy of enriching a clinical trial for testing an
intervention intended to prevent or delay an unwanted clinical event.  A
prognostically enriched trial enrolls only patients who are more likely to
experience the unwanted clinical event than the broader patient population (R.
Temple (2010) <doi:10.1038/clpt.2010.233>).  By testing the intervention in an
enriched study population, the trial may be adequately powered with a smaller
sample size, which can have both practical and ethical advantages.  This package
provides tools to evaluate biomarkers for prognostic enrichment of clinical
trials with survival/time-to-event outcomes.")
    (license license:gpl2+)))

(define-public r-biopet
  (package
    (name "r-biopet")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BioPET" version))
       (sha256
        (base32 "0zgq7i37d0rnjhlkcrl44x62vhz0njsv2fldm64j9r4wc02z1q9n"))))
    (properties `((upstream-name . "BioPET")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam r-proc r-gridextra r-ggplot2))
    (home-page "https://cran.r-project.org/package=BioPET")
    (synopsis "Biomarker Prognostic Enrichment Tool")
    (description
     "Prognostic Enrichment is a clinical trial strategy of evaluating an intervention
in a patient population with a higher rate of the unwanted event than the
broader patient population (R. Temple (2010) <DOI:10.1038/clpt.2010.233>).  A
higher event rate translates to a lower sample size for the clinical trial,
which can have both practical and ethical advantages.  This package is a tool to
help evaluate biomarkers for prognostic enrichment of clinical trials.")
    (license license:gpl2+)))

(define-public r-bionetdata
  (package
    (name "r-bionetdata")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bionetdata" version))
       (sha256
        (base32 "14ll1m85pcr0xycczk1y9jxiqxfy5daiigfhqn4f6sfk2afy07yr"))))
    (properties `((upstream-name . "bionetdata")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=bionetdata")
    (synopsis "Biological and Chemical Data Networks")
    (description
     "Data Package that includes several examples of chemical and biological data
networks, i.e.  data graph structured.")
    (license license:gpl2+)))

(define-public r-biomod2
  (package
    (name "r-biomod2")
    (version "4.2-5-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biomod2" version))
       (sha256
        (base32 "1rdlkspi0hkgnrc4qs7vgbmcpsf74nzdq8w0akg59bynp0ks8k0w"))))
    (properties `((upstream-name . "biomod2")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-sp
                             r-rpart
                             r-reshape2
                             r-reshape
                             r-proc
                             r-presenceabsence
                             r-mass
                             r-ggplot2
                             r-gbm
                             r-foreach
                             r-dplyr
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://biomodhub.github.io/biomod2/")
    (synopsis "Ensemble Platform for Species Distribution Modeling")
    (description
     "This package provides functions for species distribution modeling, calibration
and evaluation, ensemble of models, ensemble forecasting and visualization.  The
package permits to run consistently up to 10 single models on a
presence/absences (resp presences/pseudo-absences) dataset and to combine them
in ensemble models and ensemble projections.  Some bench of other evaluation and
visualisation tools are also available within the package.")
    (license license:gpl3)))

(define-public r-biometryassist
  (package
    (name "r-biometryassist")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biometryassist" version))
       (sha256
        (base32 "1gj2lfsi8rnvc61sz70rvii1xhszl84wfvy6lsnwgh26vnj890wf"))))
    (properties `((upstream-name . "biometryassist")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-stringi
                             r-scales
                             r-rlang
                             r-pracma
                             r-multcompview
                             r-lattice
                             r-ggplot2
                             r-farver
                             r-emmeans
                             r-curl
                             r-cowplot
                             r-askpass
                             r-agricolae))
    (native-inputs (list r-knitr))
    (home-page "https://biometryhub.github.io/biometryassist/")
    (synopsis
     "Functions to Assist Design and Analysis of Agronomic Experiments")
    (description
     "This package provides functions to aid in the design and analysis of agronomic
and agricultural experiments through easy access to documentation and helper
functions, especially for users who are learning these concepts.  While not
required for most functionality, this package enhances the `asreml` package
which provides a computationally efficient algorithm for fitting mixed models
using Residual Maximum Likelihood.  It is a commercial package that can be
purchased as asreml-R from VSNi <https://vsni.co.uk/>, who will supply a zip
file for local installation/updating (see <https://asreml.kb.vsni.co.uk/>).")
    (license license:expat)))

(define-public r-biomass
  (package
    (name "r-biomass")
    (version "2.1.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BIOMASS" version))
       (sha256
        (base32 "1qbx7hxzzwnwp00rdvdwqxawm54chn1pcql3009cwdydc249v0dh"))))
    (properties `((upstream-name . "BIOMASS")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-sf
                             r-rappdirs
                             r-proj4
                             r-minpack-lm
                             r-jsonlite
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/umr-amap/BIOMASS")
    (synopsis
     "Estimating Aboveground Biomass and Its Uncertainty in Tropical Forests")
    (description
     "This package contains functions to estimate aboveground biomass/carbon and its
uncertainty in tropical forests.  These functions allow to (1) retrieve and to
correct taxonomy, (2) estimate wood density and its uncertainty, (3) construct
height-diameter models, (4) manage tree and plot coordinates, (5) estimate the
aboveground biomass/carbon at the stand level with associated uncertainty.  To
cite BIOMASS', please use citation(\"BIOMASS\").  See more in the article of
RÃ©jou-MÃ©chain et al. (2017) <doi:10.1111/2041-210X.12753>.")
    (license license:gpl2)))

(define-public r-biomark
  (package
    (name "r-biomark")
    (version "0.4.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BioMark" version))
       (sha256
        (base32 "1ifc72bayy3azbilajqqzl0is6z7l1zaadchcg3n8lhmjrv5sk3m"))))
    (properties `((upstream-name . "BioMark")))
    (build-system r-build-system)
    (propagated-inputs (list r-st r-pls r-mass r-glmnet))
    (home-page "https://cran.r-project.org/package=BioMark")
    (synopsis "Find Biomarkers in Two-Class Discrimination Problems")
    (description
     "Variable selection methods are provided for several classification methods: the
lasso/elastic net, PCLDA, PLSDA, and several t-tests.  Two approaches for
selecting cutoffs can be used, one based on the stability of model coefficients
under perturbation, and the other on higher criticism.")
    (license license:gpl2+)))

(define-public r-biom2
  (package
    (name "r-biom2")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BioM2" version))
       (sha256
        (base32 "0p0gzmb3kdfpa3w48f1g5s71z14w1g3m000ka7p9ifilajy9hrnh"))))
    (properties `((upstream-name . "BioM2")))
    (build-system r-build-system)
    (propagated-inputs (list r-wordcloud2
                             r-wgcna
                             r-webshot
                             r-viridis
                             r-uwot
                             r-rocr
                             r-mlr3verse
                             r-mlr3
                             r-jiebar
                             r-igraph
                             r-htmlwidgets
                             r-ggthemes
                             r-ggstatsplot
                             r-ggsci
                             r-ggpubr
                             r-ggplot2
                             r-ggnetwork
                             r-ggforce
                             r-cmplot
                             r-caret))
    (home-page "https://cran.r-project.org/package=BioM2")
    (synopsis "Biologically Explainable Machine Learning Framework")
    (description
     "Biologically Explainable Machine Learning Framework for Phenotype Prediction
using omics data described in Chen and Schwarz (2017)
<doi:10.48550/@code{arXiv.1712.00336>.Identifying} reproducible and
interpretable biological patterns from high-dimensional omics data is a critical
factor in understanding the risk mechanism of complex disease.  As such,
explainable machine learning can offer biological insight in addition to
personalized risk scoring.In this process, a feature space of biological
pathways will be generated, and the feature space can also be subsequently
analyzed using WGCNA (Described in Horvath and Zhang (2005)
<doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008)
<doi:10.1186/1471-2105-9-559> ) methods.")
    (license license:expat)))

(define-public r-biologicalactivityindices
  (package
    (name "r-biologicalactivityindices")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biologicalActivityIndices" version))
       (sha256
        (base32 "022qip20nwv0xfxnx5507bsa7finnixjshvf60ssnagbchvgwvz9"))))
    (properties `((upstream-name . "biologicalActivityIndices")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=biologicalActivityIndices")
    (synopsis "Biological Activity Indices")
    (description
     "Ecological alteration of degraded lands can improve their sustainability by
addition of large amount of biomass to soil resulting in improved soil health.
Soil biological parameters (such as carbon, nitrogen and phosphorus cycling
enzyme activity) are reactive to minute variations in soils [Ghosh et al. (2021)
<doi:10.1016/j.ecoleng.2021.106176> ].  Hence, biological activity index
combining Urease, Alkaline Phosphatase, Dehydrogenase (DHA) & Beta-Glucosidase
activity will assist in detecting early changes in restored land use systems
[Patidar et al. (2023) <doi:10.3389/fsufs.2023.1230156>].  This package helps to
calculate Biological Activity Index (BAI) based on vectors of Land Use
System/treatment and control/reference Land Use System containing four values of
Urease, Alkaline Phosphatase, DHA & Beta-Glucosidase. (DHA), urease (URE),
fluorescein diacetate hydrolysis (FDA) and alkaline phosphatase (ALP) activities
are measured in soil samples using triphenyl tetrazolium chloride, urea,
fluorescein diacetate and p-nitro phenyl-phosphate as substrates, respectively.")
    (license license:gpl3)))

(define-public r-biolink
  (package
    (name "r-biolink")
    (version "0.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biolink" version))
       (sha256
        (base32 "03jsvzy6fqbx3w6gs7v1mnznkqwsad3ywdb4wxp0mbaq4vrsandm"))))
    (properties `((upstream-name . "biolink")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-rmysql
                             r-rentrez
                             r-memoise
                             r-glue
                             r-dbi))
    (home-page "https://cran.r-project.org/package=biolink")
    (synopsis "Create Hyperlinks to Biological Databases and Resources")
    (description
     "Generate urls and hyperlinks to commonly used biological databases and resources
based on standard identifiers.  This is primarily useful when writing dynamic
reports that reference things like gene symbols in text or tables, allowing you
to, for example, convert gene identifiers to hyperlinks pointing to their entry
in the NCBI Gene database.  Currently supports NCBI Gene, @code{PubMed}', Gene
Ontology, KEGG', CRAN and Bioconductor.")
    (license license:expat)))

(define-public r-bioinsight
  (package
    (name "r-bioinsight")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BioInsight" version))
       (sha256
        (base32 "129w9643l01y316hm0qcsmxzh5lsdmq6xmiz4rhmdh0dcnw2s93n"))))
    (properties `((upstream-name . "BioInsight")))
    (build-system r-build-system)
    (propagated-inputs (list r-wordcloud r-rcolorbrewer r-limma r-knitr
                             r-edger))
    (home-page "https://cran.r-project.org/package=BioInsight")
    (synopsis "Filter and Plot RNA Biotypes")
    (description
     "Analyze and plot the abundance of different RNA biotypes present in a count
matrix, this evaluation can be useful if you want to test different strategies
of normalization or analyze a particular biotype in a differential gene
expression analysis.")
    (license license:gpl2+)))

(define-public r-bioinactivation
  (package
    (name "r-bioinactivation")
    (version "1.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bioinactivation" version))
       (sha256
        (base32 "0gz1bbakf6b23jsbvrr8314yxajxcr1akm5srgm883lrf1p5ax13"))))
    (properties `((upstream-name . "bioinactivation")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-purrr
                             r-mass
                             r-lazyeval
                             r-ggplot2
                             r-fme
                             r-dplyr
                             r-desolve))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bioinactivation")
    (synopsis "Mathematical Modelling of (Dynamic) Microbial Inactivation")
    (description
     "This package provides functions for modelling microbial inactivation under
isothermal or dynamic conditions.  The calculations are based on several
mathematical models broadly used by the scientific community and industry.
Functions enable to make predictions for cases where the kinetic parameters are
known.  It also implements functions for parameter estimation for isothermal and
dynamic conditions.  The model fitting capabilities include an Adaptive Monte
Carlo method for a Bayesian approach to parameter estimation.")
    (license license:gpl3)))

(define-public r-bioimagetools
  (package
    (name "r-bioimagetools")
    (version "1.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bioimagetools" version))
       (sha256
        (base32 "1b7nxjis0p1hiv3w0cr3cyc2xz6sn9qbpssvlp2d7c5gm8x2gnj7"))))
    (properties `((upstream-name . "bioimagetools")))
    (build-system r-build-system)
    (propagated-inputs (list r-tiff r-httr r-ebimage))
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "https://bioimaginggroup.github.io/bioimagetools/")
    (synopsis "Tools for Microscopy Imaging")
    (description
     "This package provides tools for 3D imaging, mostly for biology/microscopy.  Read
and write TIFF stacks.  Functions for segmentation, filtering and analyzing 3D
point patterns.")
    (license license:gpl3)))

(define-public r-bioi
  (package
    (name "r-bioi")
    (version "0.2.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Bioi" version))
       (sha256
        (base32 "1ay8889490nfhxhh72fvk9z4h58xknm2b145xhbx2wbzj65zf9ib"))))
    (properties `((upstream-name . "Bioi")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-igraph r-dplyr r-assertthat))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=Bioi")
    (synopsis "Biological Image Analysis")
    (description
     "Single linkage clustering and connected component analyses are often performed
on biological images.  Bioi provides a set of functions for performing these
tasks.  This functionality is implemented in several key functions that can
extend to from 1 to many dimensions.  The single linkage clustering method
implemented here can be used on n-dimensional data sets, while connected
component analyses are limited to 3 or fewer dimensions.")
    (license license:gpl3)))

(define-public r-biogrowth
  (package
    (name "r-biogrowth")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biogrowth" version))
       (sha256
        (base32 "19czyp7n4ip01psl8hbiihfhj52vdp6nsq0h7cnl7p9h0ax9wcj5"))))
    (properties `((upstream-name . "biogrowth")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-mass
                             r-lifecycle
                             r-lamw
                             r-ggplot2
                             r-formula-tools
                             r-fme
                             r-dplyr
                             r-desolve
                             r-cowplot))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=biogrowth")
    (synopsis "Modelling of Population Growth")
    (description
     "Modelling of population growth under static and dynamic environmental
conditions.  Includes functions for model fitting and making prediction under
isothermal and dynamic conditions.  The methods (algorithms & models) are based
on predictive microbiology (See Perez-Rodriguez and Valero (2012,
ISBN:978-1-4614-5519-6)).")
    (license license:gpl3)))

(define-public r-biogram
  (package
    (name "r-biogram")
    (version "1.6.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biogram" version))
       (sha256
        (base32 "1y7yaa6xwv11b6a4qf4qb3d7rsz0yiiq1k5mb0dbm3avydnl9c4b"))))
    (properties `((upstream-name . "biogram")))
    (build-system r-build-system)
    (propagated-inputs (list r-slam r-partitions r-entropy r-combinat))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/michbur/biogram")
    (synopsis "N-Gram Analysis of Biological Sequences")
    (description
     "This package provides tools for extraction and analysis of various n-grams
(k-mers) derived from biological sequences (proteins or nucleic acids).
Contains @code{QuiPT} (quick permutation test) for fast feature-filtering of the
n-gram data.")
    (license license:gpl3)))

(define-public r-biogeom
  (package
    (name "r-biogeom")
    (version "1.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biogeom" version))
       (sha256
        (base32 "0wzj99ldk0654s1a8d1b4q57mahv4mjzndx5bj1hakmsdrwa6q8b"))))
    (properties `((upstream-name . "biogeom")))
    (build-system r-build-system)
    (propagated-inputs (list r-spatstat-geom))
    (home-page "https://cran.r-project.org/package=biogeom")
    (synopsis "Biological Geometries")
    (description
     "Is used to simulate and fit biological geometries.  biogeom incorporates several
novel universal parametric equations that can generate the profiles of bird
eggs, flowers, linear and lanceolate leaves, seeds, starfish, and tree-rings
(Gielis (2003) <doi:10.3732/ajb.90.3.333>; Shi et al. (2020)
<doi:10.3390/sym12040645>), three growth-rate curves representing the
ontogenetic growth trajectories of animals and plants against time, and the
axially symmetrical and integral forms of all these functions (Shi et al. (2017)
<doi:10.1016/j.ecolmodel.2017.01.012>; Shi et al. (2021)
<doi:10.3390/sym13081524>).  The optimization method proposed by Nelder and Mead
(1965) <doi:10.1093/comjnl/7.4.308> was used to estimate model parameters.
biogeom includes several real data sets of the boundary coordinates of natural
shapes, including avian eggs, fruit, lanceolate and ovate leaves, tree rings,
seeds, and sea stars,and can be potentially applied to other natural shapes.
biogeom can quantify the conspecific or interspecific similarity of natural
outlines, and provides information with important ecological and evolutionary
implications for the growth and form of living organisms.  Please see Shi et al.
(2022) <doi:10.1111/nyas.14862> for details.")
    (license license:gpl2+)))

(define-public r-biogas
  (package
    (name "r-biogas")
    (version "1.23.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biogas" version))
       (sha256
        (base32 "05g6nyi872skvr34kw5c71ggnnjc3fv95imc327yjqhmqmwx3m19"))))
    (properties `((upstream-name . "biogas")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=biogas")
    (synopsis "Process Biogas Data and Predict Biogas Production")
    (description
     "High- and low-level functions for processing biogas data and predicting biogas
production.  Molar mass and calculated oxygen demand (COD') can be determined
from a chemical formula.  Measured gas volume can be corrected for water vapor
and to (possibly user-defined) standard temperature and pressure.  Gas quantity
can be converted between volume, mass, and moles.  Gas composition, cumulative
production, or other variables can be interpolated to a specified time.
Cumulative biogas and methane production (and rates) can be calculated using
volumetric, manometric, gravimetric, or gas density methods for any number of
bottles.  With cumulative methane production data and data on bottle contents,
biochemical methane potential (BMP) can be calculated and summarized, including
subtraction of the inoculum contribution and normalization by substrate mass.
Cumulative production and production rates can be summarized in several
different ways (e.g., omitting normalization) using the same function.  Biogas
quantity and composition can be predicted from substrate composition and
additional, optional data.  Lastly, inoculum and substrate mass can be
determined for planning BMP experiments.")
    (license license:gpl2)))

(define-public r-biodry
  (package
    (name "r-biodry")
    (version "0.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BIOdry" version))
       (sha256
        (base32 "1j6fbrvhiaz04fpybdldjk2prg3n5ayzvxqbx0g0vbna54kd1vz6"))))
    (properties `((upstream-name . "BIOdry")))
    (build-system r-build-system)
    (propagated-inputs (list r-nlme r-ecodist))
    (home-page "https://cran.r-project.org/package=BIOdry")
    (synopsis "Multilevel Modeling of Dendroclimatical Fluctuations")
    (description
     "Multilevel ecological data series (MEDS) are sequences of observations ordered
according to temporal/spatial hierarchies that are defined by sample designs,
with sample variability confined to ecological factors.  Dendroclimatic MEDS of
tree rings and climate are modeled into normalized fluctuations of tree growth
and aridity.  Modeled fluctuations (model frames) are compared with Mantel
correlograms on multiple levels defined by sample design.  Package
implementation can be understood by running examples in @code{modelFrame}(), and
@code{muleMan}() functions.")
    (license license:gpl3)))

(define-public r-biodosetools
  (package
    (name "r-biodosetools")
    (version "3.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biodosetools" version))
       (sha256
        (base32 "013nqdqrv5h3swzqss2kazsp06wrs57zri3aaidy92wrd6v24bsh"))))
    (properties `((upstream-name . "biodosetools")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-shinywidgets
                             r-shinydashboard
                             r-shiny
                             r-rmarkdown
                             r-rlang
                             r-rhandsontable
                             r-msm
                             r-mixtools
                             r-maxlik
                             r-mass
                             r-magrittr
                             r-golem
                             r-ggplot2
                             r-dplyr
                             r-config
                             r-cli
                             r-bsplus))
    (native-inputs (list r-knitr))
    (home-page "https://biodosetools-team.github.io/biodosetools/")
    (synopsis "An R Shiny Application for Biological Dosimetry")
    (description
     "This package provides a tool to perform all different statistical tests and
calculations needed by Biological Dosimetry Laboratories.")
    (license license:gpl3)))

(define-public r-biodiversityr
  (package
    (name "r-biodiversityr")
    (version "2.16-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BiodiversityR" version))
       (sha256
        (base32 "059kj0cnb3fpqngww6560mqg6c4dsh5w6484v23wnmng064rf1fi"))))
    (properties `((upstream-name . "BiodiversityR")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan r-rcmdr r-ggplot2))
    (home-page
     "http://www.worldagroforestry.org/output/tree-diversity-analysis")
    (synopsis "Package for Community Ecology and Suitability Analysis")
    (description
     "Graphical User Interface (via the R-Commander) and utility functions (often
based on the vegan package) for statistical analysis of biodiversity and
ecological communities, including species accumulation curves, diversity
indices, Renyi profiles, GLMs for analysis of species abundance and
presence-absence, distance matrices, Mantel tests, and cluster, constrained and
unconstrained ordination analysis.  A book on biodiversity and community ecology
analysis is available for free download from the website.  In 2012, methods for
(ensemble) suitability modelling and mapping were expanded in the package.")
    (license license:gpl3)))

(define-public r-biodem
  (package
    (name "r-biodem")
    (version "0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Biodem" version))
       (sha256
        (base32 "1phnq6zq96i6z7jj196png3spjpbbx5p2s8iya0vvbfik12ivzkc"))))
    (properties `((upstream-name . "Biodem")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=Biodem")
    (synopsis "Biodemography Functions")
    (description
     "The Biodem package provides a number of functions for Biodemographic analysis.")
    (license license:gpl2)))

(define-public r-biocro
  (package
    (name "r-biocro")
    (version "3.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BioCro" version))
       (sha256
        (base32 "1r0q6rwn6qv8wh5j0qyf49dzfl1vy211wbnjkcjhg27jahf553fc"))))
    (properties `((upstream-name . "BioCro")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/biocro/biocro")
    (synopsis "Modular Crop Growth Simulations")
    (description
     "This package provides a cross-platform representation of models as sets of
equations that facilitates modularity in model building and allows users to
harness modern techniques for numerical integration and data visualization.
Documentation is provided by several vignettes included in this package; also
see Lochocki et al. (2022) <doi:10.1093/insilicoplants/diac003>.")
    (license license:expat)))

(define-public r-biocompute
  (package
    (name "r-biocompute")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biocompute" version))
       (sha256
        (base32 "102n8y83z8568lwil2fb28wrjkymwmw9rzvk0a4zgsx5a3l7wl01"))))
    (properties `((upstream-name . "biocompute")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-uuid
                             r-stringr
                             r-rmarkdown
                             r-magrittr
                             r-jsonvalidate
                             r-jsonlite
                             r-httr
                             r-digest
                             r-curl
                             r-crayon
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://sbg.github.io/biocompute/")
    (synopsis "Create and Manipulate BioCompute Objects")
    (description
     "This package provides tools to create, validate, and export @code{BioCompute}
Objects described in King et al. (2019) <doi:10.17605/osf.io/h59uh>.  Users can
encode information in data frames, and compose @code{BioCompute} Objects from
the domains defined by the standard.  A checksum validator and a JSON schema
validator are provided.  This package also supports exporting @code{BioCompute}
Objects as JSON, PDF, HTML, or Word documents, and exporting to cloud-based
platforms.")
    (license license:agpl3)))

(define-public r-bioclim
  (package
    (name "r-bioclim")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bioclim" version))
       (sha256
        (base32 "15a2v5l4ydn2zsnbwkrwpfvcj5xzhwj1diqrr9vp1i42w9ci55h2"))))
    (properties `((upstream-name . "bioclim")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra r-rmarkdown r-reshape2 r-ggplot2
                             r-berryfunctions))
    (home-page "https://cran.r-project.org/package=bioclim")
    (synopsis "Bioclimatic Analysis and Classification")
    (description
     "Using numeric or raster data, this package contains functions to calculate:
complete water balance, bioclimatic balance, bioclimatic intensities, reports
for individual locations, multi-layered rasters for spatial analysis.")
    (license license:gpl3)))

(define-public r-biocircos
  (package
    (name "r-biocircos")
    (version "0.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BioCircos" version))
       (sha256
        (base32 "1gl57r47p5z1wzs94xzc26j2r8z5hpqr4163b01x6jwzvbrlfwhz"))))
    (properties `((upstream-name . "BioCircos")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer r-plyr r-jsonlite r-htmlwidgets))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/lvulliard/BioCircos.R")
    (synopsis
     "Interactive Circular Visualization of Genomic Data using 'htmlwidgets' and 'BioCircos.js'")
    (description
     "Implement in R interactive Circos-like visualizations of genomic data, to map
information such as genetic variants, genomic fusions and aberrations to a
circular genome, as proposed by the @code{JavaScript} library
@code{BioCircos.js}', based on the JQuery and D3 technologies.  The output is by
default displayed in stand-alone HTML documents or in the RStudio viewer pane.
Moreover it can be integrated in R Markdown documents and Shiny applications.")
    (license (list license:gpl2
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-bioc-logs
  (package
    (name "r-bioc-logs")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bioC.logs" version))
       (sha256
        (base32 "092hdrfkdij3s8871z4c1lb0rf5pp07ggliaz76iyfxrbvy9hcxj"))))
    (properties `((upstream-name . "bioC.logs")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mponce0/bioC.logs")
    (synopsis "BioConductor Package Downloads Stats")
    (description
     "Download stats reported from the @code{BioConductor.org} stats website.")
    (license license:gpl2+)))

(define-public r-biobricks
  (package
    (name "r-biobricks")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biobricks" version))
       (sha256
        (base32 "0xa6pd7c4sdq4wzn8053drr063cjll7lpg5k3k146zz0z6rczg30"))))
    (properties `((upstream-name . "biobricks")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=biobricks")
    (synopsis "Access Data Dependencies Installed Through 'Biobricks.ai'")
    (description
     "This package provides an integrated data management solution for assets
installed via the Biobricks.ai platform.  Streamlines the process of loading and
interacting with diverse datasets in a consistent manner.  A list of bricks is
available at <https://status.biobricks.ai>.  Documentation for Biobricks.ai is
available at <https://docs.biobricks.ai>.")
    (license license:expat)))

(define-public r-biobjclass
  (package
    (name "r-biobjclass")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BiObjClass" version))
       (sha256
        (base32 "07vbs0qi60x32y0hj6g55z76zsfmc7v8dapr2g22lsnkfdnfh10c"))))
    (properties `((upstream-name . "BiObjClass")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BiObjClass")
    (synopsis "Classification of Algorithms")
    (description
     "This package implements the Bi-objective Lexicographical Classification method
and Performance Assessment Ratio at 10% metric for algorithm classification.
Constructs matrices representing algorithm performance under multiple criteria,
facilitating decision-making in algorithm selection and evaluation.  Analyzes
and compares algorithm performance based on various metrics to identify the most
suitable algorithms for specific tasks.  This package includes methods for
algorithm classification and evaluation, with examples provided in the
documentation.  Carvalho (2019) presents a statistical evaluation of algorithmic
computational experimentation with infeasible solutions
<doi:10.48550/@code{arXiv.1902.00101>}.  Moreira and Carvalho (2023) analyze
power in preprocessing methodologies for datasets with missing values
<doi:10.1080/03610918.2023.2234683>.")
    (license license:gpl3)))

(define-public r-bioacoustics
  (package
    (name "r-bioacoustics")
    (version "0.2.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bioacoustics" version))
       (sha256
        (base32 "08qhxx7xgswjamrgg82pkg1f7w50dx9ln67d50n1p3rvrxab4061"))))
    (properties `((upstream-name . "bioacoustics")))
    (build-system r-build-system)
    (inputs (list fftw))
    (propagated-inputs (list r-tuner r-stringr r-rcpp r-moments r-htmltools))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/wavx/bioacoustics/")
    (synopsis
     "Analyse Audio Recordings and Automatically Extract Animal Vocalizations")
    (description
     "This package contains all the necessary tools to process audio recordings of
various formats (e.g., WAV, WAC, MP3, ZC), filter noisy files, display audio
signals, detect and extract automatically acoustic features for further analysis
such as classification.")
    (license license:gpl3)))

(define-public r-bio-infer
  (package
    (name "r-bio-infer")
    (version "1.3-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bio.infer" version))
       (sha256
        (base32 "1hs11c9qpdsl1awgk3b34mcsnr8g1xpg3zd9dlrqrz6ppdqkgxmn"))))
    (properties `((upstream-name . "bio.infer")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=bio.infer")
    (synopsis "Predict Environmental Conditions from Biological Observations")
    (description
     "Imports benthic count data, reformats this data, and computes environmental
inferences from this data.")
    (license license:gpl2+)))

(define-public r-bintools
  (package
    (name "r-bintools")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BINtools" version))
       (sha256
        (base32 "0x97h0b2krdgrd0x266fidj77an3dq8mf9lnbas9112p4mhkmk58"))))
    (properties `((upstream-name . "BINtools")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringi
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-mvtnorm
                             r-dplyr
                             r-combinat
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BINtools")
    (synopsis "Bayesian BIN (Bias, Information, Noise) Model of Forecasting")
    (description
     "This package provides a recently proposed Bayesian BIN model disentangles the
underlying processes that enable forecasters and forecasting methods to improve,
decomposing forecasting accuracy into three components: bias, partial
information, and noise.  By describing the differences between two groups of
forecasters, the model allows the user to carry out useful inference, such as
calculating the posterior probabilities of the treatment reducing bias,
diminishing noise, or increasing information.  It also provides insight into how
much tamping down bias and noise in judgment or enhancing the efficient
extraction of valid information from the environment improves forecasting
accuracy.  This package provides easy access to the BIN model.  For further
information refer to the paper Ville A. SatopÃ¤Ã¤, Marat Salikhov, Philip E.
Tetlock, and Barbara Mellers (2021) \"Bias, Information, Noise: The BIN Model of
Forecasting\" <doi:10.1287/mnsc.2020.3882>.")
    (license license:gpl3)))

(define-public r-binst
  (package
    (name "r-binst")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binst" version))
       (sha256
        (base32 "09kzk7n1j9nzgyijwirzk33z6p6hc67zkd05hv1i9qfijy31gci4"))))
    (properties `((upstream-name . "binst")))
    (build-system r-build-system)
    (propagated-inputs (list r-rpart))
    (home-page "https://github.com/jules-and-dave/binst")
    (synopsis "Data Preprocessing, Binning for Classification and Regression")
    (description
     "Various supervised and unsupervised binning tools including using entropy,
recursive partition methods and clustering.")
    (license license:expat)))

(define-public r-binsreg
  (package
    (name "r-binsreg")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binsreg" version))
       (sha256
        (base32 "04afp5sixn51kf519sgrra50knmkz1a1gsz28xbdjph9vx5bv6jl"))))
    (properties `((upstream-name . "binsreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich r-quantreg r-matrixstats r-ggplot2))
    (home-page "https://cran.r-project.org/package=binsreg")
    (synopsis "Binscatter Estimation and Inference")
    (description
     "This package provides tools for statistical analysis using the binscatter
methods developed by Cattaneo, Crump, Farrell and Feng (2024a)
<doi:10.48550/@code{arXiv.1902.09608>}, Cattaneo, Crump, Farrell and Feng
(2024b)
<https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2024_@code{NonlinearBinscatter.pdf>}
and Cattaneo, Crump, Farrell and Feng (2024c)
<doi:10.48550/@code{arXiv.1902.09615>}.  Binscatter provides a flexible way of
describing the relationship between two variables based on partitioning/binning
of the independent variable of interest.  binsreg(), binsqreg() and binsglm()
implement binscatter least squares regression, quantile regression and
generalized linear regression respectively, with particular focus on
constructing binned scatter plots.  They also implement robust (pointwise and
uniform) inference of regression functions and derivatives thereof.  binstest()
implements hypothesis testing procedures for parametric functional forms of and
nonparametric shape restrictions on the regression function.  binspwc()
implements hypothesis testing procedures for pairwise group comparison of
binscatter estimators.  binsregselect() implements data-driven procedures for
selecting the number of bins for binscatter estimation.  All the commands allow
for covariate adjustment, smoothness restrictions and clustering.")
    (license license:gpl2)))

(define-public r-binspp
  (package
    (name "r-binspp")
    (version "0.1.26")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binspp" version))
       (sha256
        (base32 "0ah002lvimniwmwgn2syw9saxgzpdhs5vins0f3kb98p5k3gdxqk"))))
    (properties `((upstream-name . "binspp")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam
                             r-spatstat-random
                             r-spatstat-model
                             r-spatstat-geom
                             r-spatstat
                             r-rcppeigen
                             r-rcpparmadillo
                             r-rcpp
                             r-mvtnorm
                             r-cluster))
    (home-page "https://github.com/tomasmrkvicka/binspp")
    (synopsis "Bayesian Inference for Neyman-Scott Point Processes")
    (description
     "The Bayesian MCMC estimation of parameters for Thomas-type cluster point process
with various inhomogeneities.  It allows for inhomogeneity in (i) distribution
of parent points, (ii) mean number of points in a cluster, (iii) cluster spread.
 The package also allows for the Bayesian MCMC algorithm for the homogeneous
generalized Thomas process.  The cluster size is allowed to have a variance that
is greater or less than the expected value (cluster sizes are over or under
dispersed).  Details are described in DvoÅÃ¡k, RemeÅ¡, BerÃ¡nek & MrkviÄka
(2022) <@code{arXiv}: 10.48550/@code{arXiv.2205.07946>}.")
    (license license:gpl3)))

(define-public r-binsmooth
  (package
    (name "r-binsmooth")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binsmooth" version))
       (sha256
        (base32 "1sidv8ra377glaacib1j4ksg5bg759hk7h6fadrfzip3hgxgjg9x"))))
    (properties `((upstream-name . "binsmooth")))
    (build-system r-build-system)
    (propagated-inputs (list r-triangle r-pracma r-ineq))
    (home-page "https://cran.r-project.org/package=binsmooth")
    (synopsis "Generate PDFs and CDFs from Binned Data")
    (description
     "This package provides several methods for generating density functions based on
binned data.  Methods include step function, recursive subdivision, and
optimized spline.  Data are assumed to be nonnegative, the top bin is assumed to
have no upper bound, but the bin widths need be equal.  All PDF smoothing
methods maintain the areas specified by the binned data. (Equivalently, all CDF
smoothing methods interpolate the points specified by the binned data.) In
practice, an estimate for the mean of the distribution should be supplied as an
optional argument.  Doing so greatly improves the reliability of statistics
computed from the smoothed density functions.  Includes methods for estimating
the Gini coefficient, the Theil index, percentiles, and random deviates from a
smoothed distribution.  Among the three methods, the optimized spline
(splinebins) is recommended for most purposes.  The percentile and random-draw
methods should be regarded as experimental, and these methods only support
splinebins.")
    (license license:expat)))

(define-public r-binseqtest
  (package
    (name "r-binseqtest")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binseqtest" version))
       (sha256
        (base32 "14na719dsz9bnvcyhlclzbaxvm4sc8g3v41jmhdzgr8bfbqahgk4"))))
    (properties `((upstream-name . "binseqtest")))
    (build-system r-build-system)
    (propagated-inputs (list r-clinfun))
    (home-page "https://cran.r-project.org/package=binseqtest")
    (synopsis "Exact Binary Sequential Designs and Analysis")
    (description
     "For a series of binary responses, create stopping boundary with exact results
after stopping, allowing updating for missing assessments.")
    (license license:gpl3)))

(define-public r-binsegrcpp
  (package
    (name "r-binsegrcpp")
    (version "2023.8.31")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binsegRcpp" version))
       (sha256
        (base32 "1rx8rxgzjf4bjbx6gpf70pnma05g51lg6l1z0kdswj8m3pmqnk93"))))
    (properties `((upstream-name . "binsegRcpp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/tdhock/binsegRcpp")
    (synopsis "Efficient Implementation of Binary Segmentation")
    (description
     "Standard template library containers are used to implement an efficient binary
segmentation algorithm, which is log-linear on average and quadratic in the
worst case.")
    (license license:gpl3)))

(define-public r-binsegbstrap
  (package
    (name "r-binsegbstrap")
    (version "1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BinSegBstrap" version))
       (sha256
        (base32 "1ikaqhcymy27qrpf85a23dpazy8gd407yi0z0w3nl5l38clz4h8w"))))
    (properties `((upstream-name . "BinSegBstrap")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BinSegBstrap")
    (synopsis
     "Piecewise Smooth Regression by Bootstrapped Binary Segmentation")
    (description
     "This package provides methods for piecewise smooth regression.  A piecewise
smooth signal is estimated by applying a bootstrapped test recursively (binary
segmentation approach).  Each bootstrapped test decides whether the underlying
signal is smooth on the currently considered subsegment or contains at least one
further change-point.")
    (license license:gpl3)))

(define-public r-binr
  (package
    (name "r-binr")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binr" version))
       (sha256
        (base32 "1h1zkawhzp23diav6q4i2sysf4lh3pxc0gsmh4q3vgany1wj57wy"))))
    (properties `((upstream-name . "binr")))
    (build-system r-build-system)
    (home-page "https://github.com/jabiru/binr")
    (synopsis "Cut Numeric Values into Evenly Distributed Groups")
    (description
     "Implementation of algorithms for cutting numerical values exhibiting a
potentially highly skewed distribution into evenly distributed groups (bins).
This functionality can be applied for binning discrete values, such as counts,
as well as for discretization of continuous values, for example, during
generation of features used in machine learning algorithms.")
    (license license:asl2.0)))

(define-public r-binpackr
  (package
    (name "r-binpackr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binpackr" version))
       (sha256
        (base32 "1kc5cm8apijgxwncpj3vflpm2alxlyz7nj2ifa09rh4n36c17hzm"))))
    (properties `((upstream-name . "binpackr")))
    (build-system r-build-system)
    (propagated-inputs (list r-cpp11))
    (home-page "https://github.com/lschneiderbauer/binpackr")
    (synopsis "Fast 1d Bin Packing")
    (description
     "This package implements the First Fit Decreasing algorithm to achieve one
dimensional heuristic bin packing.  Runtime is of order O(n log(n)) where n is
the number of items to pack.  See \"The Art of Computer Programming Vol.  1\" by
Donald E. Knuth (1997, ISBN: 0201896834) for more details.")
    (license license:gpl3+)))

(define-public r-binovisualfields
  (package
    (name "r-binovisualfields")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binovisualfields" version))
       (sha256
        (base32 "0s2p0miqjckkj4v9l0c7zsdnnq69j08p4c5sgdxvkzlv23rnrkyd"))))
    (properties `((upstream-name . "binovisualfields")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-plotrix r-gtools))
    (native-inputs (list r-knitr))
    (home-page "https://people.eng.unimelb.edu.au/aturpin/opi/index.html")
    (synopsis "Depth-Dependent Binocular Visual Fields Simulation")
    (description
     "Simulation and visualization depth-dependent integrated visual fields.  Visual
fields are measured monocularly at a single depth, yet real-life activities
involve predominantly binocular vision at multiple depths.  The package provides
functions to simulate and visualize binocular visual field impairment in a
depth-dependent fashion from monocular visual field results based on Ping Liu,
Allison @code{McKendrick}, Anna Ma-Wyatt, Andrew Turpin (2019)
<doi:10.1167/tvst.9.3.8>.  At each location and depth plane, sensitivities are
linearly interpolated from corresponding locations in monocular visual field and
returned as the higher value of the two.  Its utility is demonstrated by
evaluating DD-IVF defects associated with 12 glaucomatous archetypes of 24-2
visual field pattern in the included shiny apps.")
    (license license:gpl3)))

(define-public r-binordnonnor
  (package
    (name "r-binordnonnor")
    (version "1.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BinOrdNonNor" version))
       (sha256
        (base32 "17981jfjjgsfzjwbv9cjgqdha846rf03nfd8kgj4wiljgz855mz9"))))
    (properties `((upstream-name . "BinOrdNonNor")))
    (build-system r-build-system)
    (propagated-inputs (list r-ordnor
                             r-mvtnorm
                             r-matrix
                             r-genord
                             r-corpcor
                             r-bb))
    (home-page "https://cran.r-project.org/package=BinOrdNonNor")
    (synopsis "Concurrent Generation of Binary, Ordinal and Continuous Data")
    (description
     "Generation of samples from a mix of binary, ordinal and continuous random
variables with a pre-specified correlation matrix and marginal distributions.
The details of the method are explained in Demirtas et al. (2012)
<DOI:10.1002/sim.5362>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-binomialtrend
  (package
    (name "r-binomialtrend")
    (version "0.0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binomialtrend" version))
       (sha256
        (base32 "1rrs8sg1dfzgb6005pm5nj3fwp5fy89pjx0af59clybj6lyqp25d"))))
    (properties `((upstream-name . "binomialtrend")))
    (build-system r-build-system)
    (propagated-inputs (list r-pheatmap))
    (home-page "https://cran.r-project.org/package=binomialtrend")
    (synopsis
     "Calculates the Statistical Significance of a Trend in a Set of Measurements")
    (description
     "Detection of a statistically significant trend in the data provided by the user.
 This is based on the a signed test based on the binomial distribution.  The
package returns a trend test value, T, and also a p-value.  A T value close to 1
indicates a rising trend, whereas a T value close to -1 indicates a decreasing
trend.  A T value close to 0 indicates no trend.  There is also a command to
visualize the trend.  A test data set called gtsa_data is also available, which
has global mean temperatures for January, April, July, and October for the years
1851 to 2022.  Reference: Walpole, Myers, Myers, Ye. (2007, ISBN:
0-13-187711-9).")
    (license license:gpl3)))

(define-public r-binomialrf
  (package
    (name "r-binomialrf")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binomialRF" version))
       (sha256
        (base32 "1liqhnp4j3fxy552fi4yz5vmfzh5blpmxx6fa3g62dgnhcm9sy18"))))
    (properties `((upstream-name . "binomialRF")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlist r-randomforest r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://www.biorxiv.org/content/10.1101/681973v1.abstract")
    (synopsis "Binomial Random Forest Feature Selection")
    (description
     "The @code{binomialRF} is a new feature selection technique for decision trees
that aims at providing an alternative approach to identify significant feature
subsets using binomial distributional assumptions (Rachid Zaim, S., et al.
(2019)) <doi:10.1101/681973>.  Treating each splitting variable selection as a
set of exchangeable correlated Bernoulli trials, @code{binomialRF} then tests
whether a feature is selected more often than by random chance.")
    (license license:gpl2)))

(define-public r-binomci
  (package
    (name "r-binomci")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binomCI" version))
       (sha256
        (base32 "078qghb8svz7jfnmdc5j2npg2lvb00j4lal3ghszysarmw21nia1"))))
    (properties `((upstream-name . "binomCI")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=binomCI")
    (synopsis "Confidence Intervals for a Binomial Proportion")
    (description
     "Twelve confidence intervals for one binomial proportion or a vector of binomial
proportions are computed.  The confidence intervals are: Jeffreys, Wald, Wald
corrected, Wald, Blyth and Still, Agresti and Coull, Wilson, Score, Score
corrected, Wald logit, Wald logit corrected, Arcsine and Exact binomial.
References include, among others: Vollset, S. E. (1993). \"Confidence intervals
for a binomial proportion\".  Statistics in Medicine, 12(9): 809-824.
<doi:10.1002/sim.4780120902>.")
    (license license:gpl2+)))

(define-public r-binnor
  (package
    (name "r-binnor")
    (version "2.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BinNor" version))
       (sha256
        (base32 "07r7nf4ygilp470z93x9dc5zddc96m2dkw6d2s4an8fbr4pljc1y"))))
    (properties `((upstream-name . "BinNor")))
    (build-system r-build-system)
    (propagated-inputs (list r-psych r-mvtnorm r-matrix r-corpcor))
    (home-page "https://cran.r-project.org/package=BinNor")
    (synopsis
     "Simultaneous Generation of Multivariate Binary and Normal Variates")
    (description
     "Generating multiple binary and normal variables simultaneously given marginal
characteristics and association structure based on the methodology proposed by
Demirtas and Doganay (2012) <DOI:10.1080/10543406.2010.521874>.")
    (license license:gpl2)))

(define-public r-binnonnor
  (package
    (name "r-binnonnor")
    (version "1.5.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BinNonNor" version))
       (sha256
        (base32 "0ijjv2w4bx0i3mks7s4vii21hc9iq69p3jld1qg68a6115njsm24"))))
    (properties `((upstream-name . "BinNonNor")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-matrix r-corpcor r-bb))
    (home-page "https://cran.r-project.org/package=BinNonNor")
    (synopsis
     "Data Generation with Binary and Continuous Non-Normal Components")
    (description
     "Generation of multiple binary and continuous non-normal variables simultaneously
given the marginal characteristics and association structure based on the
methodology proposed by Demirtas et al. (2012) <DOI:10.1002/sim.5362>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-binmto
  (package
    (name "r-binmto")
    (version "0.0-7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binMto" version))
       (sha256
        (base32 "0rg9rsdy14jx7zjv60krcz1vh0sxqimsai0wynhx2sx0kydw4ngy"))))
    (properties `((upstream-name . "binMto")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://cran.r-project.org/package=binMto")
    (synopsis "Many-to-One Comparisons of Proportions")
    (description
     "Asymptotic simultaneous confidence intervals for comparison of many treatments
with one control, for the difference of binomial proportions, allows for
Dunnett-like-adjustment, Bonferroni or unadjusted intervals.  Simulation of
power of the above interval methods, approximate calculation of any-pair-power,
and sample size iteration based on approximate any-pair power.  Exact
conditional maximum test for many-to-one comparisons to a control.")
    (license license:gpl2)))

(define-public r-binmat
  (package
    (name "r-binmat")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BinMat" version))
       (sha256
        (base32 "1brgv0zmprvwx6x670mmxxlk50nh4ds63bip48mlh0p1qv8pm9db"))))
    (properties `((upstream-name . "BinMat")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-pvclust r-mass r-ggpubr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BinMat")
    (synopsis
     "Processes Binary Data Obtained from Fragment Analysis (Such as AFLPs, ISSRs, and RFLPs)")
    (description
     "This package provides a molecular genetics tool that processes binary data from
fragment analysis.  It consolidates replicate sample pairs, outputs summary
statistics, and produces hierarchical clustering trees and @code{nMDS} plots.
This package was developed from the publication available here:
<https://www.sciencedirect.com/science/article/pii/S1049964420306538>.  The GUI
version of this package is available on the R Shiny online server at:
<https://clarkevansteenderen.shinyapps.io/BINMAT/> or it is accessible via
@code{GitHub} by typing: shiny::@code{runGitHub(\"BinMat}\",
\"clarkevansteenderen\") into the console in R. Two real-world datasets accompany
the package: an AFLP dataset of Bunias orientalis samples from Tewes et.  al.
(2017)
<https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/1365-2745.12869>,
and an ISSR dataset of Nymphaea specimens from Reid et.  al. (2021)
<https://www.sciencedirect.com/science/article/pii/S0304377021000218> .  The
authors of these publications are thanked for allowing the use of their data.")
    (license license:gpl3)))

(define-public r-binhf
  (package
    (name "r-binhf")
    (version "1.0-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binhf" version))
       (sha256
        (base32 "1vdw2s8zddp7gad8l3c4jpmnjcc0f5wpqbrp6gp9lgp1c3qa505y"))))
    (properties `((upstream-name . "binhf")))
    (build-system r-build-system)
    (propagated-inputs (list r-wavethresh r-ebayesthresh r-adlift))
    (home-page "https://cran.r-project.org/package=binhf")
    (synopsis "Haar-Fisz Functions for Binomial Data")
    (description
     "Binomial Haar-Fisz transforms for Gaussianization as in Nunes and Nason (2009).")
    (license license:gpl2+)))

(define-public r-bingsd
  (package
    (name "r-bingsd")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BinGSD" version))
       (sha256
        (base32 "0986203jjszha7sm7n63lwi9zlfwrla9g8mi1fm84dzjn9bc9dgi"))))
    (properties `((upstream-name . "BinGSD")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://cran.r-project.org/package=BinGSD")
    (synopsis
     "Calculate Boundaries and Conditional Power for Single Arm Group Sequential Test with Binary Endpoint")
    (description
     "Consider an at-most-K-stage group sequential design with only an upper bound for
the last analysis and non-binding lower bounds.With binary endpoint, two kinds
of test can be applied, asymptotic test based on normal distribution and exact
test based on binomial distribution.  This package supports the computation of
boundaries and conditional power for single-arm group sequential test with
binary endpoint, via either asymptotic or exact test.  The package also provides
functions to obtain boundary crossing probabilities given the design.")
    (license license:gpl3)))

(define-public r-bingroup2
  (package
    (name "r-bingroup2")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binGroup2" version))
       (sha256
        (base32 "000a8vci2sfxgbw412i0wajmnwgpx54zalcv4xhvyckh69i1z1db"))))
    (properties `((upstream-name . "binGroup2")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-rdpack
                             r-rcpparmadillo
                             r-rcpp
                             r-rbeta2009
                             r-partitions
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=binGroup2")
    (synopsis "Identification and Estimation using Group Testing")
    (description
     "This package provides methods for the group testing identification problem: 1)
Operating characteristics (e.g., expected number of tests) for commonly used
hierarchical and array-based algorithms, and 2) Optimal testing configurations
for these same algorithms.  Methods for the group testing estimation problem: 1)
Estimation and inference procedures for an overall prevalence, and 2) Regression
modeling for commonly used hierarchical and array-based algorithms.")
    (license license:gpl3+)))

(define-public r-bingroup
  (package
    (name "r-bingroup")
    (version "2.2-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binGroup" version))
       (sha256
        (base32 "0cb7j6b0s3y56mv1967awwri0kv0rf3sr3vwf9gc2zbjggxi9ffp"))))
    (properties `((upstream-name . "binGroup")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-partitions))
    (home-page "https://cran.r-project.org/package=binGroup")
    (synopsis "Evaluation and Experimental Design for Binomial Group Testing")
    (description
     "This package provides methods for estimation and hypothesis testing of
proportions in group testing designs: methods for estimating a proportion in a
single population (assuming sensitivity and specificity equal to 1 in designs
with equal group sizes), as well as hypothesis tests and functions for
experimental design for this situation.  For estimating one proportion or the
difference of proportions, a number of confidence interval methods are included,
which can deal with various different pool sizes.  Further, regression methods
are implemented for simple pooling and matrix pooling designs.  Methods for
identification of positive items in group testing designs: Optimal testing
configurations can be found for hierarchical and array-based algorithms.
Operating characteristics can be calculated for testing configurations across a
wide variety of situations.")
    (license license:gpl3+)))

(define-public r-bingat
  (package
    (name "r-bingat")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bingat" version))
       (sha256
        (base32 "1y68rgafipfad78yrzcygdszgy1d5q739kap06pzr78bn3i8hiwa"))))
    (properties `((upstream-name . "bingat")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-network
                             r-matrixstats
                             r-gplots
                             r-foreach
                             r-doparallel))
    (home-page "https://cran.r-project.org/package=bingat")
    (synopsis "Binary Graph Analysis Tools")
    (description
     "This package provides tools to analyze binary graph objects.")
    (license license:asl2.0)))

(define-public r-bingadsr
  (package
    (name "r-bingadsr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bingadsR" version))
       (sha256
        (base32 "071j30fmskfw513yf9fdcm711r17kcxpn5ly20dk11chkxk6mcsc"))))
    (properties `((upstream-name . "bingadsR")))
    (build-system r-build-system)
    (propagated-inputs (list r-jsonlite))
    (native-inputs (list r-knitr))
    (home-page "https://windsor.ai/")
    (synopsis "Get Bing Ads Data via the 'Windsor.ai' API")
    (description
     "Collect your data on digital marketing campaigns from bing Ads using the
Windsor.ai API <https://windsor.ai/api-fields/>.")
    (license license:gpl3)))

(define-public r-binfunest
  (package
    (name "r-binfunest")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binfunest" version))
       (sha256
        (base32 "0gqvk9hfsskm2h6vq80hcmy08wrricdc96np8dhx1slcyqhhwz0c"))))
    (properties `((upstream-name . "binfunest")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/PhilShea/binfunest")
    (synopsis
     "Estimates Parameters of Functions Driving Binomial Random Variables")
    (description
     "This package provides maximum likelihood estimates of the performance parameters
that drive a binomial distribution of observed errors, and takes full advantage
of zero error observations.  High performance communications systems typically
have inherent noise sources and other performance limitations that need to be
estimated.  Measurements made at high signal to noise ratios typically result in
zero errors due to limitation in available measurement time.  Package includes
theoretical performance functions for common modulation schemes (Proakis,
\"Digital Communications\" (1995, <ISBN:0-07-051726-6>)), polarization shifted
QPSK (Agrell & Karlsson (2009, <DOI:10.1109/JLT.2009.2029064>)), and utility
functions to work with the performance functions.")
    (license license:expat)))

(define-public r-binford
  (package
    (name "r-binford")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binford" version))
       (sha256
        (base32 "04mpdvynlm4gddybys04k0446gc9hyq0vaxr6a525p76ls5aw4b1"))))
    (properties `((upstream-name . "binford")))
    (build-system r-build-system)
    (home-page "http://github.com/benmarwick/binford")
    (synopsis "Binford's Hunter-Gatherer Data")
    (description
     "Binford's hunter-gatherer data includes more than 200 variables coding aspects
of hunter-gatherer subsistence, mobility, and social organization for 339
ethnographically documented groups of hunter-gatherers.")
    (license license:gpl3)))

(define-public r-binequality
  (package
    (name "r-binequality")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binequality" version))
       (sha256
        (base32 "19sm4gk7pnp0aq0bil3acm2jc6w26pkj11vvxzysgs1xlmsra7vg"))))
    (properties `((upstream-name . "binequality")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-ineq r-gamlss-dist r-gamlss-cens
                             r-gamlss))
    (home-page "https://cran.r-project.org/package=binequality")
    (synopsis "Methods for Analyzing Binned Income Data")
    (description
     "This package provides methods for model selection, model averaging, and
calculating metrics, such as the Gini, Theil, Mean Log Deviation, etc, on binned
income data where the topmost bin is right-censored.  We provide both a
non-parametric method, termed the bounded midpoint estimator (BME), which
assigns cases to their bin midpoints; except for the censored bins, where cases
are assigned to an income estimated by fitting a Pareto distribution.  Because
the usual Pareto estimate can be inaccurate or undefined, especially in small
samples, we implement a bounded Pareto estimate that yields much better results.
 We also provide a parametric approach, which fits distributions from the
generalized beta (GB) family.  Because some GB distributions can have poor fit
or undefined estimates, we fit 10 GB-family distributions and use multimodel
inference to obtain definite estimates from the best-fitting distributions.  We
also provide binned income data from all United States of America school
districts, counties, and states.")
    (license license:gpl3+)))

(define-public r-bindata
  (package
    (name "r-bindata")
    (version "0.9-21")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bindata" version))
       (sha256
        (base32 "0s1f7jvlr8xjn89ns5kq0wyvcibdvhyaw92pz1p14nbmg8lqky5d"))))
    (properties `((upstream-name . "bindata")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-e1071))
    (home-page "https://cran.r-project.org/package=bindata")
    (synopsis "Generation of Artificial Binary Data")
    (description "Generation of correlated artificial binary data.")
    (license license:gpl2)))

(define-public r-binda
  (package
    (name "r-binda")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binda" version))
       (sha256
        (base32 "0fgdpdjbw95rwv7l0acfb9cf7s57q5ld2vgdvrv1pxwkyi902nh4"))))
    (properties `((upstream-name . "binda")))
    (build-system r-build-system)
    (propagated-inputs (list r-entropy))
    (home-page "https://strimmerlab.github.io/software/binda/")
    (synopsis "Multi-Class Discriminant Analysis using Binary Predictors")
    (description
     "This package implements functions for multi-class discriminant analysis using
binary predictors, for corresponding variable selection, and for dichotomizing
continuous data.")
    (license license:gpl3+)))

(define-public r-bincor
  (package
    (name "r-bincor")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BINCOR" version))
       (sha256
        (base32 "0x2s82jql429shk70bhjdy9kamz8dz5ymsxj6kp8ga1711bpwyq6"))))
    (properties `((upstream-name . "BINCOR")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma))
    (home-page "https://cran.r-project.org/package=BINCOR")
    (synopsis "Estimate the Correlation Between Two Irregular Time Series")
    (description
     "Estimate the correlation between two irregular time series that are not
necessarily sampled on identical time points.  This program is also applicable
to the situation of two evenly spaced time series that are not on the same time
grid.  BINCOR is based on a novel estimation approach proposed by Mudelsee
(2010, 2014) to estimate the correlation between two climate time series with
different timescales.  The idea is that autocorrelation (AR1 process) allows to
correlate values obtained on different time points.  BINCOR contains four
functions: bin_cor() (the main function to build the binned time series),
plot_ts() (to plot and compare the irregular and binned time series, cor_ts()
(to estimate the correlation between the binned time series) and ccf_ts() (to
estimate the cross-correlation between the binned time series).")
    (license license:gpl2+)))

(define-public r-binb
  (package
    (name "r-binb")
    (version "0.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binb" version))
       (sha256
        (base32 "18f4l7fa3b0h1r91xy9nvplllgnsx3r765qibbbz1rz5zq6cnjpg"))))
    (properties `((upstream-name . "binb")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmarkdown r-knitr r-codetools))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/eddelbuettel/binb")
    (synopsis "'binb' is not 'Beamer'")
    (description
     "This package provides a collection of @code{LaTeX} styles using Beamer
customization for pdf-based presentation slides in RMarkdown'.  At present it
contains RMarkdown adaptations of the @code{LaTeX} themes Metropolis (formerly
mtheme') theme by Matthias Vogelgesang and others (now included in
@code{TeXLive}'), the IQSS by Ista Zahn (which is included here), and the Monash
theme by Rob J Hyndman.  Additional (free) fonts may be needed: Metropolis
prefers Fira', and IQSS requires Libertinus'.")
    (license license:gpl2+)))

(define-public r-binarymm
  (package
    (name "r-binarymm")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binaryMM" version))
       (sha256
        (base32 "0vjs3in8rih5iinlfcrilpqwqjxkdf01h6giv993zy8l3s28r9yd"))))
    (properties `((upstream-name . "binaryMM")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-mass r-fastghquad))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=binaryMM")
    (synopsis "Flexible Marginalized Models for Binary Correlated Outcomes")
    (description
     "Estimates marginalized mean and dependence model parameters for correlated
binary response data.  Dependence model may include transition and/or latent
variable terms.  Methods are described in: Schildcrout and Heagerty (2007)
<doi:10.1111/j.1541-0420.2006.00680.x>, Heagerty (1999)
<doi:10.1111/j.0006-341x.1999.00688.x>, Heagerty (2002)
<doi:10.1111/j.0006-341x.2002.00342.x>.")
    (license license:expat)))

(define-public r-binarygp
  (package
    (name "r-binarygp")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binaryGP" version))
       (sha256
        (base32 "0i4xi0gxk0lamjdygyx0azdp3kfqmva1g20nl3p65w4yx77m88d6"))))
    (properties `((upstream-name . "binaryGP")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-nloptr
                             r-logitnorm
                             r-lhs
                             r-gpfit))
    (home-page "https://cran.r-project.org/package=binaryGP")
    (synopsis
     "Fit and Predict a Gaussian Process Model with (Time-Series) Binary Response")
    (description
     "Allows the estimation and prediction for binary Gaussian process model.  The
mean function can be assumed to have time-series structure.  The estimation
methods for the unknown parameters are based on penalized
quasi-likelihood/penalized quasi-partial likelihood and restricted maximum
likelihood.  The predicted probability and its confidence interval are computed
by Metropolis-Hastings algorithm.  More details can be seen in Sung et al (2017)
<@code{arXiv:1705.02511>}.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-binaryeppm
  (package
    (name "r-binaryeppm")
    (version "3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BinaryEPPM" version))
       (sha256
        (base32 "1sjwh423vap4gd44mkpdhsmp90y9rzr3zkzzakbfczawxz3mbcrp"))))
    (properties `((upstream-name . "BinaryEPPM")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv r-lmtest r-formula r-expm))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=BinaryEPPM")
    (synopsis
     "Mean and Scale-Factor Modeling of Under- And Over-Dispersed Binary Data")
    (description
     "Under- and over-dispersed binary data are modeled using an extended Poisson
process model (EPPM) appropriate for binary data.  A feature of the model is
that the under-dispersion relative to the binomial distribution only needs to be
greater than zero, but the over-dispersion is restricted compared to other
distributional models such as the beta and correlated binomials.  Because of
this, the examples focus on under-dispersed data and how, in combination with
the beta or correlated distributions, flexible models can be fitted to data
displaying both under- and over-dispersion.  Using Generalized Linear Model
(GLM) terminology, the functions utilize linear predictors for the probability
of success and scale-factor with various link functions for p, and log link for
scale-factor, to fit a variety of models relevant to areas such as bioassay.
Details of the EPPM are in Faddy and Smith (2012) <doi:10.1002/bimj.201100214>
and Smith and Faddy (2019) <doi:10.18637/jss.v090.i08>.")
    (license license:gpl2)))

(define-public r-binaryemvs
  (package
    (name "r-binaryemvs")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BinaryEMVS" version))
       (sha256
        (base32 "1ainp6pg481yqhjc4f1pkxzxczbi3qm0kgq280vndq859ldfkpnh"))))
    (properties `((upstream-name . "BinaryEMVS")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BinaryEMVS")
    (synopsis "Variable Selection for Binary Data Using the EM Algorithm")
    (description
     "This package implements variable selection for high dimensional datasets with a
binary response variable using the EM algorithm.  Both probit and logit models
are supported.  Also included is a useful function to generate high dimensional
data with correlated variables.")
    (license license:gpl3)))

(define-public r-binarydosage
  (package
    (name "r-binarydosage")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BinaryDosage" version))
       (sha256
        (base32 "10nf11c57j0ls4znm137w831s7zmihg7bybrfg666hx7c9amspjw"))))
    (properties `((upstream-name . "BinaryDosage")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-prodlim r-digest))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BinaryDosage")
    (synopsis "Creates, Merges, and Reads Binary Dosage Files")
    (description
     "This package provides tools to create binary dosage from either VCF or GEN
files, merge binary dosage files, and read binary dosage files.")
    (license license:gpl3)))

(define-public r-binarybalancedcut
  (package
    (name "r-binarybalancedcut")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BinarybalancedCut" version))
       (sha256
        (base32 "1rs7x7ggqzaz9r2912g0fi9x7przd8gjy6pianx457w5f39fqx4v"))))
    (properties `((upstream-name . "BinarybalancedCut")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-ggplot2))
    (home-page "https://cran.r-project.org/package=BinarybalancedCut")
    (synopsis
     "Threshold Cut Point of Probability for a Binary Classifier Model")
    (description
     "Allows to view the optimal probability cut-off point at which the Sensitivity
and Specificity meets and its a best way to minimize both Type-1 and Type-2
error for a binary Classifier in determining the Probability threshold.")
    (license license:gpl2)))

(define-public r-binarize
  (package
    (name "r-binarize")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Binarize" version))
       (sha256
        (base32 "08xg3s30mk5q0hfvigysnfp8lsnq42j267ylpnhphw5vzlg5xivx"))))
    (properties `((upstream-name . "Binarize")))
    (build-system r-build-system)
    (propagated-inputs (list r-diptest))
    (home-page "https://cran.r-project.org/package=Binarize")
    (synopsis "Binarization of One-Dimensional Data")
    (description
     "This package provides methods for the binarization of one-dimensional data and
some visualization functions.")
    (license license:artistic2.0)))

(define-public r-binancer
  (package
    (name "r-binancer")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "binancer" version))
       (sha256
        (base32 "1diy0g3mvzxs0jxybpk42m3ccs5qk1ynw116aaxsn5k9ch7crj3l"))))
    (properties `((upstream-name . "binancer")))
    (build-system r-build-system)
    (propagated-inputs (list r-snakecase
                             r-logger
                             r-jsonlite
                             r-httr
                             r-digest
                             r-data-table))
    (home-page "https://daroczig.github.io/binancer/")
    (synopsis "API Client to 'Binance'")
    (description
     "R client to the Binance Public Rest API for data collection on cryptocurrencies,
portfolio management and trading:
<https://github.com/binance/binance-spot-api-docs/blob/master/rest-api.md>.")
    (license (license:fsdg-compatible "AGPL"))))

(define-public r-bimodalindex
  (package
    (name "r-bimodalindex")
    (version "1.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BimodalIndex" version))
       (sha256
        (base32 "1m1dcj0ryjicaqjlkch7aymsklmvjvn2dvnbssi1cpi54bzn8l7g"))))
    (properties `((upstream-name . "BimodalIndex")))
    (build-system r-build-system)
    (propagated-inputs (list r-oompabase r-mclust))
    (home-page "http://oompa.r-forge.r-project.org/")
    (synopsis "The Bimodality Index")
    (description
     "Defines the functions used to compute the bimodal index as defined by Wang et
al. (2009) <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2730180/>,
<doi:10.4137/CIN.S2846>.")
    (license license:asl2.0)))

(define-public r-bimets
  (package
    (name "r-bimets")
    (version "4.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bimets" version))
       (sha256
        (base32 "155radkxpjqkpdyg52qck8yiffp29nk1bplalwqkvy50lz97wrai"))))
    (properties `((upstream-name . "bimets")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-xts))
    (home-page "https://github.com/andrea-luciani/bimets")
    (synopsis "Time Series and Econometric Modeling")
    (description
     "Time series analysis, (dis)aggregation and manipulation, e.g. time series
extension, merge, projection, lag, lead, delta, moving and cumulative average
and product, selection by index, date and year-period, conversion to daily,
monthly, quarterly, (semi)annually.  Simultaneous equation models definition,
estimation, simulation and forecasting with coefficient restrictions, error
autocorrelation, exogenization, add-factors, impact and interim multipliers
analysis, conditional equation evaluation, rational expectations, endogenous
targeting and model renormalization, structural stability, stochastic simulation
and forecast, optimal control.")
    (license license:gpl3)))

(define-public r-billboard
  (package
    (name "r-billboard")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "billboard" version))
       (sha256
        (base32 "1z3y8dijhc1381y91n5zq305xzm1gpvs0g4mdpfr7zrblpa8ws39"))))
    (properties `((upstream-name . "billboard")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble))
    (home-page "https://github.com/mikkelkrogsholm/billboard")
    (synopsis "Contains Data of Billboard Hot 100 Songs")
    (description
     "This package contains data sets regarding songs on the Billboard Hot 100 list
from 1960 to 2016.  The data sets include the ranks for the given year, musical
features of a lot of the songs and lyrics for several of the songs as well.")
    (license license:expat)))

(define-public r-bild
  (package
    (name "r-bild")
    (version "1.2-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bild" version))
       (sha256
        (base32 "18fcfaavqdhx0n5l4mhg5ibg9wp01amxj1qin25immsx6pwmp9gb"))))
    (properties `((upstream-name . "bild")))
    (build-system r-build-system)
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=bild")
    (synopsis "Package for BInary Longitudinal Data")
    (description
     "This package performs logistic regression for binary longitudinal data, allowing
for serial dependence among observations from a given individual and a random
intercept term.  Estimation is via maximization of the exact likelihood of a
suitably defined model.  Missing values and unbalanced data are allowed, with
some restrictions.  M. Helena Goncalves et al.(2007) <DOI:
10.18637/jss.v046.i09>.")
    (license license:gpl2+)))

(define-public r-bikm1
  (package
    (name "r-bikm1")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bikm1" version))
       (sha256
        (base32 "1ffw7vfl7cpj6n6c3hrnn4pjnwmw623xdj469fh61q6c2ig5sq87"))))
    (properties `((upstream-name . "bikm1")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2
                             r-pracma
                             r-lpsolve
                             r-gtools
                             r-ggplot2
                             r-ade4))
    (home-page "https://cran.r-project.org/package=bikm1")
    (synopsis
     "Co-Clustering Adjusted Rand Index and Bikm1 Procedure for Contingency and Binary Data-Sets")
    (description
     "Co-clustering of the rows and columns of a contingency or binary matrix, or
double binary matrices and model selection for the number of row and column
clusters.  Three models are considered: the Poisson latent block model for
contingency matrix, the binary latent block model for binary matrix and a new
model we develop: the multiple latent block model for double binary matrices.  A
new procedure named bikm1 is implemented to investigate more efficiently the
grid of numbers of clusters.  Then, the studied model selection criteria are the
integrated completed likelihood (ICL) and the Bayesian integrated likelihood
(BIC).  Finally, the co-clustering adjusted Rand index (CARI) to measure
agreement between co-clustering partitions is implemented.  Robert Valerie,
Vasseur Yann, Brault Vincent (2021) <doi:10.1007/s00357-020-09379-w>.")
    (license license:gpl2)))

(define-public r-bikeshare14
  (package
    (name "r-bikeshare14")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bikeshare14" version))
       (sha256
        (base32 "1ca8w4yfw0bfqls0g1qqh8gkyk23d0mi22kavawv9wwhydxrxxjc"))))
    (properties `((upstream-name . "bikeshare14")))
    (build-system r-build-system)
    (home-page "https://github.com/arunsrinivasan/bikeshare14")
    (synopsis "Bay Area Bike Share Trips in 2014")
    (description
     "Anonymised Bay Area bike share trip data for the year 2014.  Also contains
additional metadata on stations and weather.")
    (license license:cc0)))

(define-public r-bigvar
  (package
    (name "r-bigvar")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BigVAR" version))
       (sha256
        (base32 "1f67gk54gzdlil79gqqws0i6j0rvjqk4k0bhdb9adcvcsdxfkwwy"))))
    (properties `((upstream-name . "BigVAR")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-rcppeigen
                             r-rcpparmadillo
                             r-rcpp
                             r-mass
                             r-lattice
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/wbnicholson/BigVAR")
    (synopsis "Dimension Reduction Methods for Multivariate Time Series")
    (description
     "Estimates VAR and VARX models with Structured Penalties using the methods
developed by Nicholson et al (2017)<doi:10.1016/j.ijforecast.2017.01.003> and
Nicholson et al (2020) <doi:10.48550/@code{arXiv.1412.5250>}.")
    (license license:gpl2+)))

(define-public r-bigutilsr
  (package
    (name "r-bigutilsr")
    (version "0.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigutilsr" version))
       (sha256
        (base32 "096h0v277n39bvipfbfd730lz3qkplfnv58zmzsyy3al9f7lajxl"))))
    (properties `((upstream-name . "bigutilsr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rspectra
                             r-robustbase
                             r-rcpp
                             r-nabor
                             r-bigparallelr
                             r-bigassertr))
    (home-page "https://github.com/privefl/bigutilsr")
    (synopsis "Utility Functions for Large-scale Data")
    (description
     "Utility functions for large-scale data.  For now, package bigutilsr mainly
includes functions for outlier detection and unbiased PCA projection.")
    (license license:gpl3)))

(define-public r-bigtime
  (package
    (name "r-bigtime")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigtime" version))
       (sha256
        (base32 "1lnq8zjhvnm5q90y2aak11l8a726p1w4wgjwdvdn9pzlwgm8xia5"))))
    (properties `((upstream-name . "bigtime")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-rcppeigen
                             r-rcpparmadillo
                             r-rcpp
                             r-magrittr
                             r-ggplot2
                             r-dplyr
                             r-corrplot))
    (home-page "https://github.com/ineswilms/bigtime")
    (synopsis "Sparse Estimation of Large Time Series Models")
    (description
     "Estimation of large Vector @code{AutoRegressive} (VAR), Vector
@code{AutoRegressive} with Exogenous Variables X (VARX) and Vector
@code{AutoRegressive} Moving Average (VARMA) Models with Structured Lasso
Penalties, see Nicholson, Wilms, Bien and Matteson (2020)
<https://jmlr.org/papers/v21/19-777.html> and Wilms, Basu, Bien and Matteson
(2021) <doi:10.1080/01621459.2021.1942013>.")
    (license license:gpl2+)))

(define-public r-bigtcr
  (package
    (name "r-bigtcr")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigtcr" version))
       (sha256
        (base32 "1l03yc28afdm7glbw4ay0zsywjgqg5l90qz1hfhslsy8gg7d5wq5"))))
    (properties `((upstream-name . "bigtcr")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=bigtcr")
    (synopsis
     "Nonparametric Analysis of Bivariate Gap Time with Competing Risks")
    (description
     "For studying recurrent disease and death with competing risks, comparisons based
on the well-known cumulative incidence function can be confounded by different
prevalence rates of the competing events.  Alternatively, comparisons of the
conditional distribution of the survival time given the failure event type are
more relevant for investigating the prognosis of different patterns of
recurrence disease.  This package implements a nonparametric estimator for the
conditional cumulative incidence function and a nonparametric conditional
bivariate cumulative incidence function for the bivariate gap times proposed in
Huang et al. (2016) <doi:10.1111/biom.12494>.")
    (license license:gpl3+)))

(define-public r-bigtabulate
  (package
    (name "r-bigtabulate")
    (version "1.1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigtabulate" version))
       (sha256
        (base32 "0ws73fasmwsjbv5l63iln164ai7n3dy6wwpm8vqkpys7wl1i3xxy"))))
    (properties `((upstream-name . "bigtabulate")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-bigmemory r-biganalytics r-bh))
    (home-page "http://www.bigmemory.org")
    (synopsis
     "Table, Apply, and Split Functionality for Matrix and 'big.matrix' Objects")
    (description
     "Extend the bigmemory package with table', tapply', and split support for
big.matrix objects.  The functions may also be used with native R matrices for
improving speed and memory-efficiency.")
    (license (list license:lgpl3 license:asl2.0))))

(define-public r-bigsurvsgd
  (package
    (name "r-bigsurvsgd")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigSurvSGD" version))
       (sha256
        (base32 "08i6hwfr4rpvmxfa0vx8bi2dsa63iasj049ndwfn6zgvai6qaz0s"))))
    (properties `((upstream-name . "bigSurvSGD")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rcpp r-foreach r-doparallel
                             r-bigmemory))
    (home-page "https://cran.r-project.org/package=bigSurvSGD")
    (synopsis "Big Survival Analysis Using Stochastic Gradient Descent")
    (description
     "Fits Cox model via stochastic gradient descent.  This implementation avoids
computational instability of the standard Cox Model when dealing large datasets.
 Furthermore, it scales up with large datasets that do not fit the memory.  It
also handles large sparse datasets using proximal stochastic gradient descent
algorithm.  For more details about the method, please see Aliasghar Tarkhan and
Noah Simon (2020) <@code{arXiv:2003.00116v2>}.")
    (license license:gpl2+)))

(define-public r-bigstep
  (package
    (name "r-bigstep")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigstep" version))
       (sha256
        (base32 "1bppvib57p6m91c2v9w9lgb1k3jg8mk3w2kkbjvwgsdwy4m8fhap"))))
    (properties `((upstream-name . "bigstep")))
    (build-system r-build-system)
    (propagated-inputs (list r-speedglm
                             r-rcppeigen
                             r-r-utils
                             r-matrixstats
                             r-magrittr
                             r-bigmemory))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/pmszulc/bigstep")
    (synopsis "Stepwise Selection for Large Data Sets")
    (description
     "Selecting linear and generalized linear models for large data sets using
modified stepwise procedure and modern selection criteria (like modifications of
Bayesian Information Criterion).  Selection can be performed on data which
exceed RAM capacity.  Bogdan et al., (2004) <doi:10.1534/genetics.103.021683>.")
    (license license:gpl3)))

(define-public r-bigstatsr
  (package
    (name "r-bigstatsr")
    (version "1.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigstatsr" version))
       (sha256
        (base32 "1c445mm11gsvs2sqmrblig1d3456vwb1n3rz08mhkpxghjaf4dci"))))
    (properties `((upstream-name . "bigstatsr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-rspectra
                             r-rmio
                             r-rcpparmadillo
                             r-rcpp
                             r-ps
                             r-ggplot2
                             r-foreach
                             r-cowplot
                             r-bigparallelr
                             r-bigassertr))
    (home-page "https://privefl.github.io/bigstatsr/")
    (synopsis "Statistical Tools for Filebacked Big Matrices")
    (description
     "Easy-to-use, efficient, flexible and scalable statistical tools.  Package
bigstatsr provides and uses Filebacked Big Matrices via memory-mapping.  It
provides for instance matrix operations, Principal Component Analysis, sparse
linear supervised models, utility functions and more
<doi:10.1093/bioinformatics/bty185>.")
    (license license:gpl3)))

(define-public r-bigsplines
  (package
    (name "r-bigsplines")
    (version "1.1-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigsplines" version))
       (sha256
        (base32 "1kf04p2lglzdi1fdryk27nmj2a2jca2ii7ki8vak93sq21isb179"))))
    (properties `((upstream-name . "bigsplines")))
    (build-system r-build-system)
    (propagated-inputs (list r-quadprog))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=bigsplines")
    (synopsis "Smoothing Splines for Large Samples")
    (description
     "Fits smoothing spline regression models using scalable algorithms designed for
large samples.  Seven marginal spline types are supported: linear, cubic,
different cubic, cubic periodic, cubic thin-plate, ordinal, and nominal.  Random
effects and parametric effects are also supported.  Response can be Gaussian or
non-Gaussian: Binomial, Poisson, Gamma, Inverse Gaussian, or Negative Binomial.")
    (license license:gpl2+)))

(define-public r-bigsparser
  (package
    (name "r-bigsparser")
    (version "0.7.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigsparser" version))
       (sha256
        (base32 "0v28k0jh7nw3ms5sh2izd28b4p5b9qf4rrnps0pfwpq0xmigr4ch"))))
    (properties `((upstream-name . "bigsparser")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmio r-rcppeigen r-rcpp r-matrix r-bigassertr))
    (home-page "https://github.com/privefl/bigsparser")
    (synopsis "Sparse Matrix Format with Data on Disk")
    (description
     "Provide a sparse matrix format with data stored on disk, to be used in both R
and C++.  This is intended for more efficient use of sparse data in C++ and also
when parallelizing, since data on disk does not need copying.  Only a limited
number of features will be implemented.  For now, conversion can be performed
from a @code{dgCMatrix} or a @code{dsCMatrix} from R package Matrix'.  A new
compact format is also now available.")
    (license license:gpl3)))

(define-public r-bigsnpr
  (package
    (name "r-bigsnpr")
    (version "1.12.15")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigsnpr" version))
       (sha256
        (base32 "1cs3i68fdbihyl2p2k4dbw6c0r8mq0m66bs42npyhkgazzg1q77j"))))
    (properties `((upstream-name . "bigsnpr")))
    (build-system r-build-system)
    (inputs (list zlib))
    (propagated-inputs (list r-vctrs
                             r-runonce
                             r-roptim
                             r-rmio
                             r-rcpparmadillo
                             r-rcpp
                             r-matrix
                             r-magrittr
                             r-ggplot2
                             r-foreach
                             r-dorng
                             r-data-table
                             r-bigutilsr
                             r-bigstatsr
                             r-bigsparser
                             r-bigreadr
                             r-bigparallelr
                             r-bigassertr))
    (home-page "https://privefl.github.io/bigsnpr/")
    (synopsis "Analysis of Massive SNP Arrays")
    (description
     "Easy-to-use, efficient, flexible and scalable tools for analyzing massive SNP
arrays.  PrivÃ© et al. (2018) <doi:10.1093/bioinformatics/bty185>.")
    (license license:gpl3)))

(define-public r-bigsimr
  (package
    (name "r-bigsimr")
    (version "0.12.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigsimr" version))
       (sha256
        (base32 "1cvj6p5g2g8ldi3g6d1mvhsf8xcsxgqd71frghdqybz6f1vdbs34"))))
    (properties `((upstream-name . "bigsimr")))
    (build-system r-build-system)
    (inputs (list julia))
    (propagated-inputs (list r-juliacall))
    (home-page "https://github.com/SchisslerGroup/r-bigsimr")
    (synopsis "Fast Generation of High-Dimensional Random Vectors")
    (description
     "Simulate multivariate data with arbitrary marginal distributions.  bigsimr is a
package for simulating high-dimensional multivariate data with a target
correlation and arbitrary marginal distributions via Gaussian copula.  It
utilizes the Julia package Bigsimr.jl for its core routines.")
    (license license:gpl3)))

(define-public r-bigrquerystorage
  (package
    (name "r-bigrquerystorage")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigrquerystorage" version))
       (sha256
        (base32 "1x48msnh3lhi5x74n1v7icd3vzxwqw9yaq8llcr0a79mj6abiwar"))))
    (properties `((upstream-name . "bigrquerystorage")))
    (build-system r-build-system)
    (inputs (list zlib openssl))
    (propagated-inputs (list r-tibble
                             r-rlang
                             r-rcpp
                             r-nanoarrow
                             r-lifecycle
                             r-bit64
                             r-assertthat))
    (native-inputs (list pkg-config))
    (home-page "https://github.com/meztez/bigrquerystorage")
    (synopsis "An Interface to Google's 'BigQuery Storage' API")
    (description
     "Easily talk to Google's @code{BigQuery} Storage API from R
(<https://cloud.google.com/bigquery/docs/reference/storage/rpc>).")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-bigreg
  (package
    (name "r-bigreg")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigReg" version))
       (sha256
        (base32 "08rpkskvsnmln4583074nxlkjf2qkg61v24c0lbj27rfp2ab0wzw"))))
    (properties `((upstream-name . "bigReg")))
    (build-system r-build-system)
    (propagated-inputs (list r-uuid r-rcpparmadillo r-rcpp r-mass))
    (home-page "https://cran.r-project.org/package=bigReg")
    (synopsis "Generalized Linear Models (GLM) for Large Data Sets")
    (description
     "Allows the user to carry out GLM on very large data sets.  Data can be created
using the data_frame() function and appended to the object with
object$append(data); data_frame and data_matrix objects are available that allow
the user to store large data on disk.  The data is stored as doubles in binary
format and any character columns are transformed to factors and then stored as
numeric (binary) data while a look-up table is stored in a separate .meta_data
file in the same folder.  The data is stored in blocks and GLM regression
algorithm is modified and carries out a @code{MapReduce}- like algorithm to fit
the model.  The functions bglm(), and summary() and bglm_predict() are available
for creating and post-processing of models.  The library requires Armadillo
installed on your system.  It may not function on windows since multi-core
processing is done using mclapply() which forks R on Unix/Linux type operating
systems.")
    (license license:gpl2+)))

(define-public r-bigreadr
  (package
    (name "r-bigreadr")
    (version "0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigreadr" version))
       (sha256
        (base32 "15wy5rphk2l59k8fk68a15xsvy8nxj1srnc2c86i7y5ym04a856k"))))
    (properties `((upstream-name . "bigreadr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-parallelly r-data-table r-bigassertr))
    (home-page "https://github.com/privefl/bigreadr")
    (synopsis "Read Large Text Files")
    (description
     "Read large text files by splitting them in smaller files.  Package bigreadr also
provides some convenient wrappers around fread() and fwrite() from package
data.table'.")
    (license license:gpl3)))

(define-public r-bigquic
  (package
    (name "r-bigquic")
    (version "1.1-13")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BigQuic" version))
       (sha256
        (base32 "14mz9ry72zc4c0fhc59dima66qnksr1masvhzm1j8z8r11w7qjhd"))))
    (properties `((upstream-name . "BigQuic")))
    (build-system r-build-system)
    (propagated-inputs (list r-scalreg r-rcpp r-matrix))
    (home-page "https://www.r-project.org")
    (synopsis "Big Quadratic Inverse Covariance Estimation")
    (description
     "Use Newton's method, coordinate descent, and METIS clustering to solve the L1
regularized Gaussian MLE inverse covariance matrix estimation problem.")
    (license (list license:gpl3+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-bigqf
  (package
    (name "r-bigqf")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigQF" version))
       (sha256
        (base32 "01qr1cw5v330kkddmgwk4fdlm6msnqcbx1kfzw390yfwq5pr84sy"))))
    (properties `((upstream-name . "bigQF")))
    (build-system r-build-system)
    (propagated-inputs (list r-svd r-matrix r-coxme r-compquadform))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/tslumley/bigQF")
    (synopsis "Quadratic Forms in Large Matrices")
    (description
     "This package provides a computationally-efficient leading-eigenvalue
approximation to tail probabilities and quantiles of large quadratic forms, in
particular for the Sequence Kernel Association Test (SKAT) used in genomics
<doi:10.1002/gepi.22136>.  Also provides stochastic singular value decomposition
for dense or sparse matrices.")
    (license license:gpl2)))

(define-public r-bigparallelr
  (package
    (name "r-bigparallelr")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigparallelr" version))
       (sha256
        (base32 "14rkcig4j93c231sfm43331ykmvxjx2fiz5nhblj2rfzkxli4css"))))
    (properties `((upstream-name . "bigparallelr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rhpcblasctl
                             r-parallelly
                             r-foreach
                             r-flock
                             r-doparallel
                             r-bigassertr))
    (home-page "https://github.com/privefl/bigparallelr")
    (synopsis "Easy Parallel Tools")
    (description
     "Utility functions for easy parallelism in R. Include some reexports from other
packages, utility functions for splitting and parallelizing over blocks, and
choosing and setting the number of cores used.")
    (license license:gpl3)))

(define-public r-bignum
  (package
    (name "r-bignum")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bignum" version))
       (sha256
        (base32 "0v2hiw9kirm78fcqbm2hzrqcvndgcslz9j8gmzba1d5xaivanhkj"))))
    (properties `((upstream-name . "bignum")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs r-rlang r-cpp11 r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://davidchall.github.io/bignum/")
    (synopsis "Arbitrary-Precision Integer and Floating-Point Mathematics")
    (description
     "This package provides classes for storing and manipulating arbitrary-precision
integer vectors and high-precision floating-point vectors.  These extend the
range and precision of the integer and double data types found in R. This
package utilizes the Boost.Multiprecision C++ library.  It is specifically
designed to work well with the tidyverse collection of R packages.")
    (license license:expat)))

(define-public r-bigmds
  (package
    (name "r-bigmds")
    (version "3.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigmds" version))
       (sha256
        (base32 "1pkmzpibmch8zxlp08r460cvszwh4pshi9b8lafmkspj4pdkxlr9"))))
    (properties `((upstream-name . "bigmds")))
    (build-system r-build-system)
    (propagated-inputs (list r-svd r-pracma r-corpcor))
    (home-page "https://github.com/pachoning/bigmds")
    (synopsis "Multidimensional Scaling for Big Data")
    (description
     "MDS is a statistic tool for reduction of dimensionality, using as input a
distance matrix of dimensions n Ã n.  When n is large, classical algorithms
suffer from computational problems and MDS configuration can not be obtained.
With this package, we address these problems by means of six algorithms, being
two of them original proposals: - Landmark MDS proposed by De Silva V. and JB.
Tenenbaum (2004). - Interpolation MDS proposed by Delicado P. and C.
PachÃ³n-GarcÃ­a (2021) <@code{arXiv:2007.11919>} (original proposal). - Reduced
MDS proposed by Paradis E (2018). - Pivot MDS proposed by Brandes U. and C. Pich
(2007) - Divide-and-conquer MDS proposed by Delicado P. and C. PachÃ³n-GarcÃ­a
(2021) <@code{arXiv:2007.11919>} (original proposal). - Fast MDS, proposed by
Yang, T., J. Liu, L. @code{McMillan} and W. Wang (2006).")
    (license license:expat)))

(define-public r-bigmatch
  (package
    (name "r-bigmatch")
    (version "0.6.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigmatch" version))
       (sha256
        (base32 "1a3qiljarnv14dmpr5g2nfxzaf4wmw88md4kjrlrlgxcgmijlwm6"))))
    (properties `((upstream-name . "bigmatch")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcbalance r-plyr r-mvnfast r-liqueuer))
    (home-page "https://cran.r-project.org/package=bigmatch")
    (synopsis "Making Optimal Matching Size-Scalable Using Optimal Calipers")
    (description
     "This package implements optimal matching with near-fine balance in large
observational studies with the use of optimal calipers to get a sparse network.
The caliper is optimal in the sense that it is as small as possible such that a
matching exists.  The main functions in the bigmatch package are optcal() to
find the optimal caliper, optconstant() to find the optimal number of nearest
neighbors, and nfmatch() to find a near-fine balance match with a caliper and a
restriction on the number of nearest neighbors.  Yu, R., Silber, J. H., and
Rosenbaum, P. R. (2020). <DOI:10.1214/19-sts699>.")
    (license license:expat)))

(define-public r-bigmap
  (package
    (name "r-bigmap")
    (version "2.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigMap" version))
       (sha256
        (base32 "08c9s5sk672j70d6pnva7z52i4s3knkby06lva9al9n8a3jiwsvz"))))
    (properties `((upstream-name . "bigMap")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-rcolorbrewer
                             r-colorspace
                             r-bigmemory
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bigMap")
    (synopsis "Big Data Mapping")
    (description
     "Unsupervised clustering protocol for large scale structured data, based on a low
dimensional representation of the data.  Dimensionality reduction is performed
using a parallelized implementation of the t-Stochastic Neighboring Embedding
algorithm (Garriga J. and Bartumeus F. (2018), <@code{arXiv:1812.09869>}).")
    (license license:gpl3)))

(define-public r-bigleaf
  (package
    (name "r-bigleaf")
    (version "0.8.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigleaf" version))
       (sha256
        (base32 "1xvd1fdycyxb9da34v4ibsjwzipil5cyja3iym30q5cqcbxq47g4"))))
    (properties `((upstream-name . "bigleaf")))
    (build-system r-build-system)
    (propagated-inputs (list r-solartime r-robustbase))
    (native-inputs (list r-knitr))
    (home-page "https://bitbucket.org/juergenknauer/bigleaf")
    (synopsis
     "Physical and Physiological Ecosystem Properties from Eddy Covariance Data")
    (description
     "Calculation of physical (e.g. aerodynamic conductance, surface temperature), and
physiological (e.g. canopy conductance, water-use efficiency) ecosystem
properties from eddy covariance data and accompanying meteorological
measurements.  Calculations assume the land surface to behave like a big-leaf
and return bulk ecosystem/canopy variables.")
    (license license:gpl2+)))

(define-public r-biglasso
  (package
    (name "r-biglasso")
    (version "1.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biglasso" version))
       (sha256
        (base32 "165bc1y5byma872yld5y5nxkhyhkfn6h4gfiqv5bkfw5hn77ijlc"))))
    (properties `((upstream-name . "biglasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-ncvreg
                             r-matrix
                             r-bigmemory
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://pbreheny.github.io/biglasso/index.html")
    (synopsis "Extending Lasso Model Fitting to Big Data")
    (description
     "Extend lasso and elastic-net model fitting for ultra high-dimensional,
multi-gigabyte data sets that cannot be loaded into memory.  Designed to be more
memory- and computation-efficient than existing lasso-fitting packages like
glmnet and ncvreg', thus allowing the user to analyze big data analysis even on
an ordinary laptop.")
    (license license:gpl3)))

(define-public r-bigl
  (package
    (name "r-bigl")
    (version "1.9.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BIGL" version))
       (sha256
        (base32 "17skfw03f6ycfkqinisd9mrslwj19g5mm99s4ma65kln3xlx692j"))))
    (properties `((upstream-name . "BIGL")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-robustbase
                             r-progress
                             r-plotly
                             r-numderiv
                             r-nleqslv
                             r-minpack-lm
                             r-mass
                             r-magrittr
                             r-lifecycle
                             r-htmlwidgets
                             r-ggplot2
                             r-data-table))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/openanalytics/BIGL")
    (synopsis "Biochemically Intuitive Generalized Loewe Model")
    (description
     "Response surface methods for drug synergy analysis.  Available methods include
generalized and classical Loewe formulations as well as Highest Single Agent
methodology.  Response surfaces can be plotted in an interactive 3-D plot and
formal statistical tests for presence of synergistic effects are available.
Implemented methods and tests are described in the article \"BIGL: Biochemically
Intuitive Generalized Loewe null model for prediction of the expected combined
effect compatible with partial agonism and antagonism\" by Koen Van der Borght,
Annelies Tourny, Rytis Bagdziunas, Olivier Thas, Maxim Nazarov, Heather Turner,
Bie Verbist & Hugo Ceulemans (2017) <doi:10.1038/s41598-017-18068-5>.")
    (license license:gpl3)))

(define-public r-biggp
  (package
    (name "r-biggp")
    (version "0.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigGP" version))
       (sha256
        (base32 "0awsskpdky56llqc7flgwzs6646cvvn8q50x8gj0hkknfm8fs1rz"))))
    (properties `((upstream-name . "bigGP")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmpi))
    (home-page "https://doi.org/10.18637/jss.v063.i10")
    (synopsis "Distributed Gaussian Process Calculations")
    (description
     "Distributes Gaussian process calculations across nodes in a distributed memory
setting, using Rmpi.  The @code{bigGP} class provides high-level methods for
maximum likelihood with normal data, prediction, calculation of uncertainty
(i.e., posterior covariance calculations), and simulation of realizations.  In
addition, @code{bigGP} provides an API for basic matrix calculations with
distributed covariance matrices, including Cholesky decomposition,
back/forwardsolve, crossproduct, and matrix multiplication.")
    (license license:gpl2+)))

(define-public r-bigergm
  (package
    (name "r-bigergm")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigergm" version))
       (sha256
        (base32 "1gcb4hz399fdcrvjhmimsvzlf2rp6k79mq2jgq5hahgbc76xdg71"))))
    (properties `((upstream-name . "bigergm")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-statnet-common
                             r-rlang
                             r-reticulate
                             r-readr
                             r-rcpparmadillo
                             r-rcpp
                             r-purrr
                             r-network
                             r-memoise
                             r-matrix
                             r-magrittr
                             r-intergraph
                             r-igraph
                             r-glue
                             r-foreach
                             r-ergm-multi
                             r-ergm
                             r-dplyr
                             r-cachem))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bigergm")
    (synopsis
     "Fit, Simulate, and Diagnose Hierarchical Exponential-Family Models for Big Networks")
    (description
     "This package provides a toolbox for analyzing and simulating large networks
based on hierarchical exponential-family random graph models (HERGMs).'bigergm
implements the estimation for large networks efficiently building on the
lighthergm and hergm packages.  Moreover, the package contains tools for
simulating networks with local dependence to assess the goodness-of-fit.")
    (license license:gpl3)))

(define-public r-bigdm
  (package
    (name "r-bigdm")
    (version "0.5.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigDM" version))
       (sha256
        (base32 "183px40ah4043q2yc2811hlwx8vinqk5xxhz1wmgw4m8cig62xgw"))))
    (properties `((upstream-name . "bigDM")))
    (build-system r-build-system)
    (propagated-inputs (list r-spdep
                             r-spatialreg
                             r-sf
                             r-rlist
                             r-rdpack
                             r-rcolorbrewer
                             r-matrix
                             r-mass
                             r-geos
                             r-future-apply
                             r-future
                             r-foreach
                             r-fastdummies
                             r-doparallel
                             r-crayon))
    (home-page "https://github.com/spatialstatisticsupna/bigDM")
    (synopsis
     "Scalable Bayesian Disease Mapping Models for High-Dimensional Data")
    (description
     "This package implements several spatial and spatio-temporal scalable disease
mapping models for high-dimensional count data using the INLA technique for
approximate Bayesian inference in latent Gaussian models (Orozco-Acosta et al.,
2021 <doi:10.1016/j.spasta.2021.100496>; Orozco-Acosta et al., 2023
<doi:10.1016/j.cmpb.2023.107403> and Vicente et al., 2023
<doi:10.1007/s11222-023-10263-x>).  The creation and develpment of this package
has been supported by Project MTM2017-82553-R (AEI/FEDER, UE) and Project
PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033.  It has also been partially
funded by the Public University of Navarra (project PJUPNA2001).")
    (license license:gpl3)))

(define-public r-bigdawg
  (package
    (name "r-bigdawg")
    (version "3.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BIGDAWG" version))
       (sha256
        (base32 "1zbikvhyaksbddv2kz8nqkidjy4qsfp6a8wwyxjy5a0wcnqnwsxf"))))
    (properties `((upstream-name . "BIGDAWG")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml r-httr r-haplo-stats))
    (native-inputs (list r-knitr))
    (home-page "http://tools.immunogenomics.org/")
    (synopsis "Case-Control Analysis of Multi-Allelic Loci")
    (description
     "Data sets and functions for chi-squared Hardy-Weinberg and case-control
association tests of highly polymorphic genetic data [e.g., human leukocyte
antigen (HLA) data].  Performs association tests at multiple levels of
polymorphism (haplotype, locus and HLA amino-acids) as described in Pappas DJ,
Marin W, Hollenbach JA, Mack SJ (2016) <doi:10.1016/j.humimm.2015.12.006>.
Combines rare variants to a common class to account for sparse cells in tables
as described by Hollenbach JA, Mack SJ, Thomson G, Gourraud PA (2012)
<doi:10.1007/978-1-61779-842-9_14>.")
    (license license:gpl3+)))

(define-public r-bigdatadist
  (package
    (name "r-bigdatadist")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigdatadist" version))
       (sha256
        (base32 "16gkybwcp9sxqb0iic2fhmc4gndr44ayg14wrfcjvjaj4g5r6xs2"))))
    (properties `((upstream-name . "bigdatadist")))
    (build-system r-build-system)
    (propagated-inputs (list r-rrcov r-pdist r-mass r-fnn))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=bigdatadist")
    (synopsis
     "Distances for Machine Learning and Statistics in the Context of Big Data")
    (description
     "This package provides functions to compute distances between probability
measures or any other data object than can be posed in this way, entropy
measures for samples of curves, distances and depth measures for functional
data, and the Generalized Mahalanobis Kernel distance for high dimensional data.
 For further details about the metrics please refer to Martos et al (2014)
<doi:10.3233/IDA-140706>; Martos et al (2018) <doi:10.3390/e20010033>; Hernandez
et al (2018, submitted); Martos et al (2018, submitted).")
    (license license:gpl3+)))

(define-public r-bigd
  (package
    (name "r-bigd")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigD" version))
       (sha256
        (base32 "0pzzqqazn1nv2b613vzdyhxvr454lyqq8235jydia6r3k86fpadw"))))
    (properties `((upstream-name . "bigD")))
    (build-system r-build-system)
    (home-page "https://github.com/rich-iannone/bigD")
    (synopsis "Flexibly Format Dates and Times to a Given Locale")
    (description
     "Format dates and times flexibly and to whichever locales make sense.  Parses
dates, times, and date-times in various formats (including string-based ISO 8601
constructions).  The formatting syntax gives the user many options for
formatting the date and time output in a precise manner.  Time zones in the
input can be expressed in multiple ways and there are many options for
formatting time zones in the output as well.  Several of the provided helper
functions allow for automatic generation of locale-aware formatting patterns
based on date/time skeleton formats and standardized date/time formats with
varying specificity.")
    (license license:expat)))

(define-public r-bigchess
  (package
    (name "r-bigchess")
    (version "1.9.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigchess" version))
       (sha256
        (base32 "1vdzx185y1agh1xjf7qcfi2s6l7qzm1kx6b2kr3cbz2in88bws3n"))))
    (properties `((upstream-name . "bigchess")))
    (build-system r-build-system)
    (propagated-inputs (list r-processx))
    (home-page "https://cran.r-project.org/package=bigchess")
    (synopsis
     "Read, Write, Manipulate, Explore Chess PGN Files and R API to UCI Chess Engines")
    (description
     "This package provides functions for reading *.PGN files with more than one game,
including large files without copying it into RAM (using ff package or RSQLite
package).  Handle chess data and chess aggregated data, count figure moves
statistics, create player profile, plot winning chances, browse openings.  Set
of functions of R API to communicate with UCI-protocol based chess engines.")
    (license license:gpl3)))

(define-public r-bigbits
  (package
    (name "r-bigbits")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigBits" version))
       (sha256
        (base32 "1aajx0cg3ljjmvc50qx3v2halqksj54i2krcm7ib33cnqaxzbfbb"))))
    (properties `((upstream-name . "bigBits")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmpfr r-gmp))
    (home-page "https://cran.r-project.org/package=bigBits")
    (synopsis "Perform Boolean Operations on Large Numbers")
    (description
     "This package provides a set of Boolean operators which accept integers of any
size, in any base from 2 to 36, including 2's complement format, and perform
actions like \"AND,\" \"OR\", \"NOT\", \"SHIFTR/L\" etc.  The output can be in any base
specified.  A direct base to base converter is included.")
    (license license:lgpl3)))

(define-public r-bigassertr
  (package
    (name "r-bigassertr")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigassertr" version))
       (sha256
        (base32 "0bk11jinlc1cvm6aaq9mccs9i328b8s2lbwq63a42fgf1qng103p"))))
    (properties `((upstream-name . "bigassertr")))
    (build-system r-build-system)
    (home-page "https://github.com/privefl/bigassertr")
    (synopsis "Assertion and Message Functions")
    (description
     "Enhanced message functions (cat() / message() / warning() / error()) using
wrappers around sprintf().  Also, multiple assertion functions (e.g. to check
class, length, values, files, arguments, etc.).")
    (license license:gpl3)))

(define-public r-biganalytics
  (package
    (name "r-biganalytics")
    (version "1.1.22")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biganalytics" version))
       (sha256
        (base32 "0y9zr4gr31l4q2j78nqyh4svlgsd7f5x8jkhgsflfr6agia77gyh"))))
    (properties `((upstream-name . "biganalytics")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-foreach r-bigmemory r-biglm r-bh))
    (home-page "http://www.bigmemory.org")
    (synopsis "Utilities for 'big.matrix' Objects from Package 'bigmemory'")
    (description
     "Extend the bigmemory package with various analytics.  Functions bigkmeans and
binit may also be used with native R objects.  For tapply'-like functions, the
bigtabulate package may also be helpful.  For linear algebra support, see
bigalgebra'.  For mutex (locking) support for advanced shared-memory usage, see
synchronicity'.")
    (license (list license:lgpl3 license:asl2.0))))

(define-public r-bigalgebra
  (package
    (name "r-bigalgebra")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bigalgebra" version))
       (sha256
        (base32 "0zk0n3lxqrhxzwdm47yhvv8n0m30bp9b7bvany3gg7a6vrz43yqn"))))
    (properties `((upstream-name . "bigalgebra")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-bigmemory r-bh))
    (native-inputs (list gfortran))
    (home-page "https://fbertran.github.io/bigalgebra/")
    (synopsis
     "'BLAS' and 'LAPACK' Routines for Native R Matrices and 'big.matrix' Objects")
    (description
     "This package provides arithmetic functions for R matrix and big.matrix objects
as well as functions for QR factorization, Cholesky factorization, General
eigenvalue, and Singular value decomposition (SVD).  A method matrix
multiplication and an arithmetic method -for matrix addition, matrix difference-
allows for mixed type operation -a matrix class object and a big.matrix class
object- and pure type operation for two big.matrix class objects.")
    (license (list license:lgpl3 license:asl2.0))))

(define-public r-bifurcatingr
  (package
    (name "r-bifurcatingr")
    (version "2.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bifurcatingr" version))
       (sha256
        (base32 "0qk009q0kw8h2j5qkvlpkhcmjw9cac9xqvi1v3r721n7cmx340sx"))))
    (properties `((upstream-name . "bifurcatingr")))
    (build-system r-build-system)
    (propagated-inputs (list r-fmultivar))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bifurcatingr")
    (synopsis "Bifurcating Autoregressive Models")
    (description
     "Estimation of bifurcating autoregressive models of any order, p, BAR(p) as well
as several types of bias correction for the least squares estimators of the
autoregressive parameters as described in Zhou and Basawa (2005)
<doi:10.1016/j.spl.2005.04.024> and Elbayoumi and Mostafa (2020)
<doi:10.1002/sta4.342>.  Currently, the bias correction methods supported
include bootstrap (single, double and fast-double) bias correction and
linear-bias-function-based bias correction.  Functions for generating and
plotting bifurcating autoregressive data from any BAR(p) model are also
included.  This new version includes calculating several type of bias-corrected
and -uncorrected confidence intervals for the least squares estimators of the
autoregressive parameters as described in Elbayoumi and Mostafa (2023)
<doi:10.6339/23-JDS1092>.")
    (license license:agpl3+)))

(define-public r-bifiesurvey
  (package
    (name "r-bifiesurvey")
    (version "3.6-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BIFIEsurvey" version))
       (sha256
        (base32 "0g0v7sahh7kg84yi0gj7q21i2wld76vs8c0vzynyna3jal5a2ij7"))))
    (properties `((upstream-name . "BIFIEsurvey")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-miceadds))
    (home-page "https://github.com/alexanderrobitzsch/BIFIEsurvey")
    (synopsis "Tools for Survey Statistics in Educational Assessment")
    (description
     "This package contains tools for survey statistics (especially in educational
assessment) for datasets with replication designs (jackknife, bootstrap,
replicate weights; see Kolenikov, 2010; Pfefferman & Rao, 2009a, 2009b,
<doi:10.1016/S0169-7161(09)70003-3>, <doi:10.1016/S0169-7161(09)70037-9>); Shao,
1996, <doi:10.1080/02331889708802523>).  Descriptive statistics, linear and
logistic regression, path models for manifest variables with measurement error
correction and two-level hierarchical regressions for weighted samples are
included.  Statistical inference can be conducted for multiply imputed datasets
and nested multiply imputed datasets and is in particularly suited for the
analysis of plausible values (for details see George, Oberwimmer &
Itzlinger-Bruneforth, 2016; Bruneforth, Oberwimmer & Robitzsch, 2016; Robitzsch,
Pham & Yanagida, 2016).  The package development was supported by BIFIE (Federal
Institute for Educational Research, Innovation and Development of the Austrian
School System; Salzburg, Austria).")
    (license license:gpl2+)))

(define-public r-bife
  (package
    (name "r-bife")
    (version "0.7.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bife" version))
       (sha256
        (base32 "0xikxniwzcv6jc64rgx39hddqchxk6c43h45algssbz2vaxvc4zg"))))
    (properties `((upstream-name . "bife")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-formula r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/amrei-stammann/bife")
    (synopsis "Binary Choice Models with Fixed Effects")
    (description
     "Estimates fixed effects binary choice models (logit and probit) with potentially
many individual fixed effects and computes average partial effects.  Incidental
parameter bias can be reduced with an asymptotic bias correction proposed by
Fernandez-Val (2009) <doi:10.1016/j.jeconom.2009.02.007>.")
    (license license:gpl2+)))

(define-public r-bifactorindicescalculator
  (package
    (name "r-bifactorindicescalculator")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BifactorIndicesCalculator" version))
       (sha256
        (base32 "1vnwksw0j4lc2knlcmm5748pf4d7r44sn34s1mrv9p50hlnkk85p"))))
    (properties `((upstream-name . "BifactorIndicesCalculator")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-mplusautomation r-mnormt r-mirt
                             r-lavaan))
    (home-page "https://github.com/ddueber/BifactorIndicesCalculator")
    (synopsis "Bifactor Indices Calculator")
    (description
     "The calculator computes bifactor indices such as explained common variance
(ECV), hierarchical Omega (@code{OmegaH}), percentage of uncontaminated
correlations (PUC), item explained common variance (I-ECV), and more.  This
package is an R version of the Excel based Bifactor Indices Calculator (Dueber,
2017) <doi:10.13023/edp.tool.01> with added convenience features for directly
utilizing output from several programs that can fit confirmatory factor analysis
or item response models.")
    (license license:gpl3+)))

(define-public r-bidimregression
  (package
    (name "r-bidimregression")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BiDimRegression" version))
       (sha256
        (base32 "00zcvk5v8rn3dp02sivs5z7cch9y8csp5cjhhs4ragdgcry0c3i5"))))
    (properties `((upstream-name . "BiDimRegression")))
    (build-system r-build-system)
    (propagated-inputs (list r-formula))
    (native-inputs (list r-knitr))
    (home-page "https://CRAN.R-project.org/package=BiDimRegression/")
    (synopsis
     "Calculates the Bidimensional Regression Between Two 2D Configurations")
    (description
     "Calculates the bidimensional regression between two 2D configurations following
the approach by Tobler (1965).")
    (license license:gpl3)))

(define-public r-bidask
  (package
    (name "r-bidask")
    (version "2.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bidask" version))
       (sha256
        (base32 "1kavjvllp1vf01xj9mnngd6k1r5zx48zp5kvsv5c8qsa53l5gzxl"))))
    (properties `((upstream-name . "bidask")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-xts))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/eguidotti/bidask")
    (synopsis
     "Efficient Estimation of Bid-Ask Spreads from Open, High, Low, and Close Prices")
    (description
     "This package implements an efficient estimator of bid-ask spreads from open,
high, low, and close prices as described in Ardia, Guidotti, & Kroencke (2024)
<doi:10.1016/j.jfineco.2024.103916>.  It also provides an implementation of the
estimators described in Roll (1984) <doi:10.1111/j.1540-6261.1984.tb03897.x>,
Corwin & Schultz (2012) <doi:10.1111/j.1540-6261.2012.01729.x>, and Abdi &
Ranaldo (2017) <doi:10.1093/rfs/hhx084>.")
    (license license:gpl3)))

(define-public r-bidag
  (package
    (name "r-bidag")
    (version "2.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BiDAG" version))
       (sha256
        (base32 "0gncxn558mdasr63jvqyavd6nhg6762rk0g0a2b7hfvcivjb6bd5"))))
    (properties `((upstream-name . "BiDAG")))
    (build-system r-build-system)
    (propagated-inputs (list r-rgraphviz
                             r-rcpp
                             r-rbgl
                             r-pcalg
                             r-matrix
                             r-graph
                             r-coda))
    (home-page "https://cran.r-project.org/package=BiDAG")
    (synopsis "Bayesian Inference for Directed Acyclic Graphs")
    (description
     "Implementation of a collection of MCMC methods for Bayesian structure learning
of directed acyclic graphs (DAGs), both from continuous and discrete data.  For
efficient inference on larger DAGs, the space of DAGs is pruned according to the
data.  To filter the search space, the algorithm employs a hybrid approach,
combining constraint-based learning with search and score.  A reduced search
space is initially defined on the basis of a skeleton obtained by means of the
PC-algorithm, and then iteratively improved with search and score.  Search and
score is then performed following two approaches: Order MCMC, or Partition MCMC.
The BGe score is implemented for continuous data and the BDe score is
implemented for binary data or categorical data.  The algorithms may provide the
maximum a posteriori (MAP) graph or a sample (a collection of DAGs) from the
posterior distribution given the data.  All algorithms are also applicable for
structure learning and sampling for dynamic Bayesian networks.  References: J.
Kuipers, P. Suter, G. Moffa (2022) <doi:10.1080/10618600.2021.2020127>, N.
Friedman and D. Koller (2003) <doi:10.1023/A:1020249912095>, J. Kuipers and G.
Moffa (2017) <doi:10.1080/01621459.2015.1133426>, M. Kalisch et al. (2012)
<doi:10.18637/jss.v047.i11>, D. Geiger and D. Heckerman (2002)
<doi:10.1214/aos/1035844981>, P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel
(2023) <doi:10.18637/jss.v105.i09>.")
    (license license:gpl2+)))

(define-public r-bicorn
  (package
    (name "r-bicorn")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BICORN" version))
       (sha256
        (base32 "0mqk8vgp6jdk7f0paa06yqlibkd1y3vs69pg7i9mkvxmda7p4nkq"))))
    (properties `((upstream-name . "BICORN")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BICORN")
    (synopsis "Integrative Inference of De Novo Cis-Regulatory Modules")
    (description
     "Prior transcription factor binding knowledge and target gene expression data are
integrated in a Bayesian framework for functional cis-regulatory module
inference.  Using Gibbs sampling, we iteratively estimate transcription factor
associations for each gene, regulation strength for each binding event and the
hidden activity for each transcription factor.")
    (license license:gpl2)))

(define-public r-biclustermd
  (package
    (name "r-biclustermd")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biclustermd" version))
       (sha256
        (base32 "1knhz96k5giq3d16i3akl5gbxrzx3vxm97cb3ivaw835fr9zrmr5"))))
    (properties `((upstream-name . "biclustermd")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-phyclust
                             r-nycflights13
                             r-magrittr
                             r-ggplot2
                             r-foreach
                             r-dplyr
                             r-doparallel
                             r-biclust))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jreisner/biclustermd")
    (synopsis "Biclustering with Missing Data")
    (description
     "Biclustering is a statistical learning technique that simultaneously partitions
and clusters rows and columns of a data matrix.  Since the solution space of
biclustering is in infeasible to completely search with current computational
mechanisms, this package uses a greedy heuristic.  The algorithm featured in
this package is, to the best our knowledge, the first biclustering algorithm to
work on data with missing values.  Li, J., Reisner, J., Pham, H., Olafsson, S.,
and Vardeman, S. (2020) Biclustering with Missing Data.  Information Sciences,
510, 304â316.")
    (license license:expat)))

(define-public r-biclassify
  (package
    (name "r-biclassify")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biClassify" version))
       (sha256
        (base32 "14z3kmpmlvnkjwpjspajdfx5951cmck4binszzdd7nqbai5f1dwv"))))
    (properties `((upstream-name . "biClassify")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-mvtnorm
                             r-matrix
                             r-mass
                             r-fields
                             r-expm
                             r-daag))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=biClassify")
    (synopsis
     "Binary Classification Using Extensions of Discriminant Analysis")
    (description
     "This package implements methods for sample size reduction within Linear and
Quadratic Discriminant Analysis in Lapanowski and Gaynanova (2020)
<@code{arXiv:2005.03858>}.  Also includes methods for non-linear discriminant
analysis with simultaneous sparse feature selection in Lapanowski and Gaynanova
(2019) PMLR 89:1704-1713.")
    (license license:gpl3)))

(define-public r-bicausality
  (package
    (name "r-bicausality")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BiCausality" version))
       (sha256
        (base32 "0ryzq77j8sbic5mj8r6bpirnr2vb45wwkkkkkjmv70c15ip5409r"))))
    (properties `((upstream-name . "BiCausality")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/DarkEyes/BiCausality")
    (synopsis "Binary Causality Inference Framework")
    (description
     "This package provides a framework to infer causality on binary data using
techniques in frequent pattern mining and estimation statistics.  Given a set of
individual vectors S={x} where x(i) is a realization value of binary variable i,
the framework infers empirical causal relations of binary variables i,j from S
in a form of causal graph G=(V,E) where V is a set of nodes representing binary
variables and there is an edge from i to j in E if the variable i causes j.  The
framework determines dependency among variables as well as analyzing confounding
factors before deciding whether i causes j.  The publication of this package is
at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and
Suttipong Thajchayapong (2023) <doi:10.1016/j.heliyon.2023.e15947>.")
    (license license:expat)))

(define-public r-bibs
  (package
    (name "r-bibs")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bibs" version))
       (sha256
        (base32 "0slxqhcm4fhdmr2dj1c31zs8sgl39267179nbc59jlpb0z77z2d6"))))
    (properties `((upstream-name . "bibs")))
    (build-system r-build-system)
    (propagated-inputs (list r-gigrvg))
    (home-page "https://cran.r-project.org/package=bibs")
    (synopsis "Bayesian Inference for the Birnbaum-Saunders Distribution")
    (description
     "Developed for the following tasks.  1- Simulating and computing the maximum
likelihood estimator for the Birnbaum-Saunders (BS) distribution, 2- Computing
the Bayesian estimator for the parameters of the BS distribution based on
reference prior proposed by Xu and Tang (2010) <doi:10.1016/j.csda.2009.08.004>
and conjugate prior.  3- Computing the Bayesian estimator for the BS
distribution based on conjugate prior.  4- Computing the Bayesian estimator for
the BS distribution based on Jeffrey prior given by Achcar (1993)
<doi:10.1016/0167-9473(93)90170-X> 5- Computing the Bayesian estimator for the
BS distribution under progressive type-II censoring scheme.")
    (license license:gpl2+)))

(define-public r-bibplots
  (package
    (name "r-bibplots")
    (version "0.0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BibPlots" version))
       (sha256
        (base32 "0zvbyh8spr0ywj64b2rc8jcq4fgn87858km4mkkh17g30ympg767"))))
    (properties `((upstream-name . "BibPlots")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BibPlots")
    (synopsis "Plot Functions for Use in Bibliometrics")
    (description
     "Currently, the package provides several functions for plotting and analyzing
bibliometric data (JIF, Journal Impact Factor, and paper percentile values),
beamplots with citations and percentiles, and three plot functions to visualize
the result of a reference publication year spectroscopy (RPYS) analysis
performed in the free software CRExplorer (see <http://crexplorer.net>).
Further extension to more plot variants is planned.")
    (license (license:fsdg-compatible "EUPL"))))

(define-public r-biblioverlap
  (package
    (name "r-biblioverlap")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biblioverlap" version))
       (sha256
        (base32 "03mpw4h7z33dnmf7ain085xnlb6wjzj8vsrlr2jhm0h6fpx9880y"))))
    (properties `((upstream-name . "biblioverlap")))
    (build-system r-build-system)
    (propagated-inputs (list r-uuid
                             r-upsetr
                             r-stringdist
                             r-shiny
                             r-rlang
                             r-matrix
                             r-magrittr
                             r-ggvenndiagram
                             r-ggplot2
                             r-dplyr))
    (home-page "https://github.com/gavieira/biblioverlap")
    (synopsis "Document-Level Matching Between Bibliographic Datasets")
    (description
     "Identifies and visualizes document overlap in any number of bibliographic
datasets.  This package implements the identification of overlapping documents
through the exact match of a unique identifier (e.g. Digital Object Identifier -
DOI) and, for records where the identifier is absent, through a score calculated
from a set of fields commonly found in bibliographic datasets (Title, Source,
Authors and Publication Year).  Additionally, it provides functions to visualize
the results of the document matching through a Venn diagram and/or @code{UpSet}
plot, as well as a summary of the matching procedure.")
    (license license:gpl3+)))

(define-public r-bibliorefer
  (package
    (name "r-bibliorefer")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bibliorefer" version))
       (sha256
        (base32 "0sqj4gpdlx3ahaxh07jvgaidxa5svi3r014v2mmpmi90qym35fkl"))))
    (properties `((upstream-name . "bibliorefer")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=bibliorefer")
    (synopsis "Generator of Main Scientific References")
    (description
     "Generates a list, with a size defined by the user, containing the main
scientific references and the frequency distribution of authors and journals in
the list obtained.  The database is a dataframe with academic production
metadata made available by bibliographic collections such as Scopus, Web of
Science, etc.  The temporal evolution of scientific production on a given topic
is presented and ordered lists of articles are constructed by number of
citations and of authors and journals by level of productivity.  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>.")
    (license license:gpl3)))

(define-public r-biblionetwork
  (package
    (name "r-biblionetwork")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biblionetwork" version))
       (sha256
        (base32 "0nhx9bk7r5l0bvj98clnmwf53ihghf48zai4nj6pcgr2rb69fvjj"))))
    (properties `((upstream-name . "biblionetwork")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/agoutsmedt/biblionetwork")
    (synopsis "Create Different Types of Bibliometric Networks")
    (description
     "This package provides functions to find edges for bibliometric networks like
bibliographic coupling network, co-citation network and co-authorship network.
The weights of network edges can be calculated according to different methods,
depending on the type of networks, the type of nodes, and what you want to
analyse.  These functions are optimized to be be used on large dataset.  The
package contains functions inspired by: Leydesdorff, Loet and Park, Han Woo
(2017) <doi:10.1016/j.joi.2016.11.007>; Perianes-Rodriguez, Antonio, Ludo
Waltman, and Nees Jan Van Eck (2016) <doi:10.1016/j.joi.2016.10.006>; Sen, Subir
K. and Shymal K. Gan (1983) <http://nopr.niscair.res.in/handle/123456789/28008>;
Shen, Si, Zhu, Danhao, Rousseau, Ronald, Su, Xinning and Wang, Dongbo (2019)
<doi:10.1016/j.joi.2019.01.012>; Zhao, Dangzhi and Strotmann, Andreas (2008)
<doi:10.1002/meet.2008.1450450292>.")
    (license license:expat)))

(define-public r-bibliometrixdata
  (package
    (name "r-bibliometrixdata")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bibliometrixData" version))
       (sha256
        (base32 "0dkg5g9il95w9glzvhjdgp8z30prydspvpz5bb403vaizykwv9kl"))))
    (properties `((upstream-name . "bibliometrixData")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=bibliometrixData")
    (synopsis "Bibliometrix Example Datasets")
    (description
     "It contains some example datasets used in bibliometrix'.  The data are
bibliographic datasets exported from the SCOPUS (<https://scopus.com>) and
Clarivate Analytics Web of Science (<https://www.webofscience.com/>) databases.
They can be used to test the different features of the package bibliometrix
(<https://bibliometrix.org>).")
    (license license:expat)))

(define-public r-bibliometrix
  (package
    (name "r-bibliometrix")
    (version "4.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bibliometrix" version))
       (sha256
        (base32 "1cas914ach2d2r8pjybyx2lvhydlsx89y2bgkqn40vhg8d40zc5d"))))
    (properties `((upstream-name . "bibliometrix")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidytext
                             r-tidyr
                             r-stringi
                             r-stringdist
                             r-snowballc
                             r-shiny
                             r-rscopus
                             r-readxl
                             r-readr
                             r-pubmedr
                             r-plotly
                             r-openxlsx
                             r-openalexr
                             r-matrix
                             r-igraph
                             r-ggrepel
                             r-ggplot2
                             r-forcats
                             r-dt
                             r-dplyr
                             r-dimensionsr
                             r-ca
                             r-bibliometrixdata))
    (home-page "https://www.bibliometrix.org")
    (synopsis "Comprehensive Science Mapping Analysis")
    (description
     "Tool for quantitative research in scientometrics and bibliometrics.  It
implements the comprehensive workflow for science mapping analysis proposed in
Aria M. and Cuccurullo C. (2017) <doi:10.1016/j.joi.2017.08.007>.  bibliometrix
provides various routines for importing bibliographic data from SCOPUS',
Clarivate Analytics Web of Science (<https://www.webofknowledge.com/>), Digital
Science Dimensions (<https://www.dimensions.ai/>), @code{OpenAlex}
(<https://openalex.org/>), Cochrane Library
(<https://www.cochranelibrary.com/>), Lens (<https://lens.org>), and
@code{PubMed} (<https://pubmed.ncbi.nlm.nih.gov/>) databases, performing
bibliometric analysis and building networks for co-citation, coupling,
scientific collaboration and co-word analysis.")
    (license license:gpl3)))

(define-public r-biblio
  (package
    (name "r-biblio")
    (version "0.0.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "biblio" version))
       (sha256
        (base32 "0ml6sh0ki04kqyfixd0cxmxpdxznisifaqnjdshvrvi1yqjq12ih"))))
    (properties `((upstream-name . "biblio")))
    (build-system r-build-system)
    (propagated-inputs (list r-yamlme r-stringr r-rcrossref))
    (home-page "https://kamapu.github.io/biblio/")
    (synopsis "Interacting with BibTeX Databases")
    (description
     "Reading and writing @code{BibTeX} files using data frames in R sessions.")
    (license license:gpl2+)))

(define-public r-bibitr
  (package
    (name "r-bibitr")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BiBitR" version))
       (sha256
        (base32 "0wfwph6nw12hb43j14i9ycj8m2zn0m5ynp7afq9cray8rbgvxfv6"))))
    (properties `((upstream-name . "BiBitR")))
    (build-system r-build-system)
    (inputs (list openjdk))
    (propagated-inputs (list r-viridis
                             r-randomcolor
                             r-lattice
                             r-foreign
                             r-dendextend
                             r-cluster
                             r-biclust))
    (home-page "https://cran.r-project.org/package=BiBitR")
    (synopsis "R Wrapper for Java Implementation of BiBit")
    (description
     "This package provides a simple R wrapper for the Java @code{BiBit} algorithm
from \"A biclustering algorithm for extracting bit-patterns from binary datasets\"
from Domingo et al. (2011) <DOI:10.1093/bioinformatics/btr464>.  An simple
adaption for the @code{BiBit} algorithm which allows noise in the biclusters is
also introduced as well as a function to guide the algorithm towards given
(sub)patterns.  Further, a workflow to derive noisy biclusters from discoverd
larger column patterns is included as well.")
    (license license:gpl3)))

(define-public r-bib2df
  (package
    (name "r-bib2df")
    (version "1.1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bib2df" version))
       (sha256
        (base32 "1w7x6jj8x3rv1ms8278jwszj8d4g5b59drs7gdfsrbp1x74npcr1"))))
    (properties `((upstream-name . "bib2df")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-humaniformat r-httr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://docs.ropensci.org/bib2df/")
    (synopsis "Parse a BibTeX File to a Data Frame")
    (description
     "Parse a @code{BibTeX} file to a data.frame to make it accessible for further
analysis and visualization.")
    (license license:gpl3)))

(define-public r-biascorrector
  (package
    (name "r-biascorrector")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BiasCorrector" version))
       (sha256
        (base32 "1ligmx7c44faqpzfvcwp841wqbrbwwyh2xdr0hgp7g30qmlb5m1y"))))
    (properties `((upstream-name . "BiasCorrector")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinyjs
                             r-shinydashboard
                             r-shiny
                             r-rbiascorrection
                             r-magrittr
                             r-dt
                             r-data-table))
    (home-page "https://github.com/kapsner/BiasCorrector")
    (synopsis "GUI to Correct Measurement Bias in DNA Methylation Analyses")
    (description
     "This package provides a GUI to correct measurement bias in DNA methylation
analyses.  The @code{BiasCorrector} package just wraps the functions implemented
in the R package @code{rBiasCorrection} into a shiny web application in order to
make them more easily accessible.  Publication: Kapsner et al. (2021)
<doi:10.1002/ijc.33681>.")
    (license license:gpl3)))

(define-public r-bi
  (package
    (name "r-bi")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BI" version))
       (sha256
        (base32 "12x8l5v703b142cixnw9fz0i1j7sx6cphjcsw7ywvhrfy7z8yc35"))))
    (properties `((upstream-name . "BI")))
    (build-system r-build-system)
    (home-page "https://github.com/marcschwartz/BI")
    (synopsis
     "Blinding Assessment Indexes for Randomized, Controlled, Clinical Trials")
    (description
     "Generate the James Blinding Index, as described in James et al (1996)
<https://pubmed.ncbi.nlm.nih.gov/8841652/> and the Bang Blinding Index, as
described in Bang et al (2004) <https://pubmed.ncbi.nlm.nih.gov/15020033/>.
These are measures to assess whether or not satisfactory blinding has been
maintained in a randomized, controlled, clinical trial.  These can be generated
for trial subjects, research coordinators and principal investigators, based
upon standardized questionnaires that have been administered, to assess whether
or not they can correctly guess to which treatment arm (e.g. placebo or
treatment) subjects were assigned at randomization.")
    (license license:gpl3)))

(define-public r-bhsbvar
  (package
    (name "r-bhsbvar")
    (version "3.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BHSBVAR" version))
       (sha256
        (base32 "184dhaajjzplslbfjmdm9zyr7h39zi1h3p549lac2sy6mrkglrc0"))))
    (properties `((upstream-name . "BHSBVAR")))
    (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=BHSBVAR")
    (synopsis "Structural Bayesian Vector Autoregression Models")
    (description
     "This package provides a function for estimating the parameters of Structural
Bayesian Vector Autoregression models with the method developed by Baumeister
and Hamilton (2015) <doi:10.3982/ECTA12356>, Baumeister and Hamilton (2017)
<doi:10.3386/w24167>, and Baumeister and Hamilton (2018)
<doi:10.1016/j.jmoneco.2018.06.005>.  Functions for plotting impulse responses,
historical decompositions, and posterior distributions of model parameters are
also provided.")
    (license license:gpl3+)))

(define-public r-bhmsmafmri
  (package
    (name "r-bhmsmafmri")
    (version "2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BHMSMAfMRI" version))
       (sha256
        (base32 "0xn4ss422mkshx5kcznaym04i5z5a9vw9hvcb10r38vq031wys8q"))))
    (properties `((upstream-name . "BHMSMAfMRI")))
    (build-system r-build-system)
    (propagated-inputs (list r-wavethresh r-rcpparmadillo r-rcpp r-oro-nifti))
    (native-inputs (list r-knitr))
    (home-page "https://nilotpalsanyal.github.io/BHMSMAfMRI/")
    (synopsis
     "Bayesian Hierarchical Multi-Subject Multiscale Analysis of Functional MRI (fMRI) Data")
    (description
     "Package BHMSM@code{AfMRI} performs Bayesian hierarchical multi-subject
multiscale analysis of @code{fMRI} data as described in Sanyal & Ferreira (2012)
<DOI:10.1016/j.neuroimage.2012.08.041>, or other multiscale data, using
wavelet-based prior that borrows strength across subjects and provides posterior
smoothed images of the effect sizes and samples from the posterior distribution.")
    (license license:gpl2+)))

(define-public r-bhmbasket
  (package
    (name "r-bhmbasket")
    (version "0.9.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bhmbasket" version))
       (sha256
        (base32 "1mcgbvlnrmxlxqcjmc7ij7qn3zqfwsmbx2r2facbqrf043hmiipc"))))
    (properties `((upstream-name . "bhmbasket")))
    (build-system r-build-system)
    (propagated-inputs (list r-r2jags r-foreach r-dorng))
    (native-inputs (list r-knitr))
    (home-page "https://CRAN.R-project.org/package=bhmbasket")
    (synopsis "Bayesian Hierarchical Models for Basket Trials")
    (description
     "This package provides functions for the evaluation of basket trial designs with
binary endpoints.  Operating characteristics of a basket trial design are
assessed by simulating trial data according to scenarios, analyzing the data
with Bayesian hierarchical models (BHMs), and assessing decision probabilities
on stratum and trial-level based on Go / No-go decision making.  The package is
build for high flexibility regarding decision rules, number of interim analyses,
number of strata, and recruitment.  The BHMs proposed by Berry et al. (2013)
<doi:10.1177/1740774513497539> and Neuenschwander et al. (2016)
<doi:10.1002/pst.1730>, as well as a model that combines both approaches are
implemented.  Functions are provided to implement Bayesian decision rules as for
example proposed by Fisch et al. (2015) <doi:10.1177/2168479014533970>.  In
addition, posterior point estimates (mean/median) and credible intervals for
response rates and some model parameters can be calculated.  For simulated trial
data, bias and mean squared errors of posterior point estimates for response
rates can be provided.")
    (license license:gpl3)))

(define-public r-bhm
  (package
    (name "r-bhm")
    (version "1.18")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bhm" version))
       (sha256
        (base32 "05gr5qmd6dzfylay60wazi0iyvj9r0xhzdk7jm7wrsrag7i43z1p"))))
    (properties `((upstream-name . "bhm")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mass r-ggplot2 r-coda))
    (home-page "https://cran.r-project.org/package=bhm")
    (synopsis "Biomarker Threshold Models")
    (description
     "This package contains tools to fit both predictive and prognostic biomarker
effects using biomarker threshold models and continuous threshold models.
Evaluate the treatment effect, biomarker effect and treatment-biomarker
interaction using probability index measurement.  Test for treatment-biomarker
interaction using residual bootstrap method.")
    (license license:gpl2+)))

(define-public r-bhh2
  (package
    (name "r-bhh2")
    (version "2016.05.31")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BHH2" version))
       (sha256
        (base32 "1m4fcx979nbm97hi89vbjjix0sx6qhdzs486risck9bi7yzih5k4"))))
    (properties `((upstream-name . "BHH2")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BHH2")
    (synopsis "Useful Functions for Box, Hunter and Hunter II")
    (description
     "This package provides functions and data sets reproducing some examples in Box,
Hunter and Hunter II. Useful for statistical design of experiments, especially
factorial experiments.")
    (license license:gpl2+)))

(define-public r-bhat
  (package
    (name "r-bhat")
    (version "0.9-12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Bhat" version))
       (sha256
        (base32 "0jrn57v90anxsdasfwn3c4mxwzz8b5nbvsn5c57d71xfdza4lh6f"))))
    (properties `((upstream-name . "Bhat")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=Bhat")
    (synopsis "General Likelihood Exploration")
    (description
     "This package provides functions for Maximum Likelihood Estimation, Markov Chain
Monte Carlo, finding confidence intervals.  The implementation is heavily based
on the original Fortran source code translated to R.")
    (license license:gpl2+)))

(define-public r-bhai
  (package
    (name "r-bhai")
    (version "0.99.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BHAI" version))
       (sha256
        (base32 "1hawa55mk590is8ziav7fdszsa3a42bqb0znzqsazz5lkg4daaa7"))))
    (properties `((upstream-name . "BHAI")))
    (build-system r-build-system)
    (propagated-inputs (list r-prevtoinc r-plotrix r-mcmcpack))
    (home-page "https://cran.r-project.org/package=BHAI")
    (synopsis "Estimate the Burden of Healthcare-Associated Infections")
    (description
     "This package provides an approach which is based on the methodology of the
Burden of Communicable Diseases in Europe (B@code{CoDE}) and can be used for
large and small samples such as individual countries.  The Burden of
Healthcare-Associated Infections (BHAI) is estimated in disability-adjusted life
years, number of infections as well as number of deaths per year.  Results can
be visualized with various plotting functions and exported into tables.")
    (license license:gpl3)))

(define-public r-bgw
  (package
    (name "r-bgw")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bgw" version))
       (sha256
        (base32 "0iw36qifawm5jlsjzv4y4kl228hz4clbgzf02dpxv7579nlnibm7"))))
    (properties `((upstream-name . "bgw")))
    (build-system r-build-system)
    (native-inputs (list r-knitr gfortran))
    (home-page "https://cran.r-project.org/package=bgw")
    (synopsis "Bunch-Gay-Welsch Statistical Estimation")
    (description
     "This package performs statistical estimation and inference-related computations
by accessing and executing modified versions of Fortran subroutines originally
published in the Association for Computing Machinery (ACM) journal Transactions
on Mathematical Software (TOMS) by Bunch, Gay and Welsch (1993)
<doi:10.1145/151271.151279>.  The acronym BGW (from the authors last names) will
be used when making reference to technical content (e.g., algorithm,
methodology) that originally appeared in ACM TOMS. A key feature of BGW is that
it exploits the special structure of statistical estimation problems within a
trust-region-based optimization approach to produce an estimation algorithm that
is much more effective than the usual practice of using optimization methods and
codes originally developed for general optimization.  The bgw package bundles R
wrapper (and related) functions with modified Fortran source code so that it can
be compiled and linked in the R environment for fast execution.  This version
implements a function ('bgw_mle.R') that performs maximum likelihood estimation
(MLE) for a user-provided model object that computes probabilities (a.k.a.
probability densities).  The original motivation for producing this package was
to provide fast, efficient, and reliable MLE for discrete choice models that can
be called from the Apollo choice modelling R package ( see
<http://www.apollochoicemodelling.com>).  Starting with the release of Apollo
3.0, BGW is the default estimation package.  However, estimation can also be
performed using BGW in a stand-alone fashion without using Apollo (as shown in
simple examples included in the package).  Note also that BGW capabilities are
not limited to MLE, and future extension to other estimators (e.g., nonlinear
least squares, generalized method of moments, etc.) is possible.  The Fortran
code included in bgw was modified by one of the original BGW authors (Bunch)
under his rights as confirmed by direct consultation with the ACM Intellectual
Property and Rights Manager.  See
<https://authors.acm.org/author-resources/author-rights>.  The main requirement
is clear citation of the original publication (see above).")
    (license license:gpl3)))

(define-public r-bgvar
  (package
    (name "r-bgvar")
    (version "2.5.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BGVAR" version))
       (sha256
        (base32 "124vy9dx91sa42z6x4g4drai0gxz2l36p0nn4ng2r0hxj6z2m34x"))))
    (properties `((upstream-name . "BGVAR")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xts
                             r-stochvol
                             r-readxl
                             r-rcppprogress
                             r-rcppparallel
                             r-rcpparmadillo
                             r-rcpp
                             r-matrix
                             r-mass
                             r-knitr
                             r-gigrvg
                             r-coda
                             r-bayesm
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/mboeck11/BGVAR")
    (synopsis "Bayesian Global Vector Autoregressions")
    (description
     "Estimation of Bayesian Global Vector Autoregressions (BGVAR) with different
prior setups and the possibility to introduce stochastic volatility.  Built-in
priors include the Minnesota, the stochastic search variable selection and
Normal-Gamma (NG) prior.  For a reference see also Crespo Cuaresma, J.,
Feldkircher, M. and F. Huber (2016) \"Forecasting with Global Vector
Autoregressive Models: a Bayesian Approach\", Journal of Applied Econometrics,
Vol.  31(7), pp.  1371-1391 <doi:10.1002/jae.2504>.  Post-processing functions
allow for doing predictions, structurally identify the model with short-run or
sign-restrictions and compute impulse response functions, historical
decompositions and forecast error variance decompositions.  Plotting functions
are also available.  The package has a companion paper: Boeck, M., Feldkircher,
M. and F. Huber (2022) \"BGVAR: Bayesian Global Vector Autoregressions with
Shrinkage Priors in R\", Journal of Statistical Software, Vol.  104(9), pp.  1-28
<doi:10.18637/jss.v104.i09>.")
    (license license:gpl3)))

(define-public r-bgumbel
  (package
    (name "r-bgumbel")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bgumbel" version))
       (sha256
        (base32 "18k8dy44x49hr4s37hjrq60c3cbw0slc9gjmd8gwcbz4slix2ydk"))))
    (properties `((upstream-name . "bgumbel")))
    (build-system r-build-system)
    (inputs (list gfortran gcc))
    (propagated-inputs (list r-sparsem r-quantreg r-mcmcpack r-mass r-coda))
    (home-page "https://CRAN.R-project.org/package=bgumbel")
    (synopsis "Bimodal Gumbel Distribution")
    (description
     "Bimodal Gumbel distribution.  General functions for performing extreme value
analysis.")
    (license license:expat)))

(define-public r-bgsmtr
  (package
    (name "r-bgsmtr")
    (version "0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bgsmtr" version))
       (sha256
        (base32 "17706qims9637ppwg0vszkcm6kmvhzyrdihkgigpiv5lknhk2q3m"))))
    (properties `((upstream-name . "bgsmtr")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod
                             r-sparsemvn
                             r-rcpp
                             r-mvtnorm
                             r-mnormt
                             r-misctools
                             r-matrixcalc
                             r-matrix
                             r-laplacesdemon
                             r-inline
                             r-glmnet
                             r-edison
                             r-coda
                             r-cholwishart))
    (home-page "https://cran.r-project.org/package=bgsmtr")
    (synopsis "Bayesian Group Sparse Multi-Task Regression")
    (description
     "Implementation of Bayesian multi-task regression models and was developed within
the context of imaging genetics.  The package can currently fit two models.  The
Bayesian group sparse multi-task regression model of Greenlaw et al.
(2017)<doi:10.1093/bioinformatics/btx215> can be fit with implementation using
Gibbs sampling.  An extension of this model developed by Song, Ge et al.  to
accommodate both spatial correlation as well as correlation across brain
hemispheres can also be fit using either mean-field variational Bayes or Gibbs
sampling.  The model can also be used more generally for multivariate
(non-imaging) phenotypes with spatial correlation.")
    (license license:gpl2)))

(define-public r-bgphazard
  (package
    (name "r-bgphazard")
    (version "2.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BGPhazard" version))
       (sha256
        (base32 "0i2fsvb9pvvhl4xpp87cacg9934hsi060c6d1z3j5hylwyq2k5h9"))))
    (properties `((upstream-name . "BGPhazard")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-survival
                             r-stringr
                             r-rlang
                             r-readr
                             r-purrr
                             r-progress
                             r-magrittr
                             r-gridextra
                             r-ggthemes
                             r-ggplot2
                             r-dplyr
                             r-brobdingnag))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/EAMI91/BGPhazard")
    (synopsis "Markov Beta and Gamma Processes for Modeling Hazard Rates")
    (description
     "Computes the hazard rate estimate as described by Nieto-Barajas & Walker (2002),
Nieto-Barajas (2003), Nieto-Barajas & Walker (2007) and Nieto-Barajas & Yin
(2008).")
    (license license:gpl2+)))

(define-public r-bgms
  (package
    (name "r-bgms")
    (version "0.1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bgms" version))
       (sha256
        (base32 "0ylvr1sbn2mq2cc7lkgf8qfw08qyg2xvcwa29964qwdjl76dl05g"))))
    (properties `((upstream-name . "bgms")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-rcppprogress r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://maartenmarsman.github.io/bgms/")
    (synopsis
     "Bayesian Analysis of Networks of Binary and/or Ordinal Variables")
    (description
     "Bayesian variable selection methods for analyzing the structure of a Markov
Random Field model for a network of binary and/or ordinal variables.  Details of
the implemented methods can be found in: Marsman and Haslbeck (2023)
<doi:10.31234/osf.io/ukwrf>.")
    (license license:gpl2+)))

(define-public r-bgmm
  (package
    (name "r-bgmm")
    (version "1.8.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bgmm" version))
       (sha256
        (base32 "1qng8zlpm5pp2ilny99gxyppmp5dnrada4kmvpyg2ap06i69a9gs"))))
    (properties `((upstream-name . "bgmm")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-lattice r-combinat r-car))
    (home-page "http://bgmm.molgen.mpg.de/")
    (synopsis
     "Gaussian Mixture Modeling Algorithms and the Belief-Based Mixture Modeling")
    (description
     "Two partially supervised mixture modeling methods: soft-label and belief-based
modeling are implemented.  For completeness, we equipped the package also with
the functionality of unsupervised, semi- and fully supervised mixture modeling.
The package can be applied also to selection of the best-fitting from a set of
models with different component numbers or constraints on their structures.  For
detailed introduction see: Przemyslaw Biecek, Ewa Szczurek, Martin Vingron,
Jerzy Tiuryn (2012), The R Package bgmm: Mixture Modeling with Uncertain
Knowledge, Journal of Statistical Software <doi:10.18637/jss.v047.i03>.")
    (license license:gpl3)))

(define-public r-bgmisc
  (package
    (name "r-bgmisc")
    (version "1.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BGmisc" version))
       (sha256
        (base32 "12m6c0b0nqgxiiy9vynygx186vb1v5i6s8gfjijsqn7wzpzfj6ig"))))
    (properties `((upstream-name . "BGmisc")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-matrix r-kinship2 r-igraph
                             r-data-table))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/R-Computing-Lab/BGmisc/")
    (synopsis "An R Package for Extended Behavior Genetics Analysis")
    (description
     "The BGmisc R package offers a comprehensive suite of functions tailored for
extended behavior genetics analysis, including model identification, calculating
relatedness, pedigree conversion, pedigree simulation, and more.")
    (license license:gpl3)))

(define-public r-bgmfiles
  (package
    (name "r-bgmfiles")
    (version "0.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bgmfiles" version))
       (sha256
        (base32 "10qldfjjq5fx5jrrakdxc8k2pf0vp8ifg18nq56lvx9n28mqigim"))))
    (properties `((upstream-name . "bgmfiles")))
    (build-system r-build-system)
    (home-page "https://github.com/AustralianAntarcticDivision/bgmfiles/")
    (synopsis "Example BGM Files for the Atlantis Ecosystem Model")
    (description
     "This package provides a collection of box-geometry model (BGM) files for the
Atlantis ecosystem model.  Atlantis is a deterministic, biogeochemical,
whole-of-ecosystem model (see <http://atlantis.cmar.csiro.au/> for more
information).")
    (license license:cc0)))

(define-public r-bglr
  (package
    (name "r-bglr")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BGLR" version))
       (sha256
        (base32 "0850mzfbdbrhaahl4gv9rgw2lsbarhz783q15m18ws2a17v918p9"))))
    (properties `((upstream-name . "BGLR")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm r-mass))
    (home-page "https://cran.r-project.org/package=BGLR")
    (synopsis "Bayesian Generalized Linear Regression")
    (description "Bayesian Generalized Linear Regression.")
    (license license:gpl3)))

(define-public r-bggum
  (package
    (name "r-bggum")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bggum" version))
       (sha256
        (base32 "04vr9z81awavkhk3lbi8yasmi242lrbnq0pzqp38xlrn5xkf8b1w"))))
    (properties `((upstream-name . "bggum")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppdist r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/duckmayr/bggum")
    (synopsis
     "Bayesian Estimation of Generalized Graded Unfolding Model Parameters")
    (description
     "This package provides a Metropolis-coupled Markov chain Monte Carlo sampler,
post-processing and parameter estimation functions, and plotting utilities for
the generalized graded unfolding model of Roberts, Donoghue, and Laughlin (2000)
<doi:10.1177/01466216000241001>.")
    (license license:gpl2+)))

(define-public r-bggm
  (package
    (name "r-bggm")
    (version "2.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BGGM" version))
       (sha256
        (base32 "15yqbifnxlpwk25ki1yxfylkyb061375cxwq7dbid2381lvnslih"))))
    (properties `((upstream-name . "BGGM")))
    (build-system r-build-system)
    (propagated-inputs (list r-sna
                             r-reshape
                             r-rdpack
                             r-rcppprogress
                             r-rcppdist
                             r-rcpparmadillo
                             r-rcpp
                             r-network
                             r-mvnfast
                             r-mass
                             r-ggridges
                             r-ggplot2
                             r-ggally
                             r-bfpack))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BGGM")
    (synopsis "Bayesian Gaussian Graphical Models")
    (description
     "Fit Bayesian Gaussian graphical models.  The methods are separated into two
Bayesian approaches for inference: hypothesis testing and estimation.  There are
extensions for confirmatory hypothesis testing, comparing Gaussian graphical
models, and node wise predictability.  These methods were recently introduced in
the Gaussian graphical model literature, including Williams (2019)
<doi:10.31234/osf.io/x8dpr>, Williams and Mulder (2019)
<doi:10.31234/osf.io/ypxd8>, Williams, Rast, Pericchi, and Mulder (2019)
<doi:10.31234/osf.io/yt386>.")
    (license license:gpl2)))

(define-public r-bgge
  (package
    (name "r-bgge")
    (version "0.6.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BGGE" version))
       (sha256
        (base32 "1h76c40y45xhf2vp9g0q5j9lfwh16q9axbk5c9aqn4md008xr1j2"))))
    (properties `((upstream-name . "BGGE")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BGGE")
    (synopsis "Bayesian Genomic Linear Models Applied to GE Genome Selection")
    (description
     "Application of genome prediction for a continuous variable, focused on genotype
by environment (GE) genomic selection models (GS).  It consists a group of
functions that help to create regression kernels for some GE genomic models
proposed by JarquÃ­n et al. (2014) <doi:10.1007/s00122-013-2243-1> and
Lopez-Cruz et al. (2015) <doi:10.1534/g3.114.016097>.  Also, it computes genomic
predictions based on Bayesian approaches.  The prediction function uses an
orthogonal transformation of the data and specific priors present by Cuevas et
al. (2014) <doi:10.1534/g3.114.013094>.")
    (license license:gpl3)))

(define-public r-bgfd
  (package
    (name "r-bgfd")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BGFD" version))
       (sha256
        (base32 "0983v66bjp9v3jp7zbjyffda1gaf20nakqr94c32lvh9wmk0y1s9"))))
    (properties `((upstream-name . "BGFD")))
    (build-system r-build-system)
    (propagated-inputs (list r-adequacymodel))
    (home-page "https://cran.r-project.org/package=BGFD")
    (synopsis "Bell-G and Complementary Bell-G Family of Distributions")
    (description
     "Evaluates the probability density function, cumulative distribution function,
quantile function, random numbers, survival function, hazard rate function, and
maximum likelihood estimates for the following distributions: Bell exponential,
Bell extended exponential, Bell Weibull, Bell extended Weibull, Bell-Fisk,
Bell-Lomax, Bell Burr-XII, Bell Burr-X, complementary Bell exponential,
complementary Bell extended exponential, complementary Bell Weibull,
complementary Bell extended Weibull, complementary Bell-Fisk, complementary
Bell-Lomax, complementary Bell Burr-XII and complementary Bell Burr-X
distribution.  Related work includes: a) Fayomi A., Tahir M. H., Algarni A.,
Imran M. and Jamal F. (2022). \"A new useful exponential model with applications
to quality control and actuarial data\".  Computational Intelligence and
Neuroscience, 2022. <doi:10.1155/2022/2489998>.  b) Alanzi, A. R., Imran M.,
Tahir M. H., Chesneau C., Jamal F. Shakoor S. and Sami, W. (2023). \"Simulation
analysis, properties and applications on a new Burr XII model based on the
Bell-X functionalities\".  AIMS Mathematics, 8(3): 6970-7004.
<doi:10.3934/math.2023352>.  c) Algarni A. (2022). \"Group Acceptance Sampling
Plan Based on New Compounded Three-Parameter Weibull Model\".  Axioms, 11(9):
438. <doi:10.3390/axioms11090438>.")
    (license license:gpl2+)))

(define-public r-bgeva
  (package
    (name "r-bgeva")
    (version "0.3-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bgeva" version))
       (sha256
        (base32 "0qm4xknyab8hdyn3in2hsvm8s062cnmqqf41b5jvax1mi5hs0z8c"))))
    (properties `((upstream-name . "bgeva")))
    (build-system r-build-system)
    (propagated-inputs (list r-trust r-mgcv r-magic))
    (home-page "http://www.ucl.ac.uk/statistics/people/giampieromarra")
    (synopsis "Binary Generalized Extreme Value Additive Models")
    (description
     "Routine for fitting regression models for binary rare events with linear and
nonlinear covariate effects when using the quantile function of the Generalized
Extreme Value random variable.")
    (license license:gpl2+)))

(define-public r-bgev
  (package
    (name "r-bgev")
    (version "0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bgev" version))
       (sha256
        (base32 "1vvv44lf5vdg88fv6zkvggprxbf33msbjmsxli18m3vpwxdjxdm0"))))
    (properties `((upstream-name . "bgev")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-envstats r-deoptim))
    (home-page "https://cran.r-project.org/package=bgev")
    (synopsis "Bimodal GEV Distribution with Location Parameter")
    (description
     "Density, distribution function, quantile function random generation and
estimation of bimodal GEV distribution given in Otiniano et al. (2023)
<doi:10.1007/s10651-023-00566-7>.  This new generalization of the well-known GEV
(Generalized Extreme Value) distribution is useful for modeling heterogeneous
bimodal data from different areas.")
    (license license:gpl3)))

(define-public r-bgdata
  (package
    (name "r-bgdata")
    (version "2.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BGData" version))
       (sha256
        (base32 "1bx63spfmsn9hz3qxhkvipfvyqcdy4afqzcada3bgly265kdg10h"))))
    (properties `((upstream-name . "BGData")))
    (build-system r-build-system)
    (propagated-inputs (list r-synchronicity
                             r-symdmatrix
                             r-linkedmatrix
                             r-ff
                             r-crochet
                             r-bit
                             r-bigmemory
                             r-bedmatrix))
    (home-page "https://github.com/QuantGen/BGData")
    (synopsis "Suite of Packages for Analysis of Big Genomic Data")
    (description
     "An umbrella package providing a phenotype/genotype data structure and scalable
and efficient computational methods for large genomic datasets in combination
with several other packages: BEDMatrix', @code{LinkedMatrix}', and
@code{symDMatrix}'.")
    (license license:expat)))

(define-public r-bfw
  (package
    (name "r-bfw")
    (version "0.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bfw" version))
       (sha256
        (base32 "0qgq47axa0r9653d1sjm00fvfx49v28id5sngwyrgkppcngmbg9p"))))
    (properties `((upstream-name . "bfw")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-rvg
                             r-runjags
                             r-png
                             r-plyr
                             r-officer
                             r-mass
                             r-magrittr
                             r-ggplot2
                             r-dplyr
                             r-data-table
                             r-coda
                             r-circlize))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/oeysan/bfw/")
    (synopsis "Bayesian Framework for Computational Modeling")
    (description
     "Derived from the work of Kruschke (2015, <ISBN:9780124058880>), the present
package aims to provide a framework for conducting Bayesian analysis using
Markov chain Monte Carlo (MCMC) sampling utilizing the Just Another Gibbs
Sampler ('JAGS', Plummer, 2003, <https://mcmc-jags.sourceforge.io>).  The
initial version includes several modules for conducting Bayesian equivalents of
chi-squared tests, analysis of variance (ANOVA), multiple (hierarchical)
regression, softmax regression, and for fitting data (e.g., structural equation
modeling).")
    (license license:expat)))

(define-public r-bfsmaps
  (package
    (name "r-bfsmaps")
    (version "1.99.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bfsMaps" version))
       (sha256
        (base32 "1sjvlfwcp52l2bpnbbalrcr978lwmkrr9kz47jvnkgy3vg9ppd14"))))
    (properties `((upstream-name . "bfsMaps")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-desctools))
    (native-inputs (list r-r-rsp))
    (home-page "https://github.com/AndriSignorell/bfsMaps/")
    (synopsis "Plot Maps from Switzerland by Swiss Federal Statistical Office")
    (description
     "At the Swiss Federal Statistical Office (SFSO), spatial maps of Switzerland are
available free of charge as Cartographic bases for small-scale thematic
mapping'.  This package contains convenience functions to import ESRI
(Environmental Systems Research Institute) shape files using the package sf and
to plot them easily and quickly without having to worry too much about the
technical details.  It contains utilities to combine multiple areas to one
single polygon and to find neighbours for single regions.  For any point on a
map, a special locator can be used to determine to which municipality, district
or canton it belongs.")
    (license license:gpl2+)))

(define-public r-bfsl
  (package
    (name "r-bfsl")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bfsl" version))
       (sha256
        (base32 "10z3w1fk4va2ajvpnqp0llgw4a047iaya6xi57if1a7jd7r1jxfh"))))
    (properties `((upstream-name . "bfsl")))
    (build-system r-build-system)
    (propagated-inputs (list r-generics))
    (home-page "https://github.com/pasturm/bfsl")
    (synopsis "Best-Fit Straight Line")
    (description
     "How to fit a straight line through a set of points with errors in both
coordinates? The bfsl package implements the York regression (York, 2004
<doi:10.1119/1.1632486>).  It provides unbiased estimates of the intercept,
slope and standard errors for the best-fit straight line to independent points
with (possibly correlated) normally distributed errors in both x and y.  Other
commonly used errors-in-variables methods, such as orthogonal distance
regression, geometric mean regression or Deming regression are special cases of
the bfsl solution.")
    (license license:expat)))

(define-public r-bfs
  (package
    (name "r-bfs")
    (version "0.5.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BFS" version))
       (sha256
        (base32 "1g7zx4qy3zb2xrblywxfxc2xgg8mzgg4l406nxw0d6s634ds41hi"))))
    (properties `((upstream-name . "BFS")))
    (build-system r-build-system)
    (propagated-inputs (list r-zip
                             r-xml2
                             r-tibble
                             r-sf
                             r-rvest
                             r-rstac
                             r-pxweb
                             r-purrr
                             r-magrittr
                             r-lifecycle
                             r-janitor
                             r-httr2
                             r-fs
                             r-dplyr
                             r-curl))
    (home-page "https://felixluginbuhl.com/BFS/")
    (synopsis "Get Data from the Swiss Federal Statistical Office")
    (description
     "Search and download data from the Swiss Federal Statistical Office (BFS) APIs
<https://www.bfs.admin.ch/>.")
    (license license:gpl3+)))

(define-public r-bfpack
  (package
    (name "r-bfpack")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BFpack" version))
       (sha256
        (base32 "1ddcqcd8wzc1mc8d27kwv1xand8f4asvdirjbgncl5vs4akr924m"))))
    (properties `((upstream-name . "BFpack")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich
                             r-qrm
                             r-pracma
                             r-mvtnorm
                             r-mass
                             r-lme4
                             r-extradistr
                             r-ergm
                             r-bergm
                             r-bain))
    (native-inputs (list r-knitr gfortran))
    (home-page "https://github.com/jomulder/BFpack")
    (synopsis "Flexible Bayes Factor Testing of Scientific Expectations")
    (description
     "Implementation of default Bayes factors for testing statistical hypotheses under
various statistical models.  The package is intended for applied quantitative
researchers in the social and behavioral sciences, medical research, and related
fields.  The Bayes factor tests can be executed for statistical models such as
univariate and multivariate normal linear models, correlation analysis,
generalized linear models, special cases of linear mixed models, survival
models, relational event models.  Parameters that can be tested are location
parameters (e.g., group means, regression coefficients), variances (e.g., group
variances), and measures of association (e.g,.
polychoric/polyserial/biserial/tetrachoric/product moments correlations), among
others.  The statistical underpinnings are described in Mulder and Xin (2019)
<DOI:10.1080/00273171.2021.1904809>, Mulder and Gelissen (2019)
<DOI:10.1080/02664763.2021.1992360>, Mulder (2016)
<DOI:10.1016/j.jmp.2014.09.004>, Mulder and Fox (2019) <DOI:10.1214/18-BA1115>,
Mulder and Fox (2013) <DOI:10.1007/s11222-011-9295-3>, Boeing-Messing, van
Assen, Hofman, Hoijtink, and Mulder (2017) <DOI:10.1037/met0000116>, Hoijtink,
Mulder, van Lissa, and Gu, (2018) <DOI:10.31234/osf.io/v3shc>, Gu, Mulder, and
Hoijtink, (2018) <DOI:10.1111/bmsp.12110>, Hoijtink, Gu, and Mulder, (2018)
<DOI:10.1111/bmsp.12145>, and Hoijtink, Gu, Mulder, and Rosseel, (2018)
<DOI:10.1037/met0000187>.  When using the packages, please refer to Mulder et
al. (2021) <DOI:10.18637/jss.v100.i18>.")
    (license license:gpl3+)))

(define-public r-bfp
  (package
    (name "r-bfp")
    (version "0.0-48")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bfp" version))
       (sha256
        (base32 "0m3ika7sfksv4xhbxcysh0yyfvj7bip9g1m8n2icb2zjwfafccvn"))))
    (properties `((upstream-name . "bfp")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=bfp")
    (synopsis "Bayesian Fractional Polynomials")
    (description
     "This package implements the Bayesian paradigm for fractional polynomial models
under the assumption of normally distributed error terms, see Sabanes Bove, D.
and Held, L. (2011) <doi:10.1007/s11222-010-9170-7>.")
    (license license:gpl2+)))

(define-public r-bfi
  (package
    (name "r-bfi")
    (version "2.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BFI" version))
       (sha256
        (base32 "0szx9cjzhlxk6n83i6rm48db2lkj4zdsaqrgzipr71bwwjfk08nr"))))
    (properties `((upstream-name . "BFI")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://hassanpazira.github.io/BFI/")
    (synopsis "Bayesian Federated Inference")
    (description
     "The Bayesian Federated Inference ('BFI') method combines inference results
obtained from local data sets in the separate centers.  In this version of the
package, the BFI methodology is programmed for linear, logistic and survival
regression models.  For GLMs, see Jonker, Pazira and Coolen (2024)
<doi:10.1002/sim.10072>; for survival models, see Pazira, Massa, Weijers, Coolen
and Jonker (2024) <doi:10.48550/@code{arXiv.2404.17464>}; and for heterogeneous
populations, see Jonker, Pazira and Coolen (2024)
<doi:10.48550/@code{arXiv.2402.02898>}.")
    (license license:expat)))

(define-public r-bff
  (package
    (name "r-bff")
    (version "4.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BFF" version))
       (sha256
        (base32 "1s4f9mjhx24hxm2aan25kmb5akpxl25ggic3mkwmh1d0npbgip6a"))))
    (properties `((upstream-name . "BFF")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-matrix r-gsl r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rshudde/BFF")
    (synopsis "Bayes Factor Functions")
    (description
     "Bayes factors represent the ratio of probabilities assigned to data by competing
scientific hypotheses.  However, one drawback of Bayes factors is their
dependence on prior specifications that define null and alternative hypotheses.
Additionally, there are challenges in their computation.  To address these
issues, we define Bayes factor functions (BFFs) directly from common test
statistics.  BFFs express Bayes factors as a function of the prior densities
used to define the alternative hypotheses.  These prior densities are centered
on standardized effects, which serve as indices for the BFF. Therefore, BFFs
offer a summary of evidence in favor of alternative hypotheses that correspond
to a range of scientifically interesting effect sizes.  Such summaries remove
the need for arbitrary thresholds to determine \"statistical significance.\" BFFs
are available in closed form and can be easily computed from z, t, chi-squared,
and F statistics.  They depend on hyperparameters \"r\" and \"tau^2\", which
determine the shape and scale of the prior distributions defining the
alternative hypotheses.  Plots of BFFs versus effect size provide informative
summaries of hypothesis tests that can be easily aggregated across studies.")
    (license license:gpl2+)))

(define-public r-bfast
  (package
    (name "r-bfast")
    (version "1.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bfast" version))
       (sha256
        (base32 "1vjklk7d9b0q7j348aqvspm7rn80bh06hz66r2z1qs9436ppkx5a"))))
    (properties `((upstream-name . "bfast")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-strucchangercpp r-rdpack r-rcpp
                             r-forecast))
    (home-page "https://bfast2.github.io/")
    (synopsis "Breaks for Additive Season and Trend")
    (description
     "Decomposition of time series into trend, seasonal, and remainder components with
methods for detecting and characterizing abrupt changes within the trend and
seasonal components.  BFAST can be used to analyze different types of satellite
image time series and can be applied to other disciplines dealing with seasonal
or non-seasonal time series, such as hydrology, climatology, and econometrics.
The algorithm can be extended to label detected changes with information on the
parameters of the fitted piecewise linear models.  BFAST monitoring
functionality is described in Verbesselt et al. (2010)
<doi:10.1016/j.rse.2009.08.014>.  BFAST monitor provides functionality to detect
disturbance in near real-time based on BFAST'- type models, and is described in
Verbesselt et al. (2012) <doi:10.1016/j.rse.2012.02.022>.  BFAST Lite approach
is a flexible approach that handles missing data without interpolation, and will
be described in an upcoming paper.  Furthermore, different models can now be
used to fit the time series data and detect structural changes (breaks).")
    (license license:gpl2+)))

(define-public r-beyondwhittle
  (package
    (name "r-beyondwhittle")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "beyondWhittle" version))
       (sha256
        (base32 "0lh496w6hsdcdkn51zlnm5vw0wx1a6d60h24zqzmsl1zr4y6667z"))))
    (properties `((upstream-name . "beyondWhittle")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-mass
                             r-ltsa
                             r-forecast
                             r-bh))
    (home-page "https://cran.r-project.org/package=beyondWhittle")
    (synopsis "Bayesian Spectral Inference for Time Series")
    (description
     "Implementations of Bayesian parametric, nonparametric and semiparametric
procedures for univariate and multivariate time series.  The package is based on
the methods presented in C. Kirch et al (2018) <doi:10.1214/18-BA1126>, A. Meier
(2018) <https://opendata.uni-halle.de//handle/1981185920/13470> and Y. Tang et
al (2023) <@code{arXiv:2303.11561>}.  It was supported by DFG grants KI 1443/3-1
and KI 1443/3-2.")
    (license license:gpl3+)))

(define-public r-beyondbenford
  (package
    (name "r-beyondbenford")
    (version "1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BeyondBenford" version))
       (sha256
        (base32 "04jdx35955g44v2is6zxfw1gip7zh3li8divc7iy5hl0jrshfhv3"))))
    (properties `((upstream-name . "BeyondBenford")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2))
    (home-page "https://cran.r-project.org/package=BeyondBenford")
    (synopsis
     "Compare the Goodness of Fit of Benford's and Blondeau Da Silva's Digit Distributions to a Given Dataset")
    (description
     "Allows to compare the goodness of fit of Benford's and Blondeau Da Silva's digit
distributions in a dataset.  It is used to check whether the data distribution
is consistent with theoretical distributions highlighted by Blondeau Da Silva or
not (through the dat.distr() function): this ideal theoretical distribution must
be at least approximately followed by the data for the use of Blondeau Da
Silva's model to be well-founded.  It also enables to plot histograms of digit
distributions, both observed in the dataset and given by the two theoretical
approaches (with the digit.ditr() function).  Finally, it proposes to quantify
the goodness of fit via Pearson's chi-squared test (with the chi2() function).")
    (license license:gpl2)))

(define-public r-bexy
  (package
    (name "r-bexy")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bexy" version))
       (sha256
        (base32 "0dznrvdklfrf14p9z1pmmixac086xf0a0w8zii08lqmb3blq66lk"))))
    (properties `((upstream-name . "bexy")))
    (build-system r-build-system)
    (propagated-inputs (list r-ternary r-teachingdemos))
    (home-page "https://cran.r-project.org/package=bexy")
    (synopsis "Visualize and Parse the Output of 'BeXY'")
    (description
     "This package provides functions for summarizing and plotting the output of the
command-line tool @code{BeXY} (<https://bitbucket.org/wegmannlab/bexy>), a tool
that performs Bayesian inference of sex chromosome karyotypes and sex-linked
scaffolds from low-depth sequencing data.")
    (license license:gpl2)))

(define-public r-bevimed
  (package
    (name "r-bevimed")
    (version "5.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BeviMed" version))
       (sha256
        (base32 "0brima71g7x51zd1ifbgw54mnamjmm2wr8rrcnkzdy0q8wcnvgqj"))))
    (properties `((upstream-name . "BeviMed")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-matrix))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BeviMed")
    (synopsis
     "Bayesian Evaluation of Variant Involvement in Mendelian Disease")
    (description
     "This package provides a fast integrative genetic association test for rare
diseases based on a model for disease status given allele counts at rare variant
sites.  Probability of association, mode of inheritance and probability of
pathogenicity for individual variants are all inferred in a Bayesian framework -
A Fast Association Test for Identifying Pathogenic Variants Involved in Rare
Diseases', Greene et al 2017 <doi:10.1016/j.ajhg.2017.05.015>.")
    (license license:gpl2+)))

(define-public r-betterreg
  (package
    (name "r-betterreg")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BetterReg" version))
       (sha256
        (base32 "08qafm8j0bbh6qywyq6dr1j204n9cxrpyy2fh74mbgm3xknazxsc"))))
    (properties `((upstream-name . "BetterReg")))
    (build-system r-build-system)
    (propagated-inputs (list r-dplyr r-car))
    (home-page "https://cran.r-project.org/package=BetterReg")
    (synopsis "Better Statistics for OLS and Binomial Logistic Regression")
    (description
     "This package provides squared semi partial correlations, tolerance, Mahalanobis,
Likelihood Ratio Chi Square, and Pseudo R Square.  Aberson, C. L. (2022)
<doi:10.31234/osf.io/s2yqn>.")
    (license license:gpl3)))

(define-public r-bets-covid19
  (package
    (name "r-bets-covid19")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bets.covid19" version))
       (sha256
        (base32 "1pqa6zpkkcmnm9a4m3676css0fi2lp4ik6mmhfhnsr4v4kv2pq2s"))))
    (properties `((upstream-name . "bets.covid19")))
    (build-system r-build-system)
    (propagated-inputs (list r-rootsolve))
    (home-page "https://github.com/qingyuanzhao/bets.covid19")
    (synopsis "The BETS Model for Early Epidemic Data")
    (description
     "This package implements likelihood inference for early epidemic analysis.  BETS
is short for the four key epidemiological events being modeled: Begin of
exposure, End of exposure, time of Transmission, and time of Symptom onset.  The
package contains a dataset of the trajectory of confirmed cases during the
coronavirus disease (COVID-19) early outbreak.  More detail of the statistical
methods can be found in Zhao et al. (2020) <@code{arXiv:2004.07743>}.")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-bets
  (package
    (name "r-bets")
    (version "0.4.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BETS" version))
       (sha256
        (base32 "0daixk7mqmk2jq35i7mjaslz11gxbnnjgwxfvj8x1s88vz9l74sm"))))
    (properties `((upstream-name . "BETS")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xml2
                             r-webshot
                             r-urca
                             r-stringr
                             r-sqldf
                             r-shiny
                             r-seasonal
                             r-rvest
                             r-rstudioapi
                             r-rmysql
                             r-rmarkdown
                             r-rjson
                             r-plotly
                             r-miniui
                             r-lubridate
                             r-httr
                             r-htmltools
                             r-grnn
                             r-ggplot2
                             r-foreign
                             r-forecast
                             r-dygraphs
                             r-dt
                             r-dplyr
                             r-digest
                             r-dbi))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/nmecsys/BETS")
    (synopsis "Brazilian Economic Time Series")
    (description
     "It provides access to and information about the most important Brazilian
economic time series - from the Getulio Vargas Foundation
<http://portal.fgv.br/en>, the Central Bank of Brazil <http://www.bcb.gov.br>
and the Brazilian Institute of Geography and Statistics
<http://www.ibge.gov.br>.  It also presents tools for managing, analysing (e.g.
generating dynamic reports with a complete analysis of a series) and exporting
these time series.")
    (license license:gpl3)))

(define-public r-bethel
  (package
    (name "r-bethel")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bethel" version))
       (sha256
        (base32 "1zlkw672k1c5px47bpa2vk3w2906vkhvifz20h6xm7s51gmm64i0"))))
    (properties `((upstream-name . "bethel")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=bethel")
    (synopsis "Bethel's algorithm")
    (description "The sample size according to the Bethel's procedure.")
    (license license:gpl2+)))

(define-public r-betategarch
  (package
    (name "r-betategarch")
    (version "3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "betategarch" version))
       (sha256
        (base32 "0hqvyps3lwix2fkzk18wrkhxpqhgardvib9sq1ip8gn8sn1dsi8y"))))
    (properties `((upstream-name . "betategarch")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo))
    (home-page "http://www.sucarrat.net/")
    (synopsis
     "Simulation, Estimation and Forecasting of Beta-Skew-t-EGARCH Models")
    (description
     "Simulation, estimation and forecasting of first-order Beta-Skew-t-EGARCH models
with leverage (one-component, two-component, skewed versions).")
    (license license:gpl2)))

(define-public r-betasandwich
  (package
    (name "r-betasandwich")
    (version "1.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "betaSandwich" version))
       (sha256
        (base32 "0gd6n1xghwhqg981ibb41w2hw3wk5zzr3870wpjbic4k96cnmvyb"))))
    (properties `((upstream-name . "betaSandwich")))
    (build-system r-build-system)
    (home-page "https://github.com/jeksterslab/betaSandwich")
    (synopsis
     "Robust Confidence Intervals for Standardized Regression Coefficients")
    (description
     "Generates robust confidence intervals for standardized regression coefficients
using heteroskedasticity-consistent standard errors for models fitted by lm() as
described in Dudgeon (2017) <doi:10.1007/s11336-017-9563-z>.  The package can
also be used to generate confidence intervals for R-squared, adjusted R-squared,
and differences of standardized regression coefficients.  A description of the
package and code examples are presented in Pesigan, Sun, and Cheung (2023)
<doi:10.1080/00273171.2023.2201277>.")
    (license license:expat)))

(define-public r-betareg
  (package
    (name "r-betareg")
    (version "3.2-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "betareg" version))
       (sha256
        (base32 "1izp7nirsziwx8803ckbgwyj4r1vf4ixgx5nlin7d5pkjy13c9mv"))))
    (properties `((upstream-name . "betareg")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich r-modeltools r-lmtest r-formula
                             r-flexmix))
    (native-inputs (list r-quarto))
    (home-page "https://topmodels.R-Forge.R-project.org/betareg/")
    (synopsis "Beta Regression")
    (description
     "Beta regression for modeling beta-distributed dependent variables on the open
unit interval (0, 1), e.g., rates and proportions, see Cribari-Neto and Zeileis
(2010) <doi:10.18637/jss.v034.i02>.  Moreover, extended-support beta regression
models can accommodate dependent variables with boundary observations at 0
and/or 1.  For the classical beta regression model, alternative specifications
are provided: Bias-corrected and bias-reduced estimation, finite mixture models,
and recursive partitioning for beta regression, see GrÃ¼n, Kosmidis, and Zeileis
(2012) <doi:10.18637/jss.v048.i11>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-betaper
  (package
    (name "r-betaper")
    (version "1.1-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "betaper" version))
       (sha256
        (base32 "0gh5xjimg0wgv626g3y34mvgrji2aylnm89iwadg7d6g4s457ynp"))))
    (properties `((upstream-name . "betaper")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan))
    (home-page "https://cran.r-project.org/package=betaper")
    (synopsis
     "Taxonomic Uncertainty on Multivariate Analyses of Ecological Data")
    (description
     "Permutational method to incorporate taxonomic uncertainty and some functions to
assess its effects on parameters of some widely used multivariate methods in
ecology, as explained in Cayuela et al. (2011)
<doi:10.1111/j.1600-0587.2009.05899.x>.")
    (license license:gpl2+)))

(define-public r-betapass
  (package
    (name "r-betapass")
    (version "1.1-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BetaPASS" version))
       (sha256
        (base32 "1vx2wn2bj3xl430aw4zlv2j3m39nkmg7mfkm1pmilvpqszfx078g"))))
    (properties `((upstream-name . "BetaPASS")))
    (build-system r-build-system)
    (propagated-inputs (list r-pbapply r-ggplot2 r-betareg))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BetaPASS")
    (synopsis "Calculate Power and Sample Size with Beta Regression")
    (description
     "Power calculations are a critical component of any research study to determine
the minimum sample size necessary to detect differences between multiple groups.
 Researchers often work with data taking the form of proportions that can be
modeled with a beta distribution.  Here we present an R package,
@code{BetaPASS}', that perform power and sample size calculations for data
following a beta distribution with comparative nonparametric output.  This
package allows flexibility with multiple options for link functions to fit the
data and graphing functionality for visual comparisons.")
    (license license:gpl2+)))

(define-public r-betapart
  (package
    (name "r-betapart")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "betapart" version))
       (sha256
        (base32 "1ap1z9pvih66la7qh1bfihq0s5csnacgnx3i31gnj5ylaf72z9ds"))))
    (properties `((upstream-name . "betapart")))
    (build-system r-build-system)
    (propagated-inputs (list r-snow
                             r-rcdd
                             r-picante
                             r-minpack-lm
                             r-itertools
                             r-geometry
                             r-foreach
                             r-fastmatch
                             r-dosnow
                             r-ape))
    (home-page "https://cran.r-project.org/package=betapart")
    (synopsis
     "Partitioning Beta Diversity into Turnover and Nestedness Components")
    (description
     "This package provides functions to compute pair-wise dissimilarities (distance
matrices) and multiple-site dissimilarities, separating the turnover and
nestedness-resultant components of taxonomic (incidence and abundance based),
functional and phylogenetic beta diversity.")
    (license license:gpl2+)))

(define-public r-betanb
  (package
    (name "r-betanb")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "betaNB" version))
       (sha256
        (base32 "1qfpp4y3drqh492y7x67jkqbabkqbmsbc2yqvbzc427pd3n9ijs5"))))
    (properties `((upstream-name . "betaNB")))
    (build-system r-build-system)
    (home-page "https://github.com/jeksterslab/betaNB")
    (synopsis "Bootstrap for Regression Effect Sizes")
    (description
     "Generates nonparametric bootstrap confidence intervals (Efron & Tibshirani,
1993: <doi:10.1201/9780429246593>) for standardized regression coefficients
(beta) and other effect sizes, including multiple correlation, semipartial
correlations, improvement in R-squared, squared partial correlations, and
differences in standardized regression coefficients, for models fitted by lm().")
    (license license:expat)))

(define-public r-betamc
  (package
    (name "r-betamc")
    (version "1.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "betaMC" version))
       (sha256
        (base32 "08rw2q8dg51ygmns070qpd1204k9hizpqfzggj542sq2ssgc7byk"))))
    (properties `((upstream-name . "betaMC")))
    (build-system r-build-system)
    (home-page "https://github.com/jeksterslab/betaMC")
    (synopsis "Monte Carlo for Regression Effect Sizes")
    (description
     "Generates Monte Carlo confidence intervals for standardized regression
coefficients (beta) and other effect sizes, including multiple correlation,
semipartial correlations, improvement in R-squared, squared partial
correlations, and differences in standardized regression coefficients, for
models fitted by lm(). @code{betaMC} combines ideas from Monte Carlo confidence
intervals for the indirect effect (Pesigan and Cheung, 2023
<doi:10.3758/s13428-023-02114-4>) and the sampling covariance matrix of
regression coefficients (Dudgeon, 2017 <doi:10.1007/s11336-017-9563-z>) to
generate confidence intervals effect sizes in regression.")
    (license license:expat)))

(define-public r-betafunctions
  (package
    (name "r-betafunctions")
    (version "1.9.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "betafunctions" version))
       (sha256
        (base32 "1sm33z8nzbh4savzw3zc81bg9g0n83xfibmd5kklq02h3105d4v4"))))
    (properties `((upstream-name . "betafunctions")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=betafunctions")
    (synopsis
     "Functions for Working with Two- And Four-Parameter Beta Probability Distributions and Psychometric Analysis of Classifications")
    (description
     "Package providing a number of functions for working with Two- and Four-parameter
Beta and closely related distributions (i.e., the Gamma- Binomial-, and
Beta-Binomial distributions).  Includes, among other things: - d/p/q/r functions
for Four-Parameter Beta distributions and Generalized \"Binomial\" (continuous)
distributions, and d/p/r- functions for Beta- Binomial distributions. - d/p/q/r
functions for Two- and Four-Parameter Beta distributions parameterized in terms
of their means and variances rather than their shape-parameters. - Moment
generating functions for Binomial distributions, Beta-Binomial distributions,
and observed value distributions. - Functions for estimating classification
accuracy and consistency, making use of the Classical Test-Theory based
Livingston and Lewis (L&L) and Hanson and Brennan approaches.  A shiny app is
available, providing a GUI for the L&L approach when used for binary
classifications.  For url to the app, see documentation for the LL.CA()
function.  Livingston and Lewis (1995) <doi:10.1111/j.1745-3984.1995.tb00462.x>.
 Lord (1965) <doi:10.1007/BF02289490>.  Hanson (1991)
<https://files.eric.ed.gov/fulltext/ED344945.pdf>.")
    (license license:cc0)))

(define-public r-betadelta
  (package
    (name "r-betadelta")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "betaDelta" version))
       (sha256
        (base32 "1d9fdi4bvhqih45iism5mkpqrmfl4mgjhsqkb5jkpyyd3la059y6"))))
    (properties `((upstream-name . "betaDelta")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv))
    (home-page "https://github.com/jeksterslab/betaDelta")
    (synopsis "Confidence Intervals for Standardized Regression Coefficients")
    (description
     "Generates confidence intervals for standardized regression coefficients using
delta method standard errors for models fitted by lm() as described in Yuan and
Chan (2011) <doi:10.1007/s11336-011-9224-6> and Jones and Waller (2015)
<doi:10.1007/s11336-013-9380-y>.  The package can also be used to generate
confidence intervals for differences of standardized regression coefficients and
as a general approach to performing the delta method.  A description of the
package and code examples are presented in Pesigan, Sun, and Cheung (2023)
<doi:10.1080/00273171.2023.2201277>.")
    (license license:expat)))

(define-public r-betaclust
  (package
    (name "r-betaclust")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "betaclust" version))
       (sha256
        (base32 "1kw8ri4244z7ckn4gizz0zbqzwx7dypv6qb974lmj6dzzyjp6922"))))
    (properties `((upstream-name . "betaclust")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-proc
                             r-plotly
                             r-ggplot2
                             r-foreach
                             r-doparallel))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=betaclust")
    (synopsis
     "Family of Beta Mixture Models for Clustering Beta-Valued DNA Methylation Data")
    (description
     "This package provides a family of novel beta mixture models (BMMs) has been
developed by Majumdar et al. (2022) <doi:10.48550/@code{arXiv.2211.01938>} to
appositely model the beta-valued cytosine-guanine dinucleotide (@code{CpG})
sites, to objectively identify methylation state thresholds and to identify the
differentially methylated @code{CpG} (DMC) sites using a model-based clustering
approach.  The family of beta mixture models employs different parameter
constraints applicable to different study settings.  The EM algorithm is used
for parameter estimation, with a novel approximation during the M-step providing
tractability and ensuring computational feasibility.")
    (license license:gpl3)))

(define-public r-betacal
  (package
    (name "r-betacal")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "betacal" version))
       (sha256
        (base32 "19wgpgf9yhckl9qx9v24f4yh055wfalphcxwm7lg68px6ap2pxl9"))))
    (properties `((upstream-name . "betacal")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=betacal")
    (synopsis "Beta Calibration")
    (description
     "Fit beta calibration models and obtain calibrated probabilities from them.")
    (license license:expat)))

(define-public r-betabit
  (package
    (name "r-betabit")
    (version "2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BetaBit" version))
       (sha256
        (base32 "185bsjc1azvblg0hm39bq3r1c6c4vla9k5cz39p6kjzd1gn1zhr2"))))
    (properties `((upstream-name . "BetaBit")))
    (build-system r-build-system)
    (propagated-inputs (list r-digest))
    (home-page "https://github.com/BetaAndBit/Charts")
    (synopsis "Mini Games from Adventures of Beta and Bit")
    (description
     "Three games: proton, frequon and regression.  Each one is a console-based
data-crunching game for younger and older data scientists.  Act as a data-hacker
and find Slawomir Pietraszko's credentials to the Proton server.  In proton you
have to solve four data-based puzzles to find the login and password.  There are
many ways to solve these puzzles.  You may use loops, data filtering, ordering,
aggregation or other tools.  Only basics knowledge of R is required to play the
game, yet the more functions you know, the more approaches you can try.  In
frequon you will help to perform statistical cryptanalytic attack on a corpus of
ciphered messages.  This time seven sub-tasks are pushing the bar much higher.
Do you accept the challenge? In regression you will test your modeling skills in
a series of eight sub-tasks.  Try only if ANOVA is your close friend.  It's a
part of Beta and Bit project.  You will find more about the Beta and Bit project
at <https://github.com/@code{BetaAndBit/Charts>}.")
    (license license:gpl2)))

(define-public r-betabayes
  (package
    (name "r-betabayes")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "betaBayes" version))
       (sha256
        (base32 "1a7ng6ym47scgj47frrhs76r6ipq6mhgf3ivbg2npqpg7hjcz2ci"))))
    (properties `((upstream-name . "betaBayes")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-betareg))
    (home-page "https://cran.r-project.org/package=betaBayes")
    (synopsis "Bayesian Beta Regression")
    (description
     "This package provides a class of Bayesian beta regression models for the
analysis of continuous data with support restricted to an unknown finite
support.  The response variable is modeled using a four-parameter beta
distribution with the mean or mode parameter depending linearly on covariates
through a link function.  When the response support is known to be (0,1), the
above class of models reduce to traditional (0,1) supported beta regression
models.  Model choice is carried out via the logarithm of the pseudo marginal
likelihood (LPML), the deviance information criterion (DIC), and the
Watanabe-Akaike information criterion (WAIC).  See Zhou and Huang (2022)
<doi:10.1016/j.csda.2021.107345>.")
    (license license:gpl2+)))

(define-public r-bet
  (package
    (name "r-bet")
    (version "0.5.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BET" version))
       (sha256
        (base32 "13yc78idgdxp2sadx3pl8c8dffxinr0aywygbfmbz35zvwl7qa4r"))))
    (properties `((upstream-name . "BET")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://cran.r-project.org/package=BET")
    (synopsis "Binary Expansion Testing")
    (description
     "Nonparametric detection of nonuniformity and dependence with Binary Expansion
Testing (BET).  See Kai Zhang (2019) BET on Independence, Journal of the
American Statistical Association, 114:528, 1620-1637,
<DOI:10.1080/01621459.2018.1537921>, Kai Zhang, Wan Zhang, Zhigen Zhao, Wen
Zhou. (2023).  BEAUTY Powered BEAST, <doi:10.48550/@code{arXiv.2103.00674>} and
Wan Zhang, Zhigen Zhao, Michael Baiocchi, Yao Li, Kai Zhang. (2023)
@code{SorBET}: A Fast and Powerful Algorithm to Test Dependence of Variables,
Techinical report.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-bestsdp
  (package
    (name "r-bestsdp")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bestSDP" version))
       (sha256
        (base32 "1rff6jij9wmlpalzilvid8jvj2pczrchx0yrg0vhy00j64zg6jzs"))))
    (properties `((upstream-name . "bestSDP")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-shinywidgets
                             r-shinythemes
                             r-shinyjs
                             r-shinydashboard
                             r-shinybs
                             r-shiny
                             r-rlist
                             r-readxl
                             r-ggplot2
                             r-dt
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=bestSDP")
    (synopsis
     "Burden Estimate of Common Communicable Diseases in Settlements of Displaced Populations")
    (description
     "This package provides a practical tool for estimating the burden of common
communicable diseases in settlements of displaced populations.  An online
version of the tool can be found at
<http://who-refugee-bod.ecdf.ed.ac.uk/shiny/app/>.  Estimates of burden of
disease aim to synthesize data about cause-specific morbidity and mortality
through a systematic approach that enables evidence-based decisions and
comparisons across settings.  The focus of this tool is on four acute
communicable diseases and syndromes, including Acute respiratory infections,
Acute diarrheal diseases, Acute jaundice syndrome and Acute febrile illnesses.")
    (license license:gpl2+)))

(define-public r-bestridge
  (package
    (name "r-bestridge")
    (version "1.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bestridge" version))
       (sha256
        (base32 "10nlk1vrzmgxfia9b3skmg5594ab5s3d64k2awyxybkz2pzz7gb1"))))
    (properties `((upstream-name . "bestridge")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-rcppeigen
                             r-rcpp
                             r-pheatmap
                             r-matrix
                             r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bestridge")
    (synopsis "Comprehensive R Package for Best Subset Selection")
    (description
     "The bestridge package is designed to provide a one-stand service for users to
successfully carry out best ridge regression in various complex situations via
the primal dual active set algorithm proposed by Wen, C., Zhang, A., Quan, S.
and Wang, X. (2020) <doi:10.18637/jss.v094.i04>.  This package allows users to
perform the regression, classification, count regression and censored regression
for (ultra) high dimensional data, and it also supports advanced usages like
group variable selection and nuisance variable selection.")
    (license license:gpl3)))

(define-public r-bestree
  (package
    (name "r-bestree")
    (version "0.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BESTree" version))
       (sha256
        (base32 "0yij6xdlvhbqifckaylgbhszwwp38d9x6vbpr1nl40mwwnv4bswz"))))
    (properties `((upstream-name . "BESTree")))
    (build-system r-build-system)
    (propagated-inputs (list r-plyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BESTree")
    (synopsis "Branch-Exclusive Splits Trees")
    (description
     "Decision tree algorithm with a major feature added.  Allows for users to define
an ordering on the partitioning process.  Resulting in Branch-Exclusive Splits
Trees (BEST).  Cedric Beaulac and Jeffrey S. Rosentahl (2019)
<@code{arXiv:1804.10168>}.")
    (license license:expat)))

(define-public r-bestie
  (package
    (name "r-bestie")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Bestie" version))
       (sha256
        (base32 "1j3xnwq9faim8cjb6cz54mix7ly8dzxa5qsdglcgl2gfbvzbb9s1"))))
    (properties `((upstream-name . "Bestie")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-mvtnorm r-bidag))
    (home-page "https://cran.r-project.org/package=Bestie")
    (synopsis "Bayesian Estimation of Intervention Effects")
    (description
     "An implementation of intervention effect estimation for DAGs (directed acyclic
graphs) learned from binary or continuous data.  First, parameters are estimated
or sampled for the DAG and then interventions on each node (variable) are
propagated through the network (do-calculus).  Both exact computation (for
continuous data or for binary data up to around 20 variables) and Monte Carlo
schemes (for larger binary networks) are implemented.")
    (license license:gpl3)))

(define-public r-besthr
  (package
    (name "r-besthr")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "besthr" version))
       (sha256
        (base32 "0mg1nm014p82n9v5f929rcrpqclqs5kh8hga8gd7h29jqykgirc0"))))
    (properties `((upstream-name . "besthr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-stringr
                             r-rlang
                             r-patchwork
                             r-magrittr
                             r-ggridges
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=besthr")
    (synopsis "Generating Bootstrap Estimation Distributions of HR Data")
    (description
     "This package creates plots showing scored HR experiments and plots of
distribution of means of ranks of HR score from bootstrapping.  Authors (2019)
<doi:10.5281/zenodo.3374507>.")
    (license license:expat)))

(define-public r-bestglm
  (package
    (name "r-bestglm")
    (version "0.37.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bestglm" version))
       (sha256
        (base32 "1zncy958hlwyyxrp6yvq1inq6mpyrbr6cdkf8maijfirr5j8f0ya"))))
    (properties `((upstream-name . "bestglm")))
    (build-system r-build-system)
    (propagated-inputs (list r-pls r-leaps r-lattice r-grpreg r-glmnet))
    (home-page "https://cran.r-project.org/package=bestglm")
    (synopsis "Best Subset GLM and Regression Utilities")
    (description
     "Best subset glm using information criteria or cross-validation, carried by using
leaps algorithm (Furnival and Wilson, 1974) <doi:10.2307/1267601> or complete
enumeration (Morgan and Tatar, 1972) <doi:10.1080/00401706.1972.10488918>.
Implements PCR and PLS using AIC/BIC. Implements one-standard deviation rule for
use with the caret package.")
    (license license:gpl2+)))

(define-public r-bessel
  (package
    (name "r-bessel")
    (version "0.6-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Bessel" version))
       (sha256
        (base32 "0n2k33n4r1hx20qr2hh5i6a2rcyikd6iwihhknzz8nvx8f2ms9yh"))))
    (properties `((upstream-name . "Bessel")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmpfr))
    (home-page "https://specfun.r-forge.r-project.org/")
    (synopsis "Computations and Approximations for Bessel Functions")
    (description
     "Computations for Bessel function for complex, real and partly mpfr (arbitrary
precision) numbers; notably interfacing TOMS 644; approximations for large
arguments, experiments, etc.")
    (license license:gpl2+)))

(define-public r-bess
  (package
    (name "r-bess")
    (version "2.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BeSS" version))
       (sha256
        (base32 "1y45lqw6b0qzzg9j3cs18fvzd4q7l6njhjkzpcrik2lgwkzamc8a"))))
    (properties `((upstream-name . "BeSS")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rcppeigen r-rcpp r-matrix r-glmnet))
    (home-page "https://cran.r-project.org/package=BeSS")
    (synopsis "Best Subset Selection in Linear, Logistic and CoxPH Models")
    (description
     "An implementation of best subset selection in generalized linear model and Cox
proportional hazard model via the primal dual active set algorithm proposed by
Wen, C., Zhang, A., Quan, S. and Wang, X. (2020) <doi:10.18637/jss.v094.i04>.
The algorithm formulates coefficient parameters and residuals as primal and dual
variables and utilizes efficient active set selection strategies based on the
complementarity of the primal and dual variables.")
    (license license:gpl3)))

(define-public r-bespatial
  (package
    (name "r-bespatial")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bespatial" version))
       (sha256
        (base32 "1sbgi4dcddjwgs4dzwn3zf91kx07kr5vc9dblvijgfasqhpfjv5m"))))
    (properties `((upstream-name . "bespatial")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-terra
                             r-rcpparmadillo
                             r-rcpp
                             r-landscapemetrics
                             r-comat
                             r-belg))
    (home-page "https://jakubnowosad.com/bespatial/")
    (synopsis "Boltzmann Entropy for Spatial Data")
    (description
     "Calculates several entropy metrics for spatial data inspired by Boltzmann's
entropy formula.  It includes metrics introduced by Cushman for landscape
mosaics (Cushman (2015) <doi:10.1007/s10980-015-0305-2>), and landscape
gradients and point patterns (Cushman (2021) <doi:10.3390/e23121616>); by Zhao
and Zhang for landscape mosaics (Zhao and Zhang (2019)
<doi:10.1007/s10980-019-00876-x>); and by Gao et al.  for landscape gradients
(Gao et al. (2018) <doi:10.1111/tgis.12315>; Gao and Li (2019)
<doi:10.1007/s10980-019-00854-3>).")
    (license license:expat)))

(define-public r-berryfunctions
  (package
    (name "r-berryfunctions")
    (version "1.22.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "berryFunctions" version))
       (sha256
        (base32 "1kd1r8ha1dggqmpiiwxvgfk5igshkfaqnkhkrjn7s79xqz2bsq1k"))))
    (properties `((upstream-name . "berryFunctions")))
    (build-system r-build-system)
    (propagated-inputs (list r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/brry/berryFunctions")
    (synopsis "Function Collection Related to Plotting and Hydrology")
    (description
     "Draw horizontal histograms, color scattered points by 3rd dimension, enhance
date- and log-axis plots, zoom in X11 graphics, trace errors and warnings, use
the unit hydrograph in a linear storage cascade, convert lists to data.frames
and arrays, fit multiple functions.")
    (license license:gpl2+)))

(define-public r-bernadette
  (package
    (name "r-bernadette")
    (version "1.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Bernadette" version))
       (sha256
        (base32 "08q0jarv737p4mis7kql1ny9hrm86w49kdn47r94id4895l052ck"))))
    (properties `((upstream-name . "Bernadette")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-scales
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-magrittr
                             r-gridextra
                             r-ggplot2
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://bernadette-eu.github.io/")
    (synopsis
     "Bayesian Inference and Model Selection for Stochastic Epidemics")
    (description
     "Bayesian analysis for stochastic extensions of non-linear dynamic systems using
advanced computational algorithms.  Described in Bouranis, L., Demiris, N.,
Kalogeropoulos, K., and Ntzoufras, I. (2022) <@code{arXiv:2211.15229>}.")
    (license license:gpl3+)))

(define-public r-bergm
  (package
    (name "r-bergm")
    (version "5.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Bergm" version))
       (sha256
        (base32 "13wdkbjnqj3dk8fpk8vblkf4z79j87gdszjdzwysq9i10l6yl6ir"))))
    (properties `((upstream-name . "Bergm")))
    (build-system r-build-system)
    (propagated-inputs (list r-statnet-common
                             r-rglpk
                             r-network
                             r-mvtnorm
                             r-mcmcpack
                             r-matrixcalc
                             r-matrix
                             r-ergm
                             r-coda))
    (home-page "https://acaimo.github.io/Bergm/")
    (synopsis "Bayesian Exponential Random Graph Models")
    (description
     "Bayesian analysis for exponential random graph models using advanced
computational algorithms.  More information can be found at:
<https://acaimo.github.io/Bergm/>.")
    (license license:gpl2+)))

(define-public r-bequt
  (package
    (name "r-bequt")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BeQut" version))
       (sha256
        (base32 "065hjpz4162j2ykcwnri25j4mkqf7yl5bf9ja3xn7yvszif0pjh9"))))
    (properties `((upstream-name . "BeQut")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mass r-lqmm r-jagsui))
    (home-page "https://cran.r-project.org/package=BeQut")
    (synopsis "Bayesian Estimation for Quantile Regression Mixed Models")
    (description
     "Using a Bayesian estimation procedure, this package fits linear quantile
regression models such as linear quantile models, linear quantile mixed models,
quantile regression joint models for time-to-event and longitudinal data.  The
estimation procedure is based on the asymmetric Laplace distribution and the
JAGS software is used to get posterior samples (Yang, Luo, @code{DeSantis}
(2019) <doi:10.1177/0962280218784757>).")
    (license license:gpl2+)))

(define-public r-benthos
  (package
    (name "r-benthos")
    (version "1.3-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "benthos" version))
       (sha256
        (base32 "0pznxnf4wl47wc926diaf4h6x12grc9vxbzd8d4d5j18dp3wvr5z"))))
    (properties `((upstream-name . "benthos")))
    (build-system r-build-system)
    (propagated-inputs (list r-readr r-lazyeval r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=benthos")
    (synopsis "Marine Benthic Ecosystem Analysis")
    (description
     "Preprocessing tools and biodiversity measures (species abundance, species
richness, population heterogeneity and sensitivity) for analysing marine benthic
data.  See Van Loon et al. (2015) <doi:10.1016/j.seares.2015.05.002> for an
application of these tools.")
    (license license:gpl3+)))

(define-public r-bentcablear
  (package
    (name "r-bentcablear")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bentcableAR" version))
       (sha256
        (base32 "1d65xaqz0v2wjm6nxl0lyzjynrgizdvzpj2cqa0nhlczcg6xi62r"))))
    (properties `((upstream-name . "bentcableAR")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=bentcableAR")
    (synopsis
     "Bent-Cable Regression for Independent Data or Autoregressive Time Series")
    (description
     "Included are two main interfaces, bentcable.ar() and bentcable.dev.plot(), for
fitting and diagnosing bent-cable regressions for autoregressive time-series
data (Chiu and Lockhart 2010, <doi:10.1002/cjs.10070>) or independent data (time
series or otherwise - Chiu, Lockhart and Routledge 2006,
<doi:10.1198/016214505000001177>).  Some components in the package can also be
used as stand-alone functions.  The bent cable (linear-quadratic-linear)
generalizes the broken stick (linear-linear), which is also handled by this
package.  Version 0.2 corrected a glitch in the computation of confidence
intervals for the CTP. References that were updated from Versions 0.2.1 and
0.2.2 appear in Version 0.2.3 and up.  Version 0.3.0 improved robustness of the
error-message producing mechanism.  Version 0.3.1 improves the NAMESPACE file of
the package.  It is the author's intention to distribute any future updates via
@code{GitHub}.")
    (license license:gpl3+)))

(define-public r-bennu
  (package
    (name "r-bennu")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bennu" version))
       (sha256
        (base32 "04vgpk549fr5wwg11f5979iqd40yqamdch46vwxb57wxhraphcxy"))))
    (properties `((upstream-name . "bennu")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tidybayes
                             r-stanheaders
                             r-scales
                             r-rstantools
                             r-rstan
                             r-rlang
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-magrittr
                             r-lifecycle
                             r-glue
                             r-ggplot2
                             r-dplyr
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://sempwn.github.io/bennu/")
    (synopsis "Bayesian Estimation of Naloxone Kit Number Under-Reporting")
    (description
     "Bayesian model and associated tools for generating estimates of total naloxone
kit numbers distributed and used from naloxone kit orders data.  Provides
functions for generating simulated data of naloxone kit use and functions for
generating samples from the posterior.")
    (license license:expat)))

(define-public r-benmmi
  (package
    (name "r-benmmi")
    (version "4.3-7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BENMMI" version))
       (sha256
        (base32 "1v96g4l1zkpvvx8jdifnafyaz4ml8v1bxbl97r1n5n6mgw88gk77"))))
    (properties `((upstream-name . "BENMMI")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-tidyr
                             r-readr
                             r-purrr
                             r-markdown
                             r-knitr
                             r-jsonlite
                             r-ggplot2
                             r-dplyr
                             r-benthos))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BENMMI")
    (synopsis "Benthic Multi-Metric Index")
    (description
     "Analysis tool for evaluating benthic multimetric indices (BENMMIs).  It
generates reproducible reports on the analysis of benthic data, e.g., validation
and correction of species names, sample pooling, automatic conversion of genus
to species names, outlier detection, benthic indicator calculation, optimization
of single and multimetric indicators against a pressure gradient, and spatial
aggregation of benthic indicators.  One of its use cases was the development of
a common benthic indicator for <https://www.ospar.org> (publication accepted by
Ecological Indicators).  See Van Loon et al. (2018)
<doi:10.1016/j.ecolind.2017.09.029> for details.")
    (license license:gpl3+)))

(define-public r-benfordtests
  (package
    (name "r-benfordtests")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BenfordTests" version))
       (sha256
        (base32 "1nnj0w0zwcmg7maqmmpixx7alvsyxva370ssc26ahg6kxy5a621w"))))
    (properties `((upstream-name . "BenfordTests")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BenfordTests")
    (synopsis "Statistical Tests for Evaluating Conformity to Benford's Law")
    (description
     "Several specialized statistical tests and support functions for determining if
numerical data could conform to Benford's law.")
    (license license:gpl3)))

(define-public r-benford-analysis
  (package
    (name "r-benford-analysis")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "benford.analysis" version))
       (sha256
        (base32 "0y0c7l2r9s7lg9bw4ndcqwisa5l6a2cpydn1vmz88h3yva0l68cg"))))
    (properties `((upstream-name . "benford.analysis")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table))
    (home-page "http://github.com/carloscinelli/benford.analysis")
    (synopsis "Benford Analysis for Data Validation and Forensic Analytics")
    (description
     "This package provides tools that make it easier to validate data using Benford's
Law.")
    (license license:gpl3)))

(define-public r-benford
  (package
    (name "r-benford")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "benford" version))
       (sha256
        (base32 "0phy3bk89bn4p69mym9lrb04cfavdb1rl47yy44qp4g2w1028dqw"))))
    (properties `((upstream-name . "benford")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=benford")
    (synopsis "Benford's Analysis on Large Data Sets")
    (description
     "Perform the Benford's Analysis to a data set in order to evaluate if it contains
human fabricated data.  For more details on the method see Moreau, 2021, Model
Assist.  Statist.  Appl., 16 (2021) 73â79. <doi:10.3233/MAS-210517>.")
    (license (license:fsdg-compatible
              "Creative Commons Attribution 4.0 International License"))))

(define-public r-bender
  (package
    (name "r-bender")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bender" version))
       (sha256
        (base32 "07npksrj094h884lli0fjvwjc1lhcwzlsk9wx82761mka5xajv4v"))))
    (properties `((upstream-name . "bender")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6 r-jsonlite r-httr))
    (home-page "https://cran.r-project.org/package=bender")
    (synopsis "Bender Client")
    (description
     "R client for Bender Hyperparameters optimizer : <https://bender.dreem.com> The R
client allows you to communicate with the Bender API and therefore submit some
new trials within your R script itself.")
    (license license:expat)))

(define-public r-bend
  (package
    (name "r-bend")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BEND" version))
       (sha256
        (base32 "1d4pl73q9dr0r3albxb5mvx84smn3fffi1c5cs7f5slfaigza0mx"))))
    (properties `((upstream-name . "BEND")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjags r-label-switching r-coda))
    (home-page "https://github.com/crohlo/BEND")
    (synopsis "Bayesian Estimation of Nonlinear Data (BEND)")
    (description
     "This package provides a set of models to estimate nonlinear longitudinal data
using Bayesian estimation methods.  These models include the: 1) Bayesian
Piecewise Random Effects Model (Bayes_PREM()) which estimates a piecewise random
effects (mixture) model for a given number of latent classes and a latent number
of possible changepoints in each class, and can incorporate class and outcome
predictive covariates (see Lamm (2022) <https://hdl.handle.net/11299/252533> and
Lock et al., (2018) <doi:10.1007/s11336-017-9594-5>), 2) Bayesian Crossed Random
Effects Model (Bayes_CREM()) which estimates a linear, quadratic, exponential,
or piecewise crossed random effects models where individuals are changing groups
over time (e.g., students and schools; see Rohloff et al., (2024)
<doi:10.1111/bmsp.12334>), and 3) Bayesian Bivariate Piecewise Random Effects
Model (Bayes_BPREM()) which estimates a bivariate piecewise random effects model
to jointly model two related outcomes (e.g., reading and math achievement; see
Peralta et al., (2022) <doi:10.1037/met0000358>).")
    (license license:expat)))

(define-public r-benchr
  (package
    (name "r-benchr")
    (version "0.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "benchr" version))
       (sha256
        (base32 "1xh1n41ll50zz3v26i8lg5f92i90rc78ywnl0rczryh621whx80n"))))
    (properties `((upstream-name . "benchr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppprogress r-rcpp))
    (home-page "https://gitlab.com/artemklevtsov/benchr")
    (synopsis "High Precise Measurement of R Expressions Execution Time")
    (description
     "This package provides infrastructure to accurately measure and compare the
execution time of R expressions.")
    (license license:gpl2+)))

(define-public r-benchmarking
  (package
    (name "r-benchmarking")
    (version "0.32")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Benchmarking" version))
       (sha256
        (base32 "0fv0i4cpsisk5wa6hgvp75r170ir1zyr2wqbkzfz3qhr43z0jayx"))))
    (properties `((upstream-name . "Benchmarking")))
    (build-system r-build-system)
    (propagated-inputs (list r-ucminf r-rcpp r-quadprog r-lpsolveapi))
    (home-page "https://cran.r-project.org/package=Benchmarking")
    (synopsis "Benchmark and Frontier Analysis Using DEA and SFA")
    (description
     "This package provides methods for frontier analysis, Data Envelopment Analysis
(DEA), under different technology assumptions (fdh, vrs, drs, crs, irs, add/frh,
and fdh+), and using different efficiency measures (input based, output based,
hyperbolic graph, additive, super, and directional efficiency).  Peers and
slacks are available, partial price information can be included, and optimal
cost, revenue and profit can be calculated.  Evaluation of mergers is also
supported.  Methods for graphing the technology sets are also included.  There
is also support for comparative methods based on Stochastic Frontier Analyses
(SFA) and for convex nonparametric least squares of convex functions (STONED).
In general, the methods can be used to solve not only standard models, but also
many other model variants.  It complements the book, Bogetoft and Otto,
Benchmarking with DEA, SFA, and R, Springer-Verlag, 2011, but can of course also
be used as a stand-alone package.")
    (license license:gpl2+)))

(define-public r-benchden
  (package
    (name "r-benchden")
    (version "1.0.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "benchden" version))
       (sha256
        (base32 "01fhqdkkdm6ph965vj3m274f3zrbrjs5abcwpsp78nm5qz9kxbn8"))))
    (properties `((upstream-name . "benchden")))
    (build-system r-build-system)
    (home-page "https://github.com/thmild/benchden")
    (synopsis "28 Benchmark Densities from Berlinet/Devroye (1994)")
    (description
     "Full implementation of the 28 distributions introduced as benchmarks for
nonparametric density estimation by Berlinet and Devroye (1994)
<https://hal.science/hal-03659919>.  Includes densities, cdfs, quantile
functions and generators for samples as well as additional information on
features of the densities.  Also contains the 4 histogram densities used in
Rozenholc/Mildenberger/Gather (2010) <doi:10.1016/j.csda.2010.04.021>.")
    (license license:gpl2+)))

(define-public r-bellreg
  (package
    (name "r-bellreg")
    (version "0.0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bellreg" version))
       (sha256
        (base32 "0576yipaj3a7j159nvp5hmlarvrkxm8pa990iasymg5s4vjfi3n7"))))
    (properties `((upstream-name . "bellreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rdpack
                             r-rcppeigen
                             r-rcpp
                             r-purrr
                             r-numbers
                             r-mass
                             r-magic
                             r-loo
                             r-lambertw
                             r-formula
                             r-extradistr
                             r-dplyr
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/fndemarqui/bellreg")
    (synopsis "Count Regression Models Based on the Bell Distribution")
    (description
     "Bell regression models for count data with overdispersion.  The implemented
models account for ordinary and zero-inflated regression models under both
frequentist and Bayesian approaches.  Theoretical details regarding the models
implemented in the package can be found in Castellares et al. (2018)
<doi:10.1016/j.apm.2017.12.014> and Lemonte et al. (2020)
<doi:10.1080/02664763.2019.1636940>.")
    (license license:expat)))

(define-public r-belikelihood
  (package
    (name "r-belikelihood")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BElikelihood" version))
       (sha256
        (base32 "1v7bcyzrgaf4ih7pc0xq2zl8iwqgm5cjrz0x4ghvlai7w3008ihq"))))
    (properties `((upstream-name . "BElikelihood")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BElikelihood")
    (synopsis "Likelihood Method for Evaluating Bioequivalence")
    (description
     "This package provides a likelihood method is implemented to present evidence for
evaluating bioequivalence (BE).  The functions use bioequivalence data [area
under the blood concentration-time curve (AUC) and peak concentration (Cmax)]
from various crossover designs commonly used in BE studies including a fully
replicated, a partially replicated design, and a conventional 2x2 crossover
design.  They will calculate the profile likelihoods for the mean difference,
total standard deviation ratio, and within subject standard deviation ratio for
a test and a reference drug.  A plot of a standardized profile likelihood can be
generated along with the maximum likelihood estimate and likelihood intervals,
which present evidence for bioequivalence.  See Liping Du and Leena Choi (2015)
<doi:10.1002/pst.1661>.")
    (license license:gpl3+)))

(define-public r-belg
  (package
    (name "r-belg")
    (version "1.5.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "belg" version))
       (sha256
        (base32 "12yfqzz98c4377xjqw9biq83mp8gb009xy4fnnsgxrqb8d4r6i2i"))))
    (properties `((upstream-name . "belg")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://r-spatialecology.github.io/belg/")
    (synopsis "Boltzmann Entropy of a Landscape Gradient")
    (description
     "Calculates the Boltzmann entropy of a landscape gradient.  This package uses the
analytical method created by Gao, P., Zhang, H. and Li, Z., 2018
(<doi:10.1111/tgis.12315>) and by Gao, P. and Li, Z., 2019
(<doi:10.1007/s10980-019-00854-3>).  It also extend the original ideas by
allowing calculations on data with missing values.")
    (license license:expat)))

(define-public r-belex
  (package
    (name "r-belex")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "belex" version))
       (sha256
        (base32 "1563yngc1lvncmx3h6kgsj1r6k3hvxidh6h9rb7apxs2rq5k32ms"))))
    (properties `((upstream-name . "belex")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml))
    (home-page "https://cran.r-project.org/package=belex")
    (synopsis "Download Historical Data from the Belgrade Stock Exchange")
    (description
     "This package provides tools for downloading historical financial data from the
www.belex.rs.")
    (license license:gpl3)))

(define-public r-bekks
  (package
    (name "r-bekks")
    (version "1.4.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BEKKs" version))
       (sha256
        (base32 "1ai475d1ya8j8kl4w6qdpdmsarz74p1799fc9hl0if2r3gv211vn"))))
    (properties `((upstream-name . "BEKKs")))
    (build-system r-build-system)
    (propagated-inputs (list r-xts
                             r-reshape2
                             r-rcpparmadillo
                             r-rcpp
                             r-pbapply
                             r-numderiv
                             r-moments
                             r-mathjaxr
                             r-lubridate
                             r-ks
                             r-gridextra
                             r-ggplot2
                             r-ggfortify
                             r-gas
                             r-future-apply
                             r-future))
    (home-page "https://cran.r-project.org/package=BEKKs")
    (synopsis "Multivariate Conditional Volatility Modelling and Forecasting")
    (description
     "This package provides methods and tools for estimating, simulating and
forecasting of so-called BEKK-models (named after Baba, Engle, Kraft and Kroner)
based on the fast BerndtâHallâHallâHausman (BHHH) algorithm described in
Hafner and Herwartz (2008) <doi:10.1007/s00184-007-0130-y>.")
    (license license:expat)))

(define-public r-behavr
  (package
    (name "r-behavr")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "behavr" version))
       (sha256
        (base32 "01ny099m2zmvlalwiq3nqkgynnxn1mdspch15lkawwd40q8s9s4p"))))
    (properties `((upstream-name . "behavr")))
    (build-system r-build-system)
    (propagated-inputs (list r-data-table))
    (home-page "https://github.com/rethomics/behavr")
    (synopsis "Canonical Data Structure for Behavioural Data")
    (description
     "This package implements an S3 class based on data.table to store and process
efficiently ethomics (high-throughput behavioural) data.")
    (license license:gpl3)))

(define-public r-behaviorchange
  (package
    (name "r-behaviorchange")
    (version "0.5.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "behaviorchange" version))
       (sha256
        (base32 "1nvmrl5nif10kw1risfsmvdlc4q6bkcfxpw26vwanm6ks3lamdc3"))))
    (properties `((upstream-name . "behaviorchange")))
    (build-system r-build-system)
    (propagated-inputs (list r-yum
                             r-viridis
                             r-ufs
                             r-rmdpartials
                             r-knitr
                             r-gtable
                             r-gridextra
                             r-googlesheets4
                             r-ggplot2
                             r-diagrammersvg
                             r-diagrammer
                             r-data-tree
                             r-biasedurn))
    (native-inputs (list r-knitr))
    (home-page "https://r-packages.gitlab.io/behaviorchange")
    (synopsis "Tools for Behavior Change Researchers and Professionals")
    (description
     "This package contains specialised analyses and visualisation tools for behavior
change science.  These facilitate conducting determinant studies (for example,
using confidence interval-based estimation of relevance, CIBER, or CIBERlite
plots, see Crutzen, Noijen & Peters (2017) <doi:10/ghtfz9>), systematically
developing, reporting, and analysing interventions (for example, using Acyclic
Behavior Change Diagrams), and reporting about intervention effectiveness (for
example, using the Numbers Needed for Change, see Gruijters & Peters (2017)
<doi:10/jzkt>), and computing the required sample size (using the Meaningful
Change Definition, see Gruijters & Peters (2020) <doi:10/ghpnx8>).  This package
is especially useful for researchers in the field of behavior change or health
psychology and to behavior change professionals such as intervention developers
and prevention workers.")
    (license license:gpl3+)))

(define-public r-befproj
  (package
    (name "r-befproj")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "befproj" version))
       (sha256
        (base32 "194kicmfxamy061zx0ljmvrlh8gm56fcmhj1j2qfrihklpab4xhx"))))
    (properties `((upstream-name . "befproj")))
    (build-system r-build-system)
    (propagated-inputs (list r-dplyr))
    (home-page "https://cran.r-project.org/package=befproj")
    (synopsis "Makes a Local Population Projection")
    (description
     "This is a sub national population projection model for calculating population
development.  The model uses a cohort component method.  Further reading:
Stanley K. Smith: A Practitioner's Guide to State and Local Population
Projections.  2013. <doi:10.1007/978-94-007-7551-0>.")
    (license license:gpl3)))

(define-public r-beezdiscounting
  (package
    (name "r-beezdiscounting")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "beezdiscounting" version))
       (sha256
        (base32 "07m1mjswrjcq7573drq511vzpgcj8gp8jwzzxvnc6bl4a58wh8y0"))))
    (properties `((upstream-name . "beezdiscounting")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-psych
                             r-magrittr
                             r-gtools
                             r-dplyr))
    (home-page "https://github.com/brentkaplan/beezdiscounting")
    (synopsis "Behavioral Economic Easy Discounting")
    (description
     "Facilitates some of the analyses performed in studies of behavioral economic
discounting.  The package supports scoring of the 27-Item Monetary Choice
Questionnaire (see Kaplan et al., 2016; <doi:10.1007/s40614-016-0070-9>) and
scoring of the minute discounting task (see Koffarnus & Bickel, 2014;
<doi:10.1037/a0035973>) using the Qualtrics 5-trial discounting template (see
the Qualtrics Minute Discounting User Guide; <doi:10.13140/RG.2.2.26495.79527>),
which is also available as a .qsf file in this package.")
    (license license:gpl2+)))

(define-public r-beezdemand
  (package
    (name "r-beezdemand")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "beezdemand" version))
       (sha256
        (base32 "0rzs0mhl8dyflfz69c98ibk5bnsaf651k72wpx4ihl1im2scapfw"))))
    (properties `((upstream-name . "beezdemand")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2
                             r-optimx
                             r-nlstools
                             r-nlsr
                             r-nls2
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/brentkaplan/beezdemand")
    (synopsis "Behavioral Economic Easy Demand")
    (description
     "Facilitates many of the analyses performed in studies of behavioral economic
demand.  The package supports commonly-used options for modeling operant demand
including (1) data screening proposed by Stein, Koffarnus, Snider, Quisenberry,
& Bickel (2015; <doi:10.1037/pha0000020>), (2) fitting models of demand such as
linear (Hursh, Raslear, Bauman, & Black, 1989,
<doi:10.1007/978-94-009-2470-3_22>), exponential	(Hursh & Silberberg, 2008,
<doi:10.1037/0033-295X.115.1.186>) and modified exponential (Koffarnus, Franck,
Stein, & Bickel, 2015, <doi:10.1037/pha0000045>), and (3) calculating numerous
measures	relevant to applied behavioral economists (Intensity, Pmax, Omax).
Also	supports plotting and comparing data.")
    (license (list license:gpl2
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-beepr
  (package
    (name "r-beepr")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "beepr" version))
       (sha256
        (base32 "0xq4qs06f61m1q6gb157n4c3j7pf7wanh27nr2lr228s7k4mw3k2"))))
    (properties `((upstream-name . "beepr")))
    (build-system r-build-system)
    (propagated-inputs (list r-audio))
    (home-page "https://github.com/rasmusab/beepr")
    (synopsis "Easily Play Notification Sounds on any Platform")
    (description
     "The main function of this package is beep(), with the purpose to make it easy to
play notification sounds on whatever platform you are on.  It is intended to be
useful, for example, if you are running a long analysis in the background and
want to know when it is ready.")
    (license license:gpl3)))

(define-public r-beeguts
  (package
    (name "r-beeguts")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BeeGUTS" version))
       (sha256
        (base32 "1880bsgwal06i7q3la1f8alrhj43mnsim6n8inrnaqcbqgi69v3n"))))
    (properties `((upstream-name . "BeeGUTS")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-odeguts
                             r-magrittr
                             r-gridextra
                             r-ggplot2
                             r-dplyr
                             r-data-table
                             r-cowplot
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bgoussen/BeeGUTS")
    (synopsis
     "General Unified Threshold Model of Survival for Bees using Bayesian Inference")
    (description
     "This package provides tools to calibrate, validate, and make predictions with
the General Unified Threshold model of Survival adapted for Bee species.  The
model is presented in the publication from Baas, J., Goussen, B., Miles, M.,
Preuss, T.G., Roessing, I. (2022) <doi:10.1002/etc.5423> and is based on the
GUTS framework Jager, T., Albert, C., Preuss, T.G. and Ashauer, R. (2011)
<doi:10.1021/es103092a>.  The authors are grateful to Bayer A.G. for its
financial support.")
    (license license:gpl3)))

(define-public r-beeca
  (package
    (name "r-beeca")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "beeca" version))
       (sha256
        (base32 "1q20chw6l690fjpyaagl8h12dzna2kjyfq3l676s3w65sba7gyh0"))))
    (properties `((upstream-name . "beeca")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich r-lifecycle r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://openpharma.github.io/beeca/")
    (synopsis "Binary Endpoint Estimation with Covariate Adjustment")
    (description
     "This package performs estimation of marginal treatment effects for binary
outcomes when using logistic regression working models with covariate adjustment
(see discussions in Magirr et al (2024) <https://osf.io/9mp58/>).  Implements
the variance estimators of Ge et al (2011) <doi:10.1177/009286151104500409> and
Ye et al (2023) <doi:10.1080/24754269.2023.2205802>.")
    (license license:lgpl3+)))

(define-public r-beebdc
  (package
    (name "r-beebdc")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BeeBDC" version))
       (sha256
        (base32 "1x2inx2rvlskzpjgivw0wndihsbbbw85nm0avg2csfk8388qwx9c"))))
    (properties `((upstream-name . "BeeBDC")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-stringr
                             r-sf
                             r-rnaturalearth
                             r-readr
                             r-paletteer
                             r-openxlsx
                             r-mgsub
                             r-lubridate
                             r-igraph
                             r-here
                             r-ggspatial
                             r-ggplot2
                             r-forcats
                             r-dplyr
                             r-cowplot
                             r-coordinatecleaner
                             r-circlize))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BeeBDC")
    (synopsis "Occurrence Data Cleaning")
    (description
     "Flags and checks occurrence data that are in Darwin Core format.  The package
includes generic functions and data as well as some that are specific to bees.
This package is meant to build upon and be complimentary to other excellent
occurrence cleaning packages, including bdc and @code{CoordinateCleaner}'.  This
package uses datasets from several sources and particularly from the Discover
Life Website, created by Ascher and Pickering (2020).  For further information,
please see the original publication and package website.  Publication - Dorey et
al. (2023) <doi:10.1101/2023.06.30.547152> and package website - Dorey et al.
(2023) <https://github.com/jbdorey/@code{BeeBDC>}.")
    (license license:gpl3+)))

(define-public r-bedmatrix
  (package
    (name "r-bedmatrix")
    (version "2.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BEDMatrix" version))
       (sha256
        (base32 "0dxkhag4jxjlbv8vx59l30rc9vc2m1szalapsxgm4mqd56llp52k"))))
    (properties `((upstream-name . "BEDMatrix")))
    (build-system r-build-system)
    (propagated-inputs (list r-crochet))
    (home-page "https://github.com/QuantGen/BEDMatrix")
    (synopsis "Extract Genotypes from a PLINK .bed File")
    (description
     "This package provides a matrix-like data structure that allows for efficient,
convenient, and scalable subsetting of binary genotype/phenotype files generated
by PLINK (<https://www.cog-genomics.org/plink2>), the whole genome association
analysis toolset, without loading the entire file into memory.")
    (license license:expat)))

(define-public r-bedassle
  (package
    (name "r-bedassle")
    (version "1.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BEDASSLE" version))
       (sha256
        (base32 "1zjgcfpsvghxz62qsg4s1w6gwpd804s8hfnwy5hifg6sy4phw9d7"))))
    (properties `((upstream-name . "BEDASSLE")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrixcalc r-mass r-emdbook))
    (home-page "https://cran.r-project.org/package=BEDASSLE")
    (synopsis
     "Quantifies Effects of Geo/Eco Distance on Genetic Differentiation")
    (description
     "This package provides functions that allow users to quantify the relative
contributions of geographic and ecological distances to empirical patterns of
genetic differentiation on a landscape.  Specifically, we use a custom Markov
chain Monte Carlo (MCMC) algorithm, which is used to estimate the parameters of
the inference model, as well as functions for performing MCMC diagnosis and
assessing model adequacy.")
    (license license:gpl2+)))

(define-public r-bed
  (package
    (name "r-bed")
    (version "1.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BED" version))
       (sha256
        (base32 "0js227vn6mvn7zk2q0vw9w9cp2ayjzqja6mrmpw9wnx82fp0amcg"))))
    (properties `((upstream-name . "BED")))
    (build-system r-build-system)
    (propagated-inputs (list r-visnetwork
                             r-stringr
                             r-shiny
                             r-rstudioapi
                             r-readr
                             r-neo2r
                             r-miniui
                             r-dt
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://patzaw.github.io/BED/")
    (synopsis "Biological Entity Dictionary (BED)")
    (description
     "An interface for the Neo4j database providing mapping between different
identifiers of biological entities.  This Biological Entity Dictionary (BED) has
been developed to address three main challenges.  The first one is related to
the completeness of identifier mappings.  Indeed, direct mapping information
provided by the different systems are not always complete and can be enriched by
mappings provided by other resources.  More interestingly, direct mappings not
identified by any of these resources can be indirectly inferred by using
mappings to a third reference.  For example, many human Ensembl gene ID are not
directly mapped to any Entrez gene ID but such mappings can be inferred using
respective mappings to HGNC ID. The second challenge is related to the mapping
of deprecated identifiers.  Indeed, entity identifiers can change from one
resource release to another.  The identifier history is provided by some
resources, such as Ensembl or the NCBI, but it is generally not used by mapping
tools.  The third challenge is related to the automation of the mapping process
according to the relationships between the biological entities of interest.
Indeed, mapping between gene and protein ID scopes should not be done the same
way than between two scopes regarding gene ID. Also, converting identifiers from
different organisms should be possible using gene orthologs information.  The
method has been published by Godard and van Eyll (2018)
<doi:10.12688/f1000research.13925.3>.")
    (license license:gpl3)))

(define-public r-beaver
  (package
    (name "r-beaver")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "beaver" version))
       (sha256
        (base32 "0l33g09df9vqigfrafwwsi71qrfb2rrnlp7qbx7rjpqi8dspj2rd"))))
    (properties `((upstream-name . "beaver")))
    (build-system r-build-system)
    (propagated-inputs (list r-yodel
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-rjags
                             r-purrr
                             r-ggplot2
                             r-fs
                             r-ellipsis
                             r-dplyr
                             r-checkmate))
    (home-page "https://github.com/rich-payne/beaver")
    (synopsis
     "Bayesian Model Averaging of Covariate Adjusted Negative-Binomial Dose-Response")
    (description
     "Dose-response modeling for negative-binomial distributed data with a variety of
dose-response models.  Covariate adjustment and Bayesian model averaging is
supported.  Functions are provided to easily obtain inference on the
dose-response relationship and plot the dose-response curve.")
    (license license:expat)))

(define-public r-beautier
  (package
    (name "r-beautier")
    (version "2.6.12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "beautier" version))
       (sha256
        (base32 "116k8lkbf872db3b377l6ls2pymmdvshyxlijyplph4gvy8y5vgn"))))
    (properties `((upstream-name . "beautier")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-seqinr
                             r-rlang
                             r-rappdirs
                             r-purrr
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page "https://docs.ropensci.org/beautier/")
    (synopsis "'BEAUti' from R")
    (description
     "BEAST2 (<https://www.beast2.org>) is a widely used Bayesian phylogenetic tool,
that uses DNA/RNA/protein data and many model priors to create a posterior of
jointly estimated phylogenies and parameters.  BEAUti 2 (which is part of
BEAST2') is a GUI tool that allows users to specify the many possible setups and
generates the XML file BEAST2 needs to run.  This package provides a way to
create BEAST2 input files without active user input, but using R function calls
instead.")
    (license license:gpl3)))

(define-public r-beastt
  (package
    (name "r-beastt")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "beastt" version))
       (sha256
        (base32 "0xgz9xjanybnc2fi0fqc9i0zq80y87rnll62q98nk08f919h7xs1"))))
    (properties `((upstream-name . "beastt")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-rlang
                             r-purrr
                             r-mixtools
                             r-ggplot2
                             r-ggdist
                             r-generics
                             r-dplyr
                             r-distributional
                             r-cobalt
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://gsk-biostatistics.github.io/beastt/")
    (synopsis
     "Bayesian Evaluation, Analysis, and Simulation Software Tools for Trials")
    (description
     "Bayesian dynamic borrowing with covariate adjustment via inverse probability
weighting for simulations and data analyses in clinical trials.  This makes it
easy to use propensity score methods to balance covariate distributions between
external and internal data.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-beastjar
  (package
    (name "r-beastjar")
    (version "1.10.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BeastJar" version))
       (sha256
        (base32 "0581q9m7lb681d12vld231vn6wqv5s2sqkvav4fd0xllvjighlx7"))))
    (properties `((upstream-name . "BeastJar")))
    (build-system r-build-system)
    (inputs (list openjdk))
    (propagated-inputs (list r-rjava))
    (home-page "https://github.com/beast-dev/BeastJar")
    (synopsis "JAR Dependency for MCMC Using 'BEAST'")
    (description
     "This package provides JAR to perform Markov chain Monte Carlo (MCMC) inference
using the popular Bayesian Evolutionary Analysis by Sampling Trees BEAST
software library of Suchard et al (2018) <doi:10.1093/ve/vey016>.  BEAST
supports auto-tuning Metropolis-Hastings, slice, Hamiltonian Monte Carlo and
Sequential Monte Carlo sampling for a large variety of composable standard and
phylogenetic statistical models using high performance computing.  By placing
the BEAST JAR in this package, we offer an efficient distribution system for
BEAST use by other R packages using CRAN.")
    (license license:asl2.0)))

(define-public r-beastier
  (package
    (name "r-beastier")
    (version "2.5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "beastier" version))
       (sha256
        (base32 "1gn1yjy6d0wkvablwa4yz5qvjgrmx5lwn8kzmkbkzhd9xj1299q1"))))
    (properties `((upstream-name . "beastier")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-xml2
                             r-tibble
                             r-stringr
                             r-sessioninfo
                             r-rlang
                             r-rjava
                             r-readr
                             r-rappdirs
                             r-phangorn
                             r-beautier
                             r-ape))
    (native-inputs (list r-knitr))
    (home-page
     "https://docs.ropensci.org/beastier/https://github.com/ropensci/beastier/")
    (synopsis "Call 'BEAST2'")
    (description
     "BEAST2 (<https://www.beast2.org>) is a widely used Bayesian phylogenetic tool,
that uses DNA/RNA/protein data and many model priors to create a posterior of
jointly estimated phylogenies and parameters.  BEAST2 is a command-line tool.
This package provides a way to call BEAST2 from an R function call.")
    (license license:gpl3)))

(define-public r-beast
  (package
    (name "r-beast")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "beast" version))
       (sha256
        (base32 "0ikbnzdzp2lv1nh5mxxanra81v4dl6svg3ywqcqd6wgzri70a4ry"))))
    (properties `((upstream-name . "beast")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer))
    (home-page "https://cran.r-project.org/package=beast")
    (synopsis
     "Bayesian Estimation of Change-Points in the Slope of Multivariate Time-Series")
    (description
     "Assume that a temporal process is composed of contiguous segments with differing
slopes and replicated noise-corrupted time series measurements are observed.
The unknown mean of the data generating process is modelled as a piecewise
linear function of time with an unknown number of change-points.  The package
infers the joint posterior distribution of the number and position of
change-points as well as the unknown mean parameters per time-series by MCMC
sampling.  A-priori, the proposed model uses an overfitting number of mean
parameters but, conditionally on a set of change-points, only a subset of them
influences the likelihood.  An exponentially decreasing prior distribution on
the number of change-points gives rise to a posterior distribution concentrating
on sparse representations of the underlying sequence, but also available is the
Poisson distribution.  See Papastamoulis et al (2017) <@code{arXiv:1709.06111>}
for a detailed presentation of the method.")
    (license license:gpl2)))

(define-public r-bearishtrader
  (package
    (name "r-bearishtrader")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bearishTrader" version))
       (sha256
        (base32 "1w9q66v46ppbic684dp3wv22cykd8fvjznd53w0xp3h0fnzhb5v0"))))
    (properties `((upstream-name . "bearishTrader")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=bearishTrader")
    (synopsis "Trading Strategies for Bearish Outlook")
    (description
     "Stock, Options and Futures Trading Strategies for Traders and Investors with
Bearish Outlook.  The indicators, strategies, calculations, functions and all
other discussions are for academic, research, and educational purposes only and
should not be construed as investment advice and come with absolutely no
Liability.  Guy Cohen (âThe Bible of Options Strategies (2nd ed.)â, 2015,
ISBN: 9780133964028).  Juan A. Serur, Juan A. Serur (â151 Trading
Strategiesâ, 2018, ISBN: 9783030027919).  Chartered Financial Analyst
Institute (\"Chartered Financial Analyst Program Curriculum 2020 Level I Volumes
1-6. (Vol.  5, pp.  385-453)\", 2019, ISBN: 9781119593577).  John C. Hull
(âOptions, Futures, and Other Derivatives (11th ed.)â, 2022, ISBN:
9780136939979).")
    (license license:gpl3)))

(define-public r-beanz
  (package
    (name "r-beanz")
    (version "3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "beanz" version))
       (sha256
        (base32 "1rv4pil8hfykp35impw980riijwgrqxv9v528w1mmq1gyy25wjhl"))))
    (properties `((upstream-name . "beanz")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-loo
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=beanz")
    (synopsis "Bayesian Analysis of Heterogeneous Treatment Effect")
    (description
     "It is vital to assess the heterogeneity of treatment effects (HTE) when making
health care decisions for an individual patient or a group of patients.
Nevertheless, it remains challenging to evaluate HTE based on information
collected from clinical studies that are often designed and conducted to
evaluate the efficacy of a treatment for the overall population.  The Bayesian
framework offers a principled and flexible approach to estimate and compare
treatment effects across subgroups of patients defined by their characteristics.
 This package allows users to explore a wide range of Bayesian HTE analysis
models, and produce posterior inferences about HTE. See Wang et al. (2018)
<DOI:10.18637/jss.v085.i07> for further details.")
    (license license:gpl3+)))

(define-public r-beans
  (package
    (name "r-beans")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "beans" version))
       (sha256
        (base32 "12kxd6hnag93whncmab17yrqiif644lzbl1ramz8kz21mqvkjsfw"))))
    (properties `((upstream-name . "beans")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=beans")
    (synopsis "Data on Dried Beans")
    (description
     "These data contain morphological image measurements for dried beans from Koklu
and Ozkan (2020) <doi:10.1016/j.compag.2020.105507>.")
    (license license:expat)))

(define-public r-beamr
  (package
    (name "r-beamr")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BEAMR" version))
       (sha256
        (base32 "0187ka2lgkkqbvkrss4sgfnihicvkyi9zp6dk79cfxjvl3jyh2xm"))))
    (properties `((upstream-name . "BEAMR")))
    (build-system r-build-system)
    (propagated-inputs (list r-survminer
                             r-survival
                             r-stringr
                             r-rlist
                             r-purrr
                             r-mass
                             r-magrittr
                             r-logistf
                             r-ggpubr
                             r-ggplot2
                             r-ggmosaic
                             r-dplyr))
    (home-page "https://annaseffernick.github.io/BEAMR/")
    (synopsis "Bootstrap Evaluation of Association Matrices")
    (description
     "This package provides a bootstrap-based approach to integrate multiple forms of
high dimensional genomic data with multiple clinical endpoints.  This method is
used to find clinically meaningful groups of genomic features, such as genes or
pathways.  A manuscript describing this method is in preparation.")
    (license license:gpl3+)))

(define-public r-beakr
  (package
    (name "r-beakr")
    (version "0.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "beakr" version))
       (sha256
        (base32 "0l6475l5d58g5ahz4b3vrdlvl656p2rasni17gz4y1l2rd0pvvh4"))))
    (properties `((upstream-name . "beakr")))
    (build-system r-build-system)
    (propagated-inputs (list r-webutils
                             r-stringr
                             r-r6
                             r-mime
                             r-magrittr
                             r-jsonlite
                             r-httpuv
                             r-base64enc))
    (home-page "https://github.com/MazamaScience/beakr")
    (synopsis "Minimalist Web Framework for R")
    (description
     "This package provides a minimalist web framework for developing application
programming interfaces in R that provides a flexible framework for handling
common HTTP-requests, errors, logging, and an ability to integrate any R code as
server middle-ware.")
    (license license:gpl3)))

(define-public r-beadplexr
  (package
    (name "r-beadplexr")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "beadplexr" version))
       (sha256
        (base32 "0n07lxwmcqn6hx7bvrqvcy5y98c4x7wbcpjlwyq66imphj711blp"))))
    (properties `((upstream-name . "beadplexr")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-mclust
                             r-ggplot2
                             r-fpc
                             r-drc
                             r-dplyr
                             r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://gitlab.com/ustervbo/beadplexr")
    (synopsis "Analysis of Multiplex Cytometric Bead Assays")
    (description
     "Reproducible and automated analysis of multiplex bead assays such as CBA (Morgan
et al.  2004; <doi: 10.1016/j.clim.2003.11.017>), LEGENDplex (Yu et al.  2015;
<doi: 10.1084/jem.20142318>), and MACSPlex (Miltenyi Biotec 2014; Application
note: Data acquisition and analysis without the MACSQuant analyzer;
<https://www.miltenyibiotec.com/upload/assets/IM0021608.PDF>).  The package
provides functions for streamlined reading of fcs files, and identification of
bead clusters and analyte expression.  The package eases the calculation of
standard curves and the subsequent calculation of the analyte concentration.")
    (license license:expat)))

(define-public r-beach
  (package
    (name "r-beach")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BEACH" version))
       (sha256
        (base32 "0biq6c74k8vb8ykafzf3blyi08rp65x1vaw3bfpwdc1ihc9wrgjr"))))
    (properties `((upstream-name . "BEACH")))
    (build-system r-build-system)
    (propagated-inputs (list r-xtable
                             r-writexls
                             r-shiny
                             r-sas7bdat
                             r-rtf
                             r-rjava
                             r-plyr
                             r-haven
                             r-dt
                             r-devtools))
    (home-page
     "https://www.pharmasug.org/proceedings/2018/AD/PharmaSUG-2018-AD05.pdf")
    (synopsis "Biometric Exploratory Analysis Creation House")
    (description
     "This package provides a platform is provided for interactive analyses with a
goal of totally easy to develop, deploy, interact, and explore (TEDDIE).  Using
this package, users can create customized analyses and make them available to
end users who can perform interactive analyses and save analyses to RTF or HTML
files.  It allows developers to focus on R code for analysis, instead of dealing
with html or shiny code.")
    (license license:gpl2+)))

(define-public r-bea-r
  (package
    (name "r-bea-r")
    (version "1.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bea.R" version))
       (sha256
        (base32 "15pdbwz7av8862bl144n998qk0w9rqwip9m2brdm5hj7id5xdlif"))))
    (properties `((upstream-name . "bea.R")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-xtable
                             r-stringr
                             r-stringi
                             r-shinydashboard
                             r-shiny
                             r-scales
                             r-rcpp
                             r-plyr
                             r-munsell
                             r-magrittr
                             r-jsonlite
                             r-httr
                             r-httpuv
                             r-htmlwidgets
                             r-htmltools
                             r-gtable
                             r-googlevis
                             r-ggplot2
                             r-dt
                             r-data-table
                             r-colorspace
                             r-chron))
    (home-page "https://github.com/us-bea/bea.R")
    (synopsis "Bureau of Economic Analysis API")
    (description
     "This package provides an R interface for the Bureau of Economic Analysis (BEA)
API (see <http://www.bea.gov/API/bea_web_service_api_user_guide.htm> for more
information) that serves two core purposes - 1.  To Extract/Transform/Load data
[@code{beaGet}()] from the BEA API as R-friendly formats in the user's work
space [transformation done by default in @code{beaGet}() can be modified using
optional parameters; see, too, bea2List(), bea2Tab()].  2.  To enable the search
of descriptive meta data [@code{beaSearch}()].  Other features of the library
exist mainly as intermediate methods or are in early stages of development.
Important Note - You must have an API key to use this library.  Register for a
key at <http://www.bea.gov/API/signup/index.cfm> .")
    (license license:cc0)))

(define-public r-be
  (package
    (name "r-be")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BE" version))
       (sha256
        (base32 "17cm4vfvrv29rfxr6v58x04y4v1ncd23cpxfpdv7lq9saq8i82ag"))))
    (properties `((upstream-name . "BE")))
    (build-system r-build-system)
    (propagated-inputs (list r-rtf))
    (home-page "https://cran.r-project.org/package=BE")
    (synopsis "Bioequivalence Study Data Analysis")
    (description
     "Analyze bioequivalence study data with industrial strength.  Sample size could
be determined for various crossover designs, such as 2x2 design, 2x4 design, 4x4
design, Balaam design, Two-sequence dual design, and William design.  Reference:
Chow SC, Liu JP. Design and Analysis of Bioavailability and Bioequivalence
Studies.  3rd ed. (2009, ISBN:978-1-58488-668-6).")
    (license license:gpl3)))

(define-public r-bdwreg
  (package
    (name "r-bdwreg")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BDWreg" version))
       (sha256
        (base32 "0di2ll2sjj6js9fg3gx20kmjjzkqwhwq9g319jfwzzwxbclqs5ky"))))
    (properties `((upstream-name . "BDWreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-foreach r-dwreg r-doparallel r-coda))
    (home-page "https://cran.r-project.org/package=BDWreg")
    (synopsis "Bayesian Inference for Discrete Weibull Regression")
    (description
     "This package provides a Bayesian regression model for discrete response, where
the conditional distribution is modelled via a discrete Weibull distribution.
This package provides an implementation of Metropolis-Hastings and
Reversible-Jumps algorithms to draw samples from the posterior.  It covers a
wide range of regularizations through any two parameter prior.  Examples are
Laplace (Lasso), Gaussian (ridge), Uniform, Cauchy and customized priors like a
mixture of priors.  An extensive visual toolbox is included to check the
validity of the results as well as several measures of goodness-of-fit.")
    (license license:lgpl2.0+)))

(define-public r-bdsvd
  (package
    (name "r-bdsvd")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bdsvd" version))
       (sha256
        (base32 "02fhhwlnq31yzf0m3qyk14w1f9d4xn0ygnjcwx7m28z34l18547m"))))
    (properties `((upstream-name . "bdsvd")))
    (build-system r-build-system)
    (propagated-inputs (list r-irlba))
    (home-page "https://cran.r-project.org/package=bdsvd")
    (synopsis "Block Structure Detection Using Singular Vectors")
    (description
     "This package performs block diagonal covariance matrix detection using singular
vectors (BD-SVD), which can be extended to hierarchical variable clustering
(HC-SVD).  The methods are described in Bauer (202Xa)
<doi:10.48550/@code{arXiv.2211.16155>} and Bauer (202Xb)
<doi:10.48550/@code{arXiv.2308.06820>}.")
    (license license:gpl2+)))

(define-public r-bdscale
  (package
    (name "r-bdscale")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bdscale" version))
       (sha256
        (base32 "1hkkfd69g2bg2y4hicjs6bnw9f0zw74jy0dpadchnzw17lffpms3"))))
    (properties `((upstream-name . "bdscale")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "http://github.com/dvmlls/bdscale")
    (synopsis "Remove Weekends and Holidays from ggplot2 Axes")
    (description
     "This package provides a continuous date scale, omitting weekends and holidays.")
    (license license:gpl2)))

(define-public r-bdribs
  (package
    (name "r-bdribs")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bdribs" version))
       (sha256
        (base32 "19qakmdw3l8vfxkr4p1ydkyfs4cmq37lqzw7l5zx5rdn6zlhqwh4"))))
    (properties `((upstream-name . "bdribs")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjags))
    (home-page "https://cran.r-project.org/package=bdribs")
    (synopsis
     "Bayesian Detection of Potential Risk Using Inference on Blinded Safety Data")
    (description
     "This package implements Bayesian inference to detect signal from blinded
clinical trial when total number of adverse events of special concerns and total
risk exposures from all patients are available in the study.  For more details
see the article by Mukhopadhyay et.  al. (2018) titled Bayesian Detection of
Potential Risk Using Inference on Blinded Safety Data', in Pharmaceutical
Statistics (to appear).")
    (license license:gpl2)))

(define-public r-bdrc
  (package
    (name "r-bdrc")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bdrc" version))
       (sha256
        (base32 "0xkqm04q8cvrfkddw8bfsn8mh2l0h4762g6p3jg9n2psiac05yrc"))))
    (properties `((upstream-name . "bdrc")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales r-rlang r-gridextra r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bdrc")
    (synopsis "Bayesian Discharge Rating Curves")
    (description
     "Fits a discharge rating curve based on the power-law and the generalized
power-law from data on paired stage and discharge measurements in a given river
using a Bayesian hierarchical model as described in Hrafnkelsson et al. (2020)
<@code{arXiv:2010.04769>}.")
    (license license:expat)))

(define-public r-bdpv
  (package
    (name "r-bdpv")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bdpv" version))
       (sha256
        (base32 "0wcnmamqrbqwskgv2yhnxq2kb179n63c825y9hsl20rfca0ijh6m"))))
    (properties `((upstream-name . "bdpv")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=bdpv")
    (synopsis "Inference and Design for Predictive Values in Diagnostic Tests")
    (description
     "Computation of asymptotic confidence intervals for negative and positive
predictive values in binary diagnostic tests in case-control studies.
Experimental design for hypothesis tests on predictive values.")
    (license license:gpl2+)))

(define-public r-bdpar
  (package
    (name "r-bdpar")
    (version "3.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bdpar" version))
       (sha256
        (base32 "063b4wwc476gx1gsb028b0fr5agf1n9ciagl5h28zx7ap3jnxmph"))))
    (properties `((upstream-name . "bdpar")))
    (build-system r-build-system)
    (inputs (list python))
    (propagated-inputs (list r-rlist r-r6 r-digest))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/miferreiro/bdpar")
    (synopsis "Big Data Preprocessing Architecture")
    (description
     "Provide a tool to easily build customized data flows to pre-process large
volumes of information from different sources.  To this end, bdpar allows to (i)
easily use and create new functionalities and (ii) develop new data source
extractors according to the user needs.  Additionally, the package provides by
default a predefined data flow to extract and pre-process the most relevant
information (tokens, dates, ... ) from some textual sources (SMS, Email,
@code{YouTube} comments).")
    (license license:gpl3)))

(define-public r-bdp2
  (package
    (name "r-bdp2")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BDP2" version))
       (sha256
        (base32 "14m85sigx6hk0qzhfgih43m2lxp9xczmp9g3z4pping6msbnbdrz"))))
    (properties `((upstream-name . "BDP2")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinybs r-shiny r-rmarkdown))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BDP2")
    (synopsis
     "Bayesian Adaptive Designs for Phase II Trials with Binary Endpoint")
    (description
     "This package provides tools and workflow to choose design parameters in Bayesian
adaptive single-arm phase II trial designs with binary endpoint (response,
success) with possible stopping for efficacy and futility at interim analyses.
Also contains routines to determine and visualize operating characteristics.
See Kopp-Schneider et al. (2018) <doi:10.1002/bimj.201700209>.")
    (license license:gpl2)))

(define-public r-bdots
  (package
    (name "r-bdots")
    (version "1.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bdots" version))
       (sha256
        (base32 "18sciji8xy1s53d7d5q1g5zqdn25wd7w5vyiisigf54sbz13vqg6"))))
    (properties `((upstream-name . "bdots")))
    (build-system r-build-system)
    (propagated-inputs (list r-nlme r-mvtnorm r-gridextra r-ggplot2
                             r-data-table))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/collinn/bdots")
    (synopsis "Bootstrapped Differences of Time Series")
    (description
     "Analyze differences among time series curves with p-value adjustment for
multiple comparisons introduced in Oleson et al (2015)
<DOI:10.1177/0962280215607411>.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-bdlp
  (package
    (name "r-bdlp")
    (version "0.9-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bdlp" version))
       (sha256
        (base32 "14fxwgcn3g6k1cdimgfp6zmsgqi4s7sh6kapnls229qiin2nijjl"))))
    (properties `((upstream-name . "bdlp")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringdist
                             r-rsqlite
                             r-rgl
                             r-multiord
                             r-mass
                             r-genord
                             r-dbi))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bdlp")
    (synopsis "Transparent and Reproducible Artificial Data Generation")
    (description
     "The main function @code{generateDataset}() processes a user-supplied .R file
that contains metadata parameters in order to generate actual data.  The
metadata parameters have to be structured in the form of metadata objects, the
format of which is outlined in the package vignette.  This approach allows to
generate artificial data in a transparent and reproducible manner.")
    (license license:gpl2)))

(define-public r-bdlim
  (package
    (name "r-bdlim")
    (version "0.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bdlim" version))
       (sha256
        (base32 "04qgmanb198abrkqfv4n0fk0zib3nad14s7pz85n4ws0252vk10r"))))
    (properties `((upstream-name . "bdlim")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-laplacesdemon r-ggplot2 r-dplyr
                             r-bayeslogit))
    (home-page "https://anderwilson.github.io/bdlim/")
    (synopsis "Bayesian Distributed Lag Interaction Models")
    (description
     "Estimation and interpretation of Bayesian distributed lag interaction models
(BDLIMs).  A BDLIM regresses a scalar outcome on repeated measures of exposure
and allows for modification by a categorical variable under four specific
patterns of modification.  The main function is bdlim().  There are also summary
and plotting files.  Details on methodology are described in Wilson et al.
(2017) <doi:10.1093/biostatistics/kxx002>.")
    (license license:gpl3+)))

(define-public r-bdl
  (package
    (name "r-bdl")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bdl" version))
       (sha256
        (base32 "1d55i1pn8pfnv2wcmxxifqbxlni9npv9n3xnjcgfcfhs0ixnz314"))))
    (properties `((upstream-name . "bdl")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmaptools
                             r-tmap
                             r-tidyr
                             r-tibble
                             r-sf
                             r-randomcolor
                             r-purrr
                             r-progress
                             r-magrittr
                             r-jsonlite
                             r-httr
                             r-ggpubr
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://statisticspoland.github.io/R_Package_to_API_BDL/")
    (synopsis "Interface and Tools for 'BDL' API")
    (description
     "Interface to Local Data Bank ('Bank Danych Lokalnych - bdl') API
<https://api.stat.gov.pl/Home/@code{BdlApi?lang=en>} with set of useful tools
like quick plotting and map generating using data from bank.")
    (license license:gpl3)))

(define-public r-bdesize
  (package
    (name "r-bdesize")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BDEsize" version))
       (sha256
        (base32 "0dyjd5f1cqmzqnrbxz06pbxrk6gjrmsh2x6p7qllczvlmm7jdm5i"))))
    (properties `((upstream-name . "BDEsize")))
    (build-system r-build-system)
    (propagated-inputs (list r-ggplot2 r-fpow))
    (home-page "https://cran.r-project.org/package=BDEsize")
    (synopsis
     "Efficient Determination of Sample Size in Balanced Design of Experiments")
    (description
     "For a balanced design of experiments, this package calculates the sample size
required to detect a certain standardized effect size, under a significance
level.  This package also provides three graphs; detectable standardized effect
size vs power, sample size vs detectable standardized effect size, and sample
size vs power, which show the mutual relationship between the sample size, power
and the detectable standardized effect size.  The detailed procedure is
described in R. V. Lenth (2006-9)
<https://homepage.divms.uiowa.edu/~rlenth/Power/>, Y. B. Lim (1998), M. A.
Kastenbaum, D. G. Hoel and K. O. Bowman (1970) <doi:10.2307/2334851>, and
Douglas C. Montgomery (2013, ISBN: 0849323312).")
    (license license:gpl2+)))

(define-public r-bde
  (package
    (name "r-bde")
    (version "1.0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bde" version))
       (sha256
        (base32 "0idawsykqz7ybnnfcs0946wd1q8aaw1xdx2w3zzgjz7faxbvznb9"))))
    (properties `((upstream-name . "bde")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-ggplot2))
    (home-page "https://cran.r-project.org/package=bde")
    (synopsis "Bounded Density Estimation")
    (description
     "This package provides a collection of S4 classes which implements different
methods to estimate and deal with densities in bounded domains.  That is,
densities defined within the interval [lower.limit, upper.limit], where
lower.limit and upper.limit are values that can be set by the user.")
    (license license:gpl2)))

(define-public r-bddwc
  (package
    (name "r-bddwc")
    (version "0.1.15")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bdDwC" version))
       (sha256
        (base32 "0yraq8a0wgz12rzi2dx0gdcz5rx99qd2pzw972wjqyq3rs41pwx0"))))
    (properties `((upstream-name . "bdDwC")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinyjs r-shinyfiles r-shinydashboard r-shinybs
                             r-shiny))
    (home-page "https://cran.r-project.org/package=bdDwC")
    (synopsis "Darwinizer: Darwin Core (DwC) Field Names Standardization")
    (description
     "The shiny application @code{bdDwC} makes biodiversity data field names Darwin
Core compatible.")
    (license license:gpl3)))

(define-public r-bdc
  (package
    (name "r-bdc")
    (version "1.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bdc" version))
       (sha256
        (base32 "1z79bsg0jz5ffl47sjgxj2bigyhbn76m0kz9sqbzkph7srcc6vpr"))))
    (properties `((upstream-name . "bdc")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tibble
                             r-taxadb
                             r-stringr
                             r-stringi
                             r-stringdist
                             r-sf
                             r-rnaturalearth
                             r-rgnparser
                             r-readr
                             r-qs
                             r-purrr
                             r-magrittr
                             r-here
                             r-ggplot2
                             r-fs
                             r-foreach
                             r-dt
                             r-dplyr
                             r-coordinatecleaner))
    (native-inputs (list r-knitr))
    (home-page
     "https://brunobrr.github.io/bdc/https://github.com/brunobrr/bdc")
    (synopsis "Biodiversity Data Cleaning")
    (description
     "It brings together several aspects of biodiversity data-cleaning in one place.
bdc is organized in thematic modules related to different biodiversity
dimensions, including 1) Merge datasets: standardization and integration of
different datasets; 2) Pre-filter: flagging and removal of invalid or
non-interpretable information, followed by data amendments; 3) Taxonomy:
cleaning, parsing, and harmonization of scientific names from several taxonomic
groups against taxonomic databases locally stored through the application of
exact and partial matching algorithms; 4) Space: flagging of erroneous, suspect,
and low-precision geographic coordinates; and 5) Time: flagging and, whenever
possible, correction of inconsistent collection date.  In addition, it contains
features to visualize, document, and report data quality â which is essential
for making data quality assessment transparent and reproducible.  The reference
for the methodology is Bruno et al. (2022) <doi:10.1111/2041-210X.13868>.")
    (license license:gpl3+)))

(define-public r-bdalgo
  (package
    (name "r-bdalgo")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BDAlgo" version))
       (sha256
        (base32 "0f7aqbyfwyfq18z8ygjna3i38dnx2bm4wpkgvdsqwbiby9swqr6d"))))
    (properties `((upstream-name . "BDAlgo")))
    (build-system r-build-system)
    (propagated-inputs (list r-inflection))
    (home-page "https://cran.r-project.org/package=BDAlgo")
    (synopsis "Bloom Detecting Algorithm")
    (description
     "The Bloom Detecting Algorithm enables the detection of blooms within a time
series of species abundance and extracts 22 phenological variables.  For
details, see Karasiewicz et al. (2022) <doi:10.3390/jmse10020174>.")
    (license license:gpl2+)))

(define-public r-bda
  (package
    (name "r-bda")
    (version "18.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bda" version))
       (sha256
        (base32 "0kdm46rq939pqk0hv9x0fk9nkv45d8k2bakyhm0p4l9jcjb0vwfa"))))
    (properties `((upstream-name . "bda")))
    (build-system r-build-system)
    (propagated-inputs (list r-boot))
    (home-page "https://cran.r-project.org/package=bda")
    (synopsis "Binned Data Analysis")
    (description
     "Algorithms developed for binned data analysis, gene expression data analysis and
measurement error models for ordinal data analysis.")
    (license (license:fsdg-compatible "Unlimited"))))

(define-public r-bcv
  (package
    (name "r-bcv")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bcv" version))
       (sha256
        (base32 "1fs7pj8lsn96cr1r5bagp9mq869q3pj9k71r7j1xi0i5l06pz90y"))))
    (properties `((upstream-name . "bcv")))
    (build-system r-build-system)
    (home-page "https://github.com/michbur/bcv")
    (synopsis "Cross-Validation for the SVD (Bi-Cross-Validation)")
    (description
     "This package provides methods for choosing the rank of an SVD (singular value
decomposition) approximation via cross validation.  The package provides both
Gabriel-style \"block\" holdouts and Wold-style \"speckled\" holdouts.  It also
includes an implementation of the SVDImpute algorithm.  For more information
about Bi-cross-validation, see Owen & Perry's 2009 @code{AoAS} article (at
<@code{arXiv:0908.2062>}) and Perry's 2009 @code{PhD} thesis (at
<@code{arXiv:0909.3052>}).")
    (license license:bsd-3)))

(define-public r-bct
  (package
    (name "r-bct")
    (version "1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BCT" version))
       (sha256
        (base32 "0fs6wh59njgj7ybnkf5ki4g8phmzrchvml0f8jyhhxdarkj5vr5d"))))
    (properties `((upstream-name . "BCT")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rcpp r-igraph))
    (home-page "https://cran.r-project.org/package=BCT")
    (synopsis "Bayesian Context Trees for Discrete Time Series")
    (description
     "An implementation of a collection of tools for exact Bayesian inference with
discrete times series.  This package contains functions that can be used for
prediction, model selection, estimation, segmentation/change-point detection and
other statistical tasks.  Specifically, the functions provided can be used for
the exact computation of the prior predictive likelihood of the data, for the
identification of the a posteriori most likely (MAP) variable-memory Markov
models, for calculating the exact posterior probabilities and the AIC and BIC
scores of these models, for prediction with respect to log-loss and 0-1 loss and
segmentation/change-point detection.  Example data sets from finance, genetics,
animal communication and meteorology are also provided.  Detailed descriptions
of the underlying theory and algorithms can be found in [Kontoyiannis et al.
Bayesian Context Trees: Modelling and exact inference for discrete time series.
Journal of the Royal Statistical Society: Series B (Statistical Methodology),
April 2022.  Available at: <@code{arXiv:2007.14900>} [stat.ME], July 2020] and
[Lungu et al.  Change-point Detection and Segmentation of Discrete Data using
Bayesian Context Trees <@code{arXiv:2203.04341>} [stat.ME], March 2022].")
    (license license:gpl2+)))

(define-public r-bcsub
  (package
    (name "r-bcsub")
    (version "0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BCSub" version))
       (sha256
        (base32 "0c8dlxsx23qfyygmajg2amj78ax01kb3808d9hvy7g3hkgp2i2fp"))))
    (properties `((upstream-name . "BCSub")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-nfactors r-mcclust
                             r-mass))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BCSub")
    (synopsis
     "Bayesian Semiparametric Factor Analysis Model for Subtype Identification (Clustering)")
    (description
     "Gene expression profiles are commonly utilized to infer disease subtypes and
many clustering methods can be adopted for this task.  However, existing
clustering methods may not perform well when genes are highly correlated and
many uninformative genes are included for clustering.  To deal with these
challenges, we develop a novel clustering method in the Bayesian setting.  This
method, called BCSub, adopts an innovative semiparametric Bayesian factor
analysis model to reduce the dimension of the data to a few factor scores for
clustering.  Specifically, the factor scores are assumed to follow the Dirichlet
process mixture model in order to induce clustering.")
    (license license:gpl2)))

(define-public r-bcrypt
  (package
    (name "r-bcrypt")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bcrypt" version))
       (sha256
        (base32 "0n1zkhjq4q16smp672617w6bb506f74ifljaddn50djk0s1cb8ca"))))
    (properties `((upstream-name . "bcrypt")))
    (build-system r-build-system)
    (propagated-inputs (list r-openssl))
    (home-page "https://jeroen.r-universe.dev/bcrypt")
    (synopsis "'Blowfish' Key Derivation and Password Hashing")
    (description
     "Bindings to the blowfish password hashing algorithm
<https://www.openbsd.org/papers/bcrypt-paper.pdf> derived from the
@code{OpenBSD} implementation.")
    (license license:bsd-2)))

(define-public r-bcrocsurface
  (package
    (name "r-bcrocsurface")
    (version "1.0-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bcROCsurface" version))
       (sha256
        (base32 "0g0z4z3dw4mjp4dpa0d6bz46700jibixhlh2jj62z2l7qz5140cy"))))
    (properties `((upstream-name . "bcROCsurface")))
    (build-system r-build-system)
    (propagated-inputs (list r-rgl r-rcpparmadillo r-rcpp r-nnet r-boot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/toduckhanh/bcROCsurface")
    (synopsis
     "Bias-Corrected Methods for Estimating the ROC Surface of Continuous Diagnostic Tests")
    (description
     "The bias-corrected estimation methods for the receiver operating characteristics
ROC surface and the volume under ROC surfaces (VUS) under missing at random
(MAR) assumption.")
    (license license:gpl3)))

(define-public r-bcrm
  (package
    (name "r-bcrm")
    (version "0.5.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bcrm" version))
       (sha256
        (base32 "1j8bfa3x7h77vs41k4nqbqlb961szp1fkhc181wzb3zpbnzmjhhy"))))
    (properties `((upstream-name . "bcrm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-mvtnorm r-knitr r-ggplot2))
    (home-page "https://github.com/mikesweeting/bcrm")
    (synopsis
     "Bayesian Continual Reassessment Method for Phase I Dose-Escalation Trials")
    (description
     "This package implements a wide variety of one- and two-parameter Bayesian CRM
designs.  The program can run interactively, allowing the user to enter outcomes
after each cohort has been recruited, or via simulation to assess operating
characteristics.  See Sweeting et al. (2013): <doi:10.18637/jss.v054.i13>.")
    (license license:gpl2+)))

(define-public r-bcra
  (package
    (name "r-bcra")
    (version "2.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BCRA" version))
       (sha256
        (base32 "17h0ara1y3fh2xyhjarbw3b549v6kq108bg946ndh7kjykara9dd"))))
    (properties `((upstream-name . "BCRA")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BCRA")
    (synopsis "Breast Cancer Risk Assessment")
    (description
     "This package provides functions provide risk projections of invasive breast
cancer based on Gail model according to National Cancer Institute's Breast
Cancer Risk Assessment Tool algorithm for specified race/ethnic groups and age
intervals.  Gail MH, Brinton LA, et al (1989) <doi:10.1093/jnci/81.24.1879>.
Marthew PB, Gail MH, et al (2016) <doi:10.1093/jnci/djw215>.")
    (license license:gpl2+)))

(define-public r-bcputility
  (package
    (name "r-bcputility")
    (version "0.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bcputility" version))
       (sha256
        (base32 "11wp0llr8wk96ifl93vw1hadrda61zdkmbazp1khimbx0qgr31gx"))))
    (properties `((upstream-name . "bcputility")))
    (build-system r-build-system)
    (propagated-inputs (list r-sf r-data-table))
    (home-page "https://bcputility.delveds.com")
    (synopsis "Wrapper for SQL Server bcp Utility")
    (description
     "This package provides functions to utilize a command line utility that does bulk
inserts and exports from SQL Server databases.")
    (license license:expat)))

(define-public r-bcpa
  (package
    (name "r-bcpa")
    (version "1.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bcpa" version))
       (sha256
        (base32 "09n4grdz3vvm3744bggxv9qvnjbifh1hj31sc6s7a3189d1zhl4v"))))
    (properties `((upstream-name . "bcpa")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-plyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bcpa")
    (synopsis "Behavioral Change Point Analysis of Animal Movement")
    (description
     "The Behavioral Change Point Analysis (BCPA) is a method of identifying hidden
shifts in the underlying parameters of a time series, developed specifically to
be applied to animal movement data which is irregularly sampled.  The method is
based on: E. Gurarie, R. Andrews and K. Laidre A novel method for identifying
behavioural changes in animal movement data (2009) Ecology Letters 12:5 395-408.
 A development version is on <https://github.com/@code{EliGurarie/bcpa>}.  NOTE:
the BCPA method may be useful for any univariate, irregularly sampled Gaussian
time-series, but animal movement analysts are encouraged to apply correlated
velocity change point analysis as implemented in the smoove package, as of this
writing on @code{GitHub} at <https://github.com/@code{EliGurarie/smoove>}.  An
example of a univariate analysis is provided in the @code{UnivariateBCPA}
vignette.")
    (license (license:fsdg-compatible "Unlimited"))))

(define-public r-bcmixed
  (package
    (name "r-bcmixed")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bcmixed" version))
       (sha256
        (base32 "0am6amk1axfxkxgp9nmfys4fp4snplfncwkzh02im7a6zr3v1m63"))))
    (properties `((upstream-name . "bcmixed")))
    (build-system r-build-system)
    (propagated-inputs (list r-nlme r-mass))
    (home-page "https://cran.r-project.org/package=bcmixed")
    (synopsis "Mixed Effect Model with the Box-Cox Transformation")
    (description
     "Inference on the marginal model of the mixed effect model with the Box-Cox
transformation and on the model median differences between treatment groups for
longitudinal randomized clinical trials.  These statistical methods are proposed
by Maruo et al. (2017) <doi:10.1002/sim.7279>.")
    (license license:gpl2+)))

(define-public r-bcmaps
  (package
    (name "r-bcmaps")
    (version "2.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bcmaps" version))
       (sha256
        (base32 "0cpghf8acq7dpmiaw314n1h2aa310513f3qwnl8p74pyr940gwvn"))))
    (properties `((upstream-name . "bcmaps")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-sf
                             r-rappdirs
                             r-progress
                             r-lifecycle
                             r-jsonlite
                             r-httr
                             r-bcdata))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bcgov/bcmaps")
    (synopsis "Map Layers and Spatial Utilities for British Columbia")
    (description
     "Various layers of B.C., including administrative boundaries, natural resource
management boundaries, census boundaries etc.  All layers are available in BC
Albers (<https://spatialreference.org/ref/epsg/3005/>) equal-area projection,
which is the B.C. government standard.  The layers are sourced from the British
Columbia and Canadian government under open licenses, including B.C. Data
Catalogue (<https://data.gov.bc.ca>), the Government of Canada Open Data Portal
(<https://open.canada.ca/en/using-open-data>), and Statistics Canada
(<https://www.statcan.gc.ca/en/reference/licence>).")
    (license (list license:asl2.0
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-bclustlong
  (package
    (name "r-bclustlong")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BClustLonG" version))
       (sha256
        (base32 "1p6fm60vhs9l3b120h2l7nz4rf4ckhka7124kzwjd7znax2a336g"))))
    (properties `((upstream-name . "BClustLonG")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mcclust r-mass r-lme4))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BClustLonG")
    (synopsis
     "Dirichlet Process Mixture Model for Clustering Longitudinal Gene Expression Data")
    (description
     "Many clustering methods have been proposed, but most of them cannot work for
longitudinal gene expression data.  B@code{ClustLonG} is a package that allows
us to perform clustering analysis for longitudinal gene expression data.  It
adopts a linear-mixed effects framework to model the trajectory of genes over
time, while clustering is jointly conducted based on the regression coefficients
obtained from all genes.  To account for the correlations among genes and
alleviate the high dimensionality challenges, factor analysis models are adopted
for the regression coefficients.  The Dirichlet process prior distribution is
utilized for the means of the regression coefficients to induce clustering.
This package allows users to specify which variables to use for clustering
(intercepts or slopes or both) and whether a factor analysis model is desired.
More details about this method can be found in Jiehuan Sun, et al. (2017)
<doi:10.1002/sim.7374>.")
    (license license:gpl2)))

(define-public r-bchron
  (package
    (name "r-bchron")
    (version "4.7.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Bchron" version))
       (sha256
        (base32 "1s8bx9a5rpcrw0msnjf565crz3d2fykvy5fj1chbqxzjh7qm865w"))))
    (properties `((upstream-name . "Bchron")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-scales
                             r-purrr
                             r-mclust
                             r-mass
                             r-magrittr
                             r-ggridges
                             r-ggplot2
                             r-ggforce
                             r-dplyr
                             r-coda
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://andrewcparnell.github.io/Bchron/")
    (synopsis
     "Radiocarbon Dating, Age-Depth Modelling, Relative Sea Level Rate Estimation, and Non-Parametric Phase Modelling")
    (description
     "Enables quick calibration of radiocarbon dates under various calibration curves
(including user generated ones); age-depth modelling as per the algorithm of
Haslett and Parnell (2008) <DOI:10.1111/j.1467-9876.2008.00623.x>; Relative sea
level rate estimation incorporating time uncertainty in polynomial regression
models (Parnell and Gehrels 2015) <DOI:10.1002/9781118452547.ch32>;
non-parametric phase modelling via Gaussian mixtures as a means to determine the
activity of a site (and as an alternative to the Oxcal function SUM; currently
unpublished), and reverse calibration of dates from calibrated into
un-calibrated years (also unpublished).")
    (license license:gpl2+)))

(define-public r-bchm
  (package
    (name "r-bchm")
    (version "1.00")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BCHM" version))
       (sha256
        (base32 "1nyf65grcyf74sixixzl12gylk2rzh7n5id4mbhim4lzdm4j272i"))))
    (properties `((upstream-name . "BCHM")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-rjags
                             r-plyr
                             r-knitr
                             r-crayon
                             r-coda
                             r-cluster))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BCHM")
    (synopsis "Clinical Trial Calculation Based on BCHM Design")
    (description
     "Users can estimate the treatment effect for multiple subgroups basket trials
based on the Bayesian Cluster Hierarchical Model (BCHM).  In this model, a
Bayesian non-parametric method is applied to dynamically calculate the number of
clusters by conducting the multiple cluster classification based on subgroup
outcomes.  Hierarchical model is used to compute the posterior probability of
treatment effect with the borrowing strength determined by the Bayesian
non-parametric clustering and the similarities between subgroups.  To use this
package, JAGS software and rjags package are required, and users need to
pre-install them.")
    (license license:lgpl2.0)))

(define-public r-bcgee
  (package
    (name "r-bcgee")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BCgee" version))
       (sha256
        (base32 "09p2523hslfhg0qryfn81cd5mn8nskiv98yw3s0ajfywfnwrb68k"))))
    (properties `((upstream-name . "BCgee")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BCgee")
    (synopsis
     "Bias-Corrected Estimates for Generalized Linear Models for Dependent Data")
    (description
     "This package provides bias-corrected estimates for the regression coefficients
of a marginal model estimated with generalized estimating equations.  Details
about the bias formula used are in Lunardon, N., Scharfstein, D. (2017)
<doi:10.1002/sim.7366>.")
    (license license:gpl2)))

(define-public r-bcgam
  (package
    (name "r-bcgam")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bcgam" version))
       (sha256
        (base32 "1dg2fcjw8xal77irviiz260qpar2iqkjvi2k4qfm71jyq0ir0axk"))))
    (properties `((upstream-name . "bcgam")))
    (build-system r-build-system)
    (propagated-inputs (list r-nimble r-igraph r-coda))
    (home-page "https://cran.r-project.org/package=bcgam")
    (synopsis "Bayesian Constrained Generalised Linear Models")
    (description
     "Fits generalised partial linear regression models using a Bayesian approach,
where shape and smoothness constraints are imposed on nonparametrically modelled
predictors through shape-restricted splines, and no constraints are imposed on
optional parametrically modelled covariates.  See Meyer et al. (2011)
<doi/10.1080/10485252.2011.597852> for more details.  IMPORTANT: before
installing bcgam', you need to install Rtools (Windows) or Xcode (Mac OS X).
These are required for the correct installation of nimble
(<https://r-nimble.org/download>).")
    (license license:gpl2+)))

(define-public r-bcfrailphdv
  (package
    (name "r-bcfrailphdv")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bcfrailphdv" version))
       (sha256
        (base32 "1amfpyb9y7ngfq1npnjwzgbs6g3dmpp979q7ajf0khjgj55fkirw"))))
    (properties `((upstream-name . "bcfrailphdv")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-bcfrailph))
    (home-page "https://cran.r-project.org/package=bcfrailphdv")
    (synopsis "Bivariate Correlated Frailty Models with Varied Variances")
    (description
     "Fit and simulate bivariate correlated frailty models with proportional hazard
structure.  Frailty distributions, such as gamma and lognormal models are
supported for semiparametric procedures.  Frailty variances of the two subjects
can be varied or equal.  Details on the models are available in book of Wienke
(2011,ISBN:978-1-4200-7388-1).  Bivariate gamma fit is obtained using the
approach given in Iachine (1995) with modifications.  Lognormal fit is based on
the approach by Ripatti and Palmgren (2000)
<doi:10.1111/j.0006-341X.2000.01016.x>.  Frailty distributions, such as gamma,
inverse gaussian and power variance frailty models are supported for parametric
approach.")
    (license license:gpl2+)))

(define-public r-bcfrailph
  (package
    (name "r-bcfrailph")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bcfrailph" version))
       (sha256
        (base32 "0qq885wfd7sr8cjxygw33l321q7jk4b6996p0j6inzlwycs8d6bc"))))
    (properties `((upstream-name . "bcfrailph")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "https://cran.r-project.org/package=bcfrailph")
    (synopsis "Semiparametric Bivariate Correlated Frailty Models Fit")
    (description
     "Fit and simulate a semiparametric bivariate correlated frailty models with
proportional hazard structure.  Frailty distributions, such as gamma and
lognormal models are supported.  Bivariate gamma fit is obtained using the
approach given in Iachine (1995) and lognormal fit is based on the approach by
Ripatti and Palmgren (2000) <doi:10.1111/j.0006-341X.2000.01016.x>.")
    (license license:gpl2+)))

(define-public r-bcf
  (package
    (name "r-bcf")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bcf" version))
       (sha256
        (base32 "0a7279h7f3v23zbzqkpaxl9s2q1igfc3c5wcjpins8d7nk5hgfj4"))))
    (properties `((upstream-name . "bcf")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppparallel
                             r-rcpparmadillo
                             r-rcpp
                             r-matrixstats
                             r-hmisc
                             r-foreach
                             r-doparallel
                             r-coda))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bcf")
    (synopsis "Causal Inference using Bayesian Causal Forests")
    (description
     "Causal inference for a binary treatment and continuous outcome using Bayesian
Causal Forests.  See Hahn, Murray and Carvalho (2020) <doi:10.1214/19-BA1195>
for additional information.  This implementation relies on code originally
accompanying Pratola et.  al. (2013) <@code{arXiv:1309.1906>}.")
    (license license:gpl3)))

(define-public r-bcee
  (package
    (name "r-bcee")
    (version "1.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BCEE" version))
       (sha256
        (base32 "181khl93rsrqjm8cmn14a6bb7s47dp3xc31gm9rk088ixfjadq7h"))))
    (properties `((upstream-name . "BCEE")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-leaps r-boot r-bma))
    (home-page "https://cran.r-project.org/package=BCEE")
    (synopsis "The Bayesian Causal Effect Estimation Algorithm")
    (description
     "This package provides a Bayesian model averaging approach to causal effect
estimation based on the BCEE algorithm.  Currently supports binary or continuous
exposures and outcomes.  For more details, see Talbot et al. (2015)
<doi:10.1515/jci-2014-0035> Talbot and Beaudoin (2022)
<doi:10.1515/jci-2021-0023>.")
    (license license:gpl2+)))

(define-public r-bcea
  (package
    (name "r-bcea")
    (version "2.4.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BCEA" version))
       (sha256
        (base32 "0qmnwh3j0mksgvnfnbkym7fpcngdnf1j83lqfilp5gn46akdqyvn"))))
    (properties `((upstream-name . "BCEA")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-rstan
                             r-rlang
                             r-reshape2
                             r-rdpack
                             r-purrr
                             r-mcmcvis
                             r-matrix
                             r-mass
                             r-gridextra
                             r-ggplot2
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://gianluca.statistica.it/software/bcea/")
    (synopsis "Bayesian Cost Effectiveness Analysis")
    (description
     "This package produces an economic evaluation of a sample of suitable variables
of cost and effectiveness / utility for two or more interventions, e.g. from a
Bayesian model in the form of MCMC simulations.  This package computes the most
cost-effective alternative and produces graphical summaries and probabilistic
sensitivity analysis, see Baio et al (2017) <doi:10.1007/978-3-319-55718-2>.")
    (license license:gpl3)))

(define-public r-bcdiag
  (package
    (name "r-bcdiag")
    (version "1.0.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BcDiag" version))
       (sha256
        (base32 "1gyinmx5wn2kk70hiy28ghilkhfirfjbfqdrqq5h3wfb4khnq6pz"))))
    (properties `((upstream-name . "BcDiag")))
    (build-system r-build-system)
    (propagated-inputs (list))
    (home-page "https://cran.r-project.org/package=BcDiag")
    (synopsis "Diagnostics Plots for Bicluster Data")
    (description
     "Diagnostic tools based on two-way anova and median-polish residual plots for
Bicluster output obtained from packages; \"biclust\" by Kaiser et al.(2008),\"isa2\"
by Csardi et al. (2010) and \"fabia\" by Hochreiter et al. (2010).  Moreover, It
provides visualization tools for bicluster output and corresponding
non-bicluster rows- or columns outcomes.  It has also extended the idea of
Kaiser et al.(2008) which is, extracting bicluster output in a text format, by
adding two bicluster methods from the fabia and isa2 R packages.")
    (license license:gpl3)))

(define-public r-bcdating
  (package
    (name "r-bcdating")
    (version "0.9.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BCDating" version))
       (sha256
        (base32 "1px9fimg3fqx8bfkzq6rl34xclc9gcjxqjvj5islpdy4f1ifwph3"))))
    (properties `((upstream-name . "BCDating")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BCDating")
    (synopsis "Business Cycle Dating and Plotting Tools")
    (description
     "This package provides tools for Dating Business Cycles using Harding-Pagan
(Quarterly Bry-Boschan) method and various plotting features.")
    (license license:gpl2)))

(define-public r-bcdata
  (package
    (name "r-bcdata")
    (version "0.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bcdata" version))
       (sha256
        (base32 "05izx4bpk60icwnsy4698cm4dyxmmffss59dca8zrrh4i4pql72f"))))
    (properties `((upstream-name . "bcdata")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tidyselect
                             r-tibble
                             r-sf
                             r-rlang
                             r-readxl
                             r-readr
                             r-purrr
                             r-leaflet-extras
                             r-leaflet
                             r-jsonlite
                             r-glue
                             r-dplyr
                             r-dbplyr
                             r-dbi
                             r-crul
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://bcgov.github.io/bcdata/")
    (synopsis "Search and Retrieve Data from the BC Data Catalogue")
    (description
     "Search, query, and download tabular and geospatial data from the British
Columbia Data Catalogue (<https://catalogue.data.gov.bc.ca/>).  Search catalogue
data records based on keywords, data licence, sector, data format, and B.C.
government organization.  View metadata directly in R, download many data
formats, and query geospatial data available via the B.C. government Web Feature
Service ('WFS') using dplyr syntax.")
    (license license:asl2.0)))

(define-public r-bcdag
  (package
    (name "r-bcdag")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BCDAG" version))
       (sha256
        (base32 "1w6b72vcv9gdwxm5ynpy9jrw2qpbfi4qn8272qdbv4jz1p3k605n"))))
    (properties `((upstream-name . "BCDAG")))
    (build-system r-build-system)
    (propagated-inputs (list r-rgraphviz r-mvtnorm r-lattice r-grbase r-graph))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/alesmascaro/BCDAG")
    (synopsis
     "Bayesian Structure and Causal Learning of Gaussian Directed Graphs")
    (description
     "This package provides a collection of functions for structure learning of causal
networks and estimation of joint causal effects from observational Gaussian
data.  Main algorithm consists of a Markov chain Monte Carlo scheme for
posterior inference of causal structures, parameters and causal effects between
variables.  References: F. Castelletti and A. Mascaro (2021)
<doi:10.1007/s10260-021-00579-1>, F. Castelletti and A. Mascaro (2022)
<doi:10.48550/@code{arXiv.2201.12003>}.")
    (license license:expat)))

(define-public r-bccp
  (package
    (name "r-bccp")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bccp" version))
       (sha256
        (base32 "0g9yzqzf977rx97c77krdhl7c499r58xcbzx0nihk73143ynf17w"))))
    (properties `((upstream-name . "bccp")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma))
    (home-page "https://cran.r-project.org/package=bccp")
    (synopsis "Bias Correction under Censoring Plan")
    (description
     "Developed for the following tasks.  Simulating, computing maximum likelihood
estimator, computing the Fisher information matrix, computing goodness-of-fit
measures, and correcting bias of the ML estimator for a wide range of
distributions fitted to units placed on progressive type-I interval censoring
and progressive type-II censoring plans.  The methods of Cox and Snell (1968)
<doi:10.1111/j.2517-6161.1968.tb00724.x> and bootstrap method for computing the
bias-corrected maximum likelihood estimator.")
    (license license:gpl2+)))

(define-public r-bcclong
  (package
    (name "r-bcclong")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BCClong" version))
       (sha256
        (base32 "1ighfc6zsgclvpfan0vdi0k896y4lnwkvm9g3fj1zmmzf2pym3mj"))))
    (properties `((upstream-name . "BCClong")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncdist
                             r-rmpfr
                             r-rcpparmadillo
                             r-rcpp
                             r-nnet
                             r-mvtnorm
                             r-mixak
                             r-mcmcpack
                             r-mclust
                             r-mass
                             r-lme4
                             r-laplacesdemon
                             r-label-switching
                             r-gridextra
                             r-ggplot2
                             r-coda
                             r-cluster
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BCClong")
    (synopsis
     "Bayesian Consensus Clustering for Multiple Longitudinal Features")
    (description
     "It is very common nowadays for a study to collect multiple features and
appropriately integrating multiple longitudinal features simultaneously for
defining individual clusters becomes increasingly crucial to understanding
population heterogeneity and predicting future outcomes.  BCClong implements a
Bayesian consensus clustering (BCC) model for multiple longitudinal features via
a generalized linear mixed model.  Compared to existing packages, several key
features make the BCClong package appealing: (a) it allows simultaneous
clustering of mixed-type (e.g., continuous, discrete and categorical)
longitudinal features, (b) it allows each longitudinal feature to be collected
from different sources with measurements taken at distinct sets of time points
(known as irregularly sampled longitudinal data), (c) it relaxes the assumption
that all features have the same clustering structure by estimating the
feature-specific (local) clusterings and consensus (global) clustering.")
    (license license:expat)))

(define-public r-bcc1997
  (package
    (name "r-bcc1997")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BCC1997" version))
       (sha256
        (base32 "1lqbivjkc7858jn54av1v9bzp0as43klgndbzkr59dc3l128xp12"))))
    (properties `((upstream-name . "BCC1997")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BCC1997")
    (synopsis "Calculation of Option Prices Based on a Universal Solution")
    (description
     "Calculates the prices of European options based on the universal solution
provided by Bakshi, Cao and Chen (1997)
<doi:10.1111/j.1540-6261.1997.tb02749.x>.  This solution considers stochastic
volatility, stochastic interest and random jumps.  Please cite their work if
this package is used.")
    (license license:gpl2+)))

(define-public r-bcc
  (package
    (name "r-bcc")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bcc" version))
       (sha256
        (base32 "1wvly81rg5ximrsp01jpl8rn6rkickzfaq3ybp1i8bbcyvdj1d7d"))))
    (properties `((upstream-name . "bcc")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=bcc")
    (synopsis "Beta Control Charts")
    (description
     "Applies Beta Control Charts to defined values.  The Beta Chart presents control
limits based on the Beta probability distribution, making it suitable for
monitoring fraction data from a Binomial distribution as a replacement for
p-Charts.  The Beta Chart has been applied in three real studies and compared
with control limits from three different schemes.  The comparative analysis
showed that: (i) the Beta approximation to the Binomial distribution is more
appropriate for values confined within the [0, 1] interval; and (ii) the
proposed charts are more sensitive to the average run length (ARL) in both
in-control and out-of-control process monitoring.  Overall, the Beta Charts
outperform the Shewhart control charts in monitoring fraction data.  For more
details, see Ãngelo MÃ¡rcio Oliveira SantâAnna and Carla Schwengber ten Caten
(2012) <doi:10.1016/j.eswa.2012.02.146>.")
    (license license:gpl3)))

(define-public r-bcbcsf
  (package
    (name "r-bcbcsf")
    (version "1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BCBCSF" version))
       (sha256
        (base32 "0hvhnra68i0x78n57nlbxmz0qwl2flng9w47089jw6f9hzkq9r7n"))))
    (properties `((upstream-name . "BCBCSF")))
    (build-system r-build-system)
    (propagated-inputs (list r-abind))
    (home-page "http://www.r-project.org")
    (synopsis "Bias-Corrected Bayesian Classification with Selected Features")
    (description
     "Fully Bayesian Classification with a subset of high-dimensional features, such
as expression levels of genes.  The data are modeled with a hierarchical
Bayesian models using heavy-tailed t distributions as priors.  When a large
number of features are available, one may like to select only a subset of
features to use, typically those features strongly correlated with the response
in training cases.  Such a feature selection procedure is however invalid since
the relationship between the response and the features has be exaggerated by
feature selection.  This package provides a way to avoid this bias and yield
better-calibrated predictions for future cases when one uses F-statistic to
select features.")
    (license license:gpl2+)))

(define-public r-bcaboot
  (package
    (name "r-bcaboot")
    (version "0.2-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bcaboot" version))
       (sha256
        (base32 "0b1m4lwi9hwd4xswabzyy2svc91ppiym8bi9f1wj79w5sg2hxpn7"))))
    (properties `((upstream-name . "bcaboot")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://bnaras.github.io/bcaboot/")
    (synopsis "Bias Corrected Bootstrap Confidence Intervals")
    (description
     "Computation of bootstrap confidence intervals in an almost automatic fashion as
described in Efron and Narasimhan (2020, <doi:10.1080/10618600.2020.1714633>).")
    (license license:gpl2+)))

(define-public r-bca1sg
  (package
    (name "r-bca1sg")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BCA1SG" version))
       (sha256
        (base32 "14jvi5gn19f72szcnyhfpdld00cc7gyih5qrm4bpzvp553f1svvk"))))
    (properties `((upstream-name . "BCA1SG")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-logofgamma))
    (home-page "https://cran.r-project.org/package=BCA1SG")
    (synopsis
     "Block Coordinate Ascent with One-Step Generalized Rosen Algorithm")
    (description
     "Implementing the Block Coordinate Ascent with One-Step Generalized Rosen
(BCA1SG) algorithm on the semiparametric models for panel count data,
interval-censored survival data, and degradation data.  A comprehensive
description of the BCA1SG algorithm can be found in Wang et al. (2020)
<https://github.com/yudongstat/BCA1SG/blob/master/BCA1SG.pdf>.  For details of
the semiparametric models for panel count data, interval-censored survival data,
and degradation data, please see Wellner and Zhang (2007)
<doi:10.1214/009053607000000181>, Huang and Wellner (1997)
<ISBN:978-0-387-94992-5>, and Wang and Xu (2010) <doi:10.1198/TECH.2009.08197>,
respectively.")
    (license license:gpl2)))

(define-public r-bc3net
  (package
    (name "r-bc3net")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bc3net" version))
       (sha256
        (base32 "15dghd0v1s71h77iw8b49v5h7wcda5mb4540lfyibsdipbashv2h"))))
    (properties `((upstream-name . "bc3net")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrix r-lattice r-infotheo r-igraph r-c3net))
    (home-page "https://cran.r-project.org/package=bc3net")
    (synopsis "Gene Regulatory Network Inference with Bc3net")
    (description
     "Implementation of the BC3NET algorithm for gene regulatory network inference (de
Matos Simoes and Frank Emmert-Streib, Bagging Statistical Network Inference from
Large-Scale Gene Expression Data, P@code{LoS} ONE 7(3): e33624,
<doi:10.1371/journal.pone.0033624>).")
    (license license:gpl2+)))

(define-public r-bbw
  (package
    (name "r-bbw")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bbw" version))
       (sha256
        (base32 "0gycjqn36msa6cl4zdrjv2bpv14xm8m6nsc3rfjm8mw9zjjzrw6g"))))
    (properties `((upstream-name . "bbw")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr r-car))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/rapidsurveys/bbw")
    (synopsis "Blocked Weighted Bootstrap")
    (description
     "The blocked weighted bootstrap (BBW) is an estimation technique for use with
data from two-stage cluster sampled surveys in which either prior weighting
(e.g. population-proportional sampling or PPS as used in Standardized Monitoring
and Assessment of Relief and Transitions or SMART surveys) or posterior
weighting (e.g. as used in rapid assessment method or RAM and simple spatial
sampling method or S3M surveys) is implemented.  See Cameron et al (2008)
<doi:10.1162/rest.90.3.414> for application of bootstrap to cluster samples.
See Aaron et al (2016) <doi:10.1371/journal.pone.0163176> and Aaron et al (2016)
<doi:10.1371/journal.pone.0162462> for application of the blocked weighted
bootstrap to estimate indicators from two-stage cluster sampled surveys.")
    (license license:gpl3)))

(define-public r-bbssl
  (package
    (name "r-bbssl")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BBSSL" version))
       (sha256
        (base32 "0vb3afbx8p3frliy92sf2wlmx32rasxj39i8d023m2dvsblbh50x"))))
    (properties `((upstream-name . "BBSSL")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncnorm
                             r-svmisc
                             r-statmod
                             r-rmutil
                             r-mvnfast
                             r-matrix
                             r-greybox
                             r-glmnet))
    (home-page "https://cran.r-project.org/package=BBSSL")
    (synopsis "Bayesian Bootstrap Spike-and-Slab LASSO")
    (description
     "Posterior sampling for Spike-and-Slab LASSO prior in linear models from Nie and
Rockova <@code{arXiv:2011.14279>}.")
    (license license:gpl3)))

(define-public r-bbreg
  (package
    (name "r-bbreg")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bbreg" version))
       (sha256
        (base32 "0mi2834v4cvrhvpnzkb4lgkqq993c3c1yzsxdnmny4p6yxw1q547"))))
    (properties `((upstream-name . "bbreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod r-pbapply r-formula r-expint))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bbreg")
    (synopsis
     "Bessel and Beta Regressions via Expectation-Maximization Algorithm for Continuous Bounded Data")
    (description
     "This package provides functions to fit, via Expectation-Maximization (EM)
algorithm, the Bessel and Beta regressions to a data set with a bounded
continuous response variable.  The Bessel regression is a new and robust
approach proposed in the literature.  The EM version for the well known Beta
regression is another major contribution of this package.  See details in the
references Barreto-Souza, Mayrink and Simas (2022) <doi:10.1111/anzs.12354> and
Barreto-Souza, Mayrink and Simas (2020) <@code{arXiv:2003.05157>}.")
    (license license:gpl2)))

(define-public r-bbnet
  (package
    (name "r-bbnet")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bbnet" version))
       (sha256
        (base32 "1g7hkiz6485200qabnpx31br6ixcr1jwgvzzdznz4qs8nwhswxqf"))))
    (properties `((upstream-name . "bbnet")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-igraph r-ggplot2 r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/vda1r22/bbnet")
    (synopsis "Create Simple Predictive Models on Bayesian Belief Networks")
    (description
     "This package provides a system to build, visualise and evaluate Bayesian belief
networks.  The methods are described in Stafford et al. (2015)
<doi:10.12688/f1000research.5981.1>.")
    (license license:gpl2+)))

(define-public r-bbmix
  (package
    (name "r-bbmix")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bbmix" version))
       (sha256
        (base32 "088878222dvqmqrg5d4ny3rrpbvjw7npzrk0fmagvhj3z5dvd6la"))))
    (properties `((upstream-name . "bbmix")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-rstan
                             r-rmutil
                             r-rcppeigen
                             r-rcpp
                             r-r-utils
                             r-data-table
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bbmix")
    (synopsis "Bayesian Model for Genotyping using RNA-Seq")
    (description
     "The method models RNA-seq reads using a mixture of 3 beta-binomial distributions
to generate posterior probabilities for genotyping bi-allelic single nucleotide
polymorphisms.  Elena Vigorito, Anne Barton, Costantino Pitzalis, Myles J. Lewis
and Chris Wallace (2023) <doi:10.1093/bioinformatics/btad393> \"BBmix: a Bayesian
beta-binomial mixture model for accurate genotyping from RNA-sequencing.\".")
    (license license:gpl2)))

(define-public r-bbl
  (package
    (name "r-bbl")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bbl" version))
       (sha256
        (base32 "135i619lliy93dvgb7ymrcdh20y2b1j7s0y6awzcs3w0hbyd8j5z"))))
    (properties `((upstream-name . "bbl")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-rcolorbrewer r-proc))
    (home-page "https://cran.r-project.org/package=bbl")
    (synopsis "Boltzmann Bayes Learner")
    (description
     "Supervised learning using Boltzmann Bayes model inference, which extends naive
Bayes model to include interactions.  Enables classification of data into
multiple response groups based on a large number of discrete predictors that can
take factor values of heterogeneous levels.  Either pseudo-likelihood or mean
field inference can be used with L2 regularization, cross-validation, and
prediction on new data. <doi:10.18637/jss.v101.i05>.")
    (license license:gpl2+)))

(define-public r-bbknnr
  (package
    (name "r-bbknnr")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bbknnR" version))
       (sha256
        (base32 "0zwsk491pgfr0dxbpkw1zqa4fi4d8j1bdfiridxq6f07aypx8nmg"))))
    (properties `((upstream-name . "bbknnR")))
    (build-system r-build-system)
    (propagated-inputs (list r-uwot
                             r-tidytable
                             r-seuratobject
                             r-seurat
                             r-rtsne
                             r-rlang
                             r-reticulate
                             r-rcppannoy
                             r-rcpp
                             r-matrix
                             r-magrittr
                             r-glmnet
                             r-future))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ycli1995/bbknnR")
    (synopsis "Perform Batch Balanced KNN in R")
    (description
     "This package provides a fast and intuitive batch effect removal tool for
single-cell data.  BBKNN is originally used in the scanpy python package, and
now can be used with Seurat seamlessly.")
    (license license:expat)))

(define-public r-bbk
  (package
    (name "r-bbk")
    (version "0.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bbk" version))
       (sha256
        (base32 "1dnr024app4xp7qj777vzia72jjc8ak128dcd27879cqfzixail5"))))
    (properties `((upstream-name . "bbk")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-httr2))
    (home-page "https://m-muecke.github.io/bbk/")
    (synopsis
     "Client for the Deutsche Bundesbank and European Central Bank APIs")
    (description
     "Download data and metadata from the Bundesbank SDMX Web Service API found at
<https://www.bundesbank.de/en/statistics/time-series-databases/help-for-sdmx-web-service/web-service-interface-data>
and the ECB Data Portal API found at
<https://data.ecb.europa.eu/help/api/overview>.")
    (license license:expat)))

(define-public r-bbi
  (package
    (name "r-bbi")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BBI" version))
       (sha256
        (base32 "167sndfp3776p8n9zdpihc9xhqqi8wygzpl49dzkfj53gwa77rnn"))))
    (properties `((upstream-name . "BBI")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan))
    (home-page "https://github.com/trtcrd/BBI")
    (synopsis "Benthic Biotic Indices Calculation from Composition Data")
    (description
     "Set of functions to calculate Benthic Biotic Indices from composition data,
obtained whether from morphotaxonomic inventories or sequencing data.  Based on
reference ecological weights publicly available for a set of commonly used
marine biotic indices, such as AMBI (A Marine Biotic Index, Borja et al., 2000)
<doi:10.1016/S0025-326X(00)00061-8> NSI (Norwegian Sensitivity Index) and ISI
(Indicator Species Index) (Rygg 2013, <ISBN:978-82-577-6210-0>).  It provides
the ecological quality status of the samples based on each BBI as well as the
normalized Ecological Quality Ratio.")
    (license (list license:agpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-bbest
  (package
    (name "r-bbest")
    (version "0.1-8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BBEST" version))
       (sha256
        (base32 "0j3ykxsmxima75h40mhqm32l2ffc27pk11rfzjyvv1cm6q2q3gbh"))))
    (properties `((upstream-name . "BBEST")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-reshape2 r-ggplot2 r-deoptim r-aws))
    (home-page "https://cran.r-project.org/package=BBEST")
    (synopsis
     "Bayesian Estimation of Incoherent Neutron Scattering Backgrounds")
    (description
     "We implemented a Bayesian-statistics approach for subtraction of incoherent
scattering from neutron total-scattering data.  In this approach, the estimated
background signal associated with incoherent scattering maximizes the posterior
probability, which combines the likelihood of this signal in reciprocal and real
spaces with the prior that favors smooth lines.  The description of the
corresponding approach could be found at Gagin and Levin (2014)
<DOI:10.1107/S1600576714023796>.")
    (license license:gpl3)))

(define-public r-bbdetection
  (package
    (name "r-bbdetection")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bbdetection" version))
       (sha256
        (base32 "1j6xcbfsrg3wcr1rpddj7ia33szn5bs0wk4mzq42d761clsr5qq8"))))
    (properties `((upstream-name . "bbdetection")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo r-xtable r-rcpp r-ggplot2))
    (home-page "https://cran.r-project.org/package=bbdetection")
    (synopsis "Identification of Bull and Bear States of the Market")
    (description
     "This package implements two algorithms of detecting Bull and Bear markets in
stock prices: the algorithm of Pagan and Sossounov (2002, <doi:10.1002/jae.664>)
and the algorithm of Lunde and Timmermann (2004,
<doi:10.1198/073500104000000136>).  The package also contains functions for
printing out the dating of the Bull and Bear states of the market, the
descriptive statistics of the states, and functions for plotting the results.
For the sake of convenience, the package includes the monthly and daily data on
the prices (not adjusted for dividends) of the S&P 500 stock market index.")
    (license license:gpl3)))

(define-public r-bazar
  (package
    (name "r-bazar")
    (version "1.0.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bazar" version))
       (sha256
        (base32 "1q2w5pvlb51lzbb5k719sf1lrhffandkfqlcvi2prw4dmq7qjxv9"))))
    (properties `((upstream-name . "bazar")))
    (build-system r-build-system)
    (propagated-inputs (list r-kimisc))
    (home-page "https://github.com/paulponcet/bazar")
    (synopsis "Miscellaneous Basic Functions")
    (description
     "This package provides a collection of miscellaneous functions for copying
objects to the clipboard ('Copy'); manipulating strings ('concat', mgsub',
trim', verlan'); loading or showing packages ('library_with_dep',
require_with_dep', @code{sessionPackages}'); creating or testing for named lists
('nlist', as.nlist', is.nlist'), formulas ('is.formula'), empty objects
('as.empty', is.empty'), whole numbers ('as.wholenumber', is.wholenumber');
testing for equality ('almost.equal', almost.zero') and computing uniqueness
('almost.unique'); getting modified versions of usual functions ('rle2',
@code{sumNA}'); making a pause or a stop ('pause', stopif'); converting into a
function ('as.fun'); providing a C like ternary operator ('condition %?% true
%:% false'); finding packages and functions ('get_all_pkgs', get_all_funs'); and
others ('erase', %nin%', unwhich', top', bot', normalize').")
    (license license:gpl3)))

(define-public r-baytrends
  (package
    (name "r-baytrends")
    (version "2.0.12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "baytrends" version))
       (sha256
        (base32 "05gd8i6y5fvkx12v55drff0fqpkg7bs3qjcaqmmcid9sl4168ms8"))))
    (properties `((upstream-name . "baytrends")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-sessioninfo
                             r-readxl
                             r-plyr
                             r-pander
                             r-mgcv
                             r-memoise
                             r-lubridate
                             r-knitr
                             r-fitdistrplus
                             r-dplyr
                             r-digest
                             r-dataretrieval))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/tetratech/baytrends")
    (synopsis "Long Term Water Quality Trend Analysis")
    (description
     "Enable users to evaluate long-term trends using a Generalized Additive Modeling
(GAM) approach.  The model development includes selecting a GAM structure to
describe nonlinear seasonally-varying changes over time, incorporation of
hydrologic variability via either a river flow or salinity, the use of an
intervention to deal with method or laboratory changes suspected to impact data
values, and representation of left- and interval-censored data.  The approach
has been applied to water quality data in the Chesapeake Bay, a major estuary on
the east coast of the United States to provide insights to a range of
management- and research-focused questions.  Methodology described in Murphy
(2019) <doi:10.1016/j.envsoft.2019.03.027>.")
    (license license:gpl3)))

(define-public r-baystar
  (package
    (name "r-baystar")
    (version "0.2-10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BAYSTAR" version))
       (sha256
        (base32 "1yn79nkwi33d6qsiklay3ngk26dvi44lap59zp545gidvn9fbir6"))))
    (properties `((upstream-name . "BAYSTAR")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-coda))
    (home-page "https://cran.r-project.org/package=BAYSTAR")
    (synopsis "On Bayesian Analysis of Threshold Autoregressive Models")
    (description
     "Fit two-regime threshold autoregressive (TAR) models by Markov chain Monte Carlo
methods.")
    (license license:gpl2+)))

(define-public r-baystability
  (package
    (name "r-baystability")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "baystability" version))
       (sha256
        (base32 "11ac74mb9dasf4m3qqd0j89bbjq2zk2jynljs86c2mlqack3jivj"))))
    (properties `((upstream-name . "baystability")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-scales
                             r-rstiefel
                             r-rlang
                             r-mass
                             r-lme4
                             r-ggplot2
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=baystability")
    (synopsis
     "Bayesian Stability Analysis of Genotype by Environment Interaction (GEI)")
    (description
     "This package performs general Bayesian estimation method of linearâbilinear
models for genotype Ã environment interaction.  The method is explained in
Perez-Elizalde, S., Jarquin, D., and Crossa, J. (2011)
(<doi:10.1007/s13253-011-0063-9>).")
    (license license:gpl2)))

(define-public r-baymedr
  (package
    (name "r-baymedr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "baymedr" version))
       (sha256
        (base32 "0lsp9g9xjwwf2znh4vld41pgdn55ncvmdsyg84ifhdqzjrsgwc6f"))))
    (properties `((upstream-name . "baymedr")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr r-rlang))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/maxlinde/baymedr")
    (synopsis "Computation of Bayes Factors for Common Biomedical Designs")
    (description
     "BAYesian inference for MEDical designs in R. Functions for the computation of
Bayes factors for common biomedical research designs.  Implemented are functions
to test the equivalence (equiv_bf), non-inferiority (infer_bf), and superiority
(super_bf) of an experimental group compared to a control group on a continuous
outcome measure.  Bayes factors for these three tests can be computed based on
raw data (x, y) or summary statistics (n_x, n_y, mean_x, mean_y, sd_x, sd_y [or
ci_margin and ci_level]).")
    (license license:gpl3)))

(define-public r-baymds
  (package
    (name "r-baymds")
    (version "2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayMDS" version))
       (sha256
        (base32 "1l5srpaichn598a3a9pj7pwp35jvbwb65d37l9ngvdq3rja8xm0s"))))
    (properties `((upstream-name . "bayMDS")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinythemes
                             r-shiny
                             r-rcpparmadillo
                             r-rcpp
                             r-progress
                             r-ggpubr
                             r-ggplot2))
    (home-page "https://cran.r-project.org/package=bayMDS")
    (synopsis "Bayesian Multidimensional Scaling and Choice of Dimension")
    (description
     "Bayesian approach to multidimensional scaling.  The package consists of
implementations of the methods of Oh and Raftery (2001)
<doi:10.1198/016214501753208690>.")
    (license license:gpl2+)))

(define-public r-baylum
  (package
    (name "r-baylum")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayLum" version))
       (sha256
        (base32 "1vkykrwjmm810m29i2l3yps8snysc8s2qqfbgprr4a0a1qyj4nrg"))))
    (properties `((upstream-name . "BayLum")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-runjags
                             r-rjags
                             r-luminescence
                             r-kernsmooth
                             r-hexbin
                             r-coda
                             r-cli))
    (native-inputs (list r-r-rsp))
    (home-page "https://CRAN.r-project.org/package=BayLum")
    (synopsis
     "Chronological Bayesian Models Integrating Optically Stimulated Luminescence and Radiocarbon Age Dating")
    (description
     "Bayesian analysis of luminescence data and C-14 age estimates.  Bayesian models
are based on the following publications: Combes, B. & Philippe, A. (2017)
<doi:10.1016/j.quageo.2017.02.003> and Combes et al. (2015)
<doi:10.1016/j.quageo.2015.04.001>.  This includes, amongst others, data import,
export, application of age models and palaeodose model.")
    (license license:gpl3)))

(define-public r-baygel
  (package
    (name "r-baygel")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "baygel" version))
       (sha256
        (base32 "05s9wnsk4fi6pczjd4jg803dwss71m7yng3ck54n3b0pm27c37yd"))))
    (properties `((upstream-name . "baygel")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppprogress r-rcpparmadillo r-rcpp))
    (home-page "https://github.com/Jarod-Smithy/baygel")
    (synopsis
     "Bayesian Shrinkage Estimators for Precision Matrices in Gaussian Graphical Models")
    (description
     "This R package offers block Gibbs samplers for the Bayesian (adaptive) graphical
lasso, ridge, and naive elastic net priors.  These samplers facilitate the
simulation of the posterior distribution of precision matrices for Gaussian
distributed data and were originally proposed by: Wang (2012)
<doi:10.1214/12-BA729>; Smith et al. (2022)
<doi:10.48550/@code{arXiv.2210.16290>} and Smith et al. (2023)
<doi:10.48550/@code{arXiv.2306.14199>}, respectively.")
    (license license:gpl3+)))

(define-public r-bayfoxr
  (package
    (name "r-bayfoxr")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayfoxr" version))
       (sha256
        (base32 "1295296mbjpmd0bg1pfxvyp0az3sry6gsq9ir3l8x64w5a4qrzd5"))))
    (properties `((upstream-name . "bayfoxr")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/brews/bayfoxr/")
    (synopsis "Global Bayesian Foraminifera Core Top Calibration")
    (description
     "This package provides a Bayesian, global planktic foraminifera core top
calibration to modern sea-surface temperatures.  Includes four calibration
models, considering species-specific calibration parameters and seasonality.")
    (license license:gpl3+)))

(define-public r-bayeszib
  (package
    (name "r-bayeszib")
    (version "0.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesZIB" version))
       (sha256
        (base32 "1265vnwmxxgahdccw81sirjb782z5pl3finf5gvmky3zgvj2p8kc"))))
    (properties `((upstream-name . "bayesZIB")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppeigen
                             r-rcpp
                             r-ggplot2
                             r-bh))
    (home-page "https://cran.r-project.org/package=bayesZIB")
    (synopsis "Bayesian Zero-Inflated Bernoulli Regression Model")
    (description
     "Fits a Bayesian zero-inflated Bernoulli regression model handling (potentially)
different covariates for the zero-inflated and non zero-inflated parts.  See
MoriÃ±a D, Puig P, Navarro A. (2021) <doi:10.1186/s12874-021-01427-2>.")
    (license license:gpl2+)))

(define-public r-bayesxsrc
  (package
    (name "r-bayesxsrc")
    (version "3.0-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesXsrc" version))
       (sha256
        (base32 "0wv432bmq7vidbccli1lgzyj7d9nl2ykfhr654bhxnsfkbaaqdpg"))))
    (properties `((upstream-name . "BayesXsrc")))
    (build-system r-build-system)
    (home-page "https://www.uni-goettingen.de/de/bayesx/550513.html")
    (synopsis "Distribution of the 'BayesX' C++ Sources")
    (description
     "@code{BayesX} performs Bayesian inference in structured additive regression
(STAR) models.  The R package @code{BayesXsrc} provides the @code{BayesX}
command line tool for easy installation.  A convenient R interface is provided
in package R2@code{BayesX}.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-bayesx
  (package
    (name "r-bayesx")
    (version "0.3-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesX" version))
       (sha256
        (base32 "1yxj13nyzhwl2ak9y4y7cdcrrrfb78wdynggs6v76n94pghc9dgp"))))
    (properties `((upstream-name . "BayesX")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-shapefiles
                             r-sf
                             r-interp
                             r-colorspace
                             r-coda))
    (home-page "https://cran.r-project.org/package=BayesX")
    (synopsis "R Utilities Accompanying the Software Package BayesX")
    (description
     "This package provides functions for exploring and visualising estimation results
obtained with @code{BayesX}, a free software for estimating structured additive
regression models (<https://www.uni-goettingen.de/de/bayesx/550513.html>).  In
addition, functions that allow to read, write and manipulate map objects that
are required in spatial analyses performed with @code{BayesX}.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-bayeswatch
  (package
    (name "r-bayeswatch")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesWatch" version))
       (sha256
        (base32 "04kfx68cq5cjg1ykzl8i55prnw2mvqgnmgbjav5nv2rmh9psny61"))))
    (properties `((upstream-name . "bayesWatch")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen
                             r-rcpparmadillo
                             r-rcpp
                             r-matrix
                             r-mass
                             r-hotelling
                             r-gridextra
                             r-ggplot2
                             r-ess
                             r-cholwishart
                             r-bh
                             r-bdgraph))
    (home-page "https://cran.r-project.org/package=bayesWatch")
    (synopsis
     "Bayesian Change-Point Detection for Process Monitoring with Fault Detection")
    (description
     "Bayes Watch fits an array of Gaussian Graphical Mixture Models to groupings of
homogeneous data in time, called regimes, which are modeled as the observed
states of a Markov process with unknown transition probabilities.  In doing so,
Bayes Watch defines a posterior distribution on a vector of regime assignments,
which gives meaningful expressions on the probability of every possible
change-point.  Bayes Watch also allows for an effective and efficient fault
detection system that assesses what features in the data where the most
responsible for a given change-point.  For further details, see: Alexander C.
Murph et al. (2023) <@code{arXiv:2310.02940>}.")
    (license license:gpl3)))

(define-public r-bayesvl
  (package
    (name "r-bayesvl")
    (version "0.8.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesvl" version))
       (sha256
        (base32 "1gb2in8hjiqb3daqz6phn1639i6p2w641kxrm3zh4rm3d6hg6hzr"))))
    (properties `((upstream-name . "bayesvl")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridis
                             r-stanheaders
                             r-rstan
                             r-reshape2
                             r-ggplot2
                             r-dplyr
                             r-coda
                             r-bnlearn
                             r-bayesplot))
    (home-page "https://github.com/sshpa/bayesvl")
    (synopsis
     "Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan'")
    (description
     "This package provides users with its associated functions for pedagogical
purposes in visually learning Bayesian networks and Markov chain Monte Carlo
(MCMC) computations.  It enables users to: a) Create and examine the (starting)
graphical structure of Bayesian networks; b) Create random Bayesian networks
using a dataset with customized constraints; c) Generate Stan code for
structures of Bayesian networks for sampling the data and learning parameters;
d) Plot the network graphs; e) Perform Markov chain Monte Carlo computations and
produce graphs for posteriors checks.  The package refers to one reference item,
which describes the methods and algorithms: Vuong, Quan-Hoang and La,
Viet-Phuong (2019) <doi:10.31219/osf.io/w5dx6> The bayesvl R package.  Open
Science Framework (May 18).")
    (license license:gpl3+)))

(define-public r-bayesvarsel
  (package
    (name "r-bayesvarsel")
    (version "2.2.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesVarSel" version))
       (sha256
        (base32 "1hp8cy9dbna32r57pjpsi9kx9vm18s0xr3gk1ndj0rbzn4193jdn"))))
    (properties `((upstream-name . "BayesVarSel")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-mass))
    (home-page "https://github.com/comodin19/BayesVarSel")
    (synopsis
     "Bayes Factors, Model Choice and Variable Selection in Linear Models")
    (description
     "Bayes factors and posterior probabilities in Linear models, aimed at provide a
formal Bayesian answer to testing and variable selection problems.")
    (license license:gpl2)))

(define-public r-bayestwin
  (package
    (name "r-bayestwin")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesTwin" version))
       (sha256
        (base32 "026blnhihbx9bpd63mxhbp0kpvj0lwbj7mg72alyfr2jnrh1n3lp"))))
    (properties `((upstream-name . "BayesTwin")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjags r-matrixstats r-foreign r-coda))
    (home-page "http://www.ingaschwabe.com")
    (synopsis "Bayesian Analysis of Item-Level Twin Data")
    (description
     "Bayesian analysis of item-level hierarchical twin data using an integrated item
response theory model.  Analyses are based on Schwabe & van den Berg (2014)
<doi:10.1007/s10519-014-9649-7>, Molenaar & Dolan (2014)
<doi:10.1007/s10519-014-9647-9>, Schwabe, Jonker & van den Berg (2016)
<doi:10.1007/s10519-015-9768-9> and Schwabe, Boomsma & van den Berg (2016)
<doi:10.1016/j.lindif.2017.01.018>.")
    (license license:gpl2+)))

(define-public r-bayestreeprior
  (package
    (name "r-bayestreeprior")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesTreePrior" version))
       (sha256
        (base32 "13igmvw1wj8kzfijzsrhdqr52kv4vqrm91i71ay6f7jnkry3spaw"))))
    (properties `((upstream-name . "BayesTreePrior")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BayesTreePrior")
    (synopsis "Bayesian Tree Prior Simulation")
    (description
     "This package provides a way to simulate from the prior distribution of Bayesian
trees by Chipman et al. (1998) <DOI:10.2307/2669832>.  The prior distribution of
Bayesian trees is highly dependent on the design matrix X, therefore using the
suggested hyperparameters by Chipman et al. (1998) <DOI:10.2307/2669832> is not
recommended and could lead to unexpected prior distribution.  This work is part
of my master thesis (expected 2016).")
    (license license:gpl3)))

(define-public r-bayestree
  (package
    (name "r-bayestree")
    (version "0.3-1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesTree" version))
       (sha256
        (base32 "0nq0587j9n7c83m3mw2wglz58dayfsbgrlvyfslf8jmfhgi5lqqx"))))
    (properties `((upstream-name . "BayesTree")))
    (build-system r-build-system)
    (propagated-inputs (list r-nnet))
    (home-page "https://www.r-project.org")
    (synopsis "Bayesian Additive Regression Trees")
    (description
     "This is an implementation of BART:Bayesian Additive Regression Trees, by
Chipman, George, @code{McCulloch} (2010).")
    (license license:gpl2+)))

(define-public r-bayestools
  (package
    (name "r-bayestools")
    (version "0.2.17")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesTools" version))
       (sha256
        (base32 "1vwc64s1a10d2haw7bi5896hhipbb1y7aks4mzjpfrp4zavbzlpn"))))
    (properties `((upstream-name . "BayesTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-rdpack
                             r-mvtnorm
                             r-ggplot2
                             r-extradistr
                             r-coda
                             r-bridgesampling))
    (native-inputs (list r-knitr))
    (home-page "https://fbartos.github.io/BayesTools/")
    (synopsis "Tools for Bayesian Analyses")
    (description
     "This package provides tools for conducting Bayesian analyses and Bayesian model
averaging (Kass and Raftery, 1995, <doi:10.1080/01621459.1995.10476572>, Hoeting
et al., 1999, <doi:10.1214/ss/1009212519>).  The package contains functions for
creating a wide range of prior distribution objects, mixing posterior samples
from JAGS and Stan models, plotting posterior distributions, and etc...  The
tools for working with prior distribution span from visualization, generating
JAGS and bridgesampling syntax to basic functions such as rng, quantile, and
distribution functions.")
    (license license:gpl3)))

(define-public r-bayestfr
  (package
    (name "r-bayestfr")
    (version "7.4-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesTFR" version))
       (sha256
        (base32 "09fbyc4656dil6mqfzx7nlxxp0b95fmc73ibci60jbbzggafb51s"))))
    (properties `((upstream-name . "bayesTFR")))
    (build-system r-build-system)
    (propagated-inputs (list r-wpp2019
                             r-mvtnorm
                             r-mass
                             r-lifecycle
                             r-data-table
                             r-coda))
    (home-page "https://bayespop.csss.washington.edu")
    (synopsis "Bayesian Fertility Projection")
    (description
     "Making probabilistic projections of total fertility rate for all countries of
the world, using a Bayesian hierarchical model <doi:10.1007/s13524-011-0040-5>
<doi:10.18637/jss.v106.i08>.  Subnational probabilistic projections are also
supported <doi:10.4054/@code{DemRes.2018.38.60>}.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-bayest
  (package
    (name "r-bayest")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayest" version))
       (sha256
        (base32 "1vx0avy5a3ndy76pvgf71hxpmy7g9l0l1lmc4cgczzdmf55lrksa"))))
    (properties `((upstream-name . "bayest")))
    (build-system r-build-system)
    (propagated-inputs (list r-mcmcpack))
    (home-page "https://cran.r-project.org/package=bayest")
    (synopsis
     "Effect Size Targeted Bayesian Two-Sample t-Tests via Markov Chain Monte Carlo in Gaussian Mixture Models")
    (description
     "This package provides an Markov-Chain-Monte-Carlo algorithm for Bayesian t-tests
on the effect size.  The underlying Gibbs sampler is based on a two-component
Gaussian mixture and approximates the posterior distributions of the effect
size, the difference of means and difference of standard deviations.  A
posterior analysis of the effect size via the region of practical equivalence is
provided, too.  For more details about the Gibbs sampler see Kelter (2019)
<@code{arXiv:1906.07524>}.")
    (license license:gpl3)))

(define-public r-bayessurvive
  (package
    (name "r-bayessurvive")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesSurvive" version))
       (sha256
        (base32 "1m7acw40hvjpgjzyz8fpqzvkz1dadq829klba0343g8h0hmfcd65"))))
    (properties `((upstream-name . "BayesSurvive")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-riskregression
                             r-rcpparmadillo
                             r-rcpp
                             r-mvtnorm
                             r-ggplot2
                             r-ggally))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ocbe-uio/BayesSurvive")
    (synopsis "Bayesian Survival Models for High-Dimensional Data")
    (description
     "An implementation of Bayesian survival models with graph-structured selection
priors for sparse identification of omics features predictive of survival
(Madjar et al., 2021 <doi:10.1186/s12859-021-04483-z>) and its extension to use
a fixed graph via a Markov Random Field (MRF) prior for capturing known
structure of omics features, e.g. disease-specific pathways from the Kyoto
Encyclopedia of Genes and Genomes database.")
    (license license:gpl3)))

(define-public r-bayessurvival
  (package
    (name "r-bayessurvival")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesSurvival" version))
       (sha256
        (base32 "1kpp0dfcmk86grg13b817py49v071q6p0dh8z22g6x2i8zl1vdfi"))))
    (properties `((upstream-name . "BayesSurvival")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BayesSurvival")
    (synopsis "Bayesian Survival Analysis for Right Censored Data")
    (description
     "This package performs unadjusted Bayesian survival analysis for right censored
time-to-event data.  The main function, @code{BayesSurv}(), computes the
posterior mean and a credible band for the survival function and for the
cumulative hazard, as well as the posterior mean for the hazard, starting from a
piecewise exponential (histogram) prior with Gamma distributed heights that are
either independent, or have a Markovian dependence structure.  A function,
@code{PlotBayesSurv}(), is provided to easily create plots of the posterior
means of the hazard, cumulative hazard and survival function, with a credible
band accompanying the latter two.  The priors and samplers are described in more
detail in Castillo and Van der Pas (2020) \"Multiscale Bayesian survival
analysis\" <@code{arXiv:2005.02889>}.  In that paper it is also shown that the
credible bands for the survival function and the cumulative hazard can be
considered confidence bands (under mild conditions) and thus offer reliable
uncertainty quantification.")
    (license license:gpl3)))

(define-public r-bayessurv
  (package
    (name "r-bayessurv")
    (version "3.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesSurv" version))
       (sha256
        (base32 "0yys80bps7567av16yw6ndj6vvx1kn8pnd7wa4xz521bjby4wsjz"))))
    (properties `((upstream-name . "bayesSurv")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-smoothsurv r-coda))
    (home-page "https://msekce.karlin.mff.cuni.cz/~komarek/")
    (synopsis
     "Bayesian Survival Regression with Flexible Error and Random Effects Distributions")
    (description
     "This package contains Bayesian implementations of the Mixed-Effects Accelerated
Failure Time (MEAFT) models for censored data.  Those can be not only
right-censored but also interval-censored, doubly-interval-censored or
misclassified interval-censored.  The methods implemented in the package have
been published in KomÃ¡rek and Lesaffre (2006, Stat.  Modelling)
<doi:10.1191/1471082X06st107oa>, KomÃ¡rek, Lesaffre and Legrand (2007, Stat.  in
Medicine) <doi:10.1002/sim.3083>, KomÃ¡rek and Lesaffre (2007, Stat.  Sinica)
<https://www3.stat.sinica.edu.tw/statistica/oldpdf/A17n27.pdf>, KomÃ¡rek and
Lesaffre (2008, JASA) <doi:10.1198/016214507000000563>, GarcÃ­a-Zattera, Jara
and KomÃ¡rek (2016, Biometrics) <doi:10.1111/biom.12424>.")
    (license license:gpl2+)))

(define-public r-bayessur
  (package
    (name "r-bayessur")
    (version "2.2-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesSUR" version))
       (sha256
        (base32 "0zm1591y4iwz006p6a6vjv1q0vl8jdzr5v1cfqixqvi8wd3rs1s4"))))
    (properties `((upstream-name . "BayesSUR")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tikzdevice
                             r-rcpparmadillo
                             r-rcpp
                             r-matrix
                             r-igraph))
    (native-inputs (list r-rmarkdown r-r-rsp r-knitr))
    (home-page "https://github.com/mbant/BayesSUR")
    (synopsis
     "Bayesian Seemingly Unrelated Regression Models in High-Dimensional Settings")
    (description
     "Bayesian seemingly unrelated regression with general variable selection and
dense/sparse covariance matrix.  The sparse seemingly unrelated regression is
described in Bottolo et al. (2021) <doi:10.1111/rssc.12490>, the software paper
is in Zhao et al. (2021) <doi:10.18637/jss.v100.i11>, and the model with random
effects is described in Zhao et al. (2024) <doi:10.1093/jrsssc/qlad102>.")
    (license license:expat)))

(define-public r-bayessenmc
  (package
    (name "r-bayessenmc")
    (version "0.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesSenMC" version))
       (sha256
        (base32 "16949bcakx4a77md1rm6364mzsjxr7gpmz2rxnslhz2szx4cnnaj"))))
    (properties `((upstream-name . "BayesSenMC")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-lme4
                             r-ggplot2
                             r-dplyr
                             r-bh))
    (home-page "https://github.com/formidify/BayesSenMC")
    (synopsis
     "Different Models of Posterior Distributions of Adjusted Odds Ratio")
    (description
     "Generates different posterior distributions of adjusted odds ratio under
different priors of sensitivity and specificity, and plots the models for
comparison.  It also provides estimations for the specifications of the models
using diagnostics of exposure status with a non-linear mixed effects model.  It
implements the methods that are first proposed in
<doi:10.1016/j.annepidem.2006.04.001> and <doi:10.1177/0272989X09353452>.")
    (license license:gpl2)))

(define-public r-bayessampling
  (package
    (name "r-bayessampling")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesSampling" version))
       (sha256
        (base32 "1nwwf195b7hly7kjfikizyvgc975g74z6hsm89c1ibfgxlj51bcw"))))
    (properties `((upstream-name . "BayesSampling")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrixcalc r-matrix r-mass))
    (native-inputs (list r-knitr))
    (home-page
     "https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886")
    (synopsis "Bayes Linear Estimators for Finite Population")
    (description
     "Allows the user to apply the Bayes Linear approach to finite population with the
Simple Random Sampling - BLE_SRS() - and the Stratified Simple Random Sampling
design - BLE_SSRS() - (both without replacement), to the Ratio estimator (using
auxiliary information) - BLE_Ratio() - and to categorical data -
BLE_Categorical().  The Bayes linear estimation approach is applied to a general
linear regression model for finite population prediction in BLE_Reg() and it is
also possible to achieve the design based estimators using vague prior
distributions.  Based on GonÃ§alves, K.C.M, Moura, F.A.S and Migon, H.S.(2014)
<https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886>.")
    (license license:gpl3)))

(define-public r-bayess5
  (package
    (name "r-bayess5")
    (version "1.41")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesS5" version))
       (sha256
        (base32 "0vs6s50h4gpxda9lbxfgn76k9kp81l62a6bv22r7ixsjlnd4baql"))))
    (properties `((upstream-name . "BayesS5")))
    (build-system r-build-system)
    (propagated-inputs (list r-splines2 r-snowfall r-matrix r-abind))
    (home-page "https://arxiv.org/abs/1507.07106v4")
    (synopsis
     "Bayesian Variable Selection Using Simplified Shotgun Stochastic Search with Screening (S5)")
    (description
     "In p >> n settings, full posterior sampling using existing Markov chain Monte
Carlo (MCMC) algorithms is highly inefficient and often not feasible from a
practical perspective.  To overcome this problem, we propose a scalable
stochastic search algorithm that is called the Simplified Shotgun Stochastic
Search (S5) and aimed at rapidly explore interesting regions of model space and
finding the maximum a posteriori(MAP) model.  Also, the S5 provides an
approximation of posterior probability of each model (including the marginal
inclusion probabilities).  This algorithm is a part of an article titled
\"Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in
Ultrahigh-dimensional Settings\" (2018) by Minsuk Shin, Anirban Bhattacharya, and
Valen E. Johnson and \"Nonlocal Functional Priors for Nonparametric Hypothesis
Testing and High-dimensional Model Selection\" (2020+) by Minsuk Shin and Anirban
Bhattacharya.")
    (license license:gpl2+)))

(define-public r-bayess
  (package
    (name "r-bayess")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayess" version))
       (sha256
        (base32 "0kqk4mq7q4cp2pgxw6b261fj3kl47a0dx44b7b5siwqv45nzymiz"))))
    (properties `((upstream-name . "bayess")))
    (build-system r-build-system)
    (propagated-inputs (list r-mnormt r-gplots r-combinat))
    (home-page "https://www.r-project.org")
    (synopsis "Bayesian Essentials with R")
    (description
     "Allows the reenactment of the R programs used in the book Bayesian Essentials
with R without further programming.  R code being available as well, they can be
modified by the user to conduct one's own simulations.  Marin J.-M. and Robert
C. P. (2014) <doi:10.1007/978-1-4614-8687-9>.")
    (license license:gpl2)))

(define-public r-bayesrules
  (package
    (name "r-bayesrules")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesrules" version))
       (sha256
        (base32 "0a1a1aq91ary985xd0qqc1sgjlyfcrjh7ngzcy4f7j09a7ch3j4b"))))
    (properties `((upstream-name . "bayesrules")))
    (build-system r-build-system)
    (propagated-inputs (list r-rstanarm
                             r-purrr
                             r-magrittr
                             r-janitor
                             r-groupdata2
                             r-ggplot2
                             r-e1071
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://bayes-rules.github.io/bayesrules/docs/")
    (synopsis "Datasets and Supplemental Functions from Bayes Rules! Book")
    (description
     "This package provides datasets and functions used for analysis and
visualizations in the Bayes Rules! book (<https://www.bayesrulesbook.com>).  The
package contains a set of functions that summarize and plot Bayesian models from
some conjugate families and another set of functions for evaluation of some
Bayesian models.")
    (license license:gpl3+)))

(define-public r-bayesrs
  (package
    (name "r-bayesrs")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesRS" version))
       (sha256
        (base32 "0fhvnckabp0z1cdsbjgv3ijnzaxwhrk83fwcflgqbhvm3a9lzr77"))))
    (properties `((upstream-name . "BayesRS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjags r-reshape r-metrology r-ggplot2 r-coda))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=BayesRS")
    (synopsis
     "Bayes Factors for Hierarchical Linear Models with Continuous Predictors")
    (description
     "Runs hierarchical linear Bayesian models.  Samples from the posterior
distributions of model parameters in JAGS (Just Another Gibbs Sampler; Plummer,
2017, <http://mcmc-jags.sourceforge.net>).  Computes Bayes factors for group
parameters of interest with the Savage-Dickey density ratio (Wetzels,
Raaijmakers, Jakab, Wagenmakers, 2009, <doi:10.3758/PBR.16.4.752>).")
    (license license:gpl2+)))

(define-public r-bayesroe
  (package
    (name "r-bayesroe")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesROE" version))
       (sha256
        (base32 "1y5nvvwyhr27268prahjif71y89mbhgyc51gc5nqhmy5d7qwimwj"))))
    (properties `((upstream-name . "bayesROE")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinybs
                             r-shiny
                             r-scales
                             r-golem
                             r-ggplot2
                             r-config
                             r-colourpicker))
    (home-page "https://github.com/waidschrat/bayesROE")
    (synopsis "Bayesian Regions of Evidence")
    (description
     "Computation and visualization of Bayesian Regions of Evidence to systematically
evaluate the sensitivity of a superiority or non-inferiority claim against any
prior assumption of its assessors.  Methodological details are elaborated by
Hoefler and Miller (<https://osf.io/jxnsv>).  Besides generic functions, the
package also provides an intuitive Shiny application, that can be run in local R
environments.")
    (license license:gpl3+)))

(define-public r-bayesrgmm
  (package
    (name "r-bayesrgmm")
    (version "2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesRGMM" version))
       (sha256
        (base32 "0yvjl5kzmh2c7c00jk38cs5g6g0yhrc86p5jn8zf10349md3dpcv"))))
    (properties `((upstream-name . "BayesRGMM")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape
                             r-rdpack
                             r-rcppdist
                             r-rcpparmadillo
                             r-rcpp
                             r-plyr
                             r-mvtnorm
                             r-msm
                             r-mass
                             r-batchmeans
                             r-abind))
    (home-page "https://sites.google.com/view/kuojunglee/r-packages")
    (synopsis "Bayesian Robust Generalized Mixed Models for Longitudinal Data")
    (description
     "To perform model estimation using MCMC algorithms with Bayesian methods for
incomplete longitudinal studies on binary and ordinal outcomes that are measured
repeatedly on subjects over time with drop-outs.  Details about the method can
be found in the vignette or
<https://sites.google.com/view/kuojunglee/r-packages/bayesrgmm>.")
    (license license:gpl2)))

(define-public r-bayesreversepllh
  (package
    (name "r-bayesreversepllh")
    (version "1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesReversePLLH" version))
       (sha256
        (base32 "08zx8paaqg8zkf4d2rfparbi54p1i9wr05iqp7nqy7xv3isq1zfp"))))
    (properties `((upstream-name . "BayesReversePLLH")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://cran.r-project.org/package=BayesReversePLLH")
    (synopsis "Fits the Bayesian Piecewise Linear Log-Hazard Model")
    (description
     "This package contains posterior samplers for the Bayesian piecewise linear
log-hazard and piecewise exponential hazard models, including Cox models.
Posterior mean restricted survival times are also computed for non-Cox an Cox
models with only treatment indicators.  The @code{ApproxMean}() function can be
used to estimate restricted posterior mean survival times given a vector of
patient covariates in the Cox model.  Functions included to return the posterior
mean hazard and survival functions for the piecewise exponential and piecewise
linear log-hazard models.  Chapple, AG, Peak, T, Hemal, A (2020).  Under
Revision.")
    (license license:gpl2)))

(define-public r-bayesrepdesign
  (package
    (name "r-bayesrepdesign")
    (version "0.42")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesRepDesign" version))
       (sha256
        (base32 "0xm3ap8lwg52760kyfp71i52mpkkh32753fprb4dfy1ry7g2qmyv"))))
    (properties `((upstream-name . "BayesRepDesign")))
    (build-system r-build-system)
    (propagated-inputs (list r-lamw))
    (home-page "https://github.com/SamCH93/BayesRepDesign")
    (synopsis "Bayesian Design of Replication Studies")
    (description
     "This package provides functionality for determining the sample size of
replication studies using Bayesian design approaches in the normal-normal
hierarchical model (Pawel et al., 2022) <doi:10.48550/@code{arXiv.2211.02552>}.")
    (license license:gpl3)))

(define-public r-bayesrep
  (package
    (name "r-bayesrep")
    (version "0.42.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesRep" version))
       (sha256
        (base32 "16ddkaz0qqyy0pid614j5hbvmhih5xxzachyz07xw36cjzz4d2i8"))))
    (properties `((upstream-name . "BayesRep")))
    (build-system r-build-system)
    (propagated-inputs (list r-lamw r-hypergeo))
    (home-page "https://github.com/SamCH93/BayesRep")
    (synopsis "Bayesian Analysis of Replication Studies")
    (description
     "This package provides tools for the analysis of replication studies using Bayes
factors (Pawel and Held, 2022) <doi:10.1111/rssb.12491>.")
    (license license:gpl3)))

(define-public r-bayesrel
  (package
    (name "r-bayesrel")
    (version "0.7.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Bayesrel" version))
       (sha256
        (base32 "0c6vpkljhisdznxw1g204gyfvpy2zs8yqymdbp35zxddygfdchgd"))))
    (properties `((upstream-name . "Bayesrel")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack
                             r-rcpparmadillo
                             r-rcpp
                             r-mass
                             r-lavaan
                             r-laplacesdemon
                             r-coda))
    (home-page "https://github.com/juliuspfadt/Bayesrel")
    (synopsis "Bayesian Reliability Estimation")
    (description
     "Functionality for reliability estimates.  For unidimensional tests: Coefficient
alpha, Guttman's lambda-2/-4/-6, the Greatest lower bound and coefficient
omega_u ('unidimensional') in a Bayesian and a frequentist version.  For
multidimensional tests: omega_t (total) and omega_h (hierarchical).  The results
include confidence and credible intervals, the probability of a coefficient
being larger than a cutoff, and a check for the factor models, necessary for the
omega coefficients.  The method for the Bayesian unidimensional estimates,
except for omega_u, is sampling from the posterior inverse Wishart for the
covariance matrix based measures (see Murphy', 2007,
<https://groups.seas.harvard.edu/courses/cs281/papers/murphy-2007.pdf>.  The
Bayesian omegas (u, t, and h) are obtained by Gibbs sampling from the
conditional posterior distributions of (1) the single factor model, (2) the
second-order factor model, (3) the bi-factor model, (4) the correlated factor
model ('Lee', 2007, <doi:10.1002/9780470024737>).")
    (license license:gpl3)))

(define-public r-bayesreg
  (package
    (name "r-bayesreg")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesreg" version))
       (sha256
        (base32 "0xx49lqmrxcygbl4znjdlamjqfc6raskm5k54lfp9l8lfd5rmq91"))))
    (properties `((upstream-name . "bayesreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-pgdraw r-foreach r-doparallel))
    (home-page "https://cran.r-project.org/package=bayesreg")
    (synopsis "Bayesian Regression Models with Global-Local Shrinkage Priors")
    (description
     "Fits linear or generalized linear regression models using Bayesian global-local
shrinkage prior hierarchies as described in Polson and Scott (2010)
<doi:10.1093/acprof:oso/9780199694587.003.0017>.  Provides an efficient
implementation of ridge, lasso, horseshoe and horseshoe+ regression with
logistic, Gaussian, Laplace, Student-t, Poisson or geometric distributed targets
using the algorithms summarized in Makalic and Schmidt (2016)
<doi:10.48550/@code{arXiv.1611.06649>}.")
    (license license:gpl3+)))

(define-public r-bayesrecon
  (package
    (name "r-bayesrecon")
    (version "0.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesRecon" version))
       (sha256
        (base32 "1vnnf709m5mq2bbf9z2287paymv25pm6jzncm99zkpwqjilq9rh2"))))
    (properties `((upstream-name . "bayesRecon")))
    (build-system r-build-system)
    (propagated-inputs (list r-lpsolve))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bayesRecon")
    (synopsis "Probabilistic Reconciliation via Conditioning")
    (description
     "This package provides methods for probabilistic reconciliation of hierarchical
forecasts of time series.  The available methods include analytical Gaussian
reconciliation (Corani et al., 2021) <doi:10.1007/978-3-030-67664-3_13>, MCMC
reconciliation of count time series (Corani et al., 2024)
<doi:10.1016/j.ijforecast.2023.04.003>, Bottom-Up Importance Sampling (Zambon et
al., 2024) <doi:10.1007/s11222-023-10343-y>, methods for the reconciliation of
mixed hierarchies (Mix-Cond and TD-cond) (Zambon et al., 2024.  The 40th
Conference on Uncertainty in Artificial Intelligence, accepted).")
    (license license:lgpl3+)))

(define-public r-bayesqr
  (package
    (name "r-bayesqr")
    (version "2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesQR" version))
       (sha256
        (base32 "1ci81pishhf9iv08gsnyfmx9kq9d4bswfvnwx3whykrkrlgifzmi"))))
    (properties `((upstream-name . "bayesQR")))
    (build-system r-build-system)
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=bayesQR")
    (synopsis "Bayesian Quantile Regression")
    (description
     "Bayesian quantile regression using the asymmetric Laplace distribution, both
continuous as well as binary dependent variables are supported.  The package
consists of implementations of the methods of Yu & Moyeed (2001)
<doi:10.1016/S0167-7152(01)00124-9>, Benoit & Van den Poel (2012)
<doi:10.1002/jae.1216> and Al-Hamzawi, Yu & Benoit (2012)
<doi:10.1177/1471082X1101200304>.  To speed up the calculations, the Markov
Chain Monte Carlo core of all algorithms is programmed in Fortran and called
from R.")
    (license license:gpl2+)))

(define-public r-bayesproject
  (package
    (name "r-bayesproject")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesProject" version))
       (sha256
        (base32 "0b1xqw7f9kfbmr8m6fl9d2fpwr364s4a5vdw57l6i5rm6lszyfh5"))))
    (properties `((upstream-name . "BayesProject")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-rcppeigen r-rcpp))
    (home-page "https://cran.r-project.org/package=BayesProject")
    (synopsis
     "Fast Projection Direction for Multivariate Changepoint Detection")
    (description
     "Implementations in cpp of the @code{BayesProject} algorithm (see G. Hahn, P.
Fearnhead, I.A. Eckley (2020) <doi:10.1007/s11222-020-09966-2>) which implements
a fast approach to compute a projection direction for multivariate changepoint
detection, as well as the sum-cusum and max-cusum methods, and a wild binary
segmentation wrapper for all algorithms.")
    (license license:gpl2+)))

(define-public r-bayesppdsurv
  (package
    (name "r-bayesppdsurv")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesPPDSurv" version))
       (sha256
        (base32 "1n954qsld509hf47hz7m0998lb1pc8mj0r8i67v0qx9qvr39jmm2"))))
    (properties `((upstream-name . "BayesPPDSurv")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-rcppdist r-rcpparmadillo r-rcpp r-dplyr))
    (home-page "https://cran.r-project.org/package=BayesPPDSurv")
    (synopsis "Bayesian Power Prior Design for Survival Data")
    (description
     "Bayesian power/type I error calculation and model fitting using the power prior
and the normalized power prior for proportional hazards models with piecewise
constant hazard.  The methodology and examples of applying the package are
detailed in <doi:10.48550/@code{arXiv.2404.05118>}.  The Bayesian clinical trial
design methodology is described in Chen et al. (2011)
<doi:10.1111/j.1541-0420.2011.01561.x>, and Psioda and Ibrahim (2019)
<doi:10.1093/biostatistics/kxy009>.  The proportional hazards model with
piecewise constant hazard is detailed in Ibrahim et al. (2001)
<doi:10.1007/978-1-4757-3447-8>.")
    (license license:gpl3+)))

(define-public r-bayesppd
  (package
    (name "r-bayesppd")
    (version "1.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesPPD" version))
       (sha256
        (base32 "0c015l8ih1ck1qb31vvnn95fivf5r3ns58pdlm4x5bf7giprjxf4"))))
    (properties `((upstream-name . "BayesPPD")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppnumerical r-rcppeigen r-rcpparmadillo
                             r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BayesPPD")
    (synopsis "Bayesian Power Prior Design")
    (description
     "Bayesian power/type I error calculation and model fitting using the power prior
and the normalized power prior for generalized linear models.  Detailed examples
of applying the package are available at <doi:10.32614/RJ-2023-016>.  The
Bayesian clinical trial design methodology is described in Chen et al. (2011)
<doi:10.1111/j.1541-0420.2011.01561.x>, and Psioda and Ibrahim (2019)
<doi:10.1093/biostatistics/kxy009>.  The normalized power prior is described in
Duan et al. (2006) <doi:10.1002/env.752> and Ibrahim et al. (2015)
<doi:10.1002/sim.6728>.")
    (license license:gpl3+)))

(define-public r-bayespostest
  (package
    (name "r-bayespostest")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesPostEst" version))
       (sha256
        (base32 "0r05by4w80j6hb22v3ibj686ixxbj7lkxq499fciz5lqs4lxzf3h"))))
    (properties `((upstream-name . "BayesPostEst")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-tidyr
                             r-texreg
                             r-runjags
                             r-rstanarm
                             r-rocr
                             r-rlang
                             r-rjags
                             r-reshape2
                             r-r2winbugs
                             r-r2jags
                             r-mcmcpack
                             r-hdinterval
                             r-ggridges
                             r-ggplot2
                             r-dplyr
                             r-coda
                             r-catools
                             r-cardata
                             r-brms))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ShanaScogin/BayesPostEst")
    (synopsis
     "Generate Postestimation Quantities for Bayesian MCMC Estimation")
    (description
     "An implementation of functions to generate and plot postestimation quantities
after estimating Bayesian regression models using Markov chain Monte Carlo
(MCMC).  Functionality includes the estimation of the Precision-Recall curves
(see Beger, 2016 <doi:10.2139/ssrn.2765419>), the implementation of the observed
values method of calculating predicted probabilities by Hanmer and Kalkan (2013)
<doi:10.1111/j.1540-5907.2012.00602.x>, the implementation of the average value
method of calculating predicted probabilities (see King, Tomz, and Wittenberg,
2000 <doi:10.2307/2669316>), and the generation and plotting of first
differences to summarize typical effects across covariates (see Long 1997,
ISBN:9780803973749; King, Tomz, and Wittenberg, 2000 <doi:10.2307/2669316>).
This package can be used with MCMC output generated by any Bayesian estimation
tool including JAGS', BUGS', MCMCpack', and Stan'.")
    (license license:gpl3)))

(define-public r-bayespop
  (package
    (name "r-bayespop")
    (version "10.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesPop" version))
       (sha256
        (base32 "0jzxsqnrzvhz85wravjxbjwf6panwmylqgz99b0zknvgbf0vx6zh"))))
    (properties `((upstream-name . "bayesPop")))
    (build-system r-build-system)
    (propagated-inputs (list r-wpp2019
                             r-wpp2012
                             r-rworldmap
                             r-reshape2
                             r-plyr
                             r-mortcast
                             r-googlevis
                             r-fields
                             r-data-table
                             r-bayestfr
                             r-bayeslife
                             r-abind))
    (home-page "https://bayespop.csss.washington.edu")
    (synopsis "Probabilistic Population Projection")
    (description
     "Generating population projections for all countries of the world using several
probabilistic components, such as total fertility rate and life expectancy
(Raftery et al., 2012 <doi:10.1073/pnas.1211452109>).")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-bayespo
  (package
    (name "r-bayespo")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesPO" version))
       (sha256
        (base32 "0dav6kjasb7p0ks8fwd3r8pb7q8il39rmvpbgdb80c7k49azirhd"))))
    (properties `((upstream-name . "bayesPO")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppprogress r-rcppeigen r-rcpp r-coda))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bayesPO")
    (synopsis "Bayesian Inference for Presence-Only Data")
    (description
     "Presence-Only data is best modelled with a Point Process Model.  The work of
Moreira and Gamerman (2022) <doi:10.1214/21-AOAS1569> provides a way to use
exact Bayesian inference to model this type of data, which is implemented in
this package.")
    (license license:gpl3)))

(define-public r-bayespm
  (package
    (name "r-bayespm")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayespm" version))
       (sha256
        (base32 "0w965fn7qvxq93bb1iiwzzkz6vc5k7gz87xfnwnywsqcm0bj2khb"))))
    (properties `((upstream-name . "bayespm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmutil r-invgamma r-gridextra r-ggplot2
                             r-extradistr))
    (home-page "https://cran.r-project.org/package=bayespm")
    (synopsis "Bayesian Statistical Process Monitoring")
    (description
     "The R-package bayespm implements Bayesian Statistical Process Control and
Monitoring (SPC/M) methodology.  These methods utilize available prior
information and/or historical data, providing efficient online quality
monitoring of a process, in terms of identifying moderate/large transient shifts
(i.e., outliers) or persistent shifts of medium/small size in the process.
These self-starting, sequentially updated tools can also run under complete
absence of any prior information.  The Predictive Control Charts (PCC) are
introduced for the quality monitoring of data from any discrete or continuous
distribution that is a member of the regular exponential family.  The Predictive
Ratio CUSUMs (PRC) are introduced for the Binomial, Poisson and Normal data (a
later version of the library will cover all the remaining distributions from the
regular exponential family).  The PCC targets transient process shifts of
typically large size (a.k.a.  outliers), while PRC is focused in detecting
persistent (structural) shifts that might be of medium or even small size.
Apart from monitoring, both PCC and PRC provide the sequentially updated
posterior inference for the monitored parameter.  Bourazas K., Kiagias D. and
Tsiamyrtzis P. (2022) \"Predictive Control Charts (PCC): A Bayesian approach in
online monitoring of short runs\" <doi:10.1080/00224065.2021.1916413>, Bourazas
K., Sobas F. and Tsiamyrtzis, P. 2023. \"Predictive ratio CUSUM (PRC): A Bayesian
approach in online change point detection of short runs\"
<doi:10.1080/00224065.2022.2161434>, Bourazas K., Sobas F. and Tsiamyrtzis, P.
2023. \"Design and properties of the predictive ratio cusum (PRC) control charts\"
<doi:10.1080/00224065.2022.2161435>.")
    (license license:gpl2+)))

(define-public r-bayesplay
  (package
    (name "r-bayesplay")
    (version "0.9.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesplay" version))
       (sha256
        (base32 "1gc5gfyd03rir3yrjsqj6448wvbw49n6c789hzfqwignk3vjc555"))))
    (properties `((upstream-name . "bayesplay")))
    (build-system r-build-system)
    (propagated-inputs (list r-gginnards))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bayesplay/bayesplay")
    (synopsis "The Bayes Factor Playground")
    (description
     "This package provides a lightweight modelling syntax for defining likelihoods
and priors and for computing Bayes factors for simple one parameter models.  It
includes functionality for computing and plotting priors, likelihoods, and model
predictions.  Additional functionality is included for computing and plotting
posteriors.")
    (license license:expat)))

(define-public r-bayespiecehazselect
  (package
    (name "r-bayespiecehazselect")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesPieceHazSelect" version))
       (sha256
        (base32 "0znbmv7crqycdx75jh02h6z8d6cifjszbrldv2yva4mkjxsydag5"))))
    (properties `((upstream-name . "BayesPieceHazSelect")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://cran.r-project.org/package=BayesPieceHazSelect")
    (synopsis
     "Variable Selection in a Hierarchical Bayesian Model for a Hazard Function")
    (description
     "Fits a piecewise exponential hazard to survival data using a Hierarchical
Bayesian model with an Intrinsic Conditional Autoregressive formulation for the
spatial dependency in the hazard rates for each piece.  This function uses
Metropolis- Hastings-Green MCMC to allow the number of split points to vary and
also uses Stochastic Search Variable Selection to determine what covariates
drive the risk of the event.  This function outputs trace plots depicting the
number of split points in the hazard and the number of variables included in the
hazard.  The function saves all posterior quantities to the desired path.")
    (license license:gpl2)))

(define-public r-bayesorddesign
  (package
    (name "r-bayesorddesign")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesOrdDesign" version))
       (sha256
        (base32 "1417zd1n5sip999n6q6bgs85c0000ksl73a4p94y0lmdn27i8pmj"))))
    (properties `((upstream-name . "BayesOrdDesign")))
    (build-system r-build-system)
    (propagated-inputs (list r-superdiag
                             r-schoolmath
                             r-rjmcmc
                             r-rjags
                             r-r2jags
                             r-ordinal
                             r-madness
                             r-gsdesign
                             r-ggplot2
                             r-coda))
    (home-page "https://cran.r-project.org/package=BayesOrdDesign")
    (synopsis "Bayesian Group Sequential Design for Ordinal Data")
    (description
     "The proposed group-sequential trial design is based on Bayesian methods for
ordinal endpoints, including three methods, the proportional-odds-model
(PO)-based, non-proportional-odds-model (NPO)-based, and PO/NPO
switch-model-based designs, which makes our proposed methods generic to be able
to deal with various scenarios.  Richard J. Barker, William A. Link (2013)
<doi:10.1080/00031305.2013.791644>.  Thomas A. Murray, Ying Yuan, Peter F.
Thall, Joan H. Elizondo, Wayne L.Hofstetter (2018) <doi:10.1111/biom.12842>.
Chengxue Zhong, Haitao Pan, Hongyu Miao (2021) <@code{arXiv:2108.06568>}.")
    (license license:gpl2)))

(define-public r-bayesnsgp
  (package
    (name "r-bayesnsgp")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesNSGP" version))
       (sha256
        (base32 "0va4gvyfwfdk3yvhdyxc49qlmzxfblwlmwqh79b93zkv8239kkk1"))))
    (properties `((upstream-name . "BayesNSGP")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmatch r-nimble r-matrix r-fnn))
    (home-page "https://cran.r-project.org/package=BayesNSGP")
    (synopsis "Bayesian Analysis of Non-Stationary Gaussian Process Models")
    (description
     "Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian
process modeling.  The approach to nonstationary modeling involves a
closed-form, convolution-based covariance function with spatially-varying
parameters; these parameter processes can be specified either deterministically
(using covariates or basis functions) or stochastically (using approximate
Gaussian processes).  Stationary Gaussian processes are a special case of our
methodology, and we furthermore implement approximate Gaussian process inference
to account for very large spatial data sets (Finley, et al (2017)
<@code{arXiv:1702.00434v2>}).  Bayesian inference is carried out using Markov
chain Monte Carlo methods via the nimble package, and posterior prediction for
the Gaussian process at unobserved locations is provided as a post-processing
step.")
    (license license:gpl3)))

(define-public r-bayesnetbp
  (package
    (name "r-bayesnetbp")
    (version "1.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesNetBP" version))
       (sha256
        (base32 "1c6a82qlcrpbb2c4zp3gk24wcgw6x62lm8hm6n2l1r307dxm2rpb"))))
    (properties `((upstream-name . "BayesNetBP")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcolorbrewer
                             r-igraph
                             r-graph
                             r-fields
                             r-doby
                             r-bnlearn))
    (home-page "https://cran.r-project.org/package=BayesNetBP")
    (synopsis "Bayesian Network Belief Propagation")
    (description
     "Belief propagation methods in Bayesian Networks to propagate evidence through
the network.  The implementation of these methods are based on the article:
Cowell, RG (2005).  Local Propagation in Conditional Gaussian Bayesian Networks
<https://www.jmlr.org/papers/v6/cowell05a.html>.  For details please see Yu et.
al. (2020) @code{BayesNetBP}: An R Package for Probabilistic Reasoning in
Bayesian Networks <doi:10.18637/jss.v094.i03>.  The optional @code{cyjShiny}
package for running the Shiny app is available at
<https://github.com/cytoscape/@code{cyjShiny>}.  Please see the example in the
documentation of @code{runBayesNetApp} function for installing @code{cyjShiny}
package from @code{GitHub}.")
    (license license:gpl2+)))

(define-public r-bayesnec
  (package
    (name "r-bayesnec")
    (version "2.1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesnec" version))
       (sha256
        (base32 "0pac8s3zqg6z2x3jf4j2kkvra6mpc5aa723b4mp2pygs9x6sjn41"))))
    (properties `((upstream-name . "bayesnec")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyselect
                             r-tidyr
                             r-tibble
                             r-rlang
                             r-purrr
                             r-loo
                             r-ggplot2
                             r-formula-tools
                             r-evaluate
                             r-dplyr
                             r-chk
                             r-brms))
    (native-inputs (list r-knitr))
    (home-page "https://open-aims.github.io/bayesnec/")
    (synopsis "Bayesian No-Effect- Concentration (NEC) Algorithm")
    (description
     "Implementation of No-Effect-Concentration estimation that uses brms (see Burkner
(2017)<doi:10.18637/jss.v080.i01>; Burkner (2018)<doi:10.32614/RJ-2018-017>;
Carpenter et al. (2017)<doi:10.18637/jss.v076.i01> to fit
concentration(dose)-response data using Bayesian methods for the purpose of
estimating ECx values, but more particularly NEC (see Fox
(2010)<doi:10.1016/j.ecoenv.2009.09.012>), NSEC (see Fisher and Fox
(2023)<doi:10.1002/etc.5610>), and N(S)EC (see Fisher et al.
2023<doi:10.1002/ieam.4809>).  A full description of this package can be found
in Fisher et al. (2024)<doi:10.18637/jss.v110.i05>.  This package expands and
supersedes an original version implemented in R2jags (see Su and Yajima
(2020)<https://CRAN.R-project.org/package=R2jags>; Fisher et al.
(2020)<doi:10.5281/ZENODO.3966864>).")
    (license license:gpl2)))

(define-public r-bayesmultmeta
  (package
    (name "r-bayesmultmeta")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesMultMeta" version))
       (sha256
        (base32 "09x41r5lhr2dq56b05f0aj8v4s7084d4z90qjqwdkv8zgk664ki0"))))
    (properties `((upstream-name . "BayesMultMeta")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack r-assertthat))
    (home-page "https://cran.r-project.org/package=BayesMultMeta")
    (synopsis "Bayesian Multivariate Meta-Analysis")
    (description
     "Objective Bayesian inference procedures for the parameters of the multivariate
random effects model with application to multivariate meta-analysis.  The
posterior for the model parameters, namely the overall mean vector and the
between-study covariance matrix, are assessed by constructing Markov chains
based on the Metropolis-Hastings algorithms as developed in Bodnar and Bodnar
(2021) (<@code{arXiv:2104.02105>}).  The Metropolis-Hastings algorithm is
designed under the assumption of the normal distribution and the t-distribution
when the Berger and Bernardo reference prior and the Jeffreys prior are assigned
to the model parameters.  Convergence properties of the generated Markov chains
are investigated by the rank plots and the split hat-R estimate based on the
rank normalization, which are proposed in Vehtari et al. (2021)
(<DOI:10.1214/20-BA1221>).")
    (license license:expat)))

(define-public r-bayesmultimode
  (package
    (name "r-bayesmultimode")
    (version "0.7.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesMultiMode" version))
       (sha256
        (base32 "0ij1igdnbk3wy01mdimk9k5ajxmx2plla0ykvsmijzz5mcmqyrii"))))
    (properties `((upstream-name . "BayesMultiMode")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-sn
                             r-rdpack
                             r-posterior
                             r-mvtnorm
                             r-mcmcglmm
                             r-magrittr
                             r-gtools
                             r-ggpubr
                             r-ggplot2
                             r-dplyr
                             r-bayesplot
                             r-assertthat))
    (home-page "https://github.com/paullabonne/BayesMultiMode")
    (synopsis "Bayesian Mode Inference")
    (description
     "This package provides a two-step Bayesian approach for mode inference following
Cross, Hoogerheide, Labonne and van Dijk (2024)
<doi:10.1016/j.econlet.2024.111579>).  First, a mixture distribution is fitted
on the data using a sparse finite mixture (SFM) Markov chain Monte Carlo (MCMC)
algorithm.  The number of mixture components does not have to be known; the size
of the mixture is estimated endogenously through the SFM approach.  Second, the
modes of the estimated mixture at each MCMC draw are retrieved using algorithms
specifically tailored for mode detection.  These estimates are then used to
construct posterior probabilities for the number of modes, their locations and
uncertainties, providing a powerful tool for mode inference.")
    (license license:gpl3+)))

(define-public r-bayesmrm
  (package
    (name "r-bayesmrm")
    (version "2.4.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesMRM" version))
       (sha256
        (base32 "1qdc4y0l6v4axp47pnxcnq96hl7kbi9r4jiknd5i934smcr2bsza"))))
    (properties `((upstream-name . "bayesMRM")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinythemes
                             r-shiny
                             r-rjags
                             r-rgl
                             r-gridextra
                             r-ggplot2
                             r-coda))
    (home-page "https://cran.r-project.org/package=bayesMRM")
    (synopsis "Bayesian Multivariate Receptor Modeling")
    (description
     "Bayesian analysis of multivariate receptor modeling.  The package consists of
implementations of the methods of Park and Oh (2015)
<doi:10.1016/j.chemolab.2015.08.021>.The package uses JAGS'(Just Another Gibbs
Sampler) to generate Markov chain Monte Carlo samples of parameters.")
    (license license:gpl2+)))

(define-public r-bayesmove
  (package
    (name "r-bayesmove")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesmove" version))
       (sha256
        (base32 "1l6i8qh7l53x0133gjc5jvri4vnnfviq9y3mcr1kfa5j84gfnmsf"))))
    (properties `((upstream-name . "bayesmove")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tictoc
                             r-shiny
                             r-sf
                             r-rlang
                             r-rcpparmadillo
                             r-rcpp
                             r-purrr
                             r-progressr
                             r-progress
                             r-mcmcpack
                             r-magrittr
                             r-lubridate
                             r-leaflet
                             r-ggplot2
                             r-future
                             r-furrr
                             r-dygraphs
                             r-dplyr))
    (home-page "https://github.com/joshcullen/bayesmove")
    (synopsis "Non-Parametric Bayesian Analyses of Animal Movement")
    (description
     "This package provides methods for assessing animal movement from telemetry and
biologging data using non-parametric Bayesian methods.  This includes features
for pre- processing and analysis of data, as well as the visualization of
results from the models.  This framework does not rely on standard parametric
density functions, which provides flexibility during model fitting.  Further
details regarding part of this framework can be found in Cullen et al. (2021)
<doi:10.1101/2020.11.05.369702>.")
    (license license:gpl3)))

(define-public r-bayesmortalityplus
  (package
    (name "r-bayesmortalityplus")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesMortalityPlus" version))
       (sha256
        (base32 "15g87wpaj7rsm8dnz7w57vw9bpkj0aj5f6hi8v7m8kamki8m7chm"))))
    (properties `((upstream-name . "BayesMortalityPlus")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-scales
                             r-progress
                             r-mvtnorm
                             r-mass
                             r-magrittr
                             r-ggplot2
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=BayesMortalityPlus")
    (synopsis "Bayesian Mortality Modelling")
    (description
     "Fit Bayesian graduation mortality using the Heligman-Pollard model, as seen in
Heligman, L., & Pollard, J. H. (1980) <doi:10.1017/S0020268100040257> and
Dellaportas, Petros, et al. (2001) <doi:10.1111/1467-985X.00202>, and dynamic
linear model (Campagnoli, P., Petris, G., and Petrone, S. (2009)
<doi:10.1007/b135794_2>).  While Heligman-Pollard has parameters with a
straightforward interpretation yielding some rich analysis, the dynamic linear
model provides a very flexible adjustment of the mortality curves by controlling
the discount factor value.  Closing methods for both Heligman-Pollard and
dynamic linear model were also implemented according to Dodd, Erengul, et al.
(2018) <https://www.jstor.org/stable/48547511>.  The Bayesian Lee-Carter model
is also implemented to fit historical mortality tables time series to predict
the mortality in the following years and to do improvement analysis, as seen in
Lee, R. D., & Carter, L. R. (1992) <doi:10.1080/01621459.1992.10475265> and
Pedroza, C. (2006) <doi:10.1093/biostatistics/kxj024>.")
    (license license:gpl3)))

(define-public r-bayesmlogit
  (package
    (name "r-bayesmlogit")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesmlogit" version))
       (sha256
        (base32 "1958kyagp41ji9xlwr03g6yv4jrd68kzc7g2hm0wgjdrksifmwp8"))))
    (properties `((upstream-name . "bayesmlogit")))
    (build-system r-build-system)
    (propagated-inputs (list r-magrittr r-ggplot2 r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bayesmlogit")
    (synopsis
     "Multistate Life Table (MSLT) Methodology Based on Bayesian Approach")
    (description
     "Create life tables with a Bayesian approach, which can be very useful for
modelling a complex health process when considering multiple predisposing
factors and multiple coexisting health conditions.  Details for this method can
be found in: Lynch, Scott, et al., (2022) <doi:10.1177/00811750221112398>; Zang,
Emma, et al., (2022) <doi:10.1093/geronb/gbab149>.")
    (license license:gpl3+)))

(define-public r-bayesmixsurv
  (package
    (name "r-bayesmixsurv")
    (version "0.9.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesMixSurv" version))
       (sha256
        (base32 "19kf39881q00pap9afwvvggk4s4w3qpz17b7065nig1mvk8dnp8r"))))
    (properties `((upstream-name . "BayesMixSurv")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival))
    (home-page "https://cran.r-project.org/package=BayesMixSurv")
    (synopsis
     "Bayesian Mixture Survival Models using Additive Mixture-of-Weibull Hazards, with Lasso Shrinkage and Stratification")
    (description
     "Bayesian Mixture Survival Models using Additive Mixture-of-Weibull Hazards, with
Lasso Shrinkage and Stratification.  As a Bayesian dynamic survival model, it
relaxes the proportional-hazard assumption.  Lasso shrinkage controls
overfitting, given the increase in the number of free parameters in the model
due to presence of two Weibull components in the hazard function.")
    (license license:gpl2+)))

(define-public r-bayesmix
  (package
    (name "r-bayesmix")
    (version "0.7-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesmix" version))
       (sha256
        (base32 "0gzzzgjbz4aw2zrd5grvd1r0zlycxkgyq4hj1mcbwkfs2dd00msk"))))
    (properties `((upstream-name . "bayesmix")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjags r-coda))
    (home-page "https://statmath.wu.ac.at/~gruen/BayesMix/")
    (synopsis "Bayesian Mixture Models with JAGS")
    (description
     "Fits finite mixture models of univariate Gaussian distributions using JAGS
within a Bayesian framework.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-bayesmig
  (package
    (name "r-bayesmig")
    (version "0.4-6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesMig" version))
       (sha256
        (base32 "1b4pxg22fx898s529gigdfcdsmzlrpqhpnq96lghvsvmv5802z4n"))))
    (properties `((upstream-name . "bayesMig")))
    (build-system r-build-system)
    (propagated-inputs (list r-wpp2019 r-truncnorm r-coda r-bayestfr))
    (home-page "http://bayespop.csss.washington.edu")
    (synopsis "Bayesian Projection of Migration")
    (description
     "Producing probabilistic projections of net migration rate for all countries of
the world or for subnational units using a Bayesian hierarchical model by Azose
an Raftery (2015) <doi:10.1007/s13524-015-0415-0>.")
    (license license:gpl2+)))

(define-public r-bayesmfsurv
  (package
    (name "r-bayesmfsurv")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesMFSurv" version))
       (sha256
        (base32 "1yxxzwjh1fgfh7qkzf9dfm3x0vbgb0v4p6bf6b5w14kvm06rzhkg"))))
    (properties `((upstream-name . "BayesMFSurv")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-mvtnorm
                             r-mcmcpack
                             r-fastgp
                             r-coda))
    (home-page "https://cran.r-project.org/package=BayesMFSurv")
    (synopsis "Bayesian Misclassified-Failure Survival Model")
    (description
     "This package contains a split population survival estimator that models the
misclassification probability of failure versus right-censored events.  The
split population survival estimator is described in Bagozzi et al. (2019)
<doi:10.1017/pan.2019.6>.")
    (license license:expat)))

(define-public r-bayesmeta
  (package
    (name "r-bayesmeta")
    (version "3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesmeta" version))
       (sha256
        (base32 "1w9xz0wix4nggp6d1az8i9m1r7s288n1cksnnrdan0f3lpk5aai4"))))
    (properties `((upstream-name . "bayesmeta")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv r-mvtnorm r-metafor r-forestplot))
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "https://gitlab.gwdg.de/croever/bayesmeta")
    (synopsis "Bayesian Random-Effects Meta-Analysis and Meta-Regression")
    (description
     "This package provides a collection of functions allowing to derive the posterior
distribution of the model parameters in random-effects meta-analysis or
meta-regression, and providing functionality to evaluate joint and marginal
posterior probability distributions, predictive distributions, shrinkage
effects, posterior predictive p-values, etc.; For more details, see also Roever
C (2020) <doi:10.18637/jss.v093.i06>, or Roever C and Friede T (2022)
<doi:10.1016/j.cmpb.2022.107303>.")
    (license license:gpl2+)))

(define-public r-bayesmeanscale
  (package
    (name "r-bayesmeanscale")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesMeanScale" version))
       (sha256
        (base32 "0zkfv6f50z83d8iih2f9p0dkla2bffcfjhfbmpicybicfi35f480"))))
    (properties `((upstream-name . "bayesMeanScale")))
    (build-system r-build-system)
    (propagated-inputs (list r-posterior r-magrittr r-data-table r-bayestestr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/dalenbe2/bayesMeanScale")
    (synopsis "Bayesian Post-Estimation on the Mean Scale")
    (description
     "Computes Bayesian posterior distributions of predictions, marginal effects, and
differences of marginal effects for various generalized linear models.
Importantly, the posteriors are on the mean (response) scale, allowing for more
natural interpretation than summaries on the link scale.  Also, predictions and
marginal effects of the count probabilities for Poisson and negative binomial
models can be computed.")
    (license license:gpl3+)))

(define-public r-bayesmallows
  (package
    (name "r-bayesmallows")
    (version "2.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesMallows" version))
       (sha256
        (base32 "1jgv654k0qkspardlma2z7a854zw7zxk4fflc027wf7mlvs29b4h"))))
    (properties `((upstream-name . "BayesMallows")))
    (build-system r-build-system)
    (propagated-inputs (list r-testthat
                             r-sets
                             r-rlang
                             r-relations
                             r-rdpack
                             r-rcpparmadillo
                             r-rcpp
                             r-ggplot2))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/ocbe-uio/BayesMallows")
    (synopsis "Bayesian Preference Learning with the Mallows Rank Model")
    (description
     "An implementation of the Bayesian version of the Mallows rank model (Vitelli et
al., Journal of Machine Learning Research, 2018
<https://jmlr.org/papers/v18/15-481.html>; Crispino et al., Annals of Applied
Statistics, 2019 <doi:10.1214/18-AOAS1203>; Sorensen et al., R Journal, 2020
<doi:10.32614/RJ-2020-026>; Stein, @code{PhD} Thesis, 2023
<https://eprints.lancs.ac.uk/id/eprint/195759>).  Both Metropolis-Hastings and
sequential Monte Carlo algorithms for estimating the models are available.
Cayley, footrule, Hamming, Kendall, Spearman, and Ulam distances are supported
in the models.  The rank data to be analyzed can be in the form of complete
rankings, top-k rankings, partially missing rankings, as well as consistent and
inconsistent pairwise preferences.  Several functions for plotting and studying
the posterior distributions of parameters are provided.  The package also
provides functions for estimating the partition function (normalizing constant)
of the Mallows rank model, both with the importance sampling algorithm of
Vitelli et al.  and asymptotic approximation with the IPFP algorithm (Mukherjee,
Annals of Statistics, 2016 <doi:10.1214/15-AOS1389>).")
    (license license:gpl3)))

(define-public r-bayeslongitudinal
  (package
    (name "r-bayeslongitudinal")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayeslongitudinal" version))
       (sha256
        (base32 "0g45ikpnbry1albb3asrzab5z3sy98yf74c64qn02d65xgafifwg"))))
    (properties `((upstream-name . "bayeslongitudinal")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-mass r-learnbayes))
    (home-page "https://cran.r-project.org/package=bayeslongitudinal")
    (synopsis
     "Adjust Longitudinal Regression Models Using Bayesian Methodology")
    (description
     "Adjusts longitudinal regression models using Bayesian methodology for covariance
structures of composite symmetry (SC), autoregressive ones of order 1 AR (1) and
autoregressive moving average of order (1,1) ARMA (1,1).")
    (license license:gpl2+)))

(define-public r-bayeslogit
  (package
    (name "r-bayeslogit")
    (version "2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesLogit" version))
       (sha256
        (base32 "0w3bzk39sxdskwikcz5i0xwfsll4sqgkpr8vw8jvzlcy6dl3yhis"))))
    (properties `((upstream-name . "BayesLogit")))
    (build-system r-build-system)
    (home-page "https://github.com/jwindle/BayesLogit")
    (synopsis "PolyaGamma Sampling")
    (description
     "This package provides tools for sampling from the @code{PolyaGamma} distribution
based on Polson, Scott, and Windle (2013) <doi:10.1080/01621459.2013.829001>.
Useful for logistic regression.")
    (license license:gpl3+)))

(define-public r-bayesln
  (package
    (name "r-bayesln")
    (version "0.2.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesLN" version))
       (sha256
        (base32 "0p8g0h4zqz5njr7lkkv2hhq9qas9lvrxamg8xb9hkqrxfxd0d3nn"))))
    (properties `((upstream-name . "BayesLN")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen
                             r-rcpp
                             r-optimx
                             r-matrix
                             r-mass
                             r-lme4
                             r-gsl
                             r-generalizedhyperbolic
                             r-data-table
                             r-coda))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BayesLN")
    (synopsis "Bayesian Inference for Log-Normal Data")
    (description
     "Bayesian inference under log-normality assumption must be performed very
carefully.  In fact, under the common priors for the variance, useful quantities
in the original data scale (like mean and quantiles) do not have posterior
moments that are finite (Fabrizi et al.  2012 <doi:10.1214/12-BA733>).  This
package allows to easily carry out a proper Bayesian inferential procedure by
fixing a suitable distribution (the generalized inverse Gaussian) as prior for
the variance.  Functions to estimate several kind of means (unconditional,
conditional and conditional under a mixed model) and quantiles (unconditional
and conditional) are provided.")
    (license license:gpl3)))

(define-public r-bayeslm
  (package
    (name "r-bayeslm")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayeslm" version))
       (sha256
        (base32 "0mnzxzdz456qxfszl8vhnvb1875hxn8b57wi6dhr9g668da2zdin"))))
    (properties `((upstream-name . "bayeslm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppparallel r-rcpparmadillo r-rcpp r-coda))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/JingyuHe/bayeslm")
    (synopsis
     "Efficient Sampling for Gaussian Linear Regression with Arbitrary Priors")
    (description
     "Efficient sampling for Gaussian linear regression with arbitrary priors, Hahn,
He and Lopes (2018) <@code{arXiv:1806.05738>}.")
    (license license:lgpl2.0+)))

(define-public r-bayeslife
  (package
    (name "r-bayeslife")
    (version "5.2-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesLife" version))
       (sha256
        (base32 "116wa3sqxkp8ywc0s83clg1pp81bf1rhddpvjbcw9pyim57h4kxm"))))
    (properties `((upstream-name . "bayesLife")))
    (build-system r-build-system)
    (propagated-inputs (list r-wpp2019
                             r-hett
                             r-data-table
                             r-coda
                             r-car
                             r-bayestfr))
    (home-page "https://bayespop.csss.washington.edu")
    (synopsis "Bayesian Projection of Life Expectancy")
    (description
     "Making probabilistic projections of life expectancy for all countries of the
world, using a Bayesian hierarchical model <doi:10.1007/s13524-012-0193-x>.
Subnational projections are also supported.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-bayeslca
  (package
    (name "r-bayeslca")
    (version "1.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesLCA" version))
       (sha256
        (base32 "11ji5s1njip57zkr4bxwp3agx1ddv7yagm5prn9ckcbasan4rbx1"))))
    (properties `((upstream-name . "BayesLCA")))
    (build-system r-build-system)
    (propagated-inputs (list r-nlme r-mcmcpack r-fields r-e1071 r-coda))
    (home-page "https://cran.r-project.org/package=BayesLCA")
    (synopsis "Bayesian Latent Class Analysis")
    (description
     "Bayesian Latent Class Analysis using several different methods.")
    (license license:gpl2+)))

(define-public r-bayesimages
  (package
    (name "r-bayesimages")
    (version "0.6-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesImageS" version))
       (sha256
        (base32 "0hz6ynvrhsxp097zswdlascza5n27cq4hfaqdc3r8b7crmh7lrim"))))
    (properties `((upstream-name . "bayesImageS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (native-inputs (list r-knitr))
    (home-page "https://bitbucket.org/Azeari/bayesimages")
    (synopsis "Bayesian Methods for Image Segmentation using a Potts Model")
    (description
     "Various algorithms for segmentation of 2D and 3D images, such as computed
tomography and satellite remote sensing.  This package implements Bayesian image
analysis using the hidden Potts model with external field prior of Moores et al.
(2015) <doi:10.1016/j.csda.2014.12.001>.  Latent labels are sampled using
chequerboard updating or Swendsen-Wang.  Algorithms for the smoothing parameter
include pseudolikelihood, path sampling, the exchange algorithm, approximate
Bayesian computation (ABC-MCMC and ABC-SMC), and the parametric functional
approximate Bayesian (PFAB) algorithm.  Refer to
<doi:10.1007/978-3-030-42553-1_6> for an overview and also to
<doi:10.1007/s11222-014-9525-6> and <doi:10.1214/18-BA1130> for further details
of specific algorithms.")
    (license (list license:gpl2+
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-bayesianvars
  (package
    (name "r-bayesianvars")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesianVARs" version))
       (sha256
        (base32 "1c9m45b6szcxy1igi0vs9gpnxyn23a6s376zhl3m72c7x5ghyhz6"))))
    (properties `((upstream-name . "bayesianVARs")))
    (build-system r-build-system)
    (propagated-inputs (list r-stochvol
                             r-scales
                             r-rcppprogress
                             r-rcpparmadillo
                             r-rcpp
                             r-mvtnorm
                             r-mass
                             r-gigrvg
                             r-factorstochvol
                             r-colorspace))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/luisgruber/bayesianVARs")
    (synopsis "MCMC Estimation of Bayesian Vectorautoregressions")
    (description
     "Efficient Markov Chain Monte Carlo (MCMC) algorithms for the fully Bayesian
estimation of vectorautoregressions (VARs) featuring stochastic volatility (SV).
 Implements state-of-the-art shrinkage priors following Gruber & Kastner (2023)
<doi:10.48550/@code{arXiv.2206.04902>}.  Efficient equation-per-equation
estimation following Kastner & Huber (2020) <doi:10.1002/for.2680> and Carrerio
et al. (2021) <doi:10.1016/j.jeconom.2021.11.010>.")
    (license license:gpl3+)))

(define-public r-bayesiantreg
  (package
    (name "r-bayesiantreg")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Bayesiantreg" version))
       (sha256
        (base32 "1bg6dqvpmsx4pnp0bv16m1rpgz68yjmcw8c832xn649fyk5dmanx"))))
    (properties `((upstream-name . "Bayesiantreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-matrix r-mass))
    (home-page "https://cran.r-project.org/package=Bayesiantreg")
    (synopsis "Bayesian t Regression for Modeling Mean and Scale Parameters")
    (description
     "This package performs Bayesian t Regression where mean and scale parameters are
modeling by lineal regression structures, and the degrees of freedom parameters
are estimated.")
    (license license:gpl2+)))

(define-public r-bayesiantools
  (package
    (name "r-bayesiantools")
    (version "0.1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesianTools" version))
       (sha256
        (base32 "1ldx75g4ws2yrnq4xyj3j83xjhakhgbhn174lwqzviqynvbbshzm"))))
    (properties `((upstream-name . "BayesianTools")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmvtnorm
                             r-rcpp
                             r-numderiv
                             r-mvtnorm
                             r-msm
                             r-matrix
                             r-mass
                             r-idpmisc
                             r-gap
                             r-emulator
                             r-ellipse
                             r-dharma
                             r-coda
                             r-bridgesampling))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/florianhartig/BayesianTools")
    (synopsis
     "General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics")
    (description
     "General-purpose MCMC and SMC samplers, as well as plot and diagnostic functions
for Bayesian statistics, with a particular focus on calibrating complex system
models.  Implemented samplers include various Metropolis MCMC variants
(including adaptive and/or delayed rejection MH), the T-walk, two differential
evolution MCMCs, two DREAM MCMCs, and a sequential Monte Carlo (SMC) particle
filter.")
    (license license:gpl3)))

(define-public r-bayesianreasoning
  (package
    (name "r-bayesianreasoning")
    (version "0.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesianReasoning" version))
       (sha256
        (base32 "0bl0crj9z0grgfsqzc403jd74f411ib827gkfqis0hbdi5y57gyw"))))
    (properties `((upstream-name . "BayesianReasoning")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-scales
                             r-reshape2
                             r-png
                             r-magrittr
                             r-gt
                             r-ggtext
                             r-ggplot2
                             r-ggforce
                             r-dplyr
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/gorkang/BayesianReasoning")
    (synopsis "Plot Positive and Negative Predictive Values for Medical Tests")
    (description
     "This package provides functions to plot and help understand positive and
negative predictive values (PPV and NPV), and their relationship with
sensitivity, specificity, and prevalence.  See Akobeng, A.K. (2007)
<doi:10.1111/j.1651-2227.2006.00180.x> for a theoretical overview of the
technical concepts and Navarrete et al. (2015) for a practical explanation about
the importance of their understanding <doi:10.3389/fpsyg.2015.01327>.")
    (license license:cc0)))

(define-public r-bayesianpower
  (package
    (name "r-bayesianpower")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesianPower" version))
       (sha256
        (base32 "00rn3wmqrhk2pssbq5j2prl8m6cgfarr49672pnksp2dcbi47p20"))))
    (properties `((upstream-name . "BayesianPower")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BayesianPower")
    (synopsis
     "Sample Size and Power for Comparing Inequality Constrained Hypotheses")
    (description
     "This package provides a collection of methods to determine the required sample
size for the evaluation of inequality constrained hypotheses by means of a Bayes
factor.  Alternatively, for a given sample size, the unconditional error
probabilities or the expected conditional error probabilities can be determined.
 Additional material on the methods in this package is available in Klaassen,
F., Hoijtink, H. & Gu, X. (2019) <doi:10.31219/osf.io/d5kf3>.")
    (license license:lgpl3)))

(define-public r-bayesianplatformdesigntimetrend
  (package
    (name "r-bayesianplatformdesigntimetrend")
    (version "1.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesianPlatformDesignTimeTrend" version))
       (sha256
        (base32 "1v06airw3zzkvb5qmn0abyhkdp51wjmax101ljj9ibx01nj01zdv"))))
    (properties `((upstream-name . "BayesianPlatformDesignTimeTrend")))
    (build-system r-build-system)
    (propagated-inputs (list r-stringr
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-reshape
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-rcolorbrewer
                             r-matrixstats
                             r-lhs
                             r-lagp
                             r-iterators
                             r-ggpubr
                             r-ggplot2
                             r-foreach
                             r-doparallel
                             r-boot
                             r-biocmanager
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ZXW834/BayesianPlatformDesignTimeTrend")
    (synopsis "Simulate and Analyse Bayesian Platform Trial with Time Trend")
    (description
     "Simulating the sequential multi-arm multi-stage or platform trial with Bayesian
approach using the rstan package, which provides the R interface for the Stan.
This package supports fixed ratio and Bayesian adaptive randomization approaches
for randomization.  Additionally, it allows for the study of time trend problems
in platform trials.  There are demos available for a multi-arm multi-stage trial
with two different null scenarios, as well as for Bayesian trial cutoff
screening.  The Bayesian adaptive randomisation approaches are described in:
Trippa et al. (2012) <doi:10.1200/JCO.2011.39.8420> and Wathen et al. (2017)
<doi:10.1177/1740774517692302>.  The randomisation algorithm is described in:
Zhao W <doi:10.1016/j.cct.2015.06.008>.  The analysis methods of time trend
effect in platform trial are described in: Saville et al. (2022)
<doi:10.1177/17407745221112013> and Bofill Roig et al. (2022)
<doi:10.1186/s12874-022-01683-w>.")
    (license license:expat)))

(define-public r-bayesiannetwork
  (package
    (name "r-bayesiannetwork")
    (version "0.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesianNetwork" version))
       (sha256
        (base32 "1j0mgjib2v60sfjic8kli69lcbdkakp0fcgx2dhh7p1rq4xx88j1"))))
    (properties `((upstream-name . "BayesianNetwork")))
    (build-system r-build-system)
    (propagated-inputs (list r-shinywidgets
                             r-shinydashboard
                             r-shinyace
                             r-shiny
                             r-rintrojs
                             r-plotly
                             r-networkd3
                             r-lattice
                             r-heatmaply
                             r-bnlearn))
    (home-page "https://github.com/paulgovan/bayesiannetwork")
    (synopsis "Bayesian Network Modeling and Analysis")
    (description
     "This package provides a \"Shiny\"\" web application for creating interactive
Bayesian Network models, learning the structure and parameters of Bayesian
networks, and utilities for classic network analysis.")
    (license (license:fsdg-compatible "Apache License"))))

(define-public r-bayesianmediationa
  (package
    (name "r-bayesianmediationa")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesianMediationA" version))
       (sha256
        (base32 "1ygc0xhk7mk7pkqn0v9n23j1m75rr1r2ar30r386c1av3jh7krnw"))))
    (properties `((upstream-name . "BayesianMediationA")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-r2jags r-lattice r-gplots r-car))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BayesianMediationA")
    (synopsis "Bayesian Mediation Analysis")
    (description
     "We perform general mediation analysis in the Bayesian setting using the methods
described in Yu and Li (2022, ISBN:9780367365479).  With the package, the
mediation analysis can be performed on different types of outcomes (e.g.,
continuous, binary, categorical, or time-to-event), with default or user-defined
priors and predictive models.  The Bayesian estimates and credible sets of
mediation effects are reported as analytic results.")
    (license license:gpl2+)))

(define-public r-bayesianmcpmod
  (package
    (name "r-bayesianmcpmod")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesianMCPMod" version))
       (sha256
        (base32 "1767rjyd5vl73lqknpxkgcv38rhxdm837zr7c3d5d6f9rdh8rgyw"))))
    (properties `((upstream-name . "BayesianMCPMod")))
    (build-system r-build-system)
    (propagated-inputs (list r-rbest r-nloptr r-ggplot2 r-dosefinding
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Boehringer-Ingelheim/BayesianMCPMod")
    (synopsis
     "Simulate, Evaluate, and Analyze Dose Finding Trials with Bayesian MCPMod")
    (description
     "Bayesian MCPMod (Fleischer et al. (2022) <doi:10.1002/pst.2193>) is an
innovative method that improves the traditional MCPMod by systematically
incorporating historical data, such as previous placebo group data.  This R
package offers functions for simulating, analyzing, and evaluating Bayesian
MCPMod trials with normally distributed endpoints.  It enables the assessment of
trial designs incorporating historical data across various true dose-response
relationships and sample sizes.  Robust mixture prior distributions, such as
those derived with the Meta-Analytic-Predictive approach (Schmidli et al. (2014)
<doi:10.1111/biom.12242>), can be specified for each dose group.  Resulting
mixture posterior distributions are used in the Bayesian Multiple Comparison
Procedure and modeling steps.  The modeling step also includes a weighted model
averaging approach (Pinheiro et al. (2014) <doi:10.1002/sim.6052>).  Estimated
dose-response relationships can be bootstrapped and visualized.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-bayesianlaterality
  (package
    (name "r-bayesianlaterality")
    (version "0.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesianLaterality" version))
       (sha256
        (base32 "1dy00xgz9cc65aj39nf76crhfxfyackpcniwadrh38mk8zpbka42"))))
    (properties `((upstream-name . "BayesianLaterality")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmvtnorm
                             r-tidyr
                             r-rlang
                             r-rdpack
                             r-purrr
                             r-dplyr))
    (home-page "https://github.com/LCBC-UiO/BayesianLaterality")
    (synopsis
     "Predict Brain Asymmetry Based on Handedness and Dichotic Listening")
    (description
     "Functional differences between the cerebral hemispheres are a fundamental
characteristic of the human brain.  Researchers interested in studying these
differences often infer underlying hemispheric dominance for a certain function
(e.g., language) from laterality indices calculated from observed performance or
brain activation measures .  However, any inference from observed measures to
latent (unobserved) classes has to consider the prior probability of class
membership in the population.  The provided functions implement a Bayesian model
for predicting hemispheric dominance from observed laterality indices (Sorensen
and Westerhausen, Laterality: Asymmetries of Body, Brain and Cognition, 2020,
<doi:10.1080/1357650X.2020.1769124>).")
    (license license:gpl3)))

(define-public r-bayesianglasso
  (package
    (name "r-bayesianglasso")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesianGLasso" version))
       (sha256
        (base32 "09yb1qqx6qlsspk3ndrcqxy0956iqznw0rmyvqxgxxp3zd3y21xp"))))
    (properties `((upstream-name . "BayesianGLasso")))
    (build-system r-build-system)
    (propagated-inputs (list r-statmod r-mass))
    (home-page "https://cran.r-project.org/package=BayesianGLasso")
    (synopsis "Bayesian Graphical Lasso")
    (description
     "This package implements a data-augmented block Gibbs sampler for simulating the
posterior distribution of concentration matrices for specifying the topology and
parameterization of a Gaussian Graphical Model (GGM).  This sampler was
originally proposed in Wang (2012) <doi:10.1214/12-BA729>.")
    (license license:gpl3)))

(define-public r-bayesiangammareg
  (package
    (name "r-bayesiangammareg")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Bayesiangammareg" version))
       (sha256
        (base32 "0b0fqmig7vgmlrq2884q0apfd89nn5k4dj8rqjizkkbw4qpzmylb"))))
    (properties `((upstream-name . "Bayesiangammareg")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm))
    (home-page "https://www.r-project.org")
    (synopsis "Bayesian Gamma Regression: Joint Mean and Shape Modeling")
    (description
     "Adjust the Gamma regression models from a Bayesian perspective described by
Cepeda and Urdinola (2012) <doi:10.1080/03610918.2011.600500>, modeling the
parameters of mean and shape and using different link functions for the
parameter associated to the mean.  And calculates different adjustment
statistics such as the Akaike information criterion and Bayesian information
criterion.")
    (license license:gpl2+)))

(define-public r-bayesianfactorzoo
  (package
    (name "r-bayesianfactorzoo")
    (version "0.0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesianFactorZoo" version))
       (sha256
        (base32 "026mfyfx8dzj1y97v7gi0kx3iq2fnrlyjd3pcszz1324fi87rpa5"))))
    (properties `((upstream-name . "BayesianFactorZoo")))
    (build-system r-build-system)
    (propagated-inputs (list r-timeseries
                             r-reshape2
                             r-rdpack
                             r-nse
                             r-mvtnorm
                             r-mcmcpack
                             r-matrixcalc
                             r-matrix
                             r-mass
                             r-ggplot2
                             r-coda))
    (home-page "https://cran.r-project.org/package=BayesianFactorZoo")
    (synopsis
     "Bayesian Solutions for the Factor Zoo: We Just Ran Two Quadrillion Models")
    (description
     "This package contains the functions to use the econometric methods in the paper
Bryzgalova, Huang, and Julliard (2023) <doi:10.1111/jofi.13197>.  In this
package, we provide a novel Bayesian framework for analyzing linear asset
pricing models: simple, robust, and applicable to high-dimensional problems.
For a stand-alone model, we provide functions including @code{BayesianFM}() and
@code{BayesianSDF}() to deliver reliable price of risk estimates for both
tradable and nontradable factors.  For competing factors and possibly nonnested
models, we provide functions including continuous_ss_sdf(),
continuous_ss_sdf_v2(), and dirac_ss_sdf_pvalue() to analyze high-dimensional
models.  If you use this package, please cite the paper.  We are thankful to
Yunan Ding and Jingtong Zhang for their research assistance.  Any errors or
omissions are the responsibility of the authors.")
    (license license:gpl3)))

(define-public r-bayesianetas
  (package
    (name "r-bayesianetas")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesianETAS" version))
       (sha256
        (base32 "0nbif0b6lcik2kh948zg5ska5mvkdsfr0dg8ndnfpscm2mp7y1dg"))))
    (properties `((upstream-name . "bayesianETAS")))
    (build-system r-build-system)
    (home-page "http://www.gordonjross.co.uk/bayesianetas.pdf")
    (synopsis
     "Bayesian Estimation of the ETAS Model for Earthquake Occurrences")
    (description
     "The Epidemic Type Aftershock Sequence (ETAS) model is one of the best-performing
methods for modeling and forecasting earthquake occurrences.  This package
implements Bayesian estimation routines to draw samples from the full posterior
distribution of the model parameters, given an earthquake catalog.  The paper on
which this package is based is Gordon J. Ross - Bayesian Estimation of the ETAS
Model for Earthquake Occurrences (2016), available from the below URL.")
    (license license:gpl3)))

(define-public r-bayesian
  (package
    (name "r-bayesian")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesian" version))
       (sha256
        (base32 "17d08700gwijpzv325pp7pndrz4mv1j1c4hll8s7ry15s1z82fmf"))))
    (properties `((upstream-name . "bayesian")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble
                             r-rlang
                             r-purrr
                             r-parsnip
                             r-dplyr
                             r-brms))
    (native-inputs (list r-knitr))
    (home-page "https://hsbadr.github.io/bayesian/")
    (synopsis "Bindings for Bayesian TidyModels")
    (description
     "Fit Bayesian models using brms'/'Stan with parsnip'/'tidymodels via bayesian
<doi:10.5281/zenodo.4426836>.  tidymodels is a collection of packages for
machine learning; see Kuhn and Wickham (2020) <https://www.tidymodels.org>).
The technical details of brms and Stan are described in BÃ¼rkner (2017)
<doi:10.18637/jss.v080.i01>, BÃ¼rkner (2018) <doi:10.32614/RJ-2018-017>, and
Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.")
    (license license:expat)))

(define-public r-bayesgwqs
  (package
    (name "r-bayesgwqs")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesGWQS" version))
       (sha256
        (base32 "1vxln01194ipfhzid48x95hrpzkwylhr3nx1dh38rkfsg9fpl2af"))))
    (properties `((upstream-name . "BayesGWQS")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-stringr r-rjags r-plyr r-coda))
    (home-page "https://cran.r-project.org/package=BayesGWQS")
    (synopsis "Bayesian Grouped Weighted Quantile Sum Regression")
    (description
     "Fits Bayesian grouped weighted quantile sum (BGWQS) regressions for one or more
chemical groups with binary outcomes.  Wheeler DC et al. (2019)
<doi:10.1016/j.sste.2019.100286>.")
    (license license:gpl3)))

(define-public r-bayesgrowth
  (package
    (name "r-bayesgrowth")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesGrowth" version))
       (sha256
        (base32 "0snjl40x8h8v8y6wm8lby4h4wm78qgbaq2jx45s96lz9cmfhdz2c"))))
    (properties `((upstream-name . "BayesGrowth")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidybayes
                             r-tibble
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-loo
                             r-ggplot2
                             r-dplyr
                             r-bh
                             r-bayesplot
                             r-aquaticlifehistory))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jonathansmart/BayesGrowth")
    (synopsis "Estimate Fish Growth Using MCMC Analysis")
    (description
     "Estimate fish length-at-age models using MCMC analysis with rstan models.  This
package allows a multimodel approach to growth fitting to be applied to
length-at-age data and is supported by further analyses to determine model
selection and result presentation.  The core methods of this package are
presented in Smart and Grammer (2021) \"Modernising fish and shark growth curves
with Bayesian length-at-age models\".  PLOS ONE 16(2): e0246734
<doi:10.1371/journal.pone.0246734>.")
    (license license:gpl3)))

(define-public r-bayesgpfit
  (package
    (name "r-bayesgpfit")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesGPfit" version))
       (sha256
        (base32 "08ayk6cxhqwymmr559rrmg35s2i2kbvrxd408vy6xrsyvidjppi5"))))
    (properties `((upstream-name . "BayesGPfit")))
    (build-system r-build-system)
    (propagated-inputs (list r-lattice))
    (home-page "https://cran.r-project.org/package=BayesGPfit")
    (synopsis "Fast Bayesian Gaussian Process Regression Fitting")
    (description
     "Bayesian inferences on nonparametric regression via Gaussian Processes with a
modified exponential square kernel using a basis expansion approach.")
    (license license:gpl2)))

(define-public r-bayesgof
  (package
    (name "r-bayesgof")
    (version "5.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesGOF" version))
       (sha256
        (base32 "17wm0228xq3db431abyi9jr0q1q86nnajsn7hmy4dwzgvrlz5s0i"))))
    (properties `((upstream-name . "BayesGOF")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam r-orthopolynom r-nleqslv r-bolstad2))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BayesGOF")
    (synopsis "Bayesian Modeling via Frequentist Goodness-of-Fit")
    (description
     "This package provides a Bayesian data modeling scheme that performs four
interconnected tasks: (i) characterizes the uncertainty of the elicited
parametric prior; (ii) provides exploratory diagnostic for checking prior-data
conflict; (iii) computes the final statistical prior density estimate; and (iv)
executes macro- and micro-inference.  Primary reference is Mukhopadhyay, S. and
Fletcher, D. 2018 paper \"Generalized Empirical Bayes via Frequentist Goodness of
Fit\" (<https://www.nature.com/articles/s41598-018-28130-5 >).")
    (license license:gpl2)))

(define-public r-bayesgmed
  (package
    (name "r-bayesgmed")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesGmed" version))
       (sha256
        (base32 "1bfrvrpf0qiwy720hjrmp007z7xd7ylpl68v9gr2g0i4mjz3szdi"))))
    (properties `((upstream-name . "BayesGmed")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BayesGmed")
    (synopsis "Bayesian Causal Mediation Analysis using 'Stan'")
    (description
     "This package performs parametric mediation analysis using the Bayesian g-formula
approach for binary and continuous outcomes.  The methodology is based on
Comment (2018) <doi:10.5281/zenodo.1285275> and a demonstration of its
application can be found at Yimer et al. (2022)
<doi:10.48550/@code{arXiv.2210.08499>}.")
    (license license:expat)))

(define-public r-bayesgarch
  (package
    (name "r-bayesgarch")
    (version "2.1.10")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesGARCH" version))
       (sha256
        (base32 "1jb7jfyxp210pc8myv9lnwb55hy19d39m10kkhs5lyjwc2lv0401"))))
    (properties `((upstream-name . "bayesGARCH")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-coda))
    (home-page "https://github.com/ArdiaD/bayesGARCH")
    (synopsis
     "Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations")
    (description
     "This package provides the @code{bayesGARCH}() function which performs the
Bayesian estimation of the GARCH(1,1) model with Student's t innovations as
described in Ardia (2008) <doi:10.1007/978-3-540-78657-3>.")
    (license license:gpl2+)))

(define-public r-bayesgam
  (package
    (name "r-bayesgam")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesGAM" version))
       (sha256
        (base32 "13zs68d26c66hkm8mfhcygk8rw6sdg4rf4a4az254lf2xvypbh5d"))))
    (properties `((upstream-name . "bayesGAM")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-semipar
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-mlbench
                             r-mass
                             r-loo
                             r-gridextra
                             r-ggplot2
                             r-geometry
                             r-corrplot
                             r-cluster
                             r-boot
                             r-bh
                             r-bayesplot))
    (home-page "https://cran.r-project.org/package=bayesGAM")
    (synopsis
     "Fit Multivariate Response Generalized Additive Models using Hamiltonian Monte Carlo")
    (description
     "The @code{bayesGAM} package is designed to provide a user friendly option to fit
univariate and multivariate response Generalized Additive Models (GAM) using
Hamiltonian Monte Carlo (HMC) with few technical burdens.  The functions in this
package use rstan (Stan Development Team 2020) to call Stan routines that run
the HMC simulations.  The Stan code for these models is already pre-compiled for
the user.  The programming formulation for models in @code{bayesGAM} is designed
to be familiar to analysts who fit statistical models in R'.  Carpenter, B.,
Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., ... &
Riddell, A. (2017).  Stan: A probabilistic programming language.  Journal of
statistical software, 76(1).  Stan Development Team.  2018.  RStan: the R
interface to Stan.  R package version 2.17.3. <https://mc-stan.org/> Neal,
Radford (2011) \"Handbook of Markov Chain Monte Carlo\" ISBN: 978-1420079418.
Betancourt, Michael, and Mark Girolami. \"Hamiltonian Monte Carlo for
hierarchical models.\" Current trends in Bayesian methodology with applications
79.30 (2015): 2-4.  Thomas, S., Tu, W. (2020) \"Learning Hamiltonian Monte Carlo
in R\" <@code{arXiv:2006.16194>}, Gelman, A., Carlin, J. B., Stern, H. S.,
Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) \"Bayesian Data Analysis\" ISBN:
978-1439840955, Agresti, Alan (2015) \"Foundations of Linear and Generalized
Linear Models ISBN: 978-1118730034, Pinheiro, J., Bates, D. (2006)
\"Mixed-effects Models in S and S-Plus\" ISBN: 978-1441903174.  Ruppert, D., Wand,
M. P., & Carroll, R. J. (2003).  Semiparametric regression (No.  12).  Cambridge
university press.  ISBN: 978-0521785167.")
    (license license:gpl3)))

(define-public r-bayesforecast
  (package
    (name "r-bayesforecast")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesforecast" version))
       (sha256
        (base32 "0wnz6qblm3ib7lpj860s4lhrs4gvmyrmk779r38rybfvxw4xssii"))))
    (properties `((upstream-name . "bayesforecast")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-prophet
                             r-mass
                             r-lubridate
                             r-loo
                             r-gridextra
                             r-ggplot2
                             r-forecast
                             r-bridgesampling
                             r-bh
                             r-bayesplot
                             r-astsa))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bayesforecast")
    (synopsis "Bayesian Time Series Modeling with Stan")
    (description
     "Fit Bayesian time series models using Stan for full Bayesian inference.  A wide
range of distributions and models are supported, allowing users to fit Seasonal
ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH
models, asymmetric GARCH, Random Walks, stochastic volatility models for
univariate time series.  Prior specifications are flexible and explicitly
encourage users to apply prior distributions that actually reflect their
beliefs.  Model fit can easily be assessed and compared with typical
visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes
factor and leave-one-out cross-validation methods.  References: Hyndman (2017)
<doi:10.18637/jss.v027.i03>; Carpenter et al. (2017)
<doi:10.18637/jss.v076.i01>.")
    (license license:gpl2)))

(define-public r-bayesfmri
  (package
    (name "r-bayesfmri")
    (version "0.3.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesfMRI" version))
       (sha256
        (base32 "19yn8d3jbh1fpxdmqvz3yflnml4fdxb966w44yqha4wjhzfp6hym"))))
    (properties `((upstream-name . "BayesfMRI")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp
                             r-rcppeigen
                             r-rcpp
                             r-matrixstats
                             r-matrix
                             r-mass
                             r-foreach
                             r-fmritools
                             r-excursions
                             r-ciftitools))
    (home-page "https://github.com/mandymejia/BayesfMRI")
    (synopsis "Spatial Bayesian Methods for Task Functional MRI Studies")
    (description
     "This package performs a spatial Bayesian general linear model (GLM) for task
functional magnetic resonance imaging (@code{fMRI}) data on the cortical
surface.  Additional models include group analysis and inference to detect
thresholded areas of activation.  Includes direct support for the CIFTI
neuroimaging file format.  For more information see A. F. Mejia, Y. R. Yue, D.
Bolin, F. Lindgren, M. A. Lindquist (2020) <doi:10.1080/01621459.2019.1611582>
and D. Spencer, Y. R. Yue, D. Bolin, S. Ryan, A. F. Mejia (2022)
<doi:10.1016/j.neuroimage.2022.118908>.")
    (license license:gpl3)))

(define-public r-bayesfm
  (package
    (name "r-bayesfm")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesFM" version))
       (sha256
        (base32 "1iiahwlzxizs6d2f59amfhhca3vh612y5n9ijzpr4fnhpm1q9df1"))))
    (properties `((upstream-name . "BayesFM")))
    (build-system r-build-system)
    (inputs (list gfortran))
    (propagated-inputs (list r-plyr r-gridextra r-ggplot2 r-coda r-checkmate))
    (native-inputs (list gfortran))
    (home-page "https://cran.r-project.org/package=BayesFM")
    (synopsis "Bayesian Inference for Factor Modeling")
    (description
     "Collection of procedures to perform Bayesian analysis on a variety of factor
models.  Currently, it includes: \"Bayesian Exploratory Factor Analysis\" (befa)
from G. Conti, S. FrÃ¼hwirth-Schnatter, J.J. Heckman, R. Piatek (2014)
<doi:10.1016/j.jeconom.2014.06.008>, an approach to dedicated factor analysis
with stochastic search on the structure of the factor loading matrix.  The
number of latent factors, as well as the allocation of the manifest variables to
the factors, are not fixed a priori but determined during MCMC sampling.")
    (license license:gpl3)))

(define-public r-bayesfluxr
  (package
    (name "r-bayesfluxr")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesFluxR" version))
       (sha256
        (base32 "0kj7hxj39k0k6njf24ldb6w10qdwambd8hm6xpjfa1brb807lwkj"))))
    (properties `((upstream-name . "BayesFluxR")))
    (build-system r-build-system)
    (propagated-inputs (list r-juliacall))
    (home-page "https://cran.r-project.org/package=BayesFluxR")
    (synopsis "Implementation of Bayesian Neural Networks")
    (description
     "Implementation of @code{BayesFlux.jl} for R; It extends the famous Flux.jl
machine learning library to Bayesian Neural Networks.  The goal is not to have
the fastest production ready library, but rather to allow more people to be able
to use and research on Bayesian Neural Networks.")
    (license license:expat)))

(define-public r-bayesfbhborrow
  (package
    (name "r-bayesfbhborrow")
    (version "2.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesFBHborrow" version))
       (sha256
        (base32 "0kaw4pmy2mjawsfs8s0h2m32p0cvg8wg33vfznpzvc40xymy2g4l"))))
    (properties `((upstream-name . "BayesFBHborrow")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-mvtnorm
                             r-magrittr
                             r-invgamma
                             r-ggplot2
                             r-dplyr
                             r-checkmate))
    (home-page "https://cran.r-project.org/package=BayesFBHborrow")
    (synopsis
     "Bayesian Dynamic Borrowing with Flexible Baseline Hazard Function")
    (description
     "Allows Bayesian borrowing from a historical dataset for time-to- event data.  A
flexible baseline hazard function is achieved via a piecewise exponential
likelihood with time varying split points and smoothing prior on the historic
baseline hazards.  The method is described in Scott and Lewin (2024)
<doi:10.48550/@code{arXiv.2401.06082>}, and the software paper is in Axillus et
al. (2024) <doi:10.48550/@code{arXiv.2408.04327>}.")
    (license (license:fsdg-compatible "Apache License (>= 2)"))))

(define-public r-bayesess
  (package
    (name "r-bayesess")
    (version "0.1.19")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesESS" version))
       (sha256
        (base32 "023r5pxqa2ll3vyfmchdlcg6ay0qixphz24078g1immqp43mlrxm"))))
    (properties `((upstream-name . "BayesESS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppeigen
                             r-rcpparmadillo
                             r-rcpp
                             r-mcmcpack
                             r-matrixmodels
                             r-mass
                             r-laplacesdemon
                             r-dfcrm))
    (home-page "https://cran.r-project.org/package=BayesESS")
    (synopsis "Determining Effective Sample Size")
    (description
     "Determines effective sample size of a parametric prior distribution in Bayesian
models.  For a web-based Shiny application related to this package, see
<https://implement.shinyapps.io/bayesess/>.")
    (license license:gpl2+)))

(define-public r-bayeseo
  (package
    (name "r-bayeseo")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesEO" version))
       (sha256
        (base32 "0zs4qqf8881x01wblzmnw7m3zmh4ifrim3a8ws454r2lxf4y7mj5"))))
    (properties `((upstream-name . "bayesEO")))
    (build-system r-build-system)
    (propagated-inputs (list r-yaml
                             r-tmap
                             r-tidyr
                             r-tibble
                             r-terra
                             r-stars
                             r-rcpparmadillo
                             r-rcpp
                             r-purrr
                             r-ggplot2
                             r-dplyr))
    (home-page "https://github.com/e-sensing/bayesEO/")
    (synopsis "Bayesian Smoothing of Remote Sensing Image Classification")
    (description
     "This package provides a Bayesian smoothing method for post-processing of remote
sensing image classification which refines the labelling in a classified image
in order to enhance its classification accuracy.  Combines pixel-based
classification methods with a spatial post-processing method to remove outliers
and misclassified pixels.")
    (license license:gpl3)))

(define-public r-bayesdp
  (package
    (name "r-bayesdp")
    (version "1.3.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesDP" version))
       (sha256
        (base32 "0li063187icw96shrbawfa00vkb42m5y38a4j2aahryajjlsva57"))))
    (properties `((upstream-name . "bayesDP")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rcpparmadillo r-rcpp r-mcmcpack
                             r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/graemeleehickey/bayesDP")
    (synopsis
     "Implementation of the Bayesian Discount Prior Approach for Clinical Trials")
    (description
     "This package provides functions for data augmentation using the Bayesian
discount prior method for single arm and two-arm clinical trials, as described
in Haddad et al. (2017) <doi:10.1080/10543406.2017.1300907>.  The discount power
prior methodology was developed in collaboration with the The Medical Device
Innovation Consortium (MDIC) Computer Modeling & Simulation Working Group.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-bayesdlmfmri
  (package
    (name "r-bayesdlmfmri")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesDLMfMRI" version))
       (sha256
        (base32 "06806wckw629gl6p8jbxpg8lh6wsy8nhs2017qcfc3wwxb6dd3jc"))))
    (properties `((upstream-name . "BayesDLMfMRI")))
    (build-system r-build-system)
    (propagated-inputs (list r-rdpack
                             r-rcppdist
                             r-rcpparmadillo
                             r-rcpp
                             r-pbapply
                             r-oro-nifti
                             r-neurobase
                             r-mathjaxr
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/JohnatanLAB/BayesDLMfMRI/")
    (synopsis "Statistical Analysis for Task-Based Fmri Data")
    (description
     "The @code{BayesDLMfMRI} package performs statistical analysis for task-based
functional magnetic resonance imaging (@code{fMRI}) data at both individual and
group levels.  The analysis to detect brain activation at the individual level
is based on modeling the @code{fMRI} signal using Matrix-Variate Dynamic Linear
Models (MDLM).  The analysis for the group stage is based on posterior
distributions of the state parameter obtained from the modeling at the
individual level.  In this way, this package offers several R functions with
different algorithms to perform inference on the state parameter to assess brain
activation for both individual and group stages.  Those functions allow for
parallel computation when the analysis is performed for the entire brain as well
as analysis at specific voxels when it is required.  References:
Cardona-JimÃ©nez (2021) <doi:10.1016/j.csda.2021.107297>; Cardona-JimÃ©nez
(2021) <@code{arXiv:2111.01318>}.")
    (license license:gpl2)))

(define-public r-bayesdistreg
  (package
    (name "r-bayesdistreg")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesdistreg" version))
       (sha256
        (base32 "04slvxzbqdi2ak3dlw4lfx55rhw28js8yjmvjpy8vvgq39vcx3dq"))))
    (properties `((upstream-name . "bayesdistreg")))
    (build-system r-build-system)
    (propagated-inputs (list r-sandwich r-mass))
    (home-page "https://cran.r-project.org/package=bayesdistreg")
    (synopsis "Bayesian Distribution Regression")
    (description
     "This package implements Bayesian Distribution Regression methods.  This package
contains functions for three estimators (non-asymptotic, semi-asymptotic and
asymptotic) and related routines for Bayesian Distribution Regression in Huang
and Tsyawo (2018) <doi:10.2139/ssrn.3048658> which is also the recommended
reference to cite for this package.  The functions can be grouped into three (3)
categories.  The first computes the logit likelihood function and posterior
densities under uniform and normal priors.  The second contains Independence and
Random Walk Metropolis-Hastings Markov Chain Monte Carlo (MCMC) algorithms as
functions and the third category of functions are useful for semi-asymptotic and
asymptotic Bayesian distribution regression inference.")
    (license license:gpl2)))

(define-public r-bayesdissolution
  (package
    (name "r-bayesdissolution")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesDissolution" version))
       (sha256
        (base32 "1x3jn4mdp7csxm6h8rk33h9hlflhkjssqnfzcl3hbkwv4pb97ifl"))))
    (properties `((upstream-name . "BayesDissolution")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny
                             r-pscl
                             r-mnormt
                             r-mcmcpack
                             r-geor
                             r-coda))
    (home-page "https://cran.r-project.org/package=BayesDissolution")
    (synopsis "Bayesian Models for Dissolution Testing")
    (description
     "Fits Bayesian models (amongst others) to dissolution data sets that can be used
for dissolution testing.  The package was originally constructed to include only
the Bayesian models outlined in Pourmohamad et al. (2022)
<doi:10.1111/rssc.12535>.  However, additional Bayesian and non-Bayesian models
(based on bootstrapping and generalized pivotal quanties) have also been added.
More models may be added over time.")
    (license license:gpl2)))

(define-public r-bayesdip
  (package
    (name "r-bayesdip")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesDIP" version))
       (sha256
        (base32 "05kz0qik76gk1v6ldhlbzmylpsv5zmbqbjgf9sc8c5zkyjdawgj8"))))
    (properties `((upstream-name . "BayesDIP")))
    (build-system r-build-system)
    (home-page "<https://github.com/chenw10/BayesDIP>")
    (synopsis
     "Bayesian Decreasingly Informative Priors for Early Termination Phase II Trials")
    (description
     "Provide early termination phase II trial designs with a decreasingly informative
prior (DIP) or a regular Bayesian prior chosen by the user.  The program can
determine the minimum planned sample size necessary to achieve the
user-specified admissible designs.  The program can also perform power and
expected sample size calculations for the tests in early termination Phase II
trials.  See Wang C and Sabo RT (2022) <doi:10.18203/2349-3259.ijct20221110>;
Sabo RT (2014) <doi:10.1080/10543406.2014.888441>.")
    (license license:gpl2+)))

(define-public r-bayesdfa
  (package
    (name "r-bayesdfa")
    (version "1.3.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesdfa" version))
       (sha256
        (base32 "1xmvpkg30nk4sldzy3rp12ivnj4phzdw08hmk5nh5b70mqapvkra"))))
    (properties `((upstream-name . "bayesdfa")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridislite
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rlang
                             r-reshape2
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-mgcv
                             r-loo
                             r-ggplot2
                             r-dplyr
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://fate-ewi.github.io/bayesdfa/")
    (synopsis "Bayesian Dynamic Factor Analysis (DFA) with 'Stan'")
    (description
     "This package implements Bayesian dynamic factor analysis with Stan'.  Dynamic
factor analysis is a dimension reduction tool for multivariate time series.
bayesdfa extends conventional dynamic factor models in several ways.  First,
extreme events may be estimated in the latent trend by modeling process error
with a student-t distribution.  Second, alternative constraints (including
proportions are allowed).  Third, the estimated dynamic factors can be analyzed
with hidden Markov models to evaluate support for latent regimes.")
    (license license:gpl3+)))

(define-public r-bayesdesign
  (package
    (name "r-bayesdesign")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesDesign" version))
       (sha256
        (base32 "1h69a18wp2z96a002x154d2vd1imzap3zm7yfssm5dh7igha18zc"))))
    (properties `((upstream-name . "BayesDesign")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BayesDesign")
    (synopsis "Bayesian Single-Arm Design with Survival Endpoints")
    (description
     "The proposed event-driven approach for Bayesian two-stage single-arm phase II
trial design is a novel clinical trial design and can be regarded as an
extension of the Simonâs two-stage design with the time-to-event endpoint.
This design is motivated by cancer clinical trials with immunotherapy and
molecularly targeted therapy, in which time-to-event endpoint is often a desired
endpoint.")
    (license license:gpl2)))

(define-public r-bayesdccgarch
  (package
    (name "r-bayesdccgarch")
    (version "3.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesDccGarch" version))
       (sha256
        (base32 "1wv36yv43m8fkpk59hfrw3672p95lzhapr0d4n3jynsfhf6ivpm6"))))
    (properties `((upstream-name . "bayesDccGarch")))
    (build-system r-build-system)
    (propagated-inputs (list r-numderiv r-coda))
    (home-page
     "https://ui.adsabs.harvard.edu/abs/2014arXiv1412.2967F/abstract")
    (synopsis
     "Methods and Tools for Bayesian Dynamic Conditional Correlation GARCH(1,1) Model")
    (description
     "Bayesian estimation of dynamic conditional correlation GARCH model for
multivariate time series volatility (Fioruci, J.A., Ehlers, R.S. and
Andrade-Filho, M.G., (2014). <doi:10.1080/02664763.2013.839635>.")
    (license license:gpl2+)))

(define-public r-bayesda
  (package
    (name "r-bayesda")
    (version "2012.04-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesDA" version))
       (sha256
        (base32 "0fp27cmhw8dsxr4mc1flm6qh907476kph8ch2889g9p31xm1psjc"))))
    (properties `((upstream-name . "BayesDA")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BayesDA")
    (synopsis "Functions and Datasets for the book \"Bayesian Data Analysis\"")
    (description
     "This package provides functions for Bayesian Data Analysis, with datasets from
the book \"Bayesian data Analysis (second edition)\" by Gelman, Carlin, Stern and
Rubin.  Not all datasets yet, hopefully completed soon.")
    (license license:gpl2+)))

(define-public r-bayescvi
  (package
    (name "r-bayescvi")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesCVI" version))
       (sha256
        (base32 "0j2kaq0ssbs9d0y17kmj8p89g7gh8dpaa8wrf6ylm3jy27g8b48j"))))
    (properties `((upstream-name . "BayesCVI")))
    (build-system r-build-system)
    (propagated-inputs (list r-universalcvi r-mclust r-ggplot2 r-e1071))
    (home-page "https://cran.r-project.org/package=BayesCVI")
    (synopsis "Bayesian Cluster Validity Index")
    (description
     "Algorithms for computing and generating plots with and without error bars for
Bayesian cluster validity index (BCVI) (O. Preedasawakul, and N. Wiroonsri, A
Bayesian Cluster Validity Index, Computational Statistics & Data Analysis, 202,
108053, 2025. <doi:10.1016/j.csda.2024.108053>) based on several underlying
cluster validity indexes (CVIs) including Calinski-Harabasz, Chou-Su-Lai,
Davies-Bouldin, Dunn, Pakhira-Bandyopadhyay-Maulik, Point biserial correlation,
the score function, Starczewski, and Wiroonsri indices for hard clustering, and
Correlation Cluster Validity, the generalized C, HF, KWON, KWON2, Modified
Pakhira-Bandyopadhyay-Maulik, Pakhira-Bandyopadhyay-Maulik, Tang,
Wiroonsri-Preedasawakul, Wu-Li, and Xie-Beni indices for soft clustering.  The
package is compatible with K-means, fuzzy C means, EM clustering, and
hierarchical clustering (single, average, and complete linkage).  Though BCVI is
compatible with any underlying existing CVIs, we recommend users to use either
WI or WP as the underlying CVI.")
    (license license:gpl3+)))

(define-public r-bayescureratemodel
  (package
    (name "r-bayescureratemodel")
    (version "1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesCureRateModel" version))
       (sha256
        (base32 "0h3sq3yydzkr9f0j0n32w8x8an0ckl5q85yiz7c63c535wxhhdg0"))))
    (properties `((upstream-name . "bayesCureRateModel")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam
                             r-survival
                             r-rcpparmadillo
                             r-rcpp
                             r-mclust
                             r-hdinterval
                             r-foreach
                             r-flexsurv
                             r-doparallel
                             r-coda
                             r-calculus))
    (home-page "https://github.com/mqbssppe/Bayesian_cure_rate_model")
    (synopsis "Bayesian Cure Rate Modeling for Time-to-Event Data")
    (description
     "This package provides a fully Bayesian approach in order to estimate a general
family of cure rate models under the presence of covariates, see Papastamoulis
and Milienos (2024) <doi:10.1007/s11749-024-00942-w>.  The promotion time can be
modelled (a) parametrically using typical distributional assumptions for time to
event data (including the Weibull, Exponential, Gompertz, log-Logistic
distributions), or (b) semiparametrically using finite mixtures of
distributions.  In both cases, user-defined families of distributions are
allowed under some specific requirements.  Posterior inference is carried out by
constructing a Metropolis-coupled Markov chain Monte Carlo (MCMC) sampler, which
combines Gibbs sampling for the latent cure indicators and Metropolis-Hastings
steps with Langevin diffusion dynamics for parameter updates.  The main MCMC
algorithm is embedded within a parallel tempering scheme by considering heated
versions of the target posterior distribution.")
    (license license:gpl2)))

(define-public r-bayesctdesign
  (package
    (name "r-bayesctdesign")
    (version "0.6.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesCTDesign" version))
       (sha256
        (base32 "1277w6cv6b1s0wm492a79qrj9a4jzq1gizlpwkvwnvxzdl9f84r3"))))
    (properties `((upstream-name . "BayesCTDesign")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-reshape2 r-ggplot2 r-eha))
    (home-page "https://github.com/begglest/BayesCTDesign")
    (synopsis
     "Two Arm Bayesian Clinical Trial Design with and Without Historical Control Data")
    (description
     "This package provides a set of functions to help clinical trial researchers
calculate power and sample size for two-arm Bayesian randomized clinical trials
that do or do not incorporate historical control data.  At some point during the
design process, a clinical trial researcher who is designing a basic two-arm
Bayesian randomized clinical trial needs to make decisions about power and
sample size within the context of hypothesized treatment effects.  Through
simulation, the simple_sim() function will estimate power and other user
specified clinical trial characteristics at user specified sample sizes given
user defined scenarios about treatment effect,control group characteristics, and
outcome.  If the clinical trial researcher has access to historical control
data, then the researcher can design a two-arm Bayesian randomized clinical
trial that incorporates the historical data.  In such a case, the researcher
needs to work through the potential consequences of historical and randomized
control differences on trial characteristics, in addition to working through
issues regarding power in the context of sample size, treatment effect size, and
outcome.  If a researcher designs a clinical trial that will incorporate
historical control data, the researcher needs the randomized controls to be from
the same population as the historical controls.  What if this is not the case
when the designed trial is implemented? During the design phase, the researcher
needs to investigate the negative effects of possible historic/randomized
control differences on power, type one error, and other trial characteristics.
Using this information, the researcher should design the trial to mitigate these
negative effects.  Through simulation, the historic_sim() function will estimate
power and other user specified clinical trial characteristics at user specified
sample sizes given user defined scenarios about historical and randomized
control differences as well as treatment effects and outcomes.  The results from
historic_sim() and simple_sim() can be printed with print_table() and graphed
with plot_table() methods.  Outcomes considered are Gaussian, Poisson,
Bernoulli, Lognormal, Weibull, and Piecewise Exponential.  The methods are
described in Eggleston et al. (2021) <doi:10.18637/jss.v100.i21>.")
    (license license:gpl3)))

(define-public r-bayesct
  (package
    (name "r-bayesct")
    (version "0.99.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesCT" version))
       (sha256
        (base32 "0ragig1x34syhsnbnd58l2s7g23j7fb0cl9wkasvxq8cicw5hqgn"))))
    (properties `((upstream-name . "bayesCT")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-purrr r-magrittr r-dplyr r-bayesdp))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/thevaachandereng/bayesCT/")
    (synopsis "Simulation and Analysis of Adaptive Bayesian Clinical Trials")
    (description
     "Simulation and analysis of Bayesian adaptive clinical trials for binomial,
Gaussian, and time-to-event data types, incorporates historical data and allows
early stopping for futility or early success.  The package uses novel and
efficient Monte Carlo methods for estimating Bayesian posterior probabilities,
evaluation of loss to follow up, and imputation of incomplete data.  The package
has the functionality for dynamically incorporating historical data into the
analysis via the power prior or non-informative priors.")
    (license license:gpl3)))

(define-public r-bayescr
  (package
    (name "r-bayescr")
    (version "2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesCR" version))
       (sha256
        (base32 "1qav7l64iqqdvr1v6c7hlzvbx7zbvsa55pyisvkq5rsayy1rdsl1"))))
    (properties `((upstream-name . "BayesCR")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncdist r-rootsolve r-mvtnorm r-mnormt))
    (home-page "https://cran.r-project.org/package=BayesCR")
    (synopsis
     "Bayesian Analysis of Censored Regression Models Under Scale Mixture of Skew Normal Distributions")
    (description
     "Propose a parametric fit for censored linear regression models based on SMSN
distributions, from a Bayesian perspective.  Also, generates SMSN random
variables.")
    (license license:gpl3)))

(define-public r-bayescount
  (package
    (name "r-bayescount")
    (version "0.9.99-9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayescount" version))
       (sha256
        (base32 "1m9rsyxzf76079yzacgg1wqvylqgvmvwag043csaykn1m42kzdm3"))))
    (properties `((upstream-name . "bayescount")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-runjags r-rjags r-coda))
    (home-page "https://bayescount.sourceforge.net")
    (synopsis
     "Power Calculations and Bayesian Analysis of Count Distributions and FECRT Data using MCMC")
    (description
     "This package provides a set of functions to allow analysis of count data (such
as faecal egg count data) using Bayesian MCMC methods.  Returns information on
the possible values for mean count, coefficient of variation and zero inflation
(true prevalence) present in the data.  A complete faecal egg count reduction
test (FECRT) model is implemented, which returns inference on the true efficacy
of the drug from the pre- and post-treatment data provided, using non-parametric
bootstrapping as well as using Bayesian MCMC. Functions to perform power
analyses for faecal egg counts (including FECRT) are also provided.")
    (license license:gpl2)))

(define-public r-bayescopulareg
  (package
    (name "r-bayescopulareg")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayescopulareg" version))
       (sha256
        (base32 "0jzj1iwd8g1kpypg6dl3anhay7kjvznfkyzri3sdf538s6vr88m5"))))
    (properties `((upstream-name . "bayescopulareg")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppdist r-rcpparmadillo r-rcpp r-mvtnorm))
    (home-page "https://github.com/ethan-alt/bayescopulareg")
    (synopsis "Bayesian Copula Regression")
    (description
     "This package provides tools for Bayesian copula generalized linear models
(GLMs).  The sampling scheme is based on Pitt, Chan, and Kohn (2006)
<doi:10.1093/biomet/93.3.537>.  Regression parameters (including coefficients
and dispersion parameters) are estimated via the adaptive random walk Metropolis
approach developed by Haario, Saksman, and Tamminen (1999)
<doi:10.1007/s001800050022>.  The prior for the correlation matrix is based on
Hoff (2007) <doi:10.1214/07-AOAS107>.")
    (license license:gpl2+)))

(define-public r-bayescomm
  (package
    (name "r-bayescomm")
    (version "0.1-2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesComm" version))
       (sha256
        (base32 "1rrbvwcfm93cw0m33g0zn6nyshfjc97kb3fby9cga0zaixc0a8rk"))))
    (properties `((upstream-name . "BayesComm")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-mvtnorm r-coda r-abind))
    (home-page "https://cran.r-project.org/package=BayesComm")
    (synopsis "Bayesian Community Ecology Analysis")
    (description
     "Bayesian multivariate binary (probit) regression models for analysis of
ecological communities.")
    (license license:gpl2+)))

(define-public r-bayescombo
  (package
    (name "r-bayescombo")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesCombo" version))
       (sha256
        (base32 "01rzjja0xzvqyjivl8a9da13v13ylm4dwv4f27cmgz2znakfg5y8"))))
    (properties `((upstream-name . "BayesCombo")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/stanlazic/BayesCombo")
    (synopsis "Bayesian Evidence Combination")
    (description
     "Combine diverse evidence across multiple studies to test a high level scientific
theory.  The methods can also be used as an alternative to a standard
meta-analysis.")
    (license license:gpl3)))

(define-public r-bayescace
  (package
    (name "r-bayescace")
    (version "1.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesCACE" version))
       (sha256
        (base32 "12vw401d692x2692sykhngi5fk8l6z5724lym97zz9z2wyj29zyk"))))
    (properties `((upstream-name . "BayesCACE")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjags
                             r-rdpack
                             r-metafor
                             r-lme4
                             r-forestplot
                             r-coda))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=BayesCACE")
    (synopsis "Bayesian Model for CACE Analysis")
    (description
     "This package performs CACE (Complier Average Causal Effect analysis) on either a
single study or meta-analysis of datasets with binary outcomes, using either
complete or incomplete noncompliance information.  Our package implements the
Bayesian methods proposed in Zhou et al. (2019) <doi:10.1111/biom.13028>, which
introduces a Bayesian hierarchical model for estimating CACE in meta-analysis of
clinical trials with noncompliance, and Zhou et al. (2021)
<doi:10.1080/01621459.2021.1900859>, with an application example on Epidural
Analgesia.")
    (license license:gpl2+)))

(define-public r-bayesbp
  (package
    (name "r-bayesbp")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesBP" version))
       (sha256
        (base32 "043msccrins1smaizmf7qc6q1cyjdzirw2ygnvi56lskib1cls7m"))))
    (properties `((upstream-name . "BayesBP")))
    (build-system r-build-system)
    (propagated-inputs (list r-openxlsx r-iterators))
    (home-page "https://cran.r-project.org/package=BayesBP")
    (synopsis
     "Bayesian Estimation using Bernstein Polynomial Fits Rate Matrix")
    (description
     "Smoothed lexis diagrams with Bayesian method specifically tailored to cancer
incidence data.  Providing to calculating slope and constructing credible
interval.  LC Chien et al. (2015) <doi:10.1080/01621459.2015.1042106>.  LH Chien
et al. (2017) <doi:10.1002/cam4.1102>.")
    (license license:gpl2+)))

(define-public r-bayesboot
  (package
    (name "r-bayesboot")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesboot" version))
       (sha256
        (base32 "0976ryd0gbw3kpmxg2qxyp1m2swnrpa86vdhvqrqxp7fcrs8cs2z"))))
    (properties `((upstream-name . "bayesboot")))
    (build-system r-build-system)
    (propagated-inputs (list r-plyr r-hdinterval))
    (home-page "https://github.com/rasmusab/bayesboot")
    (synopsis "An Implementation of Rubin's (1981) Bayesian Bootstrap")
    (description
     "This package provides functions for performing the Bayesian bootstrap as
introduced by Rubin (1981) <doi:10.1214/aos/1176345338> and for summarizing the
result.  The implementation can handle both summary statistics that works on a
weighted version of the data and summary statistics that works on a resampled
data set.")
    (license license:expat)))

(define-public r-bayesbio
  (package
    (name "r-bayesbio")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesbio" version))
       (sha256
        (base32 "08qa4lzkrcwin7n3kzfqfdlnlqahdmjl1lxpdh29n780cgyjfvs0"))))
    (properties `((upstream-name . "bayesbio")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=bayesbio")
    (synopsis
     "Miscellaneous Functions for Bioinformatics and Bayesian Statistics")
    (description
     "This package provides a hodgepodge of hopefully helpful functions.  Two of these
perform shrinkage estimation: one using a simple weighted method where the user
can specify the degree of shrinkage required, and one using James-Stein
shrinkage estimation for the case of unequal variances.")
    (license license:gpl3)))

(define-public r-bayesbinmix
  (package
    (name "r-bayesbinmix")
    (version "1.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesBinMix" version))
       (sha256
        (base32 "1b18qg0mjbmrilwqffrq33gw04fzask2xgj1bp4cli51cjf2slf7"))))
    (properties `((upstream-name . "BayesBinMix")))
    (build-system r-build-system)
    (propagated-inputs (list r-label-switching r-foreach r-doparallel r-coda))
    (home-page "https://cran.r-project.org/package=BayesBinMix")
    (synopsis
     "Bayesian Estimation of Mixtures of Multivariate Bernoulli Distributions")
    (description
     "Fully Bayesian inference for estimating the number of clusters and related
parameters to heterogeneous binary data.")
    (license license:gpl2)))

(define-public r-bayesbekk
  (package
    (name "r-bayesbekk")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesBEKK" version))
       (sha256
        (base32 "1vlljv7xdfvbrbxqdi91xrz1aga04aa1pb23kvnmahm2s261map1"))))
    (properties `((upstream-name . "BayesBEKK")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-mts r-coda))
    (home-page "https://cran.r-project.org/package=BayesBEKK")
    (synopsis "Bayesian Estimation of Bivariate Volatility Model")
    (description
     "The Multivariate Generalized Autoregressive Conditional Heteroskedasticity
(MGARCH) models are used for modelling the volatile multivariate data sets.  In
this package a variant of MGARCH called BEKK (Baba, Engle, Kraft, Kroner)
proposed by Engle and Kroner (1995) <http://www.jstor.org/stable/3532933> has
been used to estimate the bivariate time series data using Bayesian technique.")
    (license license:gpl3)))

(define-public r-bayesassurance
  (package
    (name "r-bayesassurance")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesassurance" version))
       (sha256
        (base32 "0y62q0340j3781f0l9azxdn6k4isj1zds6y50fg38a73pi7mbfbm"))))
    (properties `((upstream-name . "bayesassurance")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang
                             r-plotly
                             r-plot3d
                             r-pbapply
                             r-mathjaxr
                             r-mass
                             r-ggplot2
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/jpan928/bayesassurance_rpackage")
    (synopsis "Bayesian Assurance Computation")
    (description
     "Computes Bayesian assurance under various settings characterized by different
assumptions and objectives, including precision-based conditions, credible
intervals, and goal functions.  All simulation-based functions included in this
package rely on a two-stage Bayesian method that assigns two distinct priors to
evaluate the probability of observing a positive outcome, which addresses subtle
limitations that take place when using the standard single-prior approach.  For
more information, please refer to Pan and Banerjee (2021)
<@code{arXiv:2112.03509>}.")
    (license license:gpl2+)))

(define-public r-bayesarimax
  (package
    (name "r-bayesarimax")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BayesARIMAX" version))
       (sha256
        (base32 "1rcfq0r8sqiqs65mbpcr8i4667cdb50k6bx6wdh9spr0djh8gfv0"))))
    (properties `((upstream-name . "BayesARIMAX")))
    (build-system r-build-system)
    (propagated-inputs (list r-forecast r-coda))
    (home-page "https://cran.r-project.org/package=BayesARIMAX")
    (synopsis "Bayesian Estimation of ARIMAX Model")
    (description
     "The Autoregressive Integrated Moving Average (ARIMA) model is very popular
univariate time series model.  Its application has been widened by the
incorporation of exogenous variable(s) (X) in the model and modified as ARIMAX
by Bierens (1987) <doi:10.1016/0304-4076(87)90086-8>.  In this package we
estimate the ARIMAX model using Bayesian framework.")
    (license license:gpl3)))

(define-public r-bayesanova
  (package
    (name "r-bayesanova")
    (version "1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesanova" version))
       (sha256
        (base32 "07zmg4y57ysfg7my709x1z35izb4jij0fm5qm0kacdkfvz20mgj8"))))
    (properties `((upstream-name . "bayesanova")))
    (build-system r-build-system)
    (propagated-inputs (list r-mcmcpack))
    (home-page "https://cran.r-project.org/package=bayesanova")
    (synopsis
     "Bayesian Inference in the Analysis of Variance via Markov Chain Monte Carlo in Gaussian Mixture Models")
    (description
     "This package provides a Bayesian version of the analysis of variance based on a
three-component Gaussian mixture for which a Gibbs sampler produces posterior
draws.  For details about the Bayesian ANOVA based on Gaussian mixtures, see
Kelter (2019) <@code{arXiv:1906.07524>}.")
    (license license:gpl2)))

(define-public r-bayesammi
  (package
    (name "r-bayesammi")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesammi" version))
       (sha256
        (base32 "1vff8gbfp9nsxrv43fhc02vsl935cv2cff3jih0i5rj0pwz5gvhf"))))
    (properties `((upstream-name . "bayesammi")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmvtnorm
                             r-tidyr
                             r-tibble
                             r-scales
                             r-rstiefel
                             r-rlang
                             r-mvtnorm
                             r-mass
                             r-magrittr
                             r-lme4
                             r-ggplot2
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=bayesammi")
    (synopsis
     "Bayesian Estimation of the Additive Main Effects and Multiplicative Interaction Model")
    (description
     "This package performs Bayesian estimation of the additive main effects and
multiplicative interaction (AMMI) model.  The method is explained in Crossa, J.,
Perez-Elizalde, S., Jarquin, D., Cotes, J.M., Viele, K., Liu, G. and Cornelius,
P.L. (2011) (<doi:10.2135/cropsci2010.06.0343>).")
    (license license:gpl2)))

(define-public r-bayesab
  (package
    (name "r-bayesab")
    (version "1.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayesAB" version))
       (sha256
        (base32 "1mwwbkd8mqm22mkbgrp5mq7i52qqjr9if09fnhb3ybdpqq3sdg73"))))
    (properties `((upstream-name . "bayesAB")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-rcpp r-ggplot2))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/FrankPortman/bayesAB")
    (synopsis "Fast Bayesian Methods for AB Testing")
    (description
     "This package provides a suite of functions that allow the user to analyze A/B
test data in a Bayesian framework.  Intended to be a drop-in replacement for
common frequentist hypothesis test such as the t-test and chi-sq test.")
    (license license:expat)))

(define-public r-bayes4psy
  (package
    (name "r-bayes4psy")
    (version "1.2.12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayes4psy" version))
       (sha256
        (base32 "1i6yc871bbygj6sw2v2l0diiz03n3n4mw46n905fsjkpdrx8rfhj"))))
    (properties `((upstream-name . "bayes4psy")))
    (build-system r-build-system)
    (propagated-inputs (list r-stanheaders
                             r-rstantools
                             r-rstan
                             r-reshape
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-metrology
                             r-mcmcse
                             r-ggplot2
                             r-emg
                             r-dplyr
                             r-cowplot
                             r-circular
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/bstatcomp/bayes4psy")
    (synopsis "User Friendly Bayesian Data Analysis for Psychology")
    (description
     "This package contains several Bayesian models for data analysis of psychological
tests.  A user friendly interface for these models should enable students and
researchers to perform professional level Bayesian data analysis without
advanced knowledge in programming and Bayesian statistics.  This package is
based on the Stan platform (Carpenter et el.  2017 <doi:10.18637/jss.v076.i01>).")
    (license license:gpl3+)))

(define-public r-bayenet
  (package
    (name "r-bayenet")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Bayenet" version))
       (sha256
        (base32 "0h0b6m3lpwjax5vwbmqmfqx55hx3sgbb87jby2lwn3d2v138bpks"))))
    (properties `((upstream-name . "Bayenet")))
    (build-system r-build-system)
    (propagated-inputs (list r-vgam
                             r-suppdists
                             r-rcpparmadillo
                             r-rcpp
                             r-mcmcpack
                             r-mass
                             r-hbmem
                             r-gsl))
    (home-page "https://cran.r-project.org/package=Bayenet")
    (synopsis "Bayesian Quantile Elastic Net for Genetic Study")
    (description
     "As heavy-tailed error distribution and outliers in the response variable widely
exist, models which are robust to data contamination are highly demanded.  Here,
we develop a novel robust Bayesian variable selection method with elastic net
penalty for quantile regression in genetic analysis.  In particular, the
spike-and-slab priors have been incorporated to impose sparsity.  An efficient
Gibbs sampler has been developed to facilitate computation.The core modules of
the package have been developed in C++ and R.")
    (license license:gpl2)))

(define-public r-bayefdr
  (package
    (name "r-bayefdr")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayefdr" version))
       (sha256
        (base32 "178s559p1mgzp2b23plq3dcl5p0mg1d5cy6477knbaq711awzz3j"))))
    (properties `((upstream-name . "bayefdr")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2 r-ggplot2 r-ggextra r-cowplot
                             r-assertthat))
    (home-page "https://github.com/VallejosGroup/bayefdr")
    (synopsis
     "Bayesian Estimation and Optimisation of Expected False Discovery Rate")
    (description
     "This package implements the Bayesian FDR control described by Newton et al.
(2004), <doi:10.1093/biostatistics/5.2.155>.  Allows optimisation and
visualisation of expected error rates based on tail posterior probability tests.
 Based on code written by Catalina Vallejos for BA@code{SiCS}, see Beyond
comparisons of means: understanding changes in gene expression at the
single-cell level Vallejos et al. (2016) <doi:10.1186/s13059-016-0930-3>.")
    (license license:gpl3)))

(define-public r-baycn
  (package
    (name "r-baycn")
    (version "1.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "baycn" version))
       (sha256
        (base32 "1ykz1brs3mz94n86d5qaq25vf24j5adyzpddxqmn7qklbxxr6fh5"))))
    (properties `((upstream-name . "baycn")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-igraph r-gtools r-ggplot2 r-egg))
    (home-page "https://cran.r-project.org/package=baycn")
    (synopsis "Bayesian Inference for Causal Networks")
    (description
     "This package provides a Bayesian hybrid approach for inferring Directed Acyclic
Graphs (DAGs) for continuous, discrete, and mixed data.  The algorithm can use
the graph inferred by another more efficient graph inference method as input;
the input graph may contain false edges or undirected edges but can help reduce
the search space to a more manageable size.  A Bayesian Markov chain Monte Carlo
algorithm is then used to infer the probability of direction and absence for the
edges in the network.  References: Martin and Fu (2019)
<@code{arXiv:1909.10678>}.")
    (license (list license:gpl3
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-bayclumpr
  (package
    (name "r-bayclumpr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bayclumpr" version))
       (sha256
        (base32 "17yppw6m27d1smdbwrl9mja84y3p56s4sjw4c6hklxadcf4na8im"))))
    (properties `((upstream-name . "bayclumpr")))
    (build-system r-build-system)
    (propagated-inputs (list r-rstan r-loo r-isoplotr r-deming))
    (native-inputs (list r-knitr))
    (home-page "https://bayclump.tripatilab.epss.ucla.edu/")
    (synopsis "Bayesian Analysis of Clumped Isotope Datasets")
    (description
     "Simulating synthetic clumped isotope dataset, fitting linear regression models
under Bayesian and non-Bayesian frameworks, and generating temperature
reconstructions for the same two approaches.  Please note that models
implemented in this package are described in Roman-Palacios et al. (2021)
<doi:10.1002/essoar.10507995.1>.")
    (license license:expat)))

(define-public r-bawir
  (package
    (name "r-bawir")
    (version "1.3.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BAwiR" version))
       (sha256
        (base32 "138irq8f7yinjbz9ycsvvb42bh35a8a2jmncg9fcfc1gm9dlfmzb"))))
    (properties `((upstream-name . "BAwiR")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-stringi
                             r-scales
                             r-rworldmap
                             r-rvest
                             r-reshape2
                             r-readr
                             r-qdapregex
                             r-purrr
                             r-plyr
                             r-magrittr
                             r-lubridate
                             r-httr
                             r-ggplot2
                             r-dplyr
                             r-anthropometry))
    (native-inputs (list r-knitr))
    (home-page "https://www.uv.es/vivigui/basketball_platform.html")
    (synopsis "Analysis of Basketball Data")
    (description
     "Collection of tools to work with European basketball data.  Functions available
are related to friendly web scraping, data management and visualization.  Data
were obtained from <https://www.euroleaguebasketball.net/euroleague/>,
<https://www.euroleaguebasketball.net/eurocup/> and <https://www.acb.com/>,
following the instructions of their respectives robots.txt files, when
available.  Box score data are available for the three leagues.  Play-by-play
data are also available for the Spanish league.  Methods for analysis include a
population pyramid, 2D plots, circular plots of players percentiles, plots of
players monthly/yearly stats, team heatmaps, team shooting plots, team four
factors plots, cross-tables with the results of regular season games, maps of
nationalities, combinations of lineups, possessions-related variables, timeouts,
performance by periods, personal fouls and offensive rebounds.  Please see Vinue
(2020) <doi:10.1089/big.2018.0124>.")
    (license license:gpl2+)))

(define-public r-batteryreduction
  (package
    (name "r-batteryreduction")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "batteryreduction" version))
       (sha256
        (base32 "0j838q7063bplkzd50kmnxji80cgysfsq7m1qifv8z7a2zsh8c8g"))))
    (properties `((upstream-name . "batteryreduction")))
    (build-system r-build-system)
    (propagated-inputs (list r-pracma))
    (home-page "https://cran.r-project.org/package=batteryreduction")
    (synopsis "An R Package for Data Reduction by Battery Reduction")
    (description
     "Battery reduction is a method used in data reduction.  It uses Gram-Schmidt
orthogonal rotations to find out a subset of variables best representing the
original set of variables.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-batss
  (package
    (name "r-batss")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BATSS" version))
       (sha256
        (base32 "1rkzw4sz3qn09ihjwnwj35gafh196gcnl75xqjjimga5bd7y7m7s"))))
    (properties `((upstream-name . "BATSS")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-r-utils r-plyr r-cli r-abind))
    (home-page "https://batss-dev.github.io/BATSS/")
    (synopsis
     "Bayesian Adaptive Trial Simulator Software (BATSS) for Generalised Linear Models")
    (description
     "Defines operating characteristics of Bayesian Adaptive Trials considering a
generalised linear model response via Monte Carlo simulations of Bayesian GLM
fitted via integrated Laplace approximations (INLA).")
    (license license:gpl2)))

(define-public r-batsch
  (package
    (name "r-batsch")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "batsch" version))
       (sha256
        (base32 "07axa9rrl4r8wf73023v64sc7x700gl5vmsq90gagnfgg06bfqp5"))))
    (properties `((upstream-name . "batsch")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble))
    (home-page "https://github.com/ramiromagno/batsch")
    (synopsis "Real-Time PCR Data Sets by Batsch et al. (2008)")
    (description
     "Real-time quantitative polymerase chain reaction (@code{qPCR}) data sets by
Batsch et al. (2008) <doi:10.1186/1471-2105-9-95>.  This package provides five
data sets, one for each PCR target: (i) rat SLC6A14, (ii) human SLC22A13, (iii)
pig EMT, (iv) chicken ETT, and (v) human GAPDH. Each data set comprises a
five-point, four-fold dilution series.  For each concentration there are three
replicates.  Each amplification curve is 45 cycles long.  Original raw data
file:
<https://static-content.springer.com/esm/art%3A10.1186%2F1471-2105-9-95/@code{MediaObjects/12859_2007_2080_MOESM5_ESM.xls>}.")
    (license (license:fsdg-compatible "CC BY 4.0"))))

(define-public r-batman
  (package
    (name "r-batman")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "batman" version))
       (sha256
        (base32 "0ccgx506p4iri23k2ikb8jmh04dp08w66785bv52iy8kd359h43f"))))
    (properties `((upstream-name . "batman")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp))
    (home-page "https://github.com/ironholds/batman")
    (synopsis
     "Convert Categorical Representations of Logicals to Actual Logicals")
    (description
     "Survey systems and other third-party data sources commonly use non-standard
representations of logical values when it comes to qualitative data - \"Yes\",
\"No\" and \"N/A\", say.  batman is a package designed to seamlessly convert these
into logicals.  It is highly localised, and contains equivalents to boolean
values in languages including German, French, Spanish, Italian, Turkish, Chinese
and Polish.")
    (license license:expat)))

(define-public r-bate
  (package
    (name "r-bate")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bate" version))
       (sha256
        (base32 "1jrf614g5699ygw73pda7dk79ydx57sc8wpq4vg51yy98arhidc2"))))
    (properties `((upstream-name . "bate")))
    (build-system r-build-system)
    (propagated-inputs (list r-vtable
                             r-tidyselect
                             r-purrr
                             r-magrittr
                             r-latex2exp
                             r-ggplot2
                             r-dplyr
                             r-concaveman))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/dbasu-umass/bate/")
    (synopsis "Computes Bias-Adjusted Treatment Effect")
    (description
     "Compute bounds for the treatment effect after adjusting for the presence of
omitted variables in linear econometric models, according to the method of Basu
(2022) <@code{arXiv:2203.12431>}.  You supply the data, identify the outcome and
treatment variables and additional regressors.  The main functions will compute
bounds for the bias-adjusted treatment effect.  Many plot functions allow easy
visualization of results.")
    (license license:expat)))

(define-public r-batchtma
  (package
    (name "r-batchtma")
    (version "0.1.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "batchtma" version))
       (sha256
        (base32 "1ii7iiq6vhfqnhzp5xpl40g2gdmm9c7ib3kg8fbswg99f96hqhbp"))))
    (properties `((upstream-name . "batchtma")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-quantreg
                             r-purrr
                             r-nnet
                             r-magrittr
                             r-limma
                             r-ggplot2
                             r-geepack
                             r-dplyr
                             r-broom))
    (native-inputs (list r-knitr))
    (home-page "https://stopsack.github.io/batchtma/")
    (synopsis "Batch Effect Adjustments")
    (description
     "Different adjustment methods for batch effects in biomarker data, such as from
tissue microarrays.  Some methods attempt to retain differences between batches
that may be due to between-batch differences in \"biological\" factors that
influence biomarker values.")
    (license license:gpl3)))

(define-public r-batchscr
  (package
    (name "r-batchscr")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "batchscr" version))
       (sha256
        (base32 "094l2cq7lcdrlsdmsxmbal7srxdg20rxbms46h1sd414wq26d413"))))
    (properties `((upstream-name . "batchscr")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=batchscr")
    (synopsis "Batch Script Helpers")
    (description
     "Handy frameworks, such as error handling and log generation, for batch scripts.
Use case: in scripts running in remote servers, set error handling mechanism for
downloading and uploading and record operation log.")
    (license license:expat)))

(define-public r-batchmix
  (package
    (name "r-batchmix")
    (version "2.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "batchmix" version))
       (sha256
        (base32 "1vsr4p3a03dx3kr2clzcnyvf6xfzyisfk87famfvssjb4g58bzrv"))))
    (properties `((upstream-name . "batchmix")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr r-salso r-rcpparmadillo r-rcpp r-ggplot2))
    (native-inputs (list r-rmarkdown r-knitr))
    (home-page "https://github.com/stcolema/batchmix")
    (synopsis
     "Semi-Supervised Bayesian Mixture Models Incorporating Batch Correction")
    (description
     "Semi-supervised and unsupervised Bayesian mixture models that simultaneously
infer the cluster/class structure and a batch correction.  Densities available
are the multivariate normal and the multivariate t.  The model sampler is
implemented in C++.  This package is aimed at analysis of low-dimensional data
generated across several batches.  See Coleman et al. (2022)
<doi:10.1101/2022.01.14.476352> for details of the model.")
    (license license:gpl3)))

(define-public r-batchmeans
  (package
    (name "r-batchmeans")
    (version "1.0-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "batchmeans" version))
       (sha256
        (base32 "0b2npqxrv08zxj59sahj5s87inhl6dyl10bj01v0l1yr14q5g546"))))
    (properties `((upstream-name . "batchmeans")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=batchmeans")
    (synopsis
     "Consistent Batch Means Estimation of Monte Carlo Standard Errors")
    (description
     "This package provides consistent batch means estimation of Monte Carlo standard
errors.")
    (license license:gpl2+)))

(define-public r-batchllm
  (package
    (name "r-batchllm")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "batchLLM" version))
       (sha256
        (base32 "0y7s0m2gdynqcnnf4yar8ga7y53km7a03izaf4ds254349dhkk0y"))))
    (properties `((upstream-name . "batchLLM")))
    (build-system r-build-system)
    (propagated-inputs (list r-spscomps
                             r-shinywidgets
                             r-shinyjs
                             r-shinydashboard
                             r-shiny
                             r-rlang
                             r-readxl
                             r-readr
                             r-openai
                             r-jsonlite
                             r-httr
                             r-gemini-r
                             r-dt
                             r-dplyr
                             r-digest))
    (home-page "https://github.com/dylanpieper/batchLLM")
    (synopsis "Batch Process LLM Text Completions Using a Data Frame")
    (description
     "Batch process large language model (LLM) text completions using data frame rows,
with support for @code{OpenAI's} GPT (<https://chat.openai.com>), Anthropic's
Claude (<https://claude.ai>), and Google's Gemini (<https://gemini.google.com>).
 Includes features such as local storage, metadata logging, API rate limiting
delays, and a shiny app addin.")
    (license license:expat)))

(define-public r-batchgetsymbols
  (package
    (name "r-batchgetsymbols")
    (version "2.6.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BatchGetSymbols" version))
       (sha256
        (base32 "1sv4p0f8l79wy3q6jwsi3iy7zljc33z9mnyf8ijb23l64w7fvy9i"))))
    (properties `((upstream-name . "BatchGetSymbols")))
    (build-system r-build-system)
    (propagated-inputs (list r-zoo
                             r-xml
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-scales
                             r-rvest
                             r-quantmod
                             r-purrr
                             r-lubridate
                             r-lifecycle
                             r-future
                             r-furrr
                             r-dplyr
                             r-curl
                             r-crayon
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BatchGetSymbols")
    (synopsis "Downloads and Organizes Financial Data for Multiple Tickers")
    (description "Makes it easy to download financial data from Yahoo Finance
<https://finance.yahoo.com/>.")
    (license license:gpl2)))

(define-public r-batchexperiments
  (package
    (name "r-batchexperiments")
    (version "1.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BatchExperiments" version))
       (sha256
        (base32 "0g3vr2g4v968rwzja1nngji58960p2r494km8wia7x5w3y69p063"))))
    (properties `((upstream-name . "BatchExperiments")))
    (build-system r-build-system)
    (propagated-inputs (list r-rsqlite
                             r-dbi
                             r-data-table
                             r-checkmate
                             r-bbmisc
                             r-batchjobs
                             r-backports))
    (home-page "https://github.com/tudo-r/BatchExperiments")
    (synopsis "Statistical Experiments on Batch Computing Clusters")
    (description
     "Extends the @code{BatchJobs} package to run statistical experiments on batch
computing clusters.  For further details see the project web page.")
    (license license:bsd-3)))

(define-public r-batch
  (package
    (name "r-batch")
    (version "1.1-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "batch" version))
       (sha256
        (base32 "0wdgfvk2i542cqg34ikvzwlix09f2jyjb32a0f4zh9vg9nrywswq"))))
    (properties `((upstream-name . "batch")))
    (build-system r-build-system)
    (home-page "http://sites.google.com/site/thomashoffmannproject/")
    (synopsis
     "Batching Routines in Parallel and Passing Command-Line Arguments to R")
    (description
     "This package provides functions to allow you to easily pass command-line
arguments into R, and functions to aid in submitting your R code in parallel on
a cluster and joining the results afterward (e.g. multiple parameter values for
simulations running in parallel, splitting up a permutation test in parallel,
etc.).  See `@code{parseCommandArgs}(...) for the main example of how to use
this package.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-batata
  (package
    (name "r-batata")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "batata" version))
       (sha256
        (base32 "0z3pj37jydidw3lq1yvp1yv1vqbm28nhj0v4kb3n30dl00kd0hmy"))))
    (properties `((upstream-name . "batata")))
    (build-system r-build-system)
    (propagated-inputs (list r-remotes
                             r-purrr
                             r-lubridate
                             r-jsonlite
                             r-glue
                             r-fs))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/feddelegrand7/batata")
    (synopsis "Managing Packages Removal and Installation")
    (description
     "Allows the user to manage easily R packages removal and installation.  It offers
many functions to display installed packages according to specific dates and
removes them if needed.  The user is always prompted when running the removal
functions in order to confirm the required action.  It also provides functions
that will install Github starred R packages whether available on CRAN or not.")
    (license license:expat)))

(define-public r-bat
  (package
    (name "r-bat")
    (version "2.9.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BAT" version))
       (sha256
        (base32 "04wsr8sz70z7z6cqhmc4npqvh6ay9r1x5ghkp2ick58s0f6sagbf"))))
    (properties `((upstream-name . "BAT")))
    (build-system r-build-system)
    (propagated-inputs (list r-vegan
                             r-terra
                             r-phytools
                             r-nls2
                             r-mass
                             r-hypervolume
                             r-geometry
                             r-ape))
    (home-page "https://cran.r-project.org/package=BAT")
    (synopsis "Biodiversity Assessment Tools")
    (description
     "Includes algorithms to assess alpha and beta diversity in all their dimensions
(taxonomic, phylogenetic and functional).  It allows performing a number of
analyses based on species identities/abundances, phylogenetic/functional
distances, trees, convex-hulls or kernel density n-dimensional hypervolumes
depicting species relationships.  Cardoso et al. (2015)
<doi:10.1111/2041-210X.12310>.")
    (license license:gpl3)))

(define-public r-basta
  (package
    (name "r-basta")
    (version "2.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BaSTA" version))
       (sha256
        (base32 "1db4qyg6dmcq2mzmf55n3lxxqk4i7jfz9s32560754h9n0y1l92p"))))
    (properties `((upstream-name . "BaSTA")))
    (build-system r-build-system)
    (propagated-inputs (list r-snowfall))
    (home-page "https://cran.r-project.org/package=BaSTA")
    (synopsis
     "Age-Specific Bayesian Survival Trajectory Analysis from Incomplete Census or Capture-Recapture/Recovery Data")
    (description
     "Estimates survival and mortality with covariates from census or
capture-recapture/recovery data in a Bayesian framework when many individuals
are of unknown age.  It includes tools for data checking, model diagnostics and
outputs such as life-tables and plots, as described in Colchero, Jones, and
Rebke (2012) <doi:10.1111/j.2041-210X.2012.00186.x> and Colchero et al. (2021)
<doi:10.1038/s41467-021-23894-3>.")
    (license license:gpl3+)))

(define-public r-bass
  (package
    (name "r-bass")
    (version "1.3.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BASS" version))
       (sha256
        (base32 "00py6s7b9lv8lsycpa79gbwrlbghgsffd2avggb77gxpl0g8vfz0"))))
    (properties `((upstream-name . "BASS")))
    (build-system r-build-system)
    (propagated-inputs (list r-truncdist r-hypergeo))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=BASS")
    (synopsis "Bayesian Adaptive Spline Surfaces")
    (description
     "Bayesian fitting and sensitivity analysis methods for adaptive spline surfaces
described in <doi:10.18637/jss.v094.i08>.  Built to handle continuous and
categorical inputs as well as functional or scalar output.  An extension of the
methodology in Denison, Mallick and Smith (1998) <doi:10.1023/A:1008824606259>.")
    (license license:gpl3)))

(define-public r-basksim
  (package
    (name "r-basksim")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "basksim" version))
       (sha256
        (base32 "1vrav84fqkilmk0ww0gjr6hldxp7fpzgca2062jvrkzbyff05bx9"))))
    (properties `((upstream-name . "basksim")))
    (build-system r-build-system)
    (propagated-inputs (list r-progressr
                             r-hdinterval
                             r-foreach
                             r-extradistr
                             r-dofuture
                             r-bmabasket
                             r-bhmbasket
                             r-arrangements))
    (home-page "https://github.com/lbau7/basksim")
    (synopsis
     "Simulation-Based Calculation of Basket Trial Operating Characteristics")
    (description
     "This package provides a unified syntax for the simulation-based comparison of
different single-stage basket trial designs with a binary endpoint and equal
sample sizes in all baskets.  Methods include the designs by Baumann et al.
(2024) <doi:10.48550/@code{arXiv.2309.06988>}, Fujikawa et al. (2020)
<doi:10.1002/bimj.201800404>, Berry et al. (2020)
<doi:10.1177/1740774513497539>, Neuenschwander et al. (2016)
<doi:10.1002/pst.1730> and Psioda et al. (2021)
<doi:10.1093/biostatistics/kxz014>.  For the latter three designs, the functions
are mostly wrappers for functions provided by the packages bhmbasket and
bmabasket'.")
    (license license:gpl3+)))

(define-public r-baskexact
  (package
    (name "r-baskexact")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "baskexact" version))
       (sha256
        (base32 "1825avqbbf8zkk9fps0i450zwl3j0armbnblndl9q954g2wzdhs4"))))
    (properties `((upstream-name . "baskexact")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo
                             r-rcpp
                             r-ggplot2
                             r-foreach
                             r-extradistr
                             r-dofuture
                             r-arrangements))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/lbau7/baskexact")
    (synopsis
     "Analytical Calculation of Basket Trial Operating Characteristics")
    (description
     "Analytically calculates the operating characteristics of single-stage and
two-stage basket trials with equal sample sizes using the power prior design by
Baumann et al. (2024) <doi:10.48550/@code{arXiv.2309.06988>} and the design by
Fujikawa et al. (2020) <doi:10.1002/bimj.201800404>.")
    (license license:gpl3+)))

(define-public r-basketballanalyzer
  (package
    (name "r-basketballanalyzer")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BasketballAnalyzeR" version))
       (sha256
        (base32 "1bad287llg89smqg43lgz2hdkjg3l39mh4k82avgb52xwqp5gjxc"))))
    (properties `((upstream-name . "BasketballAnalyzeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-statnet-common
                             r-sp
                             r-sna
                             r-scales
                             r-rlang
                             r-readr
                             r-plyr
                             r-pbsmapping
                             r-operators
                             r-network
                             r-mass
                             r-magrittr
                             r-hexbin
                             r-gridextra
                             r-ggrepel
                             r-ggplotify
                             r-ggplot2
                             r-ggnetwork
                             r-ggally
                             r-dplyr
                             r-directlabels
                             r-dendextend
                             r-corrplot
                             r-circlize))
    (home-page "https://github.com/sndmrc/BasketballAnalyzeR")
    (synopsis "Analysis and Visualization of Basketball Data")
    (description
     "This package contains data and code to accompany the book P. Zuccolotto and M.
Manisera (2020) Basketball Data Science.  Applications with R. CRC Press.  ISBN
9781138600799.")
    (license license:gpl2+)))

(define-public r-basket
  (package
    (name "r-basket")
    (version "0.10.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "basket" version))
       (sha256
        (base32 "1kmz53v8k1wyjg33dsf05p57ym2zq3d0f373wp85bnrwx00nfr10"))))
    (properties `((upstream-name . "basket")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tidygraph
                             r-tibble
                             r-rcolorbrewer
                             r-itertools
                             r-igraph
                             r-gridextra
                             r-ggraph
                             r-ggplot2
                             r-gensa
                             r-foreach
                             r-dplyr
                             r-crayon
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/kaneplusplus/basket")
    (synopsis "Basket Trial Analysis")
    (description
     "Implementation of multisource exchangeability models for Bayesian analyses of
prespecified subgroups arising in the context of basket trial design and
monitoring.  The R basket package facilitates implementation of the binary,
symmetric multi-source exchangeability model (MEM) with posterior inference
arising from both exact computation and Markov chain Monte Carlo sampling.
Analysis output includes full posterior samples as well as posterior
probabilities, highest posterior density (HPD) interval boundaries, effective
sample sizes (ESS), mean and median estimations, posterior exchangeability
probability matrices, and maximum a posteriori MEMs. In addition to providing
\"basketwise\" analyses, the package includes similar calculations for
\"clusterwise\" analyses for which subgroups are combined into meta-baskets, or
clusters, using graphical clustering algorithms that treat the posterior
exchangeability probabilities as edge weights.  In addition plotting tools are
provided to visualize basket and cluster densities as well as their
exchangeability.  References include Hyman, D.M., Puzanov, I., Subbiah, V.,
Faris, J.E., Chau, I., Blay, J.Y., Wolf, J., Raje, N.S., Diamond, E.L.,
Hollebecque, A. and Gervais, R (2015) <doi:10.1056/NEJMoa1502309>; Hobbs, B.P.
and Landin, R. (2018) <doi:10.1002/sim.7893>; Hobbs, B.P., Kane, M.J., Hong,
D.S. and Landin, R. (2018) <doi:10.1093/annonc/mdy457>; and Kaizer, A.M.,
Koopmeiners, J.S. and Hobbs, B.P. (2017) <doi:10.1093/biostatistics/kxx031>.")
    (license license:lgpl2.0)))

(define-public r-baskepro
  (package
    (name "r-baskepro")
    (version "1.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BaSkePro" version))
       (sha256
        (base32 "1dvkn8sgrxnxi91635ic3crhz498v95i8clj7n4pq1k9fsany3jg"))))
    (properties `((upstream-name . "BaSkePro")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass))
    (home-page "https://cran.r-project.org/package=BaSkePro")
    (synopsis "Bayesian Model to Archaeological Faunal Skeletal Profiles")
    (description
     "Tool to perform Bayesian inference of carcass processing/transport strategy and
bone attrition from archaeofaunal skeletal profiles characterized by percentages
of MAU (Minimum Anatomical Units).  The approach is based on a generative model
for skeletal profiles that replicates the two phases of formation of any faunal
assemblage: initial accumulation as a function of human transport strategies and
subsequent attrition.Two parameters define this model: 1) the transport
preference (alpha), which can take any value between - 1 (mostly axial
contribution) and 1 (mostly appendicular contribution) following strategies
constructed as a function of butchering efficiency of different anatomical
elements and the results of ethnographic studies, and 2) degree of attrition
(beta), which can vary between 0 (no attrition) and 10 (maximum attrition) and
relates the survivorship of bone elements to their maximum bone density.
Starting from uniform prior probability distribution functions of alpha and
beta, a Monte Carlo Markov Chain sampling based on a random walk
Metropolis-Hasting algorithm is adopted to derive the posterior probability
distribution functions, which are then available for interpretation.  During
this process, the likelihood of obtaining the observed percentages of MAU given
a pair of parameter values is estimated by the inverse of the Chi2 statistic,
multiplied by the proportion of elements within a 1 percent of the observed
value.  See Ana B. Marin-Arroyo, David Ocio
(2018).<doi:10.1080/08912963.2017.1336620>.")
    (license license:gpl3)))

(define-public r-basinetentropy
  (package
    (name "r-basinetentropy")
    (version "0.99.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BASiNETEntropy" version))
       (sha256
        (base32 "00lid7crjjcrfkfqdnkk7i5zlp17wvnirbgb3xb5s8wlc5cyhxyq"))))
    (properties `((upstream-name . "BASiNETEntropy")))
    (build-system r-build-system)
    (propagated-inputs (list r-randomforest r-igraph r-biostrings))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BASiNETEntropy")
    (synopsis
     "Classification of RNA Sequences using Complex Network and Information Theory")
    (description
     "It makes the creation of networks from sequences of RNA, with this is done the
abstraction of characteristics of these networks with a methodology of maximum
entropy for the purpose of making a classification between the classes of the
sequences.  There are two data present in the BA@code{SiNET} package,
\"@code{mRNA}\", and \"@code{ncRNA}\" with 10 sequences.  These sequences were taken
from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU,
Zhongyin, 2014) <doi:10.1186/1471-2105-15-311>, these sequences are used to run
examples.")
    (license license:gpl3)))

(define-public r-basinet
  (package
    (name "r-basinet")
    (version "0.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BASiNET" version))
       (sha256
        (base32 "09g2h38kz1g9kkjc69si5d6z6fkayys9hinjs3p6dn7sx7zn2bx1"))))
    (properties `((upstream-name . "BASiNET")))
    (build-system r-build-system)
    (propagated-inputs (list r-rweka
                             r-rmcfs
                             r-rjava
                             r-randomforest
                             r-igraph
                             r-biostrings))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BASiNET")
    (synopsis "Classification of RNA Sequences using Complex Network Theory")
    (description
     "It makes the creation of networks from sequences of RNA, with this is done the
abstraction of characteristics of these networks with a methodology of threshold
for the purpose of making a classification between the classes of the sequences.
 There are four data present in the BA@code{SiNET} package, \"sequences\",
\"sequences2\", \"sequences-predict\" and \"sequences2-predict\" with 11, 10, 11 and
11 sequences respectively.  These sequences were taken from the data set used in
the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014)
<doi:10.1186/1471-2105-15-311>, these sequences are used to run examples.  The
BA@code{SiNET} was published on Nucleic Acids Research, (ITO, Eric; KATAHIRA,
Isaque; VICENTE, FÃ¡bio; PEREIRA, Felipe; LOPES, FabrÃ­cio, 2018)
<doi:10.1093/nar/gky462>.")
    (license license:gpl3)))

(define-public r-basifor
  (package
    (name "r-basifor")
    (version "0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "basifoR" version))
       (sha256
        (base32 "1clnmb57g7aclgw8ibj92pfixaism0af85w4kn0208nbm75c1kvf"))))
    (properties `((upstream-name . "basifoR")))
    (build-system r-build-system)
    (propagated-inputs (list r-rodbc
                             r-measurements
                             r-httr
                             r-hmisc
                             r-foreign
                             r-curl))
    (home-page "https://cran.r-project.org/package=basifoR")
    (synopsis
     "Retrieval and Processing of the Spanish National Forest Inventory")
    (description
     "Data sets of the Spanish National Forest Inventory
<https://www.miteco.gob.es/es/biodiversidad/servicios/banco-datos-naturaleza/informacion-disponible/>
are processed to compute tree metrics and statistics.  Function metrics2Vol()
controls most of the routines.")
    (license license:gpl3)))

(define-public r-basictabler
  (package
    (name "r-basictabler")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "basictabler" version))
       (sha256
        (base32 "0d5gpvf3la0fky786z74i8q7il1mmb2prg8y9wslg2gbngnkvkvm"))))
    (properties `((upstream-name . "basictabler")))
    (build-system r-build-system)
    (propagated-inputs (list r-r6 r-htmlwidgets r-htmltools r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "http://www.basictabler.org.uk/")
    (synopsis "Construct Rich Tables for Output to 'HTML'/'Excel'")
    (description
     "Easily create tables from data frames/matrices.  Create/manipulate tables
row-by-row, column-by-column or cell-by-cell.  Use common formatting/styling to
output rich tables as HTML', HTML widgets or to Excel'.")
    (license license:gpl3)))

(define-public r-basicmcmcplots
  (package
    (name "r-basicmcmcplots")
    (version "0.2.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "basicMCMCplots" version))
       (sha256
        (base32 "02lwkbpr9yj5q7l3ing1v47c4ifvwaxn8yyr3b34fral08cgpn9x"))))
    (properties `((upstream-name . "basicMCMCplots")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=basicMCMCplots")
    (synopsis
     "Trace Plots, Density Plots and Chain Comparisons for MCMC Samples")
    (description
     "This package provides methods for examining posterior MCMC samples from a single
chain using trace plots and density plots, and from multiple chains by comparing
posterior medians and credible intervals from each chain.  These plotting
functions have a variety of options, such as figure sizes, legends, parameters
to plot, and saving plots to file.  Functions interface with the NIMBLE software
package, see de Valpine, Turek, Paciorek, Anderson-Bergman, Temple Lang and
Bodik (2017) <doi:10.1080/10618600.2016.1172487>.")
    (license license:gpl3)))

(define-public r-basicdrm
  (package
    (name "r-basicdrm")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "basicdrm" version))
       (sha256
        (base32 "130s8h54760lnjh0m2i7pjs9cx2sc6xxjkm3x93727azxzgxpk09"))))
    (properties `((upstream-name . "basicdrm")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=basicdrm")
    (synopsis "Fit Hill Dose Response Models")
    (description
     "Evaluate, fit, and analyze Hill dose response models (Goutelle et al., 2008
<doi:10.1111/j.1472-8206.2008.00633.x>), also sometimes referred to as
four-parameter log-logistic models.  Includes tools to invert Hill models,
select models based on the Akaike information criterion (Akaike, 1974
<doi:10.1109/TAC.1974.1100705>) or Bayesian information criterion (Schwarz, 1978
<https://www.jstor.org/stable/2958889>), and construct bootstrapped confidence
intervals both on the Hill model parameters and values derived from the Hill
model parameters.")
    (license license:gpl3+)))

(define-public r-basf
  (package
    (name "r-basf")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "basf" version))
       (sha256
        (base32 "0l3y5p51jic3b8h1jclhhma4myll1g3vsxr8rj0dx1bvjfimkryw"))))
    (properties `((upstream-name . "basf")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble r-sf r-raster))
    (home-page "https://github.com/mdsumner/basf")
    (synopsis "Plot Simple Features with 'base' Sensibilities")
    (description
     "Resurrects the standard plot for shapes established by the base and graphics
packages.  This is suited to workflows that require plotting using the
established and traditional idioms of plotting spatially coincident data where
it belongs.  This package depends on sf and only replaces the plot method.")
    (license license:gpl3)))

(define-public r-basetheme
  (package
    (name "r-basetheme")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "basetheme" version))
       (sha256
        (base32 "0g1dsim688zl309svbv29g5ya23x71al4kribmy279vwqiddzl8z"))))
    (properties `((upstream-name . "basetheme")))
    (build-system r-build-system)
    (home-page "https://github.com/karoliskoncevicius/basetheme")
    (synopsis "Themes for Base Graphics Plots")
    (description
     "This package provides functions to create and select graphical themes for the
base plotting system.  Contains: 1) several custom pre-made themes 2) mechanism
for creating new themes by making persistent changes to the graphical parameters
of base plots.")
    (license license:gpl2)))

(define-public r-basetempseed
  (package
    (name "r-basetempseed")
    (version "0.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BaseTempSeed" version))
       (sha256
        (base32 "07b1611k639whrc32xcjdlqyxjsdyd9gmpgx3f7b8hcchnhx98ay"))))
    (properties `((upstream-name . "BaseTempSeed")))
    (build-system r-build-system)
    (propagated-inputs (list r-nlcoptim))
    (home-page "https://cran.r-project.org/package=BaseTempSeed")
    (synopsis
     "Estimation of Seed Germination Base Temperature in Thermal Modelling")
    (description
     "All the seeds do not germinate at a single point in time due to physiological
mechanisms determined by temperature which vary among individual seeds in the
population.  Seeds germinate by following accumulation of thermal time in degree
days/hours, quantified by multiplying the time of germination with excess of
base temperature required by each seed for its germination, which follows
log-normal distribution.  The theoretical germination course can be obtained by
regressing the rate of germination at various fractions against temperature
(Garcia et al., 1982), where the fraction-wise regression lines intersect the
temperature axis at base temperature and the methodology of determining optimum
base temperature has been described by Ellis et al. (1987).  This package helps
to find the base temperature of seed germination using algorithms of Garcia et
al. (1982) and Ellis et al. (1982) <doi:10.1093/JXB/38.6.1033>
<doi:10.1093/jxb/33.2.288>.")
    (license license:gpl3)))

(define-public r-baseset
  (package
    (name "r-baseset")
    (version "0.9.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BaseSet" version))
       (sha256
        (base32 "0dn4qxmba4pwf5ig7wivqijr35krm2h1cxgr99z44678k9hd3ip5"))))
    (properties `((upstream-name . "BaseSet")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlang r-magrittr r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ropensci/BaseSet")
    (synopsis "Working with Sets the Tidy Way")
    (description
     "This package implements a class and methods to work with sets, doing
intersection, union, complementary sets, power sets, cartesian product and other
set operations in a \"tidy\" way.  These set operations are available for both
classical sets and fuzzy sets.  Import sets from several formats or from other
several data structures.")
    (license license:expat)))

(define-public r-baseq
  (package
    (name "r-baseq")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "baseq" version))
       (sha256
        (base32 "012p6xs9dkvpwcqkm9qgnc5v6sg6qhwpcaqxfnwx30fpim99liai"))))
    (properties `((upstream-name . "baseq")))
    (build-system r-build-system)
    (home-page "https://github.com/ambuvjyn/baseq")
    (synopsis "Basic Sequence Processing Tool for Biological Data")
    (description
     "Primarily created as an easy and understanding way to do basic sequences
surrounding the central dogma of molecular biology.")
    (license license:gpl3)))

(define-public r-basemodels
  (package
    (name "r-basemodels")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "basemodels" version))
       (sha256
        (base32 "07r61bzrpnwwfsxg9szfw6cf5aqsqa5fxz9j448rqlkqlg84r68h"))))
    (properties `((upstream-name . "basemodels")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/Ying-Ju/basemodels")
    (synopsis "Baseline Models for Classification and Regression")
    (description
     "Providing equivalent functions for the dummy classifier and regressor used in
Python scikit-learn library.  Our goal is to allow R users to easily identify
baseline performance for their classification and regression problems.  Our
baseline models use no predictors, and are useful in cases of class imbalance,
multiclass classification, and when users want to quickly identify how much
improvement their statistical and machine learning models are over several
baseline models.  We use a \"better\" default (proportional guessing) for the
dummy classifier than the Python implementation (\"prior\", which is the most
frequent class in the training set).  The functions in the package can be used
on their own, or introduce methods named dummy_regressor or dummy_classifier
that can be used within the caret package pipeline.")
    (license license:expat)))

(define-public r-basemaps
  (package
    (name "r-basemaps")
    (version "0.0.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "basemaps" version))
       (sha256
        (base32 "19cw01qs0wmrxcypr71ssbsj5266zgkxsxakig014x1wr592d3v6"))))
    (properties `((upstream-name . "basemaps")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-stars
                             r-slippymath
                             r-sf
                             r-pbapply
                             r-magick
                             r-httr
                             r-curl))
    (home-page "https://cran.r-project.org/package=basemaps")
    (synopsis "Accessing Spatial Basemaps in R")
    (description
     "This package provides a lightweight package to access spatial basemaps from open
sources such as @code{OpenStreetMap}', Carto', Mapbox and others in R.")
    (license license:gpl3)))

(define-public r-basefun
  (package
    (name "r-basefun")
    (version "1.1-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "basefun" version))
       (sha256
        (base32 "1m258ycxc9m2xdpvqzjk0hz3fg3bwbj3z2yx6raf315ws4s2h47f"))))
    (properties `((upstream-name . "basefun")))
    (build-system r-build-system)
    (propagated-inputs (list r-variables r-polynom r-orthopolynom r-matrix))
    (home-page "http://ctm.R-forge.R-project.org")
    (synopsis "Infrastructure for Computing with Basis Functions")
    (description "Some very simple infrastructure for basis functions.")
    (license license:gpl2)))

(define-public r-basedosdados
  (package
    (name "r-basedosdados")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "basedosdados" version))
       (sha256
        (base32 "18gfk7hivp68mgbgx60qbpcy28ysj4llzwdji5a57rrczwffr6p0"))))
    (properties `((upstream-name . "basedosdados")))
    (build-system r-build-system)
    (propagated-inputs (list r-writexl
                             r-typed
                             r-tibble
                             r-stringr
                             r-scales
                             r-rlang
                             r-readr
                             r-purrr
                             r-magrittr
                             r-httr
                             r-glue
                             r-fs
                             r-dplyr
                             r-dotenv
                             r-dbplyr
                             r-dbi
                             r-cli
                             r-bigrquery))
    (home-page "https://cran.r-project.org/package=basedosdados")
    (synopsis "'Base Dos Dados' R Client")
    (description
     "An R interface to the Base dos Dados API
<https:basedosdados.github.io/mais/py_reference_api/>).  Authenticate your
project, query our tables, save data to disk and memory, all from R.")
    (license license:expat)))

(define-public r-basecamb
  (package
    (name "r-basecamb")
    (version "1.1.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "basecamb" version))
       (sha256
        (base32 "0jxlh04f3pqxb0ihp8241z91fka4hi3d3g41ya927japps5dpvhr"))))
    (properties `((upstream-name . "basecamb")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-sae
                             r-purrr
                             r-mice
                             r-mass
                             r-hmisc
                             r-dplyr
                             r-assertthat))
    (home-page "https://CRAN.R-project.org/package=basecamb")
    (synopsis
     "Utilities for Streamlined Data Import, Imputation and Modelling")
    (description
     "This package provides functions streamlining the data analysis workflow:
Outsourcing data import, renaming and type casting to a *.csv.  Manipulating
imputed datasets and fitting models on them.  Summarizing models.")
    (license license:gpl3+)))

(define-public r-baseballr
  (package
    (name "r-baseballr")
    (version "1.6.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "baseballr" version))
       (sha256
        (base32 "0qsr6di5y1a3z28rxpzk6k8b5rbnh08pdcjv65qa0fci3yx1i15y"))))
    (properties `((upstream-name . "baseballr")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-stringr
                             r-rvest
                             r-rlang
                             r-rcppparallel
                             r-rcpp
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-jsonlite
                             r-janitor
                             r-httr
                             r-glue
                             r-ggplot2
                             r-dplyr
                             r-data-table
                             r-cli))
    (home-page "https://billpetti.github.io/baseballr/")
    (synopsis "Acquiring and Analyzing Baseball Data")
    (description
     "This package provides numerous utilities for acquiring and analyzing baseball
data from online sources such as Baseball Reference
<https://www.baseball-reference.com/>, @code{FanGraphs}
<https://www.fangraphs.com/>, and the MLB Stats API <https://www.mlb.com/>.")
    (license license:expat)))

(define-public r-base-rms
  (package
    (name "r-base-rms")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "base.rms" version))
       (sha256
        (base32 "0lsbbcvmq5n3dpqic9qkyr8wdbmagaw32qfdnl0ck5b6ijh2wz0a"))))
    (properties `((upstream-name . "base.rms")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rms r-do))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=base.rms")
    (synopsis "Convert Regression Between Base Function and 'rms' Package")
    (description
     "We perform linear, logistic, and cox regression using the base functions lm(),
glm(), and coxph() in the R software and the survival package.  Likewise, we can
use ols(), lrm() and cph() from the rms package for the same functionality.
Each of these two sets of commands has a different focus.  In many cases, we
need to use both sets of commands in the same situation, e.g. we need to filter
the full subset model using AIC, and we need to build a visualization graph for
the final model.  base.rms package can help you to switch between the two sets
of commands easily.")
    (license license:gpl3)))

(define-public r-basad
  (package
    (name "r-basad")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "basad" version))
       (sha256
        (base32 "0g8zfphwbvxq1ydvsaj0dvidsikrriyi80cam9i3syhx0v20vnir"))))
    (properties `((upstream-name . "basad")))
    (build-system r-build-system)
    (propagated-inputs (list r-rmutil r-rcppeigen r-rcpp))
    (home-page "https://cran.r-project.org/package=basad")
    (synopsis
     "Bayesian Variable Selection with Shrinking and Diffusing Priors")
    (description
     "This package provides a Bayesian variable selection approach using continuous
spike and slab prior distributions.  The prior choices here are motivated by the
shrinking and diffusing priors studied in Narisetty & He (2014)
<DOI:10.1214/14-AOS1207>.")
    (license license:gpl3+)))

(define-public r-bas
  (package
    (name "r-bas")
    (version "1.7.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BAS" version))
       (sha256
        (base32 "0f34mna1h0wc3b8qc76jjgwjddfkasq7b2rvg2y93dyz9bq33cpc"))))
    (properties `((upstream-name . "BAS")))
    (build-system r-build-system)
    (native-inputs (list r-knitr gfortran))
    (home-page "https://merliseclyde.github.io/BAS/")
    (synopsis
     "Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling")
    (description
     "Package for Bayesian Variable Selection and Model Averaging in linear models and
generalized linear models using stochastic or deterministic sampling without
replacement from posterior distributions.  Prior distributions on coefficients
are from Zellner's g-prior or mixtures of g-priors corresponding to the
Zellner-Siow Cauchy Priors or the mixture of g-priors from Liang et al (2008)
<DOI:10.1198/016214507000001337> for linear models or mixtures of g-priors from
Li and Clyde (2019) <DOI:10.1080/01621459.2018.1469992> in generalized linear
models.  Other model selection criteria include AIC, BIC and Empirical Bayes
estimates of g.  Sampling probabilities may be updated based on the sampled
models using sampling w/out replacement or an efficient MCMC algorithm which
samples models using a tree structure of the model space as an efficient hash
table.  See Clyde, Ghosh and Littman (2010) <DOI:10.1198/jcgs.2010.09049> for
details on the sampling algorithms.  Uniform priors over all models or
beta-binomial prior distributions on model size are allowed, and for large p
truncated priors on the model space may be used to enforce sampling models that
are full rank.  The user may force variables to always be included in addition
to imposing constraints that higher order interactions are included only if
their parents are included in the model.  This material is based upon work
supported by the National Science Foundation under Division of Mathematical
Sciences grant 1106891.  Any opinions, findings, and conclusions or
recommendations expressed in this material are those of the author(s) and do not
necessarily reflect the views of the National Science Foundation.")
    (license license:gpl3+)))

(define-public r-barulho
  (package
    (name "r-barulho")
    (version "2.1.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "baRulho" version))
       (sha256
        (base32 "0ymdz8dpkk8xw4baiz0xamwx526gmaf59w3nzbppckiqacjr6qvd"))))
    (properties `((upstream-name . "baRulho")))
    (build-system r-build-system)
    (propagated-inputs (list r-warbler
                             r-viridis
                             r-tuner
                             r-sim-diffproc
                             r-seewave
                             r-rlang
                             r-png
                             r-ohun
                             r-fftw
                             r-cli
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/ropensci/baRulho")
    (synopsis "Quantifying (Animal) Sound Degradation")
    (description
     "Intended to facilitate acoustic analysis of (animal) sound transmission
experiments, which typically aim to quantify changes in signal structure when
transmitted in a given habitat by broadcasting and re-recording animal sounds at
increasing distances.  The package offers a workflow with functions to prepare
the data set for analysis as well as to calculate and visualize several
degradation metrics, including blur ratio, signal-to-noise ratio, excess
attenuation and envelope correlation among others (Dabelsteen et al 1993
<doi:10.1121/1.406682>).")
    (license license:gpl2+)))

(define-public r-bartman
  (package
    (name "r-bartman")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bartMan" version))
       (sha256
        (base32 "10g9ymfjnjzykrxkws4iayzz69mi4a5vjba49js740ds6i1r3zq5"))))
    (properties `((upstream-name . "bartMan")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidytreatment
                             r-tidyr
                             r-tidygraph
                             r-tidybayes
                             r-tibble
                             r-scales
                             r-rrapply
                             r-rlang
                             r-rjava
                             r-purrr
                             r-patchwork
                             r-igraph
                             r-gtable
                             r-ggraph
                             r-ggplot2
                             r-ggnewscale
                             r-ggiraph
                             r-dplyr
                             r-dendser
                             r-dbarts
                             r-cowplot
                             r-colorspace
                             r-cli
                             r-bartmachine
                             r-bart))
    (home-page "https://cran.r-project.org/package=bartMan")
    (synopsis "Create Visualisations for BART Models")
    (description
     "Investigating and visualising Bayesian Additive Regression Tree (BART) (Chipman,
H. A., George, E. I., & @code{McCulloch}, R. E. 2010) <doi:10.1214/09-AOAS285>
model fits.  We construct conventional plots to analyze a modelâs performance
and stability as well as create new tree-based plots to analyze variable
importance, interaction, and tree structure.  We employ Value Suppressing
Uncertainty Palettes (VSUP) to construct heatmaps that display variable
importance and interactions jointly using colour scale to represent posterior
uncertainty.  Our visualisations are designed to work with the most popular BART
R packages available, namely BART Rodney Sparapani and Charles Spanbauer and
Robert @code{McCulloch} 2021 <doi:10.18637/jss.v097.i01>, dbarts (Vincent Dorie
2023) <https://CRAN.R-project.org/package=dbarts>, and @code{bartMachine} (Adam
Kapelner and Justin Bleich 2016) <doi:10.18637/jss.v070.i04>.")
    (license license:gpl2+)))

(define-public r-bartmachinejars
  (package
    (name "r-bartmachinejars")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bartMachineJARs" version))
       (sha256
        (base32 "1h71ikmj217wjd1w52qvxf6p3alnqxg1zn43dzhlkhmfyjn20ylz"))))
    (properties `((upstream-name . "bartMachineJARs")))
    (build-system r-build-system)
    (inputs (list openjdk))
    (propagated-inputs (list r-rjava))
    (home-page "https://cran.r-project.org/package=bartMachineJARs")
    (synopsis "bartMachine JARs")
    (description
     "These are @code{bartMachine's} Java dependency libraries.  Note: this package
has no functionality of its own and should not be installed as a standalone
package without @code{bartMachine}.")
    (license license:gpl3)))

(define-public r-bartmachine
  (package
    (name "r-bartmachine")
    (version "1.3.4.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bartMachine" version))
       (sha256
        (base32 "12abi19nd07lvclxpa73ba21sbrll67bbp0a8vz8wnj2ym8542iv"))))
    (properties `((upstream-name . "bartMachine")))
    (build-system r-build-system)
    (inputs (list openjdk))
    (propagated-inputs (list r-rjava r-randomforest r-missforest
                             r-bartmachinejars))
    (home-page "https://cran.r-project.org/package=bartMachine")
    (synopsis "Bayesian Additive Regression Trees")
    (description
     "An advanced implementation of Bayesian Additive Regression Trees with expanded
features for data analysis and visualization.")
    (license license:gpl3)))

(define-public r-bartcs
  (package
    (name "r-bartcs")
    (version "1.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bartcs" version))
       (sha256
        (base32 "0kajw5phkymvkilwb7bf3qdc24h369yvb1zmc49kk6k7bdpbxcrr"))))
    (properties `((upstream-name . "bartcs")))
    (build-system r-build-system)
    (propagated-inputs (list r-rootsolve
                             r-rlang
                             r-rcpp
                             r-mcmcpack
                             r-invgamma
                             r-ggplot2
                             r-ggcharts
                             r-coda))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/yooyh/bartcs")
    (synopsis "Bayesian Additive Regression Trees for Confounder Selection")
    (description
     "Fit Bayesian Regression Additive Trees (BART) models to select true confounders
from a large set of potential confounders and to estimate average treatment
effect.  For more information, see Kim et al. (2023) <doi:10.1111/biom.13833>.")
    (license license:gpl3+)))

(define-public r-bartcause
  (package
    (name "r-bartcause")
    (version "1.0-9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bartCause" version))
       (sha256
        (base32 "1nvb3ljzh4l8bq1nixvphr0aww1zxi3bcv6jfa54qs4iq9mgqxsv"))))
    (properties `((upstream-name . "bartCause")))
    (build-system r-build-system)
    (propagated-inputs (list r-dbarts))
    (home-page "https://github.com/vdorie/bartCause")
    (synopsis "Causal Inference using Bayesian Additive Regression Trees")
    (description
     "This package contains a variety of methods to generate typical causal inference
estimates using Bayesian Additive Regression Trees (BART) as the underlying
regression model (Hill (2012) <doi:10.1198/jcgs.2010.08162>).")
    (license license:gpl2+)))

(define-public r-bart
  (package
    (name "r-bart")
    (version "2.9.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BART" version))
       (sha256
        (base32 "1jm17n2p6n0knbjzy3sqxaljbq9qfm7mn1swb6zpgw172hr7l22v"))))
    (properties `((upstream-name . "BART")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-rcpp r-nlme))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BART")
    (synopsis "Bayesian Additive Regression Trees")
    (description
     "Bayesian Additive Regression Trees (BART) provide flexible nonparametric
modeling of covariates for continuous, binary, categorical and time-to-event
outcomes.  For more information see Sparapani, Spanbauer and @code{McCulloch}
<doi:10.18637/jss.v097.i01>.")
    (license license:gpl2+)))

(define-public r-barrks
  (package
    (name "r-barrks")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "barrks" version))
       (sha256
        (base32 "0idaq93alsjry7v63xm3mq3ap840vap8b7saanr286ssm26p61hd"))))
    (properties `((upstream-name . "barrks")))
    (build-system r-build-system)
    (propagated-inputs (list r-terra
                             r-stringr
                             r-readr
                             r-rdpack
                             r-purrr
                             r-lubridate
                             r-dplyr))
    (native-inputs (list r-knitr))
    (home-page "https://jjentschke.github.io/barrks/")
    (synopsis "Calculate Bark Beetle Phenology Using Different Models")
    (description
     "Calculate the bark beetle phenology based on raster data or point-related data.
There are multiple models implemented for two bark beetle species.  The models
can be customized and their submodels (onset of infestation, beetle development,
diapause initiation, mortality) can be combined.  The following models are
available in the package: PHENIPS-Clim (first-time release in this package),
PHENIPS (Baier et al.  2007) <doi:10.1016/j.foreco.2007.05.020>, RITY (Ogris et
al.  2019) <doi:10.1016/j.ecolmodel.2019.108775>, CHAPY (Ogris et al.  2020)
<doi:10.1016/j.ecolmodel.2020.109137>, BSO (Jakoby et al.  2019)
<doi:10.1111/gcb.14766>, Lange et al. (2008) <doi:10.1007/978-3-540-85081-6_32>,
JÃ¶nsson et al. (2011) <doi:10.1007/s10584-011-0038-4>.  The package may be
expanded by models for other bark beetle species in the future.")
    (license license:gpl3+)))

(define-public r-barnard
  (package
    (name "r-barnard")
    (version "1.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Barnard" version))
       (sha256
        (base32 "1ki58hjs5kpmp76kwj1qi6hwmdvjphqb1lvrlhnmkgqxg4hw4vqg"))))
    (properties `((upstream-name . "Barnard")))
    (build-system r-build-system)
    (home-page "https://github.com/kerguler/Barnard")
    (synopsis "Barnard's Unconditional Test")
    (description "Barnard's unconditional test for 2x2 contingency tables.")
    (license license:gpl2)))

(define-public r-bark
  (package
    (name "r-bark")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bark" version))
       (sha256
        (base32 "0kmxklp4l3yfyvh3g72zh5g1y04ahz1h7b1z3kx360yxv97098gx"))))
    (properties `((upstream-name . "bark")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://www.R-project.org")
    (synopsis "Bayesian Additive Regression Kernels")
    (description
     "Bayesian Additive Regression Kernels (BARK) provides an implementation for
non-parametric function estimation using Levy Random Field priors for functions
that may be represented as a sum of additive multivariate kernels.  Kernels are
located at every data point as in Support Vector Machines, however, coefficients
may be heavily shrunk to zero under the Cauchy process prior, or even, set to
zero.  The number of active features is controlled by priors on precision
parameters within the kernels, permitting feature selection.  For more details
see Ouyang, Z (2008) \"Bayesian Additive Regression Kernels\", Duke University.
@code{PhD} dissertation, Chapter 3 and Wolpert, R. L, Clyde, M.A, and Tu, C.
(2011) \"Stochastic Expansions with Continuous Dictionaries Levy Adaptive
Regression Kernels, Annals of Statistics Vol (39) pages 1916-1962
<doi:10.1214/11-AOS889>.")
    (license license:gpl3+)))

(define-public r-bardr
  (package
    (name "r-bardr")
    (version "0.0.9")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bardr" version))
       (sha256
        (base32 "02499b3j10j7qq3whz9qijfp3amp0s1ji8qk76c53dsnkj8f9hvs"))))
    (properties `((upstream-name . "bardr")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=bardr")
    (synopsis "Complete Works of William Shakespeare in Tidy Format")
    (description
     "This package provides R data structures for Shakespeare's complete works, as
provided by Project Gutenberg <https:www.gutenberg.org/ebooks/100>.")
    (license license:gpl3)))

(define-public r-barcodingr
  (package
    (name "r-barcodingr")
    (version "1.0-3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BarcodingR" version))
       (sha256
        (base32 "03x26mrlvg00rnfh74wacpgbdk7xr2lq4yd0bx4q2442nli54rdv"))))
    (properties `((upstream-name . "BarcodingR")))
    (build-system r-build-system)
    (propagated-inputs (list r-sp r-nnet r-class r-ape))
    (home-page "https://cran.r-project.org/package=BarcodingR")
    (synopsis "Species Identification using DNA Barcodes")
    (description "To perform species identification using DNA barcodes.")
    (license license:gpl2)))

(define-public r-barcoder
  (package
    (name "r-barcoder")
    (version "0.1.7")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "baRcodeR" version))
       (sha256
        (base32 "199cdbwphpz6ds19841s32bm2rjd7b6v484bvaskw94z0d8bcn6w"))))
    (properties `((upstream-name . "baRcodeR")))
    (build-system r-build-system)
    (propagated-inputs (list r-shiny r-rstudioapi r-qrcode r-miniui r-dt))
    (native-inputs (list r-knitr))
    (home-page
     "https://docs.ropensci.org/baRcodeR/https://github.com/ropensci/baRcodeR/")
    (synopsis
     "Label Creation for Tracking and Collecting Data from Biological Samples")
    (description
     "This package provides tools to generate unique identifier codes and printable
barcoded labels for the management of biological samples.  The creation of
unique ID codes and printable PDF files can be initiated by standard commands,
user prompts, or through a GUI addin for R Studio.  Biologically informative
codes can be included for hierarchically structured sampling designs.")
    (license license:gpl3)))

(define-public r-barcode
  (package
    (name "r-barcode")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "barcode" version))
       (sha256
        (base32 "1kk5nwh810l5vix6qzwl28cfi8rk7vc35hv73whk1bkndv2y4qx0"))))
    (properties `((upstream-name . "barcode")))
    (build-system r-build-system)
    (propagated-inputs (list r-lattice))
    (home-page "https://cran.r-project.org/package=barcode")
    (synopsis "Render Barcode Distribution Plots")
    (description
     "The function \\code{barcode()} produces a histogram-like plot of a distribution
that shows granularity in the data.")
    (license license:gpl2+)))

(define-public r-barborgradient
  (package
    (name "r-barborgradient")
    (version "1.0.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BarBorGradient" version))
       (sha256
        (base32 "0g2g7hrm23as1xbj79g48r8sj3gsj66fmi895jqlqfbqrl2fl7a5"))))
    (properties `((upstream-name . "BarBorGradient")))
    (build-system r-build-system)
    (home-page "https://cran.r-project.org/package=BarBorGradient")
    (synopsis "Function Minimum Approximator")
    (description
     "Tool to find where a function has its lowest value(minimum).  The functions can
be any dimensions.  Recommended use is with eps=10^-10, but can be run with
10^-20, although this depends on the function.  Two more methods are in this
package, simple gradient method (Gradmod) and Powell method (Powell).  These are
not recommended for use, their purpose are purely for comparison.")
    (license license:gpl3)))

(define-public r-bar
  (package
    (name "r-bar")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BAR" version))
       (sha256
        (base32 "0bcq6lvijq1vxri2dbhnqcn46f799h3ag974zgcdpx4c5apph04w"))))
    (properties `((upstream-name . "BAR")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BAR")
    (synopsis "Bayesian Adaptive Randomization")
    (description
     "Bayesian adaptive randomization is also called outcome adaptive randomization,
which is increasingly used in clinical trials.")
    (license license:gpl2)))

(define-public r-bapred
  (package
    (name "r-bapred")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bapred" version))
       (sha256
        (base32 "1pnhq1li0c4pvgvwsaxxzsgbdyjnb96s1hciqanzgbd1d4577lvp"))))
    (properties `((upstream-name . "bapred")))
    (build-system r-build-system)
    (propagated-inputs (list r-sva
                             r-mnormt
                             r-mass
                             r-lme4
                             r-glmnet
                             r-fuzzyranktests
                             r-fnn
                             r-biobase
                             r-affyplm
                             r-affy))
    (home-page "https://cran.r-project.org/package=bapred")
    (synopsis
     "Batch Effect Removal and Addon Normalization (in Phenotype Prediction using Gene Data)")
    (description
     "Various tools dealing with batch effects, in particular enabling the removal of
discrepancies between training and test sets in prediction scenarios.  Moreover,
addon quantile normalization and addon RMA normalization (Kostka & Spang, 2008)
is implemented to enable integrating the quantile normalization step into
prediction rules.  The following batch effect removal methods are implemented:
FAbatch, @code{ComBat}, (f)SVA, mean-centering, standardization, Ratio-A and
Ratio-G. For each of these we provide an additional function which enables a
posteriori ('addon') batch effect removal in independent batches ('test data').
Here, the (already batch effect adjusted) training data is not altered.  For
evaluating the success of batch effect adjustment several metrics are provided.
Moreover, the package implements a plot for the visualization of batch effects
using principal component analysis.  The main functions of the package for batch
effect adjustment are ba() and baaddon() which enable batch effect removal and
addon batch effect removal, respectively, with one of the seven methods
mentioned above.  Another important function here is bametric() which is a
wrapper function for all implemented methods for evaluating the success of batch
effect removal.  For (addon) quantile normalization and (addon) RMA
normalization the functions qunormtrain(), qunormaddon(), rmatrain() and
rmaaddon() can be used.")
    (license license:gpl2)))

(define-public r-baorista
  (package
    (name "r-baorista")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "baorista" version))
       (sha256
        (base32 "06dgqzkl9a2v3izpwlgvc31lg2b4yh3pynhs4wgjzhrn58lf6rp1"))))
    (properties `((upstream-name . "baorista")))
    (build-system r-build-system)
    (propagated-inputs (list r-nimble r-coda))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=baorista")
    (synopsis "Bayesian Aoristic Analyses")
    (description
     "This package provides an alternative approach to aoristic analyses for
archaeological datasets by fitting Bayesian parametric growth models and
non-parametric random-walk Intrinsic Conditional Autoregressive (ICAR) models on
time frequency data (Crema (2024)<doi:10.1111/arcm.12984>).  It handles event
typo-chronology based timespans defined by start/end date as well as more
complex user-provided vector of probabilities.")
    (license license:gpl2+)))

(define-public r-banxicor
  (package
    (name "r-banxicor")
    (version "0.9.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "banxicoR" version))
       (sha256
        (base32 "0vchm2hnqm45fhn8k38rcam4vkazmifcl40390s2r04bgvqr0pnl"))))
    (properties `((upstream-name . "banxicoR")))
    (build-system r-build-system)
    (propagated-inputs (list r-xml2 r-stringr r-rvest))
    (home-page "https://cran.r-project.org/package=banxicoR")
    (synopsis "Download Data from the Bank of Mexico")
    (description
     "This package provides functions to scrape IQY calls to Bank of Mexico,
downloading and ordering the data conveniently.")
    (license license:cc0)))

(define-public r-banter
  (package
    (name "r-banter")
    (version "0.9.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "banter" version))
       (sha256
        (base32 "07gzl06l7iw4czdx3il3p8kl83yw32fxa8cjx1j018z3468m1qyj"))))
    (properties `((upstream-name . "banter")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-swfscmisc
                             r-rlang
                             r-rfpermute
                             r-randomforest
                             r-magrittr
                             r-gridextra
                             r-ggplot2
                             r-dplyr))
    (home-page "https://cran.r-project.org/package=banter")
    (synopsis "BioAcoustic eveNT classifiER")
    (description
     "Create a hierarchical acoustic event species classifier out of multiple call
type detectors as described in Rankin et al (2017) <doi:10.1111/mms.12381>.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-banova
  (package
    (name "r-banova")
    (version "1.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BANOVA" version))
       (sha256
        (base32 "07ghmzsb77vcd3snmg59spp60j603gawnqs847x1x44pnxs9l4wl"))))
    (properties `((upstream-name . "BANOVA")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-runjags r-rstan r-rjags r-coda))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BANOVA")
    (synopsis "Hierarchical Bayesian ANOVA Models")
    (description
     "It covers several Bayesian Analysis of Variance (BANOVA) models used in analysis
of experimental designs in which both within- and between- subjects factors are
manipulated.  They can be applied to data that are common in the behavioral and
social sciences.  The package includes: Hierarchical Bayes ANOVA models with
normal response, t response, Binomial (Bernoulli) response, Poisson response,
ordered multinomial response and multinomial response variables.  All models
accommodate unobserved heterogeneity by including a normal distribution of the
parameters across individuals.  Outputs of the package include tables of sums of
squares, effect sizes and p-values, and tables of predictions, which are easily
interpretable for behavioral and social researchers.  The floodlight analysis
and mediation analysis based on these models are also provided.  BANOVA uses
Stan and JAGS as the computational platform.  References: Dong and Wedel (2017)
<doi:10.18637/jss.v081.i09>; Wedel and Dong (2020) <doi:10.1002/jcpy.1111>.")
    (license license:gpl2+)))

(define-public r-bannercommenter
  (package
    (name "r-bannercommenter")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bannerCommenter" version))
       (sha256
        (base32 "1i0b3v4n0xylcbrg9i3a28z037m27vjfg61xz2hl99wavn0bsl1j"))))
    (properties `((upstream-name . "bannerCommenter")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bannerCommenter")
    (synopsis "Make Banner Comments with a Consistent Format")
    (description
     "This package provides a convenience package for use while drafting code.  It
facilitates making stand-out comment lines decorated with bands of characters.
The input text strings are converted into R comment lines, suitably formatted.
These are then displayed in a console window and, if possible, automatically
transferred to a clipboard ready for pasting into an R script.  Designed to save
time when drafting R scripts that will need to be navigated and maintained by
other programmers.")
    (license license:gpl2+)))

(define-public r-bangladesh
  (package
    (name "r-bangladesh")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bangladesh" version))
       (sha256
        (base32 "1vjrashph4n8njm6yk8slywmfwxqrvdmb2ybgp23vdxjhk3rlrzl"))))
    (properties `((upstream-name . "bangladesh")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmap r-sf))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bangladesh")
    (synopsis
     "Provides Ready to Use Shapefiles for Geographical Map of Bangladesh")
    (description
     "Usually, it is difficult to plot choropleth maps for Bangladesh in R'.  The
bangladesh package provides ready-to-use shapefiles for different administrative
regions of Bangladesh (e.g., Division, District, Upazila, and Union).  This
package helps users to draw thematic maps of administrative regions of
Bangladesh easily as it comes with the sf objects for the boundaries.  It also
provides functions allowing users to efficiently get specific area maps and
center coordinates for regions.  Users can also search for a specific area and
calculate the centroids of those areas.")
    (license license:expat)))

(define-public r-bang
  (package
    (name "r-bang")
    (version "1.0.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bang" version))
       (sha256
        (base32 "0v2iwy52knvsnr2ldcdqm1z99ag047fxbch56nvs6mki6zbn1vwf"))))
    (properties `((upstream-name . "bang")))
    (build-system r-build-system)
    (propagated-inputs (list r-rust r-bayesplot))
    (native-inputs (list r-knitr))
    (home-page "https://paulnorthrop.github.io/bang/")
    (synopsis "Bayesian Analysis, No Gibbs")
    (description
     "This package provides functions for the Bayesian analysis of some simple
commonly-used models, without using Markov Chain Monte Carlo (MCMC) methods such
as Gibbs sampling.  The rust package <https://cran.r-project.org/package=rust>
is used to simulate a random sample from the required posterior distribution,
using the generalized ratio-of-uniforms method.  See Wakefield, Gelfand and
Smith (1991) <DOI:10.1007/BF01889987> for details.  At the moment three
conjugate hierarchical models are available: beta-binomial, gamma-Poisson and a
1-way analysis of variance (ANOVA).")
    (license license:gpl2+)))

(define-public r-banffit
  (package
    (name "r-banffit")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "banffIT" version))
       (sha256
        (base32 "0b37j8xg2rg7gisp2m1rqvjfffqj0yfz54bq2fl0wckmgknkz05c"))))
    (properties `((upstream-name . "banffIT")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stringr
                             r-rlang
                             r-madshapr
                             r-lubridate
                             r-fs
                             r-fabr
                             r-dplyr
                             r-crayon))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/PersonalizedTransplantCare/banffIT")
    (synopsis
     "Automatize Diagnosis Standardized Assignation Using the Banff Classification")
    (description
     "Assigns standardized diagnoses using the Banff Classification (Category 1 to 6
diagnoses, including Acute and Chronic active T-cell mediated rejection as well
as Active, Chronic active, and Chronic antibody mediated rejection).  The main
function considers a minimal dataset containing biopsies information in a
specific format (described by a data dictionary), verifies its content and
format (based on the data dictionary), assigns diagnoses, and creates a summary
report.  The package is developed on the reference guide to the Banff
classification of renal allograft pathology Roufosse C, Simmonds N, Clahsen-van
Groningen M, et al.  A (2018) <doi:10.1097/TP.0000000000002366>.  The full
description of the Banff classification is available at
<https://banfffoundation.org/>.")
    (license license:gpl3)))

(define-public r-bandsfdp
  (package
    (name "r-bandsfdp")
    (version "1.1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bandsfdp" version))
       (sha256
        (base32 "07w381i8cls1hf1mdl4qy3vxp7xx0czng3fl3bzrbk80mysjf47n"))))
    (properties `((upstream-name . "bandsfdp")))
    (build-system r-build-system)
    (home-page "https://github.com/uni-Arya/bandsfdp")
    (synopsis
     "Compute Upper Prediction Bounds on the FDP in Competition-Based Setups")
    (description
     "This package implements functions that calculate upper prediction bounds on the
false discovery proportion (FDP) in the list of discoveries returned by
competition-based setups, implementing Ebadi et al. (2022)
<@code{arXiv:2302.11837>}.  Such setups include target-decoy competition (TDC)
in computational mass spectrometry and the knockoff construction in linear
regression (note this package typically uses the terminology of TDC).  Included
is the standardized (TDC-SB) and uniform (TDC-UB) bound on TDC's FDP, and the
simultaneous standardized and uniform bands.  Requires pre-computed Monte Carlo
statistics available at <https://github.com/uni-Arya/fdpbandsdata>.  This data
can be downloaded by running the command
devtools::install_github(\"uni-Arya/fdpbandsdata\") in R and restarting R after
installation.  The size of this data is roughly 81Mb.")
    (license license:expat)))

(define-public r-banditpam
  (package
    (name "r-banditpam")
    (version "1.0-1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "banditpam" version))
       (sha256
        (base32 "05ym5x178rgfvpvqgiv46ly6xnk44d0j74ykdxkzshil7aig1xfk"))))
    (properties `((upstream-name . "banditpam")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp r-r6))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=banditpam")
    (synopsis "Almost Linear-Time k-Medoids Clustering")
    (description
     "Interface to a high-performance implementation of k-medoids clustering described
in Tiwari, Zhang, Mayclin, Thrun, Piech and Shomorony (2020) \"@code{BanditPAM}:
Almost Linear Time k-medoids Clustering via Multi-Armed Bandits\"
<https://proceedings.neurips.cc/paper/2020/file/73b817090081cef1bca77232f4532c5d-Paper.pdf>.")
    (license license:expat)))

(define-public r-bandit
  (package
    (name "r-bandit")
    (version "0.5.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bandit" version))
       (sha256
        (base32 "0zj98x4kwv8fr6j78vg0hzlsjd4w5l4nbazkpxqkn5yyd6g0v9h4"))))
    (properties `((upstream-name . "bandit")))
    (build-system r-build-system)
    (propagated-inputs (list r-gam r-boot))
    (home-page "https://cran.r-project.org/package=bandit")
    (synopsis
     "Functions for Simple a/B Split Test and Multi-Armed Bandit Analysis")
    (description
     "This package provides a set of functions for doing analysis of A/B split test
data and web metrics in general.")
    (license license:gpl3)))

(define-public r-bandicoot
  (package
    (name "r-bandicoot")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bandicoot" version))
       (sha256
        (base32 "0vb9cdp3ci00rjqb18l1a6106fbl5w8sjlj8kfs170rccziynnf9"))))
    (properties `((upstream-name . "bandicoot")))
    (build-system r-build-system)
    (propagated-inputs (list r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://tengmcing.github.io/bandicoot/")
    (synopsis "Light-Weight 'python'-Like Object-Oriented System")
    (description
     "This package provides a light-weight object-oriented system with python'-like
syntax which supports multiple inheritances and incorporates a python'-like
method resolution order.")
    (license license:expat)))

(define-public r-banam
  (package
    (name "r-banam")
    (version "0.2.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BANAM" version))
       (sha256
        (base32 "0jiw42icj0zzxpf51fxjbgqzjlp5s90nd8pgav4nlq70h4y64g2n"))))
    (properties `((upstream-name . "BANAM")))
    (build-system r-build-system)
    (propagated-inputs (list r-tmvtnorm
                             r-sna
                             r-rarpack
                             r-psych
                             r-mvtnorm
                             r-matrixcalc
                             r-matrix
                             r-extradistr
                             r-bfpack
                             r-bain))
    (home-page "https://cran.r-project.org/package=BANAM")
    (synopsis "Bayesian Analysis of the Network Autocorrelation Model")
    (description
     "The network autocorrelation model (NAM) can be used for studying the degree of
social influence regarding an outcome variable based on one or more known
networks.  The degree of social influence is quantified via the network
autocorrelation parameters.  In case of a single network, the Bayesian methods
of Dittrich, Leenders, and Mulder (2017) <DOI:10.1016/j.socnet.2016.09.002> and
Dittrich, Leenders, and Mulder (2019) <DOI:10.1177/0049124117729712> are
implemented using a normal, flat, or independence Jeffreys prior for the network
autocorrelation.  In the case of multiple networks, the Bayesian methods of
Dittrich, Leenders, and Mulder (2020) <DOI:10.1177/0081175020913899> are
implemented using a multivariate normal prior for the network autocorrelation
parameters.  Flat priors are implemented for estimating the coefficients.  For
Bayesian testing of equality and order-constrained hypotheses, the default Bayes
factor of Gu, Mulder, and Hoijtink, (2018) <DOI:10.1111/bmsp.12110> is used with
the posterior mean and posterior covariance matrix of the NAM parameters based
on flat priors as input.")
    (license license:gpl3+)))

(define-public r-bamp
  (package
    (name "r-bamp")
    (version "2.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bamp" version))
       (sha256
        (base32 "0qzcndjdwbgiwyvc7ikb7jr0n7cmn78v56h5in22j24jjj740l2z"))))
    (properties `((upstream-name . "bamp")))
    (build-system r-build-system)
    (propagated-inputs (list r-coda r-abind))
    (native-inputs (list r-r-rsp r-knitr))
    (home-page "https://volkerschmid.github.io/bamp/")
    (synopsis "Bayesian Age-Period-Cohort Modeling and Prediction")
    (description
     "Bayesian Age-Period-Cohort Modeling and Prediction using efficient Markov Chain
Monte Carlo Methods.  This is the R version of the previous BAMP software as
described in Volker Schmid and Leonhard Held (2007) <DOI:10.18637/jss.v021.i08>
Bayesian Age-Period-Cohort Modeling and Prediction - BAMP, Journal of
Statistical Software 21:8.  This package includes checks of convergence using
Gelman's R.")
    (license license:gpl3)))

(define-public r-bammtools
  (package
    (name "r-bammtools")
    (version "2.1.12")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BAMMtools" version))
       (sha256
        (base32 "0hijnx2rlfhwxm2jvyam6nzfcxyff9fq69nz7cnnw2wald8y6s7y"))))
    (properties `((upstream-name . "BAMMtools")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-gplots r-ape))
    (home-page "http://bamm-project.org/")
    (synopsis
     "Analysis and Visualization of Macroevolutionary Dynamics on Phylogenetic Trees")
    (description
     "This package provides functions for analyzing and visualizing complex
macroevolutionary dynamics on phylogenetic trees.  It is a companion package to
the command line program BAMM (Bayesian Analysis of Macroevolutionary Mixtures)
and is entirely oriented towards the analysis, interpretation, and visualization
of evolutionary rates.  Functionality includes visualization of rate shifts on
phylogenies, estimating evolutionary rates through time, comparing posterior
distributions of evolutionary rates across clades, comparing diversification
models using Bayes factors, and more.")
    (license license:gpl2+)))

(define-public r-bamm
  (package
    (name "r-bamm")
    (version "0.5.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bamm" version))
       (sha256
        (base32 "1m48bszhqhzsjnr70pv5lmx19x13gm9nfg1idcr2jrn87xcm8mlb"))))
    (properties `((upstream-name . "bamm")))
    (build-system r-build-system)
    (inputs (list sqlite geos))
    (propagated-inputs (list r-sp
                             r-rspectra
                             r-rdpack
                             r-rcpparmadillo
                             r-rcpp
                             r-raster
                             r-purrr
                             r-plotly
                             r-matrix
                             r-magrittr
                             r-leaflet
                             r-igraph
                             r-future
                             r-furrr
                             r-dplyr
                             r-crosstalk
                             r-animation))
    (native-inputs (list r-knitr))
    (home-page "https://luismurao.github.io/bamm/")
    (synopsis
     "Species Distribution Models as a Function of Biotic, Abiotic and Movement Factors (BAM)")
    (description
     "Species Distribution Modeling (SDM) is a practical methodology that aims to
estimate the area of distribution of a species.  However, most of the work has
focused on estimating static expressions of the correlation between
environmental variables.  The outputs of correlative species distribution models
can be interpreted as maps of the suitable environment for a species but not
generally as maps of its actual distribution.  SoberÃ³n and Peterson (2005)
<doi:10.17161/bi.v2i0.4> presented the BAM scheme, a heuristic framework that
states that the occupied area of a species occurs on sites that have been
accessible through dispersal (M) and have both favorable biotic (B) and abiotic
conditions (A).  The bamm package implements classes and functions to operate on
each element of the BAM and by using a cellular automata model where the
occupied area of a species at time t is estimated by the multiplication of three
binary matrices: one matrix represents movements (M), another abiotic -niche-
tolerances (A), and a third, biotic interactions (B).  The theoretical
background of the package can be found in SoberÃ³n and Osorio-Olvera (2023)
<doi:10.1111/jbi.14587>.")
    (license license:gpl3+)))

(define-public r-bamlss
  (package
    (name "r-bamlss")
    (version "1.2-5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bamlss" version))
       (sha256
        (base32 "0p7gi5biydv73mmscizxd193fzx60cd8zwv0i5gsshspi9cwfmw6"))))
    (properties `((upstream-name . "bamlss")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival
                             r-sp
                             r-mvtnorm
                             r-mgcv
                             r-mba
                             r-matrix
                             r-formula
                             r-distributions3
                             r-colorspace
                             r-coda))
    (native-inputs (list r-knitr))
    (home-page "http://www.bamlss.org/")
    (synopsis
     "Bayesian Additive Models for Location, Scale, and Shape (and Beyond)")
    (description
     "Infrastructure for estimating probabilistic distributional regression models in
a Bayesian framework.  The distribution parameters may capture location, scale,
shape, etc.  and every parameter may depend on complex additive terms (fixed,
random, smooth, spatial, etc.) similar to a generalized additive model.  The
conceptual and computational framework is introduced in Umlauf, Klein, Zeileis
(2019) <doi:10.1080/10618600.2017.1407325> and the R package in Umlauf, Klein,
Simon, Zeileis (2021) <doi:10.18637/jss.v100.i04>.")
    (license (list license:gpl2 license:gpl3))))

(define-public r-bamdit
  (package
    (name "r-bamdit")
    (version "3.4.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bamdit" version))
       (sha256
        (base32 "09gbli5jpzv9ndpbgmck8kfpb6gxafa8jnfkmsg7qls004qg6k7y"))))
    (properties `((upstream-name . "bamdit")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-rjags
                             r-r2jags
                             r-mass
                             r-gridextra
                             r-ggplot2
                             r-ggextra))
    (home-page "https://cran.r-project.org/package=bamdit")
    (synopsis "Bayesian Meta-Analysis of Diagnostic Test Data")
    (description
     "This package provides a new class of Bayesian meta-analysis models that
incorporates a model for internal and external validity bias.  In this way, it
is possible to combine studies of diverse quality and different types.  For
example, we can combine the results of randomized control trials (RCTs) with the
results of observational studies (OS).")
    (license license:gpl2+)))

(define-public r-bamboohr
  (package
    (name "r-bamboohr")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bambooHR" version))
       (sha256
        (base32 "0d4jw8ymrgi20il0ipszs18a7x7q7xb3jpqmp1zrb5gigvabb9x2"))))
    (properties `((upstream-name . "bambooHR")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-rlang
                             r-purrr
                             r-magrittr
                             r-lubridate
                             r-jsonlite
                             r-janitor
                             r-httr
                             r-glue
                             r-dplyr
                             r-curl
                             r-cli))
    (home-page "https://mangothecat.github.io/bambooHR/")
    (synopsis "Wrapper to the 'BambooHR' API")
    (description
     "Enables a user to consume the @code{BambooHR} API endpoints using R. The actual
URL of the API will depend on your company domain, and will be handled by the
package automatically once you setup the config file.  The API documentation can
be found here <https://documentation.bamboohr.com/docs>.")
    (license license:expat)))

(define-public r-bambi
  (package
    (name "r-bambi")
    (version "2.3.5")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BAMBI" version))
       (sha256
        (base32 "16pb04n30y8sl6m6qgrdxk7sck98233cravpbzw4fzl2j8h0mm3r"))))
    (properties `((upstream-name . "BAMBI")))
    (build-system r-build-system)
    (propagated-inputs (list r-scales
                             r-rcpparmadillo
                             r-rcpp
                             r-rcolorbrewer
                             r-qrng
                             r-numderiv
                             r-mvtnorm
                             r-loo
                             r-lattice
                             r-label-switching
                             r-gtools
                             r-future-apply
                             r-coda
                             r-bridgesampling))
    (home-page "https://doi.org/10.18637/jss.v099.i11")
    (synopsis "Bivariate Angular Mixture Models")
    (description
     "Fit (using Bayesian methods) and simulate mixtures of univariate and bivariate
angular distributions.  Chakraborty and Wong (2021) <doi:10.18637/jss.v099.i11>.")
    (license license:gpl3)))

(define-public r-bama
  (package
    (name "r-bama")
    (version "1.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bama" version))
       (sha256
        (base32 "1ydm9p6msmgi74rhrji2j14qy7cg7fqgg4ffm9rz293k27n5ghnn"))))
    (properties `((upstream-name . "bama")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcppdist r-rcpparmadillo r-rcpp r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/umich-cphds/bama")
    (synopsis "High Dimensional Bayesian Mediation Analysis")
    (description
     "Perform mediation analysis in the presence of high-dimensional mediators based
on the potential outcome framework.  Bayesian Mediation Analysis (BAMA),
developed by Song et al (2019) <doi:10.1111/biom.13189> and Song et al (2020)
<@code{arXiv:2009.11409>}, relies on two Bayesian sparse linear mixed models to
simultaneously analyze a relatively large number of mediators for a continuous
exposure and outcome assuming a small number of mediators are truly active.
This sparsity assumption also allows the extension of univariate mediator
analysis by casting the identification of active mediators as a variable
selection problem and applying Bayesian methods with continuous shrinkage priors
on the effects.")
    (license license:gpl3)))

(define-public r-bam
  (package
    (name "r-bam")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BaM" version))
       (sha256
        (base32 "1mfbz8hp6cd170d5y6iva2dbd31rlg6z2dgnfvjp9myqa4cjy40y"))))
    (properties `((upstream-name . "BaM")))
    (build-system r-build-system)
    (propagated-inputs (list r-mice r-mass))
    (home-page "https://cran.r-project.org/package=BaM")
    (synopsis
     "Functions and Datasets for \"Bayesian Methods: A Social and Behavioral Sciences Approach\"")
    (description
     "This package provides functions and datasets for Jeff Gill: \"Bayesian Methods: A
Social and Behavioral Sciences Approach\".  First, Second, and Third Edition.
Published by Chapman and Hall/CRC (2002, 2007, 2014) <doi:10.1201/b17888>.")
    (license license:gpl2+)))

(define-public r-ballmapper
  (package
    (name "r-ballmapper")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BallMapper" version))
       (sha256
        (base32 "1hf94d41cks8mbrb985mpd0nqgirvm096cfv7bsy0182bbxlk6s1"))))
    (properties `((upstream-name . "BallMapper")))
    (build-system r-build-system)
    (propagated-inputs (list r-testthat
                             r-stringr
                             r-scales
                             r-networkd3
                             r-igraph
                             r-fields))
    (home-page "https://cran.r-project.org/package=BallMapper")
    (synopsis "The Ball Mapper Algorithm")
    (description
     "The core algorithm is described in \"Ball mapper: a shape summary for topological
data analysis\" by Pawel Dlotko, (2019) <@code{arXiv:1901.07410>}.  Please
consult the following youtube video
<https://www.youtube.com/watch?v=M9Dm1nl_@code{zSQfor>} the idea of
functionality.  Ball Mapper provide a topologically accurate summary of a data
in a form of an abstract graph.  To create it, please provide the coordinates of
points (in the points array), values of a function of interest at those points
(can be initialized randomly if you do not have it) and the value epsilon which
is the radius of the ball in the Ball Mapper construction.  It can be understood
as the minimal resolution on which we use to create the model of the data.")
    (license (license:fsdg-compatible "MIT + file LICENCE"))))

(define-public r-balli
  (package
    (name "r-balli")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BALLI" version))
       (sha256
        (base32 "0qmjwpj56n07dvjwfknqvc02f0y5mlwh2yz4lrw7c4wqgrckcpgi"))))
    (properties `((upstream-name . "BALLI")))
    (build-system r-build-system)
    (propagated-inputs (list r-mass r-limma r-edger))
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=BALLI")
    (synopsis "Expression RNA-Seq Data Analysis Based on Linear Mixed Model")
    (description
     "Analysis of gene expression RNA-seq data using Bartlett-Adjusted
Likelihood-based LInear model (BALLI).  Based on likelihood ratio test, it
provides comparisons for effect of one or more variables.  See Kyungtaek Park
(2018) <doi:10.1101/344929> for more information.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-ball
  (package
    (name "r-ball")
    (version "1.3.13")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "Ball" version))
       (sha256
        (base32 "1bmwdjj7zsxnchq1xv64b45izn94bzayqzzib47jq03b59blg4ay"))))
    (properties `((upstream-name . "Ball")))
    (build-system r-build-system)
    (propagated-inputs (list r-survival r-mvtnorm r-gam))
    (native-inputs (list r-knitr))
    (home-page "https://mamba413.github.io/Ball/")
    (synopsis
     "Statistical Inference and Sure Independence Screening via Ball Statistics")
    (description
     "Hypothesis tests and sure independence screening (SIS) procedure based on ball
statistics, including ball divergence <doi:10.1214/17-AOS1579>, ball covariance
<doi:10.1080/01621459.2018.1543600>, and ball correlation
<doi:10.1080/01621459.2018.1462709>, are developed to analyze complex data in
metric spaces, e.g, shape, directional, compositional and symmetric positive
definite matrix data.  The ball divergence and ball covariance based
distribution-free tests are implemented to detecting distribution difference and
association in metric spaces <doi:10.18637/jss.v097.i06>.  Furthermore, several
generic non-parametric feature selection procedures based on ball correlation,
BCor-SIS and all of its variants, are implemented to tackle the challenge in the
context of ultra high dimensional data.  A fast implementation for large-scale
multiple K-sample testing with ball divergence <doi: 10.1002/gepi.22423> is
supported, which is particularly helpful for genome-wide association study.")
    (license license:gpl3)))

(define-public r-baldur
  (package
    (name "r-baldur")
    (version "0.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "baldur" version))
       (sha256
        (base32 "13nl5apdmb1pm32d58qq04vc2xngc35n6cv4sivf2s34yk4k0n4c"))))
    (properties `((upstream-name . "baldur")))
    (build-system r-build-system)
    (propagated-inputs (list r-viridislite
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rlang
                             r-rdpack
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-purrr
                             r-multidplyr
                             r-magrittr
                             r-lifecycle
                             r-ggplot2
                             r-dplyr
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/PhilipBerg/baldur")
    (synopsis "Bayesian Hierarchical Modeling for Label-Free Proteomics")
    (description
     "Statistical decision in proteomics data using a hierarchical Bayesian model.
There are two regression models for describing the mean-variance trend, a gamma
regression or a latent gamma mixture regression.  The regression model is then
used as an Empirical Bayes estimator for the prior on the variance in a peptide.
 Further, it assumes that each measurement has an uncertainty (increased
variance) associated with it that is also inferred.  Finally, it tries to
estimate the posterior distribution (by Hamiltonian Monte Carlo) for the
differences in means for each peptide in the data.  Once the posterior is
inferred, it integrates the tails to estimate the probability of error from
which a statistical decision can be made.  See Berg and Popescu for details
(<doi:10.1101/2023.05.11.540411>).")
    (license license:expat)))

(define-public r-balancedsampling
  (package
    (name "r-balancedsampling")
    (version "2.0.6")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BalancedSampling" version))
       (sha256
        (base32 "1qkbsihk776h5na7dv68kab6qqbymfci9yvq0pnrlb5zb92fw32b"))))
    (properties `((upstream-name . "BalancedSampling")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpparmadillo r-rcpp))
    (home-page "https://www.envisim.se/")
    (synopsis "Balanced and Spatially Balanced Sampling")
    (description
     "Select balanced and spatially balanced probability samples in multi-dimensional
spaces with any prescribed inclusion probabilities.  It contains fast (C++ via
Rcpp) implementations of the included sampling methods.  The local pivotal
method by GrafstrÃ¶m, LundstrÃ¶m and Schelin (2012)
<doi:10.1111/j.1541-0420.2011.01699.x> and spatially correlated Poisson sampling
by GrafstrÃ¶m (2012) <doi:10.1016/j.jspi.2011.07.003> are included.  Also the
cube method (for balanced sampling) and the local cube method (for doubly
balanced sampling) are included, see GrafstrÃ¶m and TillÃ© (2013)
<doi:10.1002/env.2194>.")
    (license license:gpl2+)))

(define-public r-balancecheck
  (package
    (name "r-balancecheck")
    (version "0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BalanceCheck" version))
       (sha256
        (base32 "0aal641vfbjk3w6ipwbqqmm9nnxs962hjmpph9wnqaqk5r6s7brs"))))
    (properties `((upstream-name . "BalanceCheck")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-ade4))
    (home-page "https://cran.r-project.org/package=BalanceCheck")
    (synopsis
     "Balance Check for Multiple Covariates in Matched Observational Studies")
    (description
     "Two practical tests are provided for assessing whether multiple covariates in a
treatment group and a matched control group are balanced in observational
studies.")
    (license license:gpl2+)))

(define-public r-bakr
  (package
    (name "r-bakr")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bakR" version))
       (sha256
        (base32 "070gv5s78381cq8pagggicxx065hab99v2vgvii996b5svs6rjic"))))
    (properties `((upstream-name . "bakR")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-purrr
                             r-magrittr
                             r-hmisc
                             r-ggplot2
                             r-dplyr
                             r-data-table
                             r-bh))
    (native-inputs (list r-knitr))
    (home-page "https://simonlabcode.github.io/bakR/")
    (synopsis
     "Analyze and Compare Nucleotide Recoding RNA Sequencing Datasets")
    (description
     "Several implementations of a novel Bayesian hierarchical statistical model of
nucleotide recoding RNA-seq experiments (NR-seq; @code{TimeLapse-seq}, SLAM-seq,
TUC-seq, etc.) for analyzing and comparing NR-seq datasets (see Vock and Simon
(2023) <doi:10.1261/rna.079451.122>).  NR-seq is a powerful extension of RNA-seq
that provides information about the kinetics of RNA metabolism (e.g., RNA
degradation rate constants), which is notably lacking in standard RNA-seq data.
The statistical model makes maximal use of these high-throughput datasets by
sharing information across transcripts to significantly improve uncertainty
quantification and increase statistical power. @code{bakR} includes a maximally
efficient implementation of this model for conservative initial investigations
of datasets. @code{bakR} also provides more highly powered implementations using
the probabilistic programming language Stan to sample from the full posterior
distribution. @code{bakR} performs multiple-test adjusted statistical inference
with the output of these model implementations to help biologists separate
signal from background.  Methods to automatically visualize key results and
detect batch effects are also provided.")
    (license license:expat)))

(define-public r-baker
  (package
    (name "r-baker")
    (version "1.0.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "baker" version))
       (sha256
        (base32 "0k564q7v6pvf6w3qs5cmdbhxjjqg2kqm9yr7ckvd23wbl0gm5rdv"))))
    (properties `((upstream-name . "baker")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-shinyfiles
                             r-shinydashboard
                             r-robcompositions
                             r-rjags
                             r-reshape2
                             r-r2jags
                             r-mvbutils
                             r-mgcv
                             r-lubridate
                             r-gridextra
                             r-ggpubr
                             r-ggplot2
                             r-coda
                             r-binom
                             r-abind))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/zhenkewu/baker")
    (synopsis "\"Nested Partially Latent Class Models\"")
    (description
     "This package provides functions to specify, fit and visualize nested
partially-latent class models ( Wu, Deloria-Knoll, Hammitt, and Zeger (2016)
<doi:10.1111/rssc.12101>; Wu, Deloria-Knoll, and Zeger (2017)
<doi:10.1093/biostatistics/kxw037>; Wu and Chen (2021) <doi:10.1002/sim.8804>)
for inference of population disease etiology and individual diagnosis.  In the
motivating Pneumonia Etiology Research for Child Health (PERCH) study, because
both quantities of interest sum to one hundred percent, the PERCH scientists
frequently refer to them as population etiology pie and individual etiology pie,
hence the name of the package.")
    (license license:expat)))

(define-public r-bakeoff
  (package
    (name "r-bakeoff")
    (version "0.2.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bakeoff" version))
       (sha256
        (base32 "0i40vnwgy4db8flfcx23zh5a7mmp4dhckhzayj34smqfmfm5xzkl"))))
    (properties `((upstream-name . "bakeoff")))
    (build-system r-build-system)
    (home-page "https://bakeoff.netlify.app/")
    (synopsis "Data from \"The Great British Bake Off\"")
    (description
     "Data about the bakers, challenges, and ratings for \"The Great British Bake Off\",
from Wikipedia <https://en.wikipedia.org/wiki/The_Great_British_Bake_Off>.")
    (license license:expat)))

(define-public r-baizer
  (package
    (name "r-baizer")
    (version "0.8.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "baizer" version))
       (sha256
        (base32 "06f7g71pzc4y2vnc3fj33s1xl6plxrkd2y73xi24pk81lm5wgf9a"))))
    (properties `((upstream-name . "baizer")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tidyr
                             r-tibble
                             r-stringr
                             r-seriation
                             r-rmarkdown
                             r-rlang
                             r-rematch2
                             r-readxl
                             r-readr
                             r-purrr
                             r-openxlsx
                             r-magrittr
                             r-dplyr
                             r-diffobj
                             r-curl))
    (home-page "https://william-swl.github.io/baizer/")
    (synopsis "Useful Functions for Data Processing")
    (description
     "In ancient Chinese mythology, Bai Ze is a divine creature that knows the needs
of everything.  baizer provides data processing functions frequently used by the
author.  Hope this package also knows what you want!")
    (license license:expat)))

(define-public r-bain
  (package
    (name "r-bain")
    (version "0.2.11")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bain" version))
       (sha256
        (base32 "137i13i39i87q7219bpja6a8l762is4sala55hvk2hlmph3qnylc"))))
    (properties `((upstream-name . "bain")))
    (build-system r-build-system)
    (propagated-inputs (list r-lavaan))
    (native-inputs (list r-knitr gfortran))
    (home-page "https://informative-hypotheses.sites.uu.nl/software/bain/")
    (synopsis "Bayes Factors for Informative Hypotheses")
    (description
     "Computes approximated adjusted fractional Bayes factors for equality,
inequality, and about equality constrained hypotheses.  For a tutorial on this
method, see Hoijtink, Mulder, van Lissa, & Gu, (2019) <doi:10.1037/met0000201>.
For applications in structural equation modeling, see: Van Lissa, Gu, Mulder,
Rosseel, Van Zundert, & Hoijtink, (2021) <doi:10.1080/10705511.2020.1745644>.
For the statistical underpinnings, see Gu, Mulder, and Hoijtink (2018)
<doi:10.1111/bmsp.12110>; Hoijtink, Gu, & Mulder, J. (2019)
<doi:10.1111/bmsp.12145>; Hoijtink, Gu, Mulder, & Rosseel, (2019)
<doi:10.31234/osf.io/q6h5w>.")
    (license license:gpl3+)))

(define-public r-bahc
  (package
    (name "r-bahc")
    (version "0.3.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bahc" version))
       (sha256
        (base32 "0yz0v33fhm38ivhr7bvajk9v7wdmhg9qg7ac8r2qr00rns82m6y3"))))
    (properties `((upstream-name . "bahc")))
    (build-system r-build-system)
    (propagated-inputs (list r-matrixstats r-fastcluster))
    (home-page "https://cran.r-project.org/package=bahc")
    (synopsis
     "Filter Covariance and Correlation Matrices with Bootstrapped-Averaged Hierarchical Ansatz")
    (description
     "This package provides a method to filter correlation and covariance matrices by
averaging bootstrapped filtered hierarchical clustering and boosting.  See Ch.
Bongiorno and D. Challet, Covariance matrix filtering with bootstrapped
hierarchies (2020) <@code{arXiv:2003.05807>} and Ch.  Bongiorno and D. Challet,
Reactive Global Minimum Variance Portfolios with k-BAHC covariance cleaning
(2020) <@code{arXiv:2005.08703>}.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-bagyo
  (package
    (name "r-bagyo")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bagyo" version))
       (sha256
        (base32 "0j3aiqqn1y9hw3ik2jlza3rwsi8rn32i218dd0fffz56k5d5hcdp"))))
    (properties `((upstream-name . "bagyo")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://panukatan.io/bagyo/")
    (synopsis "Philippine Tropical Cyclones Data")
    (description
     "The Philippines frequently experiences tropical cyclones (called bagyo in the
Filipino language) because of its geographical position.  These cyclones
typically bring heavy rainfall, leading to widespread flooding, as well as
strong winds that cause significant damage to human life, crops, and property.
Data on cyclones are collected and curated by the Philippine Atmospheric,
Geophysical, and Astronomical Services Administration or PAGASA and made
available through its website
<https://bagong.pagasa.dost.gov.ph/tropical-cyclone/publications/annual-report>.
 This package contains Philippine tropical cyclones data in a machine-readable
format.  It is hoped that this data package provides an interesting and unique
dataset for data exploration and visualisation.")
    (license license:cc0)))

(define-public r-baguette
  (package
    (name "r-baguette")
    (version "1.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "baguette" version))
       (sha256
        (base32 "01pxk9angq2yrw3j5c748qhcdq92axpml5lr9cpf74a8hpg6ddjb"))))
    (properties `((upstream-name . "baguette")))
    (build-system r-build-system)
    (propagated-inputs (list r-withr
                             r-tidyr
                             r-tibble
                             r-rsample
                             r-rpart
                             r-rlang
                             r-purrr
                             r-parsnip
                             r-magrittr
                             r-hardhat
                             r-generics
                             r-furrr
                             r-dplyr
                             r-dials
                             r-c50
                             r-butcher))
    (home-page "https://baguette.tidymodels.org")
    (synopsis "Efficient Model Functions for Bagging")
    (description
     "Tree- and rule-based models can be bagged (<doi:10.1007/BF00058655>) using this
package and their predictions equations are stored in an efficient format to
reduce the model objects size and speed.")
    (license license:expat)))

(define-public r-bagoft
  (package
    (name "r-bagoft")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BAGofT" version))
       (sha256
        (base32 "0lvjygnphb2d5cammmswlnyn77vmy3k46r8hh8f3mfr7i68gcdvz"))))
    (properties `((upstream-name . "BAGofT")))
    (build-system r-build-system)
    (propagated-inputs (list r-randomforest r-dcov))
    (home-page "https://cran.r-project.org/package=BAGofT")
    (synopsis "Binary Regression Adaptive Goodness-of-Fit Test (BAGofT)")
    (description
     "The BA@code{GofT} assesses the goodness-of-fit of binary classifiers.  Details
can be found in Zhang, Ding and Yang (2021) <@code{arXiv:1911.03063v2>}.")
    (license license:gpl3)))

(define-public r-baggr
  (package
    (name "r-baggr")
    (version "0.7.8")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "baggr" version))
       (sha256
        (base32 "18faz6b8llzxsrm60bnsanplc8d7knb8cmcz34nyai7i7022061k"))))
    (properties `((upstream-name . "baggr")))
    (build-system r-build-system)
    (propagated-inputs (list r-testthat
                             r-stanheaders
                             r-rstantools
                             r-rstan
                             r-rcppparallel
                             r-rcppeigen
                             r-rcpp
                             r-gridextra
                             r-ggrepel
                             r-ggplotify
                             r-ggplot2
                             r-forestplot
                             r-crayon
                             r-bh
                             r-bayesplot))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/wwiecek/baggr")
    (synopsis "Bayesian Aggregate Treatment Effects")
    (description
     "Running and comparing meta-analyses of data with hierarchical Bayesian models in
Stan, including convenience functions for formatting data, plotting and pooling
measures specific to meta-analysis.  This implements many models from Meager
(2019) <doi:10.1257/app.20170299>.")
    (license license:gpl3+)))

(define-public r-baggingbwsel
  (package
    (name "r-baggingbwsel")
    (version "1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "baggingbwsel" version))
       (sha256
        (base32 "0za7a7l4dzg0djzdrrz9axry61h8m41qkgay37ymy4lfkdwawjcx"))))
    (properties `((upstream-name . "baggingbwsel")))
    (build-system r-build-system)
    (propagated-inputs (list r-sm
                             r-rcpp
                             r-nor1mix
                             r-misc3d
                             r-mclust
                             r-kedd
                             r-foreach
                             r-doparallel))
    (home-page "https://rubenfcasal.github.io/baggingbwsel/")
    (synopsis
     "Bagging Bandwidth Selection in Kernel Density and Regression Estimation")
    (description
     "Bagging bandwidth selection methods for the Parzen-Rosenblatt and
Nadaraya-Watson estimators.  These bandwidth selectors can achieve greater
statistical precision than their non-bagged counterparts while being
computationally fast.  See Barreiro-Ures et al. (2020)
<doi:10.1093/biomet/asaa092> and Barreiro-Ures et al. (2021)
<doi:10.48550/@code{arXiv.2105.04134>}.")
    (license license:gpl3)))

(define-public r-bagged-outliertrees
  (package
    (name "r-bagged-outliertrees")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bagged.outliertrees" version))
       (sha256
        (base32 "0fcd54j6vci2yvlb609dck4pwbn54irkdk472cjc965rcw6dz9z7"))))
    (properties `((upstream-name . "bagged.outliertrees")))
    (build-system r-build-system)
    (propagated-inputs (list r-rlist
                             r-outliertree
                             r-foreach
                             r-dplyr
                             r-dosnow
                             r-data-table))
    (home-page "https://github.com/RafaJPSantos/bagged.outliertrees")
    (synopsis "Robust Explainable Outlier Detection Based on OutlierTree")
    (description
     "Bagged @code{OutlierTrees} is an explainable unsupervised outlier detection
method based on an ensemble implementation of the existing @code{OutlierTree}
procedure (Cortes, 2020).  This implementation takes advantage of bootstrap
aggregating (bagging) to improve robustness by reducing the possible masking
effect and subsequent high variance (similarly to Isolation Forest), hence the
name \"Bagged @code{OutlierTrees}\".  To learn more about the base procedure
@code{OutlierTree} (Cortes, 2020), please refer to <@code{arXiv:2001.00636>}.")
    (license license:expat)))

(define-public r-bage
  (package
    (name "r-bage")
    (version "0.7.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bage" version))
       (sha256
        (base32 "1pld907impj1l4sbr8ndk9dsxcy6kzlb799pni2pqldriaax1xwc"))))
    (properties `((upstream-name . "bage")))
    (build-system r-build-system)
    (propagated-inputs (list r-vctrs
                             r-tmb
                             r-tibble
                             r-rvec
                             r-rcppeigen
                             r-poputils
                             r-matrixstats
                             r-matrix
                             r-generics
                             r-cli))
    (native-inputs (list r-knitr))
    (home-page "https://bayesiandemography.github.io/bage/")
    (synopsis "Bayesian Estimation and Forecasting of Age-Specific Rates")
    (description
     "Fast Bayesian estimation and forecasting of age-specific rates, probabilities,
and means, based on Template Model Builder'.")
    (license license:expat)))

(define-public r-baffle
  (package
    (name "r-baffle")
    (version "0.2.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "baffle" version))
       (sha256
        (base32 "17307jn92sr4gindd9fvaqvk990v40mr77zp2xs0079h85vh8954"))))
    (properties `((upstream-name . "baffle")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://j-moravec.github.io/baffle/")
    (synopsis "Make Waffle Plots with Base Graphics")
    (description
     "Waffle plots are rectangular pie charts that represent a quantity or abundances
using colored squares or other symbol.  This makes them better at transmitting
information as the discrete number of squares is easier to read than the
circular area of pie charts.  While the original waffle charts were rectangular
with 10 rows and columns, with a single square representing 1%, they are
nowadays popular in various infographics to visualize any proportional ratios.")
    (license license:expat)))

(define-public r-baf
  (package
    (name "r-baf")
    (version "0.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "baf" version))
       (sha256
        (base32 "1zjn6iqxsa0qby285dhn86047gkzz05nknqqj3dw3bcdhsl91gim"))))
    (properties `((upstream-name . "baf")))
    (build-system r-build-system)
    (propagated-inputs (list r-readr
                             r-glue
                             r-fs
                             r-dplyr
                             r-curl
                             r-cli))
    (home-page "http://christophertkenny.com/baf/")
    (synopsis "Block Assignment Files")
    (description
     "Download and read US Census Bureau data relationship files.  Provides support
for cleaning and using block assignment files since 2010, as described in
<https://www.census.gov/geographies/reference-files/time-series/geo/block-assignment-files.html>.
 Also includes support for working with block equivalency files, used for years
outside of decennial census years.")
    (license license:expat)))

(define-public r-badger
  (package
    (name "r-badger")
    (version "0.2.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "badger" version))
       (sha256
        (base32 "1d41xcynl2g63mb4qmhkj8hrs193fq7n0ymh6v2ygabn2n6029kc"))))
    (properties `((upstream-name . "badger")))
    (build-system r-build-system)
    (propagated-inputs (list r-usethis r-rvcheck r-dlstats r-desc))
    (home-page "https://github.com/GuangchuangYu/badger")
    (synopsis "Badge for R Package")
    (description
     "Query information and generate badge for using in README and @code{GitHub}
Pages.")
    (license license:artistic2.0)))

(define-public r-badgen
  (package
    (name "r-badgen")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "badgen" version))
       (sha256
        (base32 "140hmw036966hff9wjqykixxjvs5c0s2zq5yfwafl8il0k10s3y1"))))
    (properties `((upstream-name . "badgen")))
    (build-system r-build-system)
    (propagated-inputs (list r-v8 r-jsonlite))
    (home-page "https://jeroen.r-universe.dev/badgen")
    (synopsis "Fast and Simple Badge Generator")
    (description
     "Bindings to badgen <https://www.npmjs.com/package/badgen> to generate beautiful
svg badges in R without internet access.  Images can be converted to png using
the rsvg package as shown in examples.")
    (license license:expat)))

(define-public r-bacr
  (package
    (name "r-bacr")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bacr" version))
       (sha256
        (base32 "14zr1v4rihx0ra3x0vsb81vsz0g8gzskkdxkg7nhiz835hp2fiy8"))))
    (properties `((upstream-name . "bacr")))
    (build-system r-build-system)
    (propagated-inputs (list r-mcmcpack))
    (home-page "https://cran.r-project.org/package=bacr")
    (synopsis "Bayesian Adjustment for Confounding")
    (description
     "Estimating the average causal effect based on the Bayesian Adjustment for
Confounding (BAC) algorithm.")
    (license license:gpl2)))

(define-public r-bacprior
  (package
    (name "r-bacprior")
    (version "2.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BACprior" version))
       (sha256
        (base32 "1z5wxkik8ziqsgiq6arxsp772scz9jfsa1kz9wz07gpfvv1pl53z"))))
    (properties `((upstream-name . "BACprior")))
    (build-system r-build-system)
    (propagated-inputs (list r-mvtnorm r-leaps r-boot))
    (home-page "https://cran.r-project.org/package=BACprior")
    (synopsis "Choice of Omega in the BAC Algorithm")
    (description
     "The Bayesian Adjustment for Confounding (BAC) algorithm (Wang et al., 2012) can
be used to estimate the causal effect of a continuous exposure on a continuous
outcome.  This package provides an approximate sensitivity analysis of BAC with
regards to the hyperparameter omega.  BACprior also provides functions to guide
the user in their choice of an appropriate omega value.  The method is based on
Lefebvre, Atherton and Talbot (2014).")
    (license license:gpl2+)))

(define-public r-bacondecomp
  (package
    (name "r-bacondecomp")
    (version "0.1.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bacondecomp" version))
       (sha256
        (base32 "1sd6pbhmx8vxz5c2cvvadbjy7p15y91gzs3n0ish7h5br1vq8fpm"))))
    (properties `((upstream-name . "bacondecomp")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://cran.r-project.org/package=bacondecomp")
    (synopsis "Goodman-Bacon Decomposition")
    (description
     "Decomposition for differences-in-differences with variation in treatment timing
from Goodman-Bacon (2018) <doi:10.3386/w25018>.")
    (license license:expat)))

(define-public r-baclava
  (package
    (name "r-baclava")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "baclava" version))
       (sha256
        (base32 "1jpb59g23cmxc8c07fx9y810sx58gm6n1v6cgkmsf8m7592di1bb"))))
    (properties `((upstream-name . "baclava")))
    (build-system r-build-system)
    (propagated-inputs (list r-tidyr
                             r-tibble
                             r-rcppnumerical
                             r-rcppeigen
                             r-rcpp
                             r-ggplot2
                             r-foreach
                             r-dplyr
                             r-doparallel
                             r-coda))
    (home-page "https://cran.r-project.org/package=baclava")
    (synopsis
     "Bayesian Analysis of Cancer Latency with Auxiliary Variable Augmentation")
    (description
     "This package provides a novel data-augmentation Markov chain Monte Carlo
sampling algorithm to fit a progressive compartmental model of disease in a
Bayesian framework Morsomme, R.N., Holloway, S.T., Ryser, M.D. and Xu J. (2024)
<doi:10.48550/@code{arXiv.2408.14625>}.")
    (license license:gpl3)))

(define-public r-backtest
  (package
    (name "r-backtest")
    (version "0.3-4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "backtest" version))
       (sha256
        (base32 "1s0mf247dz2vvyf4m3sp9xiqhv7xcs4rphyg9gdcy73060sah2ad"))))
    (properties `((upstream-name . "backtest")))
    (build-system r-build-system)
    (propagated-inputs (list r-lattice))
    (home-page "https://cran.r-project.org/package=backtest")
    (synopsis
     "Exploring Portfolio-Based Conjectures About Financial Instruments")
    (description
     "The backtest package provides facilities for exploring portfolio-based
conjectures about financial instruments (stocks, bonds, swaps, options, et
cetera).")
    (license license:gpl2+)))

(define-public r-backshift
  (package
    (name "r-backshift")
    (version "0.1.4.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "backShift" version))
       (sha256
        (base32 "1dhmq51w07vcqlngsb04ip0wqbgqq2xdfdm1w82r082f07ywqb16"))))
    (properties `((upstream-name . "backShift")))
    (build-system r-build-system)
    (propagated-inputs (list r-reshape2
                             r-matrixcalc
                             r-mass
                             r-igraph
                             r-ggplot2
                             r-clue))
    (native-inputs (list r-knitr))
    (home-page "https://github.com/christinaheinze/backShift")
    (synopsis "Learning Causal Cyclic Graphs from Unknown Shift Interventions")
    (description
     "Code for @code{backShift}', an algorithm to estimate the connectivity matrix of
a directed (possibly cyclic) graph with hidden variables.  The underlying system
is required to be linear and we assume that observations under different shift
interventions are available.  For more details, see <@code{arXiv:1506.02494>}.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-backpipe
  (package
    (name "r-backpipe")
    (version "0.2.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "backpipe" version))
       (sha256
        (base32 "12k2cv9x8h0b002m9c8g4vj5a7chp4b8jqz377ia0diqw89dydpm"))))
    (properties `((upstream-name . "backpipe")))
    (build-system r-build-system)
    (native-inputs (list r-knitr))
    (home-page "https://github.com/decisionpatterns/backpipe")
    (synopsis "Backward Pipe (Right-to-Left) Operator")
    (description
     "This package provides a backward-pipe operator for magrittr (%<%) or
@code{pipeR} (%<<%) that allows for a performing operations from right-to-left.
This allows writing more legible code where right-to-left ordering is natural.
This is common with hierarchies and nested structures such as trees, directories
or markup languages (e.g. HTML and XML).  The package also includes a R-Studio
add-in that can be bound to a keyboard shortcut.")
    (license (list license:gpl2
                   (license:fsdg-compatible "file://LICENSE")))))

(define-public r-backbone
  (package
    (name "r-backbone")
    (version "2.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "backbone" version))
       (sha256
        (base32 "05b493cfzi85hnczl56d81sc7j5xfwybsmnc4i4q984sw0gpgvll"))))
    (properties `((upstream-name . "backbone")))
    (build-system r-build-system)
    (propagated-inputs (list r-rcpp r-matrix r-igraph))
    (native-inputs (list r-knitr))
    (home-page "https://www.zacharyneal.com/backbone")
    (synopsis "Extracts the Backbone from Graphs")
    (description
     "An implementation of methods for extracting an unweighted unipartite graph (i.e.
 a backbone) from an unweighted unipartite graph, a weighted unipartite graph,
the projection of an unweighted bipartite graph, or the projection of a weighted
bipartite graph (Neal, 2022 <doi:10.1371/journal.pone.0269137>).")
    (license license:gpl3)))

(define-public r-bacistool
  (package
    (name "r-bacistool")
    (version "1.0.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "bacistool" version))
       (sha256
        (base32 "1hby2bmxbc8qgjzgkp2flv89lpzzvg3vr7s98989ayjmv4vgmqaf"))))
    (properties `((upstream-name . "bacistool")))
    (build-system r-build-system)
    (inputs (list jags))
    (propagated-inputs (list r-rjags))
    (home-page "https://cran.r-project.org/package=bacistool")
    (synopsis
     "Bayesian Classification and Information Sharing (BaCIS) Tool for the Design of Multi-Group Phase II Clinical Trials")
    (description
     "This package provides the design of multi-group phase II clinical trials with
binary outcomes using the hierarchical Bayesian classification and information
sharing (@code{BaCIS}) model.  Subgroups are classified into two clusters on the
basis of their outcomes mimicking the hypothesis testing framework.
Subsequently, information sharing takes place within subgroups in the same
cluster, rather than across all subgroups.  This method can be applied to the
design and analysis of multi-group clinical trials with binary outcomes.
Reference: Nan Chen and J. Jack Lee (2019) <doi:10.1002/bimj.201700275>.")
    (license license:gpl3+)))

(define-public r-bacct
  (package
    (name "r-bacct")
    (version "1.0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BACCT" version))
       (sha256
        (base32 "0bpm4l27vbblzi2hhfbjw2af3ic2jbnyghkvchpq8ww43k8068iv"))))
    (properties `((upstream-name . "BACCT")))
    (build-system r-build-system)
    (propagated-inputs (list r-rjags r-reshape2 r-ggplot2))
    (home-page "https://cran.r-project.org/package=BACCT")
    (synopsis "Bayesian Augmented Control for Clinical Trials")
    (description
     "This package implements the Bayesian Augmented Control (BAC, a.k.a.  Bayesian
historical data borrowing) method under clinical trial setting by calling Just
Another Gibbs Sampler ('JAGS') software.  In addition, the BACCT package
evaluates user-specified decision rules by computing the type-I error/power, or
probability of correct go/no-go decision at interim look.  The evaluation can be
presented numerically or graphically.  Users need to have JAGS 4.0.0 or newer
installed due to a compatibility issue with rjags package.  Currently, the
package implements the BAC method for binary outcome only.  Support for
continuous and survival endpoints will be added in future releases.  We would
like to thank @code{AbbVie's} Statistical Innovation group and Clinical
Statistics group for their support in developing the BACCT package.")
    (license license:gpl3+)))

(define-public r-bacco
  (package
    (name "r-bacco")
    (version "2.1-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "BACCO" version))
       (sha256
        (base32 "1xqhb91pj6gcbr2afbq8jqkilhvy12qqq74zhv9c05i7dxl3b7ak"))))
    (properties `((upstream-name . "BACCO")))
    (build-system r-build-system)
    (propagated-inputs (list r-emulator r-calibrator r-approximator))
    (home-page "https://cran.r-project.org/package=BACCO")
    (synopsis "Bayesian Analysis of Computer Code Output (BACCO)")
    (description
     "The BACCO bundle of packages is replaced by the BACCO package, which provides a
vignette that illustrates the constituent packages (emulator, approximator,
calibrator) in use.")
    (license (list license:gpl2+ license:gpl3+))))

(define-public r-babynamesil
  (package
    (name "r-babynamesil")
    (version "0.0.2")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "babynamesIL" version))
       (sha256
        (base32 "0g8p5afhsh0piip0i8jxd1s52bk4zb1z5bf3chdkcbxr8z0d42f6"))))
    (properties `((upstream-name . "babynamesIL")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble))
    (home-page "https://github.com/aviezerl/babynamesIL")
    (synopsis "Israel Baby Names 1948-2022")
    (description
     "Israeli baby names provided by Israel's Central Bureau of Statistics.  The
package contains only names used for at least 5 children in at least one gender
and sector (\"Jewish\", \"Muslim\", \"Christian\", \"Druze\" and \"Other\").  Data was
downloaded from:
<https://www.cbs.gov.il/he/publications/@code{LochutTlushim/2020/%D7%A9%D7%9E%D7%95%D7%AA-%D7%A4%D7%A8%D7%98%D7%99%D7%99%D7%9D.xlsx>}.")
    (license license:cc0)))

(define-public r-babynames
  (package
    (name "r-babynames")
    (version "1.0.1")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "babynames" version))
       (sha256
        (base32 "1gcpd6lncqwjz05z98ra6hcc8q966l2594ylkg50avndild89n3h"))))
    (properties `((upstream-name . "babynames")))
    (build-system r-build-system)
    (propagated-inputs (list r-tibble))
    (home-page "https://github.com/hadley/babynames")
    (synopsis "US Baby Names 1880-2017")
    (description
     "US baby names provided by the SSA. This package contains all names used for at
least 5 children of either sex.")
    (license license:cc0)))

(define-public r-babette
  (package
    (name "r-babette")
    (version "2.3.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "babette" version))
       (sha256
        (base32 "1hir478scnfm87j2r6f48sr9sxwv2n9b6vcmwjvq58m6s0j8mzfi"))))
    (properties `((upstream-name . "babette")))
    (build-system r-build-system)
    (inputs (list))
    (propagated-inputs (list r-xml2
                             r-tracerer
                             r-stringr
                             r-rlang
                             r-phangorn
                             r-mauricer
                             r-beautier
                             r-beastier))
    (native-inputs (list r-knitr))
    (home-page "https://docs.ropensci.org/babette/")
    (synopsis "Control 'BEAST2'")
    (description
     "BEAST2 (<https://www.beast2.org>) is a widely used Bayesian phylogenetic tool,
that uses DNA/RNA/protein data and many model priors to create a posterior of
jointly estimated phylogenies and parameters.  BEAST2 is commonly accompanied by
BEAUti 2', Tracer and @code{DensiTree}'.  babette provides for an alternative
workflow of using all these tools separately.  This allows doing complex
Bayesian phylogenetics easily and reproducibly from R'.")
    (license license:gpl3)))

(define-public r-babelmixr2
  (package
    (name "r-babelmixr2")
    (version "0.1.4")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "babelmixr2" version))
       (sha256
        (base32 "1dkc6i7dcxqc5v7w8sn54zy582dhgl5nakzndvh25qylhkakp4bc"))))
    (properties `((upstream-name . "babelmixr2")))
    (build-system r-build-system)
    (propagated-inputs (list r-rxode2
                             r-rex
                             r-rcppeigen
                             r-rcpparmadillo
                             r-rcpp
                             r-qs
                             r-nonmem2rx
                             r-nlmixr2est
                             r-nlmixr2
                             r-monolix2rx
                             r-lotri
                             r-digest
                             r-cli
                             r-checkmate))
    (native-inputs (list r-knitr))
    (home-page "https://nlmixr2.github.io/babelmixr2/")
    (synopsis
     "Use 'nlmixr2' to Interact with Open Source and Commercial Software")
    (description
     "Run other estimation and simulation software via the nlmixr2 (Fidler et al
(2019) <doi:10.1002/psp4.12445>) interface including PKNCA', NONMEM and
Monolix'.  While not required, you can get/install the @code{lixoftConnectors}
package in the Monolix installation, as described at the following url
<https://monolixsuite.slp-software.com/r-functions/2024R1/installation-and-initialization>.
 When @code{lixoftConnectors} is available, Monolix can be run directly instead
of setting up command line usage.")
    (license license:gpl3+)))

(define-public r-babel
  (package
    (name "r-babel")
    (version "0.3-0")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "babel" version))
       (sha256
        (base32 "1iwvx69051yhlxbcl6bypvc3mcih0q8bf3i29r3i79356hp12xqa"))))
    (properties `((upstream-name . "babel")))
    (build-system r-build-system)
    (propagated-inputs (list r-edger))
    (native-inputs (list r-r-rsp))
    (home-page "https://cran.r-project.org/package=babel")
    (synopsis "Ribosome Profiling Data Analysis")
    (description
     "Included here are babel routines for identifying unusual ribosome protected
fragment counts given @code{mRNA} counts.")
    (license license:lgpl2.1+)))

(define-public r-b64
  (package
    (name "r-b64")
    (version "0.1.3")
    (source
     (origin
       (method url-fetch)
       (uri (cran-uri "b64" version))
       (sha256
        (base32 "0alr0q2zfxni31rb3m1676kwhwwg91xaq9fsfhjgcycn7r06rjpl"))))
    (properties `((upstream-name . "b64")))
    (build-system r-build-system)
    (inputs (list))
    (home-page "https://extendr.github.io/b64/")
    (synopsis "Fast and Vectorized Base 64 Engine")
    (description
     "This package provides a fast, lightweight, and vectorized base 64 engine to
encode and decode character and raw vectors as well as files stored on disk.
Common base 64 alphabets are supported out of the box including the standard,
URL-safe, bcrypt, crypt, @code{BinHex}', and IMAP-modified UTF-7 alphabets.
Custom engines can be created to support unique base 64 encoding and decoding
needs.")
    (license license:expat)))

